Abstract Blood Flow Restriction Training (BFRT) is a low-load training technique that involves applying pressure to partially restrict arterial blood flow while occluding venous return. Despite its growing popularity, there is still no consensus on how combining BFRT with resistance or aerobic training influences hemodynamic responses, or on the safest and most effective methods for implementing it. This review aims to systematically identify the effects of BFRT on hemodynamic parameters. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement guidelines. The Chinese literature search was performed in the China National Knowledge Infrastructure (CNKI) database. English literature search was conducted in the Web of Science, PubMed, and Google Scholar databases. The studies included human subjects, the outcome indicators included hemodynamic evaluation indicators, and only randomized controlled trials and randomized crossover trials were considered. Non-Chinese or English literature, duplicate studies, and those with missing data were excluded. The adapted STROBE checklist was used to assess the risk of bias, 44 articles were included in this review. Results indicated that BFRT has increased heart rate and blood lactate levels, while its effect on blood oxygen saturation varies. Additionally, BFRT significantly enhances cardiac output but may either have no significant effect or cause a decrease in stroke volume. Furthermore, BFRT improves pulse wave velocity from the femur to the posterior tibia, suggesting a positive influence on cardiovascular function. BFRT induces changes in arterial structure and function, with these indicators interacting to produce both positive and negative effects on cardiovascular health. The primary mechanisms by which BFRT influences hemodynamics include the activation of the sympathetic and vagus nerves, as well as the regulation of chemical mediators in body fluids that modulate cardiovascular function. Convenient, economical, non-invasive, and easily measurable hemodynamic indicators are expected to become an efficient tool for evaluating the effects of exercise training. Further research is needed to establish the optimal compression thresholds and durations for different populations and exercise types, as well as to assess the long-term impact of BFRT on hemodynamic parameters.
Michael Kimmeyer, MD, Tilman Hees, MD, Laurens Allaart, MD
et al.
Background: Tendon transfers of the latissimus dorsi transfer (LDT) or the lower trapezius transfer (LTT) are treatment options for irreparable posterosuperior irreparable rotator cuff tears (PSIRCT). There is still no consensus on which type of tendon transfer is superior in the treatment of PSIRCT. Due to the differences in the anatomy and biomechanics, we hypothesize that there are different clinical situations in which either LDT or LTT should be preferred. The aim of this study was to evaluate the clinical and radiological outcomes of LDT and LTT in patients with PSIRCT to establish a clinical algorithm for the treatment decision. Materials and methods: This is a retrospective, single-center observational study. Included were patients who underwent arthroscopically assisted LDT (aaLDT) or arthroscopically assisted LTT (aaLTT) for PSIRCT. In all patients, range of motion (ROM), external rotation strength, visual analog scale of pain and subjective shoulder value were determined pre- and postoperatively. Constant–Murley score was evaluated at the final follow-up. The complication rate, failure of the tendon transfer, and revision rate were analyzed. Results: In total, 29 aaLDT (age 64 years, median follow-up time 45 months) and 8 aaLTT (age 54 years, median follow-up time 34 months) were included. Active ROM, visual analog scale and subjective shoulder value was significantly improved in both cohorts. At follow-up, the median Constant–Murley score was 73 (aaLDT) and 77 (aaLTT), respectively. The failure rate, including revision surgery, was 14% (aaLDT) and 13% (aaLTT), respectively. Low functional findings preoperatively were correlated to a lower functional outcome at follow-up in both groups. Painful loss of anterior elevation and loss of external rotation had no significant impact on functional outcomes in aaLDT. Conclusion: Following the treatment algorithm based on the clinical examination, clinical outcome parameters, active ROM and pain could be significantly improved. A good preoperative function was associated with a good clinical outcome in both transfers. A low failure and revision rate supports the good decision-making of the algorithm presented.
Orthopedic surgery, Diseases of the musculoskeletal system
Alicja Kot, Marta Wojtczak, Natalia Myśliwiec
et al.
