This study aims to clarify the characteristics and temporal patterns of colonic barrier damage in mice following a single simulated high-intensity exercise in hot and humid environment, and further investigate the protective effects of baicalein (BAI) intervention on colonic injury, thereby providing experimental evidence for the development of medical protection time windows and pharmaceutical intervention protocols for personnel engaged in high-intensity exercise and operations in hot and humid environments.

①Thirty SPF-grade 8-week-old male C57BL/6J mice were randomly divided into 5 groups (n=6): control group, 30 min post-exercise group, 4 h post-exercise group, 12 h post-exercise group, and 24 h post-exercise group. The mice in the exercise groups were subjected on a treadmill in a heat chamber (temperature: 38℃, humidity: RH 75%) at 80% maximal exercise velocity (Vmax) (exercise 12 min, rest 8 min, 3 cycles; slope 10°). Subsequently, the colon tissues were harvested for morphological observation by HE staining in each group; ELISA was used to detect the serum levels of inflammatory factors (TNF-α, IL-6, IL-10, and IL-1β) to observe the inflammatory response of each group. Intestinal fatty acid binding protein (I-FABP) and lipopolysaccharide (LPS) were detected to observe intestinal permeability; Microassay was performed to determine the concentrations of reduced glutathione (GSH) and malondialdehyde (MDA), as well as the activity of catalase (CAT) and superoxide dismutase (SOD) in the colon tissues; RT-qPCR was applied to detect the expression levels of intestinal tight junction protein related genes Occludin, ZO-1, Claudin-1, inflammation and oxidative stress related genes Tlr4, TNF-α, IL-1β, IL-6, IL-10, heme oxygenase-1 (HO-1), and superoxide dismutase1 (SOD1), as well as key transcription factors HSF1, NF-κB, PARP-1, and NRF2 in heat stress regulation; Western blotting was utilized to detect the protein of colonic tight junction protein Occludin. ② For BAI intervention study, 24 SPF-grade 8-week-old male C57BL/6J mice were randomly divided into 4 groups (n=6) control group, exercise group (high temperature and high humidity exercise, gavage with deionized water 200 μL), exercise+low- and high-dose intervention groups (BAI gavage 200 μL at 10 and 40 mg/kg per day). At 4 h after the end of exercise, colonic and blood samples were collected for serum levels of urea nitrogen (BUN) content, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) to evaluate the safety of BAI. Other detection sites, indicators, and methods were the same as aforementioned.

① The core body temperature was significantly elevated in the exercise group (P<0.05); the structural damage, along with inflammatory cell infiltration was observed in colonic mucosa; the expression of colon Occludin, ZO-1, and Claudin-1 was downregulated (P<0.05), and the protein level of Occludin was decreased (P<0.05), and the serum levels of I-FABP and LPS were increased (P<0.05); The serum levels of TNF-α, IL-1β, IL-6, and IL-10 were increased (P<0.05), and the expression of IL-1β and IL-10 in the colon was upregulated (P<0.05); The concentration of MDA was increased (P<0.05), while the concentration of GSH and the activities of CAT and SOD were decreased (P<0.05); The expression of HSF1, PARP-1, NF-κB, Tlr4, TNF-α, IL-6, and SOD1 in the colon was upregulated (P<0.05), while the expression of NRF2 and HO-1 was downregulated (P<0.05). These changes were present at 30 min post-exercise, most prominent at 4 h post-exercise, and markedly recovered by 24 h. ② After BAI intervention, the core body temperature was significantly decreased (P<0.05), and the serum levels of ALT and AST were decreased (P<0.05); colonic mucosal injury was alleviated; the expression of Occludin at mRNA and protein levels in the colonic tissues were increased (P<0.05); the expression of ZO-1 and Claudin-1 were upregulated (P<0.05), and serum levels of I-FABP and LPS were decreased (P<0.05); the serum level of IL-1β was decreased (P<0.05), and the expression of IL-1β in the colon was downregulated (P<0.05); The concentration of MDA was decreased (P<0.05), while the concentration of GSH and activities of CAT and SOD in the colon were increased (P<0.05); The expression of HSF1, PARP-1, NF-κB, Tlr4, TNF-α, and IL-6 in the colon was downregulated (P<0.05), while the expression of NRF2 and HO-1 was upregulated (P<0.05). These results indicated that BAI possesses anti-inflammatory and antioxidant effects, with better efficacy in the low-dose group.

Peak colonic barrier injury occurs at 4 h after simulated high-intensity exercise in a hot and humid environment, and prophylactic use of BAI (10 mg/kg per day) can significantly inhibit inflammation and oxidative stress and alleviate injury, providing evidence for time windows and intervention basis for colonic injury induced by heat stress.

To compare the hypothalamic cellular composition and neuron-neuron communication networks across 3 etiologically distinct mouse models of obesity, high-fat diet (HFD), leptin-deficient (ob/ob), and leptin receptor-deficient (db/db) so as to identify universal mechanistic clues that can be validated across etiologies and searching for etiology-specific targets applicable to stratified treatment.

