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PubMed2026

Boosting kynurenic acid in kombucha via substrate selection: metagenomic and biochemical insights.

Kombucha is gaining global popularity for its health benefits. This study explored the use of chestnut honey, a rich source of kynurenic acid (KYNA), to produce kombucha enriched with this metabolite. Five variants were prepared using different green/black tea blends and carbon sources: white sugar or acacia honey (controls) versus chestnut honey. Samples were analyzed for tryptophan metabolites, physicochemical properties, and microbial diversity. Komagataeibacter and Enterobacter were predominant bacterial genera in SCOBY. Candida and Aspergillus were predominated in the single sample analyzed for fungi. During fermentation, tryptophan decreased, while kynurenine increased. KYNA levels remained largely stable during fermentation and were mainly influenced by the fermentation substrate. No melatonin pathway derivatives were detected. On day 7, chestnut honey yielded kombucha with 381.680-739.915 μmol/L KYNA and elevated myricetin. Overall, chestnut honey-based kombucha represents a system in which substrate composition appears to be the main factor influencing KYNA levels in the final beverage.

Kynurenic Acid↗

PubMed2026

Artificial Intelligence Technologies in Nursing Clinical Decision-Making: An Umbrella Review.

AIM: To describe contemporary peer-reviewed literature on artificial intelligence in nurses' clinical decision-making. METHODS: An umbrella review of literature reviews. DATA SOURCES: Four major databases were searched for reviews published between 2019 and 2024. RESULTS: Sixteen literature reviews reported on 965 nursing artificial intelligence primary studies. The studies focused on technology development and emerging performance evaluations, whilst real-world testing or implementation in nursing clinical settings was rare. Rigorous comparative analyses were lacking. While artificial intelligence demonstrates promise in decision-making, challenges such as a lack of controlled studies, algorithmic bias, limited reproducibility and insufficient clinical trials hinder its practical impact. Ethical concerns, transparency and patient data privacy issues pose barriers to AI integration in nursing practice. Ethical and legal guidelines for patient privacy are needed and should be taught along with AI literacy training for nurses. CONCLUSIONS: Artificial intelligence has the potential to enhance clinical nursing decision-making, although evidence is limited by too few examples of nurse participation during development. Underutilisation in administrative nursing functions hinders implementation. Nurses should assume a central role in the design and development of AI applications to ensure that these technologies address the realities of nursing practice. With such improvements, artificial intelligence can transform nursing practice, improve nurses' clinical decision-making and ultimately enhance consumer healthcare outcomes. PATIENT OR PUBLIC INVOLVEMENT: No Patient or Public Involvement. REPORTING METHOD: While there is no reporting checklist for umbrella reviews, the PRISMA guide for systematic reviews was followed.

Artificial Intelligence↗

PubMed2026

Vertical distribution of accessory canals in different tooth types: A systematic review and meta-analysis.

OBJECTIVE: To systematically analyze the vertical distribution of accessory canals and propose potential root-end resection levels in different tooth types. DATA: Proportional distribution of accessory canals (PD-AC) in 1 mm intervals, cumulative proportions within 2 mm and 3 mm (CP-AC0-2 and CP-AC0-3), mean distance from accessory foramen to root apex or main foramen (MD-AF), and prevalence of accessory canals in 2D cross-sections (PR-AC-2D). SOURCES: A systematic search of electronic databases was conducted through December 25, 2025. The review was registered in PROSPERO (CRD420251107855). STUDY SELECTION: Two reviewers independently performed study selection, data extraction, and risk of bias assessment using the AQUA tool. Nineteen studies were included for qualitative synthesis, of which eleven provided sufficient data for meta-analysis. A random-effects model was used, and subgroup analyses were stratified by tooth type, accessory canal type, and country. Within 0-1 mm, 1-2 mm, 2-3 mm, and 3-4 mm from the apex, 41.5%, 34.3%, 9.9%, and 4.4% of accessory canals were located, respectively. Molars had significantly higher proportions than anterior teeth both within 2 mm (90.0% vs. 72.4%) and 3 mm (96.9% vs. 87.5%). Of apical ramifications, 86.4% were within 2 mm. The pooled MD-AF was 1.472 mm. PR-AC-2D decreased from 29.3% at 1 mm to 1.3% at 5 mm. All studies presented moderate to high risk of bias. CONCLUSIONS: A 2 mm root-end resection level may be sufficient for molars, whereas anterior teeth may require a higher level. Further randomized controlled trials are needed. CLINICAL SIGNIFICANCE: A 2 mm resection may adequately expose or remove most accessory canals in molars, potentially preserving more root length while maintaining treatment efficacy. In anterior teeth, a traditional 3 mm resection remains advisable until further evidence becomes available.

