Background: Boron Neutron Capture Therapy (BNCT) is an emerging radiotherapy. There are ongoing efforts to develop a Canadian accelerator-based BNCT center. However, it remains unclear how Canadian radiation oncologists (RO), medical physicists (MP), and their trainees perceive BNCT and its impact on radiation oncology as a discipline. Methods: A survey was created to explore knowledge of BNCT, its clinical role, and support for Canadian research. It was distributed through the Canadian Association of Radiation Oncology (CARO) and Canadian Organization of Medical Physicists (COMP). Results: We received 118 valid responses from all 10 provinces, from 70 RO (59.3%) and 48 MP (40.7%), including 9 residents. Most knew of BNCT and its indications (60.2%). Although many were unaware of reasons behind early failures (44.1%), common reasons were lack of clinical trials and inaccessibility of neutron sources (42.4%) and reactor unsuitability (34.7%). Additionally, 90.6% showed definite (66.9%) or possible (23.7%) support for Canadian BNCT research, while 89% indicated definite (56.8%) or possible (32.2%) willingness for BNCT referrals. Conclusions: Most RO and MP supported Canadian BNCT research and would refer patients. However, limited awareness and lack of experiences remain a challenge. Educational sessions are needed to realize this innovative cancer treatment in Canada.

Background and objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome ME/CFS is a common complex multi-system disease with a significant impact on the quality of life of patients and their families, yet the majority of ME/CFS patients go unrecognised or undiagnosed. For two decades the medical education establishment in the UK has been challenged to remedy these failings, but little has changed. This study was designed to ascertain the current UK medical school education on ME/CFS and to identify challenges and opportunities to inform the future of medical education. Materials and methods: A questionnaire, developed under the guidance of the Medical Schools Council, was sent to all 34 UK Medical Schools to collect data for the academic year 2018-2019. Results: Responses were provided by 22 out of a total of 34 medical schools (65%). 59% of respondents taught ME/CFS, led by specialists drawn from 6 medical disciplines. Teaching delivery was usually by lecture; however, discussion case studies and e-learning were used. 7 schools included questions on ME/CFS in their examinations and 3 schools reported likely clinical exposure to ME/CFS patients. 64% of respondents were interested in receiving further teaching aids in ME/CFS. None of the schools shared details of their teaching syllabus so it was not possible to ascertain what students were being taught. Conclusions: UK medical school teaching in ME/CFS is shown to be inadequate. Several medical disciplines, with known differences about the disease, need to set these aside to give greater clarity in teaching undergraduates so they can more easily recognise and diagnose ME/CFS. Improvements are proposed in ME/CFS medical education consistent with the international paradigm shift in biomedical understanding of this disease. Many medical schools (64% of respondents) acknowledge this need by expressing a strong appetite for the development of further teaching aids and materials. The GMC and MSC are called upon to use their considerable influence to bring about the appropriate changes to medical school curricula so future doctors can recognise, diagnose and treat ME/CFS. The GMC should also consider creating a registered speciality encompassing ME/CFS, post viral fatigue and Long Covid.

: BACKGROUND: The prevalence of smoking among students is a growing concern, particular-ly among medical students. Being associated with stress, competitiveness, and various social in-fluences, smoking can have detrimental effects on self-esteem and self-efficacy and may create a vicious cycle that may contribute to students’ well-being and academic performance out-comes.METHODS: A cross-sectional study on 456 medical students was conducted via an online survey using structured questionnaires on socio-demographic characteristics, smoking habits, nicotine dependence, self-esteem, and self-efficacy. RESULTS: The prevalence of smoking was 31.1% The scores of self-esteem and self-efficacy were positively correlated with smoking habits (W1=0.957, W2=0.975, P<0.05). Students presented rather low smoking addiction with male stu-dents having slightly higher scores than female students on self-esteem and self-efficacy, mili-tary students had higher self-esteem scores than civilians with physical activity having a posi-tive correlation with non-smoking behavior. CONCLUSION: According to our results, self-esteem and self-efficacy were slightly positively correlated with smoking habits. Furthermore, there was no statistically significant association between other social factors and smoking. Our results may develop a theoretical basis for medical students’ psychological variables studying for further implementation of university educational programs and preventive interventions em-powering students towards an overall better quality of life, addressing, among others, smoking behaviors.

Medical Trust-Network is one of the most promising fields of study in network science. Establishment of trust within medical entities ensures better treatment and increases better medical facilities. The word ‘Trust’ signifies a very important behavioral aspect between any human entities, especially among doctors and patients. To represent such relationships Trust Network Models are built to express the interactions between human entities within such networks. Though the idea of a Trust-Network has traditionally been one of the major areas of research, yet the concept of a medical trust network model is relatively a new domain. In this paper, we introduce an overall multilayered Trust Network to represent the entire healthcare architecture. More specifically our model is based on an evolutionary graph system with a discrete relationship between the three most important entities of any healthcare system, namely – Doctors, Departments, and Hospitals. Observations indicate that based on our model, the medical healthcare system is a multilayered model unlike a feed-forward model as indicated by previous studies.

Visual Question Generation (VQG) from images is a rising research topic in both fields of natural language processing and computer vision. Although there are some recent efforts towards generating questions from images in the open domain, the VQG task in the medical domain has not been well-studied so far due to the lack of labeled data. In this paper, we introduce a goal-driven VQG approach for radiology images called VQGRaD that generates questions targeting specific image aspects such as modality and abnormality. In particular, we study generating natural language questions based on the visual content of the image and on additional information such as the image caption and the question category. VQGRaD encodes the dense vectors of different inputs into two latent spaces, which allows generating, for a specific question category, relevant questions about the images, with or without their captions. We also explore the impact of domain knowledge incorporation (e.g., medical entities and semantic types) and data augmentation techniques on visual question generation in the medical domain. Experiments performed on the VQA-RAD dataset of clinical visual questions showed that VQGRaD achieves 61.86% BLEU score and outperforms strong baselines. We also performed a blinded human evaluation of the grammaticality, fluency, and relevance of the generated questions. The human evaluation demonstrated the better quality of VQGRaD outputs and showed that incorporating medical entities improves the quality of the generated questions. Using the test data and evaluation process of the ImageCLEF 2020 VQA-Med challenge, we found that relying on the proposed data augmentation technique to generate new training samples by applying different kinds of transformations, can mitigate the lack of data, avoid overfitting, and bring a substantial improvement in medical VQG.

Data sets for medical images are generally imbalanced and limited in sample size because of high data collection costs, time-consuming annotations, and patient privacy concerns. The training of deep neural network classification models on these data sets to improve the generalization ability does not produce the desired results for classifying the medical condition accurately and often overfit the data on the majority of class samples. To address the issue, we propose a framework for improving the classification performance metrics of deep neural network classification models using transfer learning: pre-trained models, such as Xception, InceptionResNet, DenseNet along with the Generative Adversarial Network (GAN) – based data augmentation. Then, we trained the network by combining traditional data augmentation techniques, such as randomly flipping the image left to right and GAN-based data augmentation, and then fine-tuned the hyper-parameters of the transfer learning models, such as the learning rate, batch size, and the number of epochs. With these configurations, the Xception model outperformed all other pre-trained models achieving a test accuracy of 98.7%, the precision of 99%, recall of 99.3%, f1-score of 99.1%, receiver operating characteristic (ROC) - area under the curve (AUC) of 98.2%.

Background: This study was conducted to investigate medical students' lifestyle habits, including sleep quality, eating and drinking pattern, physical activity, and social status. Method: This research project is part two of a multi-institutional cross-sectional observational study conducted among medical students from six medical colleges in Saudi Arabia between September and December 2019. Results: 675 medical students were enrolled electively into the lifestyle study. About half of this number were male students, and the majority aged 18-24 years. Most students (87.6%) slept between 4-8 hours a day, and over 44% were dissatisfied with their sleep. Only 28.1% had three meals a day; about 40% of them usually or always skipped breakfast. While 44% usually or always eat fast food, 44.7% drink 2 liters of water per day. Moreover, male students were significantly consuming fast foods than females, p<0.001. The majority (63.3%) revealed they usually or always drink black coffee daily. Females were significantly more inclined to regular coffee consumption than males, p<0.001. Only 4.3% exercising for 30 minutes or more daily. The majority (65%) of the students were introverted; they had few close friends. Yet, 81% were somewhat satisfied or satisfied with their social life. Male students were predicted to be significantly more satisfied with their social life than females, p=0.001. Only 4.6% smoked cigarettes daily. Whereas 7.1% smoke e-cigarette daily. In contrast, only 0.3% use shisha (hookah) daily. Male medical students were substantially more predicted to be inclined to e-cigarette use than females (p <0.001. The top five leisure activities of a medical student are surfing social media (75.9%), watching movies (61.3%), hanging out with friends (58.1%), spent time with their family (55.4%), and browsing the internet (53.6%). Female medical students were significantly more inclined to surfing social media than male medical students, p=0.022; also, watching movies was preferred for females compared to males, p=0.006. Conclusion: This study revealed that the majority of medical students in Saudi Arabia exhibited healthy lifestyles to some extent, and these health-promoting behaviors differed based on gender, especially concerning physical activity and eating patterns. The findings of this study provide relevant information for future actions that will be geared towards effectively decreasing the occurrence of chronic illnesses and improving future doctor’s well-being.

Disgust evolved as a way to protect one’s self from illness. DS-R measures disgust propensity of three kinds of disgust (Core, Animal Reminder and Contamination). Although the DS-R scale was refined mainly with young and largely female student population its impact on educational orientation has not been assessed. In the present study we examined the DS-R scoring and the choice of postgraduate studies in medical (n= 94) and psychology (n= 97) students. They responded to an anonymous web-based survey and completed the DS-R and a questionnaire on their demographics and plans for postgraduate studies. Female students outnumbered males (3:1) and scored higher in Total DS-R score (median: 59 vs. 50, p<0.05). Psychology students scored higher in all three kinds of disgust (p<0.05), indicating a higher level of disease avoidance. Medical students willing to follow Internal Medicine scored higher in Core Disgust (p<0.05) while psychology students willing to study Experimental Psychology scored lower in Animal Reminder subscale (p<0.001). Also, the higher the psychology students scored in Core Disgust scale the higher was the probability to choose Experimental Psychology. In conclusion, disgust propensity as rated by DS-R differentiates medical from psychology students and is also related to orientation preferences in postgraduate studies.

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.

Diagnostics is believed to drive about 70% of medical interventions, but this may not be true for all demographics. With more than 30% of the African population living in extreme poverty ($1.9 per day - 2022); and healthcare costs competing unfavorably against unbridled access to antibiotics and herbal concoctions, especially in Sub-Saharan Africa, how do you convince such a people to choose diagnostics first? More importantly, how do you design a medical device targeted at meeting the diagnostic needs of this demographic? In developing diagnostic devices for such a market, what is worth sacrificing? How dire is the need for advanced technologies in devices developed for such a demographic? These are questions that remain unaddressed by the technological breakthroughs and current research in medical device development, especially for infectious disease diagnostics. The objective of this paper is to underscore critical concerns which must be considered in the bid to successfully design medical diagnostic devices for Africa and perhaps, other limited-resource settings. It is also written as a suggestive guidance document for researchers whose interest is in the development of infectious disease diagnostic platforms for rural Africa and similar limited-resource environments.

