[The effect of rehabilitation and one year follow-up for a COVID-19 patient on prolonged mechanical ventilation].

We herein report the outcomes of rehabilitation intervention for a patient in his 80s with chronic obstructive pulmonary disease on prolonged mechanical ventilation after COVID-19 infection. The patient was forced to be long-term bedridden due to respirator dependence, showing notable muscle weakness and needing full assistance for all of his activities of daily living (ADL). We implemented rehabilitation for the purposes of withdrawal from mechanical ventilation and improvement of his physical function. We provided a combination program of range of motion exercise, resistance training, and gradual mobilization, such as sitting on the edge of the bed, moving between the bed and wheelchair, sitting on the wheelchair, standing and walking. After rehabilitation for 24 days, the patient was withdrawn from mechanical ventilation, his muscle strength recovered to a level of 4 (Good) on manual muscle testing (MMT) and he became able to walk using a walker. A follow-up survey one year later confirmed that he performed ADL without assistance and returned to work.

Perception of training and job perspectives among final-year residents in the specialty of Endocrinology and Nutrition.

INTRODUCTION: The medical specialisation model in Spain is carried out in the context of specialised health training, through the residency programme. The aim of the study is to analyse, by an anonymous survey, the opinion on three aspects among final-year residents in Endocrinology and Nutrition (E&N): self-assessment of the knowledge acquired, working prospects, care and training consequences arising from the pandemic COVID-19. MATERIALS AND METHODS: Cross-sectional observational study using a voluntary and anonymous online survey, shared among final-year national interns in the last year of the E&N programme, carried out between June-July 2021. RESULTS: 51 responses were obtained, 66% of the fourth-year residents. Overall perception of their knowledge was 7.8 out of 10. Most external rotations were in thyroid and nutrition areas. A total of 96.1% residents, carried out some activity associated with COVID-19, with a training deterioration of 6.9 out of 10. 88.2% cancelled their rotations and 74.5% extended their working schedule. The average negative emotional impact was 7.3 out of 10. 80.4% would like to continue in their training hospital, remaining 45.1%. 56.7% have an employment contract of less than 6 months, most of them practising Endocrinology. CONCLUSION: The perception of the knowledge acquired during the training period is a "B". Residents consider that the pandemic has led to a worsening of their training, generating a negative emotional impact. Employment outlook after completing the residency can be summarised as: temporality, practice of Endocrinology and interhospital mobility.

Medicine in the Era of Artificial Intelligence: Hey Chatbot, Write Me an H&P.

This Perspective envisions a world where artificial intelligence is integrated into health care.

Insights into the FAIRness of the German Network University Medicine: A Survey.

The need to harness large amounts of data, possibly within a short period of time, became apparent during the Covid-19 pandemic outbreak. In 2022, the Corona Data Exchange Platform (CODEX), which had been developed within the German Network University Medicine (NUM), was extended by a number of common components, including a section on FAIR science. The FAIR principles enable research networks to evaluate how well they comply with current standards in open and reproducible science. To be more transparent, but also to guide scientists on how to improve data and software reusability, we disseminated an online survey within the NUM. Here we present the outcomes and lessons learnt.

The Importance of Being FAIR and FAST - The Clinical Epidemiology and Study Platform of the German Network University Medicine (NUKLEUS).

The COVID-19 pandemic has urged the need to set up, conduct and analyze high-quality epidemiological studies within a very short time-scale to provide timely evidence on influential factors on the pandemic, e.g. COVID-19 severity and disease course. The comprehensive research infrastructure developed to run the German National Pandemic Cohort Network within the Network University Medicine is now maintained within a generic clinical epidemiology and study platform NUKLEUS. It is operated and subsequently extended to allow efficient joint planning, execution and evaluation of clinical and clinical-epidemiological studies. We aim to provide high-quality biomedical data and biospecimens and make its results widely available to the scientific community by implementing findability, accessibility, interoperability and reusability - i.e. following the FAIR guiding principles. Thus, NUKLEUS might serve as role model for FAIR and fast implementation of clinical epidemiological studies within the setting of University Medical Centers and beyond.

COVID-19, Graphic Medicine, and Thinking Beyond Data.

Datafication has allowed us to quantify every facet of the corona-virus pandemic. A significant quantity of data sets on infection and recovery rates, mortality, comorbidities, the intensity of symptoms, region-by-region statistics, vaccination, and virus variants, among other things, has been made publicly available. However, these data sets relentlessly reduce human beings to mere numbers and graph points. The present study employs a close reading of comic panels to demonstrate how graphic medicine uses data to critique, supplement, and expose its lacunae. The article draws from graphic medical narratives and panels such as Andy Warner's "The Nib Bureau of Statistics" (2020), Sarah Firth's "State of Emergency" (2021), and Randall Munroe's "Statistics" (2020). Though data visualizations and comics are both graphical representations, their treatment of COVID-19-related issues is radically different. Graphic medicine "re-draws" data visualizations through imitation, subversion, and displacement to showcase multiple temporalities, marginal agencies, and the affective nature of human existence. Furthermore, the humanistic intervention of graphic medicine deftly reclaims individual lives and attendant stories in a world dominated by technologically mediated data. This essay does not dismiss the performative force of data; instead, it insists on humanizing and contextualizing a sensitive presentation of data to convey our entangled existence and collective states.

Long-term effect of childhood pandemic experience on medical major choice: Evidence from the 2003 severe acute respiratory syndrome outbreak in China.

This study examines the long-term effect of a pandemic on a crucial human capital decision, namely college major choice. Using China's 2008-2016 major-level National College Entrance Examination (Gaokao) entry grades, we find that the 2003 severe acute respiratory syndrome (SARS) had a substantial deterrent effect on the choice of majoring in medicine among high school graduates who experienced the pandemic in their childhood. In provinces with larger intensities of SARS impact, medical majors become less popular as the average Gaokao grades of enrolled students decline. Further evidence from a nationally representative survey shows that the intensity of the SARS impact significantly decreases children's aspirations to pursue medical occupations, but does not affect their parents' expectations for their children to enter the medical profession. Our discussion on the effect mechanism suggests that the adverse influence of SARS on the popularity of medical majors likely originates from students' childhood experiences.

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach.

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.