Humans↗

PubMed2026

One Year After a Cyberattack: Lessons Learned and Dosimetric Analysis of Contingency Radiotherapy Plans.

PURPOSE: Cyberattacks on health care institutions pose significant risks to patient care, particularly in radiotherapy departments, which are heavily reliant on digital systems. This study examines the impact of a ransomware attack on our hospital and evaluates the effectiveness of the contingency measures implemented to resume radiotherapy treatments. METHODS AND MATERIALS: Following the cyberattack, our radiotherapy department faced a complete shutdown. After an initial estimate considering a shutdown of several weeks, a contingency plan was executed, including manual patient data retrieval and collaboration with a backup hospital. Contingency plans were prepared and delivered within hours, despite a partial lack of information. These plans allowed some patients to restart treatment 3 days after the attack. A dosimetric analysis was performed for the contingency plans, including various pathologies, mainly glioblastoma, head and neck cancers, and lung cancer. We compared the original and contingency plans in terms of dose coverage to the clinical target volume, biological effective dose, and their clinical impact as assessed at the 1‑year follow‑up after the cyberattack. RESULTS: Treatments resumed within 12 days at our hospital. Patients with glioblastoma showed good target coverage because of generous margins, resulting in favorable outcomes. In head and neck cases, the lack of detailed imaging led to significant target volume misses, suggesting that more conservative initial treatments could have been beneficial. Lung cases demonstrated accurate peripheral lesion targeting but faced challenges in central lesions because of the absence of positron emission tomography information. In most cases, the approach of using a contingency plan, even with limited information, led to a higher biological effective dose than would have been achieved if treatment had been stopped until full recovery at our hospital. CONCLUSIONS: The study highlights the critical importance of robust contingency planning in radiotherapy departments, emphasizing the need for backup systems and tailored approaches based on tumor location and available diagnostic information. These lessons emphasize that preparedness for digital disruptions should not focus exclusively on information and technology infrastructure.

Humans↗

PubMed2026

Protein isolation markedly enhances in vitro digestibility, nutritional quality, and bioactivity of fungal mycelial proteins.

Fungal mycelial proteins are promising sustainable protein sources, yet their nutritional utilization is often limited by structural constraints. This study systematically evaluated the effects of protein isolation on the proteomic composition, gastrointestinal digestion behavior, amino acid utilization, and bioactivity of Pleurotus citrinopileatus mycelial proteins. Quantitative proteomics identified 3591 proteins, of which 3374 were shared between mycelial flour (PCMF) and protein isolate (PCMPI), indicating that PCMPI primarily represents the soluble proteome fraction. In vitro digestion revealed that PCMPI exhibited significantly higher digestibility (93.98%) than PCMF (42.98%) (p < 0.05), reaching levels comparable to whey protein isolate. Enhanced enzymatic accessibility in PCMPI promoted rapid peptide generation during the gastric phase and efficient amino acid release during the intestinal phase, resulting in higher peptide (634.76 mg/g) and free amino acid levels (341.69 mg/g) at the digestion endpoint. Consequently, PCMPI achieved a balanced amino acid profile with a PDCAAS of 1.0. Moreover, its digestion products exhibited stronger antioxidant activity (IC₅₀ = 8.36 mg/mL) and ACE inhibitory activity (IC₅₀ = 15.65 mg/mL) compared with PCMF. Mechanistically, protein isolation disrupted the cell wall matrix, shifting digestion from a structure-limited to an accessibility-driven regime. Collectively, these findings demonstrate that protein isolation markedly enhances the digestibility, nutritional quality, and functional potential of mycelial proteins, supporting their application as high-value sustainable protein ingredients.

Digestion↗

PubMed2026

A Critical Assessment of Evidence-Based Design's Knowledge Base and Inspiration: A Systematic Review.

