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Unconfined compressive strength prediction for the ordinary Portland cement-steel slag-silica fume ternary system based on response surface methodology.

This research was undertaken to address environmental concerns associated with industrial solid waste and to reduce cement consumption in geotechnical engineering. It specifically investigates the feasibility of using steel slag (SS) and silica fume (SF) as partial substitutes for ordinary Portland cement (OPC) in soil stabilization. The effects of SS, SF, OPC, and initial moisture content on the unconfined compressive strength (UCS) of stabilized soil were investigated through single-factor experiments and response surface methodology (RSM). The results show that SS and SF can synergistically enhance the strength of stabilized soil, although their interaction effect was not statistically significant within the investigated ranges. Compared with soil stabilized solely with OPC, the addition of 18 % SS and 10 % SF reduced OPC consumption by 3 % without compromising strength. Microstructural and compositional analyses further revealed that SS mainly supplied calcium- and silica-bearing components, while SF provided highly reactive silica and micro-filling effects, jointly promoting hydration reactions and improving the compactness of the stabilized soil matrix. As a result, more hydration products were formed in the OPC/SS/SF-stabilized soil than in the OPC-stabilized soil, which contributed to pore filling and strength enhancement. This study provides useful guidance for the sustainable utilization of industrial solid waste and the low-carbon development of soil stabilization materials.

Construction Materials

Challenges and future directions in AI-driven biomaterials for microbiome-associated oral infectious diseases: A systematic review.

Oral biofilm-induced antimicrobial resistance is the core pathogenic mechanism of microbiome-associated oral infectious diseases (dental caries, periodontitis, peri-implantitis, and endodontic infection). Traditional therapies and biomaterials are limited by poor biofilm penetration, drug resistance induction, single functionality, and inadequate adaptation to dynamic oral microenvironmental changes (e.g., pH fluctuations, salivary rinsing, masticatory stimulation). Artificial intelligence (AI) has transformed the field by integrating materials science, microbiology, and stomatology data. Via machine learning, deep learning, and multi-physics simulation, AI optimizes biomaterial physicochemical properties, decodes microenvironmental signals, constructs precise sensing-response loops, and supports the full chain of material design, performance prediction, and action simulation, advancing treatment from empirical intervention to precision regulation. This systematic review retrieved literature from PubMed, Embase, and Web of Science (January 2016-January 2026) using keywords across three dimensions: AI, biomaterials, and oral microbiome. Following inclusion/exclusion criteria, 99 articles were included. It elaborates on five core mechanisms of AI-driven oral biomaterials (precise oral microbiome analysis, targeted material design/optimization, performance prediction/simulation, targeted delivery/intervention, effect evaluation/dynamic regulation), analyzes their applications in microbiome-targeted biomaterial research and development (R&D) and clinical practice for the four major oral infectious diseases, addresses technical bottlenecks (insufficient targeting specificity and precision of biomaterials, poor stability and durability in complex oral microenvironments, inadequate biofilm disruption capacity, and clinical translation obstacles), and proposes future directions (multimodal design to enhance targeting specificity, structural and component optimization to improve stability/durability, development of multi-mechanism synergistic biofilm disruption strategies, strengthening translational research for clinical application, and deep integration of AI in the full chain of biomaterial R&D). This work provides comprehensive theoretical and practical support for the R&D, optimization, and clinical translation of AI-driven microbiome-targeted oral biomaterials.

Humans

Reversed unidirectional transport in a Janus polyurethane/alginate dressing for directional postbiotic delivery to infected wounds.

