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PubMed2026

The future of pediatric vesicoureteral reflux management.

BACKGROUND AND OBJECTIVE: Vesicoureteral reflux (VUR) is a common condition in pediatric urology, yet important uncertainties persist regarding risk stratification, imaging strategies, and prevention of long-term renal damage. Emerging technologies may help address these challenges. This review provides a forward-looking overview of recent advances in artificial intelligence (AI) and immunomodulation that may influence future management of pediatric VUR. METHODS: A forward-looking literature review was performed using the PubMed database (January 2000-March 2025), focusing on studies addressing AI, immunomodulation, or vaccination in the context of VUR and urinary tract infections. Criteria of inclusion were the relevance to pediatric VUR, the novelty of the proposed concept, the potential clinical implications and, for the AI literature, the existence of a clinical evaluation of the algorithm on a dataset from patients. KEY FINDINGS AND LIMITATIONS: AI-based models show promising performance in supporting clinical decision-making, including prediction of the need for voiding cystourethrography, automated grading of VUR, estimation of recurrent urinary tract infection risk and prediction of chemoprophylaxis. These tools may facilitate more individualized diagnostic and therapeutic strategies, although current evidence is largely retrospective and requires prospective validation. Immunization and immunomodulatory approaches aim to reduce infection burden and modulate inflammatory pathways associated with renal scarring. While early experimental and adult clinical data are encouraging, pediatric-specific evidence remains limited, and clinical applicability in children with VUR is not yet established. CONCLUSION: Artificial intelligence and immunologically targeted strategies represent complementary, emerging approaches that may contribute to more personalized management of pediatric VUR. At present, both should be regarded as exploratory tools whose clinical impact will depend on further validation and appropriately designed pediatric studies.

Humans↗

PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.

OBJECTIVE: To identify plasma protein differences between type 2 diabetes mellitus (T2DM) patients with and without metabolic dysfunction-associated steatotic liver disease (MASLD), and to evaluate the diagnostic potential of X-prolyl aminopeptidase 3 (XPNPEP3) for identifying MASLD in T2DM patients. METHODS: Twenty T2DM inpatients were categorized into groups with and without MASLD and their plasma samples were analyzed using data-independent acquisition mass spectrometry, followed by bioinformatics analysis to identify differentially expressed proteins. The cohort was then expanded to 84 patients, and plasma XPNPEP3 levels were validated by enzyme-linked immunosorbent assay. Correlation between XPNPEP3 and clinical indicators were evaluated, and diagnostic performance was determined via receiver operating characteristic (ROC) analysis. Immunohistochemistry was employed to compare hepatic XPNPEP3 expression between the two groups. RESULTS: Proteomic analysis identified 176 differentially expressed proteins, with XPNPEP3 exhibiting the most significant down-regulation by fold change. In the validation cohort, plasma XPNPEP3 was significantly lower in T2DM+MASLD versus T2DM alone. XPNPEP3 levels were negatively correlated with diabetes duration, liver function markers, and triglyceride levels, and was identified as an independent factor inversely associated with MASLD in T2DM.ROC analysis demonstrated strong diagnostic performance for XPNPEP3, further enhanced when combined with BMI and diabetes duration.  Immunohistochemistry confirmed reduced hepatic XPNPEP3 expression in T2DM+MASLD patients. CONCLUSIONS: Lower plasma XPNPEP3 is independently associated with MASLD in T2DM patients and demonstrates strong diagnostic potential, positioning XPNPEP3 as a promising biomarker for diagnosing MASLD in T2DM patients and a novel target for non-invasive diagnostic tool development.

Humans↗

PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗

PubMed2026

Adjunctive intermittent theta-burst stimulation for first-episode schizophrenia: A randomized clinical trial.

