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Bioactive peptides for meat quality and preservation: Integrating peptidomics and computational screening.

Bioactive peptides generated from meat proteins, fermented meat products, and slaughter by-products have attracted increasing attention as functional molecules for improving meat quality and preservation. In meat systems, peptides can be produced through endogenous postmortem proteolysis, microbial fermentation, gastrointestinal digestion, or controlled enzymatic hydrolysis of underutilized animal by-products. These peptides are closely associated with key meat science endpoints, including postmortem tenderization, oxidative stability, color retention, flavor development, microbial inhibition, and the valorization of processing by-products. However, although high-resolution peptidomics has greatly expanded the identification of meat-derived peptide sequences, their translation into practical meat applications remains limited by matrix interactions, processing stability, sensory constraints, safety concerns, and insufficient validation in real meat systems. This review synthesizes recent advances in meat-related peptidomics and computational screening, including sequence-based prediction, machine learning, molecular docking, molecular dynamics, stability assessment, and safety-oriented filtering. Particular attention is given to how these approaches can prioritize peptides with antioxidant, antimicrobial, flavor-modulating, and preservation-related functions under meat-specific technological constraints. By integrating peptide generation pathways, mass spectrometry-based identification, in silico prioritization, and meat quality endpoints, this review proposes a stage-gated framework for translating meat-derived bioactive peptides from discovery to application. Future research should strengthen matrix-specific validation, standardized peptidomic reporting, and safety assessment to support the use of bioactive peptides in meat quality improvement, clean-label preservation, and circular utilization of meat industry by-products.

Animals

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&#x2009;years of age and having experienced their child's acute illness. Participants were assigned to an electronic document group or a video group (n&#x2009;=&#x2009;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&#x2009;<&#x2009;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

Investigation of Fatty Acid Metabolism-Associated Molecular CPOX and the Underlying Mechanism in Follicular Lymphoma.

Dysregulated lipid metabolism is a key driver of follicular lymphoma (FL). This study aimed to explore the lipid metabolism-related genes (LMRGs) and clarify the underlying roles and mechanisms in FL. Bioinformatics methods, including differential analysis, WGCNA, machine learning, and Mendelian randomization, were utilized to select the LMRGs in FL. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to investigate the function of the key LMRG. Receiver operator characteristic (ROC) was used to evaluate the diagnostic value of the key gene CPOX. A pan-cancer analysis investigated CPOX's expression level and immune correlations. In vitro experiments using FL cell lines (WSU-FSCCL, DOHH2) validated CPOX expression, and CPOX knockdown in DOHH2 cells was used to assess its impact on viability, migration, invasion, and fatty acid metabolism. CPOX was confirmed to be a risk factor, significantly overexpressed in FL, and exhibited effective diagnostic ability in FL (AUC&#x2009;=&#x2009;0.731). Functional analysis linked CPOX to mitochondrial function, oxidative phosphorylation, and heme metabolic process. Pan-cancer indicated the dysregulated CPOX across multiple cancers and closely correlation with immune characteristics. Experimentally, CPOX was higher in the more invasive DOHH2 cells; and CPOX knockdown suppressed FL progression and reduced lipid droplet formation, triglyceride, total cholesterol, and free fatty acid levels. In conclusion, this study fills the gap in understanding the significance of lipid metabolism-related molecules in FL, and innovatively proposes that CPOX is a risk factor for FL. Knockdown of CPOX inhibits the FL progression, which is regulated by fatty acid metabolism.

Lymphoma, Follicular

Decoding the spatiotemporal patterns of food spoilage microbial communities: Integrating multi-omics and artificial intelligence to enable precision preservation.

