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 construct a representative index system for evaluating pediatric orthopedic nursing quality, providing a basis for hospital pediatric orthopedic nursing quality assessment and monitoring.Methods:From April to July 2023, using the "structure-process-outcome" three-dimensional quality structure model as the theoretical framework, a literature review was conducted, and an item pool was formulated. Through two rounds of Delphi method expert consultations, the hierarchical analysis method was finally employed to determine the indicators and their weights at each level.Results:The effective recovery rates of the questionnaire of the two rounds of expert consultations were 100% (20/20), the authority coefficients of experts were 0.87 and 0.88, the coefficients of variation were 0.00 to 0.27 and 0.00 to 0.24. The Kendell harmony coefficients of the second and third indicators in the two rounds of inquiry were 0.140, 0.166 and 0.192, 0.161(all P<0.05). The final pediatric orthopedic nursing quality evaluation index system included 3 primary indicators, 21 secondary indicators and 83 tertiary indicators. Among the primary indicators, the weight of process quality was the highest at 0.493 4, followed by outcome quality at 0.310 8, and the lowest was structural quality at 0.195 8. In the secondary indicators, "assessment criteria of limb blood circulation" had the highest weight at 0.099 8. Conclusions:The constructed pediatric orthopedic nursing quality evaluation index system covers key aspects and is more operationally feasible. It provides better guidance for nursing interventions and quality control.

BACKGROUND:Myocardial patches are used as an effective way to repair damaged myocardium,and there is controversy over which cells to use to make myocardial patches and how to maximize the therapeutic effect of myocardial patches in vivo. OBJECTIVE:To find out the best way to make myocardial patches by overviewing the cellular sources of myocardial patches and strategies for perfecting them. METHODS:The first author searched PubMed and Web of Science databases by using"cell sheet,cell patch,cardiomyocytes,cardiac progenitor cells,fibroblasts,embryonic stem cell,mesenchymal stem cells"as English search terms,and searched CNKI and Wanfang databases by using"myocardial patch,biological 3D printing,myocardial"as Chinese search terms.After enrollment screening,94 articles were ultimately included in the result analysis. RESULTS AND CONCLUSION:(1)The cellular sources of myocardial patches are mainly divided into three categories:somatic cells,monoenergetic stem cells,and pluripotent stem cells,respectively.There are rich sources of cells for myocardial patches,but not all of them are suitable for making myocardial patches,e.g.,myocardial patches made from fibroblasts and skeletal myoblasts carry a risk of arrhythmogenicity,and mesenchymal stem cells have a short in vivo duration of action and ethical concerns.With the discovery of induced multifunctional stem cells,a reliable source of cells for making myocardial patches is available.(2)There are two methods of making myocardial patches.One is using cell sheet technology.The other is using biological 3D printing technology.Cell sheet technology can preserve the extracellular matrix components intact and can maximally mimic the cell growth ring in vivo.However,it is still difficult to obtain myocardial patches with three-dimensional structure by cell sheet technology.Biologicasl 3D printing technology,however,can be used to obtain myocardial patches with three-dimensional structures through computerized personalized design.(3)The strategies for perfecting myocardial patches mainly include:making myocardial patches after co-cultivation of multiple cells,improving the ink formulation and scaffold composition in biological 3D printing technology,improving the therapeutic effect of myocardial patches,suppressing immune rejection after transplantation,and perfecting the differentiation and cultivation protocols of stem cells.(4)There is no optimal cell source or method for making myocardial patches,and myocardial patches obtained from a particular cell or technique alone often do not achieve the desired therapeutic effect.Therefore,researchers need to choose the appropriate strategy for making myocardial patches based on the desired therapeutic effect before making them.

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status. Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785 participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed. Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953-0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05). Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

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 understand the changes in pain and its effects in patients with the diagnosis of herpes zoster. Methods: A total of 3 487 patients diagnosed with herpes zoster (HZ) for the first time at the outpatient department of Miyun District Hospital from January 1, 2017, to December 31, 2019, were included in the study. The information of patients was registered and issued with a record card. Patients were required to record the time of pain and rash by themselves. Telephone follow-up was conducted at 21, 90, 180 and 365 days after the onset of rashes, including hospitalization, location of rash and pain, and the time of start and end. The impact of pain on life was evaluated by the Zoster Brief Pain Inventory (ZBPI). Results: The age of 2 999 HZ patients included in the analysis were (53±16) years old, including 1 377 (45.91%) males and 1 903 (63.45%) patients aged 50 years and older. After 21 days of rash, mild, moderate and severe pain accounted for 20.87% (626 cases), 37.98% (1 139 cases) and 33.81% (1 014 cases), respectively. Only 5.07% (152 cases) had no pain or discomfort, and 2.27% (68 cases) had no pain but discomfort. Most of the pain sites were consistent with the rash sites. The chest and back and waist and abdomen were the most common, accounting for 35.58% (1 067 cases) and 29.18% (875 cases), respectively, followed by the limbs and face and neck, accounting for 16.74% (502 cases) and 16.40% (492 cases), respectively. The M (Q₁, Q₃) of pain days in the HZ patients was 14 (8, 20) days, and the incidence of post-herpetic neuralgia (PHN) was 6.63% (171/2 580) (excluding 419 patients who refused to visit or lost to visit on 90 days after the onset of rash). The pain score of HZ patients within 21 days after the rash was (5.19±2.73) points, and the pain score of PHN patients was (7.61±2.13) points, which was significantly higher than that of non-PHN patients [(5.04±2.69) points] (P<0.001). Daily activities, emotions, walking ability, work, social interaction, sleep and recreation were affected for 21 days after the rash in HZ patients, ranging from 60.79% to 83.83%, with sleep being the most affected (83.83%). The impact scores of pain and life dimensions in PHN patients ranged from 4.59 to 7.61 points on the ZBPI scale, which were higher than those in non-PHN patients (2.49-5.04) (t values ranged from 8.86 to 11.67, all P values <0.001). Conclusion: The proportion of pain in HZ patients after the diagnosis is high, and the pain is more obvious in patients with PHN and HZ patients aged 50 and older, which has a greater impact on their daily lives.
Male ; Humans ; Middle Aged ; Aged ; Adult ; Female ; Beijing ; Follow-Up Studies ; Herpes Zoster/epidemiology* ; Pain/epidemiology* ; Exanthema