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On the relationship between external and internal load variables in elite youth soccer players
Abstract The study investigated the relationship between external and internal training load measures in 25 male elite youth soccer players (age: 16.6 ± 0.9 years, VO 2max : 59 ± 4 ml/min/kg) over 3 months. External load (i.e., total distance, high metabolic power distance, high-speed running) was quantified using a local positioning system and related to subjective (RPE, sleep quality, drive (energy level)), biochemical (creatine kinase (CK), lactate dehydrogenase (LDH), C-reactive protein (CRP), urea, cortisol, transferrin), and neuromuscular (CMJ) markers. Single day workload (1DL), exponential 7-day workload (7DL), and the acute: chronic workload ratio (ACWR) were calculated. 1DL parameters were correlated (Spearman’s rho) with RPE (range r = 0.24 to 0.43, p < 0.01) and 1DL distance was negatively related to drive ( r = − 0.28, p < 0.001). LDH correlated positively with training load across all calculation methods (up to r = 0.27, p < 0.01). CK exhibited positive correlations to ACWR training load ( r = 0.23 to 0.27, p < 0.05), while transferrin (ACWR) and CRP (1DL) showed negative associations to training load ( r = − 0.21 to − 0.28, p < 0.05). CMJ eccentric mean force was negatively correlated with all ACWR training load variables ( r = − 0.22 to − 0.25, p < 0.01). In summary, subjective measures showed stronger and more consistent associations with training load than biomarkers or neuromuscular testing. Practitioners may confidently use well-structured questionnaires for load monitoring in elite youth soccer.
Variation in public hospital costs for children and young patients from priority populations: An Australian health service economic analysis
The extent to which inpatient hospital costs for children and young patients (CYP) from the priority population in Australia differ from their peers is unknown. Using a multivariate analysis approach, this study (1) investigated variations in inpatient hospital costs between priority and non-priority CYP; and (2) identified the associations between CYP patient characteristics and inpatient hospital costs. Priority populations were defined as Indigenous Australian, National Disability Insurance Scheme (NDIS) participants and refugee/asylum seeking CYP. They were identified using electronic medical records (EMRs) within the Sydney Children’s Hospitals Network (SCHN). Data from inpatient hospital admissions (public hospitals) by CYP aged 0–17 years between January 2015 and December 2019 within the SCHN were collected from the EMRs. This study employed mixed-effects generalised linear models (GLMMs) with a log-link function and gamma distribution to investigate patient factors associated with inpatient hospital costs. The model incorporated demographic characteristics, socioeconomic and location variables, and hospital admission severity as covariates. Gender (females 1.03 times higher than males), area of residence (1.12 times higher) (non-major city vs. major city) and socioeconomic status (1.04 times higher) (living in socially disadvantaged areas vs. living in less disadvantaged areas) were significantly associated with inpatient hospital costs. Priority CYP who were Indigenous Australian (1.07 times) and NDIS participants (1.02 times) were associated with substantially higher costs compared to those who were non-Indigenous Australian or non-NDIS participants, respectively. Australian CYP with multiple disadvantaged social statuses (priority CYP living in non-major cities) were associated with higher inpatient hospital costs. Reducing health disparities for priority CYP presents a potential opportunity to lower overall hospital inpatient costs in Australia.
Mitochondrial dysfunction and Ca2+ dysregulation in human iPSC-derived neurons carrying presenilin-1 mutation arise under stress via an MCU-1-independent mechanism
Piperine inhibits biofilm formation and efflux activity and dysregulates lipid metabolism in Mycobacterium abscessus
The nontuberculous Mycobacterium abscessus is a human pathogen that causes chronic lung infections and soft tissue infections. The bacterium forms biofilms and efflux pumps contribute to its tolerance of antibiotics. Efflux pumps also transport lipids and other molecules to the bacterial outer cell surface for biofilm formation. The effects of piperine, an alkaloid derived from black pepper, on biofilm formation, efflux activity and lipid biosynthesis in M. abscessus have not been reported. We report that, at sub-minimum inhibitory concentration levels, piperine inhibits biofilm formation in M. abscessus by more than 90%. We investigated lipid biosynthesis from exogenously supplied radiolabeled 14 C-palmitic acid in M. abscessus during its log-phase growth and during biofilm formation and examined the effects of piperine. We report that piperine dysregulates the biosynthesis of major lipids in M. abscessus during biofilm formation. Piperine inhibited the biosynthesis of the neutral storage lipid triacylglycerol during biofilm formation by nearly 80% and the biosynthesis of the polar lipid trehalose monomycolate by 50%. In contrast, piperine stimulated the biosynthesis of the major polar lipid phosphatidylethanolamine during biofilm formation. Piperine inhibited efflux activity in M. abscessus by nearly 70%. Piperine enhanced the efficacies of four commonly used antibiotics used to treat M. abscessus infections. The minimum inhibitory concentration of clarithromycin was decreased by more than 16-fold by piperine and that of amikacin and cefoxitin by about 5-fold. The efficacy of ciprofloxacin was improved by more than 2-fold by piperine. This is the first report on the effects of piperine on lipid biosynthesis, efflux activity and biofilm formation in M. abscessus that highlights the potential importance of piperine as an adjunct therapy to treat nontuberculous mycobacterial infections.