In the global food supply chain, food wastage caused by spoilage has resulted in significant economic losses, food shortages, and environmental pressure. This process is fundamentally driven by the spatiotemporal dynamics of microbial communities. However, traditional research methods struggle to elucidate the complex mechanisms of spatial heterogeneity, interspecies interactions, and functional succession. This limits the development of effective preservation strategies. This review systematically reviews the cutting-edge progress of integrating multi-omics technologies and artificial intelligence (AI) to study food spoilage microbial communities, breaking through this bottleneck. We propose an intelligent theoretical framework that could potentially analyze microbial metabolic activities and predict dynamic shelf life if implemented. The conceptual framework integrates multidimensional data, including spatial metabolomics, temporal metatranscriptomics, single-cell transcriptomics, and longitudinal metagenomics. It can also be combined with AI models, such as graph neural networks. The article elaborates on the principles and applications of spatio-temporal monitoring technologies, such as nano secondary ion mass spectrometry, hyperspectral imaging, and the Internet of Things sensing. Through illustrative cases of typical perishable foods, it also explores how such a multi-omics - AI system might be applied to spoilage warning and precise intervention. Additionally, the article addresses the current challenges in data coverage, model generalization, and federated learning implementation. Then the research further explores emerging areas such as engineered probiotics, edge AI, and microfluidic sensing. These areas are targeted at transforming food preservation from an empirical control approach to a data-driven, precise regulatory framework. This transformation provides theoretical support and technical approaches for developing a smart, sustainable food preservation system.

Multiomics

Automated CEAP Classification of Venous Duplex Reports Using Multimodal Artificial Intelligence.

OBJECTIVE: To develop and internally validate a prototype multimodal artificial intelligence system for automated CEAP (Clinical, Etiological, Anatomical and Pathophysiological) classification of venous duplex ultrasound (VDUS) reports, integrating natural language processing of free-text components with computer vision analysis of hand-drawn anatomical diagrams. METHODS: Single centre retrospective observational study using routinely collected clinical data. One thousand consecutive venous duplex ultrasound reports from Cambridge University Hospitals NHS Foundation Trust, UK (July 2024 - May 2025) were labelled according to the CEAP classification, excluding the Etiological component, which could not be reliably determined from duplex reports alone. Transfer learning was applied using ClinicalBERT for text and MobileNetV3 for diagrammatic data. Clinical classes were predicted from request line text. Text- and image-based pathophysiological models were developed for four anatomical territories (Great Saphenous Vein, Small Saphenous Vein, Deep system, Perforators), combined using late fusion with probability averaging. RESULTS: The clinical CEAP model achieved accuracy of 0.91, macro-F1 of 0.82, and macro-AUC of 0.98. Pathophysiological prediction varied, with text models broadly outperforming image models. Fusion yielded heterogeneous benefits, improving SSV performance but reducing Deep system accuracy. The performance of the final pathophysiological CEAP fusion models varied across anatomical territories: accuracy ranged from 0.70-0.92 and macro-AUC from 0.80-0.92. CONCLUSION: This study demonstrates the feasibility of automated CEAP classification from VDUS reports. Despite class imbalance affecting minority class predictions, the strong discriminatory performance validates this multimodal ML model for extracting clinically meaningful information from real-world data. This approach offers potential, pending external validation, to streamline vascular services through automated triage and guideline-compliant decision making.

Artificial intelligence

Virtual Reality Mastoidectomy as Precadaver Training for Novices: A Randomized Crossover Study.

OBJECTIVES: To compare cognitive load during virtual reality (VR) simulation and cadaveric dissection (CD) mastoidectomy training in novice learners. To determine whether training order influences cognitive load, characterize cognitive load progression during the procedure, and assess whether VR training improves subsequent cadaveric performance. METHODS: In this randomized crossover study, 24 core surgical trainees with no prior mastoidectomy experience performed a cortical mastoidectomy in both VR and CD settings. Participants were randomized to either VR-first or CD-first training sequences. Cognitive load was measured using a bespoke auditory reaction-time device at baseline and 10, 30, and 50&#x2009;min. Relative reaction time (RRT) served as an objective index of cognitive load. Cadaveric performance was assessed using the Modified Welling Scale by two blinded otologists. RESULTS: Cognitive load was significantly lower during VR than CD, with mean RRT rising 26% from baseline in VR versus 60% in CD (p&#x2009;<&#x2009;0.001). Training order did not affect cognitive load in either modality, and RRT increased progressively throughout mastoidectomy in both VR and CD. Participants who began with VR achieved significantly higher cadaveric performance scores than those who began with CD (mean 9.50 vs. 4.96; p&#x2009;<&#x2009;0.001), and inter-rater reliability for performance scoring was high. CONCLUSION: VR mastoidectomy reduces cognitive load and enhances subsequent cadaveric performance in novice trainees, supporting its role as a cognitively optimized precadaver training modality that complements, rather than replaces, cadaveric dissection. These findings suggest VR enhances early learning efficiency and resource utilization in novice otolaryngology training. LEVEL OF EVIDENCE: N/A.

