Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

Objective To develop and validate a machine learning-based predictive model to assess the in-hospital mortality risk of patients with myocardial infarction(MI)complicated by heart failure(HF)undergoing percutaneous coronary intervention(PCI).Methods This retrospective study analyzed MI patients with HF who underwent PCI at The First Affiliated Hospital of Xinjiang Medical University from January 2019 to January 2023.Patient data,including demographic characteristics,vital signs,laboratory test results,imaging parameters and medication use,were collected and randomly divided into a training set(70％)and a validation set(30％).The extreme gradient boosting(XGBoost)model was used to identify variables significantly associated with in-hospital mortality,and the Shapley additive explanations(SHAP)model was applied to assess feature importance.A predictive model was then constructed using univariate and multivariate Logistic regression analyses.Model performance was evaluated using receiver operating characteristic(ROC)curves,area under the curve(AUC)values,calibration curves,and decision curve analysis.Finally,a nomogram was developed for intuitive risk assessment.Results A total of 1 214 MI patients with HF were included in the study,with a median age of 64 years.The in-hospital mortality rate was 7.41％(90 deaths).XGBoost feature selection identified ten key predictive variables:age,myoglobin,albumin,fasting blood glucose,N-terminal pro-B-type natriuretic peptide(NT-proBNP),diabetes mellitus,creatinine,cystatin C,procalcitonin,and left ventricular ejection fraction.Based on these variables,a Logistic regression model was developed,with seven final predictors:age,diabetes mellitus,creatinine,fasting blood glucose,cystatin C,NT-proBNP,and albumin.The model demonstrated high predictive accuracy,with AUC value of 0.869(95％CI:0.84-0.89)in the training set and 0.827(95％CI:0.79-0.85)in the validation set.The calibration curve indicated that the predicted probabilities were consistent with the actual observed outcomes,and decision curve analysis showed that the model had a high net benefit across various decision thresholds.Conclusion This study developed a machine learning-based predictive model incorporating Logistic regression to assess the in-hospital mortality risk of MI patients with HF undergoing PCI.The model demonstrated high predictive performance and clinical utility.The nomogram derived from this model provides an intuitive tool for individualized risk assessment,aiding clinicians in the early identification of high-risk patients,optimizing intervention strategies,and improving patient outcomes.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

BACKGROUND: G protein-coupled receptor kinase 2 (GRK2) could participate in the regulation of diverse cells via interacting with non-G-protein-coupled receptors. In the present work, we explored how paroxetine, a GRK2 inhibitor, modulates the differentiation and activation of immune cells in rheumatoid arthritis (RA). METHODS: The blood samples of healthy individuals and RA patients were collected between July 2021 and March 2022 from the First Affiliated Hospital of Anhui Medical University. C57BL/6 mice were used to induce the collagen-induced arthritis (CIA) model. Flow cytometry analysis was used to characterize the differentiation and function of dendritic cells (DCs)/T cells. Co-immunoprecipitation was used to explore the specific molecular mechanism. RESULTS: In patients with RA, high expression of GRK2 in peripheral blood lymphocytes, accompanied by the increases of phosphatidylinositol 3 kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR). In animal model, a decrease in regulatory T cells (T regs ), an increase in the cluster of differentiation 8 positive (CD8 + ) T cells, and maturation of DCs were observed. Paroxetine, when used in vitro and in CIA mice, restrained the maturation of DCs and the differentiation of CD8 + T cells, and induced the proportion of T regs . Paroxetine inhibited the secretion of pro-inflammatory cytokines, the expression of C-C motif chemokine receptor 7 in DCs and T cells. Simultaneously, paroxetine upregulated the expression of programmed death ligand 1, and anti-inflammatory cytokines. Additionally, paroxetine inhibited the PI3K-AKT-mTOR metabolic pathway in both DCs and T cells. This was associated with a reduction in mitochondrial membrane potential and changes in the utilization of glucose and lipids, particularly in DCs. Paroxetine reversed PI3K-AKT pathway activation induced by 740 Y-P (a PI3K agonist) through inhibiting the interaction between GRK2 and PI3K in DCs and T cells. CONCLUSION Paroxetine exerts an immunosuppressive effect by targeting GRK2, which subsequently inhibits the metabolism-related PI3K-AKT-mTOR pathway of DCs and T cells in RA.
G-Protein-Coupled Receptor Kinase 2/metabolism* ; Arthritis, Rheumatoid/immunology* ; Animals ; Dendritic Cells/metabolism* ; Paroxetine/therapeutic use* ; Proto-Oncogene Proteins c-akt/metabolism* ; Mice ; Humans ; Mice, Inbred C57BL ; Signal Transduction/drug effects* ; Male ; Phosphatidylinositol 3-Kinases/metabolism* ; Lymphocyte Activation/drug effects* ; Female ; T-Lymphocytes/metabolism* ; Middle Aged