Probiotic-derived postbiotics exhibit significant potential for infected wound control; however, their effective and localized delivery at wound sites remains a challenge. This study developed a polyurethane/alginate composite nonwoven via electrospinning to establish a postbiotic delivery platform for Bifidobacterium bifidum BD-1 (PU/Alg/BD-1). The beaded fibrous hydrophobic PU layer and hydrophilic Alg layer form a wettability gradient, enabling reversed unidirectional fluid transport toward the wound interface while inhibiting backflow. In vitro results showed that PU/Alg/BD-1 exhibited significant antibacterial activity against Staphylococcus aureus and Escherichia coli and good cytocompatibility with a hemolysis rate of <5%. Targeted metabolomic analysis revealed multiple organic acids in the BD-1 metabolites, which contributed to its antibacterial activity. In vivo microbial analysis verified that PU/Alg/BD-1 effectively reduced the relative abundance of Staphylococcus at the wound site while increasing the proportions of Corynebacterium and Psychrobacter. This microbial modulation contributed to infection control in a rat full-thickness infected wound model, accompanied by a shift in the macrophage phenotype and the downregulation of inflammatory factors including IL-6, TNF-&#x3b1;, and TGF-&#x3b2; in the PU/Alg/BD-1 group. Compared with the blank control, conventional gauze, PU/Alg, and BD-1 groups, PU/Alg/BD-1 significantly promoted wound contraction and re-epithelialization and enhanced collagen deposition. Hence, this study provides an effective material construction strategy for the application of probiotic-derived postbiotics to promote wound healing, demonstrates the potential of BD-1 to regulate the wound microenvironment and accelerate healing, and thereby offers a novel approach for the treatment of infected wounds.

Journal Article

PdIr bimetallic nanozyme engineered metal-organic frameworks integrated dual-mode sensor toward Stx2 detection in food.

Shiga toxin II (Stx2) has attracted extensive attention due to its toxicity and pathogenicity, making the development of sensitive detection methods urgent. This study constructed a dual-mode sensing platform for the sensitive detection of Stx2 in food. Composite material UIO-66@PdIr with peroxidase-like activity and fluorescent properties was synthesized and combined with cDNA as the signal probe, while aptamer-modified magnetic beads served as the capture probe. Specific binding of Stx2 to the aptamer triggered the release of the signal probe, enabling colorimetric and fluorescence signal readout. The colorimetric mode showed a linear range of 0.05-100&#xa0;ng/mL with an LOD of 0.039&#xa0;ng/mL, and the fluorescence mode exhibited 0.01-1000&#xa0;ng/mL with an LOD of 0.0097&#xa0;ng/mL. Additionally, this method was successfully applied to the detection of Stx2 in food, and the recovery rates were 94.33%&#xa0;&#x223c;&#xa0;102.20%. It indicated that the constructed sensor holds great practical potential for Stx2 detection.

Food Contamination

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas

Recent advances in supramolecular macrocycle-based artificial light-harvesting systems.

Artificial light-harvesting systems (ALHSs) inspired by the antenna function of natural photosynthesis provide molecular platforms for collecting excitation energy and directing it to emissive or reactive acceptors. In many supramolecular ALHSs, however, practical performance is limited by poorly defined donor-acceptor orientation, aggregation-caused quenching (ACQ), interfacial defects, and limited stability in aqueous or complex media. Supramolecular macrocycles-particularly pillar[n]arenes (PAs), cucurbit[n]urils (CBs), calixarenes (CAs), cyclodextrins (CDs), and supramolecular coordination complexes (SCCs)-offer a useful design space because their cavities, pre-organized scaffolds, and reversible non-covalent interactions can confine chromophores, tune local donor/acceptor ratios, and modulate F&#xf6;rster resonance energy transfer (FRET). This Review systematically examines the unique structural advantages and assembly mechanisms of the five macrocyclic families, with an emphasis on their use in constructing ALHSs-from single-step to cascaded FRET-and in advancing aqueous photocatalysis, near-infrared bioimaging, panchromatic fluorescence modulation, and singlet oxygen generation. The resulting structure-property-application framework is intended to guide the rational design of macrocycle-assisted photofunctional materials while avoiding overextension of the photosynthesis analogy.

Journal Article

Construction of an infectious clone of Spodoptera frugiperda densovirus and its biological characteristics.

