This study proposes a statistically grounded framework for real-time win probability evaluation and player assessment in score-based team sports, based on minute-by-minute cumulative box-score data. We introduce a continuous dominance indicator (T-score) that maps final scores to real values consistent with win/lose outcomes, and formulate it as a time-evolving stochastic representation (T-process) driven by standardized cumulative statistics. This structure captures temporal game dynamics and enables sequential, analytically tractable updates of in-game win probability. Through this stochastic formulation, competitive advantage is decomposed into interpretable statistical components. Furthermore, we define a latent contribution index, STATS X, which quantifies a player's involvement in favorable dominance intervals identified by the T-process. This allows us to separate a team's baseline strength from game-specific performance fluctuations and provides a coherent, structural evaluation framework for both teams and players. While we do not implement AI methods in this paper, our framework is positioned as a foundational step toward hybrid integration with AI. By providing a structured time-series representation of dominance with an explicit probabilistic interpretation, the framework enables flexible learning mechanisms and incorporation of high-dimensional data, while preserving statistical coherence and interpretability. This work provides a basis for advancing AI-driven sports analytics.
Traditional Chinese Medicine (TCM), as an effective alternative medicine, has been receiving increasing attention. In recent years, the rapid development of large language models (LLMs) tailored for TCM has highlighted the urgent need for an objective and comprehensive evaluation framework to assess their performance on real-world tasks. However, existing evaluation datasets are limited in scope and primarily text-based, lacking a unified and standardized multimodal question-answering (QA) benchmark. To address this issue, we introduce TCM-Ladder, the first comprehensive multimodal QA dataset specifically designed for evaluating large TCM language models. The dataset covers multiple core disciplines of TCM, including fundamental theory, diagnostics, herbal formulas, internal medicine, surgery, pharmacognosy, and pediatrics. In addition to textual content, TCM-Ladder incorporates various modalities such as images and videos. The dataset was constructed using a combination of automated and manual filtering processes and comprises over 52,000 questions. These questions include single-choice, multiple-choice, fill-in-the-blank, diagnostic dialogue, and visual comprehension tasks. We trained a reasoning model on TCM-Ladder and conducted comparative experiments against nine state-of-the-art general domain and five leading TCM-specific LLMs to evaluate their performance on the dataset. Moreover, we propose Ladder-Score, an evaluation method specifically designed for TCM question answering that effectively assesses answer quality in terms of terminology usage and semantic expression. To the best of our knowledge, this is the first work to systematically evaluate mainstream general domain and TCM-specific LLMs on a unified multimodal benchmark. The datasets and leaderboard are publicly available at https://tcmladder.com and will be continuously updated.
Marcos Cezar Pitombo da Silva Junior, Enaiane Cristina Menezes, Sand Araújo Tenório
et al.
Objective: This study aimed to identify the prevalence and factors associated with perceived changes in physical activity (PA) and sedentary behavior (SB) during the COVID 19 pandemic within the university community. Methods: It is an observational, cross-sectional, multicenter study conducted with the academic community of higher education institutions in Brazil. A structured and validated questionnaire was utilized, and multinomial logistic regression was applied with a 95% confidence interval. Results: A total of 4,809 individuals participated (65.8% women and 74.0% students). It was observed that 44.6% (n= 2,136) perceived a reduction in PA, and 74.2% (n = 3,549) perceived an increase in SB. Women aged 40 and over and men in social isolation were less likely to be active (31.0% and 43.0%, respectively). Women with a good (OR = 3.33; 95% CI: 2.22 - 4.99) or fair health perception (OR = 1.98; 95% CI: 1.30 - 3.04) and men with a good health perception (OR = 2.38; 95% CI: 1.35 - 4.20) were more likely to be active. The likelihood of higher SB was lower among women with a good health perception (58.0%) or aged 30–39 (34.0%) or 40+ (50.0%), and among men with a good health perception (61.0%) or aged 30–39 (42.0%) or 40+ (54.0%). Increased SB likelihood was higher among women in isolation (OR = 1.71; 95% CI: 1.25 - 2.34), isolated for two or more months (OR = 1.43; 95% CI: 1.10 - 1.85), or with a room per capita ratio of 1.20 (OR = 1.51; 95% CI: 1.13 - 2.01); and among men in isolation (OR = 1.61; 95% CI: 1.10 - 2.34), isolated for two or more months (OR = 1.42; 95% CI: 1.02 - 1.96), and living in the Northeast (OR = 2.34; 95% CI: 1.20 - 4.57) or Southeast (OR = 2.96; 95% CI: 1.47 - 5.96) regions of Brazil. Conclusion: The pandemic led to a perceived increase in SB and a decrease in PA, especially among older women, those in isolation, and those with limited living space, as well as among men in isolation.
Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.
Generative artificial intelligence (AI) has brought revolutionary innovations in various fields, including medicine. However, it also exhibits limitations. In response, retrieval-augmented generation (RAG) provides a potential solution, enabling models to generate more accurate contents by leveraging the retrieval of external knowledge. With the rapid advancement of generative AI, RAG can pave the way for connecting this transformative technology with medical applications and is expected to bring innovations in equity, reliability, and personalization to health care.
Daniel Schwabe, Katinka Becker, Martin Seyferth
et al.
The adoption of machine learning (ML) and, more specifically, deep learning (DL) applications into all major areas of our lives is underway. The development of trustworthy AI is especially important in medicine due to the large implications for patients' lives. While trustworthiness concerns various aspects including ethical, technical and privacy requirements, we focus on the importance of data quality (training/test) in DL. Since data quality dictates the behaviour of ML products, evaluating data quality will play a key part in the regulatory approval of medical AI products. We perform a systematic review following PRISMA guidelines using the databases PubMed and ACM Digital Library. We identify 2362 studies, out of which 62 records fulfil our eligibility criteria. From this literature, we synthesise the existing knowledge on data quality frameworks and combine it with the perspective of ML applications in medicine. As a result, we propose the METRIC-framework, a specialised data quality framework for medical training data comprising 15 awareness dimensions, along which developers of medical ML applications should investigate a dataset. This knowledge helps to reduce biases as a major source of unfairness, increase robustness, facilitate interpretability and thus lays the foundation for trustworthy AI in medicine. Incorporating such systematic assessment of medical datasets into regulatory approval processes has the potential to accelerate the approval of ML products and builds the basis for new standards.
Traditional Chinese Medicine (TCM) involves complex compatibility mechanisms characterized by multi-component and multi-target interactions, which are challenging to quantify. To address this challenge, we applied graph artificial intelligence to develop a TCM multi-dimensional knowledge graph that bridges traditional TCM theory and modern biomedical science (https://zenodo.org/records/13763953 ). Using feature engineering and embedding, we processed key TCM terminology and Chinese herbal pieces (CHP), introducing medicinal properties as virtual nodes and employing graph neural networks with attention mechanisms to model and analyze 6,080 Chinese herbal formulas (CHF). Our method quantitatively assessed the roles of CHP within CHF and was validated using 215 CHF designed for COVID-19 management. With interpretable models, open-source data, and code (https://github.com/ZENGJingqi/GraphAI-for-TCM ), this study provides robust tools for advancing TCM theory and drug discovery.
Adam M Pinkoski, Matthew Davies, Mark Sommerfeldt
et al.
# Background
The National Hockey League (NHL) saw an unprecedented disruption to the competitive calendar due to the COVID-19 pandemic in March of 2020. Returning to play following an abrupt cessation of activity is a known risk factor for athletes.
# Purpose
To analyze the occurrence and severity of events (injury and illness) in the NHL and to understand any differences in occurrence and severity between pre-pandemic seasons and seasons that immediately followed.
# Study Design
Descriptive Epidemiology Study
# Methods
Using a retrospective cohort inclusive of all players on active rosters in the NHL between 2016-2023, public access injury and illness data were collected. Outcome measures included event incidence, period prevalence, and severity (mean days lost; MDL), as well as incidence rate ratio (IRR) comparing pre- and post-pandemic seasons.
# Results
IRR for illness peaked in December 2021 (IRR = 62.46; 95% CI 13.65 to 285.91). Incidence of upper body injuries was significantly higher in 2020-21 (IRR = 1.70, p = 0.001) and 2021-22 (IRR = 1.40, p = 0.044) compared to pre-pandemic seasons (Incidence = 17.58 injuries / 1000 player-hours). Injury incidence increased as the 2022-23 season progressed (p = 0.004); injury incidence was stable across all other seasons. Mean days lost (MDL) to injury was higher in 2020-21 (MDL = 18.12, p < 0.001), 2021-22 (MDL = 18.46, p = 0.015), and 2022-23 (MDL = 18.12, p < 0.001) compared to pre-pandemic seasons (MDL = 17.34).
