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.

For decades, sepsis research has been motivated by the idea of a dangerous overreaction of the immune system in sepsis. But is it true that the response to sepsis is dysregulated? This review surveys the history of sepsis trials and found that evidence for dysregulation does not exist in many of the physiologic mechanisms of sepsis. It is time to consider the alternative hypothesis, that sepsis traits are often functional, and do more harm than good. This review discusses the implications of this perspective for the future of sepsis research

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.

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.

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.

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.

A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g. ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

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.

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 advance of the medical field in the 21st century has influenced the development of many innovations, including in medical education. PBL (problem-based learning) is one of the pedagogical approaches that has been studying and implementing worldwide to attract medical students’ attention with their learning style and learning environment. China is the most significant “receiver” country of foreign students in Asia, and clinical medicine is one of the most chosen majors for undergraduate. In China, PBL has been implementing in some schools, but mostly with the local students and only a few implementations to the international students. International medical students in China faced a variety of challenges during their studies. In this review paper, we give the solution to adapt to the challenges by focusing on the implementation the integrated organ/system curriculum/syllabus with the PBL method to the school system with the support of the school stakeholders and students’ role.

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.

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: Health policy is a set of comprehensive principles and legislations that guide how healthcare should be effectively delivered in the community. Medical schools should prepare students to undertake managerial responsibilities by incorporating health policy in the curriculum to deal with the intricacies of healthcare systems and their clinical roles in their future professional careers. Objective: To examine medical students' perception at a Public University in Malaysia regarding teaching health policy and their participation in health policy roles. Material and Methods: A cross-sectional study using universal sampling was carried out among the medical students using a paper-based questionnaire to collect the data. Results: Most respondents opined their willingness to learn health policy (80.9%) and that teaching health policy (83.6%) should be compulsory for medical students. The respondents thought health policy should be introduced earlier in Year 1 or 2. The student scores on their knowledge regarding health policy and year of study were significantly associated with their involvement in the health policy roles in both the simple and multiple logistic regression. Both statistical tests reported higher participation in health policy roles with the higher year of study, though only Year 4 and 5 were significant in the simple logistic regression and only Year 5 in the multiple logistic regression compared to Year 1. On the other hand, age and type of admission show significant results only in the simple logistic regression, while the race was only significant at the multivariate level. Conclusion: This study demonstrated that most respondents showed their willingness to learn health policy, participate in the health policy programs, and recommend that health policy be considered an essential topic in the medical curriculum, which should be taught right from the first year of medical school. We recommend encouraging students’ participation in health policy activities.

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.

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.

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.

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.

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.

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.

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.

Introduction: Due to the spread of SARS CoV-2 virus responsible for COVID-19 disease, there is an urgent need to analyse COVID-19 epidemic perception in Poland. This study aims to investigate social perception of coronavirus in the Internet media during the epidemic. It is a signal report highlighting the main issues in public perception and medical commutation in real time. Methods: We study the perception of COVID-19 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet on platforms: Google, Twitter, YouTube, Wikipedia and electronic media represented by Event Registry, from January 2020 to 29.04.2020 (before and after official introduction to Poland on 04.03.20). We present trend analysis with a support of natural language processing techniques. Results: We identified seven temporal major clusters of interest on the topic COVID-19: 1) Chinese, 2) Italian, 3) Waiting, 4) Mitigations, 5) Social distancing and Lockdown, 6) Anti-crisis shield, 7) Restrictions releasing. There was an exponential increase of interest when the Polish government “declared war against disease” around 11/12.03.20 with a massive mitigation program. Later on, there was a decay in interest with additional phases related to social distancing and an anti-crisis legislation act with local peaks. We have found that declarations of mitigation strategies by the Polish prime minister or the minister of health gathered the highest attention of Internet users. So enacted or in force events do not affect interest to such extent. Traditional news agencies were ahead of social media (mainly Twitter) in dissemination of information. We have observed very weak or even negative correlations between a colloquial searching term 'antiviral mask' in Google, encyclopaedic definition in Wikipedia “SARS-CoV-2” as well official incidence series, implying different mechanisms governing the search for knowledge, panic related behaviour and actual risk of acquiring infection. Conclusions: Traditional and social media do not only reflect reality, but also create it. Risk perception in Poland is unrelated to actual physical risk of acquiring COVID-19. As traditional media are ahead of social media in time, we advise to choose traditional news media for a quick dissemination of information, however for a greater impact, social media should be used. Otherwise public information campaigns might have less impact on society than expected.