PurposeThis study examines Evidence-Based Design (EBD) as an epistemological framework for guiding design research and practice, with a particular focus on its reliance on Evidence-Based Medicine (EBM) as a source of methodological inspiration.BackgroundOver the past two decades, EBD has been promoted as a way to strengthen design processes through the systematic use of scientific evidence. Its relationship to EBM, however, remains conceptually ambiguous: EBD draws legitimacy from EBM's hierarchical conception of "best evidence" while at the same time acknowledging the specificities of design practice, which do not easily fit such a model.MethodologyA systematic review was conducted on 31 publications in the design research literature that explicitly address the tension surrounding EBD's conception of "best evidence." The criticisms raised were coded and analyzed by main topics and subtopics.ResultsThe review highlights several reasons why EBM's hierarchical view of "best evidence" is an unsuitable epistemological foundation for EBD. It imposes scientifically inappropriate and practically ineffective methodological standards, devalues important sources of design knowledge, and fails to address central epistemic challenges intrinsic to design processes.ConclusionsBy bringing together critical yet fragmented insights from the literature, this study argues for the development of an updated epistemological framework for EBD. Constructing this framework will require sustained interdisciplinary dialogue between design research and philosophy of science.

Humans↗

PubMed2025

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↗

PubMed2026

Applications of quantum AI in brain disorder diagnosis: A systematic review.

BACKGROUND AND OBJECTIVE: Brain disorder diagnosis and prediction remain challenging because neuroimaging, electrophysiological, behavioral, and multimodal data are high-dimensional, noisy, heterogeneous, and limited by small clinical cohorts. This systematic review synthesised applications of quantum artificial intelligence (QAI) for brain disorder diagnosis, prediction, detection, and monitoring. METHODS: Following PRISMA guidelines, studies published from 2016 to 13 January 2026 were retrieved from Scopus, Web of Science, and IEEE Xplore. After screening, 36 studies met the eligibility criteria and were qualitatively analysed according to disorder category, data modality, QAI method, implementation setting, validation strategy, and performance. RESULTS: At the broader disease-group level, neurodegenerative disorders were the most frequently investigated, followed by mental health and psychiatric disorders. At the individual level, Parkinson's disease and schizophrenia were the leading applications, followed by depression, anxiety, Alzheimer's disease, and stress-related tasks. MRI-based modalities were the most frequently used data source, followed by multimodal data and EEG. Methodologically, primary QAI approaches were dominated by quantum neural and QDL architectures, followed by quantum-inspired optimization or feature-selection methods and quantum-kernel/conventional QML classifiers. Qiskit/IBM Quantum and PennyLane were the most frequently reported quantum software frameworks. However, most studies relied on simulators, classical quantum-inspired implementations, or unclear implementation settings, with limited real-hardware evaluation. CONCLUSIONS: QAI shows emerging potential for brain disorder analysis, particularly through hybrid quantum-classical learning, quantum neural architectures, quantum-kernel methods, and quantum-inspired optimization. Nevertheless, current evidence remains preliminary and requires larger datasets, subject-level and external validation, fair classical benchmarking, noise-resilient circuits, real quantum hardware evaluation, explainability, and clinical validation.