# Conclusion
Incidence of upper body injuries increased in the 2020-21 and 2021-22 NHL regular seasons while it decreased significantly in the 2022-23 regular season compared with the four pre-pandemic seasons. This suggests a need to examine if modifiable risk factors exist for determining optimal return to play strategies following an abrupt cessation of play.
# Level of Evidence
3
Szymon Niemirka, Aleksandra Janiak, Kinga Dominiczak
et al.
Endometriosis is a common condition affecting approximately 10% of women of reproductive age and leads to many unpleasant symptoms, such as pelvic pain, painful intercourse, and fertility issues. Although treatment primarily involves surgical and hormonal approaches, growing evidence suggests that physical activity may play a beneficial role in alleviating symptoms of the disease. The aim of this paper is to review the literature on the impact of physical activity on the symptoms of endometriosis and evaluate the role of exercise in improving the quality of life for patients. Available research indicates that regular physical activity, including aerobic and strength exercises, can reduce pain intensity, improve well-being, and decrease the need for pain medications. Additionally, exercise positively affects mental health, reducing stress, anxiety, and depression, which, in turn, improves the course of the disease. The findings suggest that physical activity should be considered an integral part of endometriosis treatment, serving as a supportive therapeutic approach. However, further controlled studies and long-term observations are necessary to determine the optimal type, intensity, and frequency of exercise for women with endometriosis.
Introduction: Microplastics are common pollutants found in the environment and consumer products. People can be exposed to them through eating, breathing, and skin contact. The sources include contaminated food, drinks, seafood, water, salt, and particles in the air, especially in urban and industrial areas. Research suggests these microplastics may cause physical damage, chemical toxicity, inflammation, oxidative stress, and disruption of hormones [1].
Purpose of work: This systematic literature review focuses on providing a comprehensive overview of microplastics in the human body and the health implications due to their presence in human systems [2].
State of knowledge: Research on microplastics has been gaining significant attention among the scientific community. They were found in human stool, blood, and even placental tissue, suggesting they can be absorbed into the human body [3]. Microplastics can enter the body through ingestion, inhalation, and skin contact, and may accumulate in organs over time. Potential adverse health effects include inflammation, immune responses, reproductive toxicity, and translocation to other organs [4][5].
Material and methods: The methodology involved clearly outlining the objectives, a systematic literature search, and a structured process for screening and analyzing studies. The research involved a comprehensive search of scientific databases, including PubMed and Google Scholar.
Summary: Microplastics are ubiquitous in the human environment and can enter the body through various exposure pathways. They have the potential to cause a range of adverse health effects, including physical damage, chemical toxicity, inflammation, oxidative stress, and hormonal disruption. However, more research is needed to fully understand the long-term health consequences of microplastics in the human body.
Anna E van der Windt, Lisette C Langenberg, Joost W Colaris
et al.
Despite the common occurrence of radial head fractures, there is still a lack of consensus on which radial head fractures should be treated surgically.
The radial head is an important secondary stabilizer in almost all directions. An insufficient radial head can lead to increased instability in varus–valgus and posterolateral rotatory directions, especially in a ligament-deficient elbow.
The decision to perform surgery is often not dictated by the fracture pattern alone but also by the presence of associated injury. Comminution of the radial head and complete loss of cortical contact of at least one fracture fragment are associated with a high occurrence of associated injuries.
Nondisplaced and minimally displaced radial head fractures can be treated non-operatively with early mobilization. Displacement (>2 mm) of fragments in radial head fractures without a mechanical block to pronation/supination is not a clear indication for surgery.
Mechanical block to pronation/supination and comminution of the fracture are indications for surgery.
The following paper reviews the current literature and provides state-of-the-art guidance on which radial head fractures should be treated surgically.
Michael Buldo-Licciardi, B.S., Nicole D. Rynecki, M.D., Naina Rao, B.S.
et al.
