Medical Research Research Articles

Incidence of diabetes after SARS-CoV-2 infection in England and the implications of COVID-19 vaccination: a retrospective cohort study of 16 million people

Some studies have shown that the incidence of type 2 diabetes increases after a diagnosis of COVID-19, although the evidence is not conclusive. However, the effects of the COVID-19 vaccine on this association, or the effect on other diabetes subtypes, are not clear. We aimed to investigate the association between COVID-19 and incidence of type 2, type 1, gestational and non-specific diabetes, and the effect of COVID- 19 vaccination, up to 52 weeks after diagnosis. In this retrospective cohort study, we investigated the diagnoses of incident diabetes following COVID-19 diagnosis in England in a pre-vaccination, vaccinated, and unvaccinated cohort using linked electronic health records. People alive and aged between 18 years and 110 years, registered with a general practitioner for at least 6 months before baseline, and with available data for sex, region, and area deprivation were included. Those with a previous COVID-19 diagnosis were excluded. We estimated adjusted hazard ratios (aHRs) comparing diabetes incidence after COVID-19 diagnosis with diabetes incidence before or in the absence of COVID-19 up to 102 weeks after diagnosis. Results were stratified by COVID-19 severity (categorised as hospitalised or non-hospitalised) and diabetes type. 16 669 943 people were included in the pre-vaccination cohort (Jan 1, 2020-Dec 14, 2021), 12 279 669 in the vaccinated cohort, and 3 076 953 in the unvaccinated cohort (both June 1-Dec 14, 2021). In the pre-vaccination cohort, aHRs for the incidence of type 2 diabetes after COVID-19 (compared with before or in the absence of diagnosis) declined from 4·30 (95% CI 4·06-4·55) in weeks 1-4 to 1·24 (1·14-1.35) in weeks 53-102. aHRs were higher in unvaccinated people (8·76 [7·49-10·25]) than in vaccinated people (1·66 [1·50-1·84]) in weeks 1-4 and in patients hospitalised with COVID-19 (pre-vaccination cohort 28·3 [26·2-30·5]) in weeks 1-4 declining to 2·04 [1·72-2·42] in weeks 53-102) than in those who were not hospitalised (1·95 [1·78-2·13] in weeks 1-4 declining to 1·11 [1·01-1·22] in weeks 53-102). Type 2 diabetes persisted for 4 months after COVID-19 in around 60% of those diagnosed. Patterns were similar for type 1 diabetes, although excess incidence did not persist beyond 1 year after a COVID-19 diagnosis. Elevated incidence of type 2 diabetes after COVID-19 is greater, and persists for longer, in people who were hospitalised with COVID-19 than in those who were not, and is markedly less apparent in people who have been vaccinated against COVID-19. Testing for type 2 diabetes after severe COVID-19 and the promotion of vaccination are important tools in addressing this public health problem. UK National Institute for Health and Care Research, UK Research and Innovation (UKRI) Medical Research Council, UKRI Engineering and Physical Sciences Research Council, Health Data Research UK, Diabetes UK, British Heart Foundation, and the Stroke Association.

Do Interventions Meet the Needs of Caregivers of Cardiac Patients?: A Scoping Review.

Interventions targeting the needs of caregivers of cardiac patients are few, and in most cases, no effect is found on caregivers' well-being. A closer look at the existing interventions will provide a solid foundation for future efforts to develop effective interventions targeted at caregivers of cardiac patients. The study's objective was to scrutinize and discuss interventions targeting caregivers of cardiac patients and contribute to reflections that will improve future interventions. Systematic scoping of the literature within the field was conducted through a literature search in PubMed, EMBASE, CINAHL, PsycINFO, and Cochrane Library from January 2011 to May 2022. The development of search terms and inclusion criteria was inspired by the Patient problem/population, Intervention, Comparison/Control, and Outcome approach, and the Medical Research Council framework for developing and evaluating complex interventions served as the underlying basis for the analysis. Eleven articles were included. The interventions reported in the articles were generally not systematically developed and did not include field-specific, methodological, and theoretical reflections. Furthermore, the development process behind the studies seemed not to be transparent. Lack of systematic methodology and methodological transparency in the reviewed studies hinders further testing of interventions and might explain lack of evidence for effective interventions within the field. More systematic, needs-based, and well-documented interventions targeting caregivers of various kinds of cardiac patients are needed to develop the field to the benefit of caregivers, patients, and society.

