Six compounds were isolated from the roots of Ephedra sinica Stapf using various chromatographic techniques such as silica gel column chromatography, thin layer chromatography and semi-preparative HPLC. Their chemical structures were identified by analysis of physicochemical properties and spectral data, and determined as (Z)-docosanylferulate (1), (E)-docosanylferulate (2), bis (2-ethylheptyl) phythalate (3), 2,2′-oxybis (1,4-di-tert-butylbenzene) (4), diisobutyl phthalate (5), bis (2-ethylhexyl) phthalate (6). Among them, compound 1 is a new compound, compounds 2-4 were first isolated from Ephedra. A corticosterone-induced PC-12 cell injury model was used for compound activity screening. The results showed that compounds 1 and 5 significantly improved corticosterone-induced PC-12 cell injury and significantly increased 5-HT₇ receptor protein expression in the cells, indicating potential antidepressant activity.

ObjectiveTo investigate a suspected outbreak of carbapenem-resistant Acinetobacter baumannii （CRAB） nosocomial infection in an intensive care unit （ICU） and provide scientific evidence for prevention and control of multi-drug resistant nosocomial infection. MethodsClinical and epidemiological data of 4 patients with CRAB infection were retrospectively investigated in the ICU of Renji Hospital in November 2021. Environmental hygiene monitoring and multilocus sequence typing （MLST） were conducted and intervention measures were taken. ResultsA total of 4 cases with CRAB infection were identified， among which 1 case was determined to be community-acquired and3 cases were hospital-acquired. The isolated strains shared the same drug resistance， and were all classified into ST368 type. In the surface and hand samples （n=40）， 2 CRAB strains were detected in the air filter beside the bed of the first case， with a detection rate of 5%. After adopting comprehensive prevention and control strategies， including environmental cleaning and disinfection， hand hygiene， staff management and training， and supervision， no similar case with CRAB infection was found. ConclusionThis suspected outbreak of CRAB nosocomial infection may be induced by inadequate environmental cleaning and disinfection， and inadequate implementation of hand hygiene. Timely identification， investigation， and targeted measures remain crucial to effective control of possible nosocomial infection.

Coronary perforation is when a contrast agent or blood flows outside a blood vessel through a tear in a coronary artery.In this case,we reported a case of percutaneous coronary intervention for coronary calcified lesions,which led to iatrogenic coronary perforation and cardiac tamponade after the use of Shockwave balloon to treat intracoronary calcified nodules,and the management of PCI-related CAP was systematically reviewed through the literature.

Objective To propose a sperm classification model based on convolutional neural network to enhance the accuracy of sperm morphological classification.Methods A FT-EfficientNet model was constructed using EfficientNetB0 as the base model,which was fine-tuned by data preprocessing enhancement,transfer learning and cosine decay.Classification experi-ments were performed on the sperm public datasets SCIAN-Morpho and HuSHeM,and the datasets were segmented and vali-dated using 5-fold cross-validation.The classification results by the FT-EfficientNet model were compared with those by the cascade ensemble of support vector machines(CE-SVM)model,the adaptive patch-based dictionary learning(APDL)model,fine tuning of visual geometry group(FT-VGG)model,morphological classification of human sperm heads(MH-HSH)model and transfer learning(TL)model.Ablation experiments were performed in the SCIAN-Morpho dataset to verify the effect of different fine-tuning methods on the model.Results The FT-EfficientNet model proposed had the accuracy,precision and F1 score on the SCIAN-Morpho validation set being 64.1％,63.8％and 64.8％,respectively,which were better than CE-SVM,APDL,FT-VGG and MC-HSH models.The recall rate of the model proposed(65.2％)was slightly lower than that of MC-HSH model(68.0％).The accuracy,precision,F1 score and recall rate on the HuSHeM validation set was 95.4％,95.8％,95.4％and 96.0％,respectively,which were slightly lower than those of TL model while better than those of CE-SVM,APDL,FT-VGG and MC-HSH models.Ablation experiments showed the FT-EfficientNet model behaved the best in fine-tuning.Conclusion The sperm classification model based on convolutional neural network facilitates sperm morphology classification with high accuracy and performance.[Chinese Medical Equipment Journal,2024,45(10):7-13]