Introduction and purpose: Hypertension, or high blood pressure, is a condition characterized by persistently elevated
arterial pressure, leading to severe complications such as heart disease, stroke, and kidney
failure. According to the World Health Organization (WHO), hypertension affects
approximately 1.13 billion people worldwide, contributing significantly to global morbidity
and mortality. Emerging research has revealed a significant connection between gut
microbiota—the diverse community of microorganisms residing in the gastrointestinal
tract—and the regulation of blood pressure. This comprehensive review explores the intricate
relationship between gut microbiota and hypertension, delving into the underlying
mechanisms, evidence from animal and human studies, and potential therapeutic
interventions.
The state of knowledge: Key mechanisms involved into regulation of blood pressure include the production of short-
chain fatty acids (SCFAs), modulation of the renin-angiotensin system (RAS), immune system regulation, and metabolite production such as trimethylamine-N-oxide (TMAO). Animal studies using fecal microbiota transplantation (FMT) have provided strong evidence
for a causal role of gut microbiota in blood pressure regulation. Human studies have shown
associations between gut microbiota composition and hypertension, highlighting the potential
for dietary interventions, probiotics, prebiotics, physical activity and FMT as therapeutic
strategies.
Summary: This review underscores the promise of microbiota-targeted therapies in managing
hypertension and calls for further research to elucidate precise mechanisms and develop
personalized medicine approaches. Understanding the complex interactions between gut
microbiota and hypertension could pave the way for innovative treatments and improved
cardiovascular health outcomes.
Purpose: Comprehensive legal medicine documentation includes both an internal but also an external examination of the corpse. Typically, this documentation is conducted manually during conventional autopsy. A systematic digital documentation would be desirable, especially for the external examination of wounds, which is becoming more relevant for legal medicine analysis. For this purpose, RGB surface scanning has been introduced. While a manual full surface scan using a handheld camera is timeconsuming and operator dependent, floor or ceiling mounted robotic systems require substantial space and a dedicated room. Hence, we consider whether a mobile robotic system can be used for external documentation. Methods: We develop a mobile robotic system that enables full-body RGB-D surface scanning. Our work includes a detailed configuration space analysis to identify the environmental parameters that need to be considered to successfully perform a surface scan. We validate our findings through an experimental study in the lab and demonstrate the system's application in a legal medicine environment. Results: Our configuration space analysis shows that a good trade-off between coverage and time is reached with three robot base positions, leading to a coverage of 94.96 %. Experiments validate the effectiveness of the system in accurately capturing body surface geometry with an average surface coverage of 96.90 +- 3.16 % and 92.45 +- 1.43 % for a body phantom and actual corpses, respectively. Conclusion: This work demonstrates the potential of a mobile robotic system to automate RGB-D surface scanning in legal medicine, complementing the use of post-mortem CT scans for inner documentation. Our results indicate that the proposed system can contribute to more efficient and autonomous legal medicine documentation, reducing the need for manual intervention.
Recently, a growing number of experts in artificial intelligence (AI) and medicine have be-gun to suggest that the use of AI systems, particularly machine learning (ML) systems, is likely to humanise the practice of medicine by substantially improving the quality of clinician-patient relationships. In this thesis, however, I argue that medical ML systems are more likely to negatively impact these relationships than to improve them. In particular, I argue that the use of medical ML systems is likely to comprise the quality of trust, care, empathy, understanding, and communication between clinicians and patients.
In the much-celebrated book Deep Medicine, Eric Topol argues that the development of artificial intelligence for health care will lead to a dramatic shift in the culture and practice of medicine. In the next several decades, he suggests, AI will become sophisticated enough that many of the everyday tasks of physicians could be delegated to it. Topol is perhaps the most articulate advocate of the benefits of AI in medicine, but he is hardly alone in spruiking its potential to allow physicians to dedicate more of their time and attention to providing empathetic care for their patients in the future. Unfortunately, several factors suggest a radically different picture for the future of health care. Far from facilitating a return to a time of closer doctor-patient relationships, the use of medical AI seems likely to further erode therapeutic relationships and threaten professional and patient satisfaction.
