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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

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

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

Large Language Models↗

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

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

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

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↗

PubMed2025

The molecular mechanism of cuproptosis and research progress in pancreatic diseases.

PURPOSE: Cuproptosis has been proven to be a novel mode of cell death, distinct from other types of cell death such as necrosis, ferroptosis, pyroptosis, and apoptosis. This study aims to systematically review the molecular mechanisms of cuproptosis in recent years and its research progress in pancreatic diseases. METHODS: By searching PubMed and Web of Science databases, 113 key literatures were included for thematic analysis, covering the molecular mechanism of cuproptosis and its role in the occurrence and development of pancreatic cancer, acute and chronic pancreatitis, diabetes, pancreatic cyst, pancreatic injury and pancreatic neuroendocrine tumor. RESULTS: Cuproptosis refers to the accumulation of copper ions in cells, which leads to instability of ferritin and aggregation of acylated proteins, resulting in oxidative stress-related cell death. Recent studies have shown that cuproptosis plays an important role in the occurrence and development of various pancreatic diseases, such as pancreatic cancer, acute and chronic pancreatitis, diabetes, pancreatic cysts, pancreatic injuries and pancreatic neuroendocrine tumor. The inducers of cuproptosis, such as disulfiram, chloroquinolones, and perilla phenols, alleviate pancreatic cancer by promoting cell cuproptosis. Copper chelators such as tetraethylenepentamine and tetrathiomolybdate promote the recovery of pancreatic injury by inhibiting cell cuproptosis. CONCLUSIONS: Cuproptosis plays a crucial role in the pathogenesis of pancreatic diseases. Further research on the cuproptosis pathway may become a potential target for the treatment of pancreatic diseases.

Animals↗

PubMed2026

Direct background subtraction LC-MS/MS assay for human plasma progesterone: Full validation and comparative application.

OBJECTIVE: To develop and validate a liquid chromatography-tandem mass spectrometry method based on direct background subtraction for the quantification of endogenous progesterone in human plasma. METHODS: Protein precipitation was used for sample preparation with deuterated progesterone as the internal standard. Chromatographic separation was performed on an ACQUITY C18 column using gradient elution with 0.1% formic acid in water and acetonitrile at a flow rate of 0.3 mL/min. Mass spectrometry was operated in positive electrospray ionization mode with multiple reaction monitoring. Instead of using analyte-stripped matrix or surrogate matrix, authentic plasma was directly used for all validation experiments. Quantitation was achieved by subtracting the background signal, and results were compared with those from the classical method using stripped matrix. RESULTS: Excellent linearity was achieved over 0.1-100 ng/mL (R2 ≥ 0.99). Precision, accuracy, recovery, matrix effect, and stability all met FDA and ICH M10 acceptance criteria. Compared with the classical method, the bias in Cmax and AUC0-t was within ±15%, indicating no significant difference between the two methods. CONCLUSION: The direct background subtraction method avoids laborious preparation of blank matrix, eliminates matrix effect discrepancies, and is simple, efficient, and low-cost. It can serve as a general strategy for endogenous substance determination.

Humans↗

PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

PubMed2026

Efficacy of current approaches to non-invasive diagnosis of skin cancer and the potential impact of artificial intelligence: A systematic review and meta-analysis.

BACKGROUND: Skin cancer is one of the most prevalent malignancies worldwide, particularly within Caucasian populations. This systematic review and meta-analysis aimed to quantitatively review the current literature on non-invasive diagnosis of skin cancer and evaluate the current evidence to support the use of tools in addition to, or in replacement of clinician face-to-face assessment. METHODS: A literature search was conducted for publications in PubMed, Medline and Embase databases. Articles describing accuracy, sensitivity, specificity and outcomes of their mode of assessment were included. A total of 208 articles met the inclusion criteria. RESULTS AND CONCLUSION: This systematic review and meta-analysis showed that the diagnostic performance of artificial intelligence (AI) in the interpretation of dermatoscopic images was high for melanoma diagnosis, basal cell carcinoma or malignancy, in comparison to dermatoscopic assessment alone by clinicians and experts. Although AI interpretation of images demonstrated higher sensitivity for melanoma diagnosis in comparison to clinical assessment combined with dermatoscopic assessment, it is unclear if this is also the case for basal cell carcinoma and squamous cell carcinoma diagnosis. Reflectance confocal microscopy, a non-invasive high resolution imaging technique, is known to have a high sensitivity for diagnosing cutaneous malignancy, and this may have applications within secondary care. Therefore, AI could help reduce resource burden and aid in clinical assessment, particularly within primary care settings.

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

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

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

Alarms and alarm management with automated versus conventional ventilation in neurocritical care patients.

INTRODUCTION: False or clinically irrelevant alarms are a major driver of ICU alarm fatigue and nursing workload. Ventilator alarms make up a large share, and although automated ventilation modes can reduce manual adjustments, their effect on alarm burden is still unclear. This issue can be particularly relevant in neurocritical care patients, where precise ventilator and alarm management is imperative for patient safety. OBJECTIVES: This explorative post hoc analysis of a randomized clinical trial compared alarm frequency and management between automated ventilation and conventional ventilation in neurocritical care patients. METHODS: Ventilator alarms and manual ventilator changes were captured continuously from the ventilator for up to 24 h per patient. The primary endpoint was a composite of workload-relevant alarms; with alarm management interventions at the ventilator as a key secondary outcome. Additional endpoints included redundant alarms, alarm duration and ventilator management. RESULTS: 13 patients received automated ventilation and 24 received conventional ventilation. No difference was observed in workload-relevant alarm frequency between automated and conventional ventilation (3.28 [2.87 to 4.30] vs 3.73 [1.66 to 7.33] alarms per hour; P = 0.81), while alarm management interventions at the ventilator were lower with automated ventilation (0.14 [0.10 to 0.15] vs 0.21 [0.17 to 0.31] interventions per hour; P = 0.01). Other alarm frequencies, duration of alarms and ventilator management were similar. CONCLUSIONS: In this exploratory post hoc analysis of a randomized clinical trial in neurocritical care patients during the early phase of mechanical ventilation, automated ventilation did not reduce the frequency of total or workload-relevant alarms, nor their duration, but was associated with fewer alarm management interventions compared to conventional ventilation. IMPLICATIONS FOR CLINICAL PRACTICE: Automated ventilation may not reduce alarm frequency in neurocritical care patients, but the observed reduction in alarm-related bedside interventions suggests a potential benefit for nursing workload.

Humans↗

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↗