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PubMed2026

The efficacy of non-invasive brain stimulation interventions in obsessive-compulsive disorder management: A network meta-analysis of randomized controlled trials.

Non-invasive brain stimulation (NIBS) has been widely used as an alternative treatment for obsessive compulsive disorder (OCD). However, the most effective NIBS parameters are unclear. To compare the efficacy of NIBS in OCD. We conducted a systematic review and network meta-analyses (NMA) to combine direct and indirect comparisons of NIBS.Systematic searches were conducted in Cochrane CENTRAL, EMBASE, PubMed, and Web of Science from inception to June 20, 2025. Forty-two randomized sham-controlled trials (n = 1456) were included. All statistical analyses were conducted with R statistical software. Bayesian NMAs mainly using the BUGSnet package and gemtc package. Five NIBS protocols produced statistically significant reductions in Yale-Brown Obsessive Compulsive Scale (Y-BOCS) scores compared with sham stimulation: high-frequency rTMS over the FzFCz (Hf-rTMS-FzFCz; MD -11.77, 95% CrI -20.62 to -3.09), low-frequency rTMS over F3F4 (Lf-rTMS-F3F4; MD -9.93, 95% CrI -18.07 to -1.65), low-frequency rTMS over FCz (Lf-rTMS-FCz; MD -3.25, 95% CrI -6.06 to -0.40), high-frequency deep TMS over FzFC (Hf-dTMS-FzFC; MD -6.48, 95% CrI -12.32 to -0.50), and 2 mA anodal tDCS over F3 with cathodal over Fp2 (MD -9.34, 95% CrI -16.01 to -3.03).For secondary outcomes, high-frequency deep rTMS over FzFCz produced the largest reduction both in depressive symptoms (SMD -1.24, 95% CrI -1.92 to -0.55) and anxiety scores (SMD -1.88, 95% CrI -2.62 to -1.11), but had no effect on Clinical Global Impression-Severity (CGI-S) scores.Specific NIBS protocols are safe and effective adjunctive treatments for OCD, with promising yet inconclusive improvements in comorbid depressive symptoms. Further high-quality, head-to-head trials are needed.

Humans↗

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

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

Efficacy, acceptability, and related outcomes of pharmacological interventions for acute bipolar mania: a systematic review and dose-related network meta-analysis across different age groups.

BACKGROUND: Acute bipolar mania carries negative social and economic consequences. We investigated the comparative efficacy/response/acceptability of pharmacological interventions for acute bipolar mania, considering dose effects across different age groups. METHODS: We conducted a network meta-analysis (NMA) to search for randomized controlled trials (RCTs) comparing pharmacological interventions with one another or placebo in acute bipolar mania patients, indexed in PubMed/MEDLINE, Embase, Web of Science, and Scopus (from inception through 2025.12.24). Co-primary outcomes were change in manic symptoms/response/and acceptability. Tolerability/remission and rate of adverse events were secondary outcomes. Confidence-In-Network-Meta-Analysis was likewise appraised. RESULTS: 113 RCTs, encompassing 49 distinct treatment combinations, included 20,666 participants. Sensitivity analysis retaining only low-risk-of-bias studies and excluding outliers for possible effect modifiers indicated that risperidone 3 mg/day(SMD = -7.57;95%C.I. = -8.25;-5.85); tamoxifen 160 mg/day(SMD = -1.73;95%C.I. = -2.32;-1.13); rivastigmine 3 mg/day(SMD = -1.13;95%C.I. = -1.06;-0.58); haloperidol 30 mg/day(SMD = -0.96;95%C.I. = -1.25;-0.75); valproate 750 mg/day(SMD = -0.76;95%C.I. = -1.48;-0.58); tamoxifen 40 mg/day(SMD = -0.75;95%C.I. = -1.41;-0.59); celecoxib 400 mg/day(SMD = -0.74;95%C.I. = -1.20;-0.38); paliperidone extended-release 12 mg/day(SMD = -0.62; 95%C.I. = -0.91;-0.32); olanzapine 15 mg/day(SMD = -0.59;95%C.I. = -0.60;-0.38); olanzapine 20 mg/day(SMD = -0.52;95%C.I. = -0.66;-0.38); risperidone 4 mg/day(SMD = -0.53;95%C.I. = -0.76;-0.29); allopurinol 600 mg/day(SMD = -0.54;95%C.I. = -0.67;-0.22); cariprazine 12 mg/day(SMD = -0.49;95%C.I. = -0.66;-0.33); risperidone 4.2 mg/day(SMD = -0.46;95%C.I. = -0.75;-0.17); lithium 1500 mg/day(SMD = -0.42;95%C.I. = -0.57;-0.28); ziprasidone 160 mg/day(SMD = -0.49;95%C.I. = -0.68;-0.31); asenapine 20 mg/day(SMD = -0.38;95%C.I. = -0.53;-0.22); haloperidol 8 mg/day(SMD = -0.34;95%C.I. = -0.63;-0.05); aripiprazole 15 mg/day(SMD = -0.33;95%C.I. = -0.61;-0.06) outperformed placebo. Ziprasidone 160 mg/day, celecoxib 200 mg/day, asenapine 20 mg/day, and asenapine 10 mg/day proved more efficacious than placebo in children. No statistically significant differences were reported between treatments and placebo for response/remission/acceptability/tolerability, and manic/hypomanic switch. A meta-regression of efficacy effect sizes against the adapted AMSTAR-Plus content scores showed that larger SMDs were associated with lower AMSTAR scores, indicating lower study quality, warranting further caution for such large efficacy estimates. CONCLUSIONS: Our findings are consistent with previous NMAs and current guidelines, expanding the current knowledge base while concurrently appraising different drugs, doses, and age groups.

