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.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

Immmunosuppressants are mainly used to reduce rejection after solid organ transplantation,so as to improve the success rate of organ transplantation.However,long-term use of immunosuppressants can also serious-ly impair the immune function of patients,thereby increasing the risk of viral infection and postoperative complica-tions,leading to transplant failure.Therefore,patients need to use both immunosuppressants and antiviral agents.If some immunosuppressants with antiviral effects are found,the patient's burden of taking medicines will be greatly reduced.Currently,the immunosuppressants with antiviral effect have been focused by researchers.The gradual re-vealing of the antiviral mechanism of these immunosuppressants will help to optimize the treatment plan of postope-rative rehabilitation of organ transplant recipients.Based on the mechanism of rejection of transplanted organ,this paper systematically describes the types of viruses which closely related to infection of organ transplant patients and the molecular mechanism of some immunosuppressants in antiviral aspects,which further provides a new idea for clinical prevention and treatment of viral infection due to organ transplantation.

Objective:To investigate the effect of CD8+CD28-T cells on acute graft-versus-host disease(aGVHD)after haploidentical hematopoietic stem cell transplantation(haplo-HSCT).Methods:The relationship between absolute count of CD8+CD28-T cells and aGVHD in 60 patients with malignant hematological diseases was retrospectively analyzed after haplo-HSCT,and the differences in the incidence rate of chronic graft-versus host disease(cGVHD),infection and prognosis between different CD8+CD28-T absolute cells count groups were compared.Results:aGVHD occurred in 40 of 60 patients after haplo-HSCT,with an incidence rate of 66.67％.The median occurrence time of aGVHD was 32.5(20-100)days.At 30 days after the transplantation,the absolute count of CD8+CD28-T cells of aGVHD group was significantly lower than that of non-aGVHD group(P=0.03).Thus the absolute count of CD8+CD28-T cells at 30 days after transplantation can be used to predict the occurrence of aGVHD to some extent.At 30 days after transplantation,the incidence rate of aGVHD in the low cell count group(CD8+CD28-T cells absolute count＜0.06/μl)was significantly higher than that in the high cell count group(CD8+CD28-T cells absolute count ≥0.06/μl,P=0.011).Multivariate Cox regression analysis further confirmed that the absolute count of CD8+CD28-T cells at 30 days after transplantation was an independent risk factor for aGVHD,and the risk of aGVHD in the low cell count group was 2.222 times higher than that in the high cell count group(P=0.015).The incidence of cGVHD,fungal infection,EBV infection and CMV infection were not significantly different between the two groups with different CD8+CD28-T cells absolute count.The overall survival,non-recurrent mortality and relapse rates were not significantly different between different CD8+CD28-T cells absolute count groups.Conclusion:Patients with delayed CD8+CD28-T cells reconstitution after haplo-HSCT are more likely to develop aGVHD,and the absolute count of CD8+CD28-T cells can be used to predict the incidence of aGVHD to some extent.The absolute count of CD8+CD28-T cells after haplo-HSCT was not associated with cGVHD,fungal infection,EBV infection,and CMV infection,and was also not significantly associated with the prognosis after transplantation.

Aim To investigate the regulatory mecha-nism of arrhythmia of sodium channel ubiquitination af-ter MI and to study the electrophysiological remodeling mechanism of lncRNA-CCRR after MI for the preven-tion and treatment of arrhythmia after MI.Methods LncRNA-CCRR transgenic mice and C57BL/6 mice injected with lncRNA-CCRR overexpressed adeno-asso-ciated virus were used.Four weeks after infection,the left anterior descending branch of the coronary artery was ligated for 12 h to establish a mouse acute myocar-dial infarction model,and the incidence of arrhythmia was detected by programmed electrical stimulation.Ln-cRNA-CCRR overexpression/knockdown adeno-associ-ated virus and negative control were transfected into neonatal mouse cardiomyocytes(NMCMs),and the model was prepared by hypoxia for 12 h.LncRNA-CCRR expression was detected by FISH,Nav1.5 and UBA6 protein and Nav.1.5 mRNA expression were de-tected by Western blot and real-time quantitative poly-merase chain reaction(qRT-PCR),Nav1.5 and UBA6 expressions were detected by immunofluores-cence,and the relationship between lncRNA-CCRR and UBA6 was detected by RIP.INa current density af-ter CCRR overexpression and knockdown was detected by Whole-cell clamp patch.Results In MI mice,the expression of lncRNA-CCRR decreased,the incidence of arrhythmia increased,the expression of CCRR and Nav1.5 mRNA was down-regulated,the protein ex-pression of Nav1.5 was down-regulated,and the pro-tein expression of UBA6 was up-regulated compared with sham group.Overexpression of CCRR could re-verse the above changes.AAV-CCRR could reverse the down-regulated CCRR and Nav1.5 mRNA levels af-ter hypoxia,and improve the expression of Nav1.5 and UBA6 protein.The direct relationship between ln-cRNA-CCRR and UBA6 was identified by RIP analy-sis.The INa density increased after transfection with AAV-CCRR.The INa density decreased after transfec-tion with AAV-si-CCRR.Conclusions The expres-sion of lncRNA-CCRR decreases after MI,and ln-cRNA-CCRR can improve arrhythmia induced by MI by inhibiting UBA6 to increase the protein expression level of Nav1.5 and the density of INa.

Sleep occupies one-third of a person's lifetime and is a necessary condition for maintaining physiological function and health. With the increase in social and economic pressures, the growing use of electronic devices and the accelerated aging process of the population, insufficient sleep and its hazards have drawn widespread attention from researchers in China and abroad. Sleep deprivation refers to a decrease in sleep or a severe lack of sleep due to various reasons. Previous studies have found that sleep deprivation can cause extensive damage to the body, including an increased incidence and mortality rate of neuropathic diseases in the brain, cardiovascular diseases, imbalances in the gut microbiota, and other multi-organ diseases. The mechanisms underlying the occurrence of multi-system and multi-organ diseases due to sleep deprivation mainly involve oxidative stress, inflammatory responses, and impaired immune function in the body. According to traditional Chinese medicine(TCM), sleep deprivation falls into the category of sleepiness, and long-term sleepiness leads to Yin-Yang imbalance, resulting in the consumption of Qi and damage to the five Zang-organs. The appropriate treatment should focus on tonifying deficiency, reinforcing healthy Qi, and harmonizing Yin and Yang. TCM is characterized by a wide variety and abundant resources, and it has minimal side effects and a broad range of applications. Numerous studies have shown that TCM drugs and prescriptions not only improve sleep but also have beneficial effects on liver nourishment, intelligence enhancement, and kidney tonification, effectively preventing and treating the body injury caused by sleep deprivation. Given the increasing prevalence of sleep deprivation and its significant impact on body health, this article reviewed sleep deprivation-mediated body injury and its mechanism, summarized and categorized TCM compound prescriptions and single drugs for preventing and treating body injury, with the aim of laying the foundation for researchers to develop effective drugs for preventing and treating body injury caused by sleep deprivation and providing references for further exploration of the molecular mechanisms underlying the body injury caused by sleep deprivation.
Humans ; Medicine, Chinese Traditional ; Sleep Deprivation/drug therapy* ; Sleepiness ; Yin-Yang ; China ; Drugs, Chinese Herbal/therapeutic use*