Through consulting herbal medicine， medical books， and local chronicles from past dynasties to modern times， this paper systematically researched Spatholobi Caulis from name， origin， producing areas， harvesting， processing， usage， quality evaluation， functions and indications， providing a reference for the development and utilization of famous classical formulas containing Spatholobi Caulis. According to the research， Spatholobi Caulis was first recorded in the Annals of Shunning Prefecture from the Qing dynasty. It was originally a medicinal herb commonly used in Shunning， Yunnan， and was named from the red juice resembling chicken blood that flowed out after the vein was cut off. The mainstream original plants of each dynasty were Kadsura heteroclita and Spatholobus suberectus. Among them， K. heteroclita mainly focused on dispersing blood stasis and unblocking meridians， mainly treating rheumatic pain and injuries caused by falls or blows， and it is mostly used as the raw material of Jixueteng ointments. S. suberectus was commonly used as decoction pieces in decoction， which had the functions of promoting blood circulation and replenishing blood， activating meridians and collaterals， and mainly used for treating anemia， irregular menstruation， and rheumatic bone pain. The production area of Spatholobi Caulis recorded in the Qing dynasty was Yunnan. Currently， the main production area of S. suberectus is Guangxi， while the main production area of K. interior is Yunnan. In the Qing dynasty， the usage of Spatholobi Caulis was an individual prescription with other herbs before making ointments， which was usually composed of the juice of it， safflower， angelica， and glutinous rice. But in modern times， Spatholobi Caulis is mostly sliced and dried for use. The quality of Spatholobi Caulis is often determined by the number of reddish-brown concentric circles on the cut surface， with a higher number indicating better quality. Additionally， the presence of resinous secretions is also considered desirable. Based on the research findings， it is suggested that when developing famous classical formulas containing Spatholobi Caulis， the choice of the primary source should be S. suberectus or K. heteroclita， taking into consideration the therapeutic effects of the formula. It is also recommended that the latest plant classification be referenced in the next edition of Chinese Pharmacopoeia， adjusting the primary source of Kadsurae Caulis to K. heteroclita to avoid confusion caused by inconsistent original names， and the functions adjust to promote Qi circulation and relieve pain， disperse blood stasis and unblock collaterals， treating injuries caused by falls and bruises.

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.

Three carboline fluorescent probes F1-F3 were designed and synthesized, based on lead compound JYJ-19, an antifungal compound discovered previously by our group. The antifungal activity in vitro results showed that compound F1 had moderate antifungal activity (MIC₈₀ = 32 μg·mL^-1). The stokes shift of F1 is 70 nm. The fluorescent probe F1 has good optical properties and can be used for fluorescence imaging research. Subcellular localization experiments results showed that F1 was enriched in the mitochondria of fungal cells. The detection of intracellular reactive oxygen species levels shows that JYJ-19 enhances intracellular reactive oxygen species levels. The above results indicated that carboline compounds could exert antifungal effects by acting on fungal mitochondria.

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.

Objective To explore the potential mechanism of proguanil on the proliferation and apoptosis of bladder cancer cells.Methods 253J cells were randomly divided into control group(normal treatment),proguanil group(42.06 μmol·L-1 proguanil),pcDNA group(transfected with pcDNA+42.06 μmol·L-1 proguanil),FADS2 group[transfected fatty acid desaturase gene 2(FADS2)+42.06 μmol·L-1 proguanil],si-NC(transfection si-NC),si-FADS2(transfection si-FADS2),Ferrostatin-1 group(transfected with si-FADS2+10 μmol·L-1 ferrostatin-1).Real-time fluorescence quantitative polymerase chain reaction(RT-qPCR)assay was used to detect mRNA expression of related genes;Western blot assay was used to detect the expression of each protein;apoptosis was detected by TdT mediated dUDP nick end labeling(Tunel)assay;5-ethynyl-2'-deoxyuridine(EdU)assay to detect cell proliferation;the Transwell assay measures the ability of cells to migrate;Fe2+levels were determined by kit method;DCFH-DA probe was used to detect ROS levels.Results The mRNA levels of FADS2 in control group,proguanil group,pcDNA group and FADS2 group were 1.00±0.11,0.47±0.09,0.49±0.06 and 2.09±0.21,respectively;cell proliferation rate were(100.00±3.50)％,(54.31±4.90)％,(56.46±5.17)％and(78.76±6.50)％,respectively;the apoptosis rate were(3.92±0.53)％,(28.79±3.30)％,(27.20±2.90)％and(7.34±0.68)％,respectively;the migration number were 132.70±9.81,70.10±5.05,68.70±537 and 101.80±11.25,respectively;Fe2+level were(100.00±8.14)％,(201.33±17.84)％,(192.38±21.34)％and(116.70±10.90)％,respectively;GPX4 protein relative expression level were 0.77±0.05,0.31±0.05,0.34±0.05 and 0.68±0.06,respectively.The above indexes in proguanil group were compared with those in control group,the above indexes in FADS2 group were compared with those in pcDNA group,all the differences were statistically significant(all P＜0.05).The ROS levels of si-NC,si-FADS2 and Ferrostatin-1 groups were 9.72±1.18,40.94±5.63 and 13.77±1.40,respectively.Compared the si-FADS2 group with the si-NC group,Ferrostatin-1 group compared with si-FADS2 group,ROS level were significantly different(all P＜0.05).Conclusion Proguanil can induce the apoptosis of bladder cancer cells by inhibiting FADS2 expression mediated by oxidation-reduction driven ferroptosis pathway.