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

AIM To study the secondary metabolites from the endophytic fungi Candida sp.of Berberis atrocarpa Schneid.METHODS The ethyl acetate fraction and petroleum ether fraction from the secondary metabolites of Candida sp.fermentation extract were separated and purified by silica gel,Sephadex LH-20 and preparative liquid chromatography,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Eighteen compounds were isolated and identified as 1-phenyl-1,2-ethanediol(1),4-hydroxyphenethyl alcohol(2),4-hydroxybenzoic acid(3),4-hydroxyphenylacetic acid(4),3-hydroxyphenylacetic acid(5),3-methylsulfinyl propionic acid(6),phenylacetic acid(7),(S)-N-nitroso-1-amino-p-hydroxy phenylethanol(8),2-phenylacetamide(9),p-hydroxybenzaldehyde(10),ethyl 2-(4-hydroxyphenyl)acetate(11),dibutyl phthalate(12),5,5'-dimethoxybiphenyl-2,2'-diol(13),3-indolealdehyde(14),N-acetyl-L-phenylalanine(15),9-hydroxy-10E,12Z-octadecadienoic acid(16),9-hydroxy-10E,12E-octadecadienoic acid(17),(6E)-5-methylene-6-tetradecenoic acid(18).CONCLUSION Compounds 1,3-8 and 10-18 are isolated from Candida sp for the first time.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Diabetic retinopathy(DR)is a neurovascular disease caused by the neurovascular unit(NVU)impairment. Immune imbalance and inflammation are key factors that affect the normal function of NVU and lead to the progression of DR. Nucleotide-binding oligomerization domain-like receptor protein 3(NLRP3)inflammasome is indicated as an important component of the inflammatory response, and it can identify endogenous danger signals, leading to the activation of caspase-1 and then activating a series of inflammatory cytokines and pyroptosis. Early activation of inflammasome maintains and promotes innate immunity against bacterial and viral infections, while excessive inflammasome activation results in excessive expression and ongoing action of inflammatory proteins, which in turn triggers off immune disorders and an inflammatory cascade that seriously harms the body. This review summarizes the recent research progress on the mechanism of NLRP3 inflammasome in NVU impairment of DR, including the related drugs targeting NLRP3 pathways.

Abstract: Objective　To evaluate the potential transmission risk of schistosomiasis in Yunnan Province, and to provide strategic basis for the prevention and control. Methods　Based on the prevalence of schistosomiasis, the social and environmental factors that may lead to the epidemic, 1-3 villages from 3 provincial-level and 15 county-level counties (cities and districts) were selected as the evaluated villages in 2021. The risk of schistosomiasis spread was analyzed comprehensively by consulting, reviewing and collecting routine surveillance data of schistosomiasis in the villages, combined with snail and wild feces survey. The risk level was evaluated for the positive snails, positive wild feces, resident infection, average density of live snails and snail frame occurrence rate. Results　Totally 7 snail counties schistosomiasis transmission was blocked of 18 epidemic counties and the rest were eliminated counties. A total of 152 447 snail frames were investigated and 3 043 frames with snails, 15 895 snails were captured and included 15 727 live snails in the 32 evaluated villages. The total area of snail was 58.87 hm2 and the area of reoccurrence was 34.19 hm2 with snail frame occurrence rate of 2.00% and average density of live snails 0.103 2/0.11 m2, and no positive snails were found by loop-mediated isothermal amplification (LAMP) assay. A total of 1 374 wild feces were collected in 27 evaluated villages of 14 epidemic counties, mainly from cattle, dogs, sheep, equine animals, pigs and so on, all of which were negative. According to the risk assessment of epidemic spread, Yongle Village and Yongsheng Village in Eryuan County, Zhiming Village in Chuxiong City were Ⅱ risk, and the rest were Ⅲ risk. Conclusions　Although the risk of transmission is low in Yunnan Province, the risk of transmission and spread still exists. It is necessary to strengthen the risk monitoring, control of snail and effective management of livestock to prevent the rebound of the epidemic.

OBJECTIVES: To investigate the reliability and accuracy of deep learning technology in automatic sex estimation using the 3D reconstructed images of the computed tomography (CT) from the Chinese Han population. METHODS: The pelvic CT images of 700 individuals (350 males and 350 females) of the Chinese Han population aged 20 to 85 years were collected and reconstructed into 3D virtual skeletal models. The feature region images of the medial aspect of the ischiopubic ramus (MIPR) were intercepted. The Inception v4 was adopted as the image recognition model, and two methods of initial learning and transfer learning were used for training. Eighty percent of the individuals' images were randomly selected as the training and validation dataset, and the remaining were used as the test dataset. The left and right sides of the MIPR images were trained separately and combinedly. Subsequently, the models' performance was evaluated by overall accuracy, female accuracy, male accuracy, etc. RESULTS: When both sides of the MIPR images were trained separately with initial learning, the overall accuracy of the right model was 95.7%, the female accuracy and male accuracy were both 95.7%; the overall accuracy of the left model was 92.1%, the female accuracy was 88.6% and the male accuracy was 95.7%. When the left and right MIPR images were combined to train with initial learning, the overall accuracy of the model was 94.6%, the female accuracy was 92.1% and the male accuracy was 97.1%. When the left and right MIPR images were combined to train with transfer learning, the model achieved an overall accuracy of 95.7%, and the female and male accuracies were both 95.7%. CONCLUSIONS The use of deep learning model of Inception v4 and transfer learning algorithm to construct a sex estimation model for pelvic MIPR images of Chinese Han population has high accuracy and well generalizability in human remains, which can effectively estimate the sex in adults.
Adult ; Female ; Humans ; Male ; Deep Learning ; Imaging, Three-Dimensional ; Pelvis ; Reproducibility of Results ; Tomography, X-Ray Computed ; Young Adult ; Middle Aged ; Aged ; Aged, 80 and over