Citations

Nutritional supplement use is common among athletes aiming to enhance performance, recovery, and health. However, variable regulatory frameworks and limited safety oversight create risks for inadvertent doping violations. This article provides a global overview of supplement use, relevant authorities, legislation, and safety measures, with a focus on third-party testing (TPT) as a risk-mitigation strategy. Data from six global regions—Africa, Asia, Australia/New Zealand, Europe, Latin America, and North America—were synthesized from peer-reviewed studies, governmental sources, and regional expert contributions. Reported supplement use ranged from 7% to 100% among athletes (variability within regions), with protein powders, vitamins/minerals, creatine, caffeine, and sports drinks being most prevalent. High-risk products (potential anti-doping rule violations), including certain herbal blends, preworkouts, and weight-management supplements, were reported across all regions. While some countries have robust regulatory systems, most lack harmonized or enforceable safety frameworks. TPT programs, which independently verify products for prohibited substances, remain concentrated in the global northwest (Europe, North America, and Australia/New Zealand); awareness and use of TPT certification vary widely, and even in regions with established systems, athlete adherence is inconsistent. Barriers to low-risk supplement use are limited TPT availability, cost, differences in labeling (including language), and cultural factors. Firsthand experiences and perceptions highlight widespread misconceptions about supplement safety and certification. The authors recommend expanded athlete and team-around-the-athlete education, improved global access to TPT low-risk supplements, and policy initiatives to harmonize safety standards. This work emphasizes the need for coordinated international efforts to protect athlete health and integrity while allowing access to evidence-based supplementation.

ABSTRACT As artificial intelligence (AI) continues to rapidly reshape health care, there is a critical need for clear frameworks for clinicians to ensure ethical, equitable, and effective integration and use of AI in patient care. Key integrations of AI include enhancing health communications, patient engagement, clinicians' training, pharmaceutical advertising, clinical decision‐making, and automation of clinical operations and workflow. However, there are growing concerns related to regulatory gaps, the spread of misinformation, security threats, patient and data privacy leaks, and widening health disparities gaps. These concerns are exacerbated by limited institutional infrastructure and limited AI literacy of clinicians and patients. Recent policy developments reflect efforts to guide responsible AI development and use. While progress has been made, the lack of standardized human oversight remains a critical gap, particularly as policies may not fully consider the challenges and complexities at institutional, societal, technical, and individual levels. Thus, herein, the Drug Information Practice and Research Network (DI PRN) of the American College of Clinical Pharmacy (ACCP) : (1) explores current and emerging multifaceted challenges, a call to action, and opportunities of AI integration in health care including examination of the regulatory, ethical, operational, and health equity implications; and (2) provides practical recommendations for responsible use of AI through DI PRN‐developed example case‐based approaches and best practices infographics to enhance AI literacy for diverse learners including clinicians, trainees, and patients. This DI PRN opinion paper highlights the importance of proactive governance frameworks and equips and empowers diverse learners with practical AI literacy tools to confidently engage with AI technologies in patient care.

ABSTRACTWith the global rollout of COVID‐19 vaccines, vaccine safety remains a priority. Emerging concerns have raised the potential risk of a long COVID‐like syndrome following vaccination, informally called long Vax and provisionally termed post‐COVID‐19 vaccination syndrome (PCVS). Our narrative review describes the putative manifestation, pathophysiology, and therapeutic approaches of PCVS based on the available evidence, mostly from case reports/series and observational studies. Our review noted that PCVS typically manifests within days to weeks post‐vaccination, with symptoms lasting months to years. PCVS may present as recognized diagnoses such as postural orthostatic tachycardia syndrome (POTS), small‐fibre neuropathy (SFN), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), or as long‐term sequelae of myocarditis, vaccine‐induced thrombotic thrombocytopaenia (VITT), or immune thrombocytopaenia purpura (ITP). Symptomatically, PCVS overlaps with long COVID, such as fatigue and brain fog, but PCVS may involve more frequent paraesthesia and less dyspnoea. We also review pathophysiological hypotheses of PCVS, focussing on the vaccine‐derived spike protein and related immune responses. Finally, we discuss potential therapies used to treat patients with PCVS or related conditions, primarily documented in case reports/series, which could guide future clinical research. Overall, PCVS remains a poorly understood condition that requires more research to elucidate its prevalence, prognosis, risk factors, and treatments.

