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.

From the perspective of the physiological basis of liver and kidney sharing the common source in traditional Chinese medicine(TCM),and by integrating the theory of kidney dominating bone,liver dominating tendon,and meridian sinew of TCM as well as the bone resorption and collapse theory,and non-uniform settlement theory and lower-limb musculoskeletal bowstring structure theory of modern orthopedics,the pathogenesis of osteonecrosis of the femoral head(ONFH)under the system of non-uniform settlement during bone resorption and multidimensional composite bowstring working in coordination with the theory of liver-kidney and muscle-bone was explored.The key to the TCM pathogenesis of ONFH lies in the deficiency of the liver and kidney,and then the imbalance of kidney yin-yang leads to the disruption of the dynamic balance of bone formation and bone resorption mediated by osteoblasts-osteoclasts,which manifests as the elevated level of bone metabolism and the enhancement of focal bone resorption in the femoral head,and then leads to the necrosis and collapse of the femoral head.It is considered that the kidney dominates bone,liver dominates tendon,and the tendon and bone together constitute the muscle-bone-joint dynamic and static system of the hip joint.The appearance of collapse destroys the originally balanced muscle-bone-joint system.Moreover,the failure of liver blood in the nourishment of muscles and tendons further exacerbates the imbalance of the soft tissues around the hip joint,accelerates the collapse of the muscle-bone-joint dynamic and static system,speeds up the process of femoral head collapse,and ultimately results in irreversible outcomes.Based on the above pathogenesis,the systematic integrative treatment of ONFH should be based on the TCM holistic concept,focuses on the focal improvement of internal and external blood circulation of the femoral head by various approaches,so as to rebuild the coordination of joint function.Moreover,attention should be paid to the physical constitution of the patients,and therapy of tonifying the kidney and regulating the liver can be used to restore the balance between osteogenesis and osteoblastogenesis,and to reconstruct the muscle-bone-joint system,so as to effectively delay or even prevent the occurrence of ONFH.

Objective To investigate the value of magnetic resonance imaging(MRI)T2-mapping in evaluating the activity of Graves ophthalmopathy(GO).Methods A total of 64 patients with GO in the Department of Endocrinology,the First Affiliated Hospital of Chongqing Medical University from July 2019 to January 2021 were collected.Simple random grouping was performed by computer,with 49 cases as observation subjects,and 15 patients for diagnostic test.According to clinical activity score(CAS),49 GO patients were divided into active group(CAS≥3 points,48 eyes)and inactive group(CAS<3 points,50 eyes).Normal control group(NC group)included 31 patients(62 eyes).All subjects underwent 3.0T orbital MRI T2-mapping.Measuring the T2 relaxation time(T2RT)of superior rectus,inferior rectus,medial rectus,and lateral rectus on five layers behind the eyeball on T2-mapping coronal images,and select the maximum value of T2RT in the five layers for each extraocular muscle to represent the T2RT of this extraocular muscle.Finally,select the maximum T2RT values of the four extraocular muscles,expressed as extraocular muscle maximum T2RT.Compare the differences of the above 5 indicators(superior rectus T2RT,inferior rectus T2RT,medial rectus T2RT,lateral rectus T2RT,extraocular muscle maximum T2RT)between active group,inactive group and NC group.ROC curve was used to analyze the diagnostic value of the above 5 indicators for GO activity assessment,and the diagnostic threshold was obtained.Then,another 15 GO patients were performed for diagnostic tests evaluation to determine the indicators of high diagnostic efficacy and the threshold of diagnostic activity.Results The T2RT of all extraocular muscles in active group were significantly higher than those in inactive group and NC group,the difference was statistically significant(P<0.001).The threshold value of the five indicators were obtained by ROC curve analysis.The maximum T2RT cut-off values of superior rectus muscle,inferior rectus muscle,medial rectus muscle,lateral rectus muscle and extraocular muscles for judging activity were 80.200 ms,97.045 ms,94.355 ms,85.750 ms and 101.385 ms respectively.Another 15 GO patients were performed for diagnostic tests,the indexes with relatively high sensitivity,specificity,positive predictive value and negative predictive value were inferior rectus T2RT and extraocular muscle maximum T2RT,the cut-off values of GO activity were 97.045 ms and 101.385 ms,respectively;the sensitivity were 91.7％and 93.8％,respectively;the specificity all were 80.0％.Conclusions MRI T2-mapping sequence has a good value in assessment of GO activity.The inferior rectus T2RT and extraocular muscle maximum T2RT can be choosed to evaluate the activity of GO.

DNA origami is a powerful technique for generating nanostructures with dynamic properties and intelligent controllability. The precise geometric shapes, high programmability, and excellent biocompatibility make DNA origami nanostructures an emerging drug delivery vehicle. The shape, size of the carrier material, as well as the loading and release of drugs are important factors affecting the bioavailability of drugs. This paper focuses on the controllable design of DNA origami nanostructures, efficient drug loading, and intelligent drug release. It summarizes the cutting-edge applications of DNA origami technology in biomedicine, and discusses areas where researchers can contribute to further advancing the clinical application of DNA origami carriers.