Exploring a learning-to-rank approach to enhance the Retrieval Augmented Generation (RAG)-based electronic medical records search engines

BackgroundThis study addresses the challenge of enhancing Retrieval Augmented Generation (RAG) search engines for electronic medical records (EMR) by learning users' distinct search semantics. The specific aim is to develop a learning-to-rank system that improves the accuracy and relevance of search results to support RAG-based search engines. MethodsGiven a prompt or search query, the system first asks the user to label a few randomly selected documents, which contain some keywords, as relevant to the prompt or not. The system then identifies relevant sentences and adjusts word similarities by updating a medical semantic embedding. New documents are ranked by the number of relevant sentences identified by the weighted embedding. Only the top-ranked documents and sentences are provided to a Large-Language-Model (LLM) to generate answers for further review. FindingsTo evaluate our approach, four medical researchers labeled documents based on their relevance to specific diseases. We measured the information retrieval performance of our approach and two baseline methods. Results show that our approach achieved at least a 0.60 Precision-at-10 (P @ 10) score with only ten positive labels, outperforming the baseline methods. In our pilot study, we demonstrate that the learned semantic preference can transfer to the analysis of unseen datasets, boosting the accuracy of an RAG model in extracting and explaining cancer progression diagnoses from 0.14 to 0.50. InterpretationThis study demonstrates that a customized learning-to-rank method can enhance state-of-the-art natural language models, such as LLMs, by quickly adapting to users' semantics. This approach supports EMR document retrieval and helps RAG models generate clinically meaningful answers to specific questions, underscoring the potential of user-tailored learning-to-rank methods in clinical practice.

NIH funding and journal selection: trends in dermatology publication models across tiers from 2021 to 2023.

This study examines the influence of National Institutes of Health (NIH) funding on the publication choices of dermatologists, particularly in terms of journal tiers and pay-to-publish (P2P) versus free-to-publish (F2P) models. Utilizing k-means clustering for journal ranking based on SCImago Journal Rank, h-index, and Impact Factor, journals were categorized into three tiers and 54,530 dermatology publications from 2021 to 2023 were analyzed. Authors were classified as Top NIH Funded or Non-Top NIH Funded according to Blue Ridge Institute for Medical Research rankings. The study finds significant differences in publication patterns, with Top NIH Funded researchers in Tier I journals demonstrating a balanced use of P2P and F2P models, while they preferred F2P models in Tier II and III journals. Non-Top NIH Funded authors, however, opted for P2P models more frequently across all tiers. These data suggest NIH funding allows researchers greater flexibility to publish in higher-tier journals despite publication fees, while prioritizing F2P models in lower-tier journals. Such a pattern indicates that funding status plays a critical role in strategic publication decisions, potentially impacting research visibility and subsequent funding. The study's dermatology focus limits broader applicability, warranting further research to explore additional factors like geographic location, author gender, and research design.

