The results of the investigation showed that second-order kinetics govern the adsorption process, and the corresponding rate constants were found. To evaluate the parameters related to the adsorption process, the adsorption equilibrium was examined using a variety of mathematical models, such as the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models. The Langmuir isotherm was found to be the best appropriate among all models for describing the adsorption of Cu2+ and Ni2+ ions using bio-nanocomposite beads. The positive values of ΔH° indicate that the adsorption is physical and endothermic, in agreement with experimental results. The negative value of ∆G° shows that the adsorption process is spontaneous. Positive ΔS° values indicate increased randomness at the solid/liquid interface, during adsorption of Cu2+ and Ni2+ cations onto the engineered bio-nanocomposite. The maximum adsorbed amounts of metal ions by the bio-nanocomposite used were 370.37 mg/g for Ni2+ and 454.54 mg/g for Cu2+ from single system. For the binary system, according to the Langmuir isotherm, the maximum adsorbed amounts of Ni2+ and Cu2+ were 357.14 mg/g and 370,37 mg/g, respectively. There is proof that Alginate-Moroccan clay bio-nanocomposites can serve as a different, less expensive source of sorbents for the removal of metal ions from single and binary systems.

Post-menopausal women are at risk of osteoporotic vertebral fractures (OVFs), which if undetected and untreated, can lead to significant pain and morbidity. However, OVFs are often not reported by radiologists on routine chest radiographs. Artificial Intelligence (AI) is increasingly used in medical applications with improvements in detection and diagnosis. This study aims to investigate the clinical value of a newly developed AI tool, Ofeye 1.0 for automated detection of OVFs on lateral chest radiographs in post-menopausal women (>60 years) who were referred to undergo chest x-rays for other reasons. A total of 510 de-identified lateral chest radiographs from three clinical sites were retrieved and analysed by Ofeye1.0 tool. These images were then reviewed by a consultant radiologist to decide whether there is any fracture present or not with findings served as the reference standard for determining the diagnostic performance of the AI tool. The percentage of radiologist reports which included the OVF was analysed by comparing diagnostic reports with AI findings, while the accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive values (NPV) of the AI tool were calculated. Out of all the original radiologist reports, missed OVFs were found in 14% images but were detected by the AI tool. The AI tool demonstrated a relatively high specificity 92.8% (95% CI: 89.6, 95.2%), moderate accuracy 80.3% (95% CI: 76,3, 80.4%), PPV 73.7% (95% CI: 65.2, 80.8%) and NPV 81.5% (95%CI: 79, 83.8%), but low sensitivity 49% (95%CI: 40.7, 57.3%). The high false negative results were mainly due to mild OVFs with <20% vertebral height loss. The new AI software tool has high specificity with a low false positive rate of 5.1%, showing that it can be used as a complimentary tool to the routine diagnostic reports for reduction of missed OVF in elderly women. The low sensitivity with high false negative rates indicate the necessity of radiologist’s in identifying and reporting early OVFs.

The integration of artificial intelligence (AI) into medical imaging has guided in an era of transformation in healthcare. This literature review explores the latest innovations and applications of AI in the field, highlighting its profound impact on medical diagnosis and patient care. The innovation segment explores into cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis. These innovations have enabled rapid and accurate detection of abnormalities, from identifying tumors during radiological examinations to detecting early signs of eye disease in retinal images. The article also highlights various applications of AI in medical imaging, including radiology, pathology, cardiology, and more. AI-based diagnostic tools not only speed up the interpretation of complex images but also improve early detection of disease, ultimately delivering better outcomes for patients. Additionally, AI-based image processing facilitates personalized treatment plans, thereby optimizing healthcare delivery. This literature review highlights the paradigm shift that AI has brought to medical imaging, highlighting its role in revolutionizing diagnosis and patient care. By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Introduction: At the end of 2019 the Covid-19 pandemic caused a huge amount of restrictions across the world which affected teaching around the world as it was not prepared to move from traditional face-to-face to an online conversion overnight, so the aim of this paper is to determine the changes in undergraduate anatomy teaching strategies post-pandemic compared to pre-pandemic. Methods: A systematic review was conducted based on the PRISMA statement. Data were retrieved from the searches conducted in the different databases, and the articles to be retrieved were selected with the help of the Rayyan platform. Using the OPMER and JBI tools, the quality of evidence was assessed for the selection of the articles included in the review. Results: The teaching of medicine, specifically anatomy, presented serious problems due to the fact that throughout history it has been taught in the classroom by means of cadaveric dissection and when this ceased to be available, new methods arose through the use of digital resources to try to make up for the needs of the lack of dissection laboratories which lost a great deal of content in this restructuring, which have been improving with the passing of the courses taught and the development of new material that has been better adapted to the needs of the students. Discussion: The authors agree that in the future the best way to teach anatomy will continue to be face-to-face dissection without neglecting the strategies developed during this pandemic and to be taken if necessary to restructure curricula in the future to improve teaching.

Recently, Artificial Intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies as well as increasing the effectiveness of the training process. Thus, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in the medical image scan segmentation, and intelligent visual-content generation, i.e. computer-generated three-dimensional (3D) images in the context of Extended Reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper also attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. Finally, this paper concludes with possible developments and open challenges in AI application in Extended Reality-based solutions. Finally, the future lines of research and development directions of Artificial Intelligence applications both in medical image segmentation and Extended reality-based medical solutions are discussed.

Respiratory viral infections (RVI) are common reasons for healthcare consultations. The inpatient management of RVI consumes significant resources. From 2009-2014, we assessed the costs of RVI management in 4776 hospitalized chil-dren aged 0-18 years participating in a quality improvement program, where all ILI patients underwent virologic testing at the National Reference Centre followed by de-tailed recording of their clinical course. The direct (medical or non-medical) and indirect costs of inpatient management outside the ICU (“non-ICU”) versus management requiring ICU-care (“ICU”) added up to 2,767.14€ (non-ICU) vs. 29,941.71€ (ICU) for Influenza, 2,713.14€ (non-ICU) vs. 16,951.06€ (ICU) for RSV infections, and 2,767.33€ (non-ICU) vs. 14,394.02€ (ICU) for human rhinovirus (hRV) infections, respectively. Non-ICU inpatient costs were similar for all 8 RVI studied: Influenza, RSV, hRV, ade-novirus (hAdV), metapneumovirus (hMPV), parainfluenzavirus (hPIV), bocavirus (hBoV) and seasonal coronavirus (hCoV) infections. ICU-costs for Influenza however, ex-ceeded all other RVI. At the time of the study, Influenza was the only RVI with antivi-ral treatment options available for children, but only 9.8% of Influenza patients (non-ICU) and 1.5% of ICU-patients with Influenza received antivirals; only 2.9% were vac-cinated. Future studies should investigate the economic impact of treatment and pre-vention of Influenza-, COVID-19-, and RSV post vaccine introduction.

Given its exceptional attributes, aerogel is viewed as a material with immense potential. Being a natural polymer, cellulose offers the advantage of being both replenishable and capable of breaking down naturally. Cellulose-derived aerogels encompass the replenish ability, biocompatible nature, and ability to degrade naturally inherent in cellulose, along with additional benefits like minimal weight, extensive porousness, and expansive specific surface area. Even with increasing appreciation and acceptance, the undiscovered possibilities of aerogels within the textile sphere continue to be predominantly uninvestigated. In this context, we outline the latest advancements in the study of cellulose aerogel formulation and their diverse impacts on textile formations. Drawing from the latest studies, we reviewed the materials used for the creation of various kinds of cellulose-focused aerogels and their properties, analytical techniques, and multiple functionalities in relation to textiles. This comprehensive analysis extensively covers the diverse strategies employed to enhance the multi-functionality of cellulose-based aerogels in the textile industry. Additionally, we focused on the global market size of bio-derivative aerogels, companies in the industry producing goods, and prospects moving forward.

Background: Citicoline, a medical food, lacks proven efficacy for ischemic stroke and has uncertainties around its harms. The continued prescription should be reevaluated given the more robust, high-quality Evidence against that medical food. This essay is based on a recent Cochrane review and uses only one all-cause mortality as the primary outcome. Therefore, the essay should be considered as something other than an updated Cochrane review but like a reflection based on Evidence-based Medicine and Philosophy of Science analysis. Question Research: Why citicoline (a medical food) should not be prescribed to treat people with acute ischemic stroke: The certainty of the Evidence.? Objective: Demonstrate from evidence-based medicine and philosophy of science perspective that citicoline should not be prescribed for acute ischemic stroke due to lack of efficacy and harm uncertainties. Search publications: We searched in PubMed and Cochrane Library from 2020 until 30 October 2023. We, furthermore, used engineering machines Bing and Google Scholar to detect additional papers. Additionally, we also reviewed reference lists of the retrieved publications and review articles and searched the websites of the U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA). Selection criteria: We included systematic reviews, meta-analyses, randomized clinical trials, clinical guidelines focused on acute ischemic stroke and comparing citicoline versus placebo or no intervention. We excluded narrative reviews, observational studies and ongoing trials. Data collection and analysis: We identified only new randomized clinical trials and assessed the risk of bias in seven domains. The other eight trials were already included in the mentioned Cochrane review. The systematic reviews with or without meta-analyses were assessed using McMaster University guidelines. We estimated risk ratios (RRs) for that outcome. We measured statistical heterogeneity using the I² statistic. We conducted our analyses using the fixed effect model. We did not use the GRADE approach due to what is shown in a Cochrane review published in 2020 by Martí-Carvajal et al. We used the RevMan 5.4 software from Cochrane Collaboration to conduct the forest plot. We used a Trial Sequential Analysis with Copenhagen Trial Unit Software. We estimated a Bayes factor from the relative risk and 95% confidence interval. Results: We identified only one new RCT (N = 99) reported mortality data and three clinical guidelines. We conducted a new meta-analysis with nine trials (N = 4461) having a high risk of bias and showing little to no difference in mortality between citicoline and placebo (17.1% vs 18.4%; RR 0.94, 95% CI 0.82 to 1.06; I2 = 0%). The Bayes factor was 0.7, indicating weak Evidence for the null over the alternative hypothesis. Trial sequential analysis suggested sufficiency of Evidence for mortality. No guidelines recommend citicoline. Conclusions: This essay reassessed citicoline for acute ischemic stroke after the 2020 Cochrane review. Adding a new RCT further supported the lack of mortality benefit with citicoline. The overall evidence quality could be better. Analyses using evidence-based medicine and philosophy of science approaches do not support prescribing citicoline due to a lack of efficacy substantiation and potential harms.

There are four categories of medical waste that cannot be mixed as this will cause serious problems such as environmental pollution or infection. In the past, the classification of medical waste often involved a lot of human and material resources to process, with workers at risk of exposure to infectious substances. Therefore, an augmented reality (AR) and artificial intelligence (AI) medical waste classification system and method were developed. This innovative medical waste classification system and method combines AR and AI identification technology to reduce the risk of manual judgment errors by clinical staff when handling medical waste.

This research employs a spatial optimization approach customized for addressing equitable emergency medical facility location problems, through the p-dispersed-median problem (p-DIME). The p-DIME integrates two conflicting classes of spatial optimization problems, dispersion, and median problems, aiming to identify the optimal locations for emergency medical facilities to achieve a balanced spatial distribution of emergency medical services (EMS) while effectively serving demands. To demonstrate the utility of the p-DIME model, we selected Gyeongsangbuk-do in South Korea, recognized as one of the most challenging areas for providing EMS to the elderly population (aged 65 and over). This challenge arises from the significant spatial disparity in the distribution of emergency medical facilities. The results of the model assessment gauge the spatial disparity of EMS, provide significantly enhanced solutions for a more equitable EMS distribution in terms of service coverage, and offer policy implications for future EMS location planning. In addition, to address the computational challenges posed by p-DIME's inherent complexity, involving mixed-integer programming, this study introduces a solution technique through constraint formulations aimed at tightening the lower bounds of the problem's solution space. The computational results confirm the effectiveness of this approach in ensuring reliable computational performance, with significant reductions in solution times, while still producing optimal solutions.

Wildfires are a rising environmental threat which health impact remains poorly documented, in particular beyond the fire period. This ecological study investigates changes in health conditions before, during, and after two consecutive wildfires that occurred in France´s Gironde district in the summer 2022. Electronic records from the district´s emergency medical communication centre were used to identify calls with respiratory and cardiovascular diseases, and two specific subgroups of those, asthma (all ages and 0-5 years) and cardiac arrest, respectively. Compared to before the fires, respiratory disease cases were fewer during each fire (OR = 0.85, 95% CI: 0.79, 0.90 and OR = 0.62, 95% CI: 0.58, 0.67, respectively), cardiovascular disease cases, more common during the second fire, and asthma cases, higher following the fires (ORall ages= 1.85, 95% CI: 1.56, 2.21 and ORchildren= 3.52, 95% CI: 2.62, 4.80). Asthma cases also rose during the same calendar time in 2021, but to a lesser extent. In sum, except for asthma, emergency calls data in Gironde reveal few significant potentially smoke-related symptoms around fire periods, apart from post-fire asthma. Whether the effects are smoke- or season-related remains to be determined, preferably using additional data sources.

