Search

For many, the World Health Organization's (WHO) definition of health does not reflect their own understanding of health, because it lacks aspects such as spiritual wellbeing. Responding to these concerns, the WHO called in 2023 for a vision of health that integrates physical, mental, psychological, emotional, spiritual, and social wellbeing. To date, medical practitioners are often reluctant to consider spiritual aspects, because of a perceived lack of statistical evidence about the strength of relations. Research on this topic is emerging. A recent study among 800 young people living with HIV in Zimbabwe showed how study participants navigated three parallel, at times contradicting health systems (religious, traditional, medical). Conflicting approaches led to multifaceted dilemmas (= spiritual struggles), which were significantly related to poorer mental and physical health. This illustrates the need for inclusion of spiritual aspects for health and wellbeing in research, and of increased collaboration between all stakeholders in healthcare., Competing Interests: The author declares that they do not have any conflicts of interest., (Copyright © 2024 Wüthrich-Grossenbacher.)

Objectives: Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advancements in GRL methods and their applications in the biomedical field. We also highlight key challenges currently faced by GRL and outline potential directions for future research., Methods: We conducted a comprehensive search of multiple databases, including PubMed, Web of Science, IEEE Xplore, and Google Scholar, to collect relevant publications from the past two years (2021-2022). The studies selected for review were based on their relevance to the topic and the publication quality., Results: A total of 78 articles were included in our analysis. We identified three main categories of GRL methods and summarized their methodological foundations and notable models. In terms of GRL applications, we focused on two main topics: drug and disease. We analyzed the study frameworks and achievements of the prominent research. Based on the current state-of-the-art, we discussed the challenges and future directions., Conclusions: GRL methods applied in the biomedical field demonstrated several key characteristics, including the utilization of attention mechanisms to prioritize relevant features, a growing emphasis on model interpretability, and the combination of various techniques to improve model performance. There are also challenges needed to be addressed, including mitigating model bias, accommodating the heterogeneity of large-scale knowledge graphs, and improving the availability of high-quality graph data. To fully leverage the potential of GRL, future efforts should prioritize these areas of research., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (IMIA and Thieme. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).)

Considering the limitations of medical science and the risks associated with medical treatments, we need to re-examine the connotation of medical science from the perspective of philosophy. Medical science is the natural expression of human kindness and human nature of rescuing the dying and healing the wounded. It is a combination of the natural sciences, social sciences, and humanities. From the perspectives of medical philosophy and humanistic care, this article expounds the concepts and ideas of evidence-based, translational, and precision medicine in modern medicine and emphasizes the importance of avoiding new technical bureaucracy, paying attention to achieving a holistic view and systematic understanding, and avoiding biases in development because of the loss of the humanistic spirit in modern medical practice.

This themed section contributes to efforts to conceptualize medical mobility. It does so by observing medical histories within the Middle East while following concrete movements. This focus on what moves and how, rather than on largely static and fixed units of analysis on where to, is central to the studies in this issue. The location of the Middle East, as a crossroad for imperial mobilities-is ideal for exploring transnational medical movements. Bringing together historians of the Middle East and North Africa, the articles explore intersections among medicine, health, and the body and histories of cross-regional mobility. This section spans the period from the early twentieth century to the 1970s. The articles are based on primary sources in Greek, Turkish, English, French, Spanish, and Arabic, located in the national archives of the UK, Israel, and Cyprus; in French diplomatic and military archives; and in the Overseas Nursing Association's publications.

The medical auxiliary Mahboub al-Mahmoud (Tangier, 1888-ca. 1970) remains a neglected figure in the history of Morocco's medical modernization. However, his life and professional trajectory can provide a fruitful standpoint from which to question the persisting nationalist bias that has pervaded the modest postcolonial medical historiography about that farthest corner of North Africa. Mahboub's multiple mobilities-temporal, social, geographical, professional-transcend the "colonial fractures" created by the complex European partition of Morocco, which have resulted in Moroccans playing no significant role in the narratives of the origin and development of modern medicine in the country. This paper is divided into three sections, each of which deals with a distinctive phase of Mahboub's itinerary, his connections with various groups of irregular medical practitioners, and the modernizing initiatives they embodied from the times of Hassan I's late nineteenth-century reforms to the rise of Moroccan anticolonial nationalism in the 1930s.

Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

Competing Interests: POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no conflicts of interest to disclose.

Competing Interests: Competing interests: none.

Purpose: To report trends in the number and types of applicants and matched trainees to radiation oncology in comparison to other specialties participating in the National Resident Matching Program (NRMP) between 2010 and 2020., Methods and Materials: Data from the NRMP and Electronic Residency Application System (ERAS) were obtained for 18 medical specialties between 2010 and 2020. We assessed the numbers and types of applicants and matched trainees relative to available positions in the NRMP and Supplemental Offer and Acceptance Program (SOAP)., Results: In the 2020 NRMP, 122 US MD senior graduates preferentially ranked radiation oncology, a significant decrease from a median of 187 between 2010 to 2019 (interquartile range [IQR], 170-192; P < .001). Across all 18 specialties, radiation oncology experienced the greatest declines in the 2020 NRMP cycle relative to 2010 to 2019, in both the number of ERAS applicants from the United States and Canada (-31%) and the percentage of positions filled by US MD or DO senior graduates (-28%). Of 189 available positions, 81% (n = 154) filled in the NRMP prior to the SOAP, of which 65% (n = 122) were "matched" by US MD senior graduates who preferentially ranked radiation oncology as their top choice of specialty, representing a significant decrease from a median of 92% between 2010 to 2019 (IQR, 88%-94%; P = .002). The percentages of radiation oncology programs and positions unfilled in the NRMP prior to the SOAP were significantly increased in 2020 compared with 2010 to 2019 (programs: 29% vs 8% [IQR, 5%-8%; P < .001]; positions: 19% vs 4% [IQR, 2%-4%; P <.001]). Despite >99% (n = 127 of 128) of US MD or DO senior applicants preferring radiation oncology successfully matching to a radiation oncology position in the 2020 NRMP, 16 of 35 remaining unfilled positions were filled via the SOAP. Radiation oncology was the top user of the SOAP across all specialties participating in the 2020 NRMP, filling 15% of total positions versus a median of 0.9% (IQR, 0.3%-2.3%; P <.001)., Conclusions: The supply of radiation oncology residency positions now far exceeds demand by graduating US medical students. Efforts to nullify a market correction revealed by medical student behavior via continued reliance on the SOAP to fill historical levels of training positions may not be in the best of interest of trainees, individual programs, or the specialty as a whole., (Copyright © 2021 Elsevier Inc. All rights reserved.)

One of the biggest challenges of utilizing artificial intelligence (AI) in medicine is that physicians are reluctant to trust and adopt something that they do not fully understand and regarded as a "black box." Machine Learning (ML) can assist in reading radiological, endoscopic and histological pictures, suggesting diagnosis and predict disease outcome, and even recommending therapy and surgical decisions. However, clinical adoption of these AI tools has been slow because of a lack of trust. Besides clinician's doubt, patients lacking confidence with AI-powered technologies also hamper development. While they may accept the reality that human errors can occur, little tolerance of machine error is anticipated. In order to implement AI medicine successfully, interpretability of ML algorithm needs to improve. Opening the black box in AI medicine needs to take a stepwise approach. Small steps of biological explanation and clinical experience in ML algorithm can help to build trust and acceptance. AI software developers will have to clearly demonstrate that when the ML technologies are integrated into the clinical decision-making process, they can actually help to improve clinical outcome. Enhancing interpretability of ML algorithm is a crucial step in adopting AI in medicine., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

