OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

Chronic fatigue syndrome (CFS) is a common problem in military maritime navigation, which greatly affects the safety of military missions. The use of animal models to carry out research on the mechanism of CFS and treatment measures is a common method. This paper systematically introduced the construction methods of CFS models such as single-factor and multi-factor models, summarized common evaluation indicators of CFS, including behavioral and biochemical indicators, and summed up key characteristics of CFS animal models in military oceanic navigation combined with common causes of CFS in military contexts, such as prolonged continuous work, high-intensity physical activity, sleep deprivation, psychological stress, and extreme environmental conditions. The key characteristics of the animal models included, but not limited to, chronic fatigue, sleep disorders, impaired cognitive function, psychological stress responses, and abnormal biochemical indicators. Furthermore, this article identified future research directions for CFS animal models in military oceanic navigation to enhance the application value of the models and provide robust support for the health protection and disease prevention of military personnel.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor （SNRI） in inpatients with moderate and severe depression by using a machine learning method. METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024； those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate. After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression， five prediction models including support vector machine， k-nearest neighbor， random forest， lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set. Bayesian optimization was used to adjust the hyperparameters of these models. The performance of the models was evaluated in the validation set to select the optimal model. The Shapley additive explanations method was used to perform explainable analysis on the best model. RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected， comprising 285 cases in the effective group and 70 cases in the ineffective group， resulting in an overall therapeutic response rate of 80.28%. After feature variable screening， five characteristic variables for therapeutic efficacy were obtained， including Hamilton anxiety scale， blood urea nitrogen， combination of anti-anxiety drugs， drinking history， and first onset of the disease. Compared with other models， the random forest model performed the best. The area under the receiver operating characteristic curve was 0.85， the area under the precision-recall curve was 0.87， the accuracy was 0.74， and the recall rate value was 0.75. CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.

[摘 要] 目的：探索外周免疫功能状态与肺癌转移的关联，筛选可用于肺癌转移“正虚”评估的外周血免疫标志物。方法：回顾性分析2023年3月至2025年4月期间上海中医药大学附属市中医医院收治的肺癌患者治疗前的外周血免疫标志物，根据是否存在远处转移，将患者分为无转移组与转移组，比较两组间免疫细胞和细胞因子的表达差异。将单因素分析P < 0.05的外周血免疫指标纳入多因素二元Logistic回归模型，以识别肺癌转移的独立预测因素。结果：共纳入193例肺癌患者（无转移组101例，转移组92例），两组在性别、年龄、吸烟史、饮酒史、病理类型间的差异均无统计学意义（均P > 0.05）。单因素分析显示，无转移组与转移组间有多项免疫指标存在显著差异（均P < 0.05），包括：淋巴细胞计数，CD3+、CD4+、CD8+ T、CD19+ B细胞及CD3-CD16+56+ NK细胞绝对计数，Treg细胞、CD8+CD28+ Treg细胞、G-MDSC和CD3-CD16+CD56+dim NK细胞百分率，以及细胞因子IL-1β、IL-6和IL-10水平。将差异性指标行二元Logistic回归分析，提示外周血中Treg细胞和CD8+CD28+ Treg细胞百分率是肺癌发生远处转移的独立预测因素[OR = 1.193, 95% CI（1.047, 1.36）, P < 0.01; OR = 0.978, 95% CI（0.957, 0.999）, P < 0.05]。结论：外周血免疫功能紊乱是肺癌转移“正虚”的生物学基础，本研究以量化指标证实外周免疫功能状态与肺癌转移的相关性，为“正虚伏毒”和“肿瘤转移态”理论提供了实证。