Studying the distribution and transport dynamics of cations in plants is crucial for understanding their response mechanisms to saline-alkali stress conditions. However, our current understanding of how restoration measures affect cation distribution and transport in plants is surprisingly limited. To address this gap, we conducted a split-plot experiment using Medicago sativa L. cv. “Zhongmu No. 1” to investigate the combined effects of biological and chemical restoration measures—with bio-fertilizer as the primary zone and flue gas desulfurization (FGD) gypsum with humic acid as the secondary zone—on soil properties, plant growth, and the content, distribution, and transport of cations in plants. The results revealed that different restoration strategies decreased soil alkalinity and enhanced plant growth. Significantly, bio-fertilizer exhibited positive effects on plant growth and yield. On the contrary, FGD gypsum with humic acid reduced the soil's pH level, exchangeable sodium percentage, and sodium adsorption ratio, while increasing the contents of K+, Ca2+, and Mg2+ in the soil. The triple combination of FGD gypsum and humic acid with bio-fertilizer reduced Na+ transport in M. sativa by enhancing the selective absorption of beneficial ions, such as K+, Ca2+, and Mg2+ in leaves, and by facilitating the transport of Ca2+ and Mg2+ from roots and stems to the leaves. Therefore, the application of FGD gypsum and humic acid combined with bio-fertilizer has the potential to decrease soil alkalinity and the proportions of water-soluble ions in the soil, ultimately enhancing the translocation of key ionic components to leaves. This, in turn, increases the salt tolerance of plants and promotes their growth. Our results offer new insights into the interactions among measures, soil, and plants in saline-alkali land restoration, providing practical solutions for the restoration of saline-alkali soil.

Exudative-pleural-effusion (ePE) is a common presentation in pulmonology clinics. Pleural biopsy is indicated for identifying the aetiology in undiagnosed ePE especially the ones with low adenosine-deaminase levels. The access to rigid and semi-rigid medical thoracoscopy is scarce and heterogeneous in a resource limited country like India. In such circumstances, using flexible bronchoscope via intercostal-chest-tube for visualization of parietal pleura and pleural biopsy offers a way out. Here, in this retrospective study, we have presented our experience of such practice on 25 cases. Mean age was 52.4±1 years. Adhesions were present in 40% of the subjects. Most common finding was presence of nodules. On an average 6.8 passes were taken with maximum of 10. In 32% cases only tiny tissue was obtained, hence requiring multiple passes. Large tissue could be obtained in 44% of the subjects. Mean duration of the procedure was 35.6±9.0 minutes. Tissue diagnosis was established in 80% with most common being malignancy (48%) followed by tuberculosis (20%). In 3 cases, the final diagnosis was made by exclusion. No major complications were recorded. In conclusion, medical thoracoscopy using flexible video bronchoscope via intercostal-chest-tube was a feasible alternative for the diagnosis of ePE effusion in resource limited settings.

In response to global climate challenges and the increasing demand for energy, exploring renewable energy alternatives has become crucial. Bio-oils, derived from biomass pyrolysis, are emerging as potential replacements for fossil fuel-based liquid fuels. This paper shares findings from the Institute of Energy and Fuel Processing Technology on the quality of crude biomass pyrolysis bio-oil samples. These findings highlight their potential as motor liquid fuels. The article details the results of tests on the physicochemical properties of four distinct bio-oil samples. Additionally, it presents preliminary test results on the hydrodeoxygenation of bio-oils in a batch reactor. The production of homogeneous, stable mixtures using other fuel additives, such as diesel oil, rapeseed methyl ester (RME), and butanol, is also discussed.

Chitin/chitosan and collagen are two of the most important bioactive compounds with applications in the pharmaceutical, veterinary, nutraceutical, cosmetic, biomaterials, and other industries. When extracted from fish and shellfish non-edible parts, by-catches, and invasive species, their use contributes to a more sustainable and circular economy. The present article reviews the scientific knowledge and publication trends along the marine chitin/chitosan and collagen value-chains and assesses how researchers, industry players, and end-users can bridge the gap between scientific understanding and industrial applications. Overall, research on chitin/chitosan remains focused on the compound itself rather than its market applications. Still, chitin/chitosan use is expected to increase in food and biomedical applications, while that of collagen is expected to increase in biomedical, cosmetic, pharmaceutical, and nutritional applications. Sustainable practices, such as the reuse of waste materials, contribute to strengthen both value-chains; the identified weaknesses include the lack of studies considering market trends, social sustainability, and profitability, as well as insufficient examination of intellectual property rights. Government regulations, market demand, consumer preferences, technological advancements, environmental challenges, and legal frameworks play significant roles in shaping both value-chains. Addressing these factors is crucial for seizing opportunities, fostering sustainability, complying with regulations, and maintaining competitiveness in these constantly evolving value-chains.

In this paper we report on the use of an in-house developed solid-state hybrid microwave and radiofrequency generator operating at frequencies of 2.45 GHz and/or 480 kHz for cancer ablation in various tissues. The generator has demonstrated increased accuracy due to its custom-designed hardware, which incorporates the latest technologies and algorithms to control the emitted power versus temperature profile of the treated sample. In particular, the hybrid generator incorporates control systems based on impedance or reflected power measurements that allow controlled ablation without causing unwanted carbonisation and without including areas where tissue damage is not desired. Tests performed ex-vivo on chicken breast and bovine liver show that radiofrequency ablation (RFA) is very effective for performing controlled ablations with minimally invasive probes, such as cardiac pathologies, small lesions, tissues with particular composition, while microwave ablation (MWA) is optimal for performing large ablations in highly vascularised tissues, such as liver cancer, where it is necessary to achieve higher temperatures.

One of the challenges in the healthcare sector is making accurate forecasts across insurance years for claims reserve. The state-of-the-art envisages various methods for estimating the claims reserve but without being applied to systems that do not present huge variability and heterogeneity, such as the healthcare sector. The methodology is based on generalized linear models using the Overdispersed Poisson distribution as shown in the state-of-the-art. In this context, we developed a method to estimate the parameters of the quasi-likelihood function using a Gauss-Newton algorithm optimized through a genetic algorithm. The genetic algorithm plays a crucial role in glimpsing the position of the global minimum to ensure a correct convergence of the Gauss-Newton method, where the choice of the initial guess is fundamental. The proposed methodology has been entirely developed using MATLAB and has been applied to estimate the claims reserve in the healthcare system of the Tuscany Region in Italy, as case study. The results obtained were validated by comparing them with the state-of-the-art by measuring the confidence intervals of the Overdispersed Poisson distribution parameters. Therefore local healthcare authorities could use the proposed and improved methodology to allocate the resources dedicated to healthcare and global management.

Biomass as a whole offers a more diverse potential for valorisation than any other renew-able energy source. As one of the stages in the separation of bio-oil involves the liquid mixture of acetol and acetic acid, and as both components are particularly well suited for valorisation, a hybrid method has been developed for their separation with a high purity level through an approach combining liquid-liquid extraction and rectification. In order to design and simulate the flowsheet, the ChemCAD 7.0 simulation software has been used. Sensitive analyses have been carried out to investigate the influence of the different pa-rameters in the rectification columns such as the reflux ratio, the feed stage location, and the vapour/bottom molar flow ratio. The effect of different extractants and of their excess on the separation process as well as the possibility of regenerating the extractant has also been studied. Tri-n-octylamine has accordingly been selected as a separating agent which has been fully recycled. The end result for separating an initial 48/52 wt% ace-tol/acetic-acid liquid mixture has been acetol with a purity of 99.4 wt% and acetic acid with a purity of 100 wt%.

Alzheimer's disease (AD) is a progressive and evolving neurodegenerative disease with an insidious onset that can lead to memory loss and cognitive impairment. There is no effective treatment for this disease. However, early diagnosis plays an important role in treatment planning to slow down its progression, as treatment has the greatest impact in the early stages of the disease. Neurological images obtained through different imaging techniques provide powerful information and help diagnose the disease. With the wide application of deep learning techniques in disease diagnosis, especially the prominence of Convolutional Neural Networks (CNNs) in computer vision and image processing, more and more studies are proposing the use of this algorithm for the diagnosis of AD. In this paper, we first systematically introduce the impact of AD on people, detailing the biomarkers, early clinical symptoms, and risk factors of this disease. Secondly, it goes on to detail the development of CNNs, their form, and methods to help diagnose AD. It is proposed that CNNs can help diagnose AD by analyzing medical imaging data, particularly structural brain scans such as magnetic resonance imaging (MRI) and functional scans such as positron emission tomography (PET). Finally, it is concluded that CNNs are of great importance for the diagnosis of AD and that they are likely to play an increasingly important role in the early detection of the disease, the understanding of disease mechanisms, and ultimately, in the development of effective AD therapies and interventions. CNNs are playing an increasingly important role in the Their potential impact on healthcare emphasizes the importance of continued research and innovation in neural networks and medical imaging.

In recent years, antibacterial coatings have become a significant approach in the global fight against bacterial pathogens. Developments in materials science, chemistry and biochemistry have led to a plethora of materials and chemical compounds that have potential for creating antibacterial coatings. Nonetheless, there has been insufficient consideration given to the analysis of the techniques and technologies used to apply these coatings. Among the various inorganic coating techniques, Atomic Layer Deposition (ALD) is noteworthy. It enables the successful synthesis of high-purity inorganic nanocoatings on surfaces of complex shape and topography, while also providing precise control over their thickness and composition. ALD has various industrial applications, but its practical application in medicine is still limited. In recent years, a substantial number of papers have been published on the suggested implementation of thin films and coatings produced through ALD in medicine, notably those with antibacterial properties. The aim of this paper is to carefully evaluate and analyse the relevant literature on this subject. Simple oxide coatings, including TiO2, ZnO, Fe2O3, MgO, and ZrO2, were examined, as well as coatings containing metal nanoparticles such as Ag, Cu, Pt, and Au, and mixed systems such as TiO2-ZnO, TiO2-ZrO2, ZnO-Al2O3, TiO2-Ag, and ZnO-Ag. Through comparative analysis, we have been able to draw conclusions on the effectiveness of various antibacterial coatings of different compositions, including key characteristics such as thickness, morphology, and crystal structure. ALD has been analysed in its use for developing antibacterial coatings for various applications. Furthermore, assumptions regarding the most promising development areas have been presented.

Cancer is the second major cause of disease-related dead worldwide, and its accurate early diagnosis and therapeutic intervention are fundamental for saving the patient’s life. Cancer, as a complex and heterogeneous disorder, results from disruption and alteration of a wide variety of biological entities, including genes, proteins, mRNAs, miRNAs, and metabolites that eventually emerge as clinical symptoms. Traditionally, diagnosis is based on clinical examination, blood tests for biomarkers, histopathology of biopsy, and imaging (MRI, CT, PET, US). Additionally, omics biotechnologies help to further characterize the genome, metabolome, microbiome traits of the patient that could have an impact on the prognosis and patient’s response to the therapy. The integration of all these data relies on gathering of several experts and may require considerable time, and, unfortunately, it is not without the risk of error in the interpretation and therefore in the decision. Systems biology algorithms exploit Artificial Intelligence (AI) combined with omics technologies to perform a rapid and accurate analysis and integration of patient’s big data and support the physician in making diagnosis and tailoring the most appropriate therapeutic intervention. However, AI is not free from possible diagnostic and prognostic errors in the interpretation of images or biochemical-clinical data. Here, we first describe the methods used by systems biology for combining AI with omics and then discuss the potential, challenges, limitations, and critical issues in using AI in cancer research.

In this paper we introduce the concept of Nano Beta Closure, Nano Beta Interior and study the relationship between them also study some of their properties. Based on these notions we introduced the topological Approach to Decision Making and Its Application in the Medical Field" which describes the use of topological methods in decision-making, specifically through the utilization of nano beta closure and nano beta interior. These mathematical tools are applied in the medical field to improve diagnostic accuracy and aid in treatment planning. The authors demonstrate the effectiveness of this approach through a case study on the diagnosis of Chronic Kidney. The results show that the topological approach using nano beta closure and nano beta interior provides a reliable and accurate method for medical decision-making. Finally, we introduce a medical application for disease groups patients of with chronic kidney. To identify the main causative agent of kidney disease. where using this approach, the medical team can identify the specific areas of the kidney that are affected by the infection, as well as the extent of damage and any potential complications. This information is used to create an individualized treatment strategy that is customized to meet the specific requirements of the patient. and medical history. We write an algorithm that can be a helpful tool for doctors to utilize in diagnosing infection disease Chronic Kidney infections. By utilizing this approach, clinicians can provide accurate diagnoses, develop effective treatment plans, and ultimately improve patient outcomes. finally, we show that fractals hold significant importance in future nano topological generation of the human factor. Their unique properties and self-similar patterns offer insights into the complex organization of biological systems and provide a foundation for designing advanced Nanosystems.

