SLC26A4 is a major causative gene for hereditary hearing loss, its mutation spectrum shows pronounced population specificity. In Chinese populations, patients predominantly carry biallelic mutations, and compound heterozygous genotypes are prevalent, which results in a wide spectrum of auditory phenotypes. However, how different alleles interact within these contexts to shape phenotypic variability remains poorly understood. We employed cellular and mouse models to explore the allele-specific mechanisms associated with two novel mutations, a frameshift mutation and a missense mutation, in compound heterozygous that share the same splice-site pathogenic allele. In vitro, wild-type (WT) and mutant (c.574delC, c.1211C>A) SLC26A4 constructs were expressed in HeLa cells to assess pendrin localization. Both mutations reduced membrane enrichment and increased intracellular retention. In vivo, compound heterozygous knock-in mouse models (Slc26a4c.574delC/c.919-2A>G and Slc26a4c.1211C>A/c.919-2A>G) were generated using CRISPR/Cas9. The auditory function and cochlear pathology were investigated. Both compound mutants exhibited elevated ABR thresholds, with more severe hearing loss in Slc26a4c.574delC/c.919-2A>G mice. Correspondingly, these mice showed marked hair cell disruption, stereociliary loss, and cochlear structural abnormalities, whereas the Slc26a4c.1211C>A/c.919-2A>G mice displayed milder changes. Transcriptomic profiling examined by bulk RNA-sequencing revealed broader differential expression in Slc26a4c.574delC/c.919-2A>G mice, enriched in structural and developmental pathways, while the missense model showed predominantly immune-related signatures. Our findings demonstrate that allele-specific functional divergence in compound heterozygous SLC26A4 mutations leads to distinct auditory dysfunction, cochlear pathology, and transcriptional programs. These findings provide mechanistic insight into the phenotypic heterogeneity of hearing loss and may indicate future allele-specific interventions or therapeutic strategies.

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with limited treatment options. Transient receptor potential ankyrin 1 (TRPA1) has been implicated in inflammation and pain, but its role in UC remains a subject of debate. The current study investigated the effects of TRPA1 inhibition in both acute and subacute murine models of dextran sulfate sodium (DSS)-induced colitis. Genetic knockout of Trpa1 or pharmacological inhibition with A967079 significantly ameliorated inflammation in the acute model, reducing the disease activity index (DAI), colon shortening, histopathological damage, and TNF-α secretion from macrophages. In contrast, TRPA1 suppression exacerbated subacute colitis and worsened weight loss, DAI, colon shortening, and histopathology. Mechanistically, Trpa1 deletion promoted CD4+ T cell polarization toward the Th1 subtype in subacute colitis, increasing IFN-γ levels. These findings reveal a dual role for TRPA1 in colonic inflammation: it mediates pro-inflammatory effects primarily via innate immune cells in the acute phase but has anti-inflammatory effects by modulating adaptive immunity in the subacute phase. These findings provide new insights into the context-dependent roles of TRPA1 and suggest that TRPA1 may represent a context-specific and stage-dependent therapeutic target in UC.

Pancreatic cancer, and especially pancreatic ductal adenocarcinoma (PDAC), is extremely difficult to treat due to early asymptomatic stage, molecular heterogeneity, and resistance to conventional treatments, with a 5-year survival rate of less than 10%. Antibody-drug conjugates (ADCs), as an emerging precision therapy, show the potential to treat PDAC through the synergy of antibody targeting and cytotoxic drugs. Multiple targets (such as uPAR, Mesothelin, CLDN18.2, and TROP2) are highly expressed in PDAC, which has become the key direction of ADC development. However, the matrix barrier restricts drug delivery, heterogeneous expression leads to efficacy differentiation, and drug resistance mechanisms further limit the role of ADCs. To overcome these challenges, researchers are exploring high-stability single domain antibodies, more potent payloads and linkers, bystander effect mechanisms, and combined treatment strategies with immune, autophagy, DNA damage repair, and other pathways. Bispecific ADC, conditionally activated ADC, and penetration enhancement design have also been used to improve efficacy. On the whole, ADCs offer hope for the treatment of PDAC. Future research and development should focus on improving delivery efficiency, alleviating drug resistance, and individualized design.