Purpose: To compare psychological readiness to return to sport (RTS), RTS rate, level of return, and time to return between patients who underwent bilateral anterior cruciate ligament reconstruction (ACLR) and those who underwent unilateral ACLR. Methods: The electronic medical record at a single academic medical center was queried for patients who underwent ACLR from January 2012 to May 2020. The inclusion criteria were skeletally mature patients who underwent either single or sequential bilateral ACLR and who had undergone either the primary ACLR or second contralateral ACLR at least 2 years earlier. Bilateral ACLRs were matched 1:3 to unilateral reconstructions based on age, sex, and body mass index. Psychological readiness to RTS was assessed using the validated ACL Return to Sport After Injury (ACL-RSI) scale. This, along with time to return and level of RTS, was compared between the 2 cohorts. Results: In total, 170 patients were included, of whom 44 underwent bilateral ACLR and 132 underwent unilateral ACLR. At the time of the first surgical procedure, patients in the unilateral cohort were aged 28.8 ± 9.4 years and those in the bilateral cohort were aged 25.7 ± 9.8 years (P = .06). The average time difference between the first and second surgical procedures was 28.4 ± 22.3 months. There was no difference in psychological readiness to RTS (50.5 in bilateral cohort vs 48.1 in unilateral cohort, P = .66), RTS rate (78.0% in unilateral cohort vs 65.9% in bilateral cohort, P = .16), percentage of return to preinjury sport level (61.2% in unilateral cohort vs 69.0% in bilateral cohort, P = .21), or time to return (41.2 ± 29.3 weeks in unilateral cohort vs 35.2 ± 23.7 weeks in bilateral cohort, P = .31) between the 2 cohorts. Conclusions: Compared with patients who undergo unilateral ACLR, patients who undergo bilateral ACLR are equally as psychologically ready to RTS, showing equal rates of RTS, time to return, and level of return. Level of Evidence: Level III, retrospective cohort study.
Study aim: The aim of this study was to examine the mediating role of secondary school students’ physical activity attitudes between health-related fitness knowledge (HRFK) and physical activity (PA).
One of the major barriers to using large language models (LLMs) in medicine is the perception they use uninterpretable methods to make clinical decisions that are inherently different from the cognitive processes of clinicians. In this manuscript we develop novel diagnostic reasoning prompts to study whether LLMs can perform clinical reasoning to accurately form a diagnosis. We find that GPT4 can be prompted to mimic the common clinical reasoning processes of clinicians without sacrificing diagnostic accuracy. This is significant because an LLM that can use clinical reasoning to provide an interpretable rationale offers physicians a means to evaluate whether LLMs can be trusted for patient care. Novel prompting methods have the potential to expose the black box of LLMs, bringing them one step closer to safe and effective use in medicine.
Andreas Maier, Michael Hartung, Mark Abovsky
et al.
In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research.
Monte Carlo (MC) techniques are currently deemed the gold standard for internal dosimetry, since the simulations can perform full radiation transport and reach a precision level not attainable by analytical methods. In this study, a custom voxelized particle source was developed for the TOPAS-MC toolkit to be used for internal dosimetry purposes. The source was designed to allow the use of clinical functional scans data to simulate events that reproduce the patient-specific tracer biodistribution. Simulation results are very promising, showing that this can be a first step towards the extension of TOPAS-MC to nuclear medicine applications. In the future more studies are needed to further ascertain the applicability and accuracy of the developed routines.
Shishir Rajendran, Prathic Sundararajan, Ashi Awasthi
et al.
Nanorobotics offers an emerging frontier in biomedicine, holding the potential to revolutionize diagnostic and therapeutic applications through its unique capabilities in manipulating biological systems at the nanoscale. Following PRISMA guidelines, a comprehensive literature search was conducted using IEEE Xplore and PubMed databases, resulting in the identification and analysis of a total of 414 papers. The studies were filtered to include only those that addressed both nanorobotics and direct medical applications. Our analysis traces the technology's evolution, highlighting its growing prominence in medicine as evidenced by the increasing number of publications over time. Applications ranged from targeted drug delivery and single-cell manipulation to minimally invasive surgery and biosensing. Despite the promise, limitations such as biocompatibility, precise control, and ethical concerns were also identified. This review aims to offer a thorough overview of the state of nanorobotics in medicine, drawing attention to current challenges and opportunities, and providing directions for future research in this rapidly advancing field.