Green synthesis offers a superior alternative to traditional methods for producing metal and metal oxide nanoparticles. This approach is not only benign and safe but also cost-effective, scalable, and straightforward, operating under ambient conditions. Notable metals and metal oxide nanoparticles, such as manganese oxides, iron oxides, silver, and gold, have been produced using various bio-reductants derived from plant extracts. These biological agents not only expedite the reduction process but also stabilize the nanoparticles, serving dual roles as reducing and capping agents. This review presents the green synthesis of nanoparticles (NPs) obtained from biogenic wastes and plant extracts. The green-synthesized nanostructured MnO2 are evaluated as a potential photocatalyst for water treatment and as an electrode material in lithium-ion batteries and supercapacitors. The green-derived iron oxide nanoparticles are examined as promising antioxidant, anti-inflammatory, and anti-diabetic agents. Additionally, this review discusses the green synthesis of precious metal nanoparticles, specifically silver (Ag NPs) and gold (Au NPs), highlighting their potential medical applications in areas like antiviral treatments and cancer therapy.

The contemporary landscape of eHealth is characterized by major concerns related to electronic medical records (EMRs). In an EMR, critical patient health information gets its own repository, which is the core of the eHealth system. Notably, the EMR not only retrieves health information generated in ambulatory clinical settings but also includes court-generated records. These overlapping records assume the role of a foundational data source for a comprehensive electronic health record (EHR). This scholarly review sheds light on recent trends in medical records and associated challenges, while at the same time providing insights into Saudi Arabia’s targeted approaches to healthcare delivery. Additionally, the talk expands to include a description of blockchain technology and its underlying conceptual framework. This demonstration positions blockchain technology as a promising approach that requires consideration for future use in the healthcare system.

Unexpected preoperative surgery cancellations are one of the important problems in daily anesthesia practice. It's an even bigger problem if these cancellations happen in the operating room. Our aim was to draw attention to this issue and reveal the reasons. This prospective, cross-sectional study aimed to assess the prevalence and causes of elective surgery cancellations once patients are in the operating room. The study sample consisted of 7482 adult patients scheduled for elective surgeries and taken to the operating room. Sixty-seven patients whose surgeries were canceled were grouped into Group 1, while the remaining 7415 whose surgeries were completed were in Group 2. Patients were divided into two groups based on whether their surgeries were completed or canceled. Factors such as age, American Society of Anesthesiologists physical status, and surgical department were analyzed. Metrics involved comparisons between the two groups regarding age, American Society of Anesthesiologists physical status classification, surgical department, and timing of the surgery. The prevalence of elective surgery cancellations after patients were in the operating room was 0.9%. Group 1 was significantly older than Group 2 (p<0.001). The proportion of patients classified as American Society of Anesthesiologists physical status III was higher in Group 1 (p<0.001). The department with the highest cancellation rate was ophthalmology(2.5%), followed by general surgery(2.1%), urology(1.5%), and ear, nose, and throat(1.4%). Approximately 59.7% of cancellations were deemed potentially avoidable. The study suggests that many of these cancellations are potentially avoidable, underscoring the need for optimized surgical scheduling and patient assessment protocols.

Tomato quality is intricately regulated by a combination of factors, including the presence of bioactive compounds referred to as secondary metabolites and various organoleptic characteristics. These attributes are notably influenced and harmonized by the specific growing conditions, with a particular emphasis on the type of fertilization employed. Traditionally, chemical fertilizers have been favoured in crop cultivation due to their cost-effectiveness and ability to accelerate crop growth. However, in pursuit of sustainable and intelligent agricultural practices, there is a growing need for alternative fertilizers. In this context, the present study aimed to assess the impact of fertilizers derived from waste materials, specifically sulphur bentonite and orange residue (referred to as SB), on tomato quality. This assessment extended to examining qualitative and quantitative alterations in aroma-related volatile compounds and the antioxidant systems of tomatoes, in comparison to the conventional use of fertilizers such as horse manure and NPK (nitrogen, phosphorus, and potassium). The results obtained revealed distinct effects of different fertilizers on tomato quality. Notably, parameters such as TPRO (total protein), TCARB (total carbohydrate), LIC (lycopene content), TCAR (total carotenoid content), total phenols, total flavonoids, and aroma profiling exhibited significantly superior values in the group treated with SB fertilizer. These findings strongly suggest that the novel fertilizer functioned as a bio-stimulant, enhancing the nutraceutical and sensory attributes of tomatoes, with a pronounced impact on the synthesis of secondary metabolites and the aroma profile of the fruits.

A systematic and comprehensive review of critical applications of Blockchain Technol-ogy with Differential Privacy integration lies within the privacy and security enhancement. This paper aims to highlight the research issues in the e-health domain (e.g., Electronic Medical Rec-ords) and to review the current research directions in Differential Privacy integration with Blockchain Technology.(1) Background: The current state of the art in the e-health domain is identified as follows: (a) healthcare information poses a high level of security and privacy concerns due to its sensitivity; (b) due to vulnerabilities surrounding the healthcare system, a data breach is common and presents a risk for attacks by an adversary; and (c) the current privacy and security apparatus needs further fortification. (2) Methods: The methodology uses a systematic literature review (SLR) to identify and select relevant research papers and academic journals in DP and BT. (3) Results: The results are categorized into: e-Health Record Privacy, Real-Time Health Data, and Health Survey Data Protection to identify inherent issues with Differential Privacy integra-tion with Blockchain and technical challenges.(4) Conclusion: This review thoroughly surveyed and summarized Differential Privacy mechanisms in EMR privacy, real-time health data, and health survey data protection while highlighting challenges.

This work demonstrates that microbial fuel cells (MFCs), optimized for energy recovery, can be used as an effective tool to detect the presence of antibiotics in water-based environments. MFCs directly convert chemical energy of organic matter in electrical energy thanks to electroactive biofilms. The efficiency of the conversion process can be significantly affected by the presence of contaminants acting as toxicants for the biofilm. In the present work, we demonstrate that MFCs can successfully detect the presence of antibiotic residues in water and water-based electrolytes associated to the food industry, especially using honey as a new and more complex electron donor. The effectiveness of MFCs to sense antibiotics is here demonstrated for tetracycline that was added to both water and water/honey electrolytes with a minimum concentration close to 3.5 μg⁄kg. MFCs not only efficiently detect the presence of tetracycline in both the electrolytes, but also recover the same performance after each cycle of exposure, showing to a be very robust and reliable technology for both biosensing and energy recovery.

Malaria is one of the life-threatening disease caused by the Anopheles mosquitoes affecting the human red blood cells. It is the global health concern which is quite common in tropical as well as sub-tropical parts of the World. Therefore, it is an important to have an effective computer aided system in place for early detection and treatment. State of the art machine learning techniques are used to detect the infected malaria cells from the pool of images. As the visual heterogeneity of the malaria dataset is highly complex and dynamic, therefore higher number of images are needed to train the machine learning models effectively. However, hospitals as well as medical institutions do not share the medical image data for collaboration due to general data protection regulation (GDPR) and data protection act (DPA). To overcome this collaborative challenge, our research is inspired to use the real-time medical image data while using the framework of federated learning (FL) framework that will ensure the data privacy while mutual collaboration of hospitals and medical institutes. We have used the state of the art machine learning models that include the Resnet50 and densenet in a federated learning framework. We have experimented both models in different settings and our preliminary results showed that the densenet model performed better in accuracy (75%) in contrast to resnet50 (72%) while considering 8 clients, while the trend is observed common in 4 clients with the similar accuracy of 94% and 6 client showed that the densenet model performed quite well with the accuracy of 92% while resnet50 achieving only 72%.

Background The workload of nurses in the intensive care unit (ICU) can affect the quality of nursing services. Aim This study aimed to determine the relationship between the nursing activity score and missed care in patients hospitalized in the ICU in Zanjan, Iran. Methods This observational and prospective study was conducted from April 3 to September 18 in 2021. In this study, the tools used included a patient and nurses' profile questionnaire, Nursing Activity Score (NAS). Missed care was observed in the 301 patients for whom the NAS was calculated. Analysis of variance (ANOVA) was used to investigate the differences in mean levels of missed care. Logistic regression models were used to assess the association between factors and missed nursing care. Results Results show that the Medical ICU's mean NAS was 76.31 (95% CI: -13.06 -14.89). In 9 dimensions of care, the extent of missed care was 40.7%. In the care dimensions of assessment, hand hygiene, and infection control, the mean NAS had a statistically significant increase at higher levels of missed care (P<.5). Furthermore, work experience was identified as a protective factor for missed care (OR =.59, 95% CI: .37–.94, χ2= =4.97, p =.026). Conclusion The study revealed a high incidence of missed care. The study revealed that the mean workload was high in certain dimensions of care such as assessment, hand hygiene, and infection control. The increase in workload for nurses results in lost care. Nonetheless, the utilization of experienced nurses can help mitigate this problem. However, utilizing experienced nurses can help reduce this problem.

Understanding the genetic architecture of a disease is crucial for development of valid diagnostic and therapeutic interventions. The analysis of genomic variations associated with pathological conditions is the starting point for uncovering disease-causing pathways (candidate processes). However, the complexity of intergenic and genetic-environmental interactions hinders the identification of pathogenic values of genomic changes. Furthermore, heredity, epigenetics and somatic mosaicism make the interpretation of genomic data even more sophisticated. To succeed, a variety of bioinformatic techniques are applied. Here, reviewing own and literature data, knowledge-based prioritization of genomic variations is described. Theoretical basis of the knowledge-based prioritization is given with a special regard to gene ontology, heuristics, hermeneutics (genomic hermeneutics) and analytics. Practical and methodological issues of prioritization using ontology- or pathway-based systems analysis are considered in the light of optimistic and realistic scenarios of cumulative phenotypic effects of the variome (the whole set of genomic variations in an individual or specific set of genomic variations for a phenotypic outcome). In the present communication, copy number variants (CNVs) in children with neurodevelopmental diseases are used as a practical foundation for the prioritization, inasmuch as these genome variations are systematically overlooked in the so-called NGS era. Nonetheless, it is highly likely that the prioritization is applicable to almost all types of genomic variations (e.g. chromosome abnormalities, gene mutations, functional synonymous variants etc.). The present methodology seems to be a valuable addition to current biomedical science widening the opportunities for medical genomics and genetics.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present cable-driven reconfigurable robot kinematics and statics. We studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. Likewise, we propose a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporate a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We reconfigure the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Likewise, we also computed the workspace to show that the reconfigurable design fits several sizes. The data used is from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators.

Background: Advances in additive manufacturing and the development of antimicrobial biopolymers facilitate the production of critical medical devices with strong and safe biocidal properties. The purpose of this study was to assess the antimicrobial efficacy, safety, and longevity of a polylactic acid-based biopolymer supplemented with a copper-based composite additive. Methods: An antimicrobial polylactic acid-based biopolymer was tested against several inoculants including Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Material safety was evaluated according to international testing standards for in vitro cytotoxicity. Results: The main findings of the present investigation showed a strong and long-lasting biocidal effect of a polylactic acid-based biopolymer embedded with a copper-based composite additive against Staphylococcus Aureus, MRSA, E. coli, Listeria, HCoV-229E (a SARS-CoV-2 surrogate), and HIV-1. Furthermore, the cytotoxicity and safety assessment of the antimicrobial biopolymer was found to be “non-toxic” and safe for human skin contact. Conclusions: The present investigation showed that the antimicrobial biopolymer exhibits strong and long-lasting biocidal properties against an array of viral and bacterial inoculants.

In this study, a field experiment was conducted to evaluate the growth and yield responses of Sri Lankan lowland rice (Oryza sativa L.) with the application of beneficial Arbuscular mycorrhizal fungi (AMF) inoculum, and inter-cropping with highly mycorrhizal dependent vetiver grass (Chrysopogon zizanioides L.) under two different soil nutrient management systems (NMSs): conventional/chemical (CNMS) and organic (ONMS). The experiment was designed as a split plot with three blocks. Each CNMS and ONMS experiment included untreated control (T0), and three treatments—AMF inoculation (T1), vetiver intercropping (T2), and the combination of AMF and vetiver (T3). According to the results, colonization of rice roots with AMF was not affected significantly by the treatments and ranged from 0‒15.8%. The effect was very low or absent in the early stage and then higher in the later stages of the rice plant. Furthermore, plant growth was not significantly different between the two NMSs, although grain yield was significantly higher (P &lt; 0.05), with the order T1 (0.45 kg/m2) &gt;T2 (0.42 kg/m2) &gt;T3 (0.41 kg/m2) in CNMS and T2 (0.44 kg/m2) &gt;T1 (0.41 kg/m2) &gt;T3 (0.40 kg/m2), in ONMS than for the respective controls (T0), thus suggesting beneficial utilization of AMF and vetiver in the lowland rice farming system.

Antimicrobial peptides (AMPs) have emerged as a promising class of bioactive molecules with the potential to combat infections associated with medical implants and biomaterials. This review article aims to provide a comprehensive analysis of the role of antimicrobial peptides in medical implants and biomaterials, along with their diverse clinical applications. The incorporation of AMPs into various medical implants and biomaterials has shown immense potential in mitigating biofilm formation and preventing implant-related infections. We review the latest advancements in biomedical sciences and discuss the AMPs that were immobilized successfully to enhance their efficacy and stability within the implant environment. We also highlight successful examples of AMP coatings for the treatment of surgical site infections (SSIs), Contact lenses, Dental Applications, AMP-Incorporated Bone Grafts, Urinary tract infections (UTIs), Medical Implants, etc. Additionally, we discuss the potential challenges and prospects of AMPs in medical implants, such as effectiveness, instability and implant-related complications. We also discuss strategies that can be employed to overcome the limitations of AMP-coated biomaterials for prolonged longevity in clinical settings.