Background: Tele-emergency physicians (TEPs) take an increasingly important role in the need-oriented provision of emergency patient care. To improve emergency medicine in rural areas, we set up the project Land|Rettung (English: Rural|Rescue), which uses TEPs to restructure professional rescue services using information and communication technologies (ICTs) in order to reduce the therapy-free interval. Successful implementation of ICTs relies on user acceptance and knowledge sharing behavior. Methods and findings: We conducted a factorial design with active knowledge transfer and technology acceptance as a function of work satisfaction (high vs. low), workload (high vs. low) and point in time (prior to vs. after digitalization). Data were collected via machine readable questionnaires issued to 755 persons (411 pre, 344 post), of which 304 or 40.3% of these persons responded (194 pre, 115 post).Technology acceptance was higher after the implementation of TEP for nurses but not for other professional groups, and it was higher when the workload was high. Regarding active communication and knowledge sharing, employees with low work satisfaction are more likely to share their digital knowledge as compared to employees with high work satisfaction. Additional and more detailed analyses reveal that this is an effect of previous knowledge concerning digitalization. After implementing the new technology, work satisfaction increased for the more experienced employees, but not for the less experienced ones. Results are discussed considering the Dunning-Kruger effect. The Dunning-Kruger effect describes a cognitive bias. People with high expertise often underestimate their actual skill level. They have a more critical attitude towards their performance and feel the urgent need to fill possible knowledge gaps they notice. Conclusions: Our research illustrates that employees’ workload has an impact on the intention of using digital applications. The higher the workload, the more people are willing to use TEPs. Regarding active knowledge sharing, we see that employees with low work satisfaction are more likely to share their digital knowledge compared to employees with high work satisfaction. This might be attributed to the Dunning-Kruger effect. Highly knowledgeable employees initially feel uncertain about the change, which translates into temporarily lower work satisfaction. They feel the urge to fill even small knowledge gaps, which in return leads to higher work satisfaction. Those responsible need to acknowledge that digital change affects their employees’ workflow and work satisfaction. During such times, employees need time and support to gather information and knowledge in order to cope with digitally changed tasks.

Big Data in medicine includes possibly fast processing of large data sets, both current and historical in purpose supporting the diagnosis and therapy of patients' diseases. Support systems for these activities may include pre-programmed rules based on data obtained from the interview medical and automatic analysis of test results diagnostic results will lead to classification of observations to a specific disease entity. The current revolution using Big Data significantly expands the role of computer science in achieving these goals, which is why we propose a Big Data computer data processing system using artificial intelligence to analyze and process medical images.

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.

This paper aims to implement histogram pyramids with marching cubes method for 3D medical volumetric rendering. The histogram pyramids are used for feature extraction by segmenting the image into the hierarchical order like the pyramid shape. The histogram pyramids can decrease the number of sparse matrixes that will occur during voxel manipulation. The important feature of the histogram pyramids is the direction of segments in the image. This feature will be then used for connecting pixels (2D) to form up voxel (3D) during marching cubes implementation. The proposed method is fast and easy to implement and it also produces a smooth result (compared to the traditional marching cubes technique). The experimental results show that time consuming for generating 3D model can be reduced by 15.59% in average. The paper also shows the comparison between the surface rendering using the traditional marching cubes and the marching cubes with histogram pyramids. Therefore, for the volumetric rendering such as 3D medical models and terrains where a large number of lookups in 3D grids are performed, this method is a particularly good choice for generating the smooth surface of 3D object.

The Medi-test system we developed was motivated by the large number of resources available for the medical domain, as well as the number of tests needed in this field (during and after the medical school) for evaluation, promotion, certification, etc. Generating questions to support learning and user interactivity has been an interesting and dynamic topic in NLP since the availability of e-book curricula and e-learning platforms. Current e-learning platforms offer increased support for student evaluation, with an emphasis in exploiting automation in both test generation and evaluation. In this context, our system is able to evaluate a student’s academic performance for the medical domain. Using as input medical reference texts and supported by a specially designed medical ontology, Medi-test generates different types of questionnaires for Romanian language. The evaluation includes 4 types of questions (multiple-choice, fill in the blanks, true/false and match), can have customizable length and difficulty and can be automatically graded. A recent extension of our system also allows for the generation of tests which include images. We evaluated our system with a local testing team, but also with a set of medicine students, and user satisfaction questionnaires showed that the system can be used to enhance learning.

In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0– disease status and 1– reasonable control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson’s symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance.

The Internet of Things (IoT) technology and devices constitute an exciting field in computer science that is rapidly emerging worldwide. IoT devices function by connecting real-world objects to the internet, resulting in a higher number of interconnected devices than ever witnessed in history. Through internet connectivity, these devices can be utilized in various ways, such as monitoring and tracking. Their prevalence is increasing exponentially, coinciding with advancements in wireless networking technologies. The internet’s enhanced connectivity has played a vital role in fostering the proliferation of IoT devices. Presently, almost any everyday object can be network-connected. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. This paper aims to review the emergence of IoT devices, analyze their common applications, and explore the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. IoT technology can be applied to nearly any domain, and each use case has unique requirements. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Moreover, a comprehensive understanding of the prevalent methodologies will enable readers to distinguish between current practices and future methods. Given the rapid rate at which IoT technology is advancing, this paper aims to provide researchers with an understanding of the factors that have brought us to this point and the ongoing efforts to shape the future of IoT.

Chronic non-communicable diseases (NCDs) are major public health problems and a significant financial burden on public health systems. By 2030, mortality due to NCDs, such as cardiovascular diseases, cancer, respiratory diseases, and diabetes, is predicted to increase in Brazil. A peculiar aspect of NCDs involves their long-term and integrated care management. This paper proposes a chronic disease management platform based on artificial intelligence to deliver digital health services everywhere. The proposed platform is anchored and built with healthcare 4.0 technologies, such as wearable devices, the internet of medical things, and artificial intelligence cloud-based solutions that allow the deployment of a smart healthcare system. In addition, the paper presents the feasibility of the platform in a diabetes prediction study case. For the study case, an initial dataset was established with bio-impedance, oxygen concentration, pulse rate, skin impedance, and skin temperature attributes. A baseline was implemented with ten regression models to assess the prediction performance of the mean squared error, root mean squared error, and r-squared score to compare predictive findings with capillary blood glucose measurements. Results evidence that the decision tree regressor and three ensemble methods (bagging decision tree regressor, random forest regressor, and AdaBoost regressor) yielded improvement over the other models. Moreover, a comparison among those models revealed that the decision tree regressor outperforms them and presents promissory outcomes.

People must have particular personality traits and social resources, also known as health literacy, in order to access, comprehend, and use information to make decisions about their health. Patients' ability to engage in complex disease management and self-care is strongly related to their level of health literacy. It can help us stay healthy by preventing illness and effectively managing existing illnesses. People with low health literacy (LHL) may find it difficult to manage their condition and prevent illness, which may lead to increased use of healthcare services. Furthermore, LHL is associated with increased hospitalizations, increased use of emergency care, decreased use of preventative services, and a worsened ability to understand labels and health messages, a worsened state of health, higher mortality, and more expensive medical care.

Diffuse optical tomography (DOT) is an optical medical imaging method that can assess the structural properties of tissues and their functional characteristics, such as hemoglobin concentration, water content, as well as lipid concentration, via three-dimensional image reconstruction. This paper presents the theoretical basis and working principle of diffuse optical tomography. The paper explains how the optical properties of tissue can be imaged by photon migration techniques based on diffusion theory. The author presents the reasoning for near-infrared (NIR) imaging as the most effective technique in terms of quantitative recovery of spectroscopic optical parameters. This work also listed various advantages, applications, and challenges of DOT. The paper also briefly discusses current progress in near-infrared medical imaging and its future direction.

Fall prediction using machine learning has become one of the most fruitful and socially relevant applications of computer vision in gerontological research. Since its inception in the early 2000s, this subfield has proliferated into a robust body of research underpinned by various machine learning algorithms (including neural networks, support vector machines, and decision trees) as well as statistical modeling approaches (Markov chains, Gaussian mixture models, and hidden Markov models). Furthermore, some advancements have been translated into commercial and clinical practice, with companies in various stages of development capitalizing on the aging population to develop new commercially available products. Yet despite the marvel of modern machine learning-enabled fall prediction, little research has been conducted to shed light on the security and privacy concerns that such systems pose for older adults. The present study employs an interdisciplinary lens in examining privacy issues associated with machine learning fall prediction and exploring the implications of these models in elderly care and the Internet of Medical Things (IoMT). Ultimately, a justice-informed set of best practices rooted in social geroscience is suggested to help fall prediction researchers and companies continue to advance the field while preserving elderly privacy and autonomy.

This study evaluated the impact of didactic videos and service user testimonial videos on mental illness stigma among medical students. Two randomized controlled trials were conducted in Nepal. Study 1 examined stigma reduction for depression. Study 2 examined depression and psychosis. Participants were Nepali medical students (Study 1:n=94, Study 2:¬n=213) randomized to three conditions: a didactic video based on the mental health Gap Action Programme (mhGAP), a service user video about living with mental illness, or a control condition with no videos. In Study 1, videos only addressed depression. In Study 2, videos addressed depression and psychosis. In Study 1, both didactic and service user videos reduced stigma compared to the control (F2,91=6.37, p=0.003). In Study 2 (depression and psychosis), there were no differences among the three arms (F2,210=2.07, p=0.13). When comparing Study 1 and 2, there was greater stigma reduction in the service user video arm with only depression versus service user videos with depression and psychosis (t(31)=-3.04, p=0.005). In summary, didactic and service user videos were associated with decreased stigma when content addressed only depression. However, no stigma reduction was seen when including depression and psychosis. This calls for different strategies based on types of mental illnesses. ClinicalTrials.gov identifier: NCT03231761

In Thailand, the topic of medical ethics and laws related to medical professions has been one part of the national competency assessment criteria. The objective of this article was to design legal issues into the medical curriculum and to share experiences of creative legal study. Legal contents were inserted into 10 subjects and taught for year 1 to year 6 medical students. Students were divided into multi-groups or received individual tasks and then, shared their knowledge and idea for solving legal problems. The results showed they could interpret and create novel ideas for legal and ethical reconstruction, including the topic of the principle of laws, criminal laws, civil and commercial laws, public health laws, organ donation/transplantation, end of life decisions, and legal liability for the medical profession. Finally, the creative legal study can be used as a novel approach to support creativity among medical students.

