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.

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.

Objective:To establish an evaluation model for occupational hazard prevention and control levels in coal mines, to explore the combination of Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, to evaluate the overall situation of occupational hazard prevention and control in coal mines.Methods:In November 2021, Collect information of occupational hazards and their prevention and control status in 30 coal mines. AHP model was first constructed for the elements of occupational hazard prevention and control in coal mines. Then, the AHP and fuzzy comprehensive evaluation method was applied to comprehensively evaluate and grade the occupational hazard prevention and control in coal mines, using the principles of maximum membership and weighted average.Results:The hierarchical fuzzy comprehensive evaluation results for typical coal mine were basically consistent with its occupational hazard prevention and control situation. The dust prevention and control situation was good, the noise prevention and control situation is average, the occupational health monitoring situation was good, the occupational health management situation was average, and the overall level of occupational hazard prevention and control was good.Conclusion:The hierarchical fuzzy comprehensive evaluation model for occupational hazard prevention and control levels, combined with the principles of maximum membership and weighted average, can objectively evaluate and reflect the overall situation of occupational hazard prevention and control in coal mines.

Objective:To establish an evaluation model for occupational hazard prevention and control levels in coal mines, to explore the combination of Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation, to evaluate the overall situation of occupational hazard prevention and control in coal mines.Methods:In November 2021, Collect information of occupational hazards and their prevention and control status in 30 coal mines. AHP model was first constructed for the elements of occupational hazard prevention and control in coal mines. Then, the AHP and fuzzy comprehensive evaluation method was applied to comprehensively evaluate and grade the occupational hazard prevention and control in coal mines, using the principles of maximum membership and weighted average.Results:The hierarchical fuzzy comprehensive evaluation results for typical coal mine were basically consistent with its occupational hazard prevention and control situation. The dust prevention and control situation was good, the noise prevention and control situation is average, the occupational health monitoring situation was good, the occupational health management situation was average, and the overall level of occupational hazard prevention and control was good.Conclusion:The hierarchical fuzzy comprehensive evaluation model for occupational hazard prevention and control levels, combined with the principles of maximum membership and weighted average, can objectively evaluate and reflect the overall situation of occupational hazard prevention and control in coal mines.

Objective:To investigate the effect of visceral obesity on the short-term curative effect of Da Vinci robotic-assisted radical resec-tion for rectal cancers.Methods:Clinical and pathological data of patients with rectal cancer undergoing Da Vinci robotic-assisted surgery,admitted to People's Hospital of Zhengzhou University and Cancer Hospital of Zhengzhou University from November 2019 to June 2022 were retrospectively analyzed.Visceral fat area(VFA)≥100 cm2 was used as the standard to define visceral obesity.Patients were categorized in-to visceral and non-visceral obesity groups.The short-term efficacy of the two groups was evaluated,and the influencing factors of post-operative complications were analyzed using univariate and multivariate Logistic regression.Results:Among a total of 169 patients,93 were included in the visceral obesity group and 76 in the non-visceral obesity group.There was no significant difference in the baseline data between the two groups(P>0.05).There was no conversion to laparotomy in the non-visceral obesity group,and the conversion rate was 1.1%(1/93)in the visceral obesity group.The second operation rate was 2.2%(2/93)in the visceral obesity group and 1.3%(1/76)in the non-visceral obesity group with no statistical difference between the two groups.There were no significant differences in the operation dur-ation,intraoperative blood loss,number of lymph node dissections,and total postoperative complication rate between the two groups(P>0.05).Multivariate Logistic regression analysis revealed that an NRS≥3 independently contributed as a risk factor for postoperative com-plications(OR=3.190,95%CI:1.240-8.210,P=0.016).Conclusions:An NRS≥3 is an independent risk factor for complications post-robotic rad-ical rectal cancer surgery.The robotic surgical platform can overcome obesity-related limitations and is equally safe and effective for pa-tients with visceral obesity presenting with rectal cancer.

Objective:To analyze the pathogenic bacteria and epidemiological characteristics in children with respiratory tract infection in Tianjin area.Methods:Retrospective case analysis was performed on 2 392 hospitalized children in the wards of respiratory diseases, intensive care unit and special care ward of Tianjin Children′s Hospital from June 2018 to May 2019. Thirteen pathogenic bacteria in deep sputum and bronchoalveolar lavage fluid samples were detected by loop-mediated isothermal amplification. The laboratory data and clinical characteristics of the infected children were analyzed, and the comparison between groups was performed by t test or χ 2 test. Results:Among 2 392 cases, 1 407 were males and 985 females. There was no significant difference in the detection rate between males and females (72.5% (1 020/1 407) vs.74.2% (731/985), χ 2=0.87, P=0.35). A total of 1 751 strains and 12 kinds of positive respiratory pathogens were detected, with a detection rate of 73.2%. Among them, 913 (38.2%) strains were Mycoplasma pneumoniae (MP), 514 (21.5%) were Streptococcus pneumoniae (Sp), 381 (15.9%) were Methicillin-resistant Staphylococcus aureus (MRSA) and 279 (11.7%) were Hemophilus influenzae (Hi). There was significant difference in the detection rate of pathogens among different age groups (χ2=83.67, P<0.01). The positive rate of alveolar lavage fluid group was higher than that of deep sputum fluid group [81.6% (614/752) vs. 69.3% (1 137/1 640), χ 2=39.89, P<0.01]. The length of hospital stay of children infected with different pathogens was significantly different (all P<0.01). There was significant difference in duration of fever among children infected with different pathogens (χ2=228.69,103.56, 3.96, 27.38,24.50,41.66, all P<0.05). There were 63 (7.7%) cases of atelectasis, 260 (31.9%) cases of pleurisy and 120 (14.7%) cases of pleural effusion in MP children. Children with Sma were most likely to involve the heart system (2/9), and children with Eco infection had a higher incidence of complications such as those of blood (3/19), urinary (2/19), digestive systems(4/19), systemic inflammatory response syndrome and sepsis (1/19). Conclusions:The main bacterial pathogens of respiratory tract infection in children in Tianjin were MP, Sp, MRSA and Hi. It is suggested that clinicians should not only pay attention to the respiratory symptoms of children, but also pay attention to the complications caused by bacterial pathogen infection, so as to prevent the deterioration of the disease and improve the prognosis.