Development of a flexible liver phantom for hepatocellular carcinoma treatment planning: a useful tool for training & education

PurposeHepatocellular carcinoma (HCC) is one of the most common types of liver cancer that could potentially be surrounded by healthy arteries or veins that a surgeon would have to avoid during treatment. A realistic 3D liver model is an unmet need for HCC preoperative planning.MethodsThis paper presents a method to create a soft phantom model of the human liver with the help of a 3D-printed mold, silicone, ballistic gel, and a blender.ResultsFor silicone, the elastic modulus of seven different ratios of base silicone and silicone hardener are tested; while for ballistic gel, a model using 20% gelatin and 10% gelatin is created for the tumor and the rest of the liver, respectively. It is found that the silicone modulus of elasticity matches with the real liver modulus of elasticity. It is also found that the 10% gelatin part of the ballistic gel model is an excellent emulation of a healthy human liver.ConclusionThe 3D flexible liver phantom made from a 10% gelatin-to-water mixture demonstrates decent fidelity to real liver tissue in terms of texture and elasticity. It holds significant potential for improving medical training, preoperative planning, and surgical research. We believe that continued development and validation of such models could further enhance their utility and impact in the field of hepatobiliary treatment planning and education.

Mediterranean Bioethics in the bioethically lessknown Mediterranean: a struggle for identity

Primarly as a reponse to mainstream bioethics – the one narrowed-down to issues related to medical ethics and research – various kinds of regional bioethics have emerged in an attempt to incoporate values other than those typical for the Anglo-Saxon culture. Among the most promising ones, the Mediterranean Bioethics, reflecting all the colorfulness of the Mediterranean microcosm and an expanded interest to the entire bios (faithful to the original ideas of Fritz Jahr and Van Renssealer Potter), has found its footholds particularly in Spain, Italy, Croatia, and Greece. Leaving aside those four countries with abundant literature and well-known contributions to the general history of bioethics, we wish to direct attention to other cultures and countries of the Mediterranean region, revealing a more complex struggle for identity.

What public health challenges and unmet medical needs would benefit from interdisciplinary collaboration in the EU? A survey and multi-stakeholder debate.

In the past decade, significant European calls for research proposals have supported translational collaborative research on non-communicable and infectious diseases within the biomedical life sciences by bringing together interdisciplinary and multinational consortia. This research has advanced our understanding of disease pathophysiology, marking considerable scientific progress. Yet, it is crucial to retrospectively evaluate these efforts' societal impact. Research proposals should be thoughtfully designed to ensure that the research findings can be effectively translated into actionable policies. In addition, the choice of scientific methods plays a pivotal role in shaping the societal impact of research discoveries. Understanding the factors responsible for current unmet public health issues and medical needs is crucial for crafting innovative strategies for research policy interventions. A multistakeholder survey and a roundtable helped identify potential needs for consideration in the EU research and policy agenda. Based on survey findings, mental health disorders, metabolic syndrome, cancer, antimicrobial resistance, environmental pollution, and cardiovascular diseases were considered the public health challenges deserving prioritisation. In addition, early diagnosis, primary prevention, the impact of environmental pollution on disease onset and personalised medicine approaches were the most selected unmet medical needs. Survey findings enabled the formulation of some research-policies interventions (RPIs), which were further discussed during a multistakeholder online roundtable. The discussion underscored recent EU-level activities aligned with the survey-derived RPIs and facilitated an exchange of perspectives on public health and biomedical research topics ripe for interdisciplinary collaboration and warranting attention within the EU's research and policy agenda. Actionable recommendations aimed at facilitating the translation of knowledge into transformative, science-based policies are also provided.

What are Proteome Atlases good for?

The technological advances in mass spectrometry and associated computational tools have enabled the development of proteome atlases and comprehensive catalogs of proteome snapshots that have gradually transformed biomedical research. These proteome catalogs in specific biological contexts, which focused initially on model organisms, have now expanded their scope to encompass diverse organisms, tissues, and experimental conditions. These atlases, such as the Human Protein Atlas (HPA), Peptide Atlas, and Global Proteome Machine Database (GPMDB), etc. provide invaluable insights into protein expression, subcellular localization, interactions, modifications, and functions. They aid in understanding biological processes, identifying disease biomarkers, and discovering novel therapeutic targets. Despite their potential, proteome atlases face challenges like data completeness, integration with other omics data, and ethical considerations. Addressing these challenges is vital for further progress. Proteome atlases serve as indispensable resources, driving biomedical discovery and innovation.