Humans↗

PubMed2026

Infra-low-frequency neurofeedback alters EEG network efficiency: exploratory evidence from healthy volunteers.

Infra-Low-Frequency Neurofeedback (ILF-NFB) combines classic frequency-band (FB) and infra-low-frequency (ILF) EEG components in implicit training protocols and is increasingly applied in clinical contexts. Yet, the neurophysiological mechanisms underlying ILF-NFB remain to be further elucidated. In this randomized, sham-controlled and double-blind study, we explored the online impact of a one-session ILF-NFB application on EEG correlates in healthy participants (39 analyzed datasets). Continuous 31-channel EEG was recorded during verum and sham feedback in a double-blind, randomized crossover design. In this exploratory analysis approach, functional connectivity was estimated using the debiased weighted phase-lag index (dwPLI) and analyzed with graph-theoretical measures. The results revealed higher global efficiency during verum compared to sham in the Beta1 band (12-15 Hz), reaching significance in the primary comparison but not surviving Bonferroni correction across the five tested bands; block-wise follow-ups showed a significant verum-sham difference in the first half of the neurofeedback session and a directionally consistent pattern in the second half. The Condition × Block interaction was not significant. No consistent differences were observed in other frequency bands, nor for betweenness centrality. While preliminary, these exploratory results point to possible network-level effects during ILF-NFB and motivate further confirmatory work in extended training protocols and clinical populations.

Humans↗

PubMed2026

Targeting ncRNA control networks with engineered exosomes to overcome therapy resistance in thyroid cancer.

Papillary thyroid cancer (PTC) is the most prevalent endocrine malignancy, accounting for over 90% of thyroid cancers. While differentiated thyroid cancers (DTCs) typically have favorable outcomes, a significant subset progresses to radioactive iodine-refractory (RAIR) disease, characterized by impaired iodine uptake and a 10-year survival rate below 10%. Genetic alterations and dysregulated signaling pathways underlie this transition. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (lncRNAs), play critical regulatory roles in tumor biology and may be transported via exosomes, facilitating intercellular communication and contributing to RAIR-PTC. This systematic review, conducted according to PRISMA 2020 guidelines, evaluated the role of exosomal ncRNAs in RAIR-PTC. A comprehensive search of PubMed, PubMed Central, and Google Scholar identified studies published within the past 15 years in English. Following stringent quality appraisal, studies with a non-bias score above 40% were included. Of 961 identified publications, 96 high-quality studies met inclusion criteria. Evidence indicates that therapy resistance in RAIR-PTC is driven by convergent ncRNA regulatory networks that suppress sodium-iodide symporter (NIS) expression and activate oncogenic pathways, most notably MAPK, PI3K/AKT/mTOR, and Wnt/β-catenin signaling. Multiple ncRNAs converge on key regulatory nodes, forming redundant circuits that sustain dedifferentiation, metabolic adaptation, and impaired iodide transport. Several consistently dysregulated ncRNAs directly or indirectly regulate NIS expression and trafficking, highlighting actionable targets. Exosomes emerge as biologically compatible, programmable delivery vehicles capable of transporting therapeutic ncRNA payloads independent of endogenous packaging mechanisms. These findings support a precision therapeutic paradigm in which engineered exosomes reprogram ncRNA networks to restore iodine-handling pathways and overcome therapy resistance in RAIR-PTC.