Six-week-old male C57 mice were fed with HFD continuously until 18 weeks of age to establish a HFD model. In parallel, 18-week-old male ob/ob mice and db/db mice were subjected as genetic obesity models, and age-matched male C57 mice served as controls. Three mice were included per group (n=3), totaling 12 mice, were euthanized, and the hypothalamic tissues were meticulously dissected under a stereomicroscope. Single-nucleus transcriptome sequencing (snRNA-seq) was performed using the 10x Genomics v3.1 platform. The obtained sequencing data were aligned with Cell Ranger and processed in Seurat for quality control, dimensionality reduction, and clustering. Subsequently, neuron-neuron communication networks were inferred based on NeuronChat and its nervous system-specific ligand-receptor database, systematically comparing changes in differential communication networks, signal output/input strength, information flow, and interaction quantity between each obesity model and C57 controls.

Dimensionality reduction and clustering of hypothalamic snRNA-seq data from C57, HFD, ob/ob, and db/db mice identified several major cell populations, including excitatory neurons, inhibitory neurons, and multiple non-neuronal cell types, with cells of the same type showing model-dependent shifts in uniform manifold approximation and projection (UMAP) space. Neuronal subclustering resolved 7 GABAergic and 10 glutamatergic subtypes, indicating pronounced neuronal heterogeneity in the hypothalamus. Across the 3 obese models, GABAergic inhibitory incoming signaling and the neurexin-neuroligin (Nrxn-Nlgn) pathway were broadly downregulated. Meanwhile, each model exhibited a distinct communication-network signature: the HFD model was characterized by enhanced electrical synapses mediated by gap junction protein alpha-1 (Gja1-Gja1) and reduced Nrxn-Nlgn output; the db/db model showed dominant nitric oxide-soluble guanylate cyclase signaling (NO-Gucy1a2) together with enrichment of prodynorphin (Pdyn)-related pathways; the ob/ob model was primarily marked by enhanced glutamate receptor interactions. Pairwise comparisons among models further validated these patterns: HFD relatively enriched Gja1-related signaling and certain metabotropic glutamate receptor interactions; db/db relatively enriched NO-Gucy1a2 and synaptic adhesion-related pathways; ob/ob relatively enriched in ionotropic/metabotropic glutamate receptor interactions.

Distinct neural communication network patterns exist in the hypothalamus across obesity models of different etiologies: diet-induced obesity preferentially exhibits enhanced electrical synapse-related pathways, leptin receptor-deficient model is primarily characterized by increased NO-related signaling, and the leptin-deficient model is centrally distinguished by enhanced glutamatergic excitatory interactions. Notably, attenuated inhibitory regulation emerges as a common feature across models, providing novel insights into understanding the pathological mechanisms underlying obesity development.

Hypoxia in the tumor microenvironment is a critical factor contributing to chemoresistance in glioblastoma. This study preliminarily investigates the role of hypoxia-inducible factor-2α (HIF-2α) and transforming growth factor-β2 (TGF-β2) in promoting chemotherapy resistance of glioblastoma cells under hypoxic conditions.

U87 and U118 cells (n=3) were cultured under normoxic and hypoxic conditions and treated with temozolomide (TMZ) for 24, 48, and 72 h. Cell proliferation was assessed by CCK-8 assay, and apoptosis was detected by flow cytometry after 48 h of culture. Using normoxic culture as control, U87 and U118 cells (n=3) were subjected to hypoxic culture for 4, 8, 12, and 24 h, followed by RT-qPCR to measure HIF-2α and TGF-β2 mRNA expression. After further hypoxic culture for 12, 24, and 48 h, the protein levels of HIF-2α and TGF-β2 were detected by Western blotting. After knockdown of HIF-2α and TGF-β2, the cells (n=3) were cultured under hypoxia and treated with TMZ for 24, 48, and 72 h. Then, the cell proliferation was detected by CCK-8 assay. After 24 h of culture, TGF-β2 (50 ng/mL) was added to the culture medium of some cells, and the cells were further cultured under hypoxia for another 24 h, followed by flow cytometry for cell apoptosis. Western blotting was applied to detect TGF-β2 expression in HIF-2α knockdown cells after 48 h of hypoxic culture.

CCK-8 and flow cytometry assays indicated that the hypoxia+TMZ group showed increased cell viability (P<0.05) and decreased apoptotic rate compared to the normoxia+TMZ group (P<0.05). RT-qPCR revealed that compared with the normoxia group, the expression levels of HIF-2α and TGF-β2 in hypoxia-cultured cells gradually increased with the prolongation of hypoxic time (P<0.05), and Western blotting also confirmed that the protein expression of HIF-2α and TGF-β2 increased in a time-dependent manner after hypoxic culture. Lower cell viability (P<0.05) and higher apoptotic rate (P<0.05) were observed in the HIF-2α-knockdown and TGF-β2-knockdown cells compared with the control non-knockdown group. Continued hypoxic culture of HIF-2α-knockdown cells indicated decreased TGF-β2 expression compared with the control non-knockdown HIF-2α group.

HIF-2α chemoresistance in glioblastoma cells via TGF-β2.