Coronary artery disease is the most prevalent cardiovascular disease, claiming millions of lives annually around the world. The current treatment includes surgically inserting a tubular construct called as a stent inside arteries to restore blood flow. However, due to lack of patient-specific design, the commercial products cannot be used with different vessel anatomies. In this review, we have summarized the drawbacks in existing commercial metal stents which face problems of restenosis and inflammatory responses, owing to the development of neointimal hyperplasia. Further, we have highlighted the fabrication of stents using biodegradable polymers, which can circumvent most of the existing limitations. In this regard, we elaborated on utilization of new fabrication methodologies based on additive manufacturing such as three-dimensional printing to design patient-specific stents. Finally, we have discussed on the functionalization of these stent surfaces with suitable bioactive molecules which can prove to enhance their properties in preventing thrombosis and better healing of injured blood vessel lining.

In recent years, semiconductor survey meters have been developed and are in increasing demand worldwide. This study aimed to investigate whether it is possible to calculate the time constant of the semiconductor survey meter using the X-ray equipment installed in each medical facility and whether it is possible to calibrate the semiconductor survey meter. Attach an additional filter to the medical X-ray system to satisfy the standards of N-60 to N-120, add more copper plates from there, and calculate the first and second half-value layers to compare with N-60 to N-120 values. Next, we will measure the leakage dose using a medical X-ray system and calculate the time constant of the survey meter. The survey meter is then calibrated and compared with the calibration factors in industrial X-ray system. Per experimental results, it is possible to reproduce the N-60 to N-120 radiation quality using a medical X-ray system and to calculate the time constant from the measured results assuming actual leakage dosimetry using that radiation quality. We also found that the calibration factor was equivalent to that of an industrial X-ray device. It was revealed that semiconductor survey meters can be calibrated using a medical X-ray system.

This paper presents the concept of a novel adaptable sensing solution currently being developed under the EU Commission founded PHOTONGATE project. This concept will allow to quantify multiple analytes of the same or different nature (chemicals, metals, bacteria, etc.) in a single test with levels of sensitivity and selectivity at/or over those offered by current solutions. PHOTONGATE relies on two core technologies: a bio-chemical technology (molecular gates) which will confer the specificity and, therefore, the capability to be adaptable to the analyte of interest, and which combined with porous substrates will increase the sensitivity, and a photonic technology based on Local Surface Plasmonic Resonance (LSPR) structures serves as transducer for light interaction. Both technologies are in the micron range, facilitating the integration of multiple sensors within a small area (mm²). The concept will be developed for its application in health diagnosis and food safety sectors. It is thought as an easy-to-use modular concept, which will consist of the sensing module, mainly of a microfluidics cartridge that will house the photonic sensor, and a platform for fluidic handling, optical interrogation, and signal processing. The platform will include a new optical concept, fully Europe Union Made, avoiding optical fibers and expensive optical components.

Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

Based on the Flashing Ratchet Potential (FRP) and Electro-Spectroscopy detection techniques, a new detection method was experimentally examined. This method allows the detection of sub-micro particles in the human exhaled air such as viral particles and other biological agents. The detection signal created by this method is carried out by electron current buildup forming an Electric Current-Spectrum (ECS) distinguishing the contents of the exhaled air samples. The detection method was developed based on FRP and a beam of free charges passing through the electrodes of the FRP. The ECS is characterized by low-energy free charges interacting with the medium through which they pass, where numerous free charges are scattered (delayed) as a result of these interactions. The resulting ECS is characterized by FRP's capacity to drift back the delayed free charges to their original starting positions. By comparing the ECS of two exhale air samples, this new technology can be utilized to identify the presence of viral and microorganisms’ particles. The time-escalating ECS difference quickly indicates the presence of such particles. Moreover, the FRP's optimal operating settings was experimentally defined, in addition to the experimental evaluation of its functionality to confirm the FRP's promising sensing capabilities, which were statistically examined several times to ensure the reproducibility of the results.

The optical complexity of coastal waters is mostly caused by the water discharged from land carrying optical components (such as dissolved and particulate matter) into coastal bays and estuaries, and increasing the attenuation of light. This paper aims to investigate the links between in-water optical properties in four Swedish bays (from the northern Baltic proper up to the Bothnian bay) and the land use and land cover (LULC) and the hydrology in the respective catchment of each bay. The optical properties were measured in situ over the last two decades by various research and monitoring groups while the LULC in each bay was classified using the Copernicus Land Monitoring Service based on Landsat 8 and Sentinel-3 data. The absorption coefficient of coloured dissolve organic matter at 440 nm, aCDOM (440), and its spectral slope factor, SCDOM, were mostly correlated to natural land cover classes (Wetland, Meadow) acting as sources of CDOM, while Agricultural and Urban classes seem to act as sinks. The Agricultural class was also found to be a sink for suspended particulate organic matter (SPOM) whilst Coniferous and Mixed Forests as well as Meadows acted as a sources. SPOM seems to mostly originate from Natural classes, possibly due to the release of pollen and other organic matter. Overall, the methods applied here allow for a better understanding of effects of land used and land cover on the bio-optical properties, and thus coastal water quality, on a macroscopic scale.

Deep Neutral Networks (DNNs) were initially proposed towards the midst of the 20th century, motivated by the neural structure and mechanism of the human brain. Thanks to major ad-vancements in computational resources, during the past decade DNN-based systems have demonstrated unprecedented performance in terms of accuracy and speed. However, recent work has shown that such models may not be sufficiently robust during the inference process. Furthermore, due to the data-driven learning nature of DNNs, designing interpretable and gen-eralizable networks is a major challenge, especially in critical applications, such as medical Computer Aided Diagnostics (CAD) and other medical imaging tasks, including classification, regression and reconstruction. Within this context, a line of physics-driven approaches for deep learning has recently emerged, aimed at improving the stability and generalization capacity of DNNs for medical imaging applications. In this paper, we review recent work focused on phys-ics-driven or prior-information learning for a variety of imaging modalities and medical applica-tions. We discuss how the inclusion of domain-related knowledge into the learning process and networks’ design supports their stability and generalization capability. In addition, we highlight current and future challenges within this scope.

During the pandemic, nurses experienced anger that stemmed from a sense of threat, frustration or even a sense of injustice. The purpose of this study was to examine the relationship between vaccination hesitancy, anger, cynicism and medical mistrust among nurses, as there are no relevant studies in the literature. The study was conducted online by completing self-report questionnaires. The Dimensions of Anger Reactions-5, the 8-item "Cynical Distrust" scale and the Medical Mistrust Multiformat Scale were used. For vaccination hesitancy, two questions with a 5-point scale were used; one question examining hesitancy to get vaccinated with the COVID-19 vaccine, and another question examining hesitancy to get vaccinated with the influenza vaccine. 387 nurses (66 men and 321 women) participated in the study. Nurses showed statistically greater hesitancy for the COVID-19 vaccine compared to hesitancy for the influenza vaccine. The variation in Vaccine Hesitancy was explained by the scores in the Medical Mistrust Multiformat Scale, the Dimensions of Anger Reactions and the Cynical Distrust Scale. The Medical Mistrust Multiformat Scale mediated the relationship between Cynical Distrust Scale and Total Vaccine Hesitancy. The Dimensions of Anger Reactions Scale significantly moderated the indirect effect of Cynical Distrust Scale on Total Vaccine Hesitancy through the Medical Mistrust Multiformat Scale. In conclusion, it is highly likely that anger is involved in reported vaccine hesitancy both by activating schemas of distrust in others and by adopting antisystemic views of mistrust in the medical system.

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to the emergence of increasingly sophisticated large language models (LLMs) enabling their use in healthcare. The study assesses the performance of two LLMs: the GPT-3.5 and GPT-4 models in passing the medical examination for access to medical specialist training in Spain MIR. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. We studied the 2022 Spanish MIR examination after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM ChatGPT4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length and question sequence d performance. GPT-4 outperformed GPT -3.5, scoring 86.81% in Spanish (p<0.001). English translations had a slightly enhanced performance. Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, with specialties like Pharmacology, ICU, and Infectious Diseases showing lower performance. The error analysis revealed that while a 13.2% error rate existed, gravest categories like "error requiring intervention to sustain life" and "error resulting in death" had a 0% rate. Conclusions: GPT-4 performs robustly on the Spanish MIR examination, varying its capability to discriminate knoweldge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implication on patient safety.

Series of bifunctional catalysts, MoS2/Al2O3(70 wt.%)-zeolite (30 wt.%) (zeolite – ZSM-5, ZSM-12, ZSM-22 and silica aluminophosphate SAPO-11), were synthesized for hydroconversion of methyl palmitate (10 wt.% in dodecane) in a trickle-bed reactor. Mo loading was about 7 wt.%. Catalyst and supports were characterized by different physical-chemical methods (HRTEM-EDX, SEM-EDX, XRD, N2 physisorption, FTIR spectroscopy). Hydroprocessing was performed at temperature of 250-350°C, hydrogen pressure 3.0-5.0 MPa, liquid hourly space velocity (LHSV) 36 h-1, H2/feed ratio – 600 Nm3/m3. Complete conversion of oxygen-containing compounds was achieved at temperature 310°C in the presence of MoS2/Al2O3-zeolite catalysts, the selectivity for the conversion of methyl palmitate via the ‘direct’ hydrodeoxygenation (HDO) route was over 85%. The yield of iso-alkanes gradually increases in order: MoS2/Al2O3 &lt; MoS2/Al2O3-ZSM-12 &lt; MoS2/Al2O3-ZSM-5 &lt; MoS2/Al2O3-SAPO-11 &lt; MoS2/Al2O3-ZSM-22. The sample MoS2/Al2O3-ZSM-22 demonstrated the highest yield of iso-alkanes (40%). Hydroisomerization activity of the catalysts was in a good correlation with concentration of Brønsted acid sites of synthesized supports.

Castor oil may be differentiated from other non-edible vegetable oils because of its main composition of hydroxylated fatty acids. Ricinoleic acid comprises 80–90% wt. of fatty acids in castor oil (Ricinus communis). In this study, the thermo-oxidative stability and tribological behavior of bio-based lubricant samples synthesized from castor oil using isoamyl alcohol were evaluated. Initially, the compositional and physicochemical properties of the obtained samples were assessed using 1H NMR, FTIR, and ASTM methods. Oxidative stability of the samples was evaluated using Rancimat method at 110 °C under air flow. The final biolubricant sample (BL2), obtained after esterification, epoxidation, and oxirane rings opening reactions, presented an oxidation stability time (OST) of 14.3 h. The thermal stability was also evaluated by thermogravimetry (TG) from the mass variations under inert and oxidative atmosphere. BL2 showed higher thermal stability compared to the other samples, demonstrating higher decomposition temperatures in both inert (339.04 °C) and oxidative (338.47 °C) atmospheres, for a mass loss of 50%. The tribological properties of the samples were evaluated using a four-ball tribometer configuration. The BL1 and BL2 samples exhibited lower friction coefficients than the mineral oil sample (MOS) by 21.5% and 43.1%, respectively. Regarding wear, the observed wear scar diameter (WSD) was also lower in BL1 and BL2 compared to MOS by 5.2% and 40.4%, respectively. The results of the tribological evaluation suggest that both samples obtained in this study have promising potential for applications in lubricating machines and mechanical systems.