Syringin: a naturally occurring compound with medicinal properties.

Syringin, a phenylpropanoid glycoside, is widely distributed in various plants, such as Acanthopanax senticosus (Rupr. et Maxim.) Harms, Syringa reticulata (BL) Hara var. mandshurica (Maxim.) Hara, and Ilex rotunda Thumb. It serves as the main ingredient in numerous listed medicines, health products, and foods with immunomodulatory, anti-tumor, antihyperglycemic, and antihyperlipidemic effects. This review aims to systematically summarize syringin, including its physicochemical properties, plant sources, extraction and separation methods, total synthesis approaches, pharmacological activities, drug safety profiles, and preparations and applications. It will also cover the pharmacokinetics of syringin, followed by suggestions for future application prospects. The information on syringin was obtained from internationally recognized scientific databases through the Internet (PubMed, CNKI, Google Scholar, Baidu Scholar, Web of Science, Medline Plus, ACS Elsevier, and Flora of China) and libraries. Syringin, extraction and separation, pharmacological activities, preparations and applications, and pharmacokinetics were chosen as the keywords. According to statistics, syringin can be found in 23 families more than 60 genera, and over 100 species of plants. As a key component in many Chinese herbal medicines, syringin holds significant research value due to its unique sinapyl alcohol structure. Its diverse pharmacological effects include immunomodulatory activity, tumor suppression, hypoglycemic action, and hypolipidemic effects. Additionally, it has been shown to provide neuroprotection, liver protection, radiation protection, cardioprotection, and bone protection. Related preparations such as Aidi injection, compound cantharidin capsule, and Tanreqing injection have been widely used in clinical settings. Other studies on syringin such as extraction and isolation, total synthesis, safety profile assessment, and pharmacokinetics have also made progress. It is crucial for medical research to deeply explore its mechanism of action, especially regarding immunity and tumor therapy. Meanwhile, more robust support is needed to improve the utilization of plant resources and to develop extraction means adapted to the needs of industrial biochemistry to further promote economic development while protecting people's health.

Roadmap for Research and Scholarship in General Surgery Residency Training: Report of The Research Subcommittee of Blue Ribbon Committee II on Surgical Education and Training.

To review the current state of research training during surgical residency and make recommendations commensurate with current surgical training and academic environment. Research training has been a mainstay of academic surgical programs, yet the scientific disciplines have evolved significantly from the traditional years of bench research. It is time to reconsider how research training should prepare surgeons for future academic practice and ensure the foundational knowledge of research evidence. As part of the Blue Ribbon Committee II, a research subcommittee was tasked to make recommendations on research training during surgical residency. Our eight-member panel brought diverse perspectives of the roles and goals of research training. We also sought input from a convenience sample of current and recent surgical residents on impact of research training during their residency. We identified a lack of a common framework and foundational research training for all surgical residents. Participation in dedicated years of scholarly activity helped trainees meet several professional and personal goals. The lack of an integrated, dedicated research track may dissuade some medical school graduates from pursuing surgery. We recommend incorporating a minimum standard for all trainees and flexibility in dedicated scholarly training to meet the needs of future academic surgeons.

Enhancing electrochemical detection through machine learning-driven prediction for canine mammary tumor biomarker with green silver nanoparticles.