Densoviruses are highly pathogenic to their insect hosts and have great potential for biocontrol. Spodoptera frugiperda densovirus (SfDV) was isolated from diseased larvae of Spodoptera frugiperda, while its biological functions remain unclear. Herein, we successfully constructed an infectious clone of SfDV. The S. frugiperda larvae transfected with the infectious clone exhibited anorexia, stunted growth, and reduced activity. Histopathological analysis further showed that the epidermis, fat body and trachea were infected instead of muscle and midgut tissues. Transmission electron microscopy (TEM) revealed that numerous virions of about 22&#x202f;nm were distributed within both the nucleoplasm and cytoplasm of epidermal cells. Moreover, many virions were also found contained within vesicles in the cytoplasm. The replication kinetics of the rescued SfDV (rSfDV) was similar to that of the parental SfDV. The median lethal dose (LD50) and median lethal time (LT50) values of rSfDV were 6.63&#x202f;&#xd7;&#x202f;107 viral genome copies (vgc), 5.23&#x202f;d, respectively, which were also comparable to those of the parental SfDV. Taken together, the infectious clone of SfDV provides an important tool for further exploring the genome function, pathogenesis, and interactions with its hosts.

Animals

Construction of circRNA-miRNA-mRNA regulatory networks in the intestine of turbot (Scophthalmus maximus) following Vibrio anguillarum infection.

Circular RNAs (circRNAs) play pivotal roles in post-transcriptional regulation by acting as molecular sponges for microRNAs (miRNAs) within the competitive endogenous RNA (ceRNA) network. However, the regulatory mechanisms in teleost immune responses remain poorly understood. In this study, circRNA-miRNA-mRNA networks were investigated in turbot (Scophthalmus maximus) following Vibrio anguillarum infection to elucidate host-pathogen interactions. Through high-throughput sequencing of intestinal tissues, a total of 50 differentially expressed circRNAs (DE-circRNAs) (18 at 2 hpi, 16 at 12 hpi, 16 at 48 hpi), 212 DE-miRNAs (11 at 2 hpi, 70 at 12 hpi, 15 at 48 hpi), and 1774 DE-mRNAs were identified. Functional enrichment analyses (GO/KEGG) revealed significant associations with immune pathways, including the MAPK signaling pathway and gap junction. An integrated circRNA-miRNA-mRNA regulatory network was constructed, highlighting key interactions including novel_circ_0002573/DE-miR-27a-3p/FGB and novel_circ_0002423/novel_347/GNE, which may regulate inflammatory and antibacterial responses. The expression patterns of selected circRNAs, miRNAs and mRNAs were validated using qRT-PCR, confirming the reliability of the sequencing results. Importantly, fibrinogen beta chain (FGB) and CXCR4/CXCL12 signaling were identified as critical immune modulators. These findings provide insights of the ceRNA regulatory networks involved in teleost intestinal immunity and provide potential molecular targets for selective breeding of disease resistance in this species.

Animals

Exploring Immersive Virtual Reality as an Approach to Improve School Participation-Related Constructs in Children With ADHD.

