Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ² = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ² = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ² = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ² = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ² = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ² = 5.604, P_adjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.

At present, the cause of traditional Chinese medicine(TCM) in China has entered a new period of high-quality development. How to strengthen the foundation for the TCM industry from the source is an important issue that deserves the attention of the authorities, industry, and academia. This study systematically analyzed the regulatory system of TCM raw materials and preparations. The study took the TCM industry chain and the product life cycle as a clue and focused on the dimensions of TCM resource protection and plant cultivation(farming), production and quality supervision of TCM raw materials and preparations, and their market access and distribution. It analyzed the current situation of the regulation of TCM raw materials and preparations under the regulatory system of drugs, discussed the main problems, and put forward corresponding suggestions. The results can provide an important reference value for the subsequent improvement of the regulatory system of drugs and the construction of a prominent regulatory system of drugs in accordance with TCM characteristics.
Drugs, Chinese Herbal/economics* ; Medicine, Chinese Traditional/standards* ; China ; Quality Control ; Humans ; Plants, Medicinal/chemistry*

Objective To investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Modern Chinese medicine studies have confirmed that the interaction between traditional Chinese medicine(TCM)and intestinal flora is the key to the treatment of diseases with tradi-tional Chinese medicine.This interplay includes such activities as:traditional Chinese medicine can be metabolized by intestinal flora into effective components with different biological activities from its precursors;TCM chemicals improve the composition of gut microbiota,consequently ameliorating its dysfunction as well as associated pathological conditions;and gut microbiota mediate the interactions between the multiple chemicals in TCM.There-fore,it becomes an important way to understand the modern sci-entific connotation of traditional Chinese medicine theory to study the pharmacological mechanism of the efficacy of traditional Chi-nese medicine by targeting Gut microbiota.

Objective To investigate the feasibility of developing a grading diagnostic model for schistosomiasis-induced liver fibrosis based on B-mode ultrasonographic images and clinical laboratory indicators. Methods Ultrasound images and clinical laboratory testing data were captured from schistosomiasis patients admitted to the Second People’s Hospital of Duchang County, Jiangxi Province from 2018 to 2022. Patients with grade I schistosomiasis-induced liver fibrosis were enrolled in Group 1, and patients with grade II and III schistosomiasis-induced liver fibrosis were enrolled in Group 2. The machine learning binary classification tasks were created based on patients’radiomics and clinical laboratory data from 2018 to 2021 as the training set, and patients’radiomics and clinical laboratory data in 2022 as the validation set. The features of ultrasonographic images were labeled with the ITK-SNAP software, and the features of ultrasonographic images were extracted using the Python 3.7 package and PyRadiomics toolkit. The difference in the features of ultrasonographic images was compared between groups with t test or Mann-Whitney U test, and the key imaging features were selected with the least absolute shrinkage and selection operator (LASSO) regression algorithm. Four machine learning models were created using the Scikit-learn repository, including the support vector machine (SVM), random forest (RF), linear regression (LR) and extreme gradient boosting (XGBoost). The optimal machine learning model was screened with the receiver operating characteristic curve (ROC), and features with the greatest contributions to the differentiation features of ultrasound images in machine learning models with the SHapley Additive exPlanations (SHAP) method. Results The ultrasonographic imaging data and clinical laboratory testing data from 491 schistosomiasis patients from 2019 to 2022 were included in the study, and a total of 851 radiomics features and 54 clinical laboratory indicators were captured. Following statistical tests (t = −5.98 to 4.80, U = 6 550 to 20 994, all P values < 0.05) and screening of key features with LASSO regression, 44 features or indicators were included for the subsequent modeling. The areas under ROC curve (AUCs) were 0.763 and 0.611 for the training and validation sets of the SVM model based on clinical laboratory indicators, 0.951 and 0.892 for the training and validation sets of the SVM model based on radiomics, and 0.960 and 0.913 for the training and validation sets of the multimodal SVM model. The 10 greatest contributing features or indicators in machine learning models included 2 clinical laboratory indicators and 8 radiomics features. Conclusions The multimodal machine learning models created based on ultrasound-based radiomics and clinical laboratory indicators are feasible for intelligent identification of schistosomiasis-induced liver fibrosis, and are effective to improve the classification effect of one-class data models.

