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.

Objective To analyze the long-term trend in the disease burden of congenital birth defects(CBDs)among neonates in China from 1990 to 2021,compare the trend with global patterns,and identify key subtypes along with their association with socioeconomic status to provide evidence for public health interventions.Methods Utilizing data from the Global Burden of Disease Study 2021(GBD 2021),we extracted indicators including disability-adjusted life years(DALYs),mortality,and prevalence for the neonatal period(＜28 days)in China,encompassing ten major CBD subtypes.Joinpoint regression analysis was employed to calculate annual percent changes and estimate annual percent changes(EAPC),with comparisons of subtype composition between 1990 and 2021.Nonlinear regression was used to assess the relationship between DALYs rates and the Socio-demographic Index(SDI).Results From 1990 to 2021,DALYs rates for neonatal CBDs declined significantly both globally and in China,with China's EAPC at-4.67％[95％CI:(—5.06,—4.28)],substantially exceeding the global average of-1.70％[95％CI:(—1.75,—1.64)].Congenital heart anomalies remained the primary burden,while neural tube defects and orofacial clefts in China showed notable reductions(EAPCs of-7.25％and-11.22％,respectively).However,DALYs rates for congenital musculoskeletal and limb anomalies exceeded global expected levels.A resurgence in the prevalence was observed post-2015,with higher burdens in males.DALYs rates exhibited a negative correlation with SDI.Conclusion China has achieved significant reductions in the neonatal CBDs burden,surpassing global trends;yet challenges persist in managing congenital heart anomalies and musculoskeletal defects.Future efforts should focus on enhancing early screening,surgical interventions,and regional equity to align with global health objectives.

Objective To explore the clinical features,treatment and prognosis of traumatic subdural effusion(TSE)in infants.Methods Data of 51 cases of traumatic subdural effusion in infants admitted to the single center of Dalian Women and Children Medical Center(Group)from February 2013 to February 2020 were retrospectively analyzed,and their clinical manifestations,imaging features,treatment methods and prognosis were summarized and analyzed.Results Fifty-one cases(26 males and 25 females),ranging in age from 1 month to 3 years old of traumatic subdural effusion in infants were reviewed in our hospital,all cases were confirmed by Computed Tomography(CT)examination.31 cases were treated conservatively,29 cases were cured,and 2 cases were treated surgically due to poor conservative treatment.Surgical treatment was performed in 22 cases(including 2 cases who received surgical treatment due to poor conservative treatment).One patient underwent puncture and continuous drainage at the lateral Angle of the anterior fontanelle and was cured.Twenty-one cases underwent cranial drilling,subdural space catheterization for external drainage,and 17 cases(80.95％,17/21)were cured at one time.There were 4 cases(19.05％,4/21)of recurrence after external drainage with catheterization.Two cases were cured by external drainage with Ommaya capsule insertion and intermittent aspiration and fluid drainage.It was changed to subdural peritoneal shunt surgery,and 2 cases were cured after the operation.There was no surgical infection or death in all the children in the group.The median follow-up time ranged from 3 months to 60 months,and the conditions were all stable.Conclusion Traumatic subdural effusion is a common complication after craniocerebral injury in infants and young children.Due to its lack of self-expression,the hidden condition is often ignored.Moreover,the brain tissue of infants and young children is in the growth and development stage,which will affect the development of brain tissue after its onset.

Our previous research demonstrated that the Wenxia Changfu Formula (WCF), as a neoadjuvant therapy, inhibits M2 macrophage infiltration in the tumor microenvironment and prevents lung cancer metastasis. Given tumor-associated macrophages (TAMs) in epithelial-mesenchymal transition (EMT), this study investigated whether WCF impedes lung cancer metastasis by attenuating TAM-induced EMT in non-small cell lung cancer (NSCLC) cells. Utilizing a co-culture model treated with or without WCF, we observed that WCF downregulated cluster of differentiation 163 (CD163) expression in macrophages, reduced CCL18 levels in the conditioned medium, and inhibited the growth, invasion, and EMT of NSCLC cells induced by macrophage co-culture. Manipulation of CCL18 levels and Src overexpression in NSCLC cells revealed that WCF's effects are mediated through CCL18 and Src signaling. In vivo, WCF inhibited recombinant CCL18 (rCCL18)-induced tumor metastasis in nude mice by blocking Src signaling. These findings indicate that WCF inhibits NSCLC metastasis by impeding TAM-induced EMT via antagonistic modulation of CCL18, providing evidence for its potential development and clinical application in NSCLC patients.
Epithelial-Mesenchymal Transition/drug effects* ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Humans ; Animals ; Lung Neoplasms/metabolism* ; Chemokines, CC/antagonists & inhibitors* ; Mice ; Mice, Nude ; Drugs, Chinese Herbal/administration & dosage* ; Cell Line, Tumor ; Neoplasm Metastasis ; Tumor-Associated Macrophages/drug effects* ; Mice, Inbred BALB C ; Signal Transduction/drug effects*