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.

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

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.

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

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: 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.

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.

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.

In this article we present a SCILAB implementation of algebraic iterative reconstruction methods for discretisation of inverse problems in imaging. These so-called row action methods rely on semi-convergence for achieving the necessary regularisation of the problem. We implement this method using SCILAB and provide a few simplified test problems: medical tomography, seismic tomography and walnut tomography.Numerical results show the capability of this method for the original and perturbed right-hand side vector.

: 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.

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

The Smart Angel connected medical device allows ambulatory surgery patients to monitor their health by taking their own blood pressure and oxygen levels and by answering a health questionnaire from home. This preventive device must necessarily be "usable" by patients with different profiles. The objective of this article is, therefore, to better understand the links between certain characteristics of potential patients and usability. We conducted an experimental study involving thirty-six participants, investigating the effects of four patient characteristics (i.e. age, education, technophilia and health literacy) on usability measured in terms of effectiveness, efficiency, and satisfaction. The results show a moderate correlation between age, health literacy and usability. However, there is a weak correlation between technophilia and usability and no relationship between the level of education and usability. This study provides theoretical insights into the effects of user characteristics by means of personas in usability (ISO 9241-11).

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.

We evaluated the usefulness of the novel cholesterol-triglyceride subgroup (CTS) indices that potentially reflect the metabolic status regarding risk of coronary heart disease (CHD) using a retrospective longitudinal study of the Japanese general population. We recruited 12,373 individuals from the annual users of our healthcare center. Among them, the first onset of CHD was recorded in 131 individuals between April, 2014 and March, 2020. The multivariate Cox proportional hazards regression analyses for all normalized lipid indices revealed that the CTSqnt index showed a comparable hazard ratio for the CHD outcome to non-high-density lipoprotein cholesterol (nonHDL-c) and triglycerides. The HR of the CTSqlt index was significantly lower than for CTSqnt, but still comparable to that for low-density lipoprotein cholesterol (LDL-c). In comparison with the other indices, CTSqlt is more sensitive to risk increment while the index value increases. Linear regression analyses for the CTS indices and previously known lipid indices suggest that the CTSqnt and CTSqlt indices reflect the quantity of atherogenic lipoproteins and size of smaller and denser LDLs, respectively. Furthermore, the CTSqnt/HDL-c index can be used as a comprehensive risk indicator that may represent the status of lipid metabolism determined by the CTSqlt and CTSqnt indices and thus may be useful for screening. The CTS indices can be used to evaluate the metabolic status of individuals, which may increase the risk of future CHD.

Introduction: Travel-based continuing medical education (CME) has become a popular format for physicians looking to combine education with travel. Emergency Medicine Update Europe is a biennial accredited CME program combining high quality Emergency Medicine education with structured group activities including cycling, hiking and social activities. This unique design incorporates innovative educational practices but as a whole has not yet been evaluated. Methods: This was a participant observation-based, ethnographic-style case study of the Emergency Medicine Update Europe conference in Provence, France in 2015. Participant interviews and embedded observation methods were used to collect data. Data was then analyzed using thematic content analysis techniques. Results: We describe three phenomena from the data that we feel are highly influential in the success of the program and impact on learning. These include “social engagement and a sense of community”; “the value of a stimulating escape” and “the ‘flat’ faculty-learner relationships”. Discussion: These unique features, prioritized by participants, seem to be key to the apparent success of this model over more traditional CME approaches. To our knowledge this is the first empirical research in this area and improves our understanding of how to leverage these more sociologic components for more effective continuing medical education.

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.