Humans↗

PubMed2026

Cerebellar iTBS enhances gait adaptation by modulating cortical sensorimotor network dynamics: a randomized controlled trial.

Gait adaptation enables individuals to maintain locomotor stability under persistent perturbations. Although the cerebellum is critical for sensory prediction error-based (SPE) adaptation, how cerebellar neuromodulation reshapes cortical sensorimotor networks to enhance gait adaptation remains unclear. This study investigated the behavioral effects and underlying cortical neurodynamic mechanisms of cerebellar intermittent theta-burst stimulation (iTBS) on gait adaptation. Thirty-two healthy adults received either active or sham cerebellar iTBS. Participants performed a split-belt treadmill adaptation task before and after intervention. Cortical responsiveness was evaluated using TMS-evoked EEG over primary motor cortex (M1), while resting-state EEG was analyzed to assess spectral power and directional functional connectivity. Compared to sham, cerebellar iTBS significantly enhanced gait adaptation, evidenced by a faster adaptation rate (p = 0.035) and enhanced Early Adaptation SLS (p = 0.011), without altering initial perturbation responses or post-adaptation outcomes. The iTBS increased TMS-evoked α (p = 0.031) and γ (p = 0.022) power in M1, while the α power was correlated with faster adaptation (r = 0.526, p = 0.002). Furthermore, iTBS strengthened PPC-to-M1 directed connectivity in the β (p = 0.025) and γ (p = 0.013) bands. Enhanced parieto-motor directionality were positively associated with adaptation rate (β: r = 0.515, p = 0.003; γ: r = 0.463, p = 0.009). These findings suggest that cerebellar iTBS facilitates gait adaptation by modulating cortical responsiveness and directional sensorimotor network connectivity, providing multi-level neurodynamic evidence for the cerebello-cortical modulation during gait adaptation and offering a strong physiological rationale for targeted neuromodulation in gait rehabilitation strategies.

Humans↗

PubMed2026

Cytonuclear conflict and reticulate evolution in the Morelloid clade (Solanum, Solanaceae): Insights from genome skimming and network Phylogenomics.

The Morelloid clade (black nightshades) is one of the most strongly supported clades within the megadiverse Solanum genus. It comprises 76 globally distributed, non-spiny herbaceous and suffrutescent species. While often erroneously considered poisonous weeds, several species are economically important as orphan crops. The clade is closely related to tomato and potato but, due to a lack of focused breeding efforts, remains a putative reservoir of genetic diversity for crop improvement. Despite this potential, we lack fundamental knowledge on the evolution of the Morelloid clade. The group includes polyploid species with unknown parental origins-likely reflecting reticulate processes such as hybridization, introgression, and associated backcrossing events. Prior analyses have been unable to disentangle these processes, leaving the mechanisms underlying reticulate evolution in the Morelloid clade poorly understood. Here, we use genome skimming to produce a well-supported maximum likelihood plastid phylogeny from complete circularized plastomes and a coalescent-based species tree from combined Angiosperms353 and conserved ortholog set nuclear markers. Our dataset, composed of previously published data and deep genome skimming from herbarium samples, spans 26 Morelloid species. To investigate phylogenetic discordance, we used a nuclear phylogenetic network, multispecies coalescent simulations, a fused rooted nuclear chloroplast tree, and quantification of nuclear gene tree concordance. We show that incongruence between nuclear and plastid trees is pervasive and cannot be explained by incomplete lineage sorting alone. Instead, our results demonstrate that events consistent with repeated chloroplast capture have shaped the reticulate evolutionary history of the clade, especially among African polyploid and Pan-American diploid lineages.