Objective To investigate the safety and efficacy of super-selective arterial embolization in the treatment of abdominal wall hematoma.Methods The clinical data of 11 patients with abdominal wall hematoma,who were admitted to the Northern Jiangsu People's Hospital of China from January 2018 to December 2023,were retrospectively analyzed.All patients received angiography together with super-selective arterial embolization.The effectiveness of embolization treatment was evaluated by the technical success rate and therapeutic effect,and the safety was evaluated by the incidence of complication.Results The median age of the 11 patients was 70 years,91％were female,with a body mass index(BMI)of 25.1 kg/m2,subcutaneous fat thickness of 2.8 cm,and international normalized ratio(INR)of 1.12.DSA showed that 8 patients(72％)had active bleeding signs,3 patients(28％)had no active bleeding signs.Under DSA,a total of 18 responsible vessels,including 6 lumbar arteries(33.3％),5 deep circumflex iliac arteries(27.7％),5 inferior epigastric arteries(27.7％)and 2 iliolumbar arteries(11.3％),were identified and were treated with embolization.The median time spent for operation was 80 minutes.The technical success rate was 100％and the clinical effective rate was 91％.No operation-related major complications occurred,and the median hospital stay was 6 days.Conclusion For the abdominal wall hematoma in aged,obesity patients with underlying diseases,super-selective arterial embolization is a therapeutic method with high technical success rate,high clinical effective rate and satisfactory clinical safety.

Objective To discuss the clinical efficacy of microwave ablation(MWA)combined with percutaneous vertebroplasty(PVP),radiofrequency ablation(RFA)combined with PVP,and simple PVP in the treatment of vertebral metastatic tumors.Methods A total of 65 patients with vertebral metastatic tumors,who were admitted to the Northern Jiangsu People's Hospital of China to receive treatment from January 2019 to June 2023,were enrolled in this study.The patients were divided into MWA plus PVP group(M+P group,n=25,27 diseased vertebral bodies in total),RFA plus PVP group(R+P group,n=20,23 diseased vertebral bodies in total),and simple PVP group(P group,n=20,24 diseased vertebral bodies in total).Visual analog scale(VAS)score was used to assess the preoperative pain degree and the postoperative relief degree.Bone cement distribution and leakage at one week after surgery were evaluated.Results Successful operation was accomplished in all of the patients.No serious procedure-related complications occurred in all the patients of three groups.In R+P group,P group and M+P group,the preoperative mean VAS scores were(8.48±0.80)points,(8.57±0.98)points and(8.20±1.00)points respectively;the differences among the three groups were not statistically significant(P＞0.05).One week after operation,the pain was significantly relieved in all the patients of three groups;the mean VAS scores in R+P group,P group and M+P group were(4.10±0.85)points,(3.17±0.93)points and(2.44±1.23)points respectively,and the reduction in VAS score was most pronounced in M+P group(P＜0.05).Six months after operation;the mean VAS scores in R+P group,P group and M+P group were(1.87±0.84)points,(4.60±1.09)points and(1.48±0.71)points respectively;and the reduction in VAS score was most pronounced in the M+P group(P＜0.05).The used amount of bone cement in M+P group,R+P group and P group was(7.54±1.44)mL,(5.48±1.12)mL and(4.59±1.56)mL respectively,the difference among the three groups was statistically significant(P＜0.05).The vascular leakage rate(34.8％)and non-vascular leakage rate(52.2％)in P group were remarkably higher than those in R+P group and in M+P group(P＜0.05).No statistically significant difference in the rate of cement leakage existed between R+P group and M+P group(P＞0.05).Conclusion For the treatment of vertebral metastases,MW A plus PVP is superior to RFA plus PVP in pain relief rate.

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.