BACKGROUND: School participation is frequency and involvement from person-environment transactions, not diagnosis, per the International Classification of Functioning, Disability and Health (ICF) and the family of participation-related constructs (fPRC). In this framework, the environmental and child determinants of school participation (e.g., school routines and peer/teacher context; self-regulation, activity competence and preferences) interact bidirectionally to shape everyday participation. However, many interventions still target isolated impairments, overlooking coordinated changes in capacities and context. Grounded in this contemporary view, this study aimed to investigate the impact of an immersive virtual reality (IVR) intervention on school participation-related constructs in children with ADHD. METHODS: The study included 92 children aged between 7 and 12&#x2009;years diagnosed with ADHD. Participants were randomly assigned into intervention (n&#x2009;=&#x2009;46) and control (n&#x2009;=&#x2009;46) groups. Both groups completed the School Participation Questionnaire (SPQ) and Bruininks-Oseretsky Test of Motor Proficiency Test 2 Brief Form (BOT2-BF) assessment prior to the intervention. The intervention group received an IVR intervention program twice a week for 8 weeks. During this period, the control group did not receive additional therapy. At the end of the 8 weeks, the SPQ was readministered to both groups. RESULTS: Baseline characteristics showed no significant differences in SPQ and BOT2-BF results between groups, confirming homogeneity prior to intervention. Following the intervention, the study group demonstrated significant improvements across all domains of the SPQ (doing, being, symptoms and environment), with large effect sizes for SPQ total score (d&#x2009;=&#x2009;0.978) and subdomains (d&#x2009;=&#x2009;0.452-0.910). In contrast, the control group showed no improvements and even declines in subdomains. Post-intervention, between-group comparisons revealed significant differences favouring the study group across all domains (p&#x2009;<&#x2009;0.001), with large effect sizes (d&#x2009;=&#x2009;0.878-1.165). CONCLUSIONS: Findings suggest that the IVR program was associated with improvements in teacher-rated environmental and child determinants of school participation (SPQ domains) in children with ADHD.

Humans

A modified stomal construction technique to reduce incidence of stomal stenosis in continent catheterizable channels.

BACKGROUND: Antegrade continence enema (ACE) and catheterizable bladder channel (Mitrofanoff) procedures are routinely performed in pediatric urology patients diagnosed with a neurogenic bladder and bowel. Stomal stenosis is a common surgical complication of these procedures, occurring in approximately 10-30% of stomas. Our frustration with this complication prompted us to modify our suturing technique during stomal construction to attempt to decrease the incidence of stomal stenosis. METHODS: We compared the rates of stomal stenosis between patients with neurogenic bladder who underwent the creation of an ACE or Mitrofanoff channel using the historical techniques (prior to April 2018) versus the current technique (from April 2018 to December 2020). Our current technique for stoma creation consists of suturing full thickness bowel to only the dermal layer of the skin using interrupted 5-0 polydioxanone interrupted sutures with the knots buried. Statistics were performed using Fisher's exact t-test, with p-values <0.05 considered significant. RESULTS: There were no significant differences in demographics between patients in the 2 cohorts. Stomal stenosis occurred in 25 of 98 stomas (25.5%) after undergoing either an ACE or Mitrofanoff procedure using the historical techniques, with a median patient follow-up of 122.6 months for ACE cohort and 165.8 for Mitrofanoff cohort. The incidence of stomal stenosis was significantly decreased in the current technique, with one of the 31 stomas (3.2%) experiencing stenosis (p = 0.022), with a median follow-up of 78.4 months for ACE cohort, and 66.5 months for Mitrofanoff cohort. These follow-up durations exceed the upper limits of time-to-stenosis in the historical stomas. Stomas in the current cohort have a minimum follow-up of 4.5 years and a maximum follow-up of 7 years. CONCLUSIONS: Our current suturing technique has significantly reduced the incidence of stomal stenosis in our patients. The technique is straightforward and flexible and can be applied to any stoma placed in any position. Only one of the patients with stomas created with the current suturing technique have developed stomal stenosis, with follow-up exceeding the median time to development of stenosis of our historical cohort.

Humans

The Meaning and Significance of Breastfeeding for Biological Mothers of Children with Cleft Lip and/or Palate.