BACKGROUND: Spicy food consumption has been reported to be inversely associated with mortality from multiple diseases. However, the effect of spicy food intake on the incidence of vascular diseases in the Chinese population remains unclear. This study was conducted to explore this association. METHODS: This study was performed using the large-scale China Kadoorie Biobank (CKB) prospective cohort of 486,335 participants. The primary outcomes were vascular disease, ischemic heart disease (IHD), major coronary events (MCEs), cerebrovascular disease, stroke, and non-stroke cerebrovascular disease. A Cox proportional hazards regression model was used to assess the association between spicy food consumption and incident vascular diseases. Subgroup analysis was also performed to evaluate the heterogeneity of the association between spicy food consumption and the risk of vascular disease stratified by several basic characteristics. In addition, the joint effects of spicy food consumption and the healthy lifestyle score on the risk of vascular disease were also evaluated, and sensitivity analyses were performed to assess the reliability of the association results. RESULTS: During a median follow-up time of 12.1 years, a total of 136,125 patients with vascular disease, 46,689 patients with IHD, 10,097 patients with MCEs, 80,114 patients with cerebrovascular disease, 56,726 patients with stroke, and 40,098 patients with non-stroke cerebrovascular disease were identified. Participants who consumed spicy food 1-2 days/week (hazard ratio [HR] = 0.95, 95% confidence interval [95% CI] = [0.93, 0.97], P <0.001), 3-5 days/week (HR = 0.96, 95% CI = [0.94, 0.99], P = 0.003), and 6-7 days/week (HR = 0.97, 95% CI = [0.95, 0.99], P = 0.002) had a significantly lower risk of vascular disease than those who consumed spicy food less than once a week ( Ptrend <0.001), especially in those who were younger and living in rural areas. Notably, the disease-based subgroup analysis indicated that the inverse associations remained in IHD ( Ptrend = 0.011) and MCEs ( Ptrend = 0.002) risk. Intriguingly, there was an interaction effect between spicy food consumption and the healthy lifestyle score on the risk of IHD ( Pinteraction = 0.037). CONCLUSIONS Our findings support an inverse association between spicy food consumption and vascular disease in the Chinese population, which may provide additional dietary guidance for the prevention of vascular diseases.
Humans ; Male ; Female ; Prospective Studies ; Middle Aged ; Aged ; Vascular Diseases/etiology* ; Risk Factors ; China/epidemiology* ; Adult ; Proportional Hazards Models ; Cerebrovascular Disorders/epidemiology* ; East Asian People

BACKGROUND: Diabetic foot is a complex condition with high incidence, recurrence, mortality, and disability rates. Current treatments for diabetic foot ulcers are often insufficient. This study was conducted to identify potential therapeutic targets for diabetic foot. METHODS: Datasets related to diabetic foot and diabetic skin were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using R software. Enrichment analysis was conducted to screen for critical gene functions and pathways. A protein interaction network was constructed to identify node genes corresponding to key proteins. The DEGs and node genes were overlapped to pinpoint target genes. Plasma and chronic ulcer samples from diabetic and non-diabetic individuals were collected. Western blotting, immunohistochemistry, and enzyme-linked immunosorbent assays were performed to verify the S100 calcium binding protein A9 (S100A9), inflammatory cytokine, and related pathway protein levels. Hematoxylin and eosin staining was used to measure epidermal layer thickness. RESULTS: In total, 283 common DEGs and 42 node genes in diabetic foot ulcers were identified. Forty-three genes were differentially expressed in the skin of diabetic and non-diabetic individuals. The overlapping of the most significant DEGs and node genes led to the identification of S100A9 as a target gene. The S100A9 level was significantly higher in diabetic than in non-diabetic plasma (178.40 ± 44.65 ng/mL vs. 40.84 ± 18.86 ng/mL) and in chronic ulcers, and the wound healing time correlated positively with the plasma S100A9 level. The levels of inflammatory cytokines (tumor necrosis factor-α, interleukin [IL]-1, and IL-6) and related pathway proteins (phospho-extracellular signal regulated kinase [ERK], phospho-p38, phospho-p65, and p-protein kinase B [Akt]) were also elevated. The epidermal layer was notably thinner in chronic diabetic ulcers than in non-diabetic skin (24.17 ± 25.60 μm vs. 412.00 ± 181.60 μm). CONCLUSIONS S100A9 was significantly upregulated in diabetic foot and was associated with prolonged wound healing. S100A9 may impair diabetic wound healing by disrupting local inflammatory responses and skin re-epithelialization.
Calgranulin B/therapeutic use* ; Diabetic Foot/metabolism* ; Humans ; Datasets as Topic ; Computational Biology ; Mice, Inbred C57BL ; Animals ; Mice ; Protein Interaction Maps ; Immunohistochemistry

