Citations

Overtraining syndrome (OTS) is a significant medical condition observed predominantly in endurance athletes and characterised by a decline in performance and disruptions in multiple physiological functions. Given the increasing popularity of endurance training and the complex nature of OTS diagnosis, there is a compelling need for comprehensive case studies. The case series summarises one professional runner, one competitive runner and one recreational runner who were diagnosed with OTS. Key parameters observed include symptoms, diagnostic journey, recovery initiatives and return-to-sport outcomes. All three athletes showed typical OTS manifestations, including impaired performance and multisystem dysfunction (ie, endocrine, nervous, immune and gastrointestinal systems). Following a mandated cessation from training, active recovery strategies were instituted. Two of the three athletes successfully returned to competition. Specific recovery patterns and potential relapse indicators were also identified. The comprehensive examination of the cases underscores the complex nature of OTS diagnosis, its multifaceted symptoms and the critical importance of structured recovery. The case series elucidates the importance of heightened awareness and early intervention to diagnose and manage OTS. In addition, we propose an anecdotal Return to Running Protocol individualised to the athlete’s response.

Abstract Background Low-density lipoprotein cholesterol (LDL-C) is directly associated with coronary artery disease (CAD) risk. Subfractionation of LDL enables differentiation between large-buoyant LDL (>20.5 nm) and small-dense LDL (≤20.5 nm). Small-dense LDL reportedly increases CAD risk, as do LDL-C and LDL particle (LDL-P) concentrations. Nuclear magnetic resonance spectroscopy (NMR) reports LDL-C, LDL-P, and LDL size (LDL-s). We investigated associations between these outputs and their agreement on CAD risk information. Methods Associations between LDL-P, LDL-C, and LDL-s measured by NMR were evaluated in serum from clinically ordered samples (n = 26 710), and a subset of patients with CAD evaluation from coronary angiography (n = 356). Correlations were determined using Spearman ρ, and clinical classifications were compared using the following thresholds for increased risk: LDL-C > 160 mg/dL, LDL-P > 1600 nmol/L, and LDL-S < 20.5 nm. Results The large laboratory NMR data set showed LDL-C was highly correlated with LDL-P (ρ = 0.87), and moderately with LDL-s (ρ = 0.51). Correlation between LDL-P and LDL-s was weakest (ρ = 0.21). When comparing pairwise high- and low-risk laboratory values, concordant values were observed in 99.8%, 43%, and 25% of cases for LDL-P/LDL-C, LDL-s/LDL-P, and LDL-C/LDL-s, respectively. In patients with angiography and NMR results, CAD was diagnosed in 40% (6/15) of patients with concordant high-risk LDL-C and small-dense LDL-s in the smaller patient cohort, but only 19% (6/31) of CAD-positive patients with high-risk LDL-C had small-dense LDL-s. Conclusions LDL-s and LDL-C are frequently discordant at established LDL-C risk cutoffs. CAD diagnosis was found in similar numbers regardless of LDL-s phenotype.

AbstractThe dual burden of undernutrition and overnutrition requires a greater understanding and commitment to physician training and specialization in nutrition support. Despite the significant healthcare need and existence of organizations focused on nutrition support such as the American Society for Parenteral and Enteral Nutrition (ASPEN), physician involvement in the nutrition care of high‐risk patients remains low. In an ASPEN survey of members, inadequate training among physicians was reported along with significant barriers to involvement in nutrition support, such as insufficient protected time, reduced reimbursement and financial support, inadequate support from administrators or supervisors, as well as a nationwide reduction in number of nutrition support teams. These changes in the healthcare landscape prompted the ASPEN Physician Engagement Committee to develop a critical review focused on domains of nutrition risk assessment, enteral and parenteral access and support, scholarly activities, board certification, administrative responsibilities, education and training opportunities, coding, and reimbursement. The aim of this critical review is to capacitate physicians to engage in nutrition support and enhance their acumen in clinical nutrition to improve patient care and enrich personal and professional development.

Background: Cell-penetrating peptides cross cell membrane barriers while carrying cargoes in a functional form. Our work identified two novel lung-targeting peptides, S7A and R11A. Here, we present studies on biodistribution, the cell types targeted, and an in vitro proof of application. Methods: Studies were performed in human bronchial epithelial cells (HBECs) with and without various endocytic inhibitors, and coincubation with fluorescently labeled transferrin or endocytic markers. Cyclic R11A (cR11A) was conjugated to siRNA duplexes and anti-viral activity against SARS-CoV-2 was tested. Biodistribution studies were performed by injecting wild-type mice with fluorescently labeled peptides, and various circulation times were allowed for, as well as cross-staining of lung sections or isolated single cells with various cellular markers, followed by fluorescence-activated cell sorting or confocal microscopy. Results: cR11A showed peak uptake in 15 min, with the highest uptake in airway epithelial type II (ATII) cells, followed by p63+ basal cells and ionocytes. Cyclization increased transduction efficiencies ~100-fold. Endocytosis studies showed a decrease in peptide uptake by pre-treatment with Pitstop2 but not Amiloride or Nystatin. Endocytic marker Lamp1 showed colocalization at the earliest time point, with the escape of the peptide from endocytic vesicles later. cR11A conjugated to ant-spike and anti-envelop proteins showed anti-viral effects with an EC90 of 0.6 μM and 1.0 µM, respectively. Conclusions: We have identified a novel peptide, cR11A, that targets ATII, basal cells, and ionocytes, the cyclization of which increased transduction efficiency in vitro and in vivo. The uptake mechanism appears to be via clathrin-mediated endocytosis with escape from endocytic vesicles. cR11A can act as a vector to deliver anti-viral siRNA to epithelial cells.