Humans↗

PubMed2026

Performance of AI-Based Screening Tools for Obstructive Sleep Apnea Across Apnea-Hypopnea Index Thresholds: Systematic Review and Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent but remains substantially underdiagnosed. Polysomnography (PSG) is the reference standard, but its cost and limited availability constrain large-scale case identification. AI-based screening tools may support risk stratification and referral prioritization, but their diagnostic accuracy across apnea-hypopnea index (AHI) thresholds remains uncertain. OBJECTIVE: This review aimed to systematically evaluate the diagnostic accuracy of AI-based OSA screening tools at AHI thresholds of ≥5, ≥15, and ≥30 events/hour, with emphasis on models using non-PSG-derived inputs. METHODS: PubMed, Embase, Scopus, and Web of Science were searched for studies published from January 1, 2016, to May 3, 2026. Eligible studies included adults evaluated for suspected OSA or recruited from population-based cohorts, assessed AI-based models intended or interpretable for OSA screening, risk prediction, or screening-oriented severity classification, used PSG as the reference standard, and reported sufficient data to construct or reconstruct 2×2 contingency tables. Diagnostic accuracy was synthesized separately by AHI threshold and input source using bivariate random-effects models, with 95% CIs and prediction intervals (PIs). Risk of bias and certainty of evidence were assessed using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) and GRADE (Grading of Recommendations Assessment, Development, and Evaluation), respectively. RESULTS: A total of 60 studies were included, of which 47 contributed data to the meta-analysis. At AHI thresholds of ≥5, ≥15, and ≥30 events/hour, pooled sensitivities were 0.94 (95% CI 0.92-0.96; 95% PI 0.71-0.99), 0.87 (95% CI 0.84-0.89; 95% PI 0.66-0.96), and 0.83 (95% CI 0.79-0.87; 95% PI 0.61-0.94), respectively; the corresponding specificities were 0.77 (95% CI 0.69-0.84; 95% PI 0.30-0.96), 0.81 (95% CI 0.75-0.85; 95% PI 0.39-0.96), and 0.91 (95% CI 0.87-0.94; 95% PI 0.55-0.99), respectively. The corresponding areas under the summary receiver operating characteristic curves were 0.943, 0.907, and 0.920. For non-PSG-derived tools, sensitivities were 0.92, 0.85, and 0.81, and specificities were 0.70, 0.74, and 0.85 at the 3 thresholds, respectively. For PSG-derived models, sensitivities were 0.96, 0.90, and 0.85, and specificities were 0.82, 0.88, and 0.96, respectively. Exploratory subgroup analyses suggested performance variation across selected study and model characteristics, including region, algorithmic framework, data source, and validation method. CONCLUSIONS: AI-based tools showed generally favorable screening performance for OSA across clinically relevant AHI thresholds, although wide PIs suggest variable performance across future comparable populations and settings. By synthesizing diagnostic accuracy across 3 AHI thresholds and distinguishing non-PSG-derived from PSG-derived models, this review extends previous broad or modality-specific reviews and offers a clinically interpretable, pathway-specific basis for linking model performance to intended use. The findings may clarify potential roles for non-PSG-derived tools in front-end screening and referral prioritization and for PSG-derived models in reduced-channel assessment and sleep-laboratory workflow support. Given substantial heterogeneity, limited external validation, and low or very low certainty of evidence, prospective validation is needed before routine implementation.

Humans↗

PubMed2026

Engineering copper ferrite (CuFe2O4) nanocomposites for enhanced eco-friendly photocatalysis: a systematic critical review on mechanisms, performance, and environmental applications.

Water pollution caused by organic and inorganic contaminants, particularly dyes and pharmaceuticals, represents a major environmental challenge. Advanced oxidation processes based on photocatalysts have emerged as efficient and sustainable approaches for water and wastewater treatment. Copper ferrite (CuFe2O4) is considered a promising photocatalyst owing to its narrow bandgap, visible-light activity, chemical stability, and magnetic properties. Despite extensive experimental investigations, a comprehensive systematic comparison of CuFe2O4-based photocatalysts under diverse operational conditions has remained limited. In this study, a systematic review following PRISMA guidelines was conducted using studies published between January 2014 and November 2025 indexed in Scopus, PubMed, Web of Science, and ScienceDirect. From an initial pool of 397 studies, 98 articles met the inclusion criteria. Key parameters - including pollutant type, pH, catalyst dosage, initial pollutant concentration, irradiation time, light source, and degradation efficiency - were quantitatively compared to identify performance trends and operational optima. The results demonstrate that CuFe2O4-based nanocomposites, particularly heterojunction, Z-scheme, and S-scheme architectures combined with TiO2, g-C3N4, graphene, and metal oxides, achieve high degradation efficiencies (often >90 %) for a wide range of organic pollutants and selected inorganic contaminants (e.g., Cr(VI)). Enhanced charge separation and suppressed electron-hole recombination were identified as the primary factors contributing to improved photocatalytic activity. In addition, the intrinsic magnetic properties of these nanocomposites enable facile catalyst recovery and reuse. In conclusion, CuFe2O4-based nanocomposites, especially those incorporating advanced heterojunction architectures, emerge as highly efficient and magnetically recoverable photocatalytic platforms for sustainable water and wastewater treatment, with strong potential for scalable implementation and real-wastewater applications.