Lipopolysaccharides (LPS) are major outer membrane components of Gram-negative bacteria. Unlike most Gram-negative bacteria, Acinetobacter baumannii can still survive and acquire polymyxin resistance after complete loss of LPS. Previous studies of LPS-deficient Acinetobacter baumannii mostly focused on LPS-deficient strains induced by polymyxin, whose background was complex and unstable. To investigate LPS loss-mediated polymyxin resistant-Acinetobacter baumannii, this study constructed a stable and clear background LPS-deficient strain by knocking out lpxC gene in Acinetobacter baumannii ATCC 19606 with CIRSPR/Cas9, and then studied the phenotypic changes of the lpxC-deficient strain including morphology, growth rate, antibiotic susceptibility, virulence, membrane permeability, and membrane potential. Animal experiments were approved by the Animal Care and Welfare Committee Institute of Medicinal Biotechnology, CAMS and PUMC (approval number: IMB-20240119D₉). The results indicated that the lpxC gene of Acinetobacter baumannii ATCC 19606 was successfully knocked out. After losing the lpxC, the strain underwent the morphological change from rod-shaped to spherical. Furthermore, it leads to reduced growth rate, enhanced membrane permeability, decreased membrane potential, lower virulence, and increased antibiotic susceptibility to β-lactams, quinolones, aminoglycosides, macrolides, glycopeptides. The lpxC deletion results in significant changes in membrane homeostasis and adaptability of Acinetobacter baumannii. Understanding the phenotypic changes of colistin-resistant Acinetobacter baumannii mediated by LPS loss is useful for exploring the resistance mechanism of Acinetobacter baumannii and developing new therapeutic strategies.

Objective To evaluate the molluscicidal effect of spraying 5% niclosamide ethanolamine salt granules with drones against of Oncomelania hupensis snails in snail habitats in marshland areas. Methods From September to October, 2022, marshlands were sampled from Dantu District, Zhenjiang City, Jiangsu Province as study areas, and assigned into four groups, of approximately 3 000 m² per group. In Group A, environmental cleaning was performed, followed by spraying 5% niclosamide ethanolamine salt granules with knapsack sprayers at a dose of 40 g/m², and in Group B, 5% niclosamide ethanolamine salt granules were sprayed with knapsack sprayers at a dose of 40 g/m² without environmental cleaning, while in Group C, environmental cleaning was conducted, followed by spraying 5% niclosamide ethanolamine salt granules with drones at a dose of 40 g/m², and in Group D, 5% niclosamide ethanolamine salt granules were sprayed with drones at a dose of 40 g/m² without environmental cleaning. Then, the study areas in each group were equally divided into six blocks, with Block 1 for baseline surveys and blocks 2 to 6 for snail surveys 1, 3, 5, 7, 14 days following chemical treatment. The mortality of snails and the reduction of the density of living snails were calculated. Results A total of 132 frames were surveyed during the period from September to October 2022, and the occurrence of frames with living snails and means density of living snails were 61.36% (81/132) and 1.58 snails/0.1 m², respectively. The overall mortality rates of snails were 43.02% (77/179), 38.69% (77/199), 47.78% (86/180) and 31.02% (58/187) 14 days following chemical treatment in groups A, B, C and D, respectively (χ² = 11.646, P < 0.05), and there were differences detected in the snail mortality between group A and D, and between groups C and D (both P_adjusted values < 0.05). The adjusted mortality rates of snails were 37.42%, 36.07%, 38.85% and 40.40% in groups A, B, C and D 14 days post-treatment, and the density of living snails decreased by 48.10%, 63.29%, 67.09% and 69.62% 14 days post-treatment relative to pre-treatment, respectively. Conclusions Chemical treatment with drones is feasible for O. hupensis snail control in marshland areas; however, the molluscicidal effect of spraying 5% niclosamide ethanolamine salt granules with drones is comparable to spraying chemicals manually in marshland areas regardless of environmental cleaning.

