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Beyond antigen matching: compatibility intelligence theory for transfusion as an emergent biological system.

BACKGROUND: Despite major advances in serologic testing, extended phenotyping, and blood group genomics, clinically similar transfusion exposures may result in markedly different immune and clinical outcomes. Existing compatibility strategies do not fully explain this biological variability. OBJECTIVES: To examine transfusion compatibility as an emergent donor-recipient biological state and propose a systems-level conceptual framework that integrates established biological determinants into a testable model for future precision transfusion medicine. METHODS: This narrative review critically synthesizes current evidence from blood group genomics, recipient immunobiology, inflammation, disease-specific biology, transfusion medicine, and computational prediction. The proposed framework distinguishes Compatibility Intelligence Theory (CIT) as a biological interpretation from Precision Transfusion Intelligence (PTI) as its potential clinician-supervised translational application. RESULTS: The review argues that transfusion compatibility is shaped by interactions among donor genetics, recipient immune biology, inflammatory physiology, disease context, transfusion history, and longitudinal adaptation rather than by antigen matching alone. CIT provides an organizational framework for integrating these determinants, whereas PTI describes a possible clinician-supervised translation. To address current feasibility, the revised framework separates variables into routinely measurable, contextually available but incompletely standardized, and research-stage domains, and proposes a staged strategy for deriving rather than assuming their quantitative weights. Any clinical implementation would require comparative validation against current serologic, phenotypic, and genotype-based practice. CONCLUSIONS: Compatibility Intelligence Theory offers a testable systems-level framework for understanding transfusion compatibility without replacing established transfusion practices. The framework is not presented as a ready-to-use score: currently measurable variables can be organized for structured risk review, whereas inflammatory, immunogenetic, and multi-omic inputs require prospective standardization and validation. If future studies demonstrate incremental predictive and patient-centered benefit, CIT-informed PTI could support an adaptive, evidence-based extension of current precision transfusion practice.

Humans

Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.

AIM: To synthesise existing evidence concerning the application of AI methods in detecting depression through behavioural cues among adults in healthcare and community settings. DESIGN: This is a diagnostic accuracy systematic review. METHODS: This review included studies examining different AI methods in detecting depression among adults. Two independent reviewers screened, appraised and extracted data. Data were analysed by meta-analysis, narrative synthesis and subgroup analysis. DATA SOURCES: Published studies and grey literature were sought in 11 electronic databases. Hand search was conducted on reference lists and two journals. RESULTS: In total, 30 studies were included in this review. Twenty of which demonstrated that AI models had the potential to detect depression. Speech and facial expression showed better sensitivity, reflecting the ability to detect people with depression. Text and movement had better specificity, indicating the ability to rule out non-depressed individuals. Heterogeneity was initially high. Less heterogeneity was observed within each modality subgroup. CONCLUSIONS: This is the first systematic review examining AI models in detecting depression using all four behavioural cues: speech, texts, movement and facial expressions. IMPLICATIONS: A collaborative effort among healthcare professionals can be initiated to develop an AI-assisted depression detection system in general healthcare or community settings. IMPACT: It is challenging for general healthcare professionals to detect depressive symptoms among people in non-psychiatric settings. Our findings suggested the need for objective screening tools, such as an AI-assisted system, for screening depression. Therefore, people could receive accurate diagnosis and proper treatments for depression. REPORTING METHOD: This review followed the PRISMA checklist. PATIENTS OR PUBLIC CONTRIBUTION: No patients or public contribution.

Humans

A Qualitative Analysis of Cancer Survivors' Experience in a Time-Restricted Eating vs Control Clinical Trial to Address Cancer-Related Fatigue.