Catalysis↗

PubMed2026

Global Seroprevalence of Q Fever Antibodies to Coxiella burnetii in Children and Adolescents : A Systematic Review and Meta-analysis.

OBJECTIVE: To comprehensively determine global estimates of Q fever seroprevalence in children and adolescents by conducting a systematic review and meta-analysis. DATA SOURCES: Searches of published articles in MEDLINE, Embase and Scopus databases were conducted from inception until February 2025. STUDY SELECTION: Cross-sectional studies reporting seroprevalence of Q fever/ Coxiella burnetii antibodies, using any established laboratory test, in any population of healthy children and adolescents <20 years old were included. The quality of eligible articles was assessed using a modified Newcastle-Ottawa Scale. DATA EXTRACTION: Data from eligible articles were extracted using a standardized form, which included year of publication, year(s) the study was conducted, numbers of antibody-positive cases/specific population, age, country, geographic region, serology test used and antibody titer cutoff value. DATA SYNTHESIS: DerSimonian and Laird random effects models were used to calculate pooled seroprevalence estimates and 95% confidence intervals in data from 41 eligible articles reporting 42 studies comprising 9841 children and adolescents. Q fever seroprevalence was observed in multiple countries across 7 geographic regions, and varied markedly between countries and regions, with the highest estimate observed by an individual country in Ethiopia (45%) and by region in the Middle East (14%). Seroprevalence estimates were higher in older children and adolescents ≥10 years (15%) compared with younger children <10 years of age (8%). CONCLUSION: Despite varying geographical prevalence, our findings demonstrate that widespread exposure to Q fever antigens occurs across multiple global regions in children and adolescents to potentially serious C. burnetii infection, indicating that diagnostic surveillance and preventive measures should be considered in both endemic and previously unreported areas.

Humans↗

PubMed2026

Acceptability of capillary point-of-care testing: a systematic review.

OBJECTIVE: To identify and synthesise evidence on the acceptability and perceived experience of finger-prick point-of-care testing (POCT) among patients and clinicians across healthcare settings. DESIGN: Systematic review conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. DATA SOURCES: Medline, Embase, PsycInfo, CINAHL, Cochrane and Web of Science were searched from inception to January 2024 and re-run in July 2025, supplemented by citation tracking of relevant studies. ELIGIBILITY CRITERIA: Studies reporting patient and clinicians' experiences, perceptions, satisfaction or acceptability relating to finger-prick POCT for any health condition or blood parameter were eligible. Quantitative, qualitative and mixed-methods designs were included. DATA EXTRACTION AND SYNTHESIS: Data were extracted independently by two reviewers and synthesised using thematic analysis and narrative synthesis. Methodological quality was appraised using the Mixed-Methods Appraisal Tool. RESULTS: 21 studies met the inclusion criteria, encompassing 9128 participants (17 quantitative, 3 qualitative, 1 mixed methods). Across diverse clinical contexts, finger-prick POCT was reported as generally acceptable, less distressing and perceived as a convenient alternative to venous sampling in comparative studies. Thematic synthesis identified two major themes: (1) enhancing the patient-clinician relationship through improved engagement, communication and understanding of care and (2) clinical implications of finger-prick POCT on clinicians' workflow, confidence and skill acquisition. Finger-prick POCT was perceived to promote personalised consultations, enable immediate discussion of results and streamline decision-making. Clinicians highlighted its potential to expand task sharing, improve efficiency and strengthen continuity of care, although concerns regarding training, reliability and quality assurance were identified. CONCLUSIONS: Finger-prick POCT is generally acceptable to patients and clinicians, improving comfort, convenience, engagement and perceived efficiency. Implementation should prioritise training, infrastructure and quality assurance frameworks to maximise clinical and experiential benefits. PROSPERO REGISTRATION NUMBER: CRD42024512130.

Humans↗

PubMed2026

Polygenic risk scores in major depressive disorder: A systematic review across diagnostic, treatment, course/severity, and subtype domains.