Mark A. Glover, B.S., Jeffery D. St. Jeor, M.D., Nihir Parikh, B.S.
et al.
Purpose: To identify prognostic factors associated with a delayed return-to-sport (RTS) time in amateur athletes who return to full participation after a primary isolated anterior cruciate ligament (ACL) reconstruction. Methods: A retrospective review was performed among athletes who underwent ACL reconstruction between October 2014 and October 2021. Inclusion criteria were any amateur athletes with an ACL reconstruction who had a documented RTS and greater than 1-year follow-up. Nonathletes, those with multiligamentous knee injury, and those missing documented RTS timelines were excluded. RTS was defined as participation in athletics at a level equivalent to or greater than the preinjury level participation. Demographic and prognostic factors, including previous knee surgery, meniscal involvement, level of participation, surgical approach, and graft type, were recorded along with RTS time and analyzed via Poisson regression. Results: In total, 91 athletes, average age 18.8 (± 6.7) years, who underwent ACL reconstruction at a single institution from 2014 to 2021 were identified with an average follow-up time of 4.6 (± 2.5) years (range 1.1, 9.0). Meniscal involvement (1.11; 95% confidence interval [CI] 1.08-1.15, P < .001) and previous knee surgery (1.43; 95% CI 1.29-1.58; P < .001) were related to a delayed RTS. Quadriceps tendon and bone–patellar tendon–bone autografts, as well as allograft, showed a significant association with a longer RTS time when compared with hamstring autograft (1.16, 95% CI 1.13-1.20, P < .001; 1.04, 95% CI 1.01-1.07, P = .020; 1.11, 95% CI 1.03-1.19, P = .004, respectively), as did anteromedial portal drilling, when compared with the outside in approach for femoral drilling (1.19, 95% CI 1.16-1.23, P < .001). Conclusions: Previous knee surgery, anteromedial femoral drilling, quadriceps tendon autograft, and meniscus tear were most associated with a delayed timeline for RTS among young athletes who were able to return. Level of Evidence: Level IV, prognostic case series.
Jeffery L Heileson, Michael J Macartney, Nora L Watson
et al.
Summary: Background: Accumulating evidence has highlighted the acute and chronic impact of repetitive subconcussive head impacts (rSHIs) in contact sports. Neurofilament-light (Nf-L), a brain-derived biomarker of neuroaxonal injury, elevates in concert with rSHI. Recently, long-chain ω-3 polyunsaturated fatty acids (LC ω-3 PUFAs) supplementation has been suggested to mitigate brain injury from rSHI as reflected by attenuation of Nf-L concentrations within contact sport athletes. Objective: Using a systematic review with a meta-analysis, we aimed to determine the effect of LC ω-3 PUFA supplementation on Nf-L concentrations in athletes routinely exposed to rSHI. Methods: Electronic databases (PubMed and CINAHL) were searched from inception through January 2024. One-stage meta-analysis of individual participant-level data was used to detect changes in Nf-L concentrations between LC ω-3 PUFA and control/placebo (PL) groups from baseline to midseason (MS) and postseason (PS). Least square means (±SE) for Nf-L change from baseline were compared by treatment group for MS/PS using contrast t tests. Significance was set a priori at adjusted P ≤ 0.05. Results: Of 460 records identified, 3 studies in collegiate American football players (n = 179; LC ω-3 PUFA = 105, PL = 71) were included in the meta-analysis. Compared with PL, the change in Nf-L concentrations was statistically similar at MS [mean difference (MD) = –1.66 ± 0.82 pg·mL–1, adjusted P = 0.09] and significantly lower at PS (MD = –2.23 ± 0.83 pg·mL–1, adjusted P = 0.02) in athletes following LC ω-3 PUFA supplementation. Conclusions: Our findings demonstrate preliminary support for the prophylactic administration of LC ω-3 PUFA in contact sport athletes exposed to rSHI; however, further research is required to determine the effective dosage required.This trial was registered at OSF (DOI: https://doi.org/10.17605/OSF.IO/EY5QW).