PURPOSE: To describe cancer survivors' lived experiences in a clinical trial that tested an individualized nutrition counseling with or without time-restricted eating to address cancer-related fatigue. METHODS: The Fatigue REDuction After cancer study was a two-arm, randomized controlled trial. Participants were adult cancer survivors who were 2 months to 2 years post-treatment. All participants received individualized nutrition counseling; those in the time-restricted eating group self-selected a consistent 10-hour eating window for 12 weeks. After the study, semi-structured exit interviews were conducted to gauge participants' experiences in the trial. Interviews were transcribed and two independent coders thematically analyzed the interviews using inductive and deductive coding. NVivo software was used for data organization and analysis. RESULTS: Participants (n = 24; TRE = 11; Control = 13) were 55 ± 13 years old, 75% were female, and they had a variety of cancer types. The majority of participants found that being in the study helped them to set and achieve lifestyle goals and would therefore recommend the study to others. Participants in the time-restricted eating group noted that time-restricted eating helped them set a better routine, providing a positive sense of control. However, some noted difficulty switching to a 14-hour fasting schedule, as it can interfere with their regular routine or employment schedules. Many participants noted they were happy that cancer-related fatigue was gaining more attention, hoping to find solutions for persistent cancer-related fatigue. CONCLUSION: The majority of participants found the study useful and, regardless of their group assignment or the intervention's impact on their fatigue, found the study helped them to gain better control of their dietary habits.

Humans

Clinical applications of digital twin technology in In Vitro Fertilisation.

BACKGROUND: Digital twin technology, originating from aerospace and manufacturing industries, has emerged as a transformative tool in healthcare. In vitro fertilisation (IVF) faces persistent challenges including suboptimal embryo selection, unpredictable treatment outcomes, and limited personalisation of protocols. Despite advances in assisted reproductive technology, existing literature exhibits fragmentation: artificial intelligence applications in embryo selection, ovarian stimulation, and endometrial assessment have been developed independently without systematic integration into comprehensive treatment frameworks. Digital twin technology offers unprecedented opportunities to create virtual replicas of biological systems, enabling real-time monitoring, predictive modelling, and personalised treatment strategies. AIM: This narrative review aims to critically examine the current applications of digital twin technology in IVF, evaluate its potential benefits and limitations, synthesize existing evidence into an integrative conceptual model, and identify future directions for implementation in reproductive medicine. METHOD: A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases. A narrative review approach was selected over systematic review to accommodate the heterogeneity of evidence types in this emerging field, including theoretical frameworks, simulation studies, and proof-of-concept implementations that would be excluded from systematic reviews. Search terms included "digital twin," "IVF," "in vitro fertilisation," "assisted reproductive technology," "embryo selection," and "predictive modelling." Studies published between 2015 and 2025 were included, focusing on original research articles, systematic reviews, and proof-of-concept studies describing digital twin applications in reproductive medicine. RESULTS: Digital twin technology in IVF demonstrates significant potential across multiple domains including embryo development simulation, ovarian response prediction, endometrial receptivity modelling, and personalised stimulation protocols. Current applications integrate artificial intelligence, machine learning algorithms, time-lapse imaging, and omics data to create comprehensive virtual models. Early evidence suggests improvements in embryo selection accuracy, ovarian response prediction, and treatment protocol optimization, though large-scale randomized controlled trials remain limited. Implementation challenges include data integration complexity, computational requirements, regulatory considerations, and validation requirements. CONCLUSION: Digital twin technology represents a paradigm shift in IVF practice, offering personalised, predictive, and precision medicine approaches. This review synthesizes existing evidence to propose an integrative conceptual model for digital twin implementation across the IVF treatment spectrum, identifies critical knowledge gaps, and establishes research priorities to advance clinical translation. Despite current limitations, continued advancement promises improved success rates and patient outcomes.

Humans

Interventions to Reduce Loneliness in Informal Caregivers of People With Dementia: A Systematic Review.