INTRODUCTION: Given the functional, emotional, and symbolic challenges imposed by cleft lip and/or palate (CL/P) on exclusive breastfeeding (EBF), mothers often experience early breastfeeding cessation with impacts on maternal identity and the mother-infant bond. This study aimed to explore the meanings, feelings, and experiences of mothers of children with CL/P regarding breastfeeding in the face of these adversities. METHODS: This qualitative study applied the Clinical-Qualitative Method as proposed by Turato. Six in-depth semistructured interviews were conducted with biological mothers of children with CL/P, recruited from a specialized craniofacial reference center in Brazil. Data were analyzed using a qualitative content analysis approach, grounded in psychodynamic concepts from the Medical Psychology theoretical framework. RESULTS: Based on the analysis of the collected material, three analytical categories were identified and constructed: (1) "Existential conflict regarding the inability to fulfill the ideal maternal role"; (2) "Duality between the need and the fear of caregiving"; and (3) "The anguish experienced between tangible and intangible support during breastfeeding."Conclusions:Although the inability to EBF in children with CL/P generates emotional distress, motherhood is reimagined through adaptive forms of care and bonding, highlighting gaps in institutional support and the need for more humanized, emotionally sensitive health practices.

Humans

Artificial Intelligence for Diagnosis, Risk Stratification, and Prognosis of Neuroblastoma - A Systematic Review and Meta-Analysis.

PURPOSE: To synthesizes evidence on artificial intelligence (AI) performance in neuroblastoma (NB) diagnosis, risk stratification, prognosis, and genomic characterization. MATERIALS AND METHODS: A systematic review and meta-analysis was conducted following PRISMA 2020 guidelines (PROSPERO: CRD42024539475) across five databases. Meta-analyses used random-effects models with logit-transformed Area Under the Curve (AUCs) and cluster-robust standard errors. AI models were classified as Machine Learning Models (MLM) or Hybrid Nomograms (HN) based on their construction methodology. RESULTS: Of 3,742 articles identified, 53 were included. MLMs demonstrated higher point estimates than radiologists in differential diagnosis (AUC: 0.87 vs. 0.83), though this difference was not statistically significant and carried substantial uncertainty. HNs achieved stronger performance in risk stratification (AUC: 0.87). AI-derived nomograms (AUC: 0.9) and gene signatures (AUC: 0.8) outperformed conventional prognostic markers descriptively. Chemotherapy response prediction remained below clinical utility thresholds across all model types. Only 33.9% of models reported calibration and 24.5% underwent external validation. CONCLUSIONS: AI demonstrates proof-of-concept across multiple NB clinical domains. However, clinical adoption remains premature given persistent gaps in external validation, calibration, dataset size, and pediatric-specific model development. Future studies should test these models prospectively in multicenter pediatric cohorts, ideally through COG or SIOPEN, using shared definitions for diagnosis, risk group, treatment response, and survival outcomes.

Humans

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30&#x2011;day all&#x2011;cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged&#xa0;&#x2265;&#xa0;60&#xa0;years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K&#x2011;Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P&#xa0;<&#xa0;0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR&#xa0;=&#xa0;9.752), smoking (OR&#xa0;=&#xa0;5.171), AIP (OR&#xa0;=&#xa0;6.691), TyG index (OR&#xa0;=&#xa0;4.393), HALP score (OR&#xa0;=&#xa0;2.831), and&#xa0;&#x2265;&#xa0;2 comorbidities (OR&#xa0;=&#xa0;3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC&#xa0;=&#xa0;0.884, accuracy&#xa0;=&#xa0;0.829, sensitivity&#xa0;=&#xa0;0.812, specificity&#xa0;=&#xa0;0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30&#x2011;day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans

Effects of different training modalities on lower-limb explosive power, acceleration, 20-m sprint performance, and change-of-direction ability in youth soccer players: a systematic review and network meta-analysis.

