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PubMed2026

Providing Feedback on Previous Pain Scores Did Not Affect Weekly Pain Variability: A Cohort-Nested Randomised Study.

BACKGROUND: Spinal pain is one of the leading causes of disability worldwide and repeated symptom monitoring is increasingly used to capture its fluctuating nature. However, repeated pain assessments may be influenced by prior responses, potentially affecting longitudinal patterns of pain reporting. This study examined whether providing feedback on prior pain scores influenced within-person variability in weekly pain intensity ratings and retention over 60 weeks. METHODS: This randomised study evaluating a methodological feature of repeated pain assessment was embedded within a cohort of adults with spinal pain referred to an outpatient hospital clinic. Participants (n = 2448) were randomised 1:1 to weekly pain intensity ratings (0-10 numerical rating scale) either with feedback ('You answered [X] last week') or without feedback. Analyses included participants with ≥ 40% valid responses (n = 1883), of whom 948 received feedback and 935 did not. The primary outcome was within-person variability in pain intensity, quantified using the root mean square of successive differences. Secondary outcomes included additional fluctuation metrics and the number of weeks with missing responses. RESULTS: No meaningful between-group differences were observed for the primary outcome (mean difference -0.04 points [95% confidence interval -0.08 to 0.01]) or secondary outcomes, including retention rates. Sensitivity analyses yielded consistent findings. CONCLUSIONS: Providing participants with feedback on their previous pain score did not meaningfully influence within-person pain variability or retention during 60 weeks of weekly monitoring. These findings aid the interpretation of repeated longitudinal pain assessments by showing that the observed variability was robust to this specific study design. SIGNIFICANCE: This randomised study showed that providing participants with feedback on prior pain scores did not meaningfully alter weekly pain variability or retention during 60 weeks of longitudinal monitoring. These findings contribute to the interpretation of repeated longitudinal pain assessments in spinal pain research and suggest that weekly pain reporting patterns are robust to prior-pain feedback during long-term symptom monitoring.

Humans↗

PubMed2026

Analyzing salinity tolerance in grass carp (Ctenopharyngodon idella): Insights from genome-wide association study and genomic selection.

Grass carp (Ctenopharyngodon idella) is one of the most widely cultured freshwater fish species globally. However, the expansion of its farming scale faces severe limitation owing to freshwater scarcity; therefore, the development of strains with greater salinity tolerance is key for expanding production using brackish water resources. To investigate the genetic basis of salinity tolerance in grass carp, a genome-wide association study (GWAS) was conducted using 200 individuals representing extreme phenotypes, namely salinity-tolerant and salinity-sensitive groups. In total, 17 single nucleotide polymorphisms (SNPs) related to salinity tolerance were detected, which were distributed across 11 chromosomes. Through gene annotation, 38 candidate genes were obtained from these loci. Enrichment analysis revealed these candidate genes are primarily implicated in key biological processes, including osmotic regulation, energy metabolism, and stress responses. Analyses of different SNP densities revealed that the 5 K SNP density panel can balance prediction accuracy and computational efficiency. The BayesA model achieved the highest prediction accuracy under the GWAS_Evenly selection strategy, with substantial reductions in mean absolute error and mean square error. This study reveals the genetic mechanisms of salinity tolerance in grass carp, which might be optimized through genomic selection, and provides insights for selectively breeding new varieties with greater salinity tolerance.

Animals↗

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

A genome-wide coverage-based pipeline for the identification of host-derived candidate DNA biomarkers from cell-free blood.

We have created a new data-analysis pipeline for the discovery of host-specific candidate DNA biomarkers derived from sequencing data of cell-free blood. Unlike approaches that rely on specific molecular or genetic signatures, our method leverages the coverage distribution of cell-free DNA sequences mapped to a reference genome, applying statistical analyses to identify informative short genomic regions for biomarker discovery. The pipeline is applicable to diverse diseases and can be used to analyze cell-free DNA sequences from plasma or serum to identify candidate biomarkers that are characteristic of disease states in mammals. Core functionalities were developed in Java and integrated with open-source software tools for the preprocessing of raw sequencing data, complemented by Python scripts for the machine-learning analysis and statistical validation. The pipeline is designed for HPC use and users can access the pipeline through a Galaxy workflow, which offers a user-friendly web interface for input selection prior to execution and analysis progress monitoring. Performance tests, carried out using duplicate sets of COVID-19 samples and controls, showed linear scalability of execution time with an increasing dataset size, as well as a substantial reduction in execution time through parallelized computation, whereby each HPC node is used to process the data of one chromosome. Further statistical tests confirmed the quality of the pipeline's results by showing that the set of identified candidate biomarkers remained stable across varying dataset sizes.