Metabolic dysfunction–associated steatotic liver disease (MASLD) is the most common pediatric liver disease, affecting approximately 10% of children. Its prevalence is rising at an alarming rate, with cases increasingly identified even in early childhood. While MASLD shares key features across the lifespan, its earlier onset reflects developmental vulnerabilities and unique mechanistic drivers. Perinatal influences, including maternal obesity, gestational diabetes, and early-life nutritional exposures, play a central role by disrupting metabolic programming, driving mitochondrial dysfunction, and inducing epigenetic modifications. These early stressors interact with genetic predispositions, such as PNPLA3 and TM6SF2 variants, to amplify susceptibility and shape disease severity. Pediatric MASLD also exhibits distinct histological features, particularly predominant periportal (zone 1) steatosis, inflammation, and fibrosis, which contrast with the centrilobular or pericentral (zone 3) patterns often seen in adults. These findings provide insight into spatial heterogeneity, developmental pathophysiology, and unique disease progression trajectories in children. Addressing MASLD in children requires pediatric-specific approaches to diagnosis, risk stratification, and intervention. By integrating epidemiological trends, mechanistic insights, and translational advances, this Review highlights opportunities for targeted therapies and prevention strategies aimed at mitigating early-life drivers of MASLD, reducing disease burden, and improving long-term outcomes.

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%. The development of new treatments is dependent upon multi-institutional collaborative clinical trials requiring reproducible and accurate centralized response assessment. We present the results of the BraTS-PEDs 2023 challenge, the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors. This challenge utilized data acquired from multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. BraTS-PEDs 2023 aimed to evaluate volumetric segmentation algorithms for pediatric brain gliomas from magnetic resonance imaging using standardized quantitative performance evaluation metrics employed across the BraTS 2023 challenges. The top-performing AI approaches for pediatric tumor analysis included ensembles of nnU-Net and Swin UNETR, Auto3DSeg, or nnU-Net with a self-supervised framework. The BraTS-PEDs 2023 challenge fostered collaboration between clinicians (neuro-oncologists, neuroradiologists) and AI/imaging scientists, promoting faster data sharing and the development of automated volumetric analysis techniques. These advancements could significantly benefit clinical trials and improve the care of children with brain tumors.

ABSTRACT Purpose The natural history of biochemical recurrence (BCR) after radical prostatectomy (RP) remains understudied, with limited long‐term data from large cohorts inclusive of both salvage radiotherapy (SRT)‐treated and untreated patients. Herein, we sought to evaluate the outcomes of patients with BCR and the impact of SRT on disease progression. Materials and Methods Patients undergoing RP who developed BCR (PSA ≥ 0.20 ng/mL) were included. Patients with BCR treated with SRT were compared to untreated patients using risk‐set matching with time‐dependent propensity scores. The primary outcome was metastases, analyzed using Kaplan–Meier and Cox models. The number needed to treat (NNT) with SRT to prevent progression was derived at 5 and 15 years. Results Among 6881 patients with BCR, 2109 received SRT. At a median follow‐up of 10.2 years, 1147 patients developed metastases. The median PSA at the time of SRT was 0.50 ng/mL. After 1:1 propensity score matching (2109 patients per cohort), SRT significantly reduced the risk of metastases at 5 (12.7% vs. 19.3%, p < 0.0001) and 15 years (28.6% vs. 31.5%, p < 0.001). On multivariable analysis, SRT independently reduced metastasis risk (HR 0.75, 95% CI 0.63–0.90, p = 0.002), translating to NNT of 23 and 15 at 5 and 15 years, respectively. Interaction analyses between SRT and nodal status ( p = 0.04) showed greater metastasis risk reduction in pN+ (HR 0.41, 95% CI 0.22–0.77, p = 0.005) compared to pN− disease (HR 0.81, 95% CI 0.67–0.97, p = 0.02). Conclusions Most patients with BCR post‐RP do not progress to metastasis. For those who do progress, SRT inarguably improves the oncologic outcomes in the BCR setting. However, careful patient selection and shared decision making should be encouraged in order to limit overtreatment and side effects.

The translation of AI-generated brain metastases (BM) segmentation into clinical practice relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS-METS 2023 challenge has gained momentum for testing and benchmarking algorithms using rigorously annotated internationally compiled real-world datasets. This study presents the results of the segmentation challenge and characterizes the challenging cases that impacted the performance of the winning algorithms. Untreated brain metastases on standard anatomic MRI sequences (T1, T2, FLAIR, T1PG) from eight contributed international datasets were annotated in stepwise method: published UNET algorithms, student, neuroradiologist, final approver neuroradiologist. Segmentations were ranked based on lesion-wise Dice and Hausdorff distance (HD95) scores. False positives (FP) and false negatives (FN) were rigorously penalized, receiving a score of 0 for Dice and a fixed penalty of 374 for HD95. The mean scores for the teams were calculated. Eight datasets comprising 1303 studies were annotated, with 402 studies (3076 lesions) released on Synapse as publicly available datasets to challenge competitors. Additionally, 31 studies (139 lesions) were held out for validation, and 59 studies (218 lesions) were used for testing. Segmentation accuracy was measured as rank across subjects, with the winning team achieving a LesionWise mean score of 7.9. The Dice score for the winning team was 0.65 ± 0.25. Common errors among the leading teams included false negatives for small lesions and misregistration of masks in space. The Dice scores and lesion detection rates of all algorithms diminished with decreasing tumor size, particularly for tumors smaller than 100 mm3. In conclusion, algorithms for BM segmentation require further refinement to balance high sensitivity in lesion detection with the minimization of false positives and negatives. The BraTS-METS 2023 challenge successfully curated well- annotated, diverse datasets and identified common errors, facilitating the translation of BM segmentation across varied environments and providing the tools for future development of personalized volumetric reports to patients undergoing BM treatment.