Benzo(a)pyrene (BaP), as the most common polycyclic aromatic hydrocarbon pollutant, exhibits potent carcinogenic, teratogenic, and inflammatory toxic effects. Previous studies have demonstrated that BaP promotes invasion and migration capabilities of human bronchial epithelial cells following malignant transformation by suppressing ferroptosis; however, whether BaP can regulate tumor cell pyroptosis remains unclear. This study aims to elucidate the mechanism by which BaP promotes lung cancer invasion and migration through suppressing pyroptosis in human lung cancer cell line LTEP-a-2.

Potential target genes of BaP were retrieved from the Comparative Toxicogenomics Database (CTD), and lung cancer-related differentially expressed genes (DEGs) were screened from the Gene Expression Omnibus (GEO) database. Subsequently, an intersection analysis of these 2 gene sets was performed to obtain key candidate genes. Protein-Protein Interaction (PPI) network analysis on these candidate genes was conducted using the STRING database, and topological attribute analysis was performed for topological properties with Cytoscape software to screen key hub target genes based on high node degree values. Survival analysis of these PPI-derived hub genes was performed using the Kaplan-Meier Plotter database. Molecular docking simulations between BaP and each target protein were conducted with AutoDock Vina to predict binding conformations and calculate binding free energies. Visualization and analysis of the docking results were carried out using PyMOL software, and detailed ligand-receptor interactions were visualized and analyzed using 2D interaction diagrams generated by LigPlot+. LTEP-a-2 cells were exposed to gradient concentrations of BaP (0, 0.1, 1, and 10 μmol/L) for 48 h. Subsequently, cell viability was evaluated with CCK-8 assay, invasive and migratory capabilities were assessed with Transwell assay, cytotoxicity was determined by lactate dehydrogenase (LDH) release assay, and apoptosis was detected via Annexin V-FITC/PI flow cytometry. The concentrations of IL-1β and IL-18 in the supernatant were determined by enzyme-linked immunosorbent assay (ELISA). RT-qPCR and Western blotting were performed to measure the expression of key pyroptosis-related molecules (NLRP3, ASC, caspase-1, GSDMD, IL-1β, and IL-18) at mRNA and protein levels. Cell viability and invasive/migratory capabilities were assessed after LTEP-a-2 cells treated with BaP combined with pyroptosis agonists, as well as pyroptosis inhibitors combined with pyroptosis agonists. Concurrently, alterations in pyroptosis-related indicators, including IL-1β/IL-18 release and expression of the aforementioned key molecules, were examined.

In vitro experiments demonstrated that compared to the control group, exposure to 1 or 10 μmol/L BaP significantly promoted the proliferation, invasion, and migration of LTEP-a-2 cells (P<0.01), but did not induced obvious changes in apoptosis. Bioinformatics analysis revealed significant enrichment of BaP-related genes in the pyroptosis pathway. Treatment with different doses of BaP significantly inhibited pyroptosis, as evidenced by reduced LDH release (P<0.05), downregulated secretion levels of the inflammatory cytokines IL-1β and IL-18, and notable decreases in both mRNA and protein expression levels of key molecules in the pyroptosis pathway (NLRP3, ASC, caspase-1, GSDMD, IL-1β, and IL-18) (P<0.05). Conversely, pyroptosis agonist (compared to the control group) induced pronounced pyroptotic phenotypes and suppressed cell proliferation and invasive and migratory capacities (P<0.05). In the BaP or pyroptosis inhibitor combined with pyroptosis agonist treatment groups, the inhibitory effects of the agonists on tumor cell proliferation and invasion/migration were significantly reversed (P<0.01). Bioinformatics prediction and molecular docking analysis indicated that NLRP3 and PTGS2 may serve as potential molecular targets for BaP-mediated regulation of pyroptosis in lung cancer cells.

BaP promotes the invasion and migration of LTEP-a-2 cells by inhibiting the NLRP3-caspase-1-GSDMD pyroptosis signaling pathway.

Host genes regulate the composition and function of gut microbiota, thereby influencing the development of colorectal cancer (CRC). This study aims to investigate the mechanisms by which host gene TRPV4 deficiency exacerbates CRC in mice through remodeling gut microbiota and promoting inflammatory cytokine secretion.

TRPV4 knockout (TRPV4^-/-) mice were used to establish a CRC mouse model induced by dextran sodium sulfate (DSS) and azoxymethane (AOM). Male TRPV4^-/- and wild-type mice (6 to 8 weeks old) were stratified by body weight and randomly divided into TRPV4^-/- group (n=7) and WT group (n=7). The mouse model of CRC was established through a single intraperitoneal injection of 10 mg/kg AOM on day 1 of week 1; from day 7, drinking water containing 1.5% DSS for 7 consecutive days, followed by 2 weeks of regular water, with this DSS treatment cycle being repeated for 3 times; a second intraperitoneal injection of 10 mg/kg AOM on day 1 of week 6. CRC progression was assessed with tumor number, size, burden, colon length, spleen weight, colonic morphology (HE staining) and pathological score, and immunohistochemical detection of proliferating cell nuclear antigen (PCNA). Additionally, male TRPV4^-/- and wild-type mice (6 to 8 weeks old) were stratified by body weight and randomly divided into antibiotic cocktail (ABX)-TRPV4^-/- group (n=5) and ABX-WT group (n=5). The ABX mixture was used to deplete the gut microbiota to evaluate the microbiota-dependent effects of TRPV4 on CRC. Fecal samples were collected from the TRPV4^-/- group (n=19) and WT group (n=21) before modeling, and then subjected to 16S rRNA sequencing to determine the effect of TRPV4 gene on mouse gut microbiota. RNA sequencing was performed on mouse colonic tissue; Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was used to examine enrichment of differentially expressed genes, and fecal bacteria-cell co-incubation experiments were conducted in vitro.