The Internet of Medical Things (IoMT) is playing a pivotal role in the healthcare sector by allowing faster and more informed hospital care, personalized treatment, and medical solutions. A very effective and trustworthy solution for resource-constrained medical devices is provided by Physical Unclonable Functions (PUF) - based identity and authentication systems, however they are not yet entirely reliable. This paper proposes VXorPUF, a Vedic Principles - Based Hybrid XOR Arbiter PUF. Modeling attacks were performed on the proposed architecture and an accuracy of 49.80 % was achieved. Uniqueness, Reliability and Randomness were the figures of merit used to evaluate PUF.

: Medical gloves, along with masks and gowns, serve as the initial line of defense against poten-tially infectious microorganisms and hazardous substances in the health sector. During the COVID-19 pandemic, medical gloves played a significant role, as they were widely utilized by society in daily activities as a preventive measure. These products demonstrated their value as important personal protection equipment (PPE) and reaffirmed their relevance as infec-tion-prevention tools. This review describes the evolution of medical gloves since the discovery of vulcanization by Charles Goodyear in 1839, which fostered the development of this industry. Regarding the current market, a comparison of the main properties, benefits, and drawbacks of the most widespread types of sanitary gloves is presented. The most usual gloves are produced from natural rubber (NR), polyisoprene (IR), acrylonitrile butadiene rubber (NBR), polychloroprene (CR), polyethylene (PE), and poly(vinyl chloride) (PVC). Furthermore, the environmental impacts of the conventional natural rubber gloves manufacturing process and mitigation strate-gies, such as bioremediation and rubber recycling, are addressed. In order to create new smart medical gloves with improved properties, several biopolymers (e.g. poly(vinyl alcohol) and starch) and additives such as biodegradable fillers (e.g., cellulose and chitin), reinforcing fillers (e.g. silica and cellulose nanocrystals), and antimicrobial agents (e.g., biguanides and quaternary ammonium salts) have been evaluated. This paper covers these smart materials and describes different innovative prototypes of gloves and coatings designed with them.

The success of total hip arthroplasty depends on the experience of the surgeon, and one of the ways the surgeon currently determines the final implant insertion depth is to listen to the change in audible pitch of the hammering sound. We investigated the use of acoustic vibration emissions as a novel method for insertion quality assessment. A non-invasive contact microphone-based measurement system for insertion depth estimation, fixation and fracture detection was developed using a simplified in vitro bone/implant (n=5). 2583 audio recordings were analysed in vitro to obtain energy spectral density functions. Out of the four main resonant peaks under in vitro conditions, broach insertion depth statistically correlates to increasing 3rd and 4th peak frequencies. Degree of fixation was also observed as higher goodness of fit (0.26-0.78 vs. 0.12-0.51 between two broach sizes, the latter undersized). Finally, however, the moment of fracture could not be predicted. A cadaver in situ pilot study suggests comparable resonant frequencies in the same order of magnitudes with the bone model. Further understanding of the signal patterns are needed for an early warning system diagnostic system for imminent fractures, bone damage, improving accuracy and quality of future procedures.

Breast cancer (BC) is a heterogeneous disease representing a substantial economic burden. In order to develop policies that successfully decrease this burden, the factors affecting costs need to be fully understood. Evidence suggests that early detection in Stage I has a lower cost than late detection. We aim to provide conservative estimates of BC's stage-wise medical costs from German healthcare and the payer's perspective. To this end, we conducted a literature review of articles evaluating stage-wise costs of BC in Germany through PubMed, Web of Science, and Econ Lit databases supplemented by Google Scholar. We developed a decision tree model to estimate BC related medical costs in Germany using available treatment and cost information. The review generated seven studies; none estimated the stage-wise costs of BC. The studies were classified into two groups: (1) case scenarios (five studies) and two studies based on administrative data. The first sickness funds data study (Gruber, Stock, et al. 2012) used 1999 information to approach BC attributable cost; their results suggest a range between €3,929 and €11,787 depending on age. The second study (Kreis, Plöthner, et al. 2020) used 2011-2014 data and suggested an initial phase incremental cost of €21,499, an intermediate phase cost of €2,620, and a terminal phase cost of €34,513 per incident case. Our decision tree model based BC stage-wise cost estimates were €21,523 for Stage I, €25,679 for Stage II, €30,156 for Stage III, €42,086 for Stage IV. Alternatively, the modeled cost estimates are €20,284 for the initial phase of care, €851 for the intermediate phase of care, and €34,963 for the terminal phase of care. Our estimates for phases of care are consistent with recent German estimates provided by Kreis and Plöthner et al. Furthermore, the data collected by sickness funds are collected primarily for reimbursement purposes, where the German ICD-10 classification system defines a cancer diagnosis. As a result, claims data lack the clinical information necessary to understand stage-wise BC costs. Our model-based estimates fill the gap and inform future economic evaluations of BC interventions.

Panoramic and periapical radiograph tools help dentists diagnose the most common dental diseases. Generally, dentists identify dental caries manually by inspecting X-ray images. However, due to their heavy workload, or poor image quality, dentists may sometimes overlook some unnoticeable dental caries, which may ultimately hinder the patient treatment. The purpose of this study was to develop an algorithm that classifies the teeth X-Ray images into three categories of “Normal”, “Caries”, and “Filled”. Our study used a dataset of 116 patients and 3712 single teeth images for training, validation and testing. Images were pre-processed using a sharpening filter and an intensity color map. We used a pre-trained transfer learning model, the NASNetMobile, which served as the feature extractor and the Convolutional Neural Network (CNN) model served as the classifier. The training dataset had a “Recall” of 0.92, 0.90 and 0.91 for “Normal”, “Caries” and “Filled” respectively and the test dataset had a “Recall” of 0.86, 0.81 and 0.85 for “Normal”, “Caries” and “Filled” respectively. The classification of the teeth X-Rays was successful and can be valuable for dentist as the artificial intelligence algorithm can serve as a decision support tool to aid dentists when they need to diagnose dental treatment.

Background: During anatomy (topographic) chair, we prepared an anatomic piece of an older female pelvis (unknown causa mortis). Here, we present an anatomic piece prepared by medical students that helped us to better understand hypertrophic bladder pathophysiology. Methods: A traditional corpse dissection guided by an assistant professor. Results: Corpse dissection enhanced our personal experience during the medical course. It also helped us to better visualize how big a hypertrophic urinary bladder can become as the etiologic cause is not taken care. Conclusions: Despite virtual and 3D anatomy lessons, we believe corpse dissection must remain as a teaching strategy that can help to build a new generation of surgeons as well as honor the History of Medicine.

Seniors safety is a compelling need, which necessitates 24/7 real-time monitoring and timely dangerous action recognition. Being able to mirror characteristics of physical objects (PO) to corresponding logical objects (LO) and seamlessly monitor their footprints thus detect anomaly parameters, Digital Twins (DT) has been considered a practical way to provide virtual health services for seniors safety. Meanwhile, widely adopted Internet of Medical Things (IoMT) consisting of wearable sensors and non-contact optical cameras for self and remote health data monitoring also raises concerns on information security and privacy violation. Therefore, security of POs, LOs and reliable data sharing among healthcare professionals are challenging as constructing trust and privacy-preserving virtual health services. Thanks to characteristics of decentralization, traceability and unalterability, Blockchain is promising to enhance security and privacy properties in many areas like data analysis, finance and healthcare. This paper envisions a lightweight authentication framework (LAF) to enable secure and privacy-preserving virtual healthcare services. Leveraging Non-Fungible Token (NFT) technology to tokenize LOs and data streams on blockchain, anyone can certify the authenticity of a digital LO along with its synchronized data between PO without relying on a third-party agency. In addition, the NFT-based tokenization not only allows owners fully control their IoMT devices and data, but it also enables verifiable ownership and traceable transferability during data sharing process. Moreover, NFT only contains references to encrypted raw data that are saved on off-chain storage like local files or distributed databases, such a hybrid storage strategy ensures privacy-preservation for sensitive information. A proof-of-concept prototype is implemented and tests are conducted on a case study of seniors safety. The experimental evaluation shows the feasibility and effectiveness of the proposed LAF solution.

Introduction: Like other public safety professions, paramedicine has historically been a male-dominated occupation, both in the demography of its workforce and in its organizational culture. Although women are increasingly choosing paramedicine as a career, participation in leadership roles remains limited. Drawing on data from a recent comprehensive mental health survey, we describe the proportion of women in leadership in a single, large, urban paramedic service in Ontario, Canada. Methods: We distributed an in-person, paper-based survey during the fall 2019 - winter 2020 Continuing Medical Education (CME) sessions. Participating paramedics completed a demographic questionnaire alongside a battery of mental health screening tools. We assessed the demography of the workforce and explored differences in employment classification, provider level (e.g., primary vs. advanced care), and participation in formal leadership roles along self-reported gender lines. Results: Out of 607 paramedics attending CME, we received 600 completed surveys, with 11 excluded for missing data, leaving 589 for analysis and a 97% response rate. Women comprised 40% of the active-duty paramedic workforce, with an average of 8 years of experience. Compared to men, women were more than twice as likely to have a university degree (Odds Ratio [OR] 2.02), but almost half as likely to practice at the Advanced Care Paramedic level (OR 0.61), and somewhat less likely to be employed full-time (OR 0.77). Women were nearly 70% less likely to hold a leadership role in the service compared to men (OR 0.36), occupying just 20% of leadership positions. Conclusion: Although paramedicine is witnessing an encouraging shift in the demography of its workforce with greater participation from women, there is still work to be done, particularly in leadership. Future research should focus on identifying and ameliorating barriers to career advancement among women and other historically underrepresented people.

Having valid and reliable tools that help health professionals to assess fear in children undergoing medical procedures is essential to offer humanized and quality of care in the paediatric population. The aim of this study was to develop the cross-cultural adaptation and the evaluation of the psychometric properties of the Spanish version of the “Child Medical Fear Scale” in its shortened version (CMFS-R). The design consisted of two phases, first of cross-cultural adaptation and second of the psychometric validation of the CMFS-R with a sample of 262 children from Spain. Confirmatory factor analysis was conducted to assess construct validity and the Cronbach alpha and the adjusted item-total score correlation coefficients were performed to study reliability. The results confirmed internal consistency and construct validity of the Spanish version of the CMFS-R, indicating that the scale has an acceptable level of validity and reliability. Therefore, this study brings a new version of the scale to assess fear related to medical procedures for use in the Spanish paediatric population.

A quality marine ecosystem if it is free from GTP contaminants. Accuracy and caution are needed in the exploitation of marine resources as marine tourism destinations so that in the future, there will be no ecological hazards that cause chain effects, not only on aquatic ecosystems but also on humans. This article identifies exposure to GTP (MP, PAH, PR, HM, MW) in marine ecosystems in the MTA area and BCI waters. The combination of qualitative and quantitative analysis methods uses a combination of analytical instruments and mathematical formulas. The search results show the average total abundance of MP in seawater and fish samples (5.47 units/m3) and (7.03 units/m3), respectively, while in sediment and sponge samples (8.18 units/m3) and (8.32 units/m3). Based on the analysis of the polymer structure, it was identified that the dominant light group MP (PE, PP and PS), followed by PA and PC. Several PAH pollutants were identified in the samples, especially NL types found in all samples, followed by PN and AZ. BCI sea waters are suspected to be exposed to MW and PR. Pollutants of Pb+2 and Cu+2 around BCI were successfully calculated with average concentrations in seawater 0.164 mg/L and 0.294 mg/L, respectively, while in fish, 1.8110 µg/g and 2,452 µg/g, respectively. Based on these findings, the BCI area is not recommended as a marine tourism destination.