The achievement gap is a disparity in academic and standardized test performance that exists between White and underrepresented minority (URM) students that begins as early as preschool and worsens as students progress through the educational system. Medical education is not immune to this inequality. URM medical students are more likely to experience delayed graduation and course failure, even after accounting for science grade point average and Medical College Admission Test performance. Moreover, URM students are more likely to earn lower scores on licensing examinations, which can have a significant impact on their career trajectory, including specialty choice and residency competitiveness. After the release of preliminary recommendations from the Invitational Conference on USMLE Scoring (InCUS) and public commentary on these recommendations, the National Board of Medical Examiners and Federation of State Medical Boards announced that the United States Medical Licensing Examination (USMLE) Step 1 would transition from a 3-digit numeric score to pass/fail scoring. Given that another of InCUS's recommendations was to "minimize racial demographic differences that exist in USMLE performance," it is paramount to consider the impact of this scoring change on URM medical students specifically. Holistic admissions are a step in the right direction of acknowledging that URM students often travel a further distance to reach medical school. However, when residency programs emphasize USMLE performance (or any standardized test score) despite persistent test score gaps, medical education contributes to the disproportionate harm URM students face and bolsters segregation across medical specialties. This Perspective provides a brief explanation of the achievement gap, its psychological consequences, and its consequences in medical education; discusses the potential effect of the Step 1 scoring change on URM medical students; and provides a review of strategies to redress this disparity., (Copyright © 2020 by the Association of American Medical Colleges.)

Introduction: Human Mesenchymal Stem Cells (hMSCs) are multipotent stem cells capable of renewing themselves and differentiation in vitro into different kinds of tissues. In vivo hMSCs are sources of trophic factors modulating the immune system and inducing intrinsic stem cells to repair damaged tissues. Currently, there are multiple clinical trials (CT) using hMSCs for therapeutic purposes in a large number of clinical settings., Material and Methods: The search strategy on clinicaltrials.gov has focused on the key term "Mesenchymal Stem Cells", and the inclusion and exclusion criteria were separated into two stages. Stage 1, CT on phases 1-4: location, the field of application, phase, and status. For stage 2, CT that have published outcome results: field of application, treatment, intervention model, source, preparation methods, and results., Results: By July 2020, there were a total of 1,138 registered CT. Most studies belong to either phase 2 (61.0%) or phase 1 (30.8%); most of them focused in the fields of traumatology, neurology, cardiology, and immunology. Only 18 clinical trials had published results: the most common source of isolation was bone marrow; the treatment varied from 1-200 M hMSCs; all of them have similar preparation methods; all of them have positive results with no serious adverse effects., Conclusions: There appears to be a broad potential for the clinical use of hMSCs with no reported serious adverse events. There are many trials in progress, their future results will help to explore the therapeutic potential of these promising cellular sources of medicinal signals., (Copyright © 2020 IMSS. Published by Elsevier Inc. All rights reserved.)

Artificial Intelligence (AI), a concept which dates back to the 1950s, is increasingly being developed by many medical specialties, especially those based on imaging or surgery. While the cognitive component of AI is far superior to that of human intelligence, it lacks consciousness, feelings, intuition and adaptation to unexpected situations. Furthermore, fundamental questions arise with regard to data security, the impact on healthcare professions, and the distribution of roles between physicians and AI especially concerning consent to medical care and liability in the event of a therapeutic accident., Competing Interests: Declaration of Competing Interest The authors reported no declarations of interest., (Copyright © 2020 Elsevier Masson SAS. All rights reserved.)

This study is based on Philippe van der Voorn's master's dissertation at the Utrecht University, Department of Science, Information and Computing Sciences. The problem identified was a lack of an integrated information chain and clear governance structure for information flow in the Netherlands' health care sector. The method of Design Science was followed to construct an overview model of the chain, and towards a business process model that is intuitive for both technical and business users. An initial declaration chain was identified in the literature and presented, to be confirmed and elaborated on via eight interviews at seven different organisations in the medical specialist health care sector. Based on these interviews, the draft overview was adjusted and a Business Process Model and Notation model created that indicates the shared understanding of the data elements and activities between the organisations. The contribution of the overview of the declaration chain, in particular, can help medical specialist staff obtain an understanding of the administrative side of their work, and with a clear information infrastructure lead to better working processes and information quality. F.J., (© 2020 The Authors Health Information and Libraries Journal published by John Wiley & Sons Ltd on behalf of Health Libraries.)