Nutrition. Foods and food supply, Nutritional diseases. Deficiency diseases
Introduction. With global aging trends, understanding the nutritional status and physical fitness of older adults is crucial for promoting healthy aging and independence. Aim. The aim of this study was to assess nutritional status, physical fitness, and demographic parameters among older adults. Material and Methods. Using reliable assessment tools, data were collected from 40 participants aged 60 years and above. Assessments included the Mini Nutritional Assessment (MNA), Timed Up & Go Test (TUG), and Handgrip Strength Test (HGS). Statistical analyses were conducted to explore correlations and differences. Results. Most participants exhibited normal nutritional status (n = 38; 95%) and generally normal mobility and handgrip strength. However, no significant correlations were found between nutritional status, physical fitness, and demographic factors. Conclusions. While the majority demonstrated favorable health indicators, some individuals were at risk of malnutrition and functional limitations. Surprisingly, age did not correlate significantly with nutritional status or physical fitness. Further research with larger cohorts and sensitive assessment tools is needed to elucidate these relationships fully.
Ben Pongkanpai, Jettanong Klaewsongkram, Timothy D. Mickleborough
et al.
Introduction: Allergic rhinitis (AR) is an inflammation of the nasal mucosa triggered by allergens. Evidence suggests exercise positively affects rhinitis symptoms, but the impact of high-intensity interval exercise (HIIE) on AR has not been studied. This study aimed to determine the acute effects of HIIE on AR symptoms and fractional exhaled nitric oxide (FeNO) in AR patients. Methods: Twelve AR patients completed two running exercise protocols: both high intensity for 1 min at 85%–90% alternating with low intensity at 50%–55% of maximum heart rate for 1 min (1:1 HIIE) or 2 min (1:2 HIIE). Rhinitis symptom scores, NBF, PNIF, and FeNO were measured before and after each protocol at 0, 15, 30, 45, and 60 minutes. Two-way ANOVA determined significant differences in NBF, PNIF, and FeNO before and after exercise. The Friedman test examined rhinitis symptom scores. Differences were significant at p < 0.05. Results: NBF and FeNO were significantly lower after 1:2 HIIE than at baseline. AR symptom scores were significantly lower after both exercises. PNIF was significantly higher after both protocols than at baseline. Moreover, there was a significant difference in NBF at 0 minutes and in rhinorrhea symptoms at 0 and 15 minutes between 1:2 HIIE and 1:1 HIIE. Conclusions: This study offers valuable insights into managing allergic rhinitis through exercise. Both HIIE protocols significantly improved AR symptoms, but NBF and FeNO were lower after the 1:2 HIIE protocol. Therefore, a 1:2 work-to-rest ratio HIIE protocol is recommended for alleviating rhinitis symptoms.
Shamik Bhattacharjee, Kamlesh Marathe, Hitesh Kapoor
et al.
Fantasy sports, particularly fantasy cricket, have garnered immense popularity in India in recent years, offering enthusiasts the opportunity to engage in strategic team-building and compete based on the real-world performance of professional athletes. In this paper, we address the challenge of optimizing fantasy cricket team selection using reinforcement learning (RL) techniques. By framing the team creation process as a sequential decision-making problem, we aim to develop a model that can adaptively select players to maximize the team's potential performance. Our approach leverages historical player data to train RL algorithms, which then predict future performance and optimize team composition. This not only represents a huge business opportunity by enabling more accurate predictions of high-performing teams but also enhances the overall user experience. Through empirical evaluation and comparison with traditional fantasy team drafting methods, we demonstrate the effectiveness of RL in constructing competitive fantasy teams. Our results show that RL-based strategies provide valuable insights into player selection in fantasy sports.