BACKGROUND: The efficacy of intermittent theta-burst stimulation (iTBS) combined with pharmacotherapy and psychotherapy in first-episode schizophrenia remains unclear. This study evaluated adjunctive iTBS with risperidone and cognitive behavioral therapy (CBT) and explored serum biomarkers indicating treatment response. METHODS: In this randomized, assessor-blind trial, 100 first-episode schizophrenia patients received either iTBS plus risperidone and CBT (iTBS group, n = 50) or risperidone and CBT alone (control, n = 50) for 3 months. The primary outcome was change in PANSS total score at 4 weeks and 3 months. Response was defined as a ≥ 50 % PANSS reduction. Secondary outcomes included cognitive function (MCCB subtests) and serum GDNF, cortisol, and dehydroepiandrosterone sulfate (DHEA-S) levels. RESULTS: The iTBS group showed significantly greater reduction in PANSS total scores than controls at both 4 weeks and 3 months (mean difference at 3 months: -13.3, 95 % CI: -16.8 to -9.8; P < 0.001), with a higher responder rate (76 % vs. 48 %). Significant improvements across all cognitive domains were observed in the iTBS group (all P < 0.001). Post-treatment, the iTBS group exhibited higher GDNF and lower cortisol and DHEA-S levels (all P < 0.001). A combined biomarker panel demonstrated superior discriminative performance for treatment efficacy (AUC=0.865 after cross-validation). Adverse events were comparable between groups. CONCLUSIONS: Adding iTBS to risperidone and CBT significantly improves clinical symptoms and cognitive function in first-episode schizophrenia. The combination of GDNF, cortisol, and DHEA-S shows promise as a composite biomarker for treatment response, though sham-controlled validation is warranted.

Humans↗

PubMed2025

Quantitative Outcomes for Shared Assessment and Management in Forensic Mental Health: A Meta-Analysis and Systematic Review.

Despite leading models of mental health care encouraging user involvement, users in forensic mental health (FMH) report poor involvement given the difficulty in reconciling shared approaches with risk-averse and legally mandated settings. While previous research has demonstrated qualitative benefits to shared approaches in FMH and has led to a proliferation of self-rated assessment tools, there remains to quantify agreement on self-rated tools and to clarify the impact of shared approaches on care. This meta-analysis examines (1) the correlation between clinician and user ratings, (2) the predictive validity of self-ratings for violence, and (3) the effects of shared risk management on violence and restriction in FMH. Five databases were searched from inception to April 2024, selecting for adult FMH inpatients, shared risk assessment, needs assessment or violence management as interventions, and quantitative outcomes (correlation, agreement, predictive validity, and effect on violence or restriction rates). Fifteen quantitative evaluations were retained. One of three planned meta-analyses could be conducted, with seven records providing paired clinician-user t-tests. Eleven more records provided clinical recommendations on operationalizing shared approaches. Random-effects meta-analysis showed a significant and large paired standard difference of .95 (95% CI = [.49,1.42]) across tools, with significant differences in DUNDRUM-3, DUNDRUM-4, and CANFOR sub-models. While acknowledging between-study heterogeneity, results substantiate quantitative differences where clinicians generally rate more needs and lesser progress than users across tools, showing that self-ratings can and should be used to broach collaborative discussions on needs and progress during FMH treatment. There remains an evidence gap for quantitative benefits in care outcomes and a need to standardize agreement measures for future comparisons and clinical sub-group analyses.

Humans↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed2026

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↗

PubMed2026

Pretreatment EBV-DNA/TLG-Based Risk Stratification Is Associated With Survival Outcomes in Nonmetastatic Nasopharyngeal Carcinoma: An Exploratory Study.