AIM: To summarize the current evidence on reducing loneliness among informal caregivers of people with dementia, such as family members or friends. DESIGN: A systematic review. METHODS: The methodological quality was evaluated using the revised Cochrane risk-of-bias tool for randomized controlled trials and the revised JBI critical appraisal checklist for quasi-experimental studies. Data were extracted as predefined and synthesized narratively. The Template for Intervention Description and Replication checklist was used to report the intervention characteristics. DATA SOURCES: Six electronic databases (MEDLINE via PubMed, EMBASE, Cochrane Library, PsycINFO, CINAHL Plus, and Web of Science Core Collection) were searched for studies published in peer-reviewed English journals from the inception of each database until 28 January 2024. RESULTS: Eight studies were included in this review, published between 2002 and 2023, with three being randomized controlled trials. All included interventions were psychosocial. Only one study reported significant improvements in loneliness. Five studies utilized remote and online interventions, such as social networking, psychotherapy, and online social support. Interventions varied in their impact on secondary outcomes, including stress, depressive symptoms, anxiety, and caregiver burden. Four studies demonstrated a positive effect on caregiver stress levels. One pilot trial reported a positive impact on depressive symptoms, and another study noted potential improvements in anxiety. One pilot study reported an average improvement in caregiver burden. CONCLUSION: While the evidence is insufficient for conclusive statements, this systematic review suggests potential benefits of interventions to reduce loneliness and improve mental health among these caregivers. It highlights the promise of remote interventions in addressing loneliness among dementia caregivers. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: The findings suggest that tailored interventions, especially those delivered remotely, can enhance the support provided to caregivers, potentially improving their mental health and overall well-being. REPORTING METHOD: This systematic review adhered to the PRISMA statement. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Humans

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models

Applications of metal-organic frameworks in smart packaging for food freshness indication: a comprehensive review.

Smart packaging is extensively studied for its multifunctional capabilities in antimicrobial activity, preservation, and atmosphere modification. Recently emerged metal-organic frameworks (MOFs) freshness-indicating packaging becomes a key research direction in smart packaging owing to its distinctive functions and physicochemical properties. As multifunctional materials, the unique porous structure and tunable properties of MOFs provide a distinctive approach for developing food packaging applications dedicated to food freshness indication. Existing MOFs-based smart packaging still faces potential safety risks and technical challenges in practical applications, and there remains a lack of integrated discussion that combines synthesis strategies, packaging design, optimization, and safety assessment. This review elaborates on the application of MOFs in freshness-indicating smart packaging, focusing on diverse MOFs synthesis strategies, the formats of smart packaging, types of indicator signals, and qualitative/quantitative analytical methods. It also delves into the methodology concepts of MOFs-based smart packaging and evaluates MOFs safety in food packaging by addressing potential risks. Studies show that MOFs-based smart packaging achieves qualitative and semi-quantitative analysis of food freshness through multiple signal modalities such as visible color change, fluorescence, and photothermal effects. This review emphasizes that safe MOFs design is critically important and should comply with the overall migration limit of <10 mg/dm2 specified in Regulation (EC) No 1935/2004, lanthanide element limit of <0.05 mg/kg, and FDA threshold of 1.5 &#x3bc;g/person/day. Comprehensive safety assessment and intelligent sensing platforms will constitute pivotal directions for advancing MOFs-based smart packaging toward practical application.

Food Packaging

Effectiveness of Internet-Delivered Cognitive Behavioural Therapy (ICBT) in Improving Weight-Loss and Psychosocial Well-Being Among Adults With High BMI: A Systematic Review.