Large Language Models (LLMs) has made significant progress in a number of professional fields, including medicine, law, and finance. However, in traditional Chinese medicine (TCM), there are challenges such as the essential differences between theory and modern medicine, the lack of specialized corpus resources, and the fact that relying only on supervised fine-tuning may lead to overconfident predictions. To address these challenges, we propose a two-stage training approach that combines continuous pre-training and supervised fine-tuning. A notable contribution of our study is the processing of a 2GB corpus dedicated to TCM, constructing pre-training and instruction fine-tuning datasets for TCM, respectively. In addition, we have developed Qibo-Benchmark, a tool that evaluates the performance of LLM in the TCM on multiple dimensions, including subjective, objective, and three TCM NLP tasks. The medical LLM trained with our pipeline, named $\textbf{Qibo}$, exhibits significant performance boosts. Compared to the baselines, the average subjective win rate is 63%, the average objective accuracy improved by 23% to 58%, and the Rouge-L scores for the three TCM NLP tasks are 0.72, 0.61, and 0.55. Finally, we propose a pipline to apply Qibo to TCM consultation and demonstrate the model performance through the case study.
In this paper we introduce a new dataset containing instance segmentation masks for ten different categories of winter sports equipment, called WSESeg (Winter Sports Equipment Segmentation). Furthermore, we carry out interactive segmentation experiments on said dataset to explore possibilities for efficient further labeling. The SAM and HQ-SAM models are conceptualized as foundation models for performing user guided segmentation. In order to measure their claimed generalization capability we evaluate them on WSESeg. Since interactive segmentation offers the benefit of creating easily exploitable ground truth data during test-time, we are going to test various online adaptation methods for the purpose of exploring potentials for improvements without having to fine-tune the models explicitly. Our experiments show that our adaptation methods drastically reduce the Failure Rate (FR) and Number of Clicks (NoC) metrics, which generally leads faster to better interactive segmentation results.
Pilar Marqués-Sánchez, José Alberto Benítez-Andrades, María Dolores Calvo Sánchez
et al.
Objectives: This study analyzed adolescent physical activity, its link to overweight, and the social network structure in group sports participants, focusing on centrality measures. Setting: Conducted in 11 classrooms across 5 schools in Ponferrada, Spain. Participants: Included 235 adolescents (49.4% female), categorized as normal weight or overweight. Methods: The Physical Activity Questionnaire for Adolescents (PAQ-A) assessed physical activity levels. Social network analysis evaluated centrality in varying contact degrees. Results: 30.2% were overweight. Males scored higher in PAQ-A and were more likely to engage in group sports. No significant correlation was found between physical activity and weight in the total sample. However, overweight females reported higher exercise levels. Centrality analysis showed gender differences; women in group sports had lower centrality, whereas men had higher. Conclusions: The study highlights the importance of gender and social network centrality in designing future strategies, considering peer interaction intensity
Aaron Baughman, Stephen Hammer, Rahul Agarwal
et al.
We address the problem of scaling up the production of media content, including commentary and personalized news stories, for large-scale sports and music events worldwide. Our approach relies on generative AI models to transform a large volume of multimodal data (e.g., videos, articles, real-time scoring feeds, statistics, and fact sheets) into coherent and fluent text. Based on this approach, we introduce, for the first time, an AI commentary system, which was deployed to produce automated narrations for highlight packages at the 2023 US Open, Wimbledon, and Masters tournaments. In the same vein, our solution was extended to create personalized content for ESPN Fantasy Football and stories about music artists for the Grammy awards. These applications were built using a common software architecture achieved a 15x speed improvement with an average Rouge-L of 82.00 and perplexity of 6.6. Our work was successfully deployed at the aforementioned events, supporting 90 million fans around the world with 8 billion page views, continuously pushing the bounds on what is possible at the intersection of sports, entertainment, and AI.
Lachlan Thorpe, Lewis Bawden, Karanjot Vendal
et al.
We present a transformer decoder based sports simulation engine, SportsNGEN, trained on sports player and ball tracking sequences, that is capable of generating sustained gameplay and accurately mimicking the decision making of real players. By training on a large database of professional tennis tracking data, we demonstrate that simulations produced by SportsNGEN can be used to predict the outcomes of rallies, determine the best shot choices at any point, and evaluate counterfactual or what if scenarios to inform coaching decisions and elevate broadcast coverage. By combining the generated simulations with a shot classifier and logic to start and end rallies, the system is capable of simulating an entire tennis match. We evaluate SportsNGEN by comparing statistics of the simulations with those of real matches between the same players. We show that the model output sampling parameters are crucial to simulation realism and that SportsNGEN is probabilistically well-calibrated to real data. In addition, a generic version of SportsNGEN can be customized to a specific player by fine-tuning on the subset of match data that includes that player. Finally, we show qualitative results indicating the same approach works for football.