Objective: This study investigated the effect of the hierarchical medical system under the national health insurance program on resident’s healthcare-seeking behavior in Taiwan. Background: Healthcare authorities in Taiwan initiated the allowance reduction of outpatient visits at regional hospitals and higher hierarchical hospitals from 2018. The ultimate goal is to implement a hierarchical medical system and provide the residents accessible as well as consistent medical services. Methods: This research was conducted through the questionnaire survey method and data were collected between August and December 2018 from the records of subjects who had recently sought medical attention. A total of 1,340 valid questionnaires were returned. Results: Regarding the effect on healthcare-seeking behavior, the following factors were significant: being aged between 40 to 49 (p＜.1), subjects with an educational background of junior high school (p＜.05), those who were not aware of the policy (p＜.001), and an awareness about both the hierarchical medical system and the policy to reduce outpatient visits to large hospitals (p＜.001). Conclusion: The public should be made aware about the hierarchical medical system to improve healthcare.

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.

Interdisciplinary conferences play a vital role in advancing explainable Artificial Intelligence (AI) within increasingly intersecting healthcare and computational biology arenas. Challenges result-ing from algorithmic bias and the necessity for domain-specific Explainable AI (XAI) techniques underscore the increasing significance of transparent and interpretable AI systems used in patient care. Collaborative efforts between clinicians, data scientists, and various stakeholders are nec-essary to innovate solutions tailored to healthcare needs. Specialized conferences may foster these interdisciplinary interactions, providing platforms for knowledge exchange, networking, and collaborative partnerships. The legal, ethical, and societal dimensions of medical AI advocate for an integrative approach that aligns technological advancements with patient-centered care val-ues. The role of specialized conferences is therefore essential for shaping future directions in ex-plainable AI and computational biology that contribute to improved patient outcomes and healthcare innovations.

The aim of performed studies was to fabricate an antibacterial and degradable scaffold that may be used in the field of skin regeneration. To reach the degradation criterion the biocompatible polyurethane (PUR), obtained by using amorphous macrodiol α,ω-dihydroxy(ethylene-butylene adipate) macrodiol (PEBA), was used and processed with so-called “fast-degradable” polymer polylactide (PLA) (5 wt% or 10 wt%). To meet the antibacterial requirement obtained hybrid PUR-PLA scaffolds (HPPS) were modified with ciprofloxacin (Cipro) (2 wt% or 5 wt%), the fluoroquinolone antibiotic inhibiting growth of bacteria such as Pseudomonas aeruginosa, Escherichia Coli and Staphylococcus aureus, which are main cause of wound infections. Obtained unmodified and Cipro-modified HPPS were studied towards their chemical composition to detect presence or absence of characteristic functional groups of PUR, PLA and Cipro, and as well to indicate the participation of hydrogen bonds in the HPPS structure in dependence on PLA addition and ciprofloxacin modification. Mechanical properties were studied to determine the possible application of HPPS as a skin tissue scaffold. Scanning electron microscopy (SEM) was used to study morphology of unmodified and Cipro-modified HPPS and to performed elementary analysis by using energy-dispersive x-ray spectroscopy (EDX) of obtained materials. Finally, the microbiological tests were performed to indicate the antibacterial effect of Cipro-modified HPPS on S.aureus growth. Performed studies showed that Cipro-modified HPPS, obtained by using 5 % of PLA, possess suitable mechanical characteristic, morphology, degradation rate and demanded antimicrobial properties to be further developed as a potential scaffolds for skin tissue engineering.

In this article, two elliptical-ovel-shaped cylindrical dielectric resonator based wideband Graphene inspired reconfigurable metasurface absorber is designed on diffused quartz substrate having a relative permittivity of 3.75 and conductivity 2200Kg/m3 with height of 25μm. This metasurface having gold plated ground in microstrip with 5μm which is essential by calculating skin depth at 2.0THz frequency. Two elliptical ovel shaped patches with Graphene plated having built in potential varies 0-2.5eV and relaxation time 1.0ps at 290oK. By changing the relaxation time of Graphene 0.1ps-0.3ps its resonance frequency also varies i.e., reconfigurable property they provide without adding any active i.e., PIN diode components. The proposed MS is designed having a dimension of 100×100μm2 is designed on a microstrip. This MS works in the range of 1.6-2.38THz having more than 90% absorbance with two transmission zero at 2.1THz and 2.3THz having high Q ˃ 50 which is used to determine the sensitivity where the proposed MS used as a sensor to determine hemoglobin (Blood Glucose level) and urine (Uric acid) by changing their refractive index from 1.34-1.43. Maximum sensitivity achieved at 2000GHz/RIU and average sensitivity for hemoglobin and urine sensing achieved 833.33GHz/RIU, FOM-8.33 and 4277.7733GHz/RIU, FOM-555.55 respectively.

Despite the fact that COVID-19 is no longer a global pandemic due to development and integration of different technologies for the diagnosis and treatment of the disease. Technological advancement in the field of molecular biology, electronics, computer science, artificial intelligence, Internet of Things, nanotechnology etc. has led to the development of molecular approaches and computer aided diagnosis for the detection of COVID-19. This study provides a holistic approach on COVID-19 detection based on (1) molecular diagnosis which include RT-PCR, antigen-antibody and CRISPR-based biosensors and (2) computer aided detection based on AI-driven models which include Deep Learning and Transfer learning approach. The review also provide comparison between these 2 emerging technologies and open research issues for the development of smart-IoMT-enable platform for the detection of COVID-19.

Skilled academic/medical writing is critical to research communication. The fundamental sections of a scholarly manuscript are introduction, methods, results and discussion. The introduction of a medical manuscript is aimed at briefing readers on the clinical extent and public health context of the research problem. It must justify the essentialness of the research to the scientific community and reveal any underlying research novelty. Skillfully conceived, designed and performed research protocols that are unskillfully presented can lose scientific credibility and impact. Without research communication skills authors would not be able to display the usefulness of their research for the scientific community. Generally, research communication or medical writing training/skills is underrepresented in curricular systems of medical schools globally. This can challenge publication quality and quantity of early-career authors/researchers. The author presents the academic experience he accumulated through peer review and supervision of vast manuscripts and theses. This article aimed at presenting a comprehensive roadmap for academic writing of the introduction and at identifying its common pitfalls.

Care coordination (CC) for children with special healthcare needs and medical complexity (CSHCN-CMC) is challenging, and medication management is especially difficult for providers, parents/caregivers, and patients alike. While numerous strategies for CC have been suggested and implemented, barriers to medication optimization remain. The report describes the creation of a pediatric clinical pharmacotherapy practice, related standard operating procedures to assure consistent application of screening tools and care provision through comprehensive medication management (CMM), and establishment of a collaborative practice agreement (CPA) to guide drug therapy delegation, monitoring, and modification. The methodology of a prospective case series is also presented to highlight drug therapy problems and their resolution in CSHCN-CMC. Future opportunities to expand the practice for engagement in population health management as well as prior authorization activities on behalf of physicians will be discussed.

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.

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 US opioid overdose epidemic has risen to an all-time high. Prescription opioids often serve as a gateway to illicit opioids which have appreciable overdose potential. Recent investigations have highlighted the efficacy and safety of marijuana-based products for pain management. Providing alternative pain treatment options may help mitigate the opioid epidemic. The distribution of codeine, fentanyl, hydrocodone, morphine, and oxycodone per 100K people and by 3-digit zip codes and overdose rates from 2014 to 2018 in California, which legalized recreational marijuana in 2016, were compared to Texas, where marijuana is functionally prohibited. Drug weights were obtained from the Automation of Reports and Consolidated Orders System and converted to oral morphine milligram equivalents. Overdose data was retrieved from the Centers for Disease Control’s WONDER database. California (-43.7%) and Texas (-27.3%) showed significant reductions in cumulative opioid distribution from 2014 to 2018. Opioid distribution per 100K people decreased -38.9% in California relative to -26.4% in Texas. Opioid and heroin overdoses increased between 1999 and 2019 by +11.6% in California but +272.7% in Texas. This evidence supports marijuana legalization as a mitigating factor to the opioid epidemic and opioid misuse.

Laser therapy devices (LTDs) operating with near-infrared laser light are increasingly being used in sports medicine. For several reasons the users cannot evaluate whether or not such devices emit laser beams according to the specifications provided by the manufacturer and the settings of the device. In this study the laser beams from two different LTDs that can be used in sports medicine were thoroughly characterized by measuring the emitted power, pulse shapes and lengths, and spatial intensity distributions using professional, high-fidelity laser measurement technology. This was repeated for three units of each LDT independently to distinguish problems of individual units from potential intrinsic instrument design errors. The laser beams from the units of one LTD agreed with the settings at the device, with the measured average power for these units being within 3.3% of the set power. In contrast, the laser beams from the units of the other LTD showed large deviations between the settings and the actual emitted light. This device came with three laser diodes that could be used independently and simultaneously. The average power differed greatly between the units as well as between the laser diodes within each unit. Some laser diodes emitted essentially no light, which could lead to a lack of treatment of patients. Other laser diodes emitted much more power than set at the device (up to 230%) that could result in skin irritations or burnings of patients. These findings indicate a need for better standardization and consistency of therapeutic laser light sources.

Little is known about family medicine academic staff in Taiwan, and basic data about this workforce may aid healthcare decision makers. We analysed data on Taiwan’s 13 medical schools collected by the Taiwan Association of Family Medicine from June to September 2019. Items included medical school names and total staff, and the gender, age, degree, working title (part-time/full-time), academic level, and sub-specialty of each current family medicine faculty member. A total of 116 family medicine faculty members were reported; most were male (n= 85, 73.3%). Ages ranged between 30 and 69 years, with a mean (SD) age of 43.3 (8.09). Faculty members with a master’s degree were the largest group (n= 49, 42.2%), and most were academic lecturers (n=49, 42.2%). Additionally, only about one-fourth (n=26, 22.4%) of family medicine faculty in medical schools were full-time, while the other three-fourths (n=90, 77.6%) were part-time faculty; most were located in northern Taiwan (n=79, 68.1%) and specialized in gerontology and geriatrics (n=55, 47.4%) and hospice palliative care (n=53, 45.7%). Our research provides the most complete census of family medicine academic physicians in medical schools in Taiwan. The results inform efforts to improve the establishment and development of family medicine departments in Taiwan.