Humans

Individual differences in brain dynamics across a social cognition network induced by cortico-cerebellar tDCS in adults with autism spectrum disorder (ASD).

Autism spectrum disorder (ASD) is a neurodevelopmental condition with core diagnostic domains of social communication impairments, restricted interests and repetitive behaviors. Idiosyncratic brain organization is a potential hallmark of ASD. Previous transcranial direct current stimulation (tDCS) studies often targeted dorsolateral prefrontal cortex, with changes oin brain dynamics averaged across the cohort. We utilized a magnetoencephalographic (MEG) array to characterize individual differences in brain dynamics induced by cortico-cerebellar tDCS across nodes of a social cognition network. A randomized, sham-controlled, double-blind, within-subject clinical trial was conducted in a cohort of 24 young adults with ASD or high autistic traits. Two separate sessions of computerized social learning activities were combined with verum/sham tDCS, with anodal electrode over right temporoparietal junction (TPJ) and cathode on right deltoid. Following stimulation, theta- and alpha-band activity were evaluated within nodes of a social cognition network: bilateral TPJ, fusiform, medial prefrontal cortex and Crus I/II of cerebellum. Idiosyncratic participant-specific up- and down-regulation of theta- and alpha-band activity occurred across the network. Activity in right Crus I/II, a region inundated by the stimulation current, strongly correlated with the change of activity summed across all cerebral cortical nodes in theta- but not alpha-band. Intrinsic theta-band activity is believed to mediate input/output relationships in cerebellar cortex and to drive synaptic plasticity. These results suggest that theta-band stimulation of cerebellar cortex might be an effective therapy for individuals on the autism spectrum who present with cerebellar hyperactivity.

Humans

A systematic review of the impact of Mental Health First Aid on medical, nursing and allied healthcare professional students.

BACKGROUND: Healthcare professional (HCP) students are at high risk of mental health problems, but stigma and fear of career repercussions often deter them from seeking help. Mental Health First Aid (MHFA) is a globally disseminated course teaching the public to identify and respond to people experiencing mental health problems. MHFA training may address some of the challenges faced by HCP students, by improving mental health knowledge and by enhancing well-being and peer support. AIMS: To systematically review the available literature regarding the impact of MHFA training on HCP students' mental health literacy, confidence and intentions to provide help, stigma, peer support and self-care. METHOD: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (International Prospective Register of Systematic Reviews ID: CRD42024589509), five databases were searched. Primary studies evaluating the above outcome measures in HCP students were included. Two authors independently screened references and extracted data. Quality was assessed using the Modified Medical Education Research Study Quality Instrument and Cochrane Risk of Bias tools. A narrative synthesis was performed. RESULTS: Of 2367 records screened, 26 met inclusion criteria. Confidence in supporting others and mental health literacy showed the most consistent improvements following MHFA training, whereas evidence for changes in stigma was mixed. Peer support, self-care and student well-being were infrequently examined, although qualitative data suggested that MHFA had improved openness to help-seeking. CONCLUSIONS: MHFA shows promise in enhancing mental health literacy, confidence and intentions, and in reducing stigma, particularly when supplemented with experiential learning. HCP students may benefit from tailoring of such courses to their specific needs, fostering a culture of peer support, enhancing well-being and introducing basic concepts in mental health.

MHFA

Can host genetics transform the sustainable control of tropical theileriosis? Insights from the Tick-Theileria interface.

Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across North Africa, the Mediterranean basin, the Middle East and South Asia. Current control depends on acaricides, the theilericidal drug buparvaquone and live attenuated schizont vaccines, but acaricide resistance, buparvaquone-resistance mutations and the logistical demands of vaccination are eroding the sustainability of these tools. Host genetics offers a complementary and durable alternative. Indigenous Bos indicus breeds are consistently more resistant to ticks and tolerate T. annulata infection better than exotic Bos taurus cattle, and this advantage has a measurable heritable component. Unlike previous reviews, which treat tick resistance, T. annulata immunobiology and livestock genomic selection as separate subjects, we integrate all three and assess host genetics specifically against the failure modes of current control. We review the tick, parasite and host interface, the evidence for natural resistance, and the genetic and immunological mechanisms involved, including signal-regulatory protein, bovine major histocompatibility complex class II and inflammatory pathway genes. We then assess whether genomic selection, multi-omics, machine learning and gene editing can translate these mechanisms into resistant cattle, and we weigh the biological, economic and infrastructural barriers to implementation. The evidence indicates that host genetics will not replace existing control but could reduce reliance on acaricides and chemotherapy. That contribution remains prospective rather than demonstrated: no resistance marker for T. annulata has yet been validated, prediction accuracies are moderate and transfer poorly between breeds, and no endemic production system has implemented selection for resistance.

Animals

Ecological Restoration of the Soil-Like Function in the Bauxite Residue: Natural Microbiomes Mediated Molecular Transformation of Dissolved Organic Matter.

Soilization of bauxite residues offers a scalable route for long-term carbon management and ecological restoration. However, the microbial processes that transform exogenous organic inputs into stable soil-like carbon pools remain poorly resolved. Here, we combined cross-ecosystem meta-analysis, machine-learning prediction, native synthetic community (SynCom) construction, 13C-labeled straw microcosms, field validation, Fourier transform ion cyclotron resonance mass spectrometry, and genome-resolved metagenomics to unravel microbiome-mediated carbon transformation at the dissolved organic matter (DOM) molecular scale. Our meta-analysis revealed that alkaline industrial wastes retained soil-like DOM signatures but were enriched in microbial humic- and protein-like components, indicating active yet incomplete carbon processing. Guided by these patterns, native SynCom inoculation increased 13C incorporation into total organic carbon (TOC) and dissolved organic carbon (DOC), enlarged biodegradable and adsorbable DOC fractions, and shifted DOM from recalcitrant aromatic pools toward oxygenated carbohydrate-, tannin-, and phenolic-like molecular classes. Genome-resolved analyses linked this transformation to complementary polymer degradation and nutrient-cycling functions across fungal and bacterial guilds, including enriched carbohydrate-active enzymes in straw-carbon-utilizing metagenome-assembled genomes. Null model and thermodynamic analyses further showed that microbial communities were constrained by homogeneous selection, whereas DOM molecules were diversified through variable selection and redox-dependent transformation. Field-scale validation confirmed that SynCom promoted TOC and DOC accumulation and humic-like, high-density DOM fractions under alkaline conditions. Together, these findings establish a mechanistic framework in which functional microbiomes couple plant carbon depolymerization, DOM molecular diversification, and mineral-interactive carbon stabilization, providing a microbiome-guided strategy for carbon sequestration and soilization in the bauxite residue.

Soil

Psychotherapy training in psychiatry: a systematic review and narrative synthesis on the supervision experiences of early-career psychiatrists.