[Abstract] Objective: To observe the effect of allogeneic platelets transfusion on the invasion and metastasis of human lung cancer A549 cells, and to preliminarily explore its mechanism of action. Methods: Eighty-nine patients with advanced lung cancer, who had received platelet transfusion in the Chemotherapy Department of Fourth Hospital of Hebei Medical University between January 2017 and December 2018, were enrolled in this study. The study cells were randomized into Ctrl group (A549 cells co-incubated with culture medium), Before group, and After group (A549 cells co-incubated with plasma Before and After platelet transfusion, respectively). The migration and invasion of A549 cells co-cultured with plasma before and after platelet transfection were detected by Scratch and Transwell experiments. The expression of MMPs, TIMPs and epithelial-mesenchymal transition (EMT) related proteins E-cadherin, N-cadherin and Vimentin, as well as vascular endothelial growth factor (VEGF) and its receptor 2 (VEGFR2) were detected by Western blotting (WB) method. Results: The scratch healing ability of A549 cells in After group was significantly higher than that of Ctrl group and Before group [(73.67±2.60)% vs (58.33±2.33)%, (35.33±2.03) %; P<0.01, vs Ctrl group; P<0.05, vs Before group], and there was also a significant difference between Before group and Ctrl group (P<0.05). The results of cell migration experiment showed that the number of transmembrane cells in After group was significantly higher than that in Ctrl group and Before group [(69.67±7.84) vs (18±2.08) and (39.33±2.03), all P<0.01]. The cell invasion experiment showed that the number of transmembrane cells in After group was significantly higher than that in Ctrl group and Before group [(59.34±3.46) vs (18.34±1.56) and (37.58±2.79), all P<0.01]. When A549 cells were co-incubated with plasma before and after platelet transfusion for 48 h, it was found that the expressions of MMP9 and MMP2 were increased (P<0.05), while their inhibitors TIMP1 and TIMP2 were decreased (P<0.01); the expressions of EMT-related proteins N-cadherin and Vimentin were increased (P<0.05), but E-cadherin was decreased (P<0.01); the expressions of angiogenesis related proteins VEGF and VEGFR2 were increased (P<0.05). Conclusion: Alloplatelets transfusion can promote the invasion and metastasis of lung cancer A549 cells, which may be realized by regulation of the expressions of EMT, metallomatrix protease and vascular growth factor-related proteins.

Objective: To investigate the duration of Norovirus (NoV) shedding among infected school children during a NoV outbreak in a kindergarten,and to provide scientitic basis for epidemic prevention and control. Methods: Specimens and epidemiological data were collected from suspected cases, and specimens were detected using real-time RT-PCR to determine whether or not infecting with NoV. Specimens were collected every 3-7 days from NoV-infected children until specimens became negative for NoV. Results: A total of 14 suspected cases were reported, and 12 of them were infected with NoV. The average duration of NoV shedding was (26.58±17.94)d. The specimens among 9 from 12 Nov-infected cases were positive at 7 days, 8 NoV-intected cased remained positive at 14 days and 7 Non-infected cased at least 21 days. Conclusion Since NoV shedding duration among NoV-infected children tends to longer than their isolation time during outbreaks, reinforcement of hygiene practices among these school children is especially necessary to reduce the risk of virus secondary transmissions after their return to school.

Computer-aided diagnosis based on computed tomography (CT) image can realize the detection and classification of pulmonary nodules, and improve the survival rate of early lung cancer, which has important clinical significance. In recent years, with the rapid development of medical big data and artificial intelligence technology, the auxiliary diagnosis of lung cancer based on deep learning has gradually become one of the most active research directions in this field. In order to promote the deep learning in the detection and classification of pulmonary nodules, we reviewed the research progress in this field based on the relevant literatures published at domestic and overseas in recent years. This paper begins with a brief introduction of two widely used lung CT image databases: lung image database consortium and image database resource initiative (LIDC-IDRI) and Data Science Bowl 2017. Then, the detection and classification of pulmonary nodules based on different network structures are introduced in detail. Finally, some problems of deep learning in lung CT image nodule detection and classification are discussed and conclusions are given. The development prospect is also forecasted, which provides reference for future application research in this field.
Deep Learning ; Humans ; Lung Neoplasms ; diagnostic imaging ; Radiographic Image Interpretation, Computer-Assisted ; Reproducibility of Results ; Solitary Pulmonary Nodule ; diagnostic imaging ; Tomography, X-Ray Computed

The human brain deteriorates as we age, and the rate and the trajectories of these changes significantly vary among brain regions and among individuals. Because neuroimaging data are potentially important indicators of individual's brain health, they are commonly used in brain age prediction. In this review, we summarize brain age prediction model from neuroimaging-based studies in the last ten years. The studies are categorized based on their image modalities and feature types. The results indicate that the prediction frameworks based on neuroimaging holds promise toward individualized brain age prediction. Finally, we addressed the challenges in brain age prediction and suggested some future research directions.
Aging ; Brain ; diagnostic imaging ; physiology ; Humans ; Neuroimaging