There are complex ethical dilemmas inherent in medicine teaching, particularly in clinical prac-tice involving actual patients. Questions must be raised on fulfilling medical students' training needs while still respecting patients' fundamental rights to autonomy and privacy. We aimed to assess patients' perspectives regarding medical students' involvement in their medical care. An observational, cross-sectional study was developed, and an interview-like questionnaire was ap-plied randomly to patients waiting for a consult/admitted to three distinct departments: General Surgery, Obstetrics/Gynaecology, and Infectious Diseases. Of the 77% interviewed patients who reported previous experiences with medical students, only 59% stated that they were asked for consent for their participation and 28% that students had adequately introduced themselves. Pa-tients from Gynaecology/Obstetrics were the ones who reported lower rates of these practices and were also the ones who were most bothered by students' presence, stating that they would refuse students' participation in the future. Male patients received more explanations than female pa-tients regarding the same matters. 35% of patients stated they would feel more comfortable without the medical students' presence. The study shows a need to pay closer attention to ful-filling patients' fundamental rights.

Our primary objectives were a) to determine the need for, and the availability of point-of-care testing (POCT) for infectious diseases and b) to recommend point-of-care testing strategies and spatial care paths (SCPs) that enhance public health preparedness in regional districts of Thua Thien Hue Province (TTHP), Central Vietnam, where we conducted field surveys. Medical professionals in 7 community health centers (CHCs), 7 district hospitals (DHs) and 1 provincial hospital (PH) participated. Survey questions (English and Vietnamese) determined the status of diagnostic testing capabilities for infectious diseases and other acute medical challenges in TTHP. Infectious disease testing was limited: 6 of 7 CHCs (86%) lacked infectious disease tests. One CHC (14%, 1/7) had two forms of diagnostic tests available for the detection of Malaria. All CHCs lacked adequate microbiology laboratories. District hospitals had few diagnostic tests for infectious diseases (Tuberculosis, Syphilis), blood culture (29%, 2/7), and pathogen culture (57%, 4/7) available. The PH had broader diagnostic testing capabilities but lacked preparedness for highly infectious disease threats (e.g., Ebola, MERS-CoV, SARS, Zika, and Monkeypox). All sites reported having COVID-19 rapid antigen tests; COVID-19 RT-PCR tests were limited to higher tier hospitals. We conclude that infectious disease diagnostic testing should be improved and POC tests must be supplied near patients’ homes and in primary care settings for the early detection of infected individuals and mitigation of the spread of new COVID-19 variants and other highly infectious diseases.

In 2020, students of Pakistan had to adapt to the online environment for the very first time. This study aims to analyze the perceptions of medical, dental, and allied health students about online education in Pakistan. A descriptive, cross-sectional study was done to assess the level of acceptance of undergraduate students. A pre-validated questionnaire regarding demographics, past-experience of e-learning, advantages disadvantages of e-learning, and general perception of students towards e-learning was distributed. Descriptive statistics were computed for demographics, Mann-Witney-U test was used to compare the differences of perceptions between pre-clinical year and clinical years students. Kruskal-Wallis test was applied to compare the results of three specialties of students. Chi-square was used to compare overall category-wise positive and negative responses of students. 1200 students participated in the study. The major advantage identified by all students was the ‘comfortable environment’ in which they studied online. The major disadvantage selected by preclinical year students was ‘anxiety due to social isolation’ and that chosen by clinical year students was ‘lack of patient interaction’. Overall, 72% of students had a negative perception of e-learning. Student-teacher training, student counselling sessions, and innovative techniques need to be introduced to enhance student engagement and reduce pandemic stress.

Background: Worldwide, microbes are becoming more dangerous by acquiring virulent skills to adapt and develop antimicrobial resistance (AMR). This is a concern as this increases morbidity, mortality, and costs. Consequently, physicians need to be trained inappropriate prescribing, starting with medical students. Objective: Evaluate medical students' confidence in antimicrobial agent prescribing and drug resistance Methods: Cross-sectional study assessing medical students' knowledge, perception, and confidence in prescribing antimicrobial agents and drug resistance in a Malaysian University. A universal sampling method was used. Results: Most respondents believe that educational input regarding overall prescribing was sufficient. Regarding the principle of appropriate and accurate prescriptions, female medical students had less knowledge [Odds Ratio (OR)=0.51; 95% Confidence Interval (CI) 0.25-0.99; p=0.050]. Year-IV and Year-V students had more excellent knowledge than Year-III students regarding confidence in antibiotic prescribing. Year-V students also showed appreciably higher confidence in the broad principles of prescribing, including infectious diseases, compared to those in other years. Conclusion: Overall, medical students, gain more excellent knowledge and confidence regarding prescribing, including antimicrobials, as their academic careers progress.

Decades of research suggest that Mindfulness-Based Stress Reduction (MBSR) training supports a greater capacity to live with chronic medical conditions and contributes to lowering stress levels. This paper introduces a model for a Mindfulness-Based Recovery from Stroke (MBRfS) for promoting stroke recovery, informed by the lived experience of the author (a stroke survivor and certified MBSR instructor), the research literature regarding MBSR training, and the specific challenges of stroke recovery. Four themes emerged from the autoethnographic analysis that informed the proposed model: Readiness to accept the stroke event and the acquired brain injury; Navigating uncertainties of stroke recovery with awareness and self-responsibility for outcomes; Trusting the inherent wisdom of the body as a stroke recovery “teacher”; and Increased capacity to integrate complex emotions with self-compassion, and a sense of wholeness. A four component MBRfS model is offered, which consists of an integration amongst a modified MBSR framework, emergent attitudinal themes, and insights from the autoethnographic vignettes. The MBRfS model offers a path for providing participants with a supportive experience within stroke recovery. Recommendations and suggestions for future studies are offered to support the development of MBRfS for stroke survivors and their caregivers, as well as contributing to health care providers.

Background: Necrotizing enterocolitis (NEC) is one of the leading causes of death in newborns, however, little is understood of which patients can be treated medically or require surgery. The purpose of our study is to analyze the associated factors of surgically treated patients compared to patients requiring only medical treatment. Methods: Patients diagnosed with necrotizing enterocolitis over a period of 14 years in a single children’s hospital were retrospectively enrolled. Demographics and clinical data patients were collected and analyzed. Results: A total of 189 patients with NEC were included. Surgically treated patients had a lower gestational age (P = .018), body weight at birth (P = .034), and percentage of exclusive breast milk feeding (P= .001). They had increased comorbidity with respiratory distress syndrome (RDS) (P = .005), number of days of antibiotic use (P = .014), and length of hospital stay (P = .000). In multivariate logistic analysis, a lower percentage of exclusive breast milk feeding (OR = 0.366, 95% CI: 0.164-0.817) and a longer hospital stay (OR = 1.010, 95% CI: 1.001- 1.019) was associated with surgical NEC. Conclusion: Comparing medical and surgical NEC, a significantly lower percentage of surgical NEC patients were exclusively fed breast milk and their hospital stays were longer.

On the 30^thanniversary of the Karolinska Institutet Summer Research School for High School students an evaluation was carried out of the school’s activities. The evaluation was performed by questionnaire sent to 533 former students and also by searching the KI course registration database for all former students who went on to register in under- or postgraduate education at KI. Approximately half of all former Summer School students performed their undergraduate studies at KI, predominantly in the medical program. Those were far more likely to choose a research-oriented education than their peers. KI Medical students who previously attended the Summer School were also more likely to later register for PhD education than their peers who did not attend the summer school.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

Background: With new challenges to the health system, many new competencies within the scope of teaching public health need to be addressed in medical schools’ curricula such as disaster risk management and health system science. The aims of this study were to identify the needs of public health competencies for medical doctors in Thailand and to assess the level of integration for technical collaboration in teaching public health. Method: A total of 17 out of 21 Thai medical schools participated in the national survey. Qualitative inquiries applied focus group interviews of community representatives from ten sample villages and in-depth interviews of representatives from stakeholder organizations particularly employers. The list of public health competencies framework recommended by WHO-SEARO was applied. Quantitative analysis applied descriptive analysis using STATA 15 and qualitative findings were validated by interrelating the meaning of themes from Word Clouds created in NVivo12. Data integration applied a mixed-method Quan-qual approach. Results: 17 medical schools returned the questionnaires (80.95 % yield). The most common regionally-defined public health competencies (in over 70% of schools) were shown to be: Biostatistics, Community Medicine, Epidemiology, Family Medicine, Medical Ethics and Professional Laws, Preventive Medicine, Health Promotion, Holistic Care, and Research. The curriculum in only one medical school lacked Health Economics, whilst Disaster Management was lacking in two other schools. Discipline-based subjects were found to be more prevalent than interdisciplinary competencies. A variety of methods were being applied for teaching public health. The majority of the schools applied lecture as the main teaching method and multiple-choice questions as the main assessment method. Thai communities expect the doctors to get in touch with the community more often, lead the primary health care team through training the health professionals and community health volunteers, and educate the community for better health. Conclusion: Human resource is the main challenge in addressing interdisciplinary competencies. It is necessary to establish a collaborating mechanism among the big and small medical schools and the faculties of public health to improve the teaching of public health to undergraduate students in medical schools. There is also a need to strengthen the health system science and leadership so that future MDs can lead health service delivery according to the needs of their employers such as the Ministry of Public Health and the Rural Doctors Association. The findings of this study may help to identify a national framework of public health core competencies for medical schools and create a common platform for interdisciplinary collaborations.

This study aimed at assessing the extent to which the Saudi general population has embraced digital health medical applications to meet their health-related needs so that the Saudi Ministry of Health and government can appropriately be guided on scaling up of digital health across the country. As such, this study was guided by the question of to what extent do the Saudi people use digital health mobile-based applications?. This was a cross-sectional study utilizing snowballing sampling approach. Frequencies, Chi-square, and Spearman rank correlation statistics were used to offer descriptive and inferential analysis of the variables. The majority of the participants were economically able to afford smart devices that have medical apps, had at least an app on such devices, and highly regarded the benefits of the apps. Unfortunately, their understanding of how to use such apps was limited and this posed a barrier to embracing digital health alongside difficulty downloading apps, and medical ethical concerns. Although there is a willingness, extra effort is needed from the Saudi Ministry of Health and government to promote the uptake of digital health in Saudi Arabia.

Background: The effect of the COVID-19 pandemic has transformed medical education and is likely to have long-lasting effects on student learning, mental well-being, and eating behaviour. This study aimed to examine the learning behaviors of medical students at the American University of Integrative Sciences (AUIS), Barbados, during the COVID-19 pandemic. Methods: A cross-sectional web-based online survey was administered to medical students at AUIS from July until November 2021. The data collecting instrument recorded students’ demographic and learning behaviour information (Meo et al. 2020), and eating disorders (SCOFF questionnaire). Results: The overall response rate was 55% (n=120). In relation to learning behaviour, students agreed with the following statements: ‘deterioration in work performance and studying’ (48.4%), ‘remember subject’s contents appropriately’ (40.4%), ‘concentration on the studies’ (40.3%), ‘difficulty in performing two tasks simultaneously’ (38.7%), ‘difficulty in performing mental calculations’ (33.9%), ‘difficulty in recalling recent information’ (32.3%), and ‘difficulty in recalling old information’ (38.7%). Among the 8 dimensions of learning behaviors, deterioration in work performance or studying, and difficulties in recalling recent information were found to be significantly associated with the gender of the students. For SCOFF questionnaire, approximately 24.2% screened positive for eating disorders. Screening with the SCOFF test demonstrated that female, older (>25 years), overweight + obese, Clinical Sciences + PreMed, and non-USA-based students were at more risk of eating disorders. Conclusions: The results indicate that during the COVID-19 pandemic AUIS students have developed learning difficulties, and are likely to have eating disorders. University policymakers should take appropriate measures to support a healthy learning environment and improve students' mental well-being and eating behaviours.