The growing dependence on large language models (LLM)s highlights the urgent need to deepen trust in these technologies. Regular, rigorous validation of their expertise, especially in nuanced and intricate scenarios, is essential to ensure their readiness for clinical applications. Our study pioneers the exploration of LLM utility in the field of cardiology. We stand at the cusp of a transformative era where mature AI and LLMs, notably ChatGPT, GPT-4, and Google Bard, are poised to influence healthcare significantly. Recently, we put three available LLMs, OpenAI's ChatGPT-3.5, GPT-4.0, and Google's Bard, to the test against a significant Polish medical special-ization licensing exam (PES). The exams cover the scope of completed specialist training, focusing on diagnostic and therapeutic procedures, excluding invasive medical procedures and interven-tions. In our analysis, GPT-4 consistently outperformed the others, ranking first, with and Google Bard and ChatGPT- 3.5 following, respectively. The performance metrics underscore GPT-4's no-table potential in medical applications. Given a score improvement of over 23.5 % between two AI models released just four months apart, clinicians must stay informed and up-to-date about these rapidly evolving tools and their potential applications to clinical practice. Our results provide a snapshot of the current capabilities of these models, highlighting the nuanced performance dif-ferences when confronted with identical questions

Platinum group metals (PGMs) high-grade ore tonnage from Merensky reef keeps on depreciating forcing the metallurgical industry to explore beneficiating PGMs from upper group 2 (UG2) ore which has up to 60% content of chromite and from secondary sources. The PGMs are naturally associated with base metal sulphides and the efficacy of the PGMs extraction processes is dependent on the processing of PGMs-bearing base metal sulphides. These processes include concentrating PGMs-bearing base metal sulphides through flotation, oxidation, etc. prior the metal extraction processes. The evolving mineralogy of PGMs ores has necessitated the use of multiple reagents to enhance the efficiency of the concentration process. This has led to an increased amount of inor-ganic chemicals disposal to the environment and the processing of PGMs to be less economic. Bio-technology has been investigated as a potential low cost, ecologically safe substitute for many current minerals processing methods. The metabolites produced by microorganisms have been used for many years with great success in the leaching and flotation of metals from medium and low-grade sulphide minerals. The possible application of microorganism mainly Acidithiobacillus ferrooxidans (A. ferrooxidans) in the metallurgical processing of PGMs-bearing base metal sulphides is systematically reviewed in this paper. The main emphasis is on the use of A. ferrooxidans in bio-oxidation and bioleaching, as well as their potential to substitute inorganic reagents in the flo-tation of PGMs-bearing base metal sulphides from primary and secondary sources

With the development of bioinspired green solutions for sustainable construction over the past two decades, bio-cementation, which exploits the naturally occurring phenomenon of calcium carbonate precipitation in different environments, has drawn a lot of attention in both building construction and soil stabilization. Various types of microorganisms, along with specific enzymes derived from these microorganisms, have been utilized to harness the benefits of bio-cementation. Different application methods for incorporating this mechanism into the production process of the construction material, as well as a variety of experimental techniques for characterizing the outcomes of bio-cementation, have been developed and tested. Despite the success of bio-cementation as a sustainable method to construction has been demonstrated in a significant body of literature at the laboratory scale, the expansion of this strategy to construction sites and field application remains a pending subject. The issue may be attributed to two primary challenges. Firstly, the complexity of the bio-cementation phenomenon is influenced by a variety of factors. Secondly, the extensive body of literature examines various types of microorganisms under different conditions, leading to a wide range of outcomes. Hence, this study aims to examine the recent advancements in utilizing the most commonly employed microorganism, Sporosarcina Pasteurii, to emphasize the significance of influential factors identified in the literature, discuss the findings that have been brought to light, and outline future research directions toward scaling up the process.

Historically, admission of hematological patients in the ICU shortly after the start of a critical illness is associated with better survival rates. Early intensive interventions administered by MET could have a role in the management of hematological critically ill patients, eventually reducing ICU admission rate. In this retrospective and monocentric study, we evaluate the safety and effectiveness of intensive treatments administered by the MET in a medical ward frame. The administered interventions were mainly helmet CPAP and pharmacological cardiovascular support. Frequent reassessment by the MET at least every 8 to 12 hours was guaranteed. We analyze data from 133 hematological patients that required MET intervention. In hospital mortality was 38%; mortality doesn’t increase in patients not immediately transferred to the ICU. Only 3 patients died without a former admission in ICU; in these cases, mortality was not related to the acute illness. Moreover, 37% of patients overcame the critical episode in the hematological ward. Higher SOFA and MEWS scores were associated with a worst survival rate, while neutropenia and pharmacological immunosuppression were not. The MET approach seems to be safe and effective. SOFA and MEWS confirmed to be effective tools for prognostication.

The production of oxygenated bio-additives for traditional fuels represents a key challenge due to the depletion of fossil fuels in the next future and their contribution to environmental pollu-tion. In this context, the present study considers the synthesis of different mixtures of 1-hexanol/hexyl hexanoate produced through the catalytic hydrogenation of hexanoic acid, a car-boxylic acid obtainable from the fermentation of a wide variety of waste biomasses. In particu-lar, crude hexanoic acid deriving from the fermentation of grape pomace, an abundant Italian agrifood waste, has been taken into consideration. The reaction was carried out with the com-mercial 5 wt% Re/γ-Al2O3 catalyst, whose acidity allowed the tuning of the reaction selectivity towards the preferential formation of hexyl hexanoate instead of 1-hexanol. As consequence, the tunable composition of the obtained 1-hexanol/hexyl hexanoate mixtures was leveraged for en-gine applications, thus allowing studying each component influence on the Diesel engine per-formances and verifying their synergistic effects. The engine experimental activity highlighted that both 1-hexanol and hexyl hexanoate, as well as their mixtures, can be used in Diesel engine with a commercial Diesel fuel up to high loadings (20 vol%) without altering engine perfor-mances and significantly lowering soot and CO emissions by more than 40%.

Alveolar bone atrophy after tooth extraction presents significant difficulties when planning delayed-immediate implant treatment to replace a lost tooth. Socket augmentation is an effective therapy that limits the post extraction volumetric changes and facilitates implant placement whilst reducing the need for staged grafting. However, socket augmentation and osteoconductive graft conversion is a slow process that could take 4-6 months. PRF and its variants have significant bio-enhancement effect on wound healing and regeneration through release of platelet drived growth factors, bioactive substances, cytokines. Leukocytes in PRF also play a significant role in enhancing tissue regeneration as part of osteo-immune response. Use of PRF during socket augmentation is highly effective in enhancing extraction site healing and graft integration by angiogenesis, cell proliferation, recruitment, and differentiation through release of growth factors and bioactive substances mediated by platelets and leukocytes. PRF with SA is recommended as an adjunct to SA as a sole biomaterial or in combination with xenograft or allograft bone substitutes to enhance tissue regeneration and to reduce risk of infection, osteitis and postoperative pain in extraction sockets

The United States is plagued with the highest rates of preventable metabolic diseases it has ever seen, and while poor nutrition is increasingly recognized as a critical contributing factor, good nutrition has been shown to be a potent factor in prevention and management of these illnesses. Notably, nutrition is inextricably intertwined with farming practices and the stewardship of our environment – particularly its soil. In this proposal, we propose a method to expose medical students to basic agricultural and environmental knowledge regarding the production of food, as well as educate them in practical nutrition education within the interactive, case-based, and longitudinal preclinical curriculum at Case Western Reserve University School of Medicine (CWRU SOM). We propose a two-part approach; first: integrating relevant topics in nutrition, culinary medicine, and farming practices into the preclinical blocks through Official Learning Objectives, and second: an optional 8-week, zero credit elective for students interested in pursuing a deeper understanding of these topics. Through these interventions, we believe courses like this will support a generation of physicians able to understand health from soil to plate with a consideration for the environment in addition to exemplifying healthful lifestyles themselves. We believe these kinds of future physicians will be the most effective in treating (and ideally reversing) the chronic disease epidemic.

Image registration (IR) is a process that deforms images to align them with respect to a reference space, making it easier for medical practitioners to examine various medical images in a standardized reference frame, such as having the same rotation and scale. This document introduces image registration using a simple numeric example. It provides a definition of image registration along with a space-oriented symbolic representation. This review covers various aspects of image transformations, including affine, deformable, invertible, and bidirectional transformations, as well as medical image registration algorithms such as Voxelmorph, Demons, SyN, Iterative Closest Point, and SynthMorph. It also explores atlas-based registration and multistage image registration techniques, including coarse-fine and pyramid approaches. Furthermore, this survey paper discusses medical image registration taxonomies, datasets, evaluation measures, such as correlation-based metrics, segmentation-based metrics, processing time, and model size. It also explores applications in image-guided surgery, motion tracking, and tumor diagnosis. Finally, the document addresses future research directions, including the further development of transformers.

With the rapid increase in diabetes worldwide, the number of patients with diabetic kidney disease (DKD), a complication of diabetes mellitus, is also on the rise. DKD is a major cause of chronic kidney disease progressing to end-stage renal failure; however, current medical treatments for DKD do not achieve satisfactory outcomes. Molecular hydrogen (H2) is an antioxidant that selectively reduces hydroxyl radicals, a reactive oxygen species with a very potent oxidative capacity. H2 was recently shown to exert not only antioxidant, but also anti-inflammatory, cell lethality-regulating, and signal transduction-regulating effects, and is now being applied clinically. Many factors contribute to the onset and progression of DKD, with mitochondrial dysfunction, oxidative stress, and inflammation being strongly implicated. Recent preclinical and clinical trials reported that substances with antioxidant properties may slow the progression of DKD. Therefore, we conducted a literature review on animal models and human clinical trials in which H2 showed efficacy against various renal diseases. This literature review and our previous findings collectively suggest that H2 exerts therapeutic effects in patients with DKD by improving mitochondrial function. Future large-scale clinical studies are needed to confirm these findings.

Nurses in disaster response require comprehensive understanding, training, and col-laboration among educators, researchers, and practitioners to overcome challenges and improve their capabilities. This study evaluates the impact of simulation-based training on improving nursing students' knowledge and performance in crisis management and triage during mass casualty incidents in Saudi Arabia, aiming to enhance existing pro-tocols in disaster management. This quantitative interventional pre-post study aimed to assess the impact of a training intervention on the disaster nursing skills of nursing students at Taif University in Saudi Arabia. The study involved a random sample of 101 nursing students and utilized a realistic train accident simulation with a response team comprising healthcare professionals and emergency specialists. A detailed questionnaire was used to measure emergency management skills and knowledge, and pre-test and post-test evaluations were conducted. Data analysis was performed using SPSS, and the study was conducted on a voluntary basis with necessary approvals obtained. The findings have the potential to enhance disaster management protocols and improve the preparedness of nursing professionals in Saudi Arabia. The posttest analysis revealed that a significant portion of participants achieved excellent, very good, and good levels of performance, indicating the effectiveness of the training program. In contrast, the pretest grades showed a higher percentage of participants receiving fail level grades, high-lighting the need for improvement prior to the training intervention. This study high-lights the importance of comprehensive training and education in disaster nursing for improving emergency response and patient outcomes

The windowpane oyster Placuna placenta, lives in brackish coastal waters and has long been economically important to the Philippines because of its durable and translucent shell, which is used as a glass substitute and material for making windowpanes and handicrafts. There has been considerable degradation of the windowpane oyster fishery in past decades. Moreover, there are waste and under-exploited by-products such as the meat, which though nutritious and edible, currently has a very low value; its use being confined to dishes consumed by families in the fishing community. Historically there are instances of excellent practice in terms of regulating the windowpane oyster fishery, while in recent times there have been local initiatives to restore this and to develop high value food products from the meat. These initiatives have, however, never been followed through at a provincial or national level. Research on other molluscs and marine organisms highlights that these also contain high value pharmaceutical products. This review compiles evidence to establish the groundwork for an essential and comprehensive multidisciplinary research program centred around the windowpane oyster, which would ensure a high value for all parts of the oyster, including those currently discarded. By fostering a sustainable and circular economy within this fishery sector and its associated industry, its economic value would be amplified. This is particularly important for oyster harvesters in the Philippines, who often find themselves at the economic ladder's lower rungs. By integrating the principles of a circular economy, this initiative would not only aim to uplift the economic prospects of these harvesters but in doing so would drive the restoration of the windowpane oyster to its former range.

During the last forty years, the use of strontium isotopes in archaeology and biogeochemical research has spread widely. These isotopes, alone or in combination with others, can contribute to trace past and present environmental conditions. However, the interpretation of the isotopic values of strontium is not always simple and requires good knowledge of geochemistry and geology. This short paper on the use of strontium isotopes is aimed at those who use this tool (archaeologists, but not only) but who do not have a thorough knowledge of mineralogy, geology, and geochemistry necessary for a good understanding of natural processes involving these isotopes. We report basic knowledge and suggestions for a correct use of these isotopes. The isotopic characteristics of bio-assimilable strontium depend not so much on the isotopic characteristics of the bulk rock as, rather, on that of its more soluble minerals. Before studying human, animal and plant remains, the state of conservation and any conditions of isotopic pollution should be carefully checked. Samples should be collected according to random sampling rules. The data should be treated by a statistical approach. To make comparisons between different areas, it should be borne in mind that the study of current soils can be misleading since the mineralogical modification of the soil over time can be very rapid.

While biologic originators’ patents are expiring, biosimilars are emerging to take their place, offering significant cost savings to healthcare systems. Many challenges still need to be addressed in the clinical practice of inflammatory bowel disease (IBD). A global survey was organized to highlight physicians’ current knowledge and beliefs and to gain insight about their practical management and remaining concerns and obstacles associated with starting a biosimilar, switching from an originator to a biosimilar, or switching from one biosimilar to another (multiple switches and reverse switching). Fifteen physicians with expertise in the field of IBD from 13 countries attended a virtual international consensus meeting to develop practical guidance regarding biosimilar adoption worldwide, considering the survey results. Consensus was reached on 10 statements regarding biosimilar effectiveness, safety, indications and rationale, multiple switches, therapeutic drug monitoring of biosimilars, non-medical switching and future perspectives.