Ischemic stroke (IS) ranks as a leading cause of death and disability globally. The blood-brain barrier (BBB) poses significant challenges for effective drug delivery to brain tissues. Recent decades have seen the development of targeted nanomedicine and biomimetic technologies, sparking substantial interest in biomimetic drug delivery systems for treating IS. These systems are devised by utilizing or replicating natural cells and their derivatives, offering promising new pathways for detection and transport across the BBB. Their multifunctionality and high biocompatibility make them effective treatment options for IS. In addition, the incorporation of engineering techniques has provided these biomimetic drug delivery systems with active targeting capabilities, enhancing the accumulation of therapeutic agents in ischemic tissues and specific cell types. This improvement boosts drug transport and therapeutic efficacy. However, it is crucial to thoroughly understand the advantages and limitations of various engineering strategies employed in constructing biomimetic delivery systems. Selecting appropriate construction methods based on the characteristics of the disease is vital to achieving optimal treatment outcomes. This review summarizes recent advancements in three types of engineered biomimetic drug delivery systems, developed from natural cells and their derivatives, for treating IS. It also discusses their effectiveness in application and potential challenges in future clinical translation.
Humans ; Drug Delivery Systems/methods* ; Ischemic Stroke/drug therapy* ; Animals ; Blood-Brain Barrier/metabolism* ; Stroke/drug therapy*

This study investigated the impact of metabolic syndrome (MetS) and its components on prostate volume (PV) in the general Chinese population. In total, 43 455 participants in The First Medical Center of the Chinese PLA General Hospital (Beijing, China) from January 1, 2012, to December 31, 2022, undergoing health examinations were included in the study. Participants were categorized into four groups according to PV quartiles: Q1 (PV ≤24.94 ml), Q2 (PV >24.94 ml and ≤28.78 ml), Q3 (PV >28.78 ml and ≤34.07 ml), and Q4 (PV >34.07 ml), with Q1 serving as the reference group. Logistic regression analyses were used to examine the association between MetS and PV, with subgroup analyses conducted by age. Among the participants, 18 787 (43.2%) were diagnosed with MetS. In the multivariate analysis model, a significant correlation between MetS and PV was observed, with odds ratios (ORs) increasing as PV increased (Q2, OR = 1.203, 95% confidence interval [CI]: 1.139-1.271; Q3, OR = 1.300, 95% CI: 1.230-1.373; and Q4, OR = 1.556, 95% CI: 1.469-1.648). Analysis of MetS components revealed that all components were positively associated with PV, with abdominal obesity showing the most significant effect. The number of MetS components was identified as a dose-dependent risk factor for elevated PV. The impact of MetS, its components, and component count on PV exhibited a decreasing trend with advancing age. Overall, the influence of MetS, its components, and component count on PV was predominantly observed in the age groups of 40-49 years and 50-59 years. Early intervention targeting MetS can significantly alleviate the increase in PV, particularly benefiting individuals aged 40-59 years who have abdominal obesity.
Humans ; Male ; Metabolic Syndrome/complications* ; Middle Aged ; Cross-Sectional Studies ; Aged ; Prostate/diagnostic imaging* ; Adult ; Age Factors ; Organ Size ; China/epidemiology* ; Obesity, Abdominal ; Risk Factors

Protein arginine methyltransferase 5 (PRMT5) acts as an oncogene in liver cancer, yet its roles and in-depth molecular mechanisms within the liver cancer immune microenvironment remain mostly undefined. Here, we demonstrated that disruption of tumor-intrinsic PRMT5 enhances CD8⁺ T-cell-mediated antitumor immunity both in vivo and in vitro. Further experiments verified that this effect is achieved through downregulation of the inhibitory immune checkpoint molecule, fibrinogen-like protein 1 (FGL1). Mechanistically, PRMT5 catalyzed symmetric dimethylation of transcription factor 12 (TCF12) at arginine 554 (R554), prompting the binding of TCF12 to FGL1 promoter region, which transcriptionally activated FGL1 in tumor cells. Methylation deficiency at TCF12-R554 residue downregulated FGL1 expression, which promoted CD8⁺ T-cell-mediated antitumor immunity. Notably, combining the PRMT5 methyltransferase inhibitor GSK591 with PD-L1 blockade efficiently inhibited liver cancer growth and improved overall survival in mice. Collectively, our findings reveal the immunosuppressive role and mechanism of PRMT5 in liver cancer and highlight that targeting PRMT5 could boost checkpoint immunotherapy efficacy.