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: Media coverage of Lyme disease (LD) has led to an increase in consultations for presumed LD in Europe. However, LD is confirmed in only 10-20% of patients, with a significant number remaining in a diagnostic dead-end. Objectives: To reach a deeper understanding of how patients themselves contribute to the diagnostic process. To describe the genesis of the LD hypothesis in care pathways. Methods: In 2019, 30 patients from a prospective cohort consulting in the infectious diseases department at University Hospital in Marseille for presumed LD were recruited for semi-structured interviews. The inclusion criteria were : suffering from subjective symptoms for 6 months, no clinical or paraclinical argument suggesting current LD. The patients’ medical trajectories were collected using a biographical approach. Results: The diagnosis of Lyme disease was primarily triggered by identification with personal testimonies found on the internet. Most of patients were leading their own diagnostic investigation. The majority of participants were convinced they had LD despite the lack of medical evidence and the scepticism of their referring GP. Conclusion: GPs should first systematically explore patients’ etiologic representations in order to improve adherence to the diagnosis especially in the management of medically unexplained symptoms. Long COVID-19 syndrome challenge offers an opportunity to promote active patient involvement in diagnosis.

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.

Introduction: In an era of increasing public awareness regarding the pivotal role of nutrition in the onset and progression of chronic degenerative diseases, there is a growing demand for information and assistance pertaining to the intricate relationship between diet and disease. Nutritional practices, therefore, emerge as a fundamental element in the prevention and management of these maladies, necessitating a proactive response from healthcare professionals (HCP). This study sought to evaluate the impact of participation in a Culinary Medicine (CM) program on Mediterranean diet adherence, food management skills, culinary proficiency, and counseling confidence among HCP. Methods: A mixed-methods pilot implementation program (PIP) was meticulously designed and executed, engaging a cohort of 20 HCP recruited from Hospital Clinic Barcelona. The program unfolded at the Alícia Foundation kitchen-lab and encompassed a series of interactive, hands-on sessions that delved into the realms of gastronomy, food management, culinary skills, and culinary diet therapy, with a specific focus on prevalent diseases and their prevention, treatment, and care. Tailored questionnaires were administered to all participants both before and after the PIP. Results: Of the 20 participants, 14 successfully completed the study, comprising 86% women, 86% aged above 40, 71% nurses, and 7% medical doctors. Interestingly, 86% of the participants had previously received nutrition training, whereas only 14% had received culinary training. Following the completion of the program, there was a remarkable improvement in adherence to the Mediterranean diet (p < 0.01). However, no statistically significant improvements were observed in food management and culinary skills or counseling confidence (p > 0.5). Despite the absence of statistical significance, there was a noteworthy trend towards greater advocacy for dietary changes before resorting to medication, along with enhanced confidence in providing nutritional and culinary guidance to patients. Conclusion: The findings from this study underscore the potential of hands-on CM training as a vital component of nutrition education for HCP. While statistically significant improvements in certain domains were not observed, the program did demonstrate its ability to influence attitudes, knowledge, and behaviors related to healthy cooking and eating for both HCP themselves and their patients. Given these promising trends, future studies with a larger sample size must be performed to further elucidate the impact of CM training on HCP and its potential benefits for public health.

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.

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.

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.

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.

The Coronavirus disease 2019 (COVID-19) rapidly spread to over 180 countries and abruptly disrupted the production rates and supply chains worldwide. Since then, 3D printing also recognized as additive manufacturing (AM) and known to be a novel technique that uses layer-by-layer deposition of material to produce the intricate 3D geometry, has been engaged in reducing the distress caused by the outbreak. During the early stages of this pandemic, shortages of Personal Protection Equipment (PPE), including facemasks, shields, respirators, and other medical gears, were significantly answered by remotely 3D printing them. Amidst the growing testing requirements, the 3D printing emerged as a potential and fast solution manufacturing process to meet the production needs due to its flexibility, reliability, and rapid response capabilities. In the recent past, some of the other medical applications that have gained prominence in the scientific community include 3D printed ventilator splitters, device components, and patient-specific products. Regarding the non-medical applications, researchers have successfully developed contact-free devices to address the sanitary crisis in public places. This work aims to systematically review the applications of 3D printing or AM techniques that have been involved in producing various critical products essential to limit this deadly pandemic's progression.

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

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

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.

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%.

