[摘 要] 目的：探讨复发/转移性鼻咽癌（NPC）接受含PD-1单抗免疫治疗的临床特征和预后影响因素。方法：回顾性分析2019年3月至2024年7月期间南部战区总医院确诊的95例NPC患者的临床资料和外周血生化及免疫学指标。预后分析采用Kaplan-Meier曲线，组间比较使用Log-rank检验，采用Cox比例风险模型进行单因素和多因素分析。结果：95例患者中男性81例，女性14例，中位年龄49.72岁（16~74岁），Ⅳ期91例（95.79%），所有患者均采用免疫治疗，联合或不联合化疗方案治疗，中位无进展生存期（mPFS）为10.5个月，客观缓解率（ORR）70.53%，疾病控制率（DCR）89.47%，接受含铂治疗方案患者PFS相对更长，且差异有统计学意义。紫杉醇 + 顺铂 + 氟尿嘧啶（TPF）对比吉西他滨 + 顺铂（GP）和紫杉醇 + 顺铂（TP）显示出更长的PFS，但差异无统计学意义。不同PD-1单抗治疗组间的PFS未显示出有统计学意义的差异。单因素及多因素Cox回归分析结果显示，肿瘤复发状态、初始血浆EBV感染状态、治疗周期数、基线外周血SII是复发/转移性NPC患者接受PD-1抑制剂治疗疗效预测的独立相关因素（均P < 0.05），并且非复发患者、初始血浆EBV DNA阳性、接受 ≥ 4治疗周期、基线外周血SII < 772.81的患者接受PD-1抑制剂治疗预后相对更好。结论：在接受PD-1抑制剂治疗的复发/转移性NPC患者中，非复发患者、初始血浆EBV DNA阳性、≥ 4治疗周期且外周血SII < 772.81者PFS相对更长，可早期识别免疫治疗效果不佳患者并精准干预。

Objective To establish a stable and reliable sepsis model of rat after liver transplantation (LT) for clinical translational research and analyze its characteristics. Methods The "two-sleeve method" was used to establish the in situ LT model of SD rats, and the sepsis model was constructed through cecal ligation and puncture (CLP) at 3 d after the operation. SD rats were randomly divided into 3 groups: sham operation group (Sham group), LT group, and LT + CLP group, with 6 rats in each group. The changes in body weight, rectal temperature and survival rate were compared, and the sepsis score was used for evaluation. The levels of blood biochemical indicators [alanine aminotransferase (ALT), aspartate aminotransferase (AST), urea (Urea), creatinine (Cr), creatine kinase (CK), lactate dehydrogenase (LDH)] and inflammatory factors [interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α] in each group were detected, and the pathological changes and cell apoptosis in different organs were observed. Results Compared with the Sham group, the body weight of the LT group and LT + CLP group decreased (all P<0.05). The rectal temperature of the LT + CLP group showed a continuous downward trend after the operation, the sepsis score increased sharply after the operation, and the survival rate dropped to 16.7%, and the differences between the Sham group, LT group and LT + CLP group were statistically significant (all P<0.05). The levels of ALT, AST, Urea, Cr, CK, LDH, and serum IL-1β, IL-6, IL-10 and TNF-α in the LT + CLP group were higher than those in the Sham group and LT group rats within 72 hours after the operation(all P<0.05). The pathological examination of the LT + CLP group showed severe tissue structure destruction, necrosis and infiltration of inflammatory cells in multiple organs, and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining showed an increased level of cell apoptosis in multiple organs. Conclusions Using liver transplantation combined with CLP, a stable animal model of liver transplantation infection is successfully established, which exhibits a high mortality rate, significant multi-organ damage and intense inflammatory response, providing an ideal animal model for transplantation infection research.

Background/Aims: Dysregulated cholesterol metabolism is a hallmark of hepatocellular carcinoma (HCC) that drives tumor initiation and progression. However, clinical targeting of cholesterol metabolism has yielded limited benefits due to stringent feedback in tumor cells. Identifying a central mediator capable of restoring cholesterol homeostasis within the cell’s intrinsically fine-tuned regulatory framework is urgently needed. Methods: We integrated a proteomic dataset from patients with cholesterol-dysregulated HCC into a global cholesterol metabolic regulatory network to identify potential therapeutic targets for disrupted cholesterol homeostasis. The prognostic significance of the candidate targets was further validated in an independent cohort through immunohistochemistry. Functional and mechanistic studies were conducted in vitro using HCC cell lines and in vivo using mouse models. The pharmacological efficacy of the candidate agent was evaluated in both subcutaneous and orthotopic HCC mouse models. Results: ER lipid raft-associated 1 (ERLIN1), a pivotal regulator of cholesterol metabolism reprogramming, was identified as an independent favorable prognostic indicator in HCC. ERLIN1 constrains HCC progression both in vitro and in vivo by stabilizing the INSIG1–SCAP–SREBP2 axis and maintaining the metabolic balance of intracellular cholesterol. Under hypoxia, impaired factor-inhibiting hypoxia-1-dependent hydroxylation of ASB11 at asparagine residues 90 and 92 enhances ASB11-mediated ERLIN1 degradation. Pharmacological targeting of this axis using zoledronic acid (ZoA) attenuated HCC progression by weakening the ASB11–ERLIN1 interaction and restoring cholesterol homeostasis. Conclusions ERLIN1 represents a druggable metabolic vulnerability in cholesterol-dysregulated HCC. Targeting the ASB11–ERLIN1 axis with the clinically approved ZoA reestablishes cholesterol homeostasis and offers a promising therapeutic strategy to overcome the current limitations of cholesterol-targeted HCC therapies.