The goal of achieving elimination of schistosomiasis across all endemic counties in China by 2030 was proposed in the Outline of the Healthy China 2030 Plan. On June 16, 2023, the Action Plan to Accelerate the Elimination of Schistosomiasis in China (2023—2030) was jointly issued by National Disease Control and Prevention Administration and other 10 ministries, which deployed the targets and key tasks of the national schistosomiasis elimination programme in China. This article describes the progress of the national schistosomiasis control programme, analyzes the opportunities to eliminate schistosomiasis, and proposes targeted recommendations to tackle the challenges of schistosomiasis elimination, so as to accelerate the process towards schistosomiasis elimination and facilitate the building of a healthy China.

In the research on cancer theranostics, most environment-sensitive drug delivery systems can only achieve unidirectional and irreversible responsive changes under pathological conditions, thereby improving the targeting effect and drug release performance of the delivery system. However, such irreversible changes pose potential safety hazards when the dynamically distributed delivery system returns to the blood circulation or transports to the normal physiological environment. Intelligent reversible drug delivery systems can respond to normal physiological and pathological microenvironments to achieve bidirectional and reversible structural changes. This feature will help to precisely control the drug release of the delivery system, prolong the blood circulation time, improve the targeting efficiency, and avoid the potential safety hazards of the irreversible drug delivery system. In this review, we describe the research progress of intelligent reversible drug delivery system from two main aspects: controlled drug release and prolonged blood circulation time/enhanced cellular internalization of drug.

OBJECTIVE: To explore the cardioprotective effects of astragaloside IV (AS-IV) in heart failure (HF). METHODS: PubMed, Excerpta Medica Database (EMBASE), Cochrane Library, Web of Science, Wanfang Database, Chinese Bio-medical Literature and Retrieval System (SinoMed), China Science and Technology Journal Database (VIP), and China National Knowledge Infrastructure (CNKI) were searched from inception to November 1, 2021 for animal experiments to explore AS-IV in treating HF in rats or mice. The left ventricular ejection fraction (LVEF), left ventricular fractional shortening (LVFS), left ventricular end-diastolic dimension (LVEDD), left ventricular end-systolic dimension (LVESD), left ventricular weight-to-body weight (LVW/BW) and B-type brain natriuretic peptide (BNP) were recorded. The qualities of included studies were assessed by the risk of bias according to the Cochrane handbook. Meta-analysis was performed using Stata 13.0. RESULTS: Twenty-one articles involving 558 animals were considered. Compared with the control group, AS-IV improved cardiac function, specifically by increasing LVEF (mean difference (MD)=6.97, 95% confidence interval (CI)=5.92 to 8.03, P<0.05; fixed effects model) and LVFS (MD=7.01, 95% CI=5.84 to 8.81, P<0.05; fixed effects model), and decreasing LVEDD (MD=-4.24, 95% CI=-4.74 to -3.76, P<0.05; random effects model) and LVESD (MD=-4.18, 95% CI=-5.26 to -3.10, P<0.05; fixed effects model). In addition, the BNP and LVW/BW levels were decreased in the AS-IV treatment group (MD=-9.18, 95% CI=-14.13 to -4.22, P<0.05; random effects model; MD=-1.91, 95% CI=-2.42 to -1.39, P<0.05; random effects model). CONCLUSIONS AS-IV is a promising therapeutic agent for HF. However, this conclusion needs to be clinically validated in the future.
Animals ; Mice ; Rats ; Stroke Volume ; Ventricular Function, Left ; Heart Failure/drug therapy* ; Natriuretic Peptide, Brain