Biomarkers↗

PubMed2026

Avian egg incubation period: Revisiting existing allometric relationships via surface area-to-volume ratio of an egg.

The incubation period (I) for bird eggs varies among species and is used in establishing allometric relationships. Research on variations in I shed light on the evolutionary mechanisms that gave rise to the differentiation of embryonic development in distinct taxa of birds. Here, using a sampling of 444 images from 444 avian species, 89 families and 30 orders, we calculated their major geometric dimensions: volume (V) and surface area (S). An assessment of the relationship between I and the measured and calculated egg parameters demonstrated the closest and most significant correlation (R = -0.760) between I and the S/V ratio that was adopted as a conditional indicator and reflects the embryo's metabolic rate. Approximation of the values of these parameters made it possible to derive a power-law dependence for the prediction of I depending on the S/V value of a particular egg (R2 = 0.757). The prediction accuracy was higher (R2 = 0.783) if the eggs of the family Procellariiformes (petrels), whose I value is characterized by a longer time, were removed from the general sampling computation. We conclude that the value of the S/V ratio can characterize both the metabolism of an embryo and the conditional thermal conductivity of an egg, which aids in ensuring the temperature regime of egg incubation.

Animals↗

PubMed2026

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

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

Artificial intelligence↗

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

Molecular characterization and biological characteristics of a highly pathogenic recombinant ALV-J strain (HUE2023) with cross-clade gp85 recombination.

Avian leukosis virus subgroup J (ALV-J) has undergone extensive diversification into phylogenetically distinct clades, yet whether recombination between these clades within the gp85 envelope glycoprotein generates variants with altered pathogenicity has received little direct investigation. A field strain (HUE2023) was recovered from breeding roosters displaying vascular tumors. The viral genome was sequenced and subjected to phylogenetic and recombination analyses. The three-dimensional structure of gp85 was predicted with AlphaFold3; electrostatic surface potentials and surface hydrophobicity were computed using the Adaptive Poisson-Boltzmann Solver and the Eisenberg hydrophobicity scale, respectively. Pathogenicity and immunosuppressive effects were assessed in Hy-Line Brown chickens. Recombination analysis revealed that HUE2023 is an inter-clade recombinant derived from Clade 1.1 (major parent: JS14NT01) and Clade 1.2 (minor parent: JS09GY3). A single-residue deletion at position 61 within receptor-binding domain 1 (RBD-1), unique to the recombinant, induced a localized conformational rearrangement that generated a concentrated electronegative surface patch and a contiguous hydrophobic pocket not observed in either parental gp85. Animal challenge showed that HUE2023 is highly pathogenic: female chickens in the high-dose group reached only 61% survival and displayed significant growth retardation (P < 0.05) together with marked immunosuppression. The recombination in the RBD-1 led to local conformational rearrangement, resulting in a concentrated and negatively charged surface area as well as a continuous hydrophobic pocket, which were never present in any of the parental gp85 sequences. These results indicate that gp85 recombination across clades can yield variants with fundamentally altered receptor-binding surfaces and argue for integrating structural surveillance into ALV-J monitoring programmes.

Animals↗

PubMed2026

Granger connectivity and graph-theoretical analysis of scalp EEG across the preictal to ictal transition for presurgical evaluation.

OBJECTIVE: To assess the feasibility of estimating lateralization and localization of the epileptogenic zone (EZ) in temporal and extratemporal lobe epilepsy by combining Electric Source Imaging (ESI) with functional connectivity analysis of high-density EEG from the preictal to the ictal phase. METHODS: Adults with drug-resistant focal epilepsy and at least one recorded seizure during 40- or 64 channels EEG monitoring were retrospectively included. Granger causality and hubness centrality were computed over the 10-s preictal interval and the first 5 s of the ictal period, with ictal onset defined as the first EEG change identified by experienced epileptologists. The reference standard for EZ localization was based on resective surgical outcome or stereo-EEG findings. RESULTS: Thirteen patients (7 females; median age 35 years) were included. Connectivity analyses showed higher concordance with clinical findings during the preictal phase than during the ictal phase for both lateralization (91% vs 46%) and localization (73% vs 27%). Performance was highest in temporal (7/7 lateralization; 6/7 localization) and frontal lobe epilepsy (2/2 for both), and lower in parieto-occipital epilepsy (1/2 and 0/2, respectively). In two cases with poor surgical outcome or no surgical indication, connectivity findings were discordant with clinical estimates. CONCLUSIONS: Connectivity analysis across the preictal to ictal transition provides relevant lateralizing and localizing information, particularly in temporal and frontal lobe epilepsy, and may reveal clinically meaningful discordance. SIGNIFICANCE: Integrating high-density EEG, ESI, and functional connectivity during the phase preceding the first EEG change may support non-invasive presurgical evaluation.