Objective To explore the correlation between serum lipid levels of total cholesterol(TC)and triglyceride(TG)and the survival prognosis in postoperative patients with early lung cancer,and to observe the effect of Fuzheng Quxie Prescription on serum lipid levels in postoperative patients with early lung cancer,so as to explore the mechanism of Fuzheng Quxie Prescription in improving the survival prognosis of postoperative patients with early lung cancer.Methods The correlation of serum TC and TG levels with survival prognosis of 257 postoperative patients with early lung cancer admitted in Shanghai Municipal Hospital of Traditional Chinese Medicine from July 2010 to December 2015 was retrospectively analyzed.The changes of serum TC and TG levels in postoperative patients with early lung cancer before and after treatment with Fuzheng Quxie Prescription were statistically analyzed.From January 2017 to April 2021,a prospective analysis of the one-year,two-year,three-year and four-year disease-free survival rates and serum TC and TG levels was carried out in 281 postoperative patients with early lung cancer treated with Fuzheng Quxie Prescription orally in Shanghai Municipal Hospital of Traditional Chinese Medicine(treatment group)and in 287 postoperative patients with early lung cancer who were followed up in clinic while had no medciation in Shanghai Pulmonary Hospital(control group).Results(1)The retrospective study showed that pre-treatment TC level was correlated with progression-free survival(PFS)in postoperative patients with early lung cancer,and the patients with high TC level had longer PFS.There was no significant correlation between pre-treatment TG level and PFS in postoperative patients with early lung cancer.The patients with high TG level had higher short-term survival rate while the patients with low TG level had higher long-term survival rate.(2)The prospective study showed that there were nine cases of recurrence in the treatment group and 24 cases of recurrence in the control group till the last follow-up time on April 1,2021.The one-year,two-year,three-year and four-year disease-free survival rates in the treatment group were 99.3％,96.8％,95.7％and 95.7％,respectively,which were superior to 97.6％,92.3％,89.2％and 87.1％in the control group(P＜0.05),indicating that the recurrence and metastasis in the postoperative patients with early lung cancer treated by Fuzheng Quxie Prescription were significantly reduced when compared with the control group,and the disease-free survival rate was significantly improved.After treatment,the serum levels of TC and TG in the treatment group were increased when compared with those before treatment(P＜0.05)while the control group showed no obvious changes(P＞0.05).The intergroup comparison showed that the increase of serum TC and TG levels in the treatment group was superior to that in the control group(P＜0.05),indicating that Fuzheng Quxie Prescription had regulatory effect on the blood lipid level of patients to a certain extent.Conclusion The analysis of the correlation between pre-treatment blood lipids and PFS prognosis in postoperative patients with early lung cancer indicated that lung cancer patients with high TC level had longer PFS;Fuzheng Quxie Prescription can regulate the blood lipid level of postoperative patients with early lung adenocarcinoma.It is speculated that Fuzheng Quxie Prescription may improve the survival prognosis of postoperative patients with early lung cancer probably by regulating the blood lipid level of the patients.

Objective To investigate the effects of astragaloside Ⅳ(AS-Ⅳ)on axon growth inhibitory factor A(Nogo-A)and its downstream pathway protein RHO-associated coiled spiral kinase 2(ROCK2)in experimental autoimmune encephalomyelitis(EAE)mice,and to explore the mechanism by which it promotes axon repair and regeneration.Methods EAE model was induced in C57BL/6 female mice by subcutaneous injection of myelin oligodendrocyte glycoprotein 35-55(MOG35-55).Mice were randomly divided into EAE group and AS-Ⅳ group(n=8 per group).EAE group received intraperitoneal injection of PBS on the 3rd day post-immunization,while AS-Ⅳ group was administered AS-Ⅳ at a dosage of 30mg/(kg.d)once daily,0.2 ml per injection,for 25 consecutive days.On the 28th day post-immunization,the expression levels of growth-associated protein 43(GAP-43),neuronal core antigen(NeuN),microtubule associated protein 2(MAP-2),glial fibroacidic protein(GFAP),and Iba1 in the spinal cord were detected using immunofluorescence assay.Real-time fluorescence quantitative PCR(qRT-PCR)was conducted to detect mRNA expression levels of GAP-43,Nogo-A,and Nogo receptor(NgR)genes.Western blotting was utilized to determine the expression levels of GAP-43,Nogo-A,ROCK2,phosphorylated myosin phosphatase(p-MYPT1),B-lymphoblastoma-2(Bcl-2),and Bcl-2 associated X protein(Bax).Results Compared with EAE group,AS-Ⅳ treatment significantly reduced the positive cell expression rates of Iba1 microglia and GFAP astrocyte in spinal cord(P<0.01 and P<0.001,respectively),while it also increased the positive expression rates of NeuN and MAP-2(P<0.001 and P<0.05,respectively).The treatment also upregulated the expression level of anti-apoptotic factor Bcl-2(P<0.001)and downregulated the expression level of pro-apoptotic factor Bax(P<0.05),leading to an increase in Bcl-2/Bax ratio(P<0.05).Furthermore,AS-Ⅳ enhanced the expression of GAP-43 protein(P<0.05)and decreased the mRNA expression levels of neuroregeneration inhibitor Nogo receptor(NgR)and ROCK2 gene(P<0.001,P<0.05,respectively);as well as decreased the expression levels of Nogo-A,ROCK2 and p-MYPT1 proteins(P<0.05,P<0.001).Conclusion AS-Ⅳ may inhibit the activation of microglia and astrocytes and neuronal apoptosis in EAE mice by inhibiting Nogo-A and downstream pathway ROCK 2,thereby promoting the expression of GAP-43,NeuN and MAP-2,alleviating neuronal damage,and facilitating axon repair and regeneration.