[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 compare the performance and efficacy of prognostic models constructed by different machine learning algorithms in predicting the survival period of lung transplantation (LTx) recipients. Methods Data from 483 recipients who underwent LTx were retrospectively collected. All recipients were divided into a training set and a validation set at a ratio of 7:3. The 24 collected variables were screened based on variable importance (VIMP). Prognostic models were constructed using random survival forest (RSF) and extreme gradient boosting tree (XGBoost). The performance of the models was evaluated using the integrated area under the curve (iAUC) and time-dependent area under the curve (tAUC). Results There were no significant statistical differences in the variables between the training set and the validation set. The top 15 variables ranked by VIMP were used for modeling and the length of stay in the intensive care unit (ICU) was determined as the most important factor. Compared with the XGBoost model, the RSF model demonstrated better performance in predicting the survival period of recipients (iAUC 0.773 vs. 0.723). The RSF model also showed better performance in predicting the 6-month survival period (tAUC _{6 months} 0.884 vs. 0.809, P = 0.009) and 1-year survival period (tAUC _{1 year} 0.896 vs. 0.825, P = 0.013) of recipients. Based on the prediction cut-off values of the two algorithms, LTx recipients were divided into high-risk and low-risk groups. The survival analysis results of both models showed that the survival rate of recipients in the high-risk group was significantly lower than that in the low-risk group (P<0.001). Conclusions Compared with XGBoost, the machine learning prognostic model developed based on the RSF algorithm may preferably predict the survival period of LTx recipients.

ObjectiveTo observe the clinical effectiveness and safety of Xiaozhong Zhitong Mixture （消肿止痛合剂） combined with antibiotic bone cement in the treatment of diabetic foot ulcer （DFU） with damp-heat obstructing syndrome. MethodsA total of 72 DFU patients with damp-heat obstructing syndrome were randomly assigned to treatment group （36 cases） and the control group （36 cases）. Both groups received standard treatment and topical antibiotic bone cement for ulcer wounds， while the treatment group received oral Xiaozhong Zhitong Mixture （50 ml per time， three times daily） in additionally. Both groups underwent daily wound dressing changes for 21 consecutive days. Ulcer healing rate， serum levels of tumor necrosis factor-alpha （TNF-α）， interleukin-1 beta （IL-1β）， malondialdehyde （MDA）， superoxide dismutase （SOD）， C-reactive protein （CRP）， and white blood cell （WBC） count were observed before and after treatment， and visual analog scale （VAS） scores for wound pain， traditional Chinese medicine （TCM） syndrome scores， and the DFU Healing Scale （DMIST scale） were also compared. Liver and kidney function were evaluated before and after treatment， and adverse events such as allergic reactions， worsening ulcer pain were recorded. ResultsTotally 35 patients in the treatment group and 33 in the control group were included in the final analysis. The ulcer healing rate in the treatment group was （87.93±9.34）%， significantly higher than （81.82±12.02）% in the control group （P = 0.035）. Compared to pre-treatment levels， both groups showed significant reductions in serum CRP， WBC， MDA， IL-1β， and TNF-α levels， with an increase in SOD level （P＜0.05）. TCM syndrome scores， VAS， and DMIST scores also significantly decreased in both groups （P＜0.05）， with greater improvements in the treatment group （P＜0.05）. No significant adverse reactions were observed in either group during treatment. ConclusionXiaozhong Zhitong Mixture combined with antibiotic bone cement has significant advantages in promoting DFU healing， reducing inflammatory response， and alleviating oxidative stress in DFU patients with damp-heat obstructing syndrome， with good safety for DFU patients with damp-heat obstructing syndrome.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective To establish a rapid screening and analysis method for etomidate and its ana-logues using a portable mass spectrometer equipped with a thermal desorption-atmospheric pressure chemical ionization source-linear ion trap.Methods A 10 μL aliquot of a standard solution at a con-centration of 1 μg/mL was taken,and after the solvent evaporated,the sample was inserted into the in-let of the portable mass spectrometer for detection.By adjusting the collision-induced dissociation pa-rameters,the molecular ion peak and fragment ion peak information of the standard were obtained and used to establish a reference database.In addition,the method was applied to 29 seized liquid and plant samples.Results A screening system for etomidate and its analogues was established based on the portable mass spectrometer and the corresponding mass spectrometry library.The system enables qualitative screening analysis by identifying primary protonated molecular ions and secondary product ions of etomidate and its analogues.The limits of detection for etomidate and its 12 analogues ranged from 0.1 to 10 μg/mL.Etomidate and its analogues were detected in all 29 liquid and plant samples.However,this method could not distinguish between isomeric imidazole esters,such as isopropoxate and propoxate.Additionally,when testing 2-SH-etomidate,there was a false positive for the detection of etomidate.Conclusion This study established a rapid screening method for etomidate and its ana-logues using a portable mass spectrometer.The method combines the high sensitivity of mass spectrome-try with the on-site applicability of portable devices,significantly improving detection efficiency and meeting the on-site detection needs of etomidate and its analogues.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Thoracolumbar spine fracture often leads to severe pain, functional impairments, and neurological deficits, for which open reduction and internal fixation can effectively restore the spinal structural stability. Open decompression and reduction with internal fixation can help relieve spinal cord compression and improve spinal function in cases of concomitant cord injury. Although spinal stability can be restored through surgery, patients often face chronic pain and functional impairments postoperatively. A postoperative rehabilitation program is critical in optimizing therapeutic outcomes, reducing complications, and minimizing the risk of secondary injuries. However, current rehabilitation methods, such as physical therapy, functional training, and pain management, are confronted with problems in clinical practice, including significant variation in efficacy, poor patient adherence, and prolonged rehabilitation period. There is an urgent need for a unified rehabilitation strategy to address these problems. To this end, the Spinal Trauma Group of the Orthopedic Physicians Branch of the Chinese Medical Association and the Spine Health Professional Committee of the Chinese Human Health Technology Promotion Association organized experts from relevant fields to formulate Evidence-based guidelines for rehabilitation treatment after internal fixation of thoracolumbar spine fracture in adults ( version 2025) by integrating evidences from clinical researches and advanced rehabilitation concepts at home and abroad. A total number of 14 recommendations concerning the rehabilitation treatment with multimodal analgesia, psychological intervention, deep vein thrombosis prevention, core muscle and extremity exercise, appropriate use of braces, early weight-bearing, device-aided rehabilitation exercise, neuroregulatory therapy, rehabilitation team were put forward, aiming to standardize the post-operative rehabilitation process following internal fixation, promote the functional recovery, and enhance patients′ quality of life.