Humans↗

PubMed2026

A randomized study of antibiotic prophylaxis after hypospadias repair in children.

BACKGROUND AND OBJECTIVE: Antibiotic resistance is a challenge in contemporary world. Urethroplasty for hypospadias is one of the most common urological surgeries performed around the world, yet, there is still no consensus on the use of antibiotics pre- and post-operatively. Our objective was to analyze the effectiveness of antibiotic prophylaxis and therapy before, during, and after hypospadias repair in children. METHODS: A prospective randomized trial was carried out including patients with coronal hypospadias who underwent urethroplasty performed by one surgeon. Urethral catheters were used in all cases for 10 days. Study participants were randomly assigned in a 1:1:1 ratio to receive a single intraoperative administration of antibiotics (Group I); an intraoperative antibiotic prophylaxis and antibiotic therapy for 10 days until the removal of urethral catheter (Group II); no antibiotic administration (Group III). Randomization was performed using computer-generated permuted blocks with randomly varying block sizes, prepared by an independent statistician. The results were analyzed using the analysis of variance (ANOVA). The following criteria were compared: postoperative functional complications, such as: urethral fistula, stenosis, diverticulum; wound infection symptoms: hyperemia of surgical site, pain during palpation, and symptomatic urinary tract infection. RESULTS: A total of 300 patients were included in the study. Two patients (2%) in Group I, two patients (2%) in Group II, and four patients (4%) in Group III had urethral fistulas, requiring surgery 6 months after primary repair, yet without statistical difference between groups. We found no significant difference in frequency of symptomatic UTIs between three groups (p = 0.182). CONCLUSION: There is no effect of antibiotic prophylaxis and therapy on the frequency of postoperative surgical and infectious complications after urethroplasty for coronal hypospadias repair in children.

Humans↗

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

Test-retest reliability of spatiotemporal, kinematic, and kinetic measures in marker-based 3D gait analysis: A systematic review.

BACKGROUND: Marker-based 3D gait analysis (3DGA) is widely used to quantify impairments and evaluate treatment effects. For longitudinal clinical interpretation, clinicians and researchers need reference values for inter-session measurement error. For this purpose, this systematic review synthesized Standard Error of Measurement (SEM) values for spatiotemporal, kinematic, and kinetic (moments) outcomes obtained from marker-based 3DGA studies. METHODS: PubMed and Scopus were searched (final search: 11 December 2025). Studies reporting inter-session test-retest SEM and/or MDC for steady-state overground or treadmill walking using marker-based motion capture were included. Two authors screened records and appraised methodological/reporting quality using a custom tool informed by COSMIN, GRRAS, and biomechanics-specific items. Due to heterogeneity, results were synthesized descriptively using study-level median SEM values, stratified by joint, plane, population (healthy, pathological, single subgroups), and walking condition. Minimal Detectable Change (MDC) values were computed for all available data. RESULTS: Thirty-four studies (762 participants, 44.2% females) were included, with substantially more evidence for overground than treadmill walking. Overground spatiotemporal outcomes showed low errors (walking speed SEM of 0.06 m/s; timing typically ≤0.03 s; spatial parameters generally ≤0.03 m). For joint kinematics during overground walking, median SEMs were 2.4° (sagittal), 1.9° (frontal), and 3.3° (transverse). The corresponding joint-kinetic SEMs were approximately 0.06, 0.04, and 0.03 Nm/kg, respectively. Treadmill data followed similar patterns. SIGNIFICANCE: Marker-based 3DGA allows for accurate assessment of spatiotemporal, kinematic, and kinetic gait features. We provided detailed SEM/MDC lookup tables to support clinical decision-making. Results further offer a benchmark for validating emerging gait assessment technologies (e.g., markerless systems) against realistic limits of marker-based 3DGA.

Humans↗

PubMed2026

Discovery of NAT-6-321056 as a novel modulator of VEGFR2 signaling to suppress tumor angiogenesis.