BACKGROUND: Major depressive disorder (MDD) is heterogeneous across diagnostic, treatment-related, course/severity, and subtype domains. Polygenic risk score (PRS) studies have examined these domains, but differences in PRS sources, samples, methods, and endpoint definitions have fragmented the evidence. We synthesised findings and examined potential contributors to heterogeneity. METHODS: PubMed/MEDLINE, Embase, PsycINFO, and Web of Science were searched for studies published from January 2016 through 25 November 2025. Result records were synthesised using SWiM, and certainty was assessed with an adapted GRADE framework. RESULTS: Sixty studies contributed 493 retained records; 450 were descriptively classified as positive, null, or reverse, although records were not independent. Positive findings accounted for 44/56 diagnostic, 61/273 treatment-related, 64/100 course/severity, and 14/21 subtype records. For MDD/depression-derived PRSs and case-control MDD status, all 10 contributing studies showed higher liability in cases (exploratory exact sign test p = 0.002; FDR q = 0.004). The same PRS group showed positive findings for overall depressive symptom severity (14/18), although the study-level test was imprecise (5/5 studies; p = 0.063). Pharmacological response/remission findings for these PRSs were mostly null or directionally mixed (10 positive, 18 null, and 9 reverse). Treatment-resistant depression (TRD) findings differed by operational definition. Atypical and psychotic subtype signals arose mainly from single-study PRS and endpoint contrasts. CONCLUSIONS: PRS evidence was clearest for MDD diagnostic status and showed a tentative pattern for overall symptom burden. Treatment and subtype findings were less consistent or less replicated. Larger, ancestrally diverse studies with standardised endpoints and transparent PRS methods are needed.

Humans↗

PubMed2026

Effect of brewers' yeast or beta-glucan derived from Saccharomyces cerevisiae on breast milk supply following preterm birth: the BLOOM randomised controlled trial.

OBJECTIVE: Breast milk is the optimal source of nutrition for preterm infants; however, low breast milk production is common following a preterm birth. This study aimed to determine if taking brewers' yeast or beta-glucan improves daily expressed breast milk volume. DESIGN: Randomised, blinded, parallel, placebo-controlled trial. SETTING: Three Australian tertiary-level neonatal units. PATIENTS: Mothers with a singleton or twin pregnancy who gave birth at <34 weeks' gestation. INTERVENTIONS: Mothers were randomised within 72 hours of birth into three parallel groups in a 1:1:1 ratio to receive either brewers' yeast, beta-glucan or placebo capsules for 7 days. MAIN OUTCOME MEASURE: Total expressed breast milk volume over a 24-hour period on day 7 of intervention. RESULTS: A total of 105 mothers underwent randomisation between August 2022 and April 2024 (36 brewers' yeast, 35 beta-glucan and 34 placebo). The adjusted mean difference in daily expressed breast milk volume was 94 mL/day (95% CI -51 mL/day to 239 mL/day) between the brewers' yeast and placebo groups and -25 mL/day (95% CI -173 mL/day to 123 mL/day) between the beta-glucan and placebo groups. Maternal side effects were similar across groups. CONCLUSION: We found no clear effect of short-term administration of brewers' yeast or beta-glucan on breast-milk production following preterm birth; both interventions were well tolerated. Given the small sample size, these findings do not rule out the possibility of a clinically meaningful benefit of brewers' yeast and suggest further research with a larger sample size may be warranted to clarify the potential clinical impact. TRIAL REGISTRATION NUMBER: ACTRN12622000968774.

Intensive Care Units, Neonatal↗

PubMed2026

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 (≥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↗

PubMed2026

The application of artificial intelligence in healthcare practice: A mapping review of systematic reviews.

Artificial intelligence (AI) is rapidly transforming healthcare practice, with growing evidence supporting its use in diagnosis, prognosis, treatment planning, and operational decision-making. The proliferation of systematic reviews in recent years underscores the need for an updated synthesis of the literature to inform research, policy, and practice. We searched PubMed, Web of Science, Scopus, IEEE Xplore, and CINAHL for systematic reviews and meta-analyses published between 2019 and February 2026. Eligible reviews focused on AI applications in healthcare practice, were peer-reviewed, and written in English. A total of 368 reviews met the inclusion criteria. Publication volume increased steadily, peaking in 2025. AI research was concentrated in high-density domains, such as radiology, oncology, and critical care. Across reviews, diagnostic imaging, electronic health record (EHR) data, and biomarkers/laboratory results accounted for 68% of training data sources, though newer data types, such as wearable device and sensor data, emerged from 2022 onward. Diagnosis, prognosis, and treatment comprised over 80% of AI applications, with novel uses emerging in recent years, such as AI-assisted clinical documentation (e.g., ambient documentation tools) and patient education. Ethical concerns were reported in 78.5% of reviews, with privacy, model accuracy, data and algorithmic bias, and explainability as recurrent themes. The proportion of reviews reporting ethical concerns increased from 2021 to 2025. AI applications in healthcare are expanding in scope, diversifying in data sources, and evolving toward novel clinical and operational uses. The human-centered AI or augmented intelligence paradigm, integrating computational precision with clinical expertise, holds significant promise but will require parallel advances in governance, regulatory frameworks, and ethical oversight to ensure safe adoption.