BACKGROUND: Supervision is a fundamental component of psychotherapy training, transforming theoretical knowledge into clinical skills through real-world practice. Psychotherapy training practices vary widely between countries, training programs, and over time, including supervision. Our systematic review aimed to investigate and describe the experiences of psychotherapy supervision through early-career psychiatrists' (ECPs) views. METHODS: We systematically searched PubMed/MEDLINE, Scopus, and PubPsych for survey-based studies on ECPs' experiences of psychotherapy supervision during or after their psychiatry training and reported our findings according to the PRISMA guidelines. Of 32,877 articles screened, 29 articles were included. Each article underwent quality assessment, and results were synthesized narratively. RESULTS: Included articles published between 2000 and 2025, were from Europe (N = 16, 55.1%), the Americas (N = 5, 17.2%), Western Pacific (N = 4, 13.7%), South-East Asia (N = 2, 7%), Eastern Mediterranean (N = 1, 3.5%), and Africa (N = 1, 3.5%), with a total of 4691 participants. Supervision access rates ranged from 26.2% in Nigeria to 85.5% in Russia, with significant variation across countries and psychotherapy modalities. Most ECPs received 50-100 total hours of supervision, frequently delivered in weekly sessions. While formats, individual, group, or mixed, varied by country and training scheme, supervision was generally provided by a psychiatrist-psychotherapist. Common learning techniques included oral consultations and case discussions, followed by audio recordings or transcripts. The need to self-fund psychotherapy supervision costs was identified as a prominent barrier. CONCLUSIONS: Psychotherapy supervision is inconsistent globally, with barriers including supervisor availability and cost. There is a large implementation gap between recommendations and evaluated practice. Digital tools and competency-based frameworks may improve access and quality.

Humans

Artificial Intelligence for Diagnosing Meibomian Gland Dysfunction: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy Studies.

PURPOSE: To identify, appraise, and synthesize the performance of artificial intelligence-based meibography reading as compared with human graders in diagnosing meibomian gland dysfunction. METHODS: We followed Cochrane methodology and reporting guidelines for diagnostic test accuracy reviews. To assess potential risk of bias and applicability, we used a modified Quality Assessment of Diagnostic Accuracy Studies-2 checklist. We applied bivariate logistic models to estimate summary sensitivity and specificity when appropriate and used the GRADE framework to rate the certainty of the evidence. RESULTS: We identified 14 eligible studies involving 5511 predominantly middle-aged participants (average age: 27-55 years) who were primarily female (&#x2265;54.5%). A total of 18,926 meibography images were obtained through noncontact infrared (11 studies) or in vivo confocal microscopy (three studies). Two studies reported external validation of deep learning models, 12 reported internally validated models, and one reported both. All but one study had high risk of bias in at least one domain; 12 studies raised high or intermediate concern about applicability. Based on three external evaluations, the summary sensitivity and specificity for diagnosing meibomian gland dysfunction from normal glands were 97.5% (95% confidence interval: 77.5%-99.8%) and 85.5% (95% confidence interval: 47.3%-97.5%). Sources of heterogeneity in internally validated models included study population, case mix, and others. The overall evidence was very low to low certainty because of imprecision, high risk of bias, and concerns about applicability. CONCLUSIONS: Artificial intelligence-based meibography grading appears less accurate than human graders. Future studies should adopt rigorous designs, including a more diverse participant pool (or image set), and external validation.

Humans

How AI-supported intelligent systems support infection prevention and control training in healthcare: A systematic review of educational functions and outcomes.

AIMS: Artificial intelligence (AI)-supported intelligent systems have been increasingly incorporated into infection prevention and control (IPC) education and training, primarily to support the monitoring of observable behaviors and the provision of feedback. However, existing evidence has focused largely on short-term compliance outcomes, with limited synthesis of the educational role of AI-supported intelligent systems in supporting sustained IPC competence. This systematic review examined how AI-supported intelligent systems have been designed and used to support IPC education and training, with a focus on system characteristics, educational functions, and reported outcomes. DESIGN: A systematic literature search was conducted across the PubMed/MEDLINE, Embase, Cochrane, and CINAHL databases. DATA SOURCES: A total of 18 studies met the inclusion criteria. Findings were qualitatively synthesized according to system design characteristics, educational functions, and outcome domains. REVIEW METHODS: Methodological quality was appraised using the Mixed Methods Appraisal Tool. RESULTS: Most AI-supported intelligent systems focused on hand hygiene and relied on fully automated monitoring systems to capture behaviors and provide performance feedback. Educational functions were predominantly limited to performance assessment, automated feedback, and reminders. Outcomes were mainly measured using compliance or performance metrics, whereas sustained behavioral change and decision quality were rarely assessed. CONCLUSIONS: AI-supported intelligent systems have been used primarily to reinforce short-term IPC performance and compliance. However, their current applications for supporting sustained competence over time remain limited. The findings of this review suggest that AI-supported intelligent systems may serve as maintenance-oriented educational support by extending learning beyond initial instruction through repeated practice and feedback. Future research should prioritize outcome measures that capture the durability of performance and decision-making processes to better align AI-supported intelligent systems used in IPC education and training with the educational demands of clinical practice.