Vascular endothelial growth factor receptor 2 (VEGFR2) is a master regulator of angiogenesis and cancer progression. However, current VEGFR2 modulators face significant challenges, including off-target toxicity and acquired resistance, underscoring the urgent need for novel therapeutic agents with improved efficacy and safety profiles. Here, we reported that virtual screening of 39,442 natural products from the ZINC natural products-derived library, coupled with molecular docking and molecular dynamics (MD) simulations to evaluate the binding stability of candidate compounds, identified NAT-6-321056 as a highly promising modulator of VEGFR2 signaling. Biological evaluations demonstrated that NAT-6-321056 exerted potent inhibition on the growth of a broad spectrum of cancer cells, including both solid tumors and hematological malignancies. In EA.hy 926 endothelial cells and SK-N-DZ neuroblast cells, the compound significantly suppressed proliferation, migration, and invasion. Microscale thermophoresis (MST) confirmed direct binding of NAT-6-321056 to VEGFR2 with favorable affinity. Kinase profiling against a panel of 33 kinases indicated that NAT-6-321056 exhibited a multi-kinase modulation profile. Mechanistic studies revealed that NAT-6-321056 suppressed the expression of hypoxia-inducible factor 1-alpha (HIF-1α) and was associated with reduced VEGFR2 phosphorylation and attenuation of the downstream ERK/JNK/AKT signaling pathways. Moreover, NAT-6-321056 exhibited robust in vivo anti-angiogenic effects in both the chick chorioallantoic membrane (CAM) assay and transgenic zebrafish vascular fluorescence imaging models. Computational absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction suggested acceptable drug-like properties. Collectively, these findings demonstrated that NAT-6-321056 is a promising modulator of VEGFR2 signaling with potent anti-angiogenic activity and represents a viable candidate for cancer therapy.

Vascular Endothelial Growth Factor Receptor-2↗

PubMed2026

Effectiveness of an AI-based home exercise app for rehabilitation of rotator cuff-related shoulder pain: A randomized controlled trial.

BACKGROUND: Rotator cuff-related shoulder pain contributes to disability and healthcare use. Although therapeutic exercise is first-line treatment, limited supervision and adherence may reduce its effectiveness; digital rehabilitation with real-time feedback may address these limitations. OBJECTIVES: To evaluate the effectiveness of adding a digital rehabilitation program to standard physiotherapy on pain, function, fear-avoidance beliefs, and healthcare utilization. DESIGN: Single-center, assessor-blinded, randomized controlled trial with two parallel groups. METHOD: Forty-six adults (mean age 59 years) with rotator cuff-related shoulder pain were randomized to 12 weeks of conventional physiotherapy or physiotherapy plus an AI-based digital rehabilitation program using computer vision for real-time feedback and performance monitoring. Outcomes were assessed at baseline and at 2, 4, and 12 weeks. Pain intensity (NPRS) was primary outcome; secondary outcomes included upper limb function (QuickDASH), fear-avoidance beliefs (FABQ), and post-intervention healthcare utilization. Analyses followed an intention-to-treat approach. RESULTS: Pain reduction exceeded the MCID (1.3) at 4 and 12 weeks. Between-group differences favoured the intervention at Weeks 2 and 4 (MD -0.7; 95% CI -1.13 to -0.14 and MD -1.01; 95% CI -1.8 to -0.2, respectively). Upper limb function improved more at Week 4 (MD -7.3; 95% CI -12.3 to -2.2). FABQ scores decreased more at Week 12 (MD -7.6; 95% CI -14 to -0.5). Fewer participants in the experimental group required post-intervention healthcare (3 vs 10; p = 0.02). CONCLUSION: Adding AI-based home exercise app to conventional treatment improve pain and may improve function and reduce healthcare utilization in rotator cuff-related shoulder pain.

Humans↗

PubMed2026

Angiography-Based Index of Microcirculatory Resistance in Assessing the MVO and Infarct Size in STEMI Patients.