Mercury toxicity significantly threatens aquatic ecosystems, particularly impacting fish populations and human well-being. This article exposes the effects of mercury contamination on aquatic life and their habitats. Mercury primarily originates from natural degassing and anthropogenic activities and accumulates in aquatic organisms, most notably in predatory fish, through bio-accumulation and bio-magnification. This bio-accumulation, driven by microbial transformation to methyl-mercury, leads to elevated concentrations in top-level predators. The consequences of mercury exposure on fish physiology are stunted growth, reproductive impairments, and compromised immunity, with potential ramifications for population dynamics and ecosystem resilience. This study delves into specific impacts of mercury on fish, ranging from bone deformities to liver damage, developmental anomalies, neurotoxic effects, and disruptions in reproductive systems. The interplay between ecological, physiological, and human health effects underscores the need for a comprehensive understanding of mercury's underlying mechanisms. Monitoring mercury levels in aquatic systems emerges as a crucial strategy for ensuring fish populations' health and ecosystems' sustainability. Urgent collaborative efforts are imperative to address this global concern, promote harmonious coexistence between human activities and aquatic environments, and secure the availability of safe and nutritious fish for future generations. In conclusion, this article highlights the urgent necessity for targeted interventions and informed decision-making to mitigate the influence of mercury contamination on aquatic ecosystems.

This study evaluates the accessibility of primary care physicians (PCPs) in Eastern Quebec using an enhanced two-step floating catchment area (E2SFCA) methodology. The E2SFCA methodology considers both the population supply of PCPs and the travel time between PCPs and dissemination areas, and was found to be effective in identifying possibly underserved areas. The availability of primary care services is contingent upon the presence of adequate road infrastructure, and populations residing in areas with limited access to main road networks may experience compromised access to primary care. The study concludes that the E2SFCA methodology can facilitate the identification of areas experiencing a shortage of PCPs, and this information can be used to inform the development of regional medical workforce programs and support the establishment of rural residency initiatives.

This study explores the impact of temperature and molarity on the pyrolysis of Açaí seeds (Euterpe Oleraceae, Mart.) activated with KOH on the yield of bio-oil, hydrocarbon content of bio-oil, an-tioxidant activity of bio-oil and chemical composition of aqueous phase. The experiments were carried out at 350, 400, and 450 °C and 1.0 atmosphere, with 2.0 M KOH, and at 450 °C and 1.0 atmosphere, with 0.5 M, 1.0 M and 2.0 M KOH, in laboratory scale. The composition of bio-oils and aqueous phase determined by GC-MS, while the acid value, a physical-chemical property of fundamental importance in biofuels, of bio-oils and aqueous phases by AOCS methods. The an-tioxidant activity of bio-oils determined by the TEAC method. The solid phase (biochar) charac-terized by X-ray diffraction (XRD). The diffractograms identified the presence of Kalicinite (KHCO3) in biochar, and those higher temperatures favor the formation peaks of Kalicinite (KHCO3). The pyrolysis of Açaí seeds activated with KOH show bio-oil yields from 3.19 to 6.79 (wt.%), aqueous phase yields between 20.34 and 25.57 (wt.%), solid phase yields (coke) between 33.40 and 43.37 (wt.%), and gas yields from 31.85 to 34.45 (wt.%). The yield of bio-oil shows a smooth exponential increase with temperature. The acidity of bio-oil varied between 12.3 and 257.6 mgKOH/g, decreasing exponentially with temperature, while that of aqueous phase between 17.9 and 118.9 mgKOH/g, showing and exponential decay behavior with temperature, demonstrating that higher temperatures favor not only the yield of bio-oil but also bio-oils with lower acidity. For the experiments with KOH activation, the GC-MS of bio-oil identified the presence of hydro-carbons (alkanes, alkenes, cycloalkanes, cycloalkenes, and aromatics) and oxygenates (carboxylic acids, phenols, ketones, and esters). The concentration of hydrocarbons varied between 10.19 to 25.71 (area.%), increasing with temperature, while that of oxygenates from 52.69 to 72.15 (area.%), decreasing with temperature. For the experiments with constant temperature, the concentrations of hydrocarbons in bio-oil increase exponentially with molarity, while those of oxygenates de-crease exponentially, showing that higher molarities favor the formation of hydrocarbons in bio-oil. The antioxidant activity of bio-oils decreases with increasing temperature, as the content of phenolic compounds decreases, and decreases with increasing KOH molarity, as higher molarities favors the formation of hydrocarbons. Finally, it can be concluded that chemical activation of Açaí seeds with KOH favors the not only the yield of bio-oil but also the content of hydrocarbons. The study of process variables is of utmost importance in order to clearly assess reaction mechanisms, economic viability and design goals that could be derived from chemically activated biomass pyrolysis processes.

Diabetes mellitus is a widespread chronic metabolic disorder demanding regular blood glucose level surveillance (BGLs). Current invasive techniques, such as finger-prick tests, often result in discomfort for patients, leading to infrequent monitoring and potential health complications. The primary objective of this study was to design a novel, portable, non-invasive system for diabetes detection using breath samples, named as DiabeticSense, an affordable digital health device for early detection, encouraging immediate intervention. The device employed MOSFET-based electrochemical sensors to assess volatile organic compounds in breath samples, whose concentrations differ between diabetic and non-diabetic individuals. The system merged body vital signs with sensor voltages obtained by processing breath sample data to predict diabetic conditions. Our research used readings from 100 patients at a nationally recognised hospital to form the dataset. Data was then processed 10 using a Gradient Boosting Classifier model, and performance was cross-validated. The proposed system attained a promising accuracy of 86.6%, marking an improvement of 20.72% over an existing regression technique. The developed device introduces a non-invasive, cost-effective, and user-friendly solution for preliminary diabetes detection. It has the potential to increase patient adherence to regular monitoring.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Wastewater contaminated with antibiotics is a major environmental challenge. We developed here the green synthesis of bio-graphenes by using natural precursors (Xanthan, Chitosan, Boswellia, Tragacanth). The use of these precursors can act as templates to create 3D doped graphene structures with special morphology. Also, this method is a simple method for in-situ synthesis of doped graphenes. The elements present in the natural polymers (N) and other elements in the natural composition (P, S) are easily placed in the graphene structure and improve the catalytic activity due to the structural defects, surface charges, increased electron transfers, and the high absorption. In this mechanism, O2 dissolved in water absorbs onto the positive charged C in doped graphenes to create oxygenated radicals, which enables the degradation of antibiotic molecules. Light irradiation increases the amounts of radicals and rate of antibiotic removal. The results have shown that the hollow cubic Chitosan-derived graphene has shown the best performance due to the doping of N, S, and P. The Boswellia-derived grapheme shows the highest surface area, but lower catalytic performance, which indicates the more effective role of doping in the catalytic activity. The effect of oxygen and light were also studied to accelerate the degradation process.

The aerospace industry demands high-performance materials that can withstand extreme conditions and maintain efficiency in a variety of applications. Bio-inspired polymer coatings have emerged as a promising approach to meet these demands, by drawing inspiration from natural systems to develop new coatings that exhibit enhanced properties, such as self-cleaning, anti-icing, thermal management, and corrosion resistance. In this review, recent developments in the field of bio-inspired polymer coatings for aerospace applications are presented, covering a range of coatings and their respective properties. We discuss the recent developments and applications of bio-inspired coatings, with a focus on their advantages and challenges in aerospace applications. We also highlight the potential for future research and development in this field, including the integration of advanced technologies such as nanotechnology and additive manufacturing. The review aims to provide insights into the current state-of-the-art of bio-inspired polymer coatings for aerospace applications and to inspire further research in this rapidly growing field.

As the human-body controls depend on the brain neuro current, here assay is related to thinking nerve detection. For this purpose, the brain current was analyzed by electrochemical three-electrode systems using 0.1-mm micro wire in vivo probe, which probe was made by using carbon nanotube paste coated needle type. Here working electrode was inserted in the rat brain’s muscle core with a 0.3-mm-diameter needle type Ag/AgCl Cl-coated Ag wire reference, and counter electrode of 0.1-mm-diameter Pt micro needle was inserted 5 mm deep into the in vivo muscle skin, under anesthesia with 2.0 ~ -2.0 V potential windows, 50 mv/s cyclic scan and 1.0x10-5A chrono amperometric sensitivity on the body systems. Under the optimum conditions, diagnostic application was performed to such as smell signal, muscular strength, five sense assay and thinking neuro current.

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope, in a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with natural waste local materials. Thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters, manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives involved a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181 – 0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275 – 0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high Transmission Loss values were obtained for the natural additives (TL = 35 – 59 dB). Petiol of Palm and Stipa Tenacissima were found as the materials able to improve both thermal and acoustic properties.

Medical Self-service Terminals (MST) offer potential for process optimization and enhanced patient convenience in hospitals, including the elderly. However, the behavioral inclination (BI) of elderly Chinese individuals towards MST adoption remains suboptimal. This study aims to elucidate BI determinants for MST among the aging demographic, focusing on User Interface (UI) attributes within the extended Unified Technology Acceptance and Use Theory (UTAUT) Model. The empirical inquiry examines seven factors, comprising three key UI attributes and four fundamental UTAUT factors. Results reveal significant positive associations of Performance Expectancy (β=0.40, p&lt;0.001), Effort Expectancy (β=0.50, p&lt;0.001), and Social Influence (β=0.25, p&lt;0.05) with BI. Notably, User Interface Design positively correlates with Performance Expectancy (β=0.89, p&lt;0.001) and Effort Expectancy (β=0.81, p&lt;0.001). These findings emphasize the interplay between objective UI attributes and subjective UTAUT determinants. This research enhances understanding of UI's impact on MST BI and acceptance, particularly within the Chinese elderly population, urging MST researchers to prioritize elderly attitudes for effective technological integration in healthcare.

Spinal deformity refers to a range of disorders that are defined by anomalous curvature of the spine and may be classified as scoliosis, lordosis, or kyphosis. Among these, scoliosis stands out as the most common type of spinal deformity in human beings, and it can be distinguished by abnormal lateral spine curvature accompanied by axial rotation. Accurate identification of spinal deformity is crucial for a person's diagnosis, and numerous assessment methods have been developed by researchers. Therefore, the present study aims to systematically review recent works on spinal deformity as-assessment for scoliosis diagnosis, utilizing image processing techniques. To gather relevant studies, a search strategy was conducted on three electronic databases (Scopus, ScienceDirect, and PubMed) between 2012 and 2022, using specific keywords and focusing on scoliosis cases. A total of 17 papers fully satisfied the established criteria and were extensively evaluated. Despite variations in methodological designs across the studies, all reviewed articles obtained quality ratings higher than satisfactory. Various diagnostic approaches have been employed, including artificial intelligence mechanisms, image processing, and scoliosis diagnosis systems. These approaches have the potential to save time and, more significantly, can reduce the incidence of human error. While all assessment methods have potential in scoliosis diagnosis, they possess several limitations that can be ameliorated in forthcoming studies. Therefore, the findings of this study may serve as guidelines for the development of a more accurate spinal deformity assessment method that can aid medical personnel in the real diagnosis of scoliosis.

Early treatment is the mainstay of sepsis therapy. We suspected that early recognition of sepsis by prehospital healthcare providers may shorten time to antibiotic administration in the Emergency Department. We retrospectively evaluated all patients above 18 years of age that were diagnosed with sepsis or severe infection in our Emergency Department between 2018 and 2020. We recorded the suspected diagnosis at time of presentation, type of referring healthcare provider and time until initiation of antibiotic treatment. Differences between groups were calculated using Kruskal-Wallis rank sum test. Of the 277 patients that were diagnosed with severe infection or sepsis in the emergency department, an infection was suspected in 124 (44.8%) patients and sepsis was suspected in 31 (11.2%) patients by referring healthcare providers. Time to initiation of antibiotic treatment was shorter in patients where sepsis or infection had been suspected prior to arrival for both patients with severe infections (p = 0.022) and sepsis (p = 0.004). Given the well described outcome benefits to early sepsis therapy, recognition of sepsis needs to be improved. Appropriate scores should be used as part of routine patient assessment to reduce time to antibiotic administration and improve patient outcomes.

Recently, there has been remarkable progress in the development of smart textiles, especially knitted strain sensors, to achieve reliable sensor signals. Stable and reliable electro-mechanical properties of sensors are essential for using textile-based sensors in medical applications. How-ever, challenges associated with significant hysteresis and low gauge factor (GF) values remain for using strain sensors for motion capture. To evaluate these issues, a comprehensive investiga-tion of the cyclic electro-mechanical properties of weft-knitted strain sensors was conducted in the present study to develop a drift-free elastic strain sensor with a robust sensor signal for mo-tion capture for medical devices. Several variables were considered in the study, including the variation of the basic knit pattern, the incorporation of the electrically conductive yarn, and the size of the strain sensor. The effectiveness and feasibility of the developed knitted strain sensors are demonstrated through experimental evaluation, by determining the gauge factor, its non-linearity, hysteresis and drift. The developed knitted piezoresistive strain sensors have a GF of 2.4, a calculated drift of 50 %, 12,5 % hysteresis, and 0.3 % nonlinearity in parts.

