The First People's Hospital of Shunde

Mismatch repair-deficient (dMMR) advanced or recurrent endometrial carcinoma presents a therapeutic challenge, and the role of preoperative immunotherapy prior to its guideline incorporation (2020–2022) remained unexplored. We report three patients with dMMR endometrial carcinoma who received preoperative anti‑PD‑1 therapy (sintilimab). Case 1 (locally recurrent vaginal mass) was treated with sintilimab monotherapy, achieving a pathological complete response. Case 2 (stage IVB with bulky uterine and osseous metastases) received sintilimab combined with carboplatin/docetaxel, attaining partial response. Case 3 (recurrent disease with renal impairment) received sintilimab plus megestrol acetate, which yielded a marked biochemical response (98.6% CA125 reduction) enabling resection despite radiographic stable disease. All patients underwent R0 resection and remained recurrence‑free after a median follow‑up of 36 months, with no grade ≥ 3 adverse events. Exploratory biomarker analysis revealed cGAS‑STING pathway activation in all tumors. These findings provide early real‑world evidence that preoperative anti‑PD‑1 therapy is a feasible and effective strategy for converting unresectable dMMR endometrial carcinoma to resectable disease, supporting further investigation in larger studies.

To systematically compare the clinical efficacy and safety of four posterior surgical approaches-olecranon osteotomy (OO), triceps-reflecting approach (TRA), triceps-splitting approach (TS), and triceps tongue approach (TT)-for the treatment of AO/OTA type C complete intra-articular distal humerus fractures, and to provide evidence-based guidance for surgical approach selection. This study was conducted in accordance with the PRISMA 2020 guidelines. PubMed, EMBASE, Web of Science, the Cochrane Library, and major Chinese databases were systematically searched for randomized controlled trials published up to September 2025. Eligible studies included patients with AO/OTA type C complete intra-articular distal humerus fractures treated using OO, TRA, TS, or TT approaches. Primary outcomes included the rate of excellent or good functional outcomes, Mayo Elbow Performance Score (MEPS), operative time, intraoperative blood loss, and overall complication rate. A network meta-analysis was performed using Stata 16.0, calculating odds ratios (ORs) or mean differences (MDs) with corresponding 95% confidence intervals (CIs). Between-study heterogeneity (τ2) and network consistency were assessed, and the surface under the cumulative ranking curve (SUCRA) was used to estimate the probability ranking of each surgical approach. A total of 12 randomized controlled trials involving 1,258 patients with AO/OTA type C complete intra-articular distal humerus fractures were included. Network meta-analysis demonstrated no statistically significant differences among the four posterior approaches with respect to the rate of excellent or good functional outcomes, MEPS, operative time, or intraoperative blood loss. SUCRA rankings suggested that OO ranked relatively higher for functional outcome and operative time, while TS ranked higher for intraoperative blood loss; however, none of these comparisons reached statistical significance. Regarding overall complication rates, TRA was associated with a lower risk of complications compared with OO, whereas TS showed a higher complication rate than OO. No statistically significant differences were observed in the remaining comparisons. Current evidence indicates that OO, TRA, TS, and TT posterior approaches provide comparable overall clinical outcomes in the management of AO/OTA type C complete intra-articular distal humerus fractures, with most outcome measures showing no statistically significant differences. SUCRA rankings reflect only relative probability trends within the network model and should not be interpreted as substitutes for effect sizes or statistical significance. Surgical approach selection should therefore be individualized based on fracture characteristics, soft tissue conditions, and surgeon experience.