The overview provides basic information on non-thermal plasma, its properties, and methods of its generation. It gives examples of its use in the inactivation of bacteria including biofilms, fungi, and prions. Related applications in human medicine, namely in wound healing, antitumor therapy, dental medicine, and dermatomycosis therapy are also mentioned.

Using data from the Centers for Disease Control and Centers for Medicare and Medicaid Services, we analyzed the relationship between specialty physician location and specialty-specific mortality rates for diagnoses where access to specialty expertise could plausibly reduce death rates. After adjustment for demographic and health indicators, counties with the highest quartile specialty physician density had lower mortality rates compared to counties with the lowest quartile. The observed association in endocrinology, infectious disease, and neurology was 10.7, 2.9 and 7.2 fewer deaths per 100,000 residents, respectively. There is an inverse correlation between the distribution of select specialties and population-level mortality., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Introduction: The digital transformation has far-reaching implications for the qualification profile of medical students, which have not been addressed in medical studies so far. Teaching concept: The competence-oriented blended learning curriculum "Medicine in the digital age" has been implemented at Mainz University Medical Centre since 2017. It represents a curricular reform project of the "Curriculum 4.0" program (Stifterverband). In six modules, the qualification requirements for digital skills are addressed. Evaluation Methodology: The qualitative evaluation of the course concept took place in the form of semi-structured interviews. All 58 participants from five courses were interviewed. Results: Using the "Qualitative Content Analysis" according to Philipp Mayring, the statements were divided into deductive main categories (process, content, methodology, learning success, learning experience and conclusion). The results reflect the student's view of the curriculum and the current qualification needs that still need to be specialised. Discussion: The didactic teaching of digital skills is a relevant and highly topical component of the further development of medical studies. In this development, the focus is not only on technological skills, but also on the., Competing Interests: The authors declare that they have no competing interests., (Copyright © 2020 Kuhn et al.)

Importance: Disparities in representation between sexes have been shown at multiple career stages in medicine despite increasing representation in the overall physician workforce., Objective: To assess sex representation of applicants to the Canadian R-1 entry match for postgraduate training programs from 1995 to 2019, comparing distribution between different specialties as well as applied vs matched applicants., Design, Setting, and Participants: This cross-sectional analysis of aggregate data provided by the Canadian Resident Matching Service between 1995 and 2019 analyzed aggregate data for the Canadian R-1 residency match from 1995 through 2019., Exposures: Applicant sex as reported in the Canadian Resident Matching Service database., Main Outcomes and Measures: The sex representation of applicants was compared and the longitudinal trends in sex representation were analyzed by specialty between 1995 and 2019. The sex representation of overall applicants to the Canadian R-1 entry match were compared with matched applicants, and both were stratified by specialty., Results: A total of 48 424 applicants were identified (26 407 [54.5%] female applicants), of which 41 037 were matched applicants. Using specialty groupings, female applicants were most highly represented in obstetrics and gynecology (1776 of 2090 [85.0%]) and least represented in radiology (658 of 2055 [32.0%]). Within individual subspecialties, female applicants had the lowest representation in neurosurgery (90 of 394 [22.8%]). While female applicants represented an increasing proportion of the overall applicant population between 1995 and 2019 (z = 2.71; P = .007), significant increases were seen in some, but not all, individual specialties. Differences by sex were found among Canadian medical graduate match rates to their top-ranked specialty: female applicants had a lower likelihood of being rejected for family medicine (rejection of male applicants: OR, 0.46; 95% CI, 0.39-0.54; P < .001) and psychiatry (OR, 0.59; 95% CI, 0.46-0.76; P < .001) and were more likely to be rejected for all-encompassing surgery (acceptance of male applicants: OR, 1.19; 95% CI, 1.10-1.28; P < .001)., Conclusions and Relevance: Increasing representation of female residency applicants over time was seen in some, but not all, medical specialties in Canada, and sex-based differences in successful match rates were observed in some specialties. The reasons for these disparities require further investigation for corrective strategies to be identified.

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.