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.

Objective To construct a visual intelligent recognition model for Oncomelania hupensis robertsoni in Yunnan Province based on the EfficientNet-B4 model, and to evaluate the impact of data augmentation methods and model hyperparameters on the recognition of O. hupensis robertsoni. Methods A total of 400 O. hupensis robertsoni and 400 Tricula snails were collected from Yongsheng County, Yunnan Province in June 2024, and snail images were captured following identification and classification of 300 O. hupensis robertsoni and 300 Tricula snails. A total of 925 O. hupensis robertsoni images and 1 062 Tricula snail images were collected as a dataset and divided into a training set and a validation set at a ratio of 8:2, while 352 images captured from the remaining 100 O. hupensis robertsoni and 354 images from the remaining 100 Tricula snails served as an external test set. All acquired images were subjected to preprocessing, including cropping and resizing. Three data augmentation approaches were employed, including baseline, Mixup and Gaussian blurring, and model hyperparameters included two optimization algorithms of adaptive moment estimation (Adam) and stochastic gradient descent (SGD), two loss functions of focal loss and cross entropy loss, and two learning rate decay strategies of cosine annealing and multi-step. The intelligent recognition models of O. hupensis robertsoni and Tricula snails were constructed based on the EfficientNet-B4 model, and 7 training strategy groups were generated by combinations of different data augmentation approaches and hyperparameters. The performance of intelligent recognition models was tested with external test sets, and evaluated with accuracy, precision, recall, F1 score, loss, Youden’s index, and the area under the receiver operating characteristic curve (AUC) under different training strategies. Results The variation of loss values was comparable among intelligent recognition models with different data augmentation approaches. The Group 4 model constructed with Mixup and Gaussian blurring data augmentation approaches showed the optimal performance, with an accuracy of 90.38%, precision of 90.07%, F1 score of 89.44%, Youden’s index of 0