Author Correction: Repulsions instruct synaptic partner matching in an olfactory circuit
Early age at smoking initiation is associated with elevated cardiovascular disease and mortality risk in a nationwide population-based cohort
Gender-inclusive sexual health literacy scale for sex workers (SHL-SW): Development and validation in Thailand
Background Marginalized sex workers worldwide face persistent sexual health inequities, yet lack a validated, gender-inclusive tool to measure their sexual health literacy (SHL). This study aimed to develop and validate the Gender-inclusive Sexual Health Literacy Scale for Sex Workers (SHL-SW). Methods A mixed methods approach was employed in three stages: (1) literature review, (2) expert consultation using the Delphi technique, and (3) questionnaire development and validation. The participants included 18 experts, 5 competent evaluators, and 600 sex workers. Data were collected through interviewer-administered online questionnaires. Analyses methods included median, interquartile range, content validity index, Cronbach’s alpha coefficient, and confirmatory factor analysis (CFA). Results The final 10-item SHL-SW demonstrated high internal consistency (Cronbach’s α = 0.92). The CFA confirmed the hypothesized four-factor structure (access, understanding, appraisal, and application), with model fit indices indicating an excellent fit to the data (χ² = 20.568, p = 0.151; CFI = 0.99; TLI = 0.98; RMSEA = 0.04; SRMR = 0.03), thus establishing strong construct validity. Conclusion The SHL-SW is a reliable and valid instrument for assessing SHL among sex workers in Thailand, and is the first such instrument to be specifically validated for gender inclusivity in this population across four components: (1) access, (2) understanding, (3) appraisal, and (4) application. This tool enables public health practitioners and researchers to identify SHL gaps, design targeted rights-based interventions, and inform evidence-based policies aimed at advancing health equity.
Author Correction: Rewiring an olfactory circuit by altering cell-surface combinatorial code
Attenuation of biofilm-encoding genes and virulence attributes in clinical isolates of Acinetobacter baumannii by essential oil derived from Myroxylon balsamum
Evolutional law and elastoplastic constitutive model of structural loess considering moisture contents
The deformation properties of natural undisturbed loess are intricately linked to its structural qualities. Structural characteristics can markedly improve soil stability, allowing structured loess to sustain a greater void ratio than disturbed loess under equivalent stress circumstances. This study examines the effect of initial moisture content on structural behavior by comparing the isotropic compression curves of disturbed and undisturbed loess under varying moisture conditions, thereby identifying the structural parameters of undisturbed loess and their changes during compression. A structural attenuation coefficient is developed to enhance the stress ratio-based structural parameter, and a quantitative approach for depicting structural degradation with axial strain is proposed, facilitating an effective evaluation of structural damage. Additionally, within the modified Cam–clay model framework, the yield surface equation is adjusted by integrating structural parameters, resulting in an elastoplastic constitutive model for structured loess that uniformly characterizes structural evolution under both spherical and shear stress. Finally, the suggested structural constitutive model is implemented in ABAQUS for numerical analysis, and comparisons with triaxial tests on undisturbed loess and published data confirm the model’s accuracy and applicability. The findings indicate that the established model accurately reflects the impact of initial moisture content on the structural strength, deformation properties, and degradation behavior of loess, offering a dependable theoretical foundation for the quantitative evaluation of loess structure and for engineering assessments in loess regions.
The effect of an acceptance and commitment therapy-based self-compassion program on self-compassion and psychological flexibility in mothers
Performance of DeepSeek and ChatGPT on the Chinese Health Professional and Technical Examination: A comparative study
Background Large language models (LLMs) are increasingly applied in medical education, yet their reliability in specialized, high-stakes assessments such as the Chinese Health Professional and Technical Examination remains unclear. DeepSeek-R1, a recently released reasoning-enhanced LLM, has shown promising performance, but empirical evidence within nursing examination contexts is limited. Objective To compare the performance of DeepSeek-R1 and the GPT-4o API on the Chinese Health Professional and Technical Examination (Intermediate Nursing), focusing on accuracy, response consistency, and consistent accuracy. Methods Four hundred official practice examination multiple-choice questions were categorized into four competency units and two question types (A/B). Both models were evaluated using overall accuracy, consistency (agreement across repeated responses), and consistent accuracy (proportion of responses that were both consistent and correct). Stratified analyses were performed across units, question types, and disciplines. Chi-square tests were used for statistical comparison, and Holm–Bonferroni correction was applied for multiple comparisons. Results DeepSeek-R1 demonstrated significantly higher overall accuracy than the GPT-4o API (88.5% vs. 67.9%, P < 0.001). GPT-4o API showed higher response consistency (96.5% vs. 88.5%) but lower consistent accuracy (66.7% vs. 84.0%). After multiple-comparison correction, significant differences in consistent accuracy remained in basic knowledge, professional knowledge, professional practice ability and Type A questions, as well as in surgical and gynecological nursing disciplines, while other domains showed no statistically significant differences. Conclusion DeepSeek-R1 outperformed the GPT-4o API across multiple dimensions of nursing competency assessment, particularly in overall accuracy and consistent accuracy. GPT-4o API exhibited high response stability but a tendency toward systematic errors, underscoring the need for careful interpretation of model outputs. Further research is needed to evaluate LLM performance using open-ended clinical reasoning tasks and real-world assessment data to support safe and effective educational integration.