Violence against paramedics is increasingly recognized as an important occupational health problem, but pervasive and institutionalized underreporting hinders efforts at risk mitigation. Earlier research has shown that the organizational culture within paramedicine may contribute to underreporting and researchers have recommended involving paramedics in the development of violence prevention policies, including reporting systems. Eighteen months after the launch of a new comprehensive violence reporting system in Peel Region, Ontario, Canada, we surveyed paramedics about their willingness to report violent encounters. A total of 204 (33% of eligible) paramedics chose to participate, of whom 67% (N=137) had experienced violence since the launch of the new reporting process, with 83% (N=114) reporting the incidents at least some of the time. In choosing to report, participants cited the accessibility of the new reporting process and the desire to promote accountability among perpetrators while contributing to a safer workplace as motivating factors. Their decisions to file a report, however, could be influenced by the perceived ‘volitionality’ and severity of the violent encounters, particularly in the context of (un)supportive co-workers and supervisors. Ultimately, the participants’ belief that the report would lead to meaningful change within the service was a key driver of reporting behavior.

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.

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.

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.

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.

A substantial spike in the need for quality healthcare has resulted from people being more cognizant of health issues. With blockchain, healthcare providers may safely share patient information electronically, which is especially important given the sensitive nature of the data contained inside them. However, flaws in the current blockchain design have surfaced since the dawn of quantum computing systems. The study proposes a novel quantum inspired blockchain system (Qchain) and constructs a unique entangled quantum medical record (EQMR)system with an emphasis on privacy and security. This Qchain relies on entangled states to connect its blocks. Automated production of the chronology indicator reduces storage capacity requirements by connecting entangled BloQ (blocks with quantum properties) with controlled activities. One qubit is used to store the hash value of each block. A lot of information regarding quantum internet is included in the protocol for the entangled quantum medical record (EQMR). EQMR can be accessed in Medical Internet of Things (M-IoT) systems may be kept private and secure, and their where-abouts can be monitored in the event of an emergency. The protocol also uses quantum authentication in place of more conventional methods like encryption and digital signatures. Mathematical research shows that the quantum converged blockchain (QCB) is highly safe against attacks such as the External attack, Intercept-Measure-Repeat attack, and Entanglement-Measure attack. The reliability and auditability evaluations of the entangled BloQ, as well as the quantum circuit design for computing the hash value, are presented. There's also a comparison between the suggested approach and several other quantum blockchain designs.

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.

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.

Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..

Mobile robots can perform tasks on the move, including exploring terrain, discovering landmark features or moving a load from one place to another. This group of robots is characterized by a certain level of intelligence allowing making decisions and responding to stimuli received from the environment. As part of Industry 5.0, such mobile robots and humans are expected to co-exist and work together in a shared environment to make human work less tiring, quicker, and safer. This can only be realized when clean, dense, and economical energy sources are available. The aim of the study is to analyze the state of the art and to identify the most important directions for future developments in robotic power systems based mainly on batteries. The efficiency and performance of the battery depends on the design using different materials. Work environments and performance requirements are considered in this systematic review to classify solutions that help developers choose the best suited power system for specific application. Indirectly, the aim of the work is to generate discussion within the scientific and engineering community. A narrative review of publications from six major bibliographic databases according to preset inclusion criteria was combined with a critical analysis of current and future technologies. The main findings of the review allow answering question what is the role of modern power source technologies, artificial intelligence and ground-breaking research work in global policies related to energy saving, green policies, and sustainable development. The main opportunities and threats are discussed, and a brief feasibility analysis is carried out. Novelty of the article relates not only to the analysis of technologies, but also to approaches and their use under conditions of limited resource availability, when resource usage must be minimized. The article provides an overview of batteries, their specifications, classifications, and their advantages and disadvantages. In addition, we propose: (1) an algorithm for selecting main energy source for robot application, and (2) an algorithm for selecting electrical system power supply. Current mobile robot batteries are, in most cases, their biggest limitation. Progress in batteries development is too slow to catch up with the demand for robot autonomy and range requirements, limiting the development of mobile robots. Further intensive research and implementation work is needed to avoid years of delay in this area.

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

Burden of diseases measured as disability-adjusted life years per 100,000 people can be mined from public domain data, when they are made available by population health surveillance systems. This can be analysed to allow insightful comparisons with the national average, and to understand differences in trends between the sexes, age groups, time periods, geographic regions and sub-regions. In this illustrative case study, we have analysed the Scottish burden of disease database to understand what ailed the population of the Grampian region before the COVID-19 pandemic. We have identified selected cancers, ischaemic heart disease, Alzheimer’s disease and other dementias amongst the highest contributors to the burden; that drug use disorders and colorectal cancer are showing worsening trends and require health promotion and disease prevention measures from ages 15 and 25 respectively, especially in Aberdeen City; and that males are more vulnerable to atrial fibrillation and flutter, diabetes mellitus and oesophageal cancer, while females are to cerebrovascular disease. We demonstrate the usefulness of our analysis and methodology for the wider health system, allowing targeted medical research investments and coordinated response from public health and health service delivery. We also show the need for up-to-date surveillance data, forecasts and evidence on the impact of interventions to be made available widely.