The TRPV4^-/- group exhibited significantly increased tumor incidence (P=0.037 9), tumor burden (P=0.000 3), histopathological score (P<0.000 1), and PCNA-positive rate (P<0.000 1), and shorter colon length (P=0.001 2) compared with the WT group. After ABX treatment, no significant differences were observed between the TRPV4^-/- group and WT group in tumor incidence, tumor burden, colon length, histopathological score, or PCNA positive rate (P>0.05). 16S rRNA sequencing demonstrated that TRPV4 deficiency reshaped the gut microbiota structure, with significant differences in β diversity between the TRPV4^-/- and WT groups (P=0.001) and distinct microbiota composition profiles. Colon tissue RNA-seq results revealed 218 significantly upregulated and 152 downregulated genes in TRPV4^-/- mice. KEGG pathway enrichment showed that differentially expressed genes were significantly enriched in cytokine signaling pathways and TNF signaling pathways. In vitro co-incubation results demonstrated that the transcription levels of TNF-α (P<0.000 1), IL-1β (P<0.000 1), and IL-23 (P<0.000 1) were significantly upregulated in TRPV4^-/- microbiota-cell co-cultures.

The gut microecology formed by TRPV4 deficiency may exacerbate CRC by promoting the secretion of inflammation-related cytokines.

To investigate the synergistic antitumor effect and immunological mechanisms of a recombinant oncolytic influenza virus carrying programmed cell death 1 (PD-1) antibody (OVFlu-αhPD1) combined with radiotherapy (RT) in hepatocellular carcinoma (HCC).

A recombinant oncolytic virus was successfully rescued in the early stage using reverse genetics (RG) technology, and designated OVFlu-αhPD1, which was comprehensively characterized via chicken red blood cell hemagglutination assay and detection of replication kinetics in human hepatocellular carcinoma cells Huh7 and murine hepatocellular carcinoma cells Hepa1-6. In vitro, CCK-8 assays were used to assess the effects of OVFlu-αhPD1 at different multiplicities of infection (MOI=0.1, 1, and 3), alone or combined with radiotherapy (RT; 8 Gy), on the viability of hepatoma cells (Huh7 and Hepa1-6) and normal hepatocytes (LX2 and AML12), and a colony formation assay was performed to evaluate the inhibitory effect of OVFlu-αhPD1 plus RT on Huh7 cell proliferation. For in vivo experiments, female C57BL/6 mice (6 to 8 weeks old, weighing 17 to19 g; n=45) were subjected to establish a subcutaneous tumor-bearing model of Hepa1-6 cells, and then the model mice were randomized into 5 groups (n=9): PBS, wild-type PR8, OVFlu-αhPD1, RT (24 Gy in 3 fractions), and OVFlu-αhPD1+RT to evaluate the antitumor efficacy of the combination therapy. Tumor volumes were measured every 2 days during treatment. After the final treatment, the spleens were havested for CD4⁺CD69⁺ and CD8⁺CD69⁺ T-cell frequencies by flow cytometry. Major organs (heart, liver, lung, kidneys, and brain) were harvested for HE staining to assess histopathological changes, and the activities of serum AST and ALT were measured to evaluate the safety of oncolytic virus therapy.

The recombinant oncolytic influenza virus OVFlu-αhPD1 showed a hemagglutination titer of 2⁷ to 2⁸, and replicated efficiently in Huh7 and Hepa1-6 cells, with viral titers reaching 7 to 8 lgTCID₅₀/mL at 72 h post-infection. CCK-8 assay demonstrated that OVFlu-αhPD1 inhibited HCC cell viability in a dose-dependent manner (MOI=3 significantly decreased viability in Huh7 and Hepa1-6 cells, P<0.01), and the inhibitory effect was further enhanced after combination with RT (P<0.01). Whereas no significant toxicity to normal hepatocytes was observed. Colony formation assay indicated that combined treatment significantly reduced colony numbers of Huh7 cells (P<0.01). In the mouse tumor-bearing model, the combination group exhibited significantly smaller tumor volumes than any monotherapy group (P<0.01), accompanied by significantly increased proportions of splenic CD4⁺CD69⁺ and CD8⁺CD69⁺ T cells in spleen (P<0.01). HE staining revealed no evident pathological changes in major organs, and serum ALT and AST levels did not differ significantly among groups (P>0.05).

The combination of oncolytic influenza virus and radiotherapy significantly enhances the killing effect against HCC cells in vitro and markedly improves tumor suppression in HCC-bearing mice by activating antitumor immune responses, demonstrating a favorable synergistic antitumor effect and providing a theoretical basis for the clinical application of oncolytic virus-based combination therapy.