This study developed an innovative biosensor strategy for the sensitive and selective detection of canine mammary tumor biomarkers, cancer antigen 15-3 (CA 15-3) and mucin 1 (MUC-1), integrating green silver nanoparticles (GAgNPs) with machine learning (ML) algorithms to achieve high diagnostic accuracy and potential for noninvasive early detection. The GAgNPs-enhanced electrochemical biosensor demonstrated selective detection of CA 15-3 in serum and MUC-1 in tissue homogenates, with limits of detection (LODs) of 0.07 and 0.11 U mL-1, respectively. The nanoscale dimensions of the GAgNPs endowed them with electrochemically active surface areas, facilitating sensitive biomarker detection. Experimental studies targeted CA 15-3 and MUC-1 biomarkers in clinical samples, and the biosensor exhibited ease of use and good selectivity. Furthermore, ML algorithms were employed to analyze the electrochemical data and predict biomarker concentrations, enhancing the diagnostic accuracy. The Random Forest algorithm achieved 98% accuracy in tumor presence prediction, while an Artificial Neural Network attained 76% accuracy in CA 15-3-based tumor grade classification. The integration of ML techniques with the GAgNPs-based biosensor offers a promising approach for noninvasive, accurate, and early detection of canine mammary tumors, potentially revolutionizing veterinary diagnostics. This multilayered strategy, combining eco-friendly nanomaterials, electrochemical sensing, and ML algorithms, holds significant potential for advancing both biomedical research and clinical practice in the field of canine mammary tumor diagnostics.

Investigating empirical and theoretical calculations for intensity ratios of L-Shell X-ray transitions in atoms with 39 ≤ Z ≤ 94

The significance of theoretical, experimental, and analytical methods in calculating the intensity ratios of L-shell transitions for diverse elements lies in their widespread applications across various domains, including physical chemistry and medical research. In the present paper, empirical values for intensity ratios were computed through polynomial interpolations using experimental databases within the scope of atomic number 39 ≤ Z ≤ 92 for ILβILα and ILγILα, and extending to the range of 39 ≤ Z ≤ 94 for ILlILα. Additionally, new theoretical calculations were conducted using the Multiconfiguration Dirac–Fock Method for specific elements. The obtained results were compared with standard theoretical, experimental, and empirical values, showing a reasonable agreement with them.

Protecting cancer survivors from financial discrimination throughout the EU: A cross-European perspective

This article provides an overview of the High-Level Conference on Ending Financial Discrimination Against Cancer Survivors held on the 15th of February 2024, in Brussels, Belgium. Organised under the auspices of the Belgian Presidency of the European Council and led by the European Initiative on Ending Discrimination Against Cancer Survivors, the focus was the "right to be forgotten" (RTBF), which seeks to prevent financial discrimination against former cancer patients by ensuring their medical history is not considered by insurers and lenders 5 years after the end of treatment and without recurrence of the disease. Through detailed discussions and poignant testimonies, the conference shed light on the profound impact of financial discrimination on cancer survivors' lives and the moral and legal imperatives to address it. The article concludes with recommendations for advancing RTBF legislation at both national and European levels, emphasising the importance of political will, medical research, and strong advocacy from the cancer community.

Using the Height-for-Age Z-score (HAZ) to predict protein-energy malnutrition in patients with congenital epidermolysis bullosa through the application of machine learning methods

Congenital epidermolysis bullosa (СЕВ) is a group of genetically and clinically heterogeneous diseases characterized by a tendency to form blisters and/or erosions on the skin and mucous membranes with minimal trauma. Nutritional deficiency stands as the most common complication observed in EB, with its development influenced by a multitude of contributing factors.Purpose of the study. To construct a predictive model for the development of protein-energy malnutrition in patients with various forms of EB, identify the main features affecting the sensitivity of the predictive model, and evaluate the model’s validity based on retrospective data on the presence of protein-energy malnutrition in this patient category.Methods. The study involved 101 patients aged 3 to 18 years with simplex (n=25), junctional (n=10), and dystrophic (n=66) СEB. The Birmingham EB Severity Score, laboratory and anthropometric parameters, as well as data on the presence of gastrointestinal complications, were used for the analysis of disease progression and predictive model construction. The Scikit-learn library of the programming language Python was utilized for building the machine learning model.Results. In the construction of the predictive model, the RandomForestClassifier model showed the best results. The developed machine learning model can correctly determine whether a patient has chronic protein-energy malnutrition (class 1, HAZ < –2) or not (class 0, HAZ > –2) with an accuracy of 92%, sensitivity of 85.7%, and specificity of 100%.Conclusions. The machine learning model presented in this study predicts the values of the Height-for-Age Z-score (HAZ) and can have practical significance in medical practice and clinical research. The model can be used for early diagnosis of protein-energy malnutrition in patients with EB, which may allow healthcare professionals to timely start nutritional support and prevent possible complications of the disease, as well as develop individual nutrition and treatment plans for patients.