Abstract Background MicroRNAs (miRNAs) play a crucial role in tumorigenesis and malignant transformation. Studies indicate that miR-4652-3p is aberrantly expressed in various cancer types. However, its impact and underlying mechanisms in non-small cell lung cancer (NSCLC) have not been investigated. Methods A549 cells were stimulated with IL-1β, TNF-α, and IL-6 (each at 10 ng/ml) to mimic an inflammatory microenvironment. Metabolic status was evaluated by measuring glutamine uptake, α-ketoglutarate (α-KG), and ATP levels. Functional studies employed the glutaminase inhibitor (CB-839), the MYC inhibitor (10058-F4), SLC1A5 small interfering RNA (siSLC1A5-2), a miR-4652-3p mimic, and overexpression plasmids. Molecular interactions were validated using chromatin immunoprecipitation (ChIP), dual-luciferase reporter assays, and RNA pull-down experiment. CCK-8 and Transwell assays were used for the assessment of cell malignant phenotypes. The functional significance of miR-4652-3p was further verified in a xenograft mouse model. Results miR-4652-3p was downregulated in NSCLC, while MYC and SLC1A5 were upregulated. Inflammatory stimulation enhanced A549 cell proliferation, glutamine uptake, and α-KG/ATP production; these effects were attenuated by CB-839. ChIP and dual-luciferase assays demonstrated that MYC binds the SLC1A5 promoter and activates its transcription. Inhibiting MYC or knocking down SLC1A5 significantly reduced glutamine uptake. Mechanistic analysis revealed that miR-4652-3p directly targets both MYC and SLC1A5 mRNA. miR-4652-3p suppressed glutamine metabolism in NSCLC cells by negatively regulating the MYC/SLC1A5 axis, consequently inhibiting cell growth and tumor progression in a xenograft mouse model, an effect reversed by MYC or SLC1A5 overexpression. Conclusions miR-4652-3p blocked the inflammatory microenvironment-induced glutamine metabolic reprogramming by directly suppressing the MYC/SLC1A5 axis, thereby inhibiting NSCLC progression. The miR-4652-3p/MYC/SLC1A5 pathway represents a key regulatory mechanism for metabolic adaptation in NSCLC.

Shenling Baizhu Powder (SLBZP) is a prominent formulation widely used in the treatment of pulmonary diseases. However, studies examining the mechanisms of SLBZP for treating asthma are limited. This study aimed to clarify the efficacy and possible mechanisms of SLBZP in the context of asthma from the perspective of gut microbiota-metabolism-immune crosstalk. Key parameters including airway hyperresponsiveness, lung pathological features and the expression of inflammatory mediators from Th2 and Th17 cells were employed to validate the anti-inflammatory properties of SLBZP. The anti-asthma mechanism of SLBZP was investigated using metagenomic sequencing, metabolomics, flow cytometry, RT-qPCR, immunohistochemistry (IHC) and immunofluorescence (IF). SLBZP demonstrated significant capacity to mitigate histopathological alterations associated with ovalbumin-induced asthma and suppress the secretion of inflammatory mediators (IL-4, IL-5, IL-13 and IL-17A) in BALF. Metagenomic results demonstrated that the protective effects of SLBZP were primarily associated with Ligilactobacillus, Eubacterium and Clostridium. Additionally, metabolomics results identified that three vital metabolic pathways were substantially regulated by SLBZP in asthmatic mice, especially D-glutamine and -glutamate metabolism. Furthermore, IHC and IF results showed that SLBZP significantly inhibited the expression of GLS1 and GOT1, which inhibited the conversion of L-glutamine to α-ketoglutarate and regulated the imbalance of Th1/Th2 and Treg/Th17. RT-qPCR results showed that SLBZP promoted the expressions of T-bet, IFN-γ, IL-10 and Foxp3 mRNA, and inhibited the expression of GATA3, IL-4, IL-5, IL-13, IL-17A and RORγt mRNA. The findings from flow cytometry provided additional evidence. Thus, this modulated the imbalance of Th1/Th2 and Treg/Th17 and exerted the immunomodulatory properties of SLBZP. SLBZP exerted protective effects against OVA-induced asthma and modified the structure and functional characteristics of the gut microbiota, and serum metabolite profiles in asthmatic mice. The anti-asthma mechanism of SLBZP may be associated with the modulation of the gut microbiota and Glutamine-GLS1 pathway.