AIM: To examine the effectiveness of Internet-delivered Cognitive Behaviour Therapy (ICBT) in improving psychosocial well-being and promoting weight-loss in adults &#x2265;&#x2009;18&#x2009;years with BMI &#x2265;&#x2009;25&#x2009;kg/m2. BACKGROUND: Global obesity engenders significant physical and psychosocial health consequences. Second-wave ICBT focused on restructuring negative thoughts and behaviours has been explored as a potential intervention for elevated BMI and mental health concerns, but its effectiveness remains to be fully established, making further evaluation essential. METHODS AND DATA SOURCES: Eight databases were searched from inception to January 2025 for randomised controlled trials (RCTs), including participants &#x2265;&#x2009;18&#x2009;years with BMI &#x2265;&#x2009;25&#x2009;kg/m2 and second-wave ICBT evaluating BMI, weight, depression, eating behaviours, and self-esteem. This review followed PRISMA 2020. Study quality was assessed using the Cochrane Risk of Bias (ROB 2) and GRADE. Data were extracted using a modified Cochrane form. Random-effects meta-analysis calculated Standardised Mean Differences (SMD) with 95% confidence intervals, with subgroup analyses exploring heterogeneity. RESULTS: Nine trials with 2278 participants were included. Significant improvements were seen in BMI, weight, and depressive symptoms while self-esteem effects were small and non-significant. Compared with passive controls, ICBT showed greater improvements in BMI and weight, whereas differences versus active control were smaller and inconsistent. Face-to-face CBT demonstrated superior outcomes for depression and self-esteem. Male-tailored interventions showed greater improvements. Shorter programmes yielded larger short-term weight loss, while longer programmes supported more sustained effects. Narrative synthesis indicated improvements in emotional and external eating, with increased mindful and restrictive eating behaviours. CONCLUSION: ICBT improved weight, BMI, and depressive symptoms, with limited evidence for self-esteem. Male-tailored interventions and longer programmes may enhance sustainable outcomes. IMPACT: Future ICBT programs should integrate strategies targeting sustainable weight loss and psychosocial well-being to support long-term outcomes. NO PATIENT OR PUBLIC CONTRIBUTION: Patients or members of the public were not involved, as this study synthesised previously published data. TRIAL REGISTRATION: PROSPERO registration number: CRD42024497961.

Humans

Molecular mechanisms of natural de novo shoot organogenesis and their applications.

Natural de novo shoot organogenesis (DNSO) is the spontaneous regeneration of shoots from wound sites outside the shoot apical region through endogenous developmental programs. This regenerative capacity enables plants to recover from severe tissue damage by re-establishing the shoot-root axis. Here, we review current knowledge about the molecular mechanisms of natural DNSO, focusing on transcriptomic and physiological studies in model plants. Accumulating evidence suggests that natural DNSO proceeds through three sequential phases: (i) early wound responses, characterized by the activation of the WIND1-ESR1 module and the establishment of apical-basal auxin asymmetry; (ii) cellular proliferation driven by metabolic and cell-cycle reprogramming; and (iii) cytokinin-mediated establishment of shoot apical meristem identity. We also discuss how these mechanistic insights have been harnessed for practical applications, including tissue culture-free transformation systems such as the cut-dip-budding (CDB) method, and developmental reprogramming strategies that employ ectopic expression of developmental regulator (DR) genes to induce DNSO in otherwise recalcitrant species. Together, these advances illustrate how understanding natural regeneration can guide the development of simplified, broadly applicable plant transformation technologies.

Plant Shoots

Examining Behavioral Interventions for Infancy and Early Toddlerhood: A Systematic Review of Intervention Effects, Parameters, and Participants.

Rapid advancement is paving the way to identify children who would likely benefit from early intervention during the first years of life, prior to the onset of significant delays in development. With the widely acknowledged benefits of early intervention, key questions arise: Does behavioral intervention targeted to infancy and early toddlerhood improve developmental outcomes? What procedures might be used, and under what circumstances? Who do these interventions work for? The current review comprehensively examined the literature on behavioral interventions based in operant learning, focused on key developmental areas with children in the first two years of life. We located and synthesized 69 studies with unique participant cohorts that included 1735 children. The search revealed many studies focused on the first year of life, of which a large proportion investigated approaches to increase communication. We provide implications, limitations, and future directions on how behavioral interventions for infants and young toddlers can inform current practice and future intervention research this population.

Humans

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans

Feasibility of implementation, diagnostic accuracy, and end-user impact of an electronic health record (EHR)-based ureteral stent tracking tool in a pediatric population.