Herein we report the expansion of the chemical space available from the chitin, accessible via the biogenic N-platforms 3A5AF, M4A2C and di-HAF. The biologically active heteroaromatics furo[3,2-d]pyrimidin-4-one and furo[3,2-d]pyrimidin-4-amine can be selectively accessed from 3A5AF and M4A2C, respectively. The chiral pool synthon di-HAF is a viable substrate for the Achmatowicz rearrangement, providing streamlined access to 2-aminosugars possessing a versatile hydroxymethyl group at C5.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

Cheese whey, a significant byproduct of the dairy industry, poses a substantial environmental challenge due to its organic pollutant load and large size demands for effective and affordable valorization methods. The abundance of lactose and other organic compounds in whey contributes to its elevated Biological Oxygen Demand (BOD) and Chemical Oxygen Demand (COD), further straining natural ecosystems. This study aimed to investigate the nutritional potential of whey and its conversion into value-added products: bioethanol and probiotic drinks. To achieve this, Bacillus megaterium and Bacillus subtilis strains, isolated from soil samples in Nepal, were co-cultured with Lactobacillus and Saccharomyces cerevisiae in the whey broth for fermentation. Ethanol distillation was carried out using a Rota-evaporator to maintain the viability of the fermenting organisms within the broth. Moreover, the probiotic criteria of the fermenting strains were extensively examined. Experimental observations revealed a remarkable concentration of 9.2 g/L of ethanol, resulting in an ethanol yield of 0.23 g/L. Extrapolating this rate, it is theoretically possible to produce an annual global bioethanol output of 5 million tons using whey as a sustainable and abundant resource. Furthermore, the study explores novel advancements in the distillation process, demonstrating the successful extraction of ethanol at room temperature. Additionally, the fermentation organisms employed in this research exhibit robust viability, surviving various in-vitro stress-tolerant tests, including exposure to bile salt, pH fluctuations, and gastric juice. These resilient strains also demonstrated long-term stability in fermented whey broth, making them promising candidates for the development of probiotic beverages. This dual approach not only enables the sustainable management of organic waste but also presents an opportunity to create value-added products with positive implications for both the environment and human health.

The reagents and raw materials used, as well as the products obtained (bio-oil, reaction condensates, polyols and sugar phases) were characterized by elemental analysis, infrared spectroscopy, thermogravimetry and high performance liquid chromatography with mass spectrometry. The heating value of the bio-oils is higher than that of the original biomass (higher heating value of Eucalyptus sawdust bio-oil 29 MJ/kg versus 19.5 MJ/kg of the original Eucalyptus sawdust). The analyses of the bio-oils allowed to identify the presence of high added-value compounds, such as levulinic acid and furfural. Finally, a study of the accelerated aging of the liquefied biomass showed that the biofuel density increases with the storage time due to the occurrence of repolymerization reactions.

Live cell imaging is a powerful technique to study the dynamics and mechanics of various biological molecules like proteins, organelles, DNA, and RNA. With the rapid evolution of optical microscopy, our understanding of how these molecules is implicated in the cells’ most critical physiological roles deepens. Here in this review, we focus on how spatiotemporal nanoscale live cell imaging on a single molecule level allows for profound contributions towards new discoveries in life science. This review will start by summarizing how single molecule tracking has been used to analyze membrane dynamics, receptor ligand interactions, protein-protein interactions, inner- and extracellular transport, gene expression/transcription, and whole organelle tracking. We then move on to how current authors are trying to improve single molecule tracking and overcome current limitations by offering new ways in labeling protein of interests, multi-channel/color detection, improvements in time laps imaging, and new methods and programs to analyze colocalization and movement of targets. We later discuss how single molecule tracking can be a beneficial tool used for medical diagnosis. Finally wrapping up with limitation and future perspective of single molecule tracking and total internal refection microscopy.

The primary goal of picture compression is to reduce the amount of unused image data while still storing or transmitting it in a format that is appropriate. The compression of raw binary data is quite different from the compression of a picture, and these differences may be rather substantial. In light of this, compression is often regarded as an essential technique for the purposes of both data storage an d transmission in order to mitigate the excessive amounts of data that are generated by these images. In order to transmit enormous datasets, particularly for the purposes of telemedicine and teleradiology, one needs a significant amount of storage capacity as well as an expansive network. As a result, compression is an important aspect of medical imaging. In addition to the importance of compression, the quality of the photos themselves is also an essential factor in the success of analysis. In addition to this, the amount of time necessary to compress the photographs before sending them should be reduced.

Amidst the strong demand for wood-based products, and the clamor of environmental consciousness, more functional and green solutions arise to meet both of these goals. Pursuant to this, the study looked into the potential of a bio-waste taken from Cocos nucifera tree known as phellem—the tree’s outermost skin made of dead tissues, to be a main component of a new construction material. This study builds on existing research showing phellem is impermeable, buoyant, elastic, has a slow burn rate, and repels water during precipitation, which supports the hypothesis that phellem may be a potential substitute material for dry wall boards. To test this, three (3) formulations were developed as experimental setups to test the physico-mechanical properties of coconut palm phellem (COPAP): Formulation A (40% COPAP and 60% cement); Formulation B ( 25% COPAP and 75% cement); and lastly, Formulation C that was (10% COPAP and 90% cement). The study tested (1) which among these formulations exhibited the best physico-mechanical properties, then it (2) compared the formulations with common dry wall products available in the market, and subsequently, (3) identified the possible architectural applications of COPAP board as a new building material. Of the three setups, Formulation C exhibited the best physico-mechanical properties in terms of compressive and flexural strength, fire resistivity, and water absorption. It fared better than the particle board in terms of compressive strength, better than wafer and particle board in fire resistivity, performed best in the water absorption test, and was almost equivalent in flexural strength to fiber cement board. With these findings, the study concluded that COPAP board can be used for projects that entail a high number of interior partitions, such as offices, BPO firms and commercial stalls in shopping malls, etc. The findings were conclusive only for interior applications, as the results clearly suggest that the COPAP boards are not load-bearing. Future investigations may explore and assess other properties of COPAP boards like thermal and sound insulation, resistance to rot and other properties that may prove its competitive advantage as a construction material.

This comprehensive review explores the advancements in Orthognathic and Oral Maxillofacial Surgery, focusing on the integration of 3D Printing and Virtual Surgical Planning (VSP). Traditional surgical methods, while effective, come with inherent risks and complications, and can lead to variability in outcomes due to the reliance on the surgeon's skill and experience. The shift towards patient-centric care necessitates personalized surgical methods, which can be achieved through advanced technology. The amalgamation of 3D printing and VSP revolutionizes surgical planning and implementation by providing tactile 3D models for visualization and planning, and accurately designed surgical guides for execution. This convergence of digital planning and physical modeling facilitates a more predictable, personalized, and precise surgical process. However, the adoption of these technologies presents challenges, including the need for extensive software training and the steep learning curve associated with computer-aided design programs. Despite these challenges, the integration of 3D printing and VSP paves the way for advanced patient care in orthognathic and oral maxillofacial surgery.

Despite the efforts to deliver the best evidence-based care, in-hospital death is an inevitable event among some patients hospitalized in cardiology departments. We retrospectively evaluated mortality events from inpatient admissions to Cardiology Department between 2010-2019. Data were collected from morbidity &amp; mortality meeting presentations which evaluated comorbidities, medical history, treatments, and causes of death for the overall cohort and according to age group and sex. There were 1,182 registered deaths. The most common causes of death among patients were acute myocardial infarction (AMI) (53.0%), heart failure (HF, 11.7%), cardiac arrest (CA, 6.6%), HF with complication/defined cardiomyopathy (6.3%) and sepsis (4.4%). We observed a decline in deaths from AMI from 61.9% in 2010 to 46.7% in 2019 while there was an apparent increase in deaths from HF (11.1% in 2010 to 25.9% in 2019). Compared to patients ≥65 years, younger patients were more likely to have died from CA (15.7% vs. 4.3%, p&lt;0.001) and other cardiac reasons (3.0% vs. 0.4%, p&lt;0.001). The majority of deaths were due to AMI, HF, and CA. We observed a significant decline in the proportion of deaths due to AMI in recent years with an increase in deaths due to HF.

Patient safety is a top priority for all healthcare systems globally. Promoting the adoption of policies for reporting and learning from errors is an important strategy for improving care safety. Therefore, the aim of our study was to detect how much patient safety culture influences the reporting of adverse events and the use of the incident reporting tool. The study protocol was developed according to PRISMA guidelines. Articles were searched electronically in PubMed/MEDLINE, the COCHRANE library, and Google Scholar by two independent reviewers, and those that met the eligibility criteria were included. Synthesis of qualitative data from included studies was performed by graphical descriptive statistical analysis. The results of the systematic review showed that health care organizations' increasing focus on staff development of a safety culture has led to a significant increase in incident reporting rates over the years. Moreover, in situations where safety culture is placed at the top of the nations, there is a higher frequency of incident reporting. An efficient incident reporting system should be an essential foundation for healthcare organizations, as it allows them to collect experiences and data and provide feedback to healthcare providers and staff involved in care.

University students, particularly those in the healthcare disciplines, constitute a category of particular interest in regard to COVID-19 vaccines and the attitudes to vaccination, as their future professional role will enable them to inform and educate citizens regarding vaccination. The objectives of the study were to investigate the vaccination coverage with a COVID-19 vaccine among students from different degree programs at the Medical universities in Bulgaria behaviors and attitudes toward vaccination with a COVID-19 vaccine. A prospective cross-sectional study in the period September 2021- March 2022 was conducted. Information on demographics, university program, year of study, general attitudes and behavior towards vaccines and attitudes and personal experience with COVID-19 and COVID-19 vaccines were collected. The chi-square test was used to test for associations and binominal logistic regression was used to identify possible predictors for vaccination. A total of 3050 students with a median age of 22 years, predominantly female took part in the study. Three-thirds of the students (73.5%) have been vaccinated against COVID-19. The main reasons for vaccine hesitancy in both group of students were the fear of side effects and the doubts about the safety of the vaccines although non-vaccinated students significantly more frequently express those fears. Respondents who considered to have: a) limited access to sufficient information to inform COVID-19 vaccine uptake; b) lack of public awareness and education campaigns about effectiveness and safety of COVID-19 vaccines; c) insufficient information about COVID-19 vaccines during the University education; and d) had a positive COVID-19 diagnosis were less likely to be vaccinated. Students who have not completed the mandatory immunization cycle according to country schedule were also less likely to have completed COVID-19 vaccination. The students showed an overall positive attitude towards the COVID-19 vaccines. Promoting informational campaigns that emphasize the vaccine's safety will be more effective to further increase the vaccination coverage with COVID-19 vaccines.

The aim of this review was to summarize the available literature regarding resident and medical student education utilizing the spaced repetition program Anki. This review enables current residents and medical students to recognize and utilize evidence-proven study techniques to increase learning efficacy. A systemic review of papers across all medical journals available on Pubmed was conducted to identify studies published without time constraints. The search was for (Anki) and (spaced repetition residency). Available outcome data was collected and discussed independently for students and residents. Many studies showed a statistically significant increase in exam performance associated with Anki use and high levels of satisfaction among residents and medical students. Further research is warranted to provide high-quality evidence of Anki’s applications and there is a need for exploration in additional residency specialties. Anki use has steadily increased with both medical students and residents. The application demonstrated consistent improvement on exam-based performance and was regarded highly by users. As burnout and time constrictions threaten educational workflow, Anki may serve as a powerful tool to improve the quality of learning. Further data needs to be collected and analyzed in specialties where Anki use may already exist.

In the production of grafted vines, losses are caused by fungal pathogens during callus forming or after planting in the soil. To control or reduce natural fungal infections in nurseries, certain applications were conducted in sapling cultivation stage to analyse the efficacy of cyprodinil + fludioxonil, floupyram + tebuconazole active substances and Trichoderma harzianum biological preparation. 1103 Paulsen rootstock and Vitis viniferea L. Sultana cultivars were stored in fungicide suspensions for 60 minutes before and after grafting in the study. After grafting, the seedlings were divided into i) cutting + sawdust ii) cutting + sawdust + soil application groups and transferred to the callus room. Fungicides were applied 1 to 7 days after the callus development to wet the seedling roots 1 to 7 days after they were planted. After nine months in the nursery, the plants were uprooted, classified as diseased or healthy, and analysed for morphological and molecular diagnosis of fungal species, isolation incidence, and seedling quality and yield. After callus development, Fusarium solani was most frequently isolated pathogen in the roots (21.5%), cyprodinil + fludioxonil reduced the Ilyonectria sp. isolation rate in both shoots and roots. Botryosphaeria obtusa and I. liriodendri pathogens were not detected in sick and healthy cyprodinil + fludioxonil-treated saplings. The highest seedling yield was observed with fludioxonil + cyprodinil, cutting + sawdust + soil (78.75%) and cutting + sawdust (70.68%) applications. According to the results of this study, it was found that fungicide applications before and after grafting prevented soil-borne pathogen infections and improved sapling quality.