Humans

Identifying stakeholder behaviors for competency-based pharmacy education: A stage 1 behavior change wheel analysis.

INTRODUCTION/OBJECTIVES: Competency-Based Pharmacy Education (CBPE) is a strategic priority for preparing graduates to meet evolving healthcare needs. However, efforts to implement CBPE can stall due to behavioral challenges among faculty, administrators, preceptors, and learners. This study aimed to apply Stage 1 of the Behavior Change Wheel (BCW) to identify stakeholder-specific behaviors and associated determinants needed to implement the five core components of CBPE. METHODS: A multi-method approach grounded in the BCW, the Capability, Opportunity, Motivation - Behavior (COM-B) model, and the Theoretical Domains Framework (TDF) was used. Data were gathered through (1) targeted literature review; (2) structured focus groups with competency-based education experts and pharmacy education stakeholders; and (3) an iterative consensus process. Behaviors were mapped to the five CBPE components: (1) defined competencies, (2) developmental progression, (3) tailored instruction, (4) authentic experiential learning, and (5) programmatic assessment, and then mapped to COM-B and TDF constructs. RESULTS: Over fifty stakeholder-specific behaviors were identified and specified across the CBPE framework. This revealed shared barriers such as limited instructional design knowledge (psychological capability), insufficient assessment of infrastructure (physical opportunity), and misaligned professional identity (reflective motivation). Key TDF domains included knowledge, environmental context, beliefs about capabilities, and professional roles. The behavioral problem statements, specifications, and determinants were identified to support future intervention planning. CONCLUSION: This Stage 1 analysis provides a behaviorally grounded foundation for CBPE implementation by identifying stakeholder behaviors and conditions that enable change. These findings will inform the development of readiness-to-change assessments and targeted interventions (BCW Stages 2 and 3), supporting scalable and sustainable CBPE transformation in pharmacy education.

Education, Pharmacy

Comparative phylogenomics and transcriptional regulatory networks of AQPs, HSPs, and LEA proteins in salt-stressed Portulaca oleracea.

Soil salinization severely threatens global food security, necessitating systematic investigations of halophytes like Portulaca oleracea to decode the molecular mechanisms of environmental resilience. Utilizing an integrated framework of deep learning-based genome annotation (58,817 predicted genes; 96.5% BUSCO completeness), multi-tissue RNA-Seq, phylogenomics, and gene regulatory network (GRN) inference, the synergistic orchestration of 78 aquaporins (AQPs), 525 heat shock proteins (HSPs), and 119 late embryogenesis abundant (LEA) proteins was elucidated. The active transcriptome, encompassing 39,065 expressed loci, revealed a systemic growth-defense trade-off. Tissues displayed distinct adaptive mechanisms: leaves modulated intracellular water balance via specialized AQPs, whereas adult roots maintained proteostasis through robust HSP20/HSP70 induction. Phylogenomic clustering across 154 species demonstrated that salinity tolerance constitutes an evolutionary mosaic, identifying 81 halophyte-exclusive orthogroups and 1129 species-specific clusters. Comparative topology across six independent GRNs (4.2M-5.3&#x202f;M edges) unmasked a highly modular transcriptional reprogramming strategy governed by a core apparatus of 22 stress-exclusive regulators, with functional enrichment heavily prioritizing protein dimerization and chromatin remodeling. Theoretically, the distinct convergence of Trihelix transcription factors with guard cell differentiation pathways offers a candidate transcriptomic framework to explain the plant's characteristic C4-CAM photosynthetic plasticity under severe osmotic pressure. Practically, these evolutionary blueprints and specific master switches transcend single-gene transgenic limitations. Utilizing these root-sustained and stress-inducible targets under localized promoters provides a naturally optimized, network-level precision engineering roadmap to transfer robust, compartmentalized halotolerance to sensitive glycophytic crops.