Colorectal liver metastasis (CRLM) remains lethal, and the convergence of cellular senescence with metabolic reprogramming via epigenomic rewiring is poorly understood. We integrated genome-wide DNA methylation and RNA-seq data from 10 paired primary tumors and liver metastases (GSE213402). After calling differentially methylated genes (3,399 hyper- and 9,519 hypomethylated) and differentially expressed genes (406 DEGs), we intersected them with curated senescence (n = 866) and metabolic reprogramming (n = 948) gene sets, yielding 28 differentially expressed cellular-senescence-related genes (DE-CSRGs) and 24 metabolic-reprogramming-related genes (DE-MRRGs). Machine-learning pipelines (LASSO + SVM-RFE) converged on a five-gene signature: CXCL1, SERPINE1, NDRG1, SRM and GATM, most of which are hypomethylated and over-expressed in metastases. Gene-set enrichment analysis revealed that these genes are involved in pathways such as oxidative phosphorylation, focal adhesion, complement-coagulation cascades, and PPAR signaling. Immune de-convolution revealed strong positive correlations between signature genes and immunosuppressive subsets (MDSCs, Tregs, type-1 T-helper cells; p < 0.05). Elevated IC50 values for oxaliplatin and 5-fluorouracil in metastatic samples were positively associated with NDRG1 and negatively with SRM, indicating chemo-resistance modulation. This five-gene epigenetic-transcriptomic hub identifies a molecular signature that warrants prospective validation as a potential biomarker for patient stratification and combination therapy in CRLM.

Neurotropin, a non-protein extract widely used for the treatment of neuropathic pain, has recently been reported to protect against ischemic brain injury, enhance remyelination in demyelinating diseases, and ameliorate neuroinflammation and memory deficits. However, its role in microglial polarization and mitochondrial dysfunction in Alzheimer's disease (AD) remains poorly understood. In this study, we investigated the therapeutic potential of Neurotropin in the 5xFAD mouse model of AD. Neurotropin administration alleviated cognitive decline, reduced amyloid-β (Aβ) deposition, suppressed neuroinflammation, and preserved neuronal density. Mechanistically, Neurotropin improved mitochondrial morphology, restored ATP production, increased mitochondrial DNA copy number, and reduced oxidative stress while promoting a shift in microglial polarization from the pro-inflammatory M1 phenotype toward the anti-inflammatory M2 phenotype. Transcriptomic and molecular analyses revealed that calcium homeostasis modulator family member 2 (Calhm2) was markedly upregulated in 5xFAD mice, colocalized with microglia, and transcriptionally regulated by fused in sarcoma (FUS), while Calhm2 interacted with EF-hand domain containing protein D2 (EFhd2). Neurotropin suppressed FUS-mediated Calhm2 transcription and attenuated Calhm2-EFhd2 interaction. Importantly, overexpression of Calhm2 in both microglial cells and 5xFAD mice abolished the beneficial effects of Neurotropin, leading to exacerbated mitochondrial dysfunction, oxidative stress, and inflammatory cytokine release. Together, these findings identify Calhm2 as a critical mediator of Neurotropin's neuroprotective effects and demonstrate that Neurotropin alleviates AD pathology by suppressing FUS-dependent Calhm2 transcription and blocking the Calhm2/EFhd2 interaction. This study provides new insights into the mechanism of Neurotropin action and highlights its therapeutic potential for AD.

The prognostic significance of preoperative platelet counts among patients with hepatocellular carcinoma (HCC) undergoing curative resection remains controversial. The objective of the current study was to investigate the impact of preoperative platelet count on long-term outcomes after HCC resection. Patients who underwent curative-intent resection for HCC between 2000 and 2021 at 10 hepatobiliary centers in China were retrospectively analyzed. Patients were categorized based on platelet count within 2 weeks before surgery: thrombocytopenia (< 100 × 109/L), normal platelet count (100-299 × 109/L), and thrombocytosis (≥ 300 × 109/L). The primary outcomes were overall survival (OS) and recurrence-free survival (RFS). Among 3,116 patients, 655 (21.0%) had thrombocytopenia, 2,374 (76.2%) had normal platelet counts, and 87 (2.8%) had thrombocytosis. The 5-year OS was 52.7%, 56.0%, and 40.2% for thrombocytopenia, normal platelet count, and thrombocytosis groups, respectively (p < 0.001 among the three groups); the corresponding 5-year RFS was 39.3%, 39.3%, and 26.9%, respectively (p = 0.001 among the three groups). Multivariable analysis identified both thrombocytopenia (HR 1.215, 95% CI 1.045-1.413, p = 0.011) and thrombocytosis (HR 1.307, 95% CI 1.130-1.511, p < 0.001) as independent risk factors for worse OS, and thrombocytosis was independently associated with worse RFS (HR 1.523, 95% CI 1.196-1.939, p = 0.001). Both thrombocytopenia and thrombocytosis were associated with worse survival after HCC resection, with thrombocytosis also predicting higher risk of recurrence. Routine preoperative platelet count may serve as a valuable and practical prognostic marker for risk stratification among patients with HCC undergoing resection.