INTRODUCTION & OBJECTIVES: Ureteral stent tracking systems have reduced stent retention in adults, but their accuracy and impact in pediatrics have been minimally explored. With low event rates in children, such tools may yield high false positives, raising questions on balancing event prevention with provider burden. We aimed to evaluate the feasibility, diagnostic accuracy, and end-user impact of an Electronic Surveillance Tool for Evaluating Nephroureteral stent Tracking (eSTENT) at our institution. STUDY DESIGN: eSTENT, implemented in 1/2024, flags ureteral stents at risk for retention based on implant documentation, expected explant date, and explant documentation. Monthly reports are generated for stents missing explant documentation. We retrospectively evaluated the diagnostic performance of eSTENT from 1/2024-8/2025 at our pediatric hospital. A usability survey including a validated 1-7 implementation score (higher = easier implementation) was distributed to pediatric urologists and operating room nurses. RESULTS: Of 172 cases with ureteral stent placement, eSTENT flagged 28 events (16%) in 24 patients. Of these, 26 represented documentation gaps where explant had been appropriate. Two flags had no documentation of explant, representing near miss events that were identified. No retained stents occurred, consistent with high sensitivity and modest specificity. There were no flags in the last 6 months of the study period. Survey response rate was 100% for surgeons and 55% for nurses. Before eSTENT, stents were not routinely tracked. All surgeons and 93% of nurses reported no added burden, despite occasional misidentification of retained stents. Three surgeons found eSTENT beneficial, four were neutral, and free-text responses generally cited eSTENT's "fail safe" nature as positive. Nurses suggested improvements, including user support and integrated documentation reminders. The average implementation score among both groups was 6/7, indicating easy adoption. DISCUSSION: While the impact of stent tracking tools in adult literature has been positive, our study emphasizes the feasibility of broader adoption at a pediatric hospital. Integration of eSTENT may avoid the potentially devastating consequences of a retained stent. Prioritizing sensitivity over specificity appears acceptable for a "never event" in patient safety. Our study is limited by the retrospective nature of data collection and survey bias. CONCLUSIONS: Though no stents were retained in the study period, eSTENT appropriately flagged two cases without added burden to most end-users. Further optimization is warranted, but adoption in pediatric centers may enhance care reliability.

Humans

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

A narrative review of what cohorts have taught us and how they have laid the foundation for much of our understanding of type 2 diabetes.

This narrative review provides a historical perspective on how observational research on type 2 diabetes has been developed and consolidated over the last 50 years and how well-designed cohort studies will provide us with knowledge for research and practice in the future and aid guideline development. We have included data from a large number of cohorts from every continent that have been used to study the development and/or progression of type 2 diabetes, including cohorts that are general population-based, disease-based, intervention-based and registry-based. We have structured the results from the past 50 years based on the following themes: diagnosis and screening, complications, risk factors and pathophysiology. We also discuss the strengths and weaknesses of observational research when compared with other research designs. Finally, we discuss the emerging and future directions for type 2 diabetes research using cohorts, which include novel developments, such as artificial intelligence, precision health and the exposome. We conclude that cohort research has significantly advanced our understanding of type 2 diabetes and aided guideline development, and complements experimental work, such as human randomised controlled trials and animal studies. Both approaches are essential and complementary in our pursuit to provide a more comprehensive understanding of the development and progression of type 2 diabetes, and to change dogma, practice and policies for better outcomes.

Humans

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

Cognitive behavioural therapy-based interventions on stress outcomes in pregnant women: A systematic review and meta-analysis.