Phylogeny↗

PubMed2026

Smoking Cue Reactivity in Relation to Uncertain-Threat and Reward-Anticipation Networks: A Coordinate-Based fMRI Meta-Analysis.

BACKGROUND: Smoking is a concerning medical and social problem, yet how the brain links stress to continued smoking is still not well understood. This coordinate-based meta-analysis identified convergent activations for smoking cues, uncertain threat, and reward anticipation, and examined co-activation patterns to clarify whether smoking-cue activity in smokers relates to threat and reward activity in non-addicted controls. METHODS: We conducted a coordinate-based activation likelihood estimation (ALE) meta-analysis of 102 fMRI studies (N = 3,068), including 30 studies on smoking cue reactivity (n = 945), 48 on reward anticipation (n = 1,413), and 24 on uncertain threat processing (n = 1,621). We performed single, conjunction, and contrast analyses, followed by meta-analytic connectivity modeling (MACM) of key regions. RESULTS: Single analysis revealed smoking engaged bilateral ACC (-1.1, 46.2, -1.1), uncertain threat engaged bilateral insula (left = -33.6, 22, 4.3, right = 41.3, 22, 1), reward anticipation activated thalamus (0.9, 1.3, -3.1) and medial frontal gyrus (2.7, 6.6, 52.1). Conjunction and contrast analyses showed shared or unique activation for each task in its respective regions. MACM showed ACC co-activation with thalamus and medial frontal gyrus, while insula co-activated with ACC and inferior frontal gyrus. CONCLUSIONS: Each process converged in a separate region, with no overlap between the smoking-cue map and either the threat or reward map. The ACC nonetheless co-activated with reward-related regions and shared network membership with the threat-related insula. On this basis we hypothesize a shift in motivation from stress-driven reward toward cue-driven craving, to be tested within subjects, and identify candidate neuromodulation targets for preventing stress-precipitated relapse.

fMRI↗

PubMed2026

Genome-Wide Impact of Human DBR1 Depletion on RNA Processing Networks Reveal a Connection Between Pre-mRNA Splicing, mRNA Surveillance and Stress Granule Dynamics.

The RNA lariat debranching enzyme DBR1 is essential for intron turnover and RNA metabolism, yet its broader impact on transcriptome regulation remains incompletely defined. To elucidate the consequences of DBR1 depletion, we performed transcriptome-wide RNA sequencing of DBR1-knockdown and wild-type HEK293 cells. Differential expression analysis revealed widespread perturbations in pathways linked to RNA splicing, mRNA surveillance, translational control, and stress-granule biology. Many of the most significantly altered transcripts encode splicing factors and RNA quality-control components, underscoring DBR1's influence on post-transcriptional regulation. Alternative splicing analysis showed changes across multiple event types, with exon skipping accounting for >50% of events, followed by mutually exclusive exons, alternative 5' and 3' splice sites, and retained introns, indicating that DBR1 depletion induces pervasive splicing defects. Direct spliceosome inhibition using isoginkgetin (blocks tri-snRNP recruitment) and pladienolide B (targets SF3B1) reproduced the DBR1-KD mis-splicing patterns of cell signaling genes and factors involved in RNA metabolism, supporting a functional link between DBR1 activity and alternative splicing. Notably, DBR1 knockdown revealed a subset of transcripts that are both NMD-sensitive and enriched within stress granules. Consistent with this observation, G3BP1 immunopurification and confocal microscopy further support a role for DBR1 and UPF1 in stress-granule dynamics, suggesting that these factors may participate at distinct stages to influence mRNA fate under stress conditions. Together, these findings indicate that DBR1 functions beyond lariat RNA turnover as a common regulator of RNA processing, transcriptome stability, and stress granule homeostasis, revealing intricate crosstalk between RNA splicing and RNA quality control pathways in human cells.

Humans↗

PubMed2026

Effectiveness of passive vs. assistive robotic gait training on functional recovery and neuroplasticity post-stroke: A randomized controlled trial.