Distant metastasis after hepatectomy for hepatocellular carcinoma (HCC) significantly impairs long-term outcome. This study aimed to identify patterns, risk factors, and develop a prediction model for distant metastasis at first recurrence following HCC resection. This multi-center retrospective study included patients undergoing curative hepatectomy for HCC. Risk factors for distant metastasis were identified using Cox regression. A nomogram was constructed and validated using the concordance index (C-index) and calibration curves. Among 2,705 patients, 1,507 experienced recurrence, with 342 (22.7 per cent) developing distant metastasis. Common metastatic sites included extrahepatic vessels (36.2 per cent), lungs (26.0 per cent), and lymph nodes (20.8 per cent). Patients with distant metastasis had significantly worse 5-year overall survival compared to those with intrahepatic recurrence (9.1 versus 41.1 per cent, p < 0.001). Independent risk factors included preoperative tumor rupture, tumor size over 5.0 cm, multiple tumors, satellite nodules, macro- and microvascular invasion, narrow resection margin, and intraoperative blood transfusion. The nomogram demonstrated excellent discrimination (C-index > 0.85) and accurately stratified patients into three risk categories. In conclusion, distant metastasis at first recurrence following HCC resection was associated with poor prognosis. The proposed nomogram facilitates accurate prediction of distant metastasis, potentially informing personalized postoperative monitoring and interventions for high-risk patients.

This study investigates the use of machine learning (ML) models combined with a Synthetic Minority Over-sampling Technique (SMOTE) and its variants to predict perioperative pressure injuries (PIs) in an imbalanced dataset. PIs are a significant healthcare problem, often leading to prolonged hospitalization and increased medical costs. Conventional risk assessment scales are limited in their ability to predict PIs accurately, prompting the exploration of ML techniques to address this challenge.We utilized data from 7,292 patients admitted to a tertiary care hospital in Shanghai between May 2017 and July 2023, with a final dataset of 2,972 patients, including 158 with PIs. Seven ML algorithms-Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Extra Trees (ET), K-Nearest Neighbors (KNN), and Decision Trees (DT)-were used in conjunction with SMOTE, SMOTE+ENN, Borderline-SMOTE, ADASYN, and GAN to balance the dataset and improve model performance.Results revealed significant improvements in model performance when SMOTE and its variants were used. For instance, the XGBoost model hadan AUC of 0.996 with SMOTE, compared to 0.800 on raw data. SMOTE+ENN and Borderline-SMOTE further enhanced the models' ability to identify minority classes. External validation indicatedthat XGBoost, RF, and ET exhibited the highest stability and accuracy, with XGBoost having an AUC of 0.977. SHAP analysis revealed that factors such as anesthesia grade, age, and serum albumin levels significantly influenced model predictions.In conclusion, integrating SMOTE with ML algorithms effectively addressed a data imbalance and improved the prediction of perioperative PIs. Future work should focus on refining SMOTE techniques and exploring their application to larger, multi-center datasets to enhance the generalizability of these findings, and especially for diseaseswith a lowincidence.

Immune checkpoint inhibitors (ICIs) have been widely used in various types of cancer, but they have also led to a significant number of adverse events, including ICI-induced immune-mediated hepatitis (IMH). This study aimed to explore the risk factors for IMH in patients treated with ICIs and to develop and validate a new nomogram model to predict the risk of IMH. Detailed information was collected between January 1, 2020, and December 31, 2023. Univariate logistic regression analysis was used to assess the impact of each clinical variable on the occurrence of IMH, followed by stepwise multivariate logistic regression analysis to determine independent risk factors for IMH. A nomogram model was constructed based on the results of the multivariate analysis. The performance of the nomogram model was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) analysis. A total of 216 (8.82%) patients developed IMH. According to stepwise multivariate logistic analysis, hepatic metastasis, the TNM stage, the WBC count, LYM, ALT, TBIL, ALB, GLB, and ADA were identified as risk factors for IMH. The AUC for the nomogram model was 0.817 in the training set and 0.737 in the validation set. The calibration curves, DCA results, and CIC results indicated that the nomogram model had good predictive accuracy and clinical utility. The nomogram model is intuitive and straightforward, making it highly suitable for rapid assessment of the risk of IMH in patients receiving ICI therapy in clinical practice. Implementing this model enables early adoption of preventive and therapeutic strategies, ultimately reducing the likelihood of immune-related adverse events (IRAEs), and especially IMH.

Breast cancer liver metastasis (BCLM) presents a critical challenge in breast cancer treatment and has substantial epidemiological and clinical significance. Receptor status is pivotal in managing both primary breast cancer and its liver metastases. Moreover, shifts in these statuses can have a profound impact on patient treatment strategies and prognoses. Research has indicated that there is significant heterogeneity in receptor status between primary breast cancer and liver metastases. This variation may be influenced by a multitude of factors, such as therapeutic pressure, inherent tumor heterogeneity, clonal evolution, and the unique microenvironment of the liver. Changes in the receptor status of BCLM are crucial for adjusting treatment strategies, and liver biopsy plays an important role in the treatment process. Directions for future research targeting changes in receptor status include in-depth study of molecular mechanisms, combined treatment strategies for receptor status reversal, development of artificial intelligence deep learning models to predict receptor status in liver metastases, and clinical research on new drug development and combination therapies. That research will provide more precise treatment strategies for patients with BCLM and improve their prognosis.