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: 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 general method for the simultaneous determination of hydroxyphenyl esters and quaternary ammonium bacteriostatic agents in eye drops.Methods:The chromatographic analysis was per-formed on an Agilent C18 column(4.6 mm ×250 mm,5 μm)with 1％triethylamine solution(pH adjusted to 5.0 with phosphoric acid)as mobile phase A and methanol as mobile phase B.Gradient elution was performed at col-umn temperature of 40 ℃.The detection wavelength was 214 nm,the flow rate was 1 mL·min-1,and the injec-tion volume was 20 μL.Results:Methylparaben,ethylparaben,propylparaben,butylparaben,benzalkonium chlo-ride and benzalkonium bromide were 0.11-559.0,0.10-513.0,0.10-258.8,0.11-270.5,1.07-537.0 and 1.03-512.8 μg·mL-1,respectively.The linear range was good(r＞0.999).The average recoveries of meth-ylparaben,benzalkonium bromide and benzalkonium chloride were 104.7％(RSD=1.3％),102.6％(RSD=1.1％)and 100.9％(RSD=1.1％),respectively.The contents of bacteriostatic agent in 100 batches of eye drops from 36 varieties of 12 enterprises were determined,and the accurate results were obtained.Conclusion:This meth-od provides a reference for the content quality control and safety evaluation of bacteriostatic agents in eye drops.

China prospective multicenter birth cohort (Prospective Omics Health Atlas birth cohort, POHA birth cohort) study was officially launched in 2022. This study, in collaboration with 12 participating units, aims to establish a high-quality, multidimensional cohort comprising 20 000 naturally conceived families and assisted reproductive families. The study involves long-term follow-up of parents and offspring, with corresponding biological samples collected at key time points. Through multi-omics testing and analysis, the study aims to conduct multi-omics big data research across the entire maternal and infant life cycle. The goal is to identify new biomarkers for maternal and infant diseases and provide scientific evidence for risk prediction related to maternal diseases and neonatal health.

The biological clock(also known as the circadian rhythm)is the fundamental reliance for all organisms on Earth to adapt and survive in the Earth's rotation environment.Circadian rhythm is the most basic regulatory mechanism of life activities,and plays a key role in maintaining normal physiological and biochemical homeostasis,disease occurrence and treatment.Recent studies have shown that the biologi-cal clock plays an important role in the development of oral tissues and in the occurrence and treatment of oral diseases.Since there is cur-rently no guiding literature on the research methods of biological clock in stomatology,researchers mainly conduct research based on pub-lished references,which has led to controversy about the research methods of biological clock in stomatology,and there are many confusions about how to rationally apply the research methods of circadia rhythms.In view of this,this expert consensus summarizes the characteristics of the biological clock and analyzes the shortcomings of the current biological clock research in stomatology,and organizes relevant experts to summarize and recommend 10 principles as a reference for the rational implementation of the biological clock in stomatology research.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To evaluate the safety and efficacy of ultrasound-guided percutaneous negative pressure suction and minimally invasive rotatory excision technique for the treatment of complex encapsulated lesions.Methods:A total of 48 patients(48 lesions) with complex encapsulated lesions who underwent ultrasound-guided percutaneous negative pressure suction and minimally invasive rotatory excision technique at Xi′an Chest Hospital from January to October 2023 were retrospectively enrolled, including 39 cases of encapsulated abscess, 7 cases of encapsulated effusion, and 2 cases of encapsulated haematoma; the distribution of the bacterial flora of the abscesses were as follows: 24 cases of tuberculous abscess, 14 cases of bacterial abscess, 1 case of bacterial combined bacterial-fungal abscess, and 7 cases of encapsulated effusion were tuberculous pleurisy, and the clinical data were analysed retrospectively. The maximum upper and lower diameters, right and left diameters, and anterior and posterior diameters of the lesions were measured by ultrasound before and after the operation. The patients′ various biochemical indicators (C-reactive protein, white blood cell count, neutrophil count, erythrocyte sedimentation rate) were detected. The intraoperative and postoperative complications, postoperative outcomes, and postoperative clinical symptoms were recorded.Results:Of the 48 patients, 39 were cured and discharged after negative pressure suction and rotatory excision technique, and 9 patients were cured and discharged after surgical incision and drainage of the lesions. The overall effective rate of negative pressure suction and rotatory excision treatment reached 81.25%, and the average number of days of tube placement was (11.81±7.22) days, and the average number of days of follow-up was (35.77±19.39) days. Compared with preoperative values, the upper and lower diameters, the left and right diameters, and the anterior and posterior diameters of the lesions were all reduced after operation [5.80 (4.95, 7.95)cm vs 8.00 (6.00, 11.82)cm, 4.00 (3.25, 5.00)cm vs 5.85 (4.52, 7.65)cm, 1.80 (1.00, 2.90)cm vs 3.40 (2.50, 6.15)cm, all P<0.01]; and postoperative C-reactive protein, white blood cell count and neutrophil count all decreased (all P<0.05). Before operation there were 31 cases of local swelling, 16 cases of pain, 12 cases of activity limitation, 12 cases of fever, 7 cases of chest tightness, and 6 cases of shortness of breath, and during postoperative follow-up, there were 4 cases of local swelling, 5 cases of pain, and 4 cases of activity limitation. The symptoms of fever, chest tightness, and shortness of breath all disappeared, and there was a statistically significant difference between preoperation and postoperation (all P<0.05). There were no adverse events or complications associated with the intraoperative and postoperative follow-up of negative pressure suction and rotatory excision treatment. Conclusions:Ultrasound-guided percutaneous negative pressure suction and invasive rotatory excision technique for the treatment of complex encapsulated lesions can significantly reduce lesion size, reduce inflammatory response and improve patient symptoms, which is a safe, effective and minimally invasive technique.