OBJECTIVE: This study seeks to compare the impacts of various robotic gait training (RAGT) modes on lower limb motor function recovery in stroke patients while exploring the corresponding neural mechanisms. DESIGN: A single-blind, randomized controlled trial. SETTING: Inpatient Rehabilitation Facility. PARTICIPANTS: Forty-eight patients aged 18-80 who had experienced their first unilateral subacute stroke accompanied by walking impairments were included. INTERVENTIONS: Participants were randomly assigned to: (1) assistive mode training, (2) passive mode training, or (3) control group receiving only traditional rehabilitation. Clinical and neurological outcomes were assessed at pre-intervention (T0), and post-2-week intervention (T1). MAIN OUTCOME MEASURES: Outcomes were evaluated using the Fugl-Meyer Assessment for Lower Extremity, Berg Balance Scale, Modified Barthel Index, the Functional Ambulatory Category, and functional near-infrared spectroscopy. RESULTS: Among the 48 patients recruited, significant time effects were observed across all groups in FMA-LE scores (p < 0.001). Notable improvements were detected in the conventional group (MD = 2.69, p＜0.01) and the passive group (MD = 3.67, p < 0.001), with the assistive mode also demonstrating a significant effect (MD = 1.79, p < 0.05). BBS scores improved across all groups; however, no significant differences were noted between the groups (p = 0.11). Similarly, MBI scores showed a significant time effect (p < 0.001), without notable group differences (p = 0.29). CONCLUSION: All training modalities effectively enhanced motor function, balance, and daily living skills in stroke patients. Distinct cortical activation and connectivity patterns were observed between training modalities, which may reflect different neuroplastic mechanisms. These preliminary neural differences may help inform personalized rehabilitation strategies, although no clinical superiority of one mode over another can be concluded from the present data.

Humans↗

PubMed2026

Delphi study robot consenso: Strategies for the implementation of robotic surgery in general surgery in the Spanish hospital network.

INTRODUCTION: The implementation of robotic surgery in public hospitals presents multiple logistical, educational, and organizational challenges. In the absence of unified guidelines, a national consensus is required to optimize its safe and efficient adoption. This study aimed to establish a set of consensus-based and measurable recommendations for the implementation of robotic surgery programs in hospitals within the Spanish National Health System, based on the experience of centres with established robotic programs and intended to serve as guidance for hospitals that are initiating or planning their implementation. METHODS: A national Delphi study was conducted with the participation of robotic surgery experts from 26 public hospitals. The expert panel was composed exclusively of digestive surgeons with experience in robotic surgery. Three iterative rounds of expert panel evaluation were conducted between March 2024 and March 2025. The questions were grouped into five thematic blocks. Consensus was defined as an agreement level of ≥66.7%. Kendall's W coefficient was used to assess concordance. RESULTS: High levels of consensus were achieved on key aspects related to infrastructure, structured training, cost evaluation, and quality assurance mechanisms. Areas of disagreement were also identified, such as the need for a dedicated anaesthesiologist, purchase of accessory instruments during the initial phase, and official accreditation pathways. CONCLUSIONS: This study provides a guideline for developing a national robotic surgery strategy focused on patient safety, program sustainability, and standardized training of surgical teams. These recommendations can guide hospitals at different stages of robotic technology adoption. Given that the consensus was reached from an exclusively surgical perspective, the recommendations focus on patient safety, program sustainability, and standardized training of the surgical team, and should be interpreted in an adaptable manner according to each centre's context, case volume, and available resources.

Cirugía Asistida por Robot↗

PubMed2026

Comprehensive analysis of mRNA-microRNA-lncRNA expression profiles in post-traumatic elbow heterotopic ossification using RNA sequencing and experimental validation.