BACKGROUND: Stress symptoms were the most common psychological problem in pregnancy. Cognitive behavioural therapy-based interventions are effective for antenatal depression and anxiety symptoms; but there are fewer studies for stress symptoms. OBJECTIVE: The review aims to (1) examine the effectiveness of cognitive behavioural therapy-based interventions in reducing stress outcomes (pregnancy-specific stress symptoms, generic symptoms, and objective stress) in pregnant women, and (2) identify significant moderators affecting the effectiveness of the intervention. DESIGN: Systematic review, meta-analysis, and meta-regression analysis of randomised controlled trials. METHODS: We conducted a three-step search (12 databases, 4 clinical registries, and citation searches) in English and Chinese up to July 24, 2025, by two independent reviewers. Meta-analysis, subgroup, and meta-regression analyses were performed using the R software. Quality assessment and certainty of the evidence were assessed with the Cochrane risk-of-bias tool version 2 and Grading of Recommendations, Assessment, Development, and Evaluation criteria. Publication bias was assessed using funnel plots and Egger's test. RESULTS: We included 20 randomised controlled trials involving a total of 6966 pregnant women from nine countries. Random-effects meta-analyses found that interventions significantly alleviated pregnancy-specific stress symptoms (Hedges' g&#xa0;=&#xa0;-0.84, 95% Confidence Interval, CI -1.42, -0.26, p&#xa0;<&#xa0;.01, I2&#xa0;=&#xa0;92.3%), reduced generic stress symptoms (g&#xa0;=&#xa0;-0.64, 95% CI -1.09, -0.20, p&#xa0;<&#xa0;.01, I2&#xa0;=&#xa0;87.4%) with median and large effect sizes at post-intervention. No effect was found in lowering cortisol levels (g&#xa0;=&#xa0;-0.99, 95% CI -2.58, -0.60, p&#xa0;=&#xa0;.12, I2&#xa0;=&#xa0;84%) at post-intervention. Subgroup and meta-regression analyses indicated that region, age of participants, use of intention-to-treat, missing data management analyses, frequency, modalities, and approaches of interventions, use of different comparators, and attrition rate were significant factors affecting the effectiveness of interventions. Subgroup analyses suggested that the intensity of intervention should be more than once per week using a blended mode among Asian populations. Multivariate meta-regression analyses indicated that both younger age (&#x3b2;&#xa0;=&#xa0;0.13, p&#xa0;=&#xa0;.02) and a lower attrition rate (&#x3b2;&#xa0;=&#xa0;0.03, p&#xa0;=&#xa0;.03) significantly improved the effectiveness of interventions. The overall certainty of the evidence was rated as either very low or low. CONCLUSIONS: Cognitive behavioural therapy-based interventions can supplement antenatal care to alleviate pregnancy-specific stress symptoms and generic stress symptoms, particularly in young Asian women. However, the evidence has some uncertainties. These findings should be interpreted with caution due to substantial heterogeneity. Well-designed trials on a large-scale with long-term follow-ups were needed. REGISTRATION: PROSPERO registration ID: CRD420251115913.

Humans

Somatic mutations: recent advances in brain aging and neurodegeneration.

Somatic mutations are genetic variants that occur after the single-cell phase of development and have been implicated in disease pathogenesis. While most DNA lesions are detected and repaired, examination of healthy tissue has revealed that some lesions escape repair, leading to somatic mutations that accumulate at a consistent rate, including in human brain tissue and postmitotic neurons. Emerging methodological and analytical advances have revealed the presence of persistent mutagenic mechanisms during healthy brain aging as well as mutational pattern shifts in the context of neurodegenerative diseases. Here, we highlight recent methodological advances, summarize our current understanding of somatic mutagenesis in neurotypical brain aging, and examine the role of somatic mutations in neurodegenerative diseases.

Humans

Advanced/Novel Stenting for Pediatric Dynamic Airway Collapse.

Pediatric dynamic airway collapse is a complex condition that can impact all levels of the pediatric airway. These conditions can pose life threatening risk to pediatric patients and carry lasting impacts. While traditionally, tracheostomy has been used to address all levels of dynamic collapse, recent advances have allowed for more individualized, anatomy-specific stenting and splinting strategies for treatment. This article covers pathophysiology and the latest evidence on strategies to address nasopharyngeal, oropharyngeal, proximal trachea, and tracheobronchial dynamic collapse.

Humans