Early predictive value of ultrasound measurements of rectus femoris cross-sectional area to diagnose ICU-acquired weakness in patients undergoing invasive mechanical ventilation: a prospective cohort study

BackgroundThe diagnosis of ICU-acquired weakness (ICUAW) may be delayed due to the complexity of critically ill patients. This study aimed to investigate the value of ultrasound measurements of rectus femoris cross-sectional area (RFCSA) in predicting ICUAW in patients undergoing invasive mechanical ventilation.MethodsThis was a prospective cohort study of patients undergoing mechanical ventilation for at least 48 h. RFCSA was measured using ultrasound in patients upon ICU admission and followed until discharge. Using the Medical Research Council score as the gold standard, we evaluated the diagnostic value of ultrasound measurements in predicting ICUAW. Kaplan–Meier curves were constructed to evaluate and compare the length of ICU stay and duration of invasive mechanical ventilation between patients with and without ICUAW.ResultsAmong the 76 patients, 34 (44.7%) were diagnosed with ICUAW using the Medical Research Council score as the gold standard. The RFCSA atrophy rate between day 1 and day 3 was significantly higher in the ICUAW group (7.9 ± 2.8% vs. 4.3 ± 2.1%, p < 0.001). By utilizing a cutoff point of 6.9%, we discovered that the RFCSA atrophy rate exhibited excellent diagnostic accuracy in predicting ICUAW, with a sensitivity of 76.5% and specificity of 92.9%. In ICUAW patients diagnosed based on an RFCSA atrophy rate, the proportion of patients with an ICU stay longer than 14 days was 42.9%, which was significantly higher compared to 22.9% in the non-ICUAW group (HR: 1.768; 95% CI 1.128–2.772; p = 0.006). Similarly, the proportion of patients continuing mechanical ventilation at 14 days was 28.6% versus 4.2% between the two groups (HR: 1.988; 95% CI 1.266–3.120; p < 0.001).ConclusionUltrasound measurements of RFCSA provide a reliable method for diagnosing ICUAW and indicating prognosis in patients undergoing invasive mechanical ventilation.

Clinical effectiveness and safety of time-lapse imaging systems for embryo incubation and selection in in-vitro fertilisation treatment (TILT): a multicentre, three-parallel-group, double-blind, randomised controlled trial