This study explores the impact of temperature and molarity on the pyrolysis of Açaí seeds (Euterpe Oleraceae, Mart.) activated with KOH on the yield of bio-oil, hydrocarbon content of bio-oil, antioxidant activity of bio-oil and chemical composition of aqueous phase. The experiments were carried out at 350, 400, and 450 °C and 1.0 atmosphere, with 2.0 M KOH, and at 450 °C and 1.0 at-mosphere, with 0.5 M, 1.0 M and 2.0 M KOH, in laboratory scale. The composition of bio-oils and aqueous phase determined by GC-MS, while the acid value, a physical-chemical property of fundamental importance in biofuels, of bio-oils and aqueous phases by AOCS methods. The antioxidant activity of bio-oils determined by the TEAC method. The solid phase (biochar) characterized by X-ray diffraction (XRD). The diffractograms identified the presence of Kalicinite (KHCO3) in bio-char, and those higher temperatures favor the formation peaks of Kalicinite (KHCO3). The pyrolysis of Açaí seeds activated with KOH show bio-oil yields from 3.19 to 6.79 (wt.%), aqueous phase yields between 20.34 and 25.57 (wt.%), solid phase yields (coke) between 33.40 and 43.37 (wt.%), and gas yields from 31.85 to 34.45 (wt.%). The yield of bio-oil shows a smooth exponential increase with temperature. The acidity of bio-oil varied between 12.3 and 257.6 mgKOH/g, decreasing exponentially with temperature, while that of aqueous phase between 17.9 and 118.9 mgKOH/g, showing and exponential decay behavior with temperature, demonstrating that higher temperatures favor not only the yield of bio-oil but also bio-oils with lower acidity. For the experiments with KOH activation, the GC-MS of bio-oil identified the presence of hydrocarbons (alkanes, alkenes, cycloalkanes, cycloalkenes, and aromatics) and oxygenates (carboxylic acids, phenols, ketones, and esters). The concentration of hydrocarbons varied between 10.19 to 25.71 (area.%), increasing with temperature, while that of oxygenates from 52.69 to 72.15 (area.%), decreasing with temperature. For the experiments with constant temperature, the concentrations of hydrocarbons in bio-oil in-crease exponentially with molarity, while those of oxygenates decrease exponentially, showing that higher molarities favor the formation of hydrocarbons in bio-oil. The antioxidant activity of bio-oils decreases with increasing temperature, as the content of phenolic compounds decreases, and de-creases with increasing KOH molarity, as higher molarities favors the formation of hydrocarbons. Finally, it can be concluded that chemical activation of Açaí seeds with KOH favors the not only the yield of bio-oil but also the content of hydrocarbons. The study of process variables is of utmost importance in order to clearly assess reaction mechanisms, economic viability and design goals that could be derived from chemically activated biomass pyrolysis processes.

This study focused on the optimization of the flexural strength of bio-composite samples of palm kernel, whelks, clams, periwinkles shells and bamboo fiber, reinforced with resin for engineering applications. The aim of the study was to: formulate different samples of bio-composite reinforce with resin for engineering applications and to evaluate the flexural strength, of the fabricated composite. The hand lay-up technique was used for the composites produced by incorporating different percentage compositions of the shells/fiber (10%, 15%, 20%, 25% and 30%) into varied proportions of epoxy resin and catalyst. The cured samples after 24hours were subjected to tensile, impact, flexural and water absorption test. The experiments were conducted using Taguchi optimization method L25 (5x5) with five design parameters and five level combinations in Minitab 18 statistical software. The results showed that the average values of flexural was 114.87MPa when compared to the unreinforced of 72.33MPa bio-composite. The study recommended that agricultural waste like palm kernel shells, whelk shells, clams, periwinkle shells and bamboo fiber, should be converted into important engineering applications.

Machine learning (ML) offers potential opportunities to enhance the learning, teaching and assessments within veterinary medical education including but not limited to assisting with admissions processes as well as student progress evaluations. The purpose of this primer is to assist veterinary educators in appraising and potentially adopting these rapid upcoming advances in data science and technology. In the first section, we introduce ML concepts and highlight similarities/differences between ML and classical statistics. In the second section, we provide a step-by-step worked example using simulated veterinary student data to answer a hypothesis driven question. Python syntax with explanations is provided within the text to create a random forest ML prediction model and within each step, specific considerations such as how to manage incomplete student records are highlighted when applying ML algorithms within the veterinary education field. The results from the simulated data demonstrate how decisions by the veterinary educator during ML model creation may impact the most important features contributing to the model. These results highlight the need for the veterinary educator to be fully transparent during the creation of ML models and future research is needed to establish guidelines for handling data not missing at random in medical education, and preferred methods for model evaluation.

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as an essential smart sensing devices and played an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while construction of novel touch sensors via mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature, and also summarizes the scientific challenges and development tendency of this aspect. Firstly, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bioinspired or biomimetic materials with extraordinary properties. Then, design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures insects/animal, and special structures in human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bioinspired materials and structures is discussed.

Bioactive fractions were isolated from the leaves extract of Kalanchoe pinnata and subjected to scrutiny for their prospective anti-obesity properties. An array of in vitro phytochemical, antioxidant, and enzyme inhibition assays were executed, which dis-cerned fractions F1 and F2 as the most effective fractions. These fractions were sub-sequently studied through in vivo experiments, affirming that F2 as the most potent fraction. Further characterization of F2 was conducted via HPTLC MS-MSn tech-niques. The outcomes demonstrated that F2 produced a notable anti-obesity effect in obese mice, reducing their body weight and lipid metrics, and leading to advanta-geous changes in their organs. An analytical examination of F2 revealed the existence of four principal compounds, which were subsequently subjected to in silco molecular docking and dynamic analysis, confirming their aptitude for binding to selected pro-teins. These findings imply that the utilization of Kalanchoe pinnata leaves could pro-vide a promising therapeutic strategy for the treatment of obesity.

The utilization of lignocellulosic biomass as an alternative energy source presents a promising opportunity to achieve a future energy system that is clean and free from CO2 emissions. To realize this potential, it is crucial to develop effective techniques for converting biomass and organic solid waste into secondary energy sources. Among the available options, hydrogen production stands out due to its numerous advantages, including its cleanliness, versatility in conversion and utilization technologies, high energy efficiency, and dense energy content per unit weight. This article offers a comprehensive overview of different conversion pathways and important technologies for generating hydrogen from biomass and organic solid waste. It specifically focuses on the thermochemical conversion process, which shows promise as an economically viable approach. While certain thermochemical conversion processes are still in the developmental phase, utilizing organic biomass for hydrogen production is widely recommended due to its ability to yield higher amounts of end products and its compatibility with existing facilities. However, it should be noted that this method necessitates a substantial amount of energy due to its endothermic nature. The article also explores alternative hydrogen conversion technologies and their potential for utilizing organic biomass as a feedstock, while addressing the challenges and limitations associated with these methods.

The article reports on a patient who required a cranial protection system.Using additive manufacturing techniques and surgical planning with the help of bio-models, a patient-specific bone implant solution was proposed that allows aesthetic restoration of the affected area and provides an adequate level of protection. In addition, through a comparative analysis with finite elements, the mechanical response to external actions of the medical device, printed with two materials: polymethylmethacrylate (PMMA) and polyether-ether-ketone (PEEK), is simulated. The tested materials have recognized biocompatibility properties, but their costs on the market differ significantly. The results obtained demonstrate the similarities in the responses of both materials. It offers the possibility that low-income people can access these devices, guaranteeing adequate biomechanical safety, considering that PMMA is a much cheaper material than PEEK.

Global rapidly evolving events, e.g., COVID-19, are usually followed by countermeasures and policies. As a reaction, the public tends to express their emotions on social media platforms. Therefore, predicting emotional responses to events is critical to put a plan to avoid risky behaviors. This paper proposes a machine learning-based framework to detect public emotions based on social media posts in response to specific events. It presents a precise measurement of population-level emotions which can aid governance in monitoring public response and guide it to put in place strategies such as targeted monitoring of mental health, to react to a rise in negative emotions in response to lockdowns, or information campaigns, for instance in response to elevated rates of fear in response to vaccination programs. We evaluate our framework by extracting 15,455 tweets. We annotate and categorize the emotions into 11 categories based on Plutchik’s study of emotion and extract the features using a combination of Bag of Words and Term Frequency-Inverse Document Frequency. We filter 813 COVID-19 vaccine-related tweets and use them to demonstrate our framework’s effectiveness. Numerical evaluation of emotions prediction using Random Forest and Logistic Regression shows that our framework predicts emotions with an accuracy up to 95%.

Violence against paramedics has been described as a ‘serious public health problem’ but one that remains ‘vastly underreported’, owing to an organizational culture that stigmatizes reporting – hindering efforts at risk mitigation in addition to creating a gap in research. Leveraging a novel reporting process developed after extensive stakeholder consultation and embedded within the electronic patient care record, our objective was to provide a descriptive profile of violence against paramedics in a single paramedic service in Ontario, Canada. Between February 1, 2021, through January 31, 2023, a total of 374 paramedics in Peel Region (48% of the workforce) generated 941 violence reports, of which 40% documented physical (n=364) or sexual (n=19) assault. The violence was typically perpetrated by patients (78%) and primarily took place at the scene of the 9-1-1 call (47%); however, violent behavior frequently persisted or recurred while in transit to hospital and after arrival. Collectively, mental health, alcohol, or drug use were listed as contributing circumstances in 83% of violence reports. In all, 81 paramedics were physically harmed because of an assault. On average, our data correspond to a paramedic filing a violence report every 18 hours, being physically assaulted every 46 hours, and injured every 9 days.

Artificial intelligence-powered generative language models (GLMs), such as ChatGPT and Google Bard, have the potential to provide personalized learning, unlimited practice opportunities, and interactive engagement 24/7, with immediate feedback. However, to fully utilize GLMs, properly formulated instructions are essential. Prompt engineering is a systematic approach to effectively communicating with GLMs to achieve the desired results. Well-crafted prompts yield good responses from the GLM, while poorly constructed prompts will lead to unsatisfactory responses. Besides the challenges of prompt engineering, significant concerns are associated with using GLMs in medical education, including ensuring accuracy, mitigating bias, maintaining privacy, and avoiding excessive reliance on technology. Future directions involve developing more sophisticated prompt engineering techniques, integrating GLMs with other technologies, creating personalized learning pathways, and researching the effectiveness of GLMs in medical education.

Atrial fibrillation (AF), the most prevalent type of sustained cardiac dysrhythmia globally, confers strikingly enhanced risks for cognitive dysfunction, stroke, chronic cardiac failure, and sudden cardiovascular demise. Aggregating studies underscore the crucial roles of inherited determinants in the occurrence and perpetuation of AF. However, due to conspicuous genetic heterogeneity, the inherited defects accounting for AF remain largely indefinite. Here, via the whole-genome genotyping with genetic markers and linkage assay in a family suffering AF, a new AF-causative locus was located at human chromosome 7p14.2-p14.3, a ~4.89-cM (~4.43-Mb) interval between the markers D7S526 and D7S2250. Exome-wide sequencing assay unveiled that at the defined locus, solely the mutation in the TBX20 gene, NM_001077653.2: c.695A&gt;G; p.(His232Arg), co-segregated with AF in the family. Additionally, Sanger sequencing assay of TBX20 in another family suffering AF uncovered a novel mutation, NM_001077653.2: c.862G&gt;C; p.(Asp288His). Neither of the two mutations was observed in 600 unrelated control individuals. Functional investigations demonstrated that the two mutations both significantly reduced the transactivation of the target gene KCNH2 (a well-established AF-causing gene) and the ability to bind the promoter of KCNH2, while without effect on the nuclear distribution of TBX20. Conclusively, these findings reveal a new AF-causative locus at human chromosome 7p14.2-p14.3 and strongly indicate TBX20 as a novel AF-causative gene, shedding light on the mechanism underlying AF and suggesting clinical significance for allele-specific treatment of AF patients.

This study investigates the influence of the porous structure, on the density and the mechanical behaviour of new foam concretes incorporating hemp shives. A total of eight batches of foam concretes were manufactured and tested, made with a protein-based foam agent, containing cement, ground granulated blast furnace slag and Metakaolin as binder and hemp shives as natural aggregate. The effect of several parameters is studied, elaboration method (direct and preformed), amount of pozzolanic additions (0% and 30%) and incorporation of hemp shives (5 and 15 vol%) on the resulting physical properties, microstructure, porous structure and mechanical behaviour of the concretes. Hemp shives incorporation results in increased concrete porosity and air bubble radius, decreased uniformity and mechanical strength, and lower cohesion with the cement matrix compared to standard concretes.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

In this work, the inhibition properties of new bio-based lignine ILs on the corrosion of mild steel in an aqueous solution of 0.01 M NaCl were investigated by Potentiostatic Electrochemical Impedance Spectroscopy (PEIS), Cyclic Potentiodynamic Polarization (CPP). Moreover, the surface was characterized using SEM, EDS, and optical profilometry. The influence of cation and anion on the inhibitor properties and a possible synergistic effect were investigated. The IL choline syringate showed a promising performance, reducing the corrosion current after 24-hour immersion in 0.01 M sodium chloride, from 1.66 µA/cm2 for the control to 0.066 µA/cm2 with 10 mM of the IL present. In addition to the performance as a corrosion inhibitor, both components of this IL also meet or exceed current additional desired properties of such compounds, being readily available, economically efficient, and well tolerated in organisms and the environment.