Gene Regulatory Networks

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans

Development, feasibility, acceptability, and preliminary impact of NutriSOS&#xae;: A behavioral mobile app to promote sustainable diets.

The primary objective of this study was to describe the development of the NutriSOS&#xae; app and to evaluate its feasibility and acceptability for its use in the NutriSOS&#xae; Randomized Controlled Trial (RCT) to promote sustainable diets. A secondary objective was to explore preliminary changes in dietary and physical activity behaviors and environmental impact following app use. The NutriSOS&#xae; app integrates personalized dietary advice, educational content, self-monitoring, and social interaction features. A single-arm, pre-post pilot study was conducted in 37 young Mexican adults over four weeks. Feasibility, acceptability, quality, and usability were assessed using online surveys, alongside exploratory changes in dietary and physical activity behaviors, environmental indicators, and their association with perceived behavioral determinants. Feasibility and acceptability were high overall, with favorable responses reaching up to 100% in key components such as the nutritional guide and learning modules, and above 90% for messaging, registration, and design. Greater variability was observed in some sections, particularly the 24-h recall (41-86%). Reductions in red and processed meat and ultra-processed food consumption were observed (from 3 to 1 times/week, p&#xa0;<&#xa0;0.01), with &#x223c;60% decreases in their related environmental footprints (p&#xa0;<&#xa0;0.01) and favorable self-reported behavioral determinants (p&#xa0;<&#xa0;0.0001). Physical activity type and intensity changed (p&#xa0;<&#xa0;0.05). These findings support NutriSOS&#xae; as a feasible and acceptable tool, while highlighting areas for refinement, particularly those related to the time and effort required for data entry, prior to its implementation in the NutriSOS&#xae; RCT, in which its effectiveness will be formally evaluated.

Humans

Development and Crossover Evaluation of an Artificial Intelligence-Assisted System for Solid Pancreatic Lesion Detection and Pancreatic Parenchyma Recognition in Endoscopic Ultrasonography (With Video).

BACKGROUND AND STUDY AIMS: Pancreatobiliary endoscopic ultrasonography (EUS) is technically demanding, and supervised training opportunities are limited. We developed an artificial intelligence (AI) overlay system for detecting solid pancreatic lesions (SPL) and recognizing pancreatic parenchyma (PP) and evaluated its effect on reader performance. PATIENTS AND METHODS: Across six centers, two deep learning-based models were trained using expert-annotated EUS frames. We then conducted a randomized, two-sequence, two-period crossover reader study in which eight endosonographers (five novices and three experts) interpreted image sets with and without AI assistance. The primary endpoint was superiority of sensitivity for SPL detection among novices; key secondary endpoints included specificity and PP recognition. RESULTS: From 118 patients, 120 SPL-positive/negative image sets and 160 PP-positive/negative image sets were constructed. Among novices, AI assistance improved SPL detection sensitivity (88.7% vs. 76.8%, p&#x2009;<&#x2009;0.001) and accuracy (86.4% vs. 78.7%), while specificity met the predefined noninferiority criterion (84.2% vs. 80.5%, p&#x2009;<&#x2009;0.001). For PP recognition, sensitivity increased numerically (86.3% vs. 83.3%) but did not meet the predefined superiority criterion (p&#x2009;=&#x2009;0.095); specificity met the noninferiority criterion (87.8% vs. 81.0%), and accuracy increased from 82.1% to 87.0%. Among experts, sensitivity was maintained for both tasks, whereas specificity increased with AI assistance. CONCLUSIONS: AI assistance improved SPL detection among novice endosonographers. For PP recognition, sensitivity increased without reaching statistical superiority, whereas specificity met the predefined noninferiority criterion. These findings support a potential adjunctive role for AI in EUS interpretation.

Humans