Time-lapse imaging systems for embryo incubation and selection might improve outcomes of in-vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI) treatment due to undisturbed embryo culture conditions, improved embryo selection, or both. However, the benefit remains uncertain. We aimed to evaluate the effectiveness of time-lapse imaging systems providing undisturbed culture and embryo selection, and time-lapse imaging systems providing only undisturbed culture, and compared each with standard care without time-lapse imaging. We conducted a multicentre, three-parallel-group, double-blind, randomised controlled trial in participants undergoing IVF or ICSI at seven IVF centres in the UK and Hong Kong. Embryologists randomly assigned participants using a web-based system, stratified by clinic in a 1:1:1 ratio to the time-lapse imaging system for undisturbed culture and embryo selection (time-lapse imaging group), time-lapse imaging system for undisturbed culture alone (undisturbed culture group), and standard care without time-lapse imaging (control group). Women were required to be aged 18-42 years and men (ie, their partners) 18 years or older. Couples had to be receiving their first, second, or third IVF or ICSI treatment and could not participate if using donor gametes. Participants and trial staff were masked to group assignment, embryologists were not. The primary outcome was live birth. We performed analyses using the intention-to-treat principle and reported the main analysis in participants with primary outcome data available (full analysis set). The trial is registered on the International Trials Registry (ISRCTN17792989) and is now closed. 1575 participants were randomly assigned to treatment groups (525 participants per group) between June 21, 2018, and Sept 30, 2022. The live birth rates were 33·7% (175/520) in the time-lapse imaging group, 36·6% (189/516) in the undisturbed culture group, and 33·0% (172/522) in the standard care group. The adjusted odds ratio was 1·04 (97·5% CI 0·73 to 1·47) for time-lapse imaging arm versus control and 1·20 (0·85 to 1·70) for undisturbed culture versus control. The risk reduction for the absolute difference was 0·7 percentage points (97·5% CI -5·85 to 7·25) between the time-lapse imaging and standard care groups and 3·6 percentage points (-3·02 to 10·22) between the undisturbed culture and standard care groups. 79 serious adverse events unrelated to the trial were reported (n=28 in time-lapse imaging, n=27 in undisturbed culture, and n=24 in standard care). In women undergoing IVF or ICSI treatment, the use of time-lapse imaging systems for embryo culture and selection does not significantly increase the odds of live birth compared with standard care without time-lapse imaging. Barts Charity, Pharmasure Pharmaceuticals, Hong Kong OG Trust Fund, Hong Kong Health and Medical Research Fund, Hong Kong Matching Fund.

Experience in treating multifocal alopecia areata in a child with Jak kinase inhibitors

Alopecia Areata is an autoimmune disease characterized by hair loss mediated by CD8 + T cells. There are no proven effective treatments for alopecia. Recent research on the immunology of hair follicles and recent developments in the field of immunopatogenesis, as well as common ways of developing the disease with other autoimmune disorders have led to research on new treatments, targeted at specific immunological pathways. The so-called JAK-STAT pathway is responsible for transmitting signal from different cytokines to the nucleus of the cell, suggesting that blocking this pathway may be effective in the treatment of alopecia.Study purpose. Presentation of Clinical Case of Multi-focal Alopecia Treatment in Child Oral Tofacitinib.Materials and methods. The study was conducted on the basis of the dermatology department with the laser surgery group of the Center for Pediatric Dermatology and Laboratory of Skin Pathology in children FGAU «National Medical Research Center for Children’s Health» of the Ministry of Health of Russia from April 2021 to November 2022. Severity Alopecia Tool (SALT) was used to determine the severity of the disease in participants. The SALT scale takes into account the percentage of affected hair and divides it into 6 subclasses (S 0 %, S1 &lt;25 %, S2 25–49 %, S3 50–74 %, S4 75–99 % and C5100 %).Results. When oral tofacitinib was administered for 6 months in a 9-year-old patient, full recovery of hair growth on the scalp was observed. No significant abnormalities based on clinical and biochemical blood results were found for the period of treatment and for a year after withdrawal. There was no recurrence of hair loss after the withdrawal of the drug for 12 months.Conclusion. JAK inhibitors can be effective new treatments for alopecia in children. Additional data from randomized controlled trials are required for safe use of JAK inhibitors.

Long-term effects of once-only flexible sigmoidoscopy screening on colorectal cancer incidence and mortality: 21-year follow-up of the UK Flexible Sigmoidoscopy Screening randomised controlled trial