Artificial intelligence (AI)-based object detection in radiology can assist in clinical diagnosis and treatment planning. This article examines the AI-based object detection models currently used in many imaging modalities, including X-ray Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US). The key models from the convolutional neural network (CNN) as well as the contemporary transformer and hybrid models are analyzed based on their ability to detect pathological features, such as tumors, lesions, and tissue abnormalities. In addition, this review offers a closer look at the strengths and weaknesses of these models in terms of accuracy, robustness, and speed in real clinical settings. The common issues related to these models, including limited data, annotation quality, and interpretability of AI decisions, are discussed in detail. Moreover, the need for strong applicable models across different populations and imaging modalities are addressed. The importance of privacy and ethics in general data use as well as safety and regulations for healthcare data are emphasized. The future potential of these models lies in their accessibility in low resource settings, usability in shared learning spaces while maintaining privacy, and improvement in diagnostic accuracy through multimodal learning. This review also highlights the importance of interdisciplinary collaboration among artificial intelligence researchers, radiologists, and policymakers. Such cooperation is essential to address current challenges and to fully realize the potential of AI-based object detection in radiology.

Abstract There are limited prospective data on whether established risk factors modify breast cancer risk in women with pathogenic variants (PV) in BRCA1/2 and virtually no risk modification data for ATM, CHEK2, or PALB2. We conducted a nested case–control study in the Women’s Health Initiative (WHI), randomly selecting women with and without breast cancer for DNA sequencing. We evaluated breast cancer odds associated with obesity, family cancer history, smoking, alcohol, parity, breastfeeding, oophorectomy, tubal ligation, neighborhood socioeconomic status, and menopausal hormone therapy (MHT) with estrogen and progestin or estrogen only in PV carriers and noncarriers. In exploratory analyses, we grouped genes by established predisposition for estrogen receptor (ER)-positive (ATM and CHEK2) or ER-negative (BRCA1 and PALB2) disease. Multivariable models with interaction terms were used to assess differential risk modification by PVs. Among 12,957 WHI participants, 287 carried PVs. Breastfeeding was modestly associated with reduced risk for PALB2 [OR = 0.08; 95% confidence interval (CI), 0.00–0.92; P value = 0.042]. With one-sided 95% CI, power was sufficient to exclude OR ≥2.0 with obesity for ATM and BRCA2; smoking and alcohol for CHEK2; no breastfeeding for ATM; no oophorectomy for BRCA2 and CHEK2; no tubal ligation for CHEK2; and neighborhood socioeconomic status for all genes. Estrogen + progestin MHT was modestly associated with increased risk for ER-positive PVs (OR = 7.31; 95% CI, 1.14–64.20; P = 0.036). PVs did not modify risk (interaction P ≥ 0.05). BRCA1/2, ATM, CHEK2, and PALB2 PV carriers do not have breast cancer OR ≥2.0 with many established risk factors. However, MHT warrants additional study in PV carriers. Significance: There is limited information on whether established risk factors increase breast cancer risk from PVs. In the WHI, PV carriers had no substantial (≥2-fold) increase with most risk factors, except potentially MHT in ATM or CHEK2 carriers. The results may inform counseling and research on MHT.

Abstract The International League Against Epilepsy (ILAE) has updated the operational classification of epileptic seizures, building upon the framework established in 2017. This revision, informed by the implementation experience, involved a working group appointed by the ILAE Executive Committee. Comprising 37 members from all ILAE regions, the group utilized a modified Delphi process, requiring a consensus threshold of more than two thirds for any proposal. Following public comments, the Executive Committee appointed seven additional experts to the revision task force to address and incorporate the issues raised, as appropriate. The updated classification maintains four main seizure classes: Focal, Generalized, Unknown (whether focal or generalized), and Unclassified. Taxonomic rules distinguish classifiers, which are considered to reflect biological classes and directly impact clinical management, from descriptors, which indicate other important seizure characteristics. Focal seizures and those of unknown origin are further classified by the patient's state of consciousness (impaired or preserved) during the seizure, defined operationally through clinical assessment of awareness and responsiveness. If the state of consciousness is undetermined, the seizure is classified under the parent term, that is, the main seizure class (focal seizure or seizure of unknown origin). Generalized seizures are grouped into absence seizures, generalized tonic–clonic seizures, and other generalized seizures, now including recognition of negative myoclonus as a seizure type. Seizures are described in the basic version as with or without observable manifestations, whereas an expanded version utilizes the chronological sequence of seizure semiology. This updated classification comprises four main classes and 21 seizure types. Special emphasis was placed on ensuring translatability into languages beyond English. Its aim is to establish a common language for all health care professionals involved in epilepsy care, from resource‐limited areas to highly specialized centers, and to provide accessible terms for patients and caregivers.