Whether combining pretreatment plasma Epstein-Barr virus DNA (EBV-DNA) with 18F-FDG PET/CT-derived total lesion glycolysis (TLG) improves prognostic stratification in nonmetastatic nasopharyngeal carcinoma (NPC) is unclear, particularly in nonendemic populations. We retrospectively analyzed 86 eligible nonmetastatic NPC patients treated with definitive radiotherapy (2010-2024) at a single nonendemic-region institution. EBV-DNA (prespecified cutoff 3500 copies/mL) and TLG (cutoff 200, ROC-derived within this cohort) were dichotomized. Both were available in 59/86 patients (68.6%), who differed from the rest in nodal and overall stage and in RT technique. Baseline PET/CT was in-house in 57 of 86 patients, and a robustness analysis in that subgroup is reported. Given limited events (13 PFS, 9 OS), Cox analyses are exploratory and were supplemented with penalized regression and bootstrap validation. At a median follow-up of 75.5 months, 5-year PFS and OS for the whole cohort (n = 86) were 81.1% and 85.9%. The EBV-DNAhigh/TLGhigh subgroup remained associated with inferior PFS after adjustment in an exploratory model (adjusted HR = 3.97, 95% CI: 1.32-11.93) and, in a single-variable model, with inferior OS (HR = 4.13, 95% CI: 1.10-15.52). Discrimination was comparable to the individual-biomarker model for PFS and lower for OS. Five-year PFS fell monotonically across the four risk groups in the complete-case cohort (n = 59; 89.7%-58.3%). OS differed across groups (log-rank p = 0.044) but was not strictly monotonic, with wide, overlapping confidence intervals. This two-biomarker model is hypothesis-generating and needs prospective, multicenter validation before any consideration of risk-adapted treatment.

Epstein–Barr virus DNA↗

PubMed2026

Associations between smart infusion pump-electronic health record interoperability and healthcare outcomes: A systematic review.

OBJECTIVE: This study synthesized available evidence on the associations between smart infusion pump-electronic health record (EHR) interoperability and healthcare outcomes. METHODS: A systematic review of PubMed, CINAHL, Embase, and Scopus databases identified 901 records, which were imported into Rayyan® for duplicate removal, independent screening by three reviewers, and resolution of discrepancies. Eligible studies were peer-reviewed, data-driven, and reported associations between smart infusion pump-EHR interoperability and healthcare outcomes. Studies focused solely on technical validation or interoperability prototypes were excluded. A backward citation search identified additional studies. Two reviewers independently extracted and cross-validated study characteristics using standardized templates. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Tools. RESULTS: Twenty records of 14 full-text studies and 6 conference proceedings were included. Most records reported positive associations between smart infusion pump-EHR interoperability and outcomes related to safety (e.g., medication administration errors, safety-reported events, pump alerts, and compliance with interoperability and drug library), operational efficiency (e.g., programming and documentation time and technical issues), financial performance (e.g., charges captured, and cost avoided), and user experience domains. Most studies used observational designs, reflecting real-world interoperability implementations, where controlling confounding factors is challenging. Limited reporting of baseline characteristics, pump type, and sample sizes limited comparability across studies. CONCLUSIONS: Smart infusion pump-EHR interoperability was associated with improvements in patient safety, efficiency, charge capture, and user experience, with variable findings across studies. Future research should use rigorous methodologies and standardized measures, examine relationships across outcome domains, assess limitations of pump-EHR interoperability, and evaluate underexplored outcomes, including team communication, cognitive workload, and AI-enabled pumps. IMPLICATIONS FOR CLINICAL PRACTICE: Interoperability should be viewed as a component of a broader sociotechnical system, in which technology, user, workflow, clinical content, and organizational practices collectively determine overall effectiveness.

Humans↗

PubMed2026

Phenotypic, physiological and transcriptomic analysis of graded salt stress responses in Pyrus betulifolia Bunge and functional characterization of the hub gene PbSTY46.