Flexible sigmoidoscopy screening reduces colorectal cancer incidence and mortality; however, uncertainty exists about the duration of protection and differences by sex and age. We assessed effects of once-only flexible sigmoidoscopy screening after 21 years' follow-up. The UK Flexible Sigmoidoscopy Screening Trial is a multicentre randomised controlled trial that recruited men and women aged 55-64 years from general practices serving 14 hospitals. Among participants indicating that they would attend flexible sigmoidoscopy screening if invited, randomisation (2:1) to the control (no further contact) or intervention (invited to once-only flexible sigmoidoscopy screening) group was performed centrally in blocks of 12, stratified by centre, general practice, and household type. Masking of intervention was infeasible. Primary outcomes were colorectal cancer incidence and mortality. The Kaplan-Meier method estimated cumulative incidence. Primary analyses estimated intention-to-treat hazard ratios (HRs) and risk differences, overall and stratified by subsite, sex, and age. The trial is registered with ISRCTN, number 28352761. Among participants recruited between Nov 14, 1994, and March 30, 1999, 170 432 were eligible and 113 195 were randomly assigned to the control group and 57 237 were randomly assigned to the intervention group. 406 participants were excluded from analyses (268 in the control group and 138 in the intervention group), leaving 112 927 participants in the control group (55 336 [49%] men and 57 591 [51%] women) and 57 099 in the intervention group (27 966 [49%] men and 29 103 [51%] women). Of participants who were invited to be screened, 40 624 (71%) attended screening. Median follow-up was 21·3 years (IQR 18·0-22·2). In the invited-to-screening group, colorectal cancer incidence was reduced compared with the control group (1631 vs 4201 cases; cumulative incidence at 21 years was 3·18% [95% CI 3·03 to 3·34] vs 4·16% [4·04 to 4·29]; HR 0·76 [95% CI 0·72 to 0·81]) with 47 fewer cases per 100 000 person-years (95% CI -56 to -37). Colorectal cancer mortality was also reduced in the invited-to-screening group compared with the control group (502 vs 1329 deaths; cumulative incidence at 21 years was 0·97% [0·88 to 1·06] vs 1·33% [1·26 to 1·40]; HR 0·75 [0·67 to 0·83]) with 16 fewer deaths per 100 000 person-years (-21 to -11). Effects were particularly evident in the distal colorectum (726 incident cancer cases in the invited-to-screening group vs 2434 cases in the control group; HR 0·59 [0·54 to 0·64]; 47 fewer cases per 100 000 person-years [-54 to -41]; 196 cancer deaths in the invited-to-screening group vs 708 deaths in the control group; HR 0·55 [0·47 to 0·64]; 15 fewer deaths per 100 000 person-years [-19 to -12]) and not the proximal colon (871 incident cancer cases in the invited-to-screening group vs 1749 cases in the control group; HR 0·98 [0·91 to 1·07]; one fewer case per 100 000 person-years [-8 to 5]; 277 cancer deaths in the invited-to-screening group vs 547 deaths in the control group; HR 1·00 [0·86 to 1·15]; zero fewer deaths per 100 000 person-years [-4 to 4]). The HR for colorectal cancer incidence was lower in men (0·70 [0·65-0·76]) than women (0·86 [0·79 to 0·93]; pinteraction=0·0007) but there was no difference by age. We show that once-only flexible sigmoidoscopy screening reduces colorectal cancer incidence and mortality for two decades and provide important data to inform colorectal cancer screening guidelines. National Institute for Health and Care Research Health Technology Assessment Programme and the Medical Research Council.

The Use of AI in Medicine: Health Data, Privacy Risks and More

In the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.

Microwave Revolution: Transforming Biomedical Synthesis for Tissue Engineering Advancements

Microwave-Assisted Synthesis (MAS) has emerged as a groundbreaking technique revolutionizing the field of biomedical and tissue engineering. This review aims to explore the fundamental principles, techniques, and applications of MAS in these domains. Beginning with an overview highlighting its significance, we delve into the basic principles, mechanisms, and comparative analysis with conventional methods. Subsequently, the review explores MAS techniques in biomaterial synthesis, tissue scaffold fabrication, functionalization, and nanomaterial synthesis, along with their role in drug delivery systems. We then examine its diverse applications, including rapid biomaterial synthesis, property tailoring, biocompatibility enhancements, and tissue regeneration strategies. Furthermore, we address the challenges and future perspectives, focusing on safety considerations, understanding cellular responses, integration with advanced technologies, regulatory aspects, and future directions. This comprehensive review underscores MAS as a transformative tool driving innovations in biomedical research and therapeutic applications