BACKGROUND: Youth soccer players repeatedly perform explosive actions, short accelerations, linear sprints, decelerations, and multidirectional movements. However, the comparative effects of different structured physical-conditioning programmes remain uncertain. METHODS: Seven databases were searched from inception to 3 July 2026 using a final expanded search strategy encompassing plyometric, strength or resistance, sprint, acceleration, speed, change-of-direction, neuromuscular, multicomponent, and combined training. Randomised controlled trials involving healthy youth soccer players were eligible. Intervention arms were classified using operational, content-based node definitions. Construct-restricted primary networks and expanded sensitivity networks were analysed using frequentist random-effects network meta-analysis. Hedges' adjusted g was preferentially calculated from post-intervention or final-follow-up means, standard deviations, and sample sizes. Estimates were presented so that positive values indicated better performance. P-scores were treated as descriptive ranking summaries. Risk of bias was assessed using an adapted study-level application of the five-domain RoB 2 framework, and confidence in the evidence was assessed using CINeMA. A post hoc strict-age sensitivity analysis excluded two age-boundary studies. RESULTS: Eighty-nine studies were included in the expanded quantitative analysis, of which 74 contributed to at least one construct-restricted primary network. The primary lower-limb explosive-power, acceleration, 20-m sprint, and planned change-of-direction networks included 55, 20, 25, and 38 studies, respectively. Compared with usual soccer training, plyometric training combined with sprint and/or change-of-direction training showed favourable estimates for lower-limb explosive power (SMD 0.79, 95% CI 0.55 to 1.03), acceleration (1.19, 0.90 to 1.49), 20-m sprint performance (0.80, 0.33 to 1.28), and planned change-of-direction ability (1.46, 1.13 to 1.80). Corresponding I&#xb2; values were 34.6%, 21.8%, 65.0%, and 41.0%. Between-design inconsistency was detected in the 20-m sprint (P&#x2009;=&#x2009;0.0036) and change-of-direction (P&#x2009;=&#x2009;0.0007) networks. CINeMA confidence for these four comparisons was low, low, very low, and low, respectively. Expanded sensitivity networks showed substantially greater heterogeneity. The highest-ranked intervention differed across outcome domains but remained consistent within each outcome across the three analysis sets. Excluding the two age-boundary studies did not materially alter the principal estimates. CONCLUSIONS: Plyometric training combined with sprint and/or planned change-of-direction training produced favourable comparative estimates across the four performance outcomes. However, evidence for several nodes and active-versus-active comparisons was sparse, heterogeneity in programmes and outcomes was present, inconsistency was detected in some networks, and confidence in the evidence was low or very low. These limitations do not support a conclusion that any training category is universally superior. The findings should be interpreted as provisional category-level signals rather than definitive training prescriptions. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD420261347297, registered on 21 March 2026, https://www.crd.york.ac.uk/PROSPERO/view/CRD420261347297 .

Change-of-direction ability

A horizontally acquired gene mediates insect cocoon pigmentation in the eri silkmoth, Samia ricini.

Holometabolous insects make cocoons during larval-pupal metamorphosis to protect the pupal phase. The materials used for cocoon construction vary widely. Lepidopteran insects typically secrete silk to form cocoons, which display diverse colors. The eri silkworm, Samia cynthia ricini, is an economically important domesticated species that mostly produces white cocoons, with some varieties producing red cocoons. The enzyme kynureninase (KYNU), acquired from bacteria by horizontal gene transfer, has previously been implicated in insect coloration, while the tryptophan metabolite 3-hydroxyanthranilic acid (3-HAA) has been identified as a red pigment. However, exactly how KYNU is involved in cocoon pigmentation remains unclear. Here, we report that a horizontally transferred bacterial gene encoding KYNU regulates red cocoon formation. Metabolomic analysis revealed a high accumulation of 3-HAA in red cocoons, confirming its role as the primary pigment and associating the coloration with tryptophan metabolism. Quantitative real-time polymerase chain reaction (qPCR) analysis indicated that SrKYNU is highly expressed in the silk glands and significantly downregulated in the red cocoon strain compared to the white cocoon strain. Genomic sequencing identified a 141 bp deletion in the upstream regulatory region of KYNU in the red cocoon strain compared to the white cocoon strain. Dual-luciferase assays confirmed that this deletion significantly reduced promoter activity. CRISPR/Cas9 knockout of SrKYNU in the white-cocoon strain resulted in mutants producing red cocoons with elevated 3-HAA content. These findings reveal that the horizontally transferred gene SrKYNU exhibits tissue-specific expression and regulates cocoon coloration in S. ricini, illustrating that horizontal gene transfer can play an important role in regulating an insect physiological process.