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.

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 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.

: 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.

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.

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.

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%.

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 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.

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.

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.

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.

The universal health insurance system in Taiwan was formed with good intentions to help vulnerable groups. However, the possibility of bankrupting the system due to wasted medical resources. In this study, using the medical expenditures of the Taiwanese Government and gross domestic product (GDP) as variables, the wavelet analysis method was used to empirically study the correlations and leading-lagging relationships in quarterly data in the period from 1996 to 2016. In addition, the dependent population of the insured was used as the control variable. This population had no income and had high medical demands. Results: After the dependent population was included as a control variable, there was a period of low-frequency (one to four years short-term) linkage correlation, as well as a period of high-frequency (four to eight years long-term) linkage correlation. In addition, for more than eight years, there was also a high degree of linkage correlation, indicating that the linkage between medical expenditures and GDP occurred over the long term. Moreover, since medical expenditures positively affected GDP, one-way causality was observed. However, after 2008, regardless of whether a long or short term was examined, there was almost no linkage correlation. Before 2008, the medical expenditures of the government were positively correlated with economic growth; i.e., they enhanced economic growth. But, after 2008, this effect had already disappeared. The universal health insurance system has long been denounced as a waste of medical resources, and the waste must be immediately stopped. The government urgently needs to find a new solution.

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.

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.

This study examines the psychological repercussions of the COVID-19 pandemic on a medical team in an Israeli general hospital. The research explores the professional quality of life, burnout symptoms, secondary traumatic stress, and mindfulness among team members across three distinct phases of the pandemic. Analysis was conducted for different subgroups based on job roles and seniority, allowing for an evaluation of the phase-specific effects on the ProQOL (Professional Quality of Life) and mindfulness. Results align with established crisis trajectories, encompassing honeymoon/heroic phases, inventory, disillusionment, and recovery. The findings demonstrate a negative correlation between compassion satisfaction and burnout, as well as between mindfulness and burnout/secondary traumatic stress. Remarkably, male doctors exhibit a more pronounced negative impact on mindfulness compared to their female counterparts, primarily comprising healthcare workers. Temporal analysis reveals significant differences in compassion satisfaction and mindfulness, with baseline measurements being notably higher. Furthermore, the study sheds light on the vulnerability of male doctors, emphasizing the crucial role of hospital management in conveying the message and implementing effective measures to support professional quality of life. These findings underscore the necessity of prioritizing the well-being of medical professionals, particularly male doctors who face unique challenges in seeking assistance.

The present study aimed to evaluate the psychometric properties of the Greek version of the Jefferson Scale of Empathy—Student version (JSE-S) and its association with potential predictors among Greek-speaking undergraduate medical students. This study adopted a cross-sectional, comparative–descriptive research design. The study was conducted during October and November 2023. Cronbach’s α for the JSE-S was 0.846. Cronbach’s α values for the factors “perspective taking”, “compassionate care”, and “standing in the patient’s shoes” were 0.800, 0.715, and 0.748, respectively. The intraclass correlation coefficient for the JSE-S score in the test–retest study was 0.827 (95% CI = 0.647 to 0.915, p-value &lt; 0.001), indicating a high level of reliability. The participants showed moderate empathy levels. Females scored higher than males on the Greek version of the JSE-S (108.8 vs. 102.8, p-value &lt; 0.001). Moreover, students enrolled in the fourth academic year showed higher empathy mean scores than those enrolled in the first year (108.6 vs. 103.6, p-value = 0.012). Statistically significant empathy differences by specialty preferences or faith in God/supreme power were not found. The present study provided satisfactory evidence that the Greek JSE-S is a psychometrically sound measurement instrument. Empathy differences by gender were found in line with prior literature.

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.

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.

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

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.

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

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.

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.

This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.

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.

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.