Pyrus betulifolia Bunge is a salt‑tolerant rootstock for pear, but its salt‑tolerance mechanisms remain largely unknown. In this study, P. betulifolia seedlings were subjected to graded NaCl stress at concentrations of 0 (CK), 50 (T1), 100 (T2), and 200 (T3) mM. We integrated phenotypic observation, physiological assessment, transcriptomic profiling, and functional gene validation to systematically elucidate its salt tolerance mechanisms. Salt stress inhibited seedling growth and root traits in a concentration-dependent manner, and T3 caused the most severe damage. Osmotic solutes responded differentially: soluble sugars peaked under T2, while proline peaked under T3. Antioxidant enzymes showed tissue-specific biphasic responses and declined after prolonged T3 stress. Meanwhile, chlorophyll and photosynthesis decreased, whereas anthocyanin increased, indicating a metabolic shift from photosynthesis to photoprotection. Transcriptome analysis revealed distinct responses depending on stress intensity: mild stress induced membrane lipid remodeling, moderate stress activated circadian rhythm and hormone signaling, and severe stress enhanced phenylpropanoid biosynthesis and thiamine metabolism. Gene Set Enrichment Analysis (GSEA) further highlighted progressive enrichment of phenylpropanoid biosynthesis, heme binding, and oxidoreductase activity. Weighted Gene Co‑expression Network Analysis (WGCNA) identified a blue module significantly positively correlated with root traits, from which the hub gene PbSTY46 was identified. Functional validation via overexpression, loss‑of‑function mutants, and pharmacological interventions (MeJA/DIECA) confirmed that PbSTY46 acts through JA signaling to enhance antioxidant enzyme activities and thereby confer salt tolerance. Collectively, P. betulifolia adopts a "survival‑first" strategy that coordinates growth arrest, osmotic homeostasis, and ROS scavenging. These findings establish PbSTY46 as a key regulator that links JA signaling to antioxidant defense. Thus, PbSTY46 represents a promising candidate for marker‑assisted breeding of salt‑tolerant pear cultivars.

Salt Stress↗

PubMed2026

Effectiveness and usability of artificial intelligence-powered assistive technologies in Supporting daily activities of children with cerebral palsy: a systematic review.

BACKGROUND: Cerebral Palsy (CP) is the main cause of motor disabilities in childhood, necessitating innovative approaches to rehabilitation and assistive technology (AT). Simultaneously, artificial intelligence (AI) is increasingly being integrated into devices to create more adaptive, personalized, and effective AT. This systematic review aimed to evaluate the effectiveness and usability of AI-powered assistive technologies designed to support daily activities and rehabilitation in children with CP. MATERIALS AND METHODS: Five databases, including Scopus, Web of Science, PubMed, Embase, and IEEE Xplore, were systematically searched, and 23 articles were included in the final analysis. Articles were identified, selected, and categorized into emerging thematic areas based on the primary function and application of the technology. RESULTS: Five key thematic topics were identified: 1) AI-driven motor rehabilitation and gait training for functional mobility; 2) intelligent assessment and monitoring systems for clinical decision support; 3) AI-supported communication, social interaction, and intention recognition tools; 4) gamified and virtual reality-based interventions to enhance engagement and usability; and 5) smart assistive systems supporting daily living and independent mobility. The findings demonstrate a strong trend toward the application of AI technologies in personalized, engaging, and data-driven interventions for children with CP. However, the field is predominantly in the proof-of-concept stage, with limitations including small sample sizes, lack of long-term clinical validation, challenges in user-centered design, and usability for children with CP. CONCLUSION: AI-powered assistive technologies hold significant potential for transforming the care of children with CP by enabling highly personalized and engaging interventions. To actualize this potential, future work must realize that practical application remains challenging owing to limited clinical validation, technological integration, and usability barriers for children with CP. Future research must prioritize user-centered design and multidisciplinary collaboration to ensure that AI and robotic advancements improve the usability and quality of life for children with CP.

Humans↗

PubMed2026

Comparison between measured and synthesized posterior lead electrocardiograms during percutaneous coronary intervention-induced myocardial ischemia.