Artificial Intelligence↗

PubMed2026

An overview of the use of proteomics and peptidomics to characterize alternative protein foods.

The global protein transition is accelerating the development of alternative protein foods, mainly derived from plants, insects, algae, fungi, and cellular agriculture. Ensuring the authenticity, safety, and nutritional adequacy of these emerging protein matrices requires molecular-level characterization beyond traditional compositional analyses. Proteomics and peptidomics have emerged as transformative analytical platforms capable of decoding the molecular signatures that define protein origin, structural integrity, digestibility, functionality, and health potential. The review comprehensively examines the application of proteomics, and peptidomics for profiling alternative protein foods. Further, the source authentication strategies based on species-specific protein and peptide biomarkers, detection of adulteration in complex matrices, and allergenicity assessment is discussed. Special attention is also given to nutritional proteomics with protein digestibility, gastrointestinal peptide release, and identification of bioactive sequences. SIGNIFICANCE: The importance of this review is that proteomics and peptidomics are becoming central in the management of the fast-growing environment of alternative protein foods, such as plant-based, insect, algal, fungal, and cultured meat products. It provides an explanation of the application of mass spectrometry-based processes to decode molecular signatures defining the origin of proteins, their structural integrity, digestibility, allergenicity, and bioactive properties, and thus directly contribute to safety, nutritional analysis, and authenticity of the product. Presentation of the article includes the integration of the knowledge of traditional muscle foods with alternative systems of proteins, where validated protein and peptide biomarkers are used in authentication, fraud detection, and allergy risk assessment in a wide variety of matrices. It also indicates the role of nutritional proteomics and peptidomics in informing the formulation strategy to promote digestibility and release of health-promoting peptides. In general, this review will guide scientists, the food industry, and regulatory bodies to use modern proteomic technologies in quality assurance, and decision-making, for the advancementof sustainable protein-based foods.

Proteomics↗

PubMed2026

Efficacy of transcranial alternating current stimulation for musculoskeletal pain and sleep quality: a systematic review and meta-analysis.

BACKGROUND: Transcranial alternating current stimulation (tACS) is a non-invasive neuromodulation technique, emerging as a potential therapeutic option for musculoskeletal pain management. OBJECTIVE: To comprehensively evaluate the efficacy and safety of tACS for alleviating pain and improving sleep quality in adults with musculoskeletal pain. METHODS: We conducted a systematic review and meta-analysis of randomized controlled trials (RCTs). Seven major databases and other sources were searched from inception until April 2026. Two reviewers independently screened studies, extracted data, and assessed risk of bias. A random-effects model was used to pool standardized mean differences (SMDs). The certainty of evidence was evaluated using the Grading of Recommendation Assessment, Development, and Evaluation framework. RESULTS: Six RCTs involving 232 participants were included. Meta-analysis showed that tACS could significantly reduce pain intensity compared to control (SMD = -0.355, 95% CI: -0.625 to -0.084, p = 0.010, I² = 28.7%). However, no significant improvement was found for sleep quality (SMD = 0.004, 95% CI: -0.310-0.317, p = 0.982, I² = 0.0%). Adverse effects were mild and transient, comparable to sham stimulation. The overall certainty of evidence was rated as low for both pain and sleep quality outcomes. CONCLUSION: Current evidence suggests that tACS may be beneficial for musculoskeletal pain; however, the available evidence remains limited and should be interpreted cautiously. The effect of tACS on sleep quality remains uncertain because of the limited number of available studies. Future well-designed RCTs with standardized outcome measures, condition-specific stimulation protocols, and longer follow-up are required to establish the efficacy and long-term safety of tACS.

Humans↗