Purpose: To systematically review the literature on the effectiveness, safety, and long-term outcomes of genicular artery embolization (GAE) for knee joint osteoarthritis (OA) associated pain. Materials and Methods: After registering the protocol with the PROSPERO database, a search was conducted from inception until July 31, 2023, in MEDLINE, Cochrane Central Register of Controlled Trials, and ScienceDirect databases to gather studies evaluating GAE’s safety and efficacy in knee OA. A total of 4979 studies were identified and evaluated against the inclusion criteria. Data on study characteristics, success parameters, and adverse events were collected and synthesized. Results: Twenty-three studies, primarily single-center prospective studies with a total of 657 patients, were included. Most studies reported a 100% technical success rates, except one study reporting a rate of 84.2%. Clinical success rates, defined variably across studies, ranged from 30% to 100%, depending on the study’s follow-up period and outcome measures. The most frequent adverse events included skin discoloration without an ulcer (15.6%, n = 98) and transient post-procedural knee pain (10.2%, n = 64). Most studies were rated as fair in terms of quality, but the lack of robust randomized controlled trials highlighted the need for further comparative studies to standardize outcome reporting. Conclusion: GAE appears to be a promising option for knee OA pain, particularly for patients unresponsive to conservative treatments or ineligible for surgery. High-quality studies are needed to confirm long-term effectiveness and standardize outcome measures. Clinical Impact This study highlights the safety and efficacy of genicular artery embolization as a minimally invasive treatment for knee osteoarthritis, particularly in patients who are unresponsive to conservative treatments or unsuitable for surgery. By targeting neovascularization and reducing inflammation, genicular artery embolization provides pain relief and functional improvement. Clinicians can consider genicular artery embolization as an alternative to surgery for mild-to-moderate OA, offering a lower adverse event rate and faster recovery.

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps.

Objectives While extracorporeal shockwave therapy (ESWT) may be an efficacious adjunctive treatment option for musculoskeletal injuries, current research is limited by significant heterogeneity within treatment protocols. This study aims to establish international expert consensus recommendations on ESWT terminology, parameters, procedural considerations, contraindications and side effects in the application of ESWT to sports injuries. Methods A systematic literature search was performed on the use of ESWT for musculoskeletal and sports medicine injuries to identify potential panellists, followed by the development of a steering committee-led questionnaire. A three-stage, modified Delphi questionnaire was provided to a panel of 41 international clinical and research experts across 13 countries. Panellists had the opportunity to suggest edits to existing statements or recommend additional statements in Round 1. Consensus was defined as≥75% agreement. Results All 41 panellists completed Rounds 1, 2 and 3. Consensus was reached on 69/118 statements (58.5%), including recommendations on terminology and fundamental concepts, indications for use, procedural aspects for tendinopathy and bone pathologies, treatment correlations with imaging, periprocedural and postprocedural considerations, absolute and relative contraindications and potential side effects. Of the 49 statements that did not reach consensus, 17/49 (34.7%) were related to procedural aspects of bone pathology. Conclusion This international panel presents recommendations on ESWT terminology, indications and treatment considerations to guide ESWT use and decision-making by sports medicine clinicians. While our panel supported the use of ESWT in the treatment of bone pathologies, certain procedural aspects of ESWT specific to these injuries did not reach consensus and require further investigation.

Background: The application of artificial intelligence (AI) in cardiac imaging has rapidly evolved, offering enhanced accuracy and efficiency in the diagnosis and management of cardiovascular diseases. This bibliometric study aimed to evaluate research trends, impact, and scholarly output in this expanding field. Methods: A systematic search was conducted on 14 August 2024 using the Web of Science Core Collection database. VOSviewer, CiteSpace, and Biblioshiny were utilized for data analysis. Results: The findings revealed a significant increase in publications on AI in cardiovascular imaging, particularly from 2018 to 2023, with the United States leading in research output. England and the United States have emerged as central hubs in the global research network, highlighting their role in generating high-quality and impactful publications. The University of London was identified as the top contributing institution, while Frontiers in Cardiovascular Medicine was the most prolific journal. Keyword analysis highlighted machine learning, echocardiography, and diagnosis as the most frequently occurring terms. A time trend analysis showed a shift in research focus toward AI applications in cardiac computed tomography (CT) and magnetic resonance imaging (MRI), with recent keywords like ejection fraction, risk, and heart failure reflecting emerging areas of interest. Conclusion: Healthcare providers should consider integrating AI tools into cardiovascular imaging practice, as AI has demonstrated the potential to enhance diagnostic accuracy and improve patient outcomes. This study highlights the rising importance of AI in personalized and predictive cardiovascular care, urging healthcare providers to stay informed about these advancements to enhance clinical decision-making and patient management.