Diabetic peripheral neuropathy (DPN) is a debilitating diabetic complication marked by progressive nerve fiber loss and dysfunction. While extensive studies have focused on the onset of DPN, the mechanisms underlying its progression remain poorly understood. Once DPN progression occurs, it can render nerve damage irreversible and make treatment more challenging. Emerging evidence suggests that immune and mitochondrial metabolic dysregulation play critical roles in disease exacerbation, yet the specific cell subtype and molecular mediators driving DPN progression have not been systematically identified. Constructed a progressive DPN mouse model for bulk sequencing to explore progression-related mechanisms. Integrated Scissor and multi-omics analyses identified key cell subtypes and hub genes. TIMM23's role in DPN progression and mitochondrial function was validated in vitro in bone marrow-derived macrophages (BMDMs) and in vivo via adeno-associated virus-mediated overexpression. Mitochondrial metabolic dysfunction is a potential core mechanism underlying the progression of DPN. CD83+ macrophages were identified as the most prominent and specific subset associated with mitochondrial dysfunction and the DPN progression. Accordingly, we constructed a progressive DPN-related mitochondrial score, which enabled quantitative evaluation of DPN progression, inflammation, and immune infiltration. In vitro, high-glucose or high-fat intervention in BMDMs resulted in reduced expression of TIMM23. TIMM23 overexpression increased ATP production and mitochondrial mass, while reducing reactive oxygen species. In vivo, TIMM23 overexpression in the sciatic nerve improved nerve conduction velocity and nociceptive responses. This study highlights the first discovery of CD83+ macrophages in DPN progression and identifies TIMM23 as a potential diagnostic and therapeutic marker.

Not available.

Extracorporeal membrane oxygenation-induced coagulopathy (ECMO-IC) represents a frequent and severe complication, contributing to oxygenator replacement and unfavorable outcomes. Currently, no reliable machine learning (ML) model exists for early identification. This study comprehensively assesses routine clinical characteristics to develop a reliable, accurate, and explainable ML model for estimating ECMO-IC risk and to identify modifiable factors. This study included two center cohorts with 266 patients undergoing ECMO from 2015 to 2024. Feature selection utilized the Boruta algorithm, followed by the implementation of a distinctive ML framework incorporating 12 ML algorithms to establish a consensus prediction model (ECMO-IC index). Model and feature variable assessment employed multiple analytical methods: Bootstrapping and fivefold cross-validation, subgroup and interaction analysis, restricted cubic spline (RCS) regression, and threshold effect analysis. Model interpretation and feature quantification relied on the Shapley Additive Explanations (SHAP) methodology for visualization purposes. Through Boruta algorithm selection, 17 characteristics were identified and incorporated into 12 ML methodologies, generating 105 permutations and an optimal algorithm for identifying ECMO-IC. The ECMO-IC index comprising 9 modifiable or nonmodifiable variables, namely platelet (PLT), lactate, systemic immune-inflammation index (SII), K, total protein (TP), shock index (SI), red blood cell volume distribution width (RDWCV), acute physiology and chronic health evaluation II (APACHE II), and Ca, demonstrated strong diagnostic capabilities, achieving a mean area under the curve (AUC) of 0.815 across derivation (AUC = 0.817) and validation (AUC = 0.813) cohorts, along with notable discriminatory power, model fit, and clinical utility. SHAP elucidates the importance of ranking features (PLT, lactate, K, Ca and APACHE II) and visualises global and individual ECMO-IC risk prediction. RCS regression and threshold effect analysis suggested a nonlinear link between model features (PLT: P for nonlinearity = 0.002, SII: P for nonlinearity = 0.001, K: P for nonlinearity = 0.006, Ca: P for nonlinearity = 0.008, and lactate: P for nonlinearity = 0.004) and ECMO-IC, and generated an inflection point for features (PLT = 95 × 109/L, lactate = 5.7mmol/L, SII = 200, K = 4.4mmol/L, TP = 45.6g/L, SI = 0.8, RDWCV = 14%, APACHE II = 15, Ca = 1.03mmol/L). To provide a more flexible predictive tool, the ECMO-IC model was constructed using a free, publicly available web-based calculator ( https://genglongliu.shinyapps.io/DynNomapp/ ). An optimised explainable ML model (ECMO-IC index) incorporating several modifiable parameters was established and internally validated to deliver an readily available and accurate diagnostic tool for ECMO-IC, with potential applications in ECMO clinical management.