Recent evidence shows that Streptococcus anginosus (S.a) promotes the development of gastric cancer (GC). However, its role in GC progression, particularly its impact on tumor immune microenvironment (TIME), remains unclear. This study aims to investigate the role of S. a in advanced GC and test the hypothesis that S. a upregulates PD-L1 expression in GC cells, thereby inhibiting CD8⁺ T cell function.

Based on in-house RNA sequencing (RNA-seq) raw data (n=98), a computational pipeline was established to determine the relative abundance of S.a (S.a score). In parallel, a machine learning-based signature gene model was constructed to infer S. a abundance from gene expression matrices of public cohorts. Using these approaches, GC patients were stratified into S.a-high (S.a^high) and S.a-low (S.a^low) groups according to the median abundance. Single-cell RNA sequencing (scRNA-seq) data (n=23) were integrated to analyze differences in cellular composition within the TIME between the 2 groups. For in vitro validation, human (NUGC-3) and murine (TKM) GC cell lines were treated with S.a for 8 h, followed by quantification of PD-L1 expression via qPCR and Western blotting. Cytotoxic function of CD8⁺ T cells against S.a-pretreated GC cells was evaluated by flow cytometry in co-culture systems. where S.a-pretreated cancer cells were incubated with CD8⁺ T cells, and cytotoxicity was measured by flow cytometry. For in vivo validation, a C57BL/6 mouse orthotopic GC model was established; the experimental group received S.a bacterial suspension via oral gavage, while the control group received sterile culture medium, with continuous intervention for 6 weeks (n=6). Tumor burden was evaluated by in vivo imaging, tumor volume, and stomach weight.

The abundance of S. a was significantly higher in GC tissues than in normal gastric mucosa (P=0.000 850), and high abundance was associated with poor prognosis (HR=1.49, 95%CI: 1.09 to 2.04, P=0.013 9). Analysis of the TIME using scRNA-seq data revealed distinct cellular compositions between GC tissues with varying S.a levels. Notably, S.a^high GC tissues exhibited a significant increase in CD8⁺ T cell infiltration (P<0.000 1), yet these cells predominantly displayed an exhausted phenotype (P<0.000 1). Further analysis identified a subset of GC cells whose abundance is correlated positively with both S.a levels (Spearman’s rho=0.288, P<0.000 1) and proportion of exhausted CD8⁺ T cells (Spearman’s rho=0.522, P<0.000 1), suggesting their potential roles in modulating CD8⁺ T cell function. Mechanistically, S.a treatment significantly upregulated PD-L1 expression in GC cells (P<0.000 1). In co-culture assays, S.a-pretreated cancer cells impaired the cytotoxic function of CD8⁺ T cells (P=0.000 526). In vivo results demonstrated that the mice in the S. a gavage group exhibited significantly higher tumor bioluminescence intensity (P=0.030 6), larger tumor volume (P=0.045 3), and increased stomach weight (P=0.030 6) compared to the control group.

Enriched S.a abundance not only reshapes the TIME of GC, but also mediates CD8⁺ T cell exhaustion and facilitates tumor immune evasion by inducing high PD-L1 expression in GC cells.

Painful diabetic peripheral neuropathy (PDPN) is a common and debilitating complication of diabetes mellitus, with complex clinical manifestations and pathogenesis. From the perspective of PDPN progression, how nutrient sensing alters gene transcription represents a scientifically significant question worthy of investigation. However, the underlying molecular mechanisms, particularly the association between post-transcriptional regulatory processes in the dorsal root ganglia (DRG) and PDPN, remain poorly understood. Therefore, this study employed an integrated multi-omics approach to comprehensively profile the miRNA and mRNA expression landscape and analyze their regulatory networks in the DRG of a well-established rat model of PDPN.

Eighteen male SD rats (aged 4 to 5 weeks, weighing 90 to 100 g) were randomly divided into a vehicle control group and a PDPN group. The rat model of PDPN was established by an intraperitoneal injection of streptozotocin (STZ). The DRG tissues from L4 to L6 were harvested from both groups (n=3 per group) for high-throughput sequencing of miRNAs and mRNAs. Bioinformatic analyses were performed to identify differentially expressed miRNAs (DEmiRs) and mRNAs (DEGs), followed by GO and KEGG enrichment analyses. An integrated miRNA-mRNA regulatory network was constructed. Preliminary validation was performed using RT-qPCR, with subsequent comparative analysis with relevant human-derived data.

A total of 11 significantly upregulated and 7 significantly downregulated DEmiRs (|log2FC|>1, P<0.05), along with 416 upregulated and 110 downregulated DEGs (|log2FC|>1, P<0.05), were identified. KEGG pathway analysis revealed that DEGs were significantly enriched in the pathways closely related to pain and diabetic metabolism (threshold P<0.05), such as neuroactive ligand-receptor interaction and lipid metabolism. Through construction of the miRNA-mRNA regulatory network, the results indicated that Ms4a2, Itgax, Wnt3, Tnn, and their upstream miRNAs may serve as core hubs regulating PDPN progression.

