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PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Metabolic engineering of Candida yeasts for biotechnological applications.

Candida yeasts represent a versatile yet underexploited platform for industrial biotechnology. These yeasts utilize a remarkably broad range of carbon sources, particularly for hydrophobic carbon sources, coupled with robust growth and diverse biosynthetic capacities, making them promising hosts for sustainable production of chemicals, fuels, and proteins. Despite these advantages, industrial deployment of Candida species has been hindered by concerns regarding opportunistic pathogenicity and the historical lack of efficient genetic manipulation tools, leading to a substantial gap between metabolic potential and practical utilization. Recent advances in functional genomics, genome editing, and systems metabolic engineering are rapidly overcoming these barriers, enabling more precise and efficient strain development. In this review, we systematically summarize recent progress in the metabolic engineering of Candida species as microbial cell factories, with particular emphasis on expanding genetic toolkits, utilizting renewable and non-conventional carbon sources, and biosynthesizing high-value compounds. In addition, we propose a biosafety-oriented classification framework to support their safe industrial deployment. Finally, we discuss current challenges and emerging opportunities, emphasizing that the synergy of synthetic biology and artificial intelligence-driven design holds the key to unlocking the biotechnological potential of Candida yeasts.

Candida↗

PubMed2026

Educational Effects of Electronic Documents and Videos on Parents' Responses to Acute Illness in Young Children: A Randomized Controlled Trial.

AIM: This study compared changes associated with electronic document-based and video-based education for parents responding to acute illness in young children, focusing on self-reported knowledge, anxiety, and satisfaction. METHODS: A randomized controlled trial with pre- and post-intervention measurements was conducted among 140 adults in Japan who self-reported raising a child under 3 years of age and having experienced their child's acute illness. Participants were assigned to an electronic document group or a video group (n = 70 each). Self-reported knowledge was assessed using a researcher-developed questionnaire, and anxiety was measured using the State-Trait Anxiety Inventory. Pre-post changes and between-group differences in change scores were examined. RESULTS: Total self-reported knowledge scores increased significantly in both groups (p < 0.01), with no significant between-group difference. The video group showed significant improvements in items related to symptoms requiring attention at home and information sources, whereas the electronic document group improved in items related to symptoms requiring medical consultation and emergency calls. State and trait anxiety did not change significantly in either group. Satisfaction was high in both groups. CONCLUSIONS: Both educational formats may support parents' learning about responses to acute illness in young children, although appropriate formats may differ according to the educational content. Information provision alone may have limited effects on anxiety; therefore, future parent education should incorporate interactive and reassurance-focused approaches. TRIAL REGISTRATION: UMIN-CTR: UMIN000056457.

Humans↗

PubMed2026

Teacher- Versus Video-Delivered Classroom Activity Breaks and Student Physical Activity: The PAAC-3 Trial.

BACKGROUND: Classroom activity breaks may increase moderate-to-vigorous physical activity (MVPA); however, few studies have compared teacher and video-delivered approaches under real-world conditions. METHODS: In this cluster randomized trial, 11 elementary schools were assigned to teacher-delivered (PAAC-T; 5 schools, 192 students) or video-delivered (PAAC-V; 6 schools, 276 students) classroom activity breaks across one academic year. Teachers were trained to deliver two 10-min breaks daily. Intervention delivery was tracked via a web-based platform, and classroom MVPA was assessed using accelerometers at baseline and follow-up. RESULTS: Implementation fidelity was low and highly variable, but comparable between PAAC-T (42.3 ± 57.1 activity breaks/teacher/year) and PAAC-V (39.2 ± 33.9; p = 0.96), with teachers delivering ∼50% of the intended daily activity. Classroom MVPA increased significantly in both groups (PAAC-T: 9.8 ± 15.9; PAAC-V: 9.1 ± 15.3 min/day; p < 0.001), with no intervention arm-by-time interaction (p = 0.43). IMPLICATIONS FOR SCHOOL HEALTH POLICY, PRACTICE, AND EQUITY: Classroom physical activity breaks may increase student MVPA, but effectiveness in elementary schools appears to depend on implementation fidelity, administrative support, and equitable system-level infrastructure. CONCLUSIONS: Modest increases in classroom MVPA were observed across both delivery formats, although low and variable implementation fidelity limited conclusions regarding effectiveness and highlighted the need for stronger implementation supports. TRIAL REGISTRATION: NCT03493139.

Humans↗

PubMed2026

Pictographs: feasibility and acceptability of a novel method of newborn identification to reduce wrong-patient errors in the NICU.

Wrong-patient errors cause serious harm in newborns. These errors involve ordering and administering tests, procedures, medications, and breast milk to an unintended patient. Newborns receiving care in neonatal intensive care units (NICUs) are at particularly high risk. Although more distinct newborn naming conventions as recommended by the Joint Commission significantly reduce wrong-patient orders, name similarities among multiple-birth infants and truncation of differentiating information in some electronic health record (EHR) systems contribute to this persistent increased risk. Accordingly, novel newborn identifiers are urgently needed. We propose Pictographs - images that are appealing, recognizable, and appropriate - to serve as visual identifiers for newborns in NICUs. Pictographs are selected by caregivers, uploaded into the EHR, and displayed at bedside. As part of a multicenter randomized controlled trial assessing effectiveness of Pictographs to prevent wrong-patient order errors, we initially evaluated feasibility and acceptability of Pictographs at two study sites. Pictographs as novel visual identifiers for newborns in the NICU were generally well received by caregivers and clinicians, and the vast majority of caregivers selected a Pictograph for their infant(s), which was posted at the bedside and uploaded into the EHR. Ordering clinicians - the primary target of the intervention to prevent wrong-patient errors - recognized the potential for Pictographs to provide a visual cue when placing orders, particularly for multiple-birth infants. Here, we describe the rationale, implementation, framework, feasibility, usefulness, and acceptability of Pictographs among key stakeholders. If found effective for preventing wrong-patient errors, Pictographs could be adopted as a patient safety solution in hospitals worldwide.

Female↗