BACKGROUND: This study aimed to profile the molecular signatures of post-traumatic elbow heterotopic ossification (HO) to identify key regulators and potential therapeutic targets. METHODS: Total RNA from post-traumatic elbow HO tissues (n=4) and normal bone tissues (n=6) was subjected to high-throughput sequencing to identify differentially expressed mRNAs (DEGs), microRNAs (DEMs), and lncRNAs (DELs). Bioinformatics analyses included Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, protein-protein interaction network construction, and transcription factor (TF)-microRNA-mRNA network analysis. The expression trends of four most upregulated and four most downregulated DEGs were validated by real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: We identified 2,138 DEGs, 40 DEMs, and 905 DELs. DEGs were significantly enriched in biological process "bone mineralization," cellular component "plasma membrane," molecular function "integrin binding," and pathways including PI3K-Akt, NF-κB, JAK-STAT, and TNF signaling pathways. Hub genes with high connectivity included MMP9, IL6, MMP3, CTSK, and BGLAP. Integrated network analysis highlighted the transcription factor JUN and key microRNAs (hsa-miR-124-3p, hsa-miR-548c-3p, and hsa-miR-135b). The qRT-PCR results confirmed the expression trends of selected DEGs. CONCLUSIONS: This study, for the first time, profiled the differentially expressed mRNAs, microRNAs, and lncRNAs in post-traumatic elbow HO using high-throughput RNA sequencing. These findings provide valuable insights into the molecular mechanisms of HO following elbow trauma. The identified hub genes (MMP9, IL6, MMP3, CTSK, and BGLAP), key TF (JUN), and key microRNAs (hsa-miR-124-3p, hsa-miR-548c-3p, and hsa-miR-135b) may serve as potential therapeutic targets for preventing and treating post-traumatic elbow HO.

Humans↗

PubMed2026

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗

PubMed2026

Prenatal exome sequencing of fetuses with central nervous system anomalies based on prenatal ultrasound and magnetic resonance imaging diagnosis: A retrospective cohort study with a systematic review and meta-analysis.

INTRODUCTION: Fetal central nervous system (CNS) abnormalities have diverse etiologies, with genetic factors as a major contributor. Prenatal exome sequencing (ES) is a powerful tool for precise molecular diagnosis of CNS anomalies, but its diagnostic yield varies among studies. This study aimed to evaluate the additional diagnostic yield of prenatal ES compared with chromosomal microarray analysis (CMA) in fetuses with CNS anomalies detected by prenatal imaging. MATERIAL AND METHODS: We collected ES results from fetuses diagnosed with CNS anomalies by prenatal imaging (2019-2024) who had negative results. Subgroup analyses assessed phenotype-specific ES diagnostic yield for associated genes and variants. A systematic review and meta-analysis incorporating our data and published studies further explored the association between phenotype and diagnostic yield. RESULTS: In the cohort study of 219 cases, ES identified pathogenic/likely pathogenic single nucleotide variations in 36 cases (16%). The highest diagnostic yield of ES was in cases with multisystem malformations (25%, 14/55), followed by multiple CNS anomalies (15%, 2/13) and isolated CNS anomalies (13%, 20/151). The most commonly identified isolated CNS anomaly was agenesis of the corpus callosum (31%, 5/16). Neural tube defects with urogenital anomalies were associated with a positive ES finding in 57% (4/7) of cases. The meta-analysis of 989 cases from 22 studies showed a pooled diagnostic yield of ES of 27% (95% CI, 21%-34%). The highest diagnostic yield of ES was in cases of corpus callosum anomalies with facial abnormalities (75%, 8/11) and neural tube defects with urogenital malformations (80%, 12/15). The diagnostic yield of ES for three or more CNS abnormalities was 43% (95% CI, 31%-58%), significantly higher than that for only two abnormalities (10%, 95% CI, 4%-18%). No significant difference in diagnostic yield was found between cases identified by prenatal MRI combined with ultrasound (27%, 95% CI, 20%-36%) and those identified by ultrasound alone (25%, 95% CI, 17%-35%). CONCLUSIONS: ES provided a significantly higher diagnostic yield than CMA for fetal CNS abnormalities, with diagnostic yields varying by phenotype. The systematic review and meta-analysis confirmed that the complexity and combination of malformations are key factors associated with differences in ES diagnostic yield.

Humans↗

PubMed2026

Effects of apple phenolics on the human metabolome: modulation of key metabolic pathways.