This study provides an initial systematic depiction of the miRNA-mRNA interaction network in the DRG of a PDPN model, demonstrating that Ms4a2, Itgax, Wnt3, Tnn, and their upstream miRNAs may modulate nutrient-sensing mechanisms involved in PDPN progression. Multiple potential therapeutic targets for PDPN are identified, including let-7i-5p, miR-9a-5p, miR-543-5p, miR-199a-5p, miR-324-5p, miR-702-3p, Ikzf3, Klhl6, Igsf6, Tmc8, Tmc1, Lmx1a, Cldn16, Vwc2, and Cyp2c23. Importantly, sphingolipid metabolism is regarded as a key pathway likely connecting metabolic alterations to the development of PDPN. Our work preliminarily explores the nutrient-sensing mechanisms underlying PDPN progression from a novel perspective.

To investigate the impact of different sedation strategies on short-term outcomes in intensive care unit (ICU) patients receiving invasive mechanical ventilation (IMV), and to assess whether these effects differ in special populations such as the elderly, those with malignancy, or hepatic impairment.

This retrospective observational cohort study utilized data from 2 large critical care databases, MIMIC-IV (v2.2) and eICU-CRD (v2.0). Inclusion criteria were: age ≥18 years; endotracheal intubation and IMV within 48 h after ICU admission; ICU length of stay (LOS) ≥48 h; initiation of a single-agent sedation strategy (dexmedetomidine, propofol, or benzodiazepines) from 2 h before to 48 h after intubation. Patients receiving combined sedatives, undergoing re-intubation, or with single-drug sample sizes <100 were excluded. The primary outcome was time to extubation; secondary outcomes included in-hospital mortality, ICU LOS, incidence of delirium, and major adverse cardiovascular events (MACEs). Inverse probability of treatment weighting (IPTW) based on gradient boosting machine algorithms was employed to control confounding bias, followed by augmented inverse probability weighting (AIPW) doubly robust models to estimate average treatment effects (ATE), odds ratios (ORs), and 95% confidence intervals (CIs). Prespecified subgroup analyses were applied to examine interactions with age, malignancy, and hepatic status.

A total of 12 561 patients were enrolled, comprising 1 111 in the dexmedetomidine group, 8 021 in the propofol group, and 3 429 in the benzodiazepine group. After IPTW weighting, baseline variables achieved overall balance across groups. In the AIPW doubly robust models, compared with the dexmedetomidine group, the propofol group demonstrated prolonged time to extubation by 0.782 d (ATE=0.782 d, 95%CI: 0.769 to 0.796, P<0.001), and the benzodiazepine group by 1.791 d (ATE=1.791 d, 95%CI: 1.779 to 1.804, P<0.001); compared with benzodiazepines, the propofol group showed shortened extubation time by 1.009 d (ATE=-1.009 d, 95%CI: -1.018 to -1.000, P<0.001). For secondary outcomes, compared with dexmedetomidine, both propofol and benzodiazepines were associated with increased risk for in-hospital mortality (propofol: OR=1.905, 95%CI: 1.647 to 2.201, P<0.001; benzodiazepines: OR=2.768, 95%CI: 2.262 to 3.388, P<0.001) and for delirium (propofol: OR=1.905, 95%CI: 1.428 to 2.541, P<0.001; benzodiazepines: OR=2.382, 95%CI: 1.681 to 3.376, P<0.001). Additionally, ICU LOS was prolonged by 0.367 d and 1.012 d in the propofol and benzodiazepine groups, respectively (propofol: ATE=0.367 d, 95%CI: 0.351 to 0.382, P<0.001; benzodiazepines: ATE=1.012 d, 95%CI: 0.996 to 1.029, P<0.001), and the total incidence of MACEs increased by 6.527 and 15.199 events (propofol: ATE=6.527 events, 95%CI: 6.112 to 6.942, P<0.001; benzodiazepines: ATE=15.199 events, 95%CI: 14.727 to 15.671, P<0.001). Regarding ventilator-associated pneumonia (VAP), propofol was associated with a lower risk than dexmedetomidine (OR=0.722, 95%CI: 0.648 to 0.805, P<0.001), whereas no significant difference was observed between benzodiazepines and dexmedetomidine (OR=1.082, 95%CI: 0.934~1.252, P=1.000). Subgroup and interaction analyses revealed significant interactions between sedation strategy and malignancy (P-for-interaction=0.001) and moderate-to-severe liver disease (P-for-interaction=0.048). Among patients with malignancy, propofol showed a trend toward shorter time to extubation than dexmedetomidine (ATE=-1.326 d, 95%CI: -2.732 to -0.080, P=0.064); similarly, in patients with moderate-to-severe liver disease, propofol also demonstrated a trend toward shorter extubation time than dexmedetomidine (ATE=-1.232 d, 95%CI: -2.939 to -0.475, P=0.157), suggesting attenuated or even reversed benefits of dexmedetomidine in these special populations.

In ICU patients receiving invasive mechanical ventilation, dexmedetomidine, is associated with shorter time to extubation and lower risks of in-hospital mortality, delirium, and cardiovascular adverse events compared with propofol and benzodiazepines. However, these advantage of dexmedetomidine are diminished among patients with malignancy or moderate-to-severe liver disease.

To explore the causal relationships between plasma total testosterone (TT), bioavailable testosterone (Bio-T) levels and the pathogenesis of aortic aneurysm (AA) using Mendelian randomization (MR).