Animals

Low-burden metrics for monitoring healthy diets among nonpregnant females aged 15 to 49 years: a multicountry validation analysis using quantitative 24-hour dietary intake data.

BACKGROUND: Limited nationally representative quantitative dietary intake data and a lack of consensus on lower-burden tools and metrics hinder high-frequency monitoring of healthy diets globally. OBJECTIVES: This study aimed to evaluate the comparative construct validity and potential complementarity of low-burden metrics of a healthy diet among nonpregnant females aged 15 to 49 y. METHODS: Quantitative 24-h dietary intake data collected from 77,118 adolescent and adult females across 27 countries were used to construct low-burden metrics and reference metrics of dietary intake. Associations between mean-standardized low-burden measures or indicators and reference metrics were assessed using linear and logistic mixed-effect models, with Spearman's &#x3c1; used for survey-level rank correlations. Test characteristics identified low-burden indicators best differentiated adherence to reference indicators. RESULTS: An indicator reflecting nonconsumption of sweet foods and/or sweet beverages was most robustly associated with greater adherence to <10% energy from free sugars in upper-middle-income countries {odds ratio [OR] [95% confidence interval (CI)]: 5.35 [5.05, 5.66]}. Food group diversity score (FGDS) was most strongly associated with and differentiated higher mean adequacy ratio of micronutrients [&#x3b2; of 1-standard deviation (SD) change: &#x223c;11 percentage points (9, 12); &#x3c1;: 0.79], whereas noncommunicable disease-Protect score best reflected consumption of &#x2265;400 g/d of fruits and vegetables [range OR of 1-SD changes (95% CI): 2.56-3.01 (2.40, 3.13) in lower-middle and high-income countries, respectively; &#x3c1;: 0.56]. FGDS and Global Diet Quality Score Positive were most consistently associated with achieving &#x2265;25 g/d of fiber and &#x2265;3510 mg/d of potassium across contexts. CONCLUSIONS: Low-burden data collection tools yield valid metrics, enabling high-frequency monitoring of healthy diets across contexts. Specifically, avoiding sweet foods and/or sweet beverages is an indicator for adherence to WHO free sugar guidelines among nonpregnant females in upper-middle-income countries, whereas metrics reflecting nutritious food group diversity strongly reflect better micronutrient adequacy and adherence to WHO guidelines for fruits and vegetables, fiber, and potassium intakes within and across contexts.

Humans

Ensemble DNA methylation clock demonstrates Immune-metabolic aging signatures associated with mortality.

Aging is a multifactorial process that is best described in terms of the progressive acquisition of multiple layers of phenotypic changes, such as epigenetic modifications, inflammation, and metabolic dysregulation. DNA methylation clocks have been extensively used to construct epigenetic clocks based on the DNAm profiles that can be used to estimate biological age and predict age-associated outcomes. Nevertheless, the vast majority of clocks constructed so far have been based on linear models, which are unlikely to fully account for the heterogeneity and non-linearity of survival-related DNAm signatures. In this work, we constructed a heterogeneous stacked ensemble survival model based on DNAm data obtained from the Framingham Heart Study. We first identified 190 CpG loci using elastic net Cox regression and subsequently constructed a survival prediction model based on the fusion of five complementary survival models by means of a neural network meta-learner. The prediction power of the survival model was evaluated in an external validation cohort, where we observed strong performance for predicting all-cause mortality that significantly exceeded PhenoAge and was statistically comparable to GrimAge. These performance estimates were derived in cohorts of European ancestry and externally validated in postmenopausal women aged 50-79 years, and should therefore be interpreted as applicable only to demographically similar populations.

Humans