In this paper, we introduce InMD-X, a collection of multiple large language models specifically designed to cater to the unique characteristics and demands of Internal Medicine Doctors (IMD). InMD-X represents a groundbreaking development in natural language processing, offering a suite of language models fine-tuned for various aspects of the internal medicine field. These models encompass a wide range of medical sub-specialties, enabling IMDs to perform more efficient and accurate research, diagnosis, and documentation. InMD-X's versatility and adaptability make it a valuable tool for improving the healthcare industry, enhancing communication between healthcare professionals, and advancing medical research. Each model within InMD-X is meticulously tailored to address specific challenges faced by IMDs, ensuring the highest level of precision and comprehensiveness in clinical text analysis and decision support. This paper provides an overview of the design, development, and evaluation of InMD-X, showcasing its potential to revolutionize the way internal medicine practitioners interact with medical data and information. We present results from extensive testing, demonstrating the effectiveness and practical utility of InMD-X in real-world medical scenarios.
Ashley E. Disantis, RobRoy L. Martin, Keelan Enseki
et al.
# Background
Acetabular dysplasia (AD) is defined as a structurally deficient acetabulum and is a well-recognized cause of hip pain in young adults. While treatment of severe AD with a periacetabular osteotomy has demonstrated good long-term outcomes, a trial of non-operative management is often recommended in this population. This may be especially true in patients with milder deformities. Currently, there is a paucity of research pertaining to non-operative management of individuals with AD.
# Purpose
To present expert-driven non-operative rehabilitation guidelines for use in individuals with AD.
# Study Design
Delphi study
# Methods
A panel of 15 physiotherapists from North America who were identified as experts in non-operative rehabilitation of individuals with AD by a high-volume hip preservation surgeon participated in this Delphi study. Panelists were presented with 16 questions regarding evaluation and treatment principles of individuals with AD. A three-step Delphi method was utilized to establish consensus on non-operative rehabilitation principles for individuals presenting with AD.
# Results
Total (100%) participation was achieved for all three survey rounds. Consensus, defined a piori as > 75%, was reached for 16/16 questions regarding evaluation principles, activity modifications, appropriate therapeutic exercise progression, return to activity/sport criteria, and indications for physician referral.
# Conclusion
This North American based Delphi study presents expert-based consensus on non-operative rehabilitation principles for use in individuals with AD. Establishing guidelines for non-operative management in this population will help reduce practice variation and is the first step in stratifying individuals who would benefit from non-operative management. Future research should focus on patient-reported outcomes and rate of subsequent surgical intervention to determine the success of the guidelines reported in this study.
# Level of Evidence
Level V
Ghizal fatima, Risala H. Allami, Maitham G. Yousif
Precision medicine, tailored to individual patients based on their genetics, environment, and lifestyle, shows promise in managing complex diseases like infections. Integrating artificial intelligence (AI) into precision medicine can revolutionize disease management. This paper introduces a novel approach using AI to advance precision medicine in infectious diseases and beyond. It integrates diverse fields, analyzing patients' profiles using genomics, proteomics, microbiomics, and clinical data. AI algorithms process vast data, providing insights for precise diagnosis, treatment, and prognosis. AI-driven predictive modeling empowers healthcare providers to make personalized and effective interventions. Collaboration among experts from different domains refines AI models and ensures ethical and robust applications. Beyond infections, this AI-driven approach can benefit other complex diseases. Precision medicine powered by AI has the potential to transform healthcare into a proactive, patient-centric model. Research is needed to address privacy, regulations, and AI integration into clinical workflows. Collaboration among researchers, healthcare institutions, and policymakers is crucial in harnessing AI-driven strategies for advancing precision medicine and improving patient outcomes.