Single nucleotide polymorphisms (SNPs) strongly associated with TT and Bio-T (P<5×10^-10) and with low bias were selected from the IEU Open GWAS public genome database as instrumental variables, with AA as the outcome. The inverse variance weighted (IVW) method served as the primary analytical approach, complemented by MR-Egger regression, weighted median estimation (WME), simple mode, and weighted mode analyses. Sensitivity analyses were conducted using Cochran’s Q test, MR-Egger intercept test, MR-PRESSO test, leave-one-out analysis, and funnel plot inspection, with statistical significance set at P<0.05.

Two-sample MR analyses revealed causal associations between both TT and Bio-T levels and AA risk, with consistent directional results from MR-Egger regression and WME. MR analysis indicated heterogeneity for the causal association between TT and AA (Cochran’s Q=132.081, P=0.024); MR-PRESSO identified no outlier SNPs, prompting adoption of the random-effects model as the primary result (IVW: OR=0.856, 95%CI: 0.744 to 0.985, P=0.030). The causal effect of Bio-T (IVW: OR=0.551, 95%CI: 0.304 to 0.998, P=0.049) on AA incidence was stronger than that of TT (OR=0.856). Progressive stringent SNP selection thresholds (down to P<5×10^-33) and a suite of sensitivity analyses confirmed the robustness of the causal association between Bio-T and AA.

Both plasma TT and Bio-T levels are causally associated with AA occurrence, with Bio-T conferring a stronger and more robust causal effect. Maintaining healthy, adequate plasma testosterone levels, particularly Bio-T levels, markedly reduce AA risk, offering novel targets and insights for AA prevention and treatment strategies.

To explore the genetic characteristics of familial Parkinson’s disease (PD)-related genes and construct a model of the gene signatures to guide the prognosis and treatment of patients with skin cutaneous melanoma (CMM).

① By analyzing the TCGA and GTEx databases, the expression differences of familial PD-related genes were compared between melanoma and normal tissues. The GEPIA2 database was used to analyze the expression differences of related genes across different TNM stages and Clark levels. ② Based on the TCGA database, a signature model containing 7 key genes was established using prognostic forest plot, LASSO regression model, and risk factor analysis. Internal and external validation of this model was performed, and GO enrichment and KEGG pathway enrichment analyses were conducted for these genes. ③ Based on the TIMER database, the impacts of gene mutations in the prognostic model on immune cell infiltration in the tumor microenvironment (TME) were analyzed. ssGSEA heatmaps were employed to analyze differences in immune cell subsets between high-risk and low-risk groups. Through PD-1 immune checkpoint inhibitor treatment response analysis, external dataset validation, and half-maximal inhibitory concentration (IC50) values of different drugs, possible differences in drug treatment efficacy between high-risk and low-risk groups were investigated. ④ Western blotting and RT-qPCR were utilized to detect the protein expression and relative mRNA expression differences of the genes in this prognostic model between normal human melanocyte cell line PIG1 and human melanoma cell line A375.

① Analysis of the TCGA and GTEx databases revealed that, except for PARK14 (PLA2G6), all familial PD-related genes showed significant expression differences between melanoma tissues and normal tissues. Analysis of the HPA database found that the protein expression of familial PD-related genes was either up-regulated or down-regulated in melanoma samples compared to normal tissues. Analysis of the GEPIA2 database for the expression of these genes across different TNM stages and Clark levels also found differential expression in some genes. ② COX regression analysis based on TCGA database identified that expression levels of 13 genes could predict overall survival in melanoma patients. LASSO regression analysis of this data constructed a melanoma prognostic model containing 7 familial PD-related genes (LRRK2, UCHL1, SNCA, VPS13C, SPR, EIF4G1, FBXO7). The prognostic risk scores for all patients were calculated using a risk factor contingency plot, and then the patients were divided into high-risk and low-risk groups based on the median risk score (-0.145). The results showed that the high-risk group had a significantly worse survival rate, while the low-risk group exhibited a longer survival time. Internal validation of the model in the TCGA cohort: the calibration curves for the 3-year and 5-year nomograms showed that the predicted curves for melanoma patient prognosis were very close to the actual curves. External validation in GEO cohorts: 3 datasets from the GEO database containing complete clinical data and gene expression matrices (GSE19234, GSE22153, and GSE53118) were selected. Based on the prognostic model's risk score, the sensitivity and specificity on the ROC curve were better than TNM staging. GO and KEGG pathway enrichment analyses found that the 7 genes were mainly enriched in pathways related to tumorigenesis and tumor immunity, such as phosphorylation, programmed cell death, and cell cycle, suggesting the prognostic model is associated with tumorigenesis and immunity. ③ GSVA scoring revealed that these 7 genes were enriched in pathways related to tumorigenesis and anti-tumor immunity. CD8⁺ T cells, cytotoxic cells, and Th1 cells, which can inhibit tumor growth, were more infiltrated in the low-risk group. Meanwhile, the IC₅₀ values for nilotinib, BIBW2992, bleomycin, bortezomib, dasatinib, and MK-2206 were lower in tumor cells from low-risk group patients (P<0.05). ④ RT-qPCR and Western blotting analysis were used to verify the relative expression differences of these 7 genes at the transcriptional and translational levels in A375 and PIG1 cells. The experimental results showed that LRRK2 and UCHL1 were almost not expressed in both A375 and PIG1 cells. Meanwhile, compared to normal melanocytes, the mRNA expression of SNCA, VPS13C, SPR, eIF4G1, and FBXO7 genes was significantly up-regulated in melanoma cells (P<0.05). The protein expression of SPR, EIF4G1, and FBXO7 was significantly upregulated (P<0.05).