BACKGROUND: Posterior/inferolateral myocardial ischemia is frequently underrecognized on standard 12‑lead electrocardiography (ECG). Synthesized posterior leads derived from the standard 12‑lead ECG have been proposed as an alternative to directly measured posterior leads; however, their accuracy under controlled ischemic conditions has not been fully validated. METHODS: We prospectively enrolled 26 consecutive patients undergoing percutaneous coronary intervention (PCI) in whom simultaneously recorded measured and synthesized posterior lead ECGs (V7-V9) were obtained during balloon-induced myocardial ischemia. ST-segment deviation was measured at the ST junction (STJ), 40 ms (ST1), and 80 ms (ST2) thereafter. Agreement between measured and synthesized posterior leads was assessed using Pearson correlation and Bland-Altman analyses. As an exploratory patient-level analysis, diagnostic performance was compared with reciprocal anterior ST-segment depression (V1-V4). RESULTS: Strong correlations were observed between measured and synthesized posterior lead ST-segment deviations (V7: r = 0.89; V8: r = 0.86; V9: r = 0.83; all P < 0.001). Bland-Altman analysis demonstrated minimal systematic bias (within ±0.004 mV) and narrow limits of agreement. Synthesized posterior leads showed higher diagnostic performance than reciprocal anterior ST-segment depression (AUC 0.917 vs. 0.708), although the difference was not statistically significant (DeLong test, P = 0.197). Using a 0.05 mV threshold, synthesized posterior leads demonstrated 83.3% sensitivity, 100% specificity, and 96.2% overall accuracy. CONCLUSIONS: Synthesized posterior leads closely reproduced measured posterior lead ST-segment deviations during percutaneous coronary intervention (PCI)-induced myocardial ischemia, supporting the technical validity of posterior lead reconstruction. Larger prospective studies are warranted to determine whether synthesized posterior leads provide incremental diagnostic value beyond careful interpretation of the standard 12‑lead ECG.

Humans↗

PubMed2026

Impact of estimated total blood volume on NT-proBNP response to angiotensin receptor-neprilysin inhibition in acute heart failure: Insights from the PREMIER study.

BACKGROUND: Sacubitril/valsartan (Sac/Val) reduces N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in acute heart failure (AHF), particularly in patients with reduced ejection fraction. However, whether estimated total blood volume (TBV), calculated using anthropometric equations, is associated with heterogeneity in biomarker response remains uncertain. METHODS: This post hoc exploratory sub-analysis of the PREMIER randomized trial evaluated whether baseline estimated TBV was associated with heterogeneity in NT-proBNP reduction after Sac/Val compared with angiotensin-converting enzyme inhibitor/angiotensin receptor blocker (ACEI/ARB) therapy. Estimated TBV was calculated using validated anthropometric equations and dichotomized at the median (4.05 L). Patients were further stratified by left ventricular ejection fraction (LVEF <40% vs ≥40%). The primary endpoint was the proportional change in NT-proBNP from baseline to Week 8. RESULTS: Among 376 patients, 372 with baseline estimated TBV data were analyzed. In the high TBV group, Sac/Val was associated with greater NT-proBNP reduction than ACEI/ARB (-56% vs -32%; ratio of change, 0.67; 95% confidence interval, 0.53-0.84; P = .001), whereas no significant difference was observed in the low TBV group (P for heterogeneity = 0.063). In patients with LVEF <40%, Sac/Val was associated with greater NT-proBNP reduction in both TBV groups. In patients with LVEF ≥40%, Sac/Val was associated with greater NT-proBNP reduction in the high TBV group, whereas the point estimate in the low TBV group numerically favored ACEI/ARB. CONCLUSIONS: In this exploratory post hoc analysis, higher estimated TBV was associated with greater NT-proBNP reduction after Sac/Val, particularly among patients with LVEF ≥40%. These findings are hypothesis-generating and require external validation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT05164653; Japan Registry of Clinical Trials, jRCTs021210046.

Humans↗

PubMed2026

Transcriptomic responses of gill and intestinal tissues in Nile tilapia (Oreochromis niloticus) to bacterial infection following sequential nanoimmersion and hydrogel-based multivalent vaccination.