Metabolic associated fatty liver disease （MAFLD） is the most prevalent chronic liver disease worldwide， and its progression is closely associated with the development of liver fibrosis， liver cirrhosis， and even hepatocellular carcinoma. However， there is still a lack of effective therapies for MAFLD in clinical practice. Telomere is the protective structure at the end of chromosomes， and telomere shortening and functional impairment have been identified as one of the key factors regulating the pathological progression of MAFLD. This article systematically reviews the core mechanism of action of telomere regulation in MAFLD， including its molecular functions in nucleotide metabolism， oxidative stress， and epigenetic regulation， as well as its pathological effect in hepatocytes and hepatic stellate cells. In addition， this article explores the clinical prospects of telomeres as biomarkers and therapeutic targets for MAFLD， in order to provide a theoretical reference for improving the precise diagnosis and treatment system of MAFLD.

[This corrects the article DOI: 10.1016/j.jpha.2023.08.006.].

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

Objective:To evaluate the therapeutic efficacy of an upper limb rehabilitation robot on the recovery of elbow function recovery following arthroscopic elbow joint release.Methods:Seventy-two patients who underwent arthroscopic elbow joint release at Shanghai Sixth People’s Hospital from December 2022 to December 2023 were recruited. All patients were randomly assigned to either the conventional group (n=36; 16 males, 20 females; age 34.39±9.04 years, range 24-56; fractures: 4 intercondylar humerus, 18 olecranon, 8 radial head, 6 other) which received conventional rehabilitation postoperatively, or the robot-assisted group (n=36; 18 males, 18 females; age 33.78±9.98 years, range 20-59; fractures distribution identical to the conventional group) receiving conventional rehabilitation combined with robot-assisted upper limb rehabilitation therapy. The active range of motion (ROM) of elbow joint, Mayo elbow performance score (MEPS), visual analogue scale (VAS) of elbow joint were recorded preoperatively and at 4 weeks and 3 months postoperatively. The surface electromyography were analyzed at 3 months postoperatively.Results:All patients completed the 3-month rehabilitation program. At 4 weeks postoperatively, the conventional group exhibited a mean active ROM of 106.78°±9.91°, MEPS of 67.78±7.68, VAS of 2.11±0.74; the robot-assisted group showed active ROM of 113.72°±7.06°, MEPS of 73.33±9.28, VAS of 21.89±0.46. By 3 months postoperatively, the conventional group achieved a mean active ROM of 118.11°±6.75°, MEPS of 85.00±8.66, VAS of 0.67±0.67; robot-assisted achieved a mean active ROM of 127.61°±6.61°, MEPS of 91.11±6.57, VAS of 0.39±0.49. Both groups exhibited significant improvements in active ROM and MEPS, and significant reductions in VAS scores at 4 weeks and 3 months postoperatively compared with preoperatively values ( P<0.05). The robot-assisted group demonstrated significantly higher active ROM and MEPS at both 4 weeks and 3 months postoperatively, and a significantly lower VAS score at 3 months postoperatively, compared with the conventional group ( P<0.05). Surface electromyography at 3 months revealed significantly higher biceps brachii root mean square and significantly lower co-contraction index in the robot-assisted group compared to the conventional group ( P<0.05). No adverse symptoms were reported in the treated elbows of either group during the operation and follow-up period. Conclusion:The integration of upper limb rehabilitation robot-assisted therapy and conventional rehabilitation program significantly enhances the recovery of elbow range of motion and functional outcomes at 3 months following arthroscopic elbow joint release.

Objective:To explore the value of quantitative parameters of diffusion weighted imaging (DWI) combined with dynamic enhanced magnetic resonance imaging (DCE-MRI) in predicting the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer (LABC) .Methods:A total of 97 patients with LABC admitted to the hospital from Mar. 2020 to Mar. 2023 were studied and received neoadjuvant chemotherapy to evaluate the therapeutic effect, and DWI and DCE-MRI scans were performed before and after treatment. The difference of DWI and DCE-MRI quantitative parameters before treatment in patients with different therapeutic effects was compared, and the correlation between the difference of DWI and DCE-MRI quantitative parameters and therapeutic effect was analyzed. The predictive value of quantitative parameters of DWI and DCE-MRI before treatment was analyzed. The quantitative parameters of DWI and DCE-MRI in patients with different pathological reactions were compared before treatment, and the quantitative parameters of DWI and DCE-MRI were compared before and after treatment.Results:The apparent diffusion coefficient (ADC) of patients with effective chemotherapy before treatment was higher, but transport constant (Ktrans) , extracellular space volume percentage (Ve) and rate constant (Kep) were lower ( t=5.0, 3.27, 3.55, 3.89, P < 0.05) ; Spearman correlation analysis showed that ADC was positively correlated with chemotherapy efficacy before treatment (r=0.66; P < 0.05) , while Kep, Ve, Ktrans were negatively correlated with it (r=-0.58, -0.47, -0.60; P < 0.05) ; ROC curves showed that the area under the curve (AUC) values of ADC, Kep, Ve and Ktrans in predicting chemotherapy efficacy before treatment were 0.771, 0.797, 0.664 and 0.715, respectively, while the combined AUC value of each indicator was 0.832; Compared with patients with non-significant pathological response, ADC before treatment was higher in patients with significant pathological response, Kep, Ve and Ktrans were lower ( t=4.46, 3.32, 3.60, 3.95, P < 0.05) ; Compared with before treatment, ADC value increased after treatment, while Kep, Ve and Ktrans decreased ( t=8.77, 6.22, 9.34, 10.26, P < 0.05) . Conclusion:Quantitative parameters of DWI and DCE-MRI can reflect the changes in the condition of patients with locally advanced neoadjuvant chemotherapy, and the combination of the two can help to improve the predictive value of chemotherapy efficacy in patients.