The 7-gene signature prognostic model developed in this study can effectively assess prognosis and treatment efficacy in CMM patients.

To perform in-depth integration and analysis of cancer multi-omics data and clinical data to enhance the predictive capability for cancer prognosis.

Multi-omics data (mRNA, lncRNA, and miRNA profiles) and clinical data for 3 cancer types, namely ovarian cancer, liver cancer, and colorectal cancer, were retrieved from The Cancer Genome Atlas (TCGA) database. A novel cancer prognosis prediction model, MoACG (Multi-omics Attention Clinical Gating Model), was constructed based on the self-attention mechanism to explore potential associations among different omics layers, and gated fusion was employed to adaptively integrate multi-omics information with clinical information(age, sex, and treatment modality) . The effectiveness of the model was validated through comparison with multiple machine learning methods, and the interpretability algorithm DeepLIFT was utilized to quantify gene contributions to the model and identify core prognostic genes.

In the ovarian cancer, liver cancer, and colorectal cancer datasets, five-fold cross-validation yielded the area under curve (AUC) values of receiver operating characteristic (ROC) curve of (0.793±0.042), (0.791±0.065), and (0.789±0.086), respectively, and the AUC values of precision-recall curve (AUPR) were (0.915±0.020), (0.855±0.058), and (0.917±0.039), respectively. The comprehensive performance surpassed that of 9 other machine learning models. Ablation experiments demonstrated that the 3-omics data integration model exhibited optimal predictive performance across all cancer types. The DeepLIFT algorithm identified MED8, DLGAP4, and NABP2 as genes associated with liver cancer, showing high concordance with existing research findings and effectively stratifying patient survival risk based on expression levels (P<0.005).

Compared with previous studies, the MoACG model, constructed by integrating multi-omics and clinical data, effectively enhances the predictive performance for cancer prognosis, thereby providing a novel approach for cancer diagnosis, treatment, and prognostic research.

To investigate whether the accuracy of artificial intelligence (AI)-assisted preoperative planning for total knee arthroplasty (TKA) is non-inferior to computer-assisted manual preoperative planning completed collaboratively by 3D engineers and orthopedic surgeons, and to evaluate the impact of age on the accuracy of AI-based planning.

This study adopted a retrospective, cross-sectional comparative design. Data were retrospectively collected from 127 patients who underwent TKA at the First Affiliated Hospital of Army Medical University between December 2022 and September 2023. They were divided into a ≤60 years group (40 to 60 years old, n=22) and an >60 years group (61 to 80 years old, n=105) based on age. All patients underwent both AI-assisted preoperative planning (AI planning) and computer-assisted manual preoperative planning (conventional planning). Hip-knee-shaft angle (HKS), femoral resection difference, tibial resection difference, femoral rotation angle, and prosthesis size data from AI planning, conventional planning, and actual intraoperative measurements, as well as time, manpower, and economic costs of AI planning and conventional planning were compared between the 2 planning methods. The non-inferiority margin was set as resection angle difference ≤1°, resection thickness difference ≤1 mm, and prosthesis size difference ≤1.

Compared with the conventional planning, the AI planning showed a mean increase of 0.8°±1.2° in HKS (Cohen’s d=0.4, 95%CI: 0.2 to 0.7), a mean increase of 1.1±1.8 mm in femoral resection difference (Cohen’s d=0.5, 95%CI: 0.3 to 0.8), and a mean increase of 0.6±1.7 mm in tibial resection difference (Cohen’s d=0.3, 95%CI: 0.0 to 0.5). The lower limits of 95%CI for these above standardized mean differences were all greater than their corresponding negative non-inferiority margins. In comparison with the conventional planning, the AI planning in the ≤60 years group showed no significant differences in HKS, tibial resection difference, and femoral rotation angle (P>0.05), whereas in the >60 years group, only femoral resection difference of AI planning exceeded the non-inferiority margin (with a mean increase of 1.1 mm, P<0.05). For prosthesis size prediction, the AI planning achieved 73.2% femoral prosthesis and 87.4% tibial prosthesis predictions with size differences not exceeding 1, while conventional planning achieved 81.9% femoral prosthesis and 90.6% tibial prosthesis predictions with size differences not exceeding 1, with no significant difference between the 2 planning methods (P>0.05). The time consumed for AI planning was significantly shorter than that for conventional planning (27.4±4.1 vs 45.3±8.5 min, P<0.05). Moreover, AI planning required no additional labor and equipment costs, with no additional manpower or equipment costs (0 vs 3 000 Yuan/case).

AI-assisted preoperative planning for TKA s demonstrates non-inferior accuracy compared with conventional planning, with superior performance particularly in patients under 60 years, and significant advantages in time and economic costs.