Bacterial pathogens, including Flavobacterium oreochromis, Aeromonas veronii, Streptococcus agalactiae, and Edwardsiella tarda, represent major infectious threats to Nile tilapia (Oreochromis niloticus). A multivalent vaccination strategy integrating cationic nanoemulsion immersion with oral hydrogel boosters was developed to investigate tissue-specific immune responses at the transcriptomic level. Gill tissues were collected following immersion challenge and intestinal tissues following intraperitoneal injection challenge, reflecting the physiologically relevant infection biology of each pathogen and the mechanistic rationale of each delivery platform. RNA sequencing (RNA-seq) generated high-quality datasets (mapping rate > 81.64%) with strong concordance to quantitative real-time PCR (qRT-PCR) validation (r = 0.83). Comparative transcriptomic analysis revealed distinct yet complementary immune signatures between tissues. Gill transcriptomes were enriched in phagosome, focal adhesion, extracellular matrix-receptor interaction (ECM-receptor interaction), and cytokine-cytokine receptor interaction pathways, accompanied by increased expression of major histocompatibility complex class I/II (MHC class I/II), mannose receptor, αVβ3 integrin, and calnexin, indicating innate activation, enhanced phagocytic capacity, epithelial barrier reinforcement, and adaptive immune coordination. Intestinal transcriptomes showed predominant enrichment of adaptive immune pathways, including the intestinal immune network for immunoglobulin (Ig) production, Forkhead box O (FoxO) signaling, and mitogen-activated protein kinase (MAPK) signaling, with increased expression of T-cell receptor (TCR), inducible T-cell co-stimulator ligand (ICOS-L), C-X-C chemokine receptor type 4 (CXCR4), and polymeric immunoglobulin receptor (pIgR), reflecting T and B cell coordination, lymphocyte trafficking, and mucosal immunoglobulin transport, alongside innate engagement through phagosome pathway enrichment. Shared upregulation of MHC class II, B-cell receptor (BCR) signaling, integrin alpha M (ITGAM), and immunoglobulin-associated components across both tissues suggests coordinated mucosal immune activation through a conserved immune module, warranting direct experimental validation. Collectively, these findings provide transcriptomic evidence that this vaccination strategy elicits an integrated, tissue-specialized immune response, advancing mechanistic understanding of gill and intestinal immunity in vaccine-induced protection of teleost fish.

Animals↗

PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Candidate biomarkers for early Giardia duodenalis infection revealed by time-resolved secretome proteomics.

Giardia duodenalis is a zoonotic protozoan parasite that causes giardiasis in humans and other mammals. Early diagnosis remains challenging because current diagnostic methods, including microscopy and enzyme-linked immunosorbent assays (ELISAs), primarily detect established infections. Consequently, a critical diagnostic gap exists during the early stage of infection within the first 2-48 h following exposure. To address this limitation, we characterized the proteins released by in vitro-cultured G. duodenalis trophozoites under serum-free conditions and evaluated their potential as early diagnostic biomarkers. Proteomic analysis of culture supernatants collected during early trophozoite incubation identified 31,773 peptides corresponding to 2504 quantifiable proteins. Temporal profiling showed distinct secretion patterns, including proteins that peaked during the early stage, progressively accumulated over time, or remained persistently abundant throughout the incubation period. Based on their secretion characteristics and predicted immunogenic properties, five candidate biomarkers were selected for further evaluation. Polyclonal antibodies raised against selected candidates successfully detected the corresponding proteins in serum-free culture supernatants, providing preliminary evidence for their potential utility as early-stage diagnostic targets. These findings identify stage-associated candidate proteins that may serve as a resource for future early giardiasis diagnostic development, provide a valuable resource for investigating host-parasite interactions, and establish a foundation for future diagnostic assay development. However, further validation in clinical and biological samples is required to confirm their diagnostic applicability. SIGNIFICANCE: Giardiasis, caused by Giardia duodenalis, is a major diarrheal disease worldwide. Although enzyme-linked immunosorbent assays (ELISAs) provide rapid detection, their diagnostic utility is limited by the lack of biomarkers capable of identifying infection during its earliest stages, creating a critical gap in the detection of active infection within 2-48 h following exposure. Using data-independent acquisition proteomics, this study provides a time-resolved characterization of proteins released by G. duodenalis trophozoites into serum-free culture supernatants. Our findings reveal temporal secretion dynamics of protein secretion and identify candidate biomarkers with potential utility for the development of early-stage diagnostic assays pending rigorous biological and clinical validation. In addition, this proteomic resource provides a foundation for investigating host-parasite interactions and may facilitate the development of future point-of-care diagnostic strategies.

Giardiasis↗