Apples are widely recognized for their potential health benefits, partly attributed to their phenolic compounds. However, their impact on human metabolism remains incompletely understood. This study investigated metabolic effects of apple-derived phenolic compounds using untargeted metabolomics approach across multiple biofluids. In a crossover intervention study, 30 healthy men consumed a phenolic-rich apple juice or a placebo for two weeks. Blood, urine and saliva samples were collected before and after each intervention and analyzed by direct infusion ultra-high resolution mass spectrometry. Consumption of apple phenolic compounds resulted in significant alterations of the human metabolome, including increased levels of phenolic-derived degradation products and microbial-associated metabolites across all biofluids. Pathway enrichment analysis revealed pronounced effects on phenylalanine and tyrosine metabolism, as well as linoleic and arachidonic acid metabolism, Overall, these findings demonstrate that apple phenolic compounds induce measurable, microbiota-associated and systemic metabolic changes, providing new insights into their metabolic fate and biological relevance.

Humans↗

PubMed2026

Integrated miRNA-mRNA profiling reveals candidate regulatory relationships associated with high-fat diet-induced muscle lipid deposition in black seabream (Acanthopagrus schlegelii).

High-fat diets are increasingly used in aquaculture due to their protein-sparing effects; however, the post-transcriptional regulatory mechanisms of fish muscle in response to high-fat diets (HFD) remain unclear. In this study, juvenile black seabream were fed either a normal-fat diet (NFD) or a HFD to investigate the miRNA-mRNA regulatory network associated with diet-induced muscle lipid deposition. Oil Red O staining and biochemical analysis showed that high-fat diet feeding markedly increased lipid droplet accumulation and crude lipid content in muscle, indicating significant induction of muscle lipid deposition. Integrated mRNA and miRNA expression profiling revealed substantial transcriptomic and post-transcriptional responses to high-fat diet challenge. A total of 271 differentially expressed genes were identified, including 120 upregulated and 151 downregulated genes. Through combined target prediction and expression correlation analysis, thirteen candidate inverse miRNA-mRNA relationships were subsequently identified, and RT-qPCR supported the expression patterns of selected miRNAs and mRNAs. These pairs included miR-499-x-dmgdh, miR-499-y-gatm, miR-727-y-ass1, miR-4649-x-foxo4, miR-9129-z-myl7, and several novel miRNA-mediated interactions involving adk, chst11, lypla2, frem2, kcnc4, wars1, bag2, and capn2. Functional analysis suggested that these regulatory pairs were mainly associated with metabolic adaptation, structural remodeling, and cellular stress responses. In particular, gatm, dmgdh, ass1, and adk were associated with energy metabolism-related processes, including pathways previously linked to Ampk regulation, whereas myl7, frem2, and kcnc4 may contribute to muscle structural maintenance and excitability regulation. Overall, this study provides candidate miRNA-mRNA regulatory relationships potentially involved in high-fat diet-induced muscle lipid deposition and adaptive remodeling in black seabream, offering a basis for future functional studies on muscle metabolism and quality regulation in marine fish.

Animals↗

PubMed2026

Criteria for Safe Hospital Discharge in Bronchiolitis: A Systematic Review.

Bronchiolitis is the leading cause of hospital presentation and admission for infants in Australasia. We aimed to synthesise current evidence on the effect of discharge criteria for infants (aged < 12 months) who are presenting to or are admitted to hospital with bronchiolitis, to inform a binational guideline recommendation update. Systematic searches were conducted on MEDLINE, EMBASE, PubMed, Cochrane Library and CINAHL (last search 19 February 2025) for non-randomised studies evaluating hospital discharge criteria in bronchiolitis. The primary outcomes were length of stay (LOS) and readmission rates. The risk of bias (ROBINS-I) and certainty of the evidence (GRADE) were appraised, and findings were narratively synthesised. GRADE evidence-to-decision methodology, expert consensus voting and interest-holder consultation were used to finalise the recommendation update. Two retrospective observational studies were included (N = 2697) (low to very low quality), reporting on unique discharge criteria. In both studies, use of the discharge criteria was associated with a significant reduction in LOS relative to alternative protocols. There was no significant difference in readmission rates observed in either study. There was low to very low certainty evidence across outcomes due to risk of bias, indirectness and imprecision. The review findings informed a recommendation update for safe discharge criteria in the 2025 Australasian Bronchiolitis Guideline update. Updated, prescriptive discharge criteria and flow chart were developed, covering clinical stability, oxygen saturation/support, feeding difficulties, caregiver confidence and education on deterioration, social factors and follow-up. The revised criteria provide clinicians with increased certainty in decision-making in bronchiolitis, albeit with further research needed.

Humans↗