Have environmental microplastics levels been overestimated?
A data analytics-driven approach to backorder prediction using federated machine learning in industrial supply chains
A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer
Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the optimal opportunity for treatment. Traditional biomarkers, such as neuron-specific enolase (NSE) or pro-gastrin-releasing peptide (ProGRP), have insufficient specificity and sensitivity to meet the demands of clinical diagnosis. Exosome and its contents have become burgeoning cancer biomarkers due to their diverse molecular cargo to achieve intercellular communication. Herein, a novel machine learning strategy was reported for rapid, efficient screening of biomarkers and identified an optimal exosome RNA combination as diagnostic biomarker of SCLC. Firstly, RNA sequencing data from 111 SCLC patients and 362 healthy controls were obtained from the exoRBase 2.0 and 3.0 databases. The machine learning methods were employed to select specific RNA by using 20 iterations with 10-fold nested cross-validation for SCLC diagnosis. Then, an optimal combination of three exosome RNAs (LINC00989, CXCL5, and MAP3K7CL) was confirmed and achieved excellent diagnostic performance (area under the curve (AUC) of 0.950, sensitivity of 0.936, and specificity of 0.892). Finally, an independent validation cohort containing tissue-based RNA expression data for two biomarkers (CXCL5 and MAP3K7CL) from 79 SCLC patients and 7 standard controls was used to evaluate the diagnostic performance of the selected RNAs. The results demonstrated modest diagnostic performance in tissue samples (AUC = 0.718) with two biomarkers, indicating potential cross-tissue applicability despite the limitations of incomplete biomarker coverage. In addition, a specificity analysis of exosome RNA data, including gastric cancer, hepatocellular carcinoma, and breast cancer, demonstrated significant specificity for SCLC. Therefore, the novel biomarker screening strategy integrating nested cross-validation with multiple machine learning algorithms successfully established to offer a potentially valuable protocol for early SCLC diagnosis and other cancers.
Exclusive: key NIH review panels due to lose all members by the end of 2026
Optimization of terahertz broadband polarization insensitive metamaterial absorber using bisecting rectangles technique based on quadratic surrogates
Abstract In this paper, an optimization algorithm based on bisecting hyperrectangles and constructing quadratic models is exploited to attain the optimal design of a broadband metamaterial absorber (MMA) with small thickness and high absorption in the terahertz (THz) frequency range. The full wave vectorial finite element method is employed along with the introduced algorithm to simulate and optimize the investigated design. The presented absorber comprises of a vanadium dioxide (VO 2 ) metallic layer placed on a lossy polyimide dielectric substrate. Further, the reported design exhibits a broad bandwidth of 2.9 THz where the absorptivity exceeds 90% through a frequency range between 2.43 and 5.33 THz. In addition, the proposed design could obtain polarization insensitive absorption spectrum for the transverse electric (TE) as well as transverse magnetic (TM) polarizations. Furthermore, the absorption spectrum of the suggested absorber could realize high absorptivity (greater than 80%) for the TE and TM polarizations when the wave incident angle is varied from normal incidence to 50°. Therefore, the reported optimization algorithm is highly recommended for THZ broadband MMAs based applications.
Microbiota discovered in scorpion venom
With low nutrient availability and presence of numerous antimicrobial peptides, animal venoms have been traditionally considered to be harsh sterile environments that lack bacteria. Contrary to this assumption, recent studies of animal venom and venom-producing tissues have revealed the presence of diverse microbial communities, warranting further studies of potential microbiota in other venomous animals. In this study we used 16S rRNA amplicon sequencing to elucidate whether scorpion venom contained bacteria, to characterize the bacterial communities, and determine if venom microbiomes differed across geologically complex geographic locations. Our study compares the venom microbiome of two scorpion species, sampled from sites in the Mojave and Great Basin deserts, Paruoctonus becki (family of Vaejovidae) and Anuroctonus phaiodactylus (family of Anuroctonidae), and represents the first assessment of microbial diversity ever conducted using the venom secretion itself, rather than the venom-producing organ and its surrounding tissues.