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 paper introduces the AltumView Sentinare smart activity sensor for senior care and patient monitoring. The sensor uses an AI chip and deep learning algorithms to monitor the activity of people, collect activity statistics, and notify caregivers when emergencies such as falls are detected. To protect privacy, only skeleton (stick figure) animations are transmitted instead of videos. The sensor is highly affordable, accessible, and versatile. It was a CES 2021 Innovation Award Honoree, and has been selected by Amazon as one of only three fall detection devices integrated into its Alexa Together urgent response service, and has received very positive reviews from Amazon customers. It has also been used in different senior care settings in about ten different countries. The paper presents the main features of the system, the evidences and lessons learned from its practical applications, and future directions.

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.

This study examines demographic factors which may influence opinions concerning medical students’ self-care and substance use behaviors as a means of providing insights into how future doctors view these issues compared to Australian doctors and members of the public. We conducted national, multicenter, prospective, on-line cross-sectional surveys using hypothetical scenarios to three cohorts- Australian medical students, medical doctors, and the public. Participants’ responses were compared for the different contextual variables within the scenarios and the participants’ demographic characteristics. In total 2,602 medical students, 809 doctors and 503 members of the public participated. Compared with doctors and the public, medical students were least tolerant of alcohol intoxication, and most tolerant of using stimulants to assist with study, and cannabis for anxiety. Doctor respondents more often aligned with the public’s opinions on the acceptability of the medical students’ behaviors. Although opinions are not equivalent to behaviour, Australian students’ views on the acceptability for cannabis to help manage anxiety, and inappropriate use of prescription-only drugs are concerning; these future doctors will be responsible for prescribing drugs and managing patients with substance abuse problems. However, if current Australian medical student’s opinions on alcohol misuse persist, one of the commonest substance addictions amongst doctors may decrease in future.

The optical properties and physiology of biological tissue, as well as how near-infrared (NIR) light interacts with the tissue, both play a significant role in interpreting the tissue probing optical measurements, and in solving the inverse problem of near-infrared spectroscopy (NIRS)-based medical imaging modalities such as diffuse optical tomography and functional near-infrared spectroscopy. This paper discusses the optical properties of tissue, specifically in the NIR wavelength range, which influence NIRS measurements in NIRS-based medical imaging. There is an easy-to-understand explanation given in this paper of the NIR light-tissue interaction phenomenon. The mathematical explanation, the processes involved in the interaction, and the rationale for a few approximations are described. Various types of chromophores present in the tissue, their composition in the tissue, and how these chromophores overall affect the scattering and absorption of NIR light are presented. The absorption spectra of these chromophores are shown. Finally, the paper concludes with the author’s perspective on two NIRS-based medical imaging modalities, diffuse optical tomography, and functional near-infrared spectroscopy.

Cosmetic surgery is the subfield of plastic surgery that enhances appearance mainly for aesthetic reasons. Currently, cosmetic surgery interventions are highly considered appealing and popular all around the world. Several factors have contributed in this rapidly evolving field such as scientific and economic development, breaking down barriers and cultural norms, globalization, social media and mental disorders. Nowadays, the morality of cosmetic surgery is facing many dilemmas due to its benefits and drawbacks. It is proven that the advantages of cosmetic surgery include improved body confidence, mental health, and function. Additionally, aesthetic corrections can upscale the quality of life and create career opportunities for special targeted groups. Also, cosmetic surgery has a major impact in gender affirmation procedures. Although, herein we also describe the disadvantages of this widely developing field such as minor and major medical complications, potential postoperative risks and addiction in plastic surgeries associated with damaging economic issues. Lack in self-confidence, altered mental health status and ethical crisis consist another negative aspects of aesthetic surgery’s impact.

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.

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.

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.

This review summarizes available data on invasive procedures applied with questionable indications in Russia in the recent past: mastectomy with the removal of muscles, gastrectomy for peptic ulcers, spleno-renal anastomosis for diabetes mellitus, thoracic surgery for bronchial asthma, overuse of surgery in tuberculosis, excessive and compulsory treatments of alcohol-dependent patients, etc. Among factors contributing to the use of invasive procedures with unproven efficiency have been the partial isolation from international scientific community, insufficient consideration of the principles of professional autonomy, informed consent and scientific polemics, training of medical personnel. It is known that invasive procedures can exert a placebo effect, which might have contributed to reported efficiency of some methods discussed here. However, by definition, placebo must be free of risks.