OBJECTIVES: To evaluate angiography-based index of microcirculatory resistance (angio-IMR) in assessing microvascular obstruction (MVO) and infarct size (IS) in ST-segment elevation myocardial infarction (STEMI). BACKGROUND: The effect of thrombolysis on post-percutaneous coronary intervention (PCI) angio-IMR, and its associations with MVO and IS remains unclear. METHODS: One hundred twenty-three STEMI patients randomized to receive 5 mg intravenous bolus of recombinant staphylokinase (r-SAK) or normal saline (NS) before PCI were recruited. Angio-IMR was computed in infarct-related arteries. MVO and IS were detected by cardiac magnetic resonance imaging. RESULTS: Compared with NS group, r-SAK group exhibited numerically lower post-PCI angio-IMR (39.12 U vs. 42.57 U; p = 0.567), MVO (54.0% vs. 70.9%; p = 0.059), MVO extent (0.70% vs. 1.90%; p = 0.101) and IS (21.30% vs. 24.50%; p = 0.079). Post-PCI angio-IMR was positively correlated with MVO extent (ρ = 0.347; p < 0.001) and IS (ρ = 0.324; p < 0.001). Receiver operating characteristic analyses showed moderate diagnostic performance of angio-IMR for MVO (area under the curve [AUC] = 0.750; p < 0.001), MVO > 2.6% (AUC = 0.735; p < 0.001) and IS > 25% (AUC = 0.712; p < 0.001). The exploratory optimal cut-off values for these endpoints were approximately 40 U. CONCLUSIONS: In STEMI patients, a single bolus of r-SAK before PCI was associated with numeric reductions in post-PCI angio-IMR, MVO, MVO extent and IS. Additionally, angio-IMR exhibited a significantly positive correlation with both MVO extent and IS, demonstrating the diagnostic value of this wire-free method for assessing microvascular injury.

Humans↗

PubMed2026

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↗

PubMed2026

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

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

Humans↗

PubMed2026

Effectiveness of Mobile-Delivered Exercise and Yoga Programs on Depressive Symptom Reduction in Employees: Randomized Controlled Trial.

BACKGROUND: Mental health challenges such as stress and depression are prevalent among employees. Mobile health platforms that deliver exercise or yoga interventions offer a promising approach to improve mental health outcomes in this population. OBJECTIVE: This study aimed to assess the effectiveness of 12-session adaptive moderate-intensity exercise and yoga programs delivered via a motion-detecting digital platform in reducing stress and depressive symptoms among employees. METHODS: This was an unblinded, 3-arm, parallel-group, randomized controlled trial conducted at Seoul National University Bundang Hospital and Boramae Medical Center between November 2023 and January 2024. Eligible participants were full-time employees. Seventy-five participants were randomly assigned to an exercise, a yoga, or a cognitive behavioral therapy-based self-care control group using computer-generated randomization. The exercise and yoga groups engaged in motion-detecting, adaptive physical activity training, whereas the control group accessed mobile-based, self-directed stress management educational materials. The intervention was largely automated, with no individualized therapeutic guidance provided. Allocation was concealed until trial entry. All recruitment and outcome assessments were conducted in person at the hospitals. The primary outcomes were perceived stress and depressive symptoms, whereas the secondary outcomes included posttraumatic stress, insomnia severity, cognitive stress response, occupational stress, and burnout. Physiological outcomes were assessed using heart rate variability and electroencephalography. Measurements were collected at baseline, immediately after the intervention, and at 4-week follow-up. Data were analyzed using a multivariate linear model to evaluate the main effects of time, group, and time×group interactions. RESULTS: Of the 75 randomized participants (exercise: n=24, 32%; yoga: n=25, 33.3%; and control: n=26, 34.7%), 71 (94.7%) who completed at least 9 of the 12 sessions (≥40 min each) were included in the outcome analysis (exercise: n=21, 29.5%; yoga: n=24, 33.8%; and control: n=26, 36.6%). For the coprimary outcomes, the group×time interaction for depressive symptoms (Patient Health Questionnaire-9) approached but did not reach the Bonferroni-corrected threshold (F4,136=2.71; P=.03; adjusted α=.025); however, planned pairwise comparisons revealed significantly greater improvement in the yoga group compared to the control group at 4-week follow-up (β=-3.67; adjusted P<.001). For the Perceived Stress Scale, the interaction was not significant (P=.29), although a significant main effect of time (P<.001) indicated overall stress reduction across all groups. For secondary outcomes, a significant group×time interaction was found for the Cognitive Stress Responses Scale (P=.003), indicating differential trajectories of improvement. The yoga group showed a consistent linear decrease, whereas the exercise group showed immediate but less sustained gains. CONCLUSIONS: Digitally delivered adaptive yoga programs demonstrated superior and sustained improvements in depressive symptoms and Cognitive Stress Responses Scale scores compared with the active cognitive behavioral therapy-based self-care control group. However, the exercise program showed more modest and less sustained effects, warranting further investigation using larger samples.

Adult↗