The study was conducted on applying machine learning and data mining methods to personalizing the treatment. This allows investigating individual patient characteristics. Personalization is built on the clustering method and associative rules. It was suggested to determine the average distance between instances for optimal performance metrics finding. The formalization of the medical data pre-processing stage for finding personalized solutions based on current standards and pharmaceutical protocols is proposed. The model of patient data is built. The paper presents the novel approach to clustering built on ensemble of cluster algorithm with better than k-means algorithm Hopkins metrics. The personalized treatment usually is based on decision tree. Such approach requires a lot of computation time and cannot be paralyzed. Therefore, it is proposed to classify persons by conditions, to determine deviations of parameters from the normative parameters of the group, as well as the average parameters. This made it possible to create a personalized approach to treatment for each patient based on long-term monitoring. According to the results of the analysis, it becomes possible to predict the optimal conditions for a particular patient and to find the medicaments treatment according to personal characteristics.

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%.

No-show appointments in healthcare is a problem faced by medical centers around the world, and understand the factors associated with the no-show behavior is essential. In the last decades, artificial intelligence took place in the medical field and machine learning algorithms can work as a efficient tool to understand the patients behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on a SLR following the Kitchenham methodology, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each studies were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patients age, whether the patient missed a previous appointment, and the distance between the appointment and the patients scheduling.

Best practices in global health training prioritize leadership and engagement from investigators from low- and middle-income countries (LMICs), along with more conscientious community consultation and research that benefits local participants and autochthonous communities. However, well into the 20th century, international research and clinical care were rife with paternalism, extractive practices, and racist ideation, with race presumed to explain both vulnerability or protection from various diseases despite scientific evidence for more precise mechanisms for infectious disease. We highlight experiences in global research on health and illness among indigenous populations in LMICs, seeking to clarify what is both scientifically essential and ethically desirable in research with human subjects; we apply a critical view towards race and racism as historically distorting elements that must be acknowledged and overcome.

This study is a cross-sectional survey involving physicians around Indonesia aimed to explore physician hesitancy to treat COVID-19 patients after experiencing COVID-19 infection coupled with associated occupational risk factors. The questionnaire was distributed via contact information from the Indonesian Physician Association database. Out of 383 participants, 25.6% suffered from moderate symptoms of COVID-19 and 2.9% were admitted for critical care. Hesitancy to treat suspected, probable, or confirmed COVID-19 patients was found in 20.3% of physicians. A higher hesitancy rate was found in older physicians and those with less experience in treating COVID-19 patients. Specialist trainees and those who work in public hospitals were physicians with the lowest hesitancy in treating COVID-19 patients. There is a significant hesitancy in treating COVID-19 patients among physicians who have suffered from COVID-19 which calls for further action by management and policy makers.

The COVID-19 pandemic measures have negatively impacted education worldwide via travel restrictions, student visas, and physical attendanc,e among many other fac-tors. As one of the first places to impose strict pandemic measures and one of the last places to lift the measures, Mainland China provided a unique venue to investigate the effect of public health pandemic control policies on tertiary education. Considering the large portion of international students in medical schools and the higher susceptibility to the pandemic measures due to the natural synergy with teaching hospitals, medical schools can be an early forefront indicator of the impact of the pandemic on tertiary education. We surveyed 513 participants international medical students from Wen-zhou Medical University’s school of international studies in 2019 and 2022 about their satisfaction and demographic and cultural adaptation parameters. We found that the pandemic restrictions did not generally impact student satisfaction. Further analyzing the associated parameters, we found teaching satisfaction to be independent of age (P=.159), years in China (P=.896) and Chinese Level (P=.577) in the medical university. Teaching satisfaction is associated with the region of origin (P <0.001), acceptance of local social norms (P <0.001), satisfaction with the core values of Chinese culture (P <0.001), acclimatization to life in China (P <0.001), can easily make Chinese friends (P <0.001) among other factors. The findings of this study provide an understanding of the effects of pandemic measures on tertiary education satisfaction and other factors that can aid in international student satisfaction.

The disease COVID-19 is brought on by the acute respiratory syndrome coronavirus 2 (SARS-CoV-2) First appeared in China in December 2019 and quickly spread around the world after being declared a worldwide pandemic by the World Health Organization in March 2020. COVID-19, Medical Laboratory Professionals, SARS-COV-2, Diagnostic, Importance and difficulties for testing were the selected terms to search the databases of PubMed and Google Scholar for previously published material. Many diagnostic tests are applied in corona virus detection like Droplet Digital PCR (ddPCR), Multiplex PCR, Loop-mediated Isothermal Amplification (LAMP) and more. There is a lack of availability of molecular and serologic tests that have been clinically validated or authorized by national or international regulatory bodies. Clinical laboratory experts are tackling threats to our global defense and wellness, including infectious diseases. The opportunity to express gratitude to the unsung medical laboratory heroes and COVID0-19 pandemic allies is now greater than ever. Each year, Medical Laboratory Professionals Week should honors those who contribute key diagnostic data that help save lives. This review article explore overall summary on testing methods including Important and Difficult Tasks for Medical Laboratories which will provide good message for better public health matter.

Demand for green technologies and energy is in continuous growth in the last years. Compact efficient antennas are crucial for energy harvesting portable systems. Small antennas have low efficiency. The efficiency of communication and energy harvesting systems may be improved by using efficient passive and active antennas. The system dynamic range may be improved by connecting amplifiers to the printed antenna feed line. Design, design considerations, computed and measured results of wearable meta-materials antennas with high efficiency for energy harvesting applications are presented in this paper. All antennas were analyzed by using 3D full-wave software. The antennas electrical parameters on human body are presented. The directivity and gain of the antennas with Split-ring resonators, SRR, is higher by 2.5dB than the antennas without SRR. The resonant frequency of the antennas with SRR is lower by 5% to 10% than the antennas without SRR.

Background: The COVID-19 outbreak has accelerated the huge difference between medical care and disease prevention in Chinese medical institutions. This study aimed to investigated the relationship between the symbiotic units, environments, models, and effects of the integration of medical and disease prevention. Methods: This cross-sectional study involved 762 employees of public hospitals in 11 cities in Zhejiang Province by random stratified sampling. We analyzed the influence paths of elements in the mechanism of integration of medical and disease prevention and the mediating effect of symbiotic models among symbiotic units, symbiotic environments, and effects for on this integration. Results: The path coefficient of symbiotic unit on the symbiosis model was 0.46 (p&lt;.001), the path coefficient of symbiotic environment on symbiosis model was 0.52 (p&lt;.001). The path coefficient of the symbiotic unit and environment was 0.91 (p&lt;.001). The symbiotic models exhibited a partial mediation effect between symbiotic units and the effect of this integration. Sobel test = 3.27, β = 0.152, and the mediating effect accounted for 34.6%. Conclusions: It is suggested that Health policy-makers and public hospital managers should provide sufficient symbiotic units, establish collaborative symbiotic models, and improve the effects of integration of medical and disease prevention in public hospitals.

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.

The aim of study was to investigate epidemiology aspects of magnetic resonance imaging (MRI) during COVID-19 pandemic. The study comprised depersonalized residents of Tomsk and Tomsk Region (n = 1714). Invitations to take online survey were sent to 50,000 residents by target SMS with response rate of 1.2% (n = 727, Cohort 1). Cohort 2 comprised retrospective patients (n = 987) who underwent contrast-enhanced cardiac MRI (CMR) in 2019-2022. Referrals, clinical characteristics, diagnosis, gender, age, past COVID-19, MRI study protocols, and MRI data were analyzed. 29% of respondents in cohort 1 received MRI examination within past two years; 26% of respondents considered MRI the most informative imaging modality for detecting COVID-19 pneumonia; 12% of respondents reported MRI unavailable. Proportion of CMR among MRI studies increased during COVID-19 pandemic, and maximum incidence of cardiac diseases detected by MRI was in 2021. Incidence of myocardial fibrosis increased from ~67% in 2019 to ~84% in 2022. The rate of outpatient MRI studies significantly increased in 2020, but returned to pre-pandemic level in 2021. COVID-19 pandemic increased the need for MRI and CMR. Patients with history of COVID-19 had persistent and newly occurring symptoms of myocardial damage suggesting chronic cardiac involvement requiring continuous follow-up.

Chronic non-cancer pain is a highly debilitating condition affecting approximately 20% of the global population. Chronic pain may lead to significant physical disability, emotional distress, social isolation and financial burden. Whilst^. pharmacological therapies remain the cornerstone of pain management in non-cancerous chronic pain, factors including the current opioid epidemic have led to non-pharmacological techniques becoming a more attractive proposition. We explored the prevalence of medical device use and their treatment efficacy in non-cancer pain management. A systematic methodology was developed, peer reviewed and published in PROSPERO (CRD42021235384). Key words of medical device, pain management devices, chronic pain, lower back pain, back pain, leg pain and chronic pelvic pain using Science direct, PubMed, Web of Science, PROSPERO, MEDLINE, EMBASE, PorQuest and ClinicalTrials.gov. All clinical trials, epidemiology and mixed methods studies that reported the use of medical devices for non-cancer chronic pain management published between the 1^st of January 1990 and the 30^th of April 2022 were included. 13 studies were included in systematic review, of these 6 were used in the meta-analysis with 173 participants. Our meta-analysis for pain reduction in each study showed that transcutaneous electrical nerve stimulation combined with instrument-assisted soft tissue mobilization treatment and pulsed electromagnetic therapy produced significant treatment on chronic lower back pain patients. Pooled evidence revealed the use of medical device related interventions resulted in 0.7 degree of pain reduction under a 0-10 scale. Significant improvement in disability scores, with a 7.44 degree reduction in disability level compared to a placebo using a 50 score range was also seen. The application of medical devices in patients with chronic pain has gained popularity due to increasingly cost effective techniques, minimally invasiveness and greater awareness of risks associated with pharmacological management. Our analysis has shown that the optimal use of medical devices in a sustainable manner requires further extensive research, needing larger cohort studies with greater gender parity, in a more diverse range of geographical locations.

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.

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

Background: Emergency medical services (EMS) are the first health care contact for the majority of stroke patients. However, there is a lack of data on the current paramedics’ hospital-directed feedback and training needs across different health care settings. We aimed to evaluate paramedics’ prehospital stroke care knowledge, training needs, and current status of feedback on suspected stroke patients. Methods: We surveyed paramedics from the Vilnius region from September to November 2019, and compared the answers between the city and the district agencies. The questionnaire content included questions on paramedics’ demographic characteristics, prehospital stroke care self-assessment, knowledge on stroke mimics, stroke training needs, and the importance of hospital-directed feedback on suspected stroke patients. Results: A total number of 161 paramedics were surveyed, with more district paramedics rating their prehospital stroke care knowledge as inadequate (44.8% (95% confidence interval (CI) 32.8–57.6) vs 28.1% (95% CI 20.1–27.8), p = 0.028). In addition, more district paramedics indicated a need for additional stroke training (83.1% (95% CI 71.5–90.5) vs 69.8% (60.0–78.1), p = 0.043). However, respondents reported being the most confident while dealing with stroke (71.3%, 95% CI 63.8–77.7) compared to other time-critical conditions (p < 0.001). Vertigo (60.8%, 95% CI 53.0–68.0), brain tumours (56.3%, 95% CI 48.5–63.8), and seizures (54.4%, 95% CI 46.7–62.0) were indicated as the most common stroke mimics. Only 6.2% (95% CI 3.4–11.1) of respondents received formal feedback on the outcome of suspected stroke patients brought to the emergency department. Conclusion: A high proportion of paramedics self-perceive having inadequate stroke knowledge and an urgent need for further stroke training. The EMS staff indicate receiving an insufficient amount of feedback on suspected stroke patients, even though the usefulness is perceived to be paramount.

Cell therapy has used mesenchymal stem cells (MSCs) which in cell culture are multipotent progenitors, capable of producing a variety of cells limited to the mesoderm layer. There are two types of MSCs sources: 1) adult MSCs, which are obtained from the bone marrow, adipose tissue, peripheral blood, and dental pulp; and 2) neonatal tissue-derived MSCs, obtained from extra-embryonic tissues such as the placenta, amnion, and umbilical cord. Until April 2023, there are 1,120 registered clinical trials using MSCs therapies worldwide, but there are only 12 MSCs therapies that have been approved by regulatory agencies for commercialization. Nine of the twelve approved MSCs products are from Asia, with South Korea being the country with the most approved therapies. In the future, MSCs will play an important role in the treatment of many diseases. However, there are many issues to deal with before their application and usage in medical practice. Some strategies have been proposed to face these problems with the hope of reaching the objective of applying these MSCs therapies at optimal therapeutic levels.

Obstructive Sleep Apnea (OSA) is nowadays one of the respiratory pathologies with a higher in-cidence globally in developed countries. This situation led to an increase in the demand for medical appointments and diagnostic studies related to that condition, especially those based on poly-somnographies and cardiorespiratory polygraphies. These studies are limited in resources, causing long waiting lists with the subsequent impact on the patients’ health. Furthermore, it is necessary to mention that OSA’s symptomatology is not very specific, and it is typically present in the general population (excessive sleepiness, snore, etc.). In this regard, this paper proposes a novel intelligent clinical decision support system for the diagnosis of OSA which could be used to help medical teams, both in primary care settings and in units specialized in respiratory pathologies. The aim of the proposed system is to help discriminate the patients suspected of suffering from the pathology from those who are not. To this end, two types of information sets of heterogeneous nature are consid-ered. The first one encompasses objective data, related to the patient's health profile with infor-mation usually available in electronic health records. The second type comprises subjective data, referred to the symptomatology reported by the patient in a previous interview. To process the first group of information, a Machine Learning classification algorithm is used, Bagged Trees in this case. For processing the second information set, related with the symptomatology of the patient, a col-lection of expert systems based on fuzzy inferential systems arranged in cascade are employed. As a result, the system is able to determine two risk indicators related to the patient's risk of suffering from OSA: the Statistical Risk and the Symbolic Risk respectively. Subsequently, by interpreting both risk indicators mentioned it will be possible to determine the severity of the patients’ health, proposing a preliminary evaluation on their condition. For the initial tests of the system, a software artifact has been built using a dataset with 4,978 selected patients, suspected of suffering from OSA, from the Álvaro Cunqueiro Hospital in Vigo. The results obtained are promising, demonstrating the potential usefulness of this type of tools in medical diagnosis. Once the system has been validated with new data from clinical environments, it is considered as possible to obtain a relevant improvement in the quality of the healthcare services, and a reduction in the associated costs.

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.

The health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) technique is used to retrieve the health data back and for reducing security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.

Alzheimer’s disease has become a major concern in the healthcare domain as it is growing rapidly. Many researches have been conducted to detect it from MRI images through various deep-learning approaches. However, the problem of the availability of medical data and the privacy of the patients still exists. To mitigate this issue in Alzheimer’s disease detection, we have implemented the federated approach which is found to be more efficient, robust, and consistent compared to the conventional approach. For this, we needed deep excavation on various orientations of MRI images and transfer learning architectures. Then, we utilized the publicly available two datasets (OASIS, and ADNI), and designed various cases to evaluate the performance of the federated approach. In almost all of them, the federated approach achieved better accuracy and sensitivity. In distinguishing between two classes equally, the capability of the models was better in the found model when trained with federated approach compared to the conventional approach. In this approach. MobileNet, which is a low-cost transfer learning architecture, achieved the highest 95.24%, 81.94%, and 83.97% accuracy in OASIS, ADNI, and merged (ADNI+OASIS) test set, which was way higher than the achieved performance in Conventional approach. Moreover, in this approach, only the weights of the model were shared which keeps the original MRI images in their respective hospital or institutions, mitigating the privacy concern in the healthcare domain.

Medical Device Design process carries a high responsibility when defining the characteristics of the object for its correct interaction with users. This study presents a proposal for the improvement of the medical device design processes, in order to increase user acceptance, considering two key factors: the hierarchy of users and the relationship with the patient's health status. The goal of the study seeks to address this research gap and raise design factors with practical suggestions for the design of new medical devices. The results obtained will help medical device designers in the development stage to make more informed decisions about the functions and features required in the final product; As well, in the design process didactics, demonstrating the importance of the correct execution of the process, and how the factors considered can generate an impact on the final product. An experiment was carried out with Forty design engineering students, who designed birthing beds, with two design processes, the traditional product design process, and the new design process based on hierarchies (proposed in this study). The results showed that there is a significant increase in user acceptance with the new birthing bed that was developed with the hierarchical based design process.

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.

The presence of burnout, anxiety and depression among health professionals is a crucial issue that must be carefully addressed. The aim of the present study is to investigate levels of burnout, anxiety and depression among health professionals (medical and nursing staff) during the pandemic crisis, the association of burnout with anxiety and depression as well as the impact of occupational and demographic factors. One hundred twenty-five health professionals (medical and nursing staff) participated who were working in a public hospital in the broader area of Athens (sample of convenience). Specifically, 41 males and 84 females with the majority of them being in the category of 36-45 years of age. For the data collection, the Maslach Burnout Inventory (MBI) and the HADS questionnaire were used. The results showed that there was a statistically significant and negative correlation of emotional exhaustion to HADS total (r= -0,377, p=0,000) as well as HADS anxiety (r= -0,417, p=0,000). Also, there was a statistically significant and negative correlation of depersonalization to HADS total (r= -0,370, p=0,000) as well as HADS anxiety (r= -0,431, p=0,000). Moreover, there was a statistically significant effect (p<0,05) of occupational and demographic characteristics.

Background: Monitoring of antibiotic prescription practices in hospitals is essential to assess and facilitate appropriate use. This is relevant to halt the progression of antimicrobial resistance. Methods: Assessment of antibiotic prescribing patterns and completeness of antibiotic prescriptions among out-patients in 2021 was conducted at the University Hospital of Kwame Nkrumah University of Science and Technology in the Ashanti region of Ghana. We reviewed electronic medical records (EMR) of 49,660 patients who had 110,280 encounters in the year. Results: The patient encounters yielded 350,149 prescriptions. Every month, 33-36% of patient encounters resulted in antibiotic prescription, higher than the World Health Organization’s (WHO) recommended optimum of 27%. Almost half of the antibiotics prescribed belonged to WHO’s Watch group. Amoxicillin-clavulanic acid (50%), azithromycin (29%), ciprofloxacin (28%), metronidazole (21%), and cefuroxime (20%) were the most prescribed antibiotics. Antibiotic prescribing parameters (indication, name of drug, duration, dose, route and frequency) were documented in almost all prescriptions. Conclusions: Extending antimicrobial stewardship to the out-patient settings by developing standard treatment guidelines, an out-patient specific drug formulary and antibiograms can promote rational antibiotic use at the hospital. The EMR system of the hospital is a valuable tool for monitoring prescriptions that can be leveraged for future audits.

Innovation development in healthcare has paved the path towards the improvement in system efficiency, patient care and cost-effective healthcare services, increasing the overall efficiency of the healthcare system in a significant manner. Electronic medical record system has fostered interoperability and collaboration in healthcare departments by enabling different systems to exchange and use patient data. This trend has been accelerated dominantly by the pandemic, leading to increased use of telemedicine, referring specifically to the use of electronic communication to provide healthcare services remotely. Moreover, an increased prevalence towards the use of artificial intelligence and machine learning in healthcare has contributed effectively towards the rigorous and informed analysis of large amounts of data as well as to identify patterns or trends that may be useful for improving care delivery or identifying potential issues. A unified electronic medical record System that will generate competitive opportunities and success prospects with reference to enhanced interoperability in US healthcare departments, can contribute towards the improvement in the quality and efficiency of care by making it easier for healthcare providers to access and share patient information as well as reducing the risk of errors in effective patient care.

This study assesses the efficacy of a microwave colonoscopy algorithm to detect colorectal cancer precursors or polyps in an ex-vivo human colon model. The algorithm works with a device composed of a cylindrical ring-shaped switchable antenna array, which can be attached to the tip of a conventional colonoscope as an accessory. The accessory is connected to an external processing unit that generates an acoustic signal when a polyp is detected. Nowadays, 22% of polyps go undetected with conventional colonoscopy and the risk of cancer after a negative colonoscopy can be up to 7.9%. Fifteen ex-vivo freshly excised human colons with cancer (n=12) or polyps (n=3) were examined with the microwave-assisted colonoscopy system simulating a real colonoscopy exploration. Successive measurements of the colon were taken with the microwave-based colonoscopy device and processed with a microwave imaging algorithm. After the experiment, the dielectric properties of the specimens were measured with a coaxial probe and finally the samples underwent a pathology analysis. The results show that all the neoplasms were detected with a sensitivity of 100% and specificity of 87.4%.

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.

Background: Articles published in scholarly journals form part of the scientific evidence base. It is the responsibility of the scientific community to maintain its integrity. In 2011 the BMJ commissioned a feature article to draw attention to an article that had appeared in another journal- The Lancet 13 years previously. The Lancet had already retracted the article. These actions exemplify the best traditions of scientific record-keeping. Objective: This submission examines whether the main claims summary made in the BMJ were factual. Method: We examine what was published in the Lancet against what was published in the BMJ and verify against the findings in the GMC hearings transcripts and verdict of the UK High Court. Results: The 6 points highlighted in BMJ had errors and need to be corrected. Conclusions: There are significant differences between what was reported in the Lancet paper and what was alleged to be there by the BMJ. This article aims only to point to errors in the BMJ article, to set the record straight. It does not show there was a causal association between MMR vaccination and autism.

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.

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.

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.

Background and Objectives: Mucilaginous substances from plant extracts are known to be able to support the lubricating effect of hyaluronic acid (HA) in dry eye disease (DED). In this pilot study, the combined lubricating effect of HA and mallow extract (Malva sylvestris L.) in patients with DED was assessed. Materials and Methods: 20 patients at 5 ophthalmological practices in Italy were treated with eye drops, containing HA and mallow extract on the one hand, and with eye drops containing HA only, on the other hand, in a two-period crossover design. As primary endpoints, the tear film breakup time (TBUT), the reduction of lissamine green staining of the ocular surface (Oxford Scheme, OS), and the safety and efficacy assessment by the ophthalmologists were evaluated. As secondary variables, the patient symptom score, the ocular surface index (OSDI), and the satisfaction, preference and efficacy judgement by the patients were evaluated. All data were analysed descriptively in addition to an exploratory analysis of the target variables. Results: Both products were well tolerated. There were no statistically significant differences, with regard to the TBUT, OS and OSDI, between both treatments. Anyway, the efficacy and safety assessments by the ophthalmologists and the patients showed results in favour of the combined product. Conclusion: The addition of mallow extract to HA containing eye drops enhances the treatment of DED at least with respect to subjective measurements. Further assessments will have to be done to prove and explain this observation in terms of measurable parameters, e.g. markers for inflammatory cytokines.

Patients living with cancer often experience serious adverse events due to their condition or its treatments. Those events may lead to a critical care unit admission or even result in death. One of the most important but challenging part of care is to build a caring plan according to the patient’s wishes, meeting his goals and values. Advance directives (ADs) allow everyone to give their preferences in advance regarding life sustaining treatments, continuation, and withdrawal or withholding of treatments in case one is not able to speak his mind anymore. While the absence of ADs is associated with a greater probability of receiving unwanted intensive care around the end of his life, their existence correlates with the respect of the patient’s desires and his greater satisfaction. Although progress has been made to promote ADs’ completion, they are still scarcely used among cancer patients in many countries. Several limitations to their acceptation and use can be detected. Efforts should be made to provide tailored solutions for the identified hindrances. This narrative review aims to depict the situation of ADs in the oncology context, and to highlight the future areas of improvement.

Background: Physical and mental health of corporate employees is equally important, especially in international salespeople in the in vitro diagnostic (IVD) medical device industry. The rapid growth of the IVD market is driven by the increasing prevalence of chronic and infectious diseases. This study aims to identify the association of socio-demographic, occupational, organizational, and psychosocial factors with mental health outcomes for depression, anxiety, and somatic symptoms in Chinese IVD international salespeople. Methods: The study was a cross-sectional survey of ISs in IVD companies officially registered in China. An online survey was designed to collect data through email contact with IVD companies and social media between August 2022 and March 2023. Measured factors included effort-reward imbalance (ERI), health-promoting leadership (HPL), health climate (HC), inner strength (IS), and perceived social support (PSS). Mental health outcomes assessed using the Core Symptom Index (CSI) were depression, anxiety, and somatic symptoms. Results: A total of 244 salespeople responded to the survey. CSI scores indicated that 18.4% (n = 45) and 10.2% (n = 25) of the respondents had symptoms of major depression and anxiety, respectively. ERI was positively correlated, while the IS and PSS were negatively correlated with major depression, anxiety, and somatic symptoms (p < .01). Health climate was negatively correlated with major depression (p < .05). Education background was associated with somatic symptoms (p < .05). ERI, IS, and gender were significant predictors of major depression, anxiety, and somatic symptoms (p < .05). Conclusion: The prevalence of depression and anxiety in China’s IVD international salespeople was considered low compared with prevalence in Chinese populations during COVID-19, but higher than those before the pandemic. Effort–reward imbalance, inner strength, and gender were significant factors in major depression, anxiety, and somatic symptoms among IVD international salespeople.

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.

To evaluate the impact of a new Substance Use Disorder (SUD) education program on medical students' representations, we selected the Substance Abuse Attitude Survey (SAAS) questionnaire, which we adapted to our curriculum and cultural context. To validate this adapted version, we conducted an exploratory factor analysis following the administration of our 29-items bSAAS questionnaire to 657 medical students in Belgium (response rate: 71,1%). 23 items correlated to 3 factors namely "Stereotypes and moralism", "Treatment optimism" and "Specialised treatment" were retained (70% of total variance explained, Cronbach's alpha= 0.80) and constituted the new questionnaire called beSAAS. The factor "Specialised treatment", stood out from previous studies which could be explained by our target population and the impact of the formal, informal, and hidden curriculum in medical education. This study highlighted certain factors influencing stereotypical representations such as age, gender, origin, and personal or professional experience with substance use. Our study allowed us to retain the beSAAS as a good questionnaire to evaluate SUD stigma and highlighted interesting findings to improve SUD training in medicine. Further studies are needed to complete its validity and reliability.

The traditional manual recording of vaccination records in Germany faced challenges during the COVID-19 pandemic, prompting the introduction of a Covid smartphone app with QR codes. However, this solution brought new challenges, emphasizing the need for a centrally managed European digital vaccination record for efficiency and validity. Thus, this study assesses the feasibility of using the HL7 FHIR standard in the healthcare industry for implementing a digital vaccination pass management and monitoring system. The system aims to offer convenience and improved efficiency for both patients and healthcare providers while promoting interoperability with other healthcare systems. We developed a prototype using modern technologies, such as React, Quarkus, and Keycloak, evaluating the applicability of FHIR in promoting interoperability within the healthcare industry. Results indicate potential benefits for patients and healthcare providers, offering secure access to immunization records, personalized recommendations, and streamlined management. However, challenges in integrating nuanced vaccination processes into the standardized FHIR system, necessitating custom extensions, and issues with manual data entry and identity provider integration, pose obstacles. Despite these challenges, the study suggests that implementing HL7 FHIR can enhance efficiency, data accessibility, and accuracy in the vaccination process, supporting broader digitization efforts in the German healthcare system and beyond.

(1) Background: Rational use of medicines (RUM) and their assessment is important to ensure optimal use of resources and patient care in hospitals. These assessments are essential to identifying practice gaps for quality improvement. (2) Methods: Assessment of adherence to WHO/ International Network for Rational Use of Drugs core prescribing indicators among out-patients in 2021 was conducted at the University Hospital of the Kwame Nkrumah University of Science and Technology in the Ashanti region of Ghana. We reviewed electronic medical records (EMR) of 110,280 patient encounters in the year which resulted in 336,087 medicines prescribed. (3) Results: The average number of medicines prescribed per encounter was 3 with generics being prescribed in 76% of prescriptions. Injections were prescribed in 7% of encounters while 90% of medicines were from Ghana’s Essential Medicines List 2017. (4) Conclusions: With the exception of patient encounters with injections, all prescribing indicators assessed in this study did not meet WHO optimum levels providing targets for quality improvement in RUM. Implementing prescribing guides and policies, regular audits and feedback as well as continuous professional development training may help to improve prescribing practices in the hospital.

The objective of this study was to identify the key elements used by prehospital emergency physicians (EP) to decide whether or not to attempt advanced life support (ALS) in asystolic out-of-hospital cardiac arrest (OHCA). From 01.01.2009 to 01.01.2017, all adult victims of asystolic OHCA in Geneva, Switzerland, were retrospectively included. Patients with signs of "obvious death" or with a Do-Not-Attempt-Resuscitation order were excluded. Patients were categorized as having received ALS if this was mentioned in the medical record, or, failing that, if at least one dose of adrenaline had been administered during cardio-pulmonary resuscitation (CPR). Prognostic factors known at the time of EP's decision were included in a multivariable logistic regression model. 784 patients were included. Factors favourably influencing the decision to provide ALS were witnessed OHCA (OR=2.14, 95%CI1.43–3.20) and bystander CPR (OR=4.10, 95%CI2.28–7.39). Traumatic aetiology (OR=0.04, 95%CI0.02–0.08), age >80 years (OR=0.14, 95%CI0.09–0.24) and a Charlson comorbidity index greater than 5 (OR=0.12, 95%CI0.06–0.27) were the factors most strongly associated with the decision not to attempt ALS. Factors influencing the EP’s decision to attempt ALS in asystolic OHCA are the relatively young age of the patients, few comorbidities, presumed medical aetiology, witnessed OHCA and bystander CPR.

The development of new medical monitoring applications requires precise modeling of the human body effects as well as simulating and emulating realistic scenarios and conditions. The first aim of this paper is to develop realistic and adjustable human body 3D emulation platforms which could be used for evaluating emerging microwave-based medical monitoring/sensing applications such as the detection of brain tumors, strokes, breast cancers as well as for capsule endoscopy studies. The new phantom recipes are developed for microwave ranges for the realistic shaped phantom molds. The second aim is to validate the feasibility and reliability of the phantoms for practical scenarios with electromagnetic simulations using tissue layer models and biomedical antennas. The third aim is to investigate the impact of the water temperature in the phantom cooking phase on the dielectric properties of the stabilized phantom. The evaluations show that the dielectric properties of the developed phantoms correspond closely to those of real human tissue. The error in dielectric properties varies between 0.5-8%. In the practical scenario simulations, the differences obtained with phantoms-based simulations in S21 parameters are 0.1-13dB. However, the differences are smaller in the frequency ranges targeted for medical applications.

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.

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.

Background: Sensor usage in the classical scientific process has allowed new experimentation in medicine. We report on the design process of a MEMS sensor being used for muscle trauma evaluation in diagnosing acute compartment syndrome (ACS), a medical ailment costing society billions of dollars per year. Objectives: Modeling the disease with scientific process allows a more complete under-standing of the disease. The goal was to formulate a hypothesis that could be tested to aid in making the diagnosis of ACS accurately. Methods: Scientific process was observed throughout the disease modelling process. Background information was improved and clarified, new pre-clinical models were de-signed and verified, a hypothesis built on pressure measurement with MEMS sensors was carried out, and the testing of the model as verified against previous clinical data was accomplished. Results: Scientific process resulted in hypothesis generation around the relationship of intracompartmental pressure measurement and the disease process and therapy. This resulted in new understanding of ACS, accurate modelling, and sensor design resulting in a MEMS device that has an extremely high sensitivity and specificity (over 99%) in treating and diagnosing the disease. Conclusion: MEMS sensor technology defines the new gold standard of implanting a sensor in a muscle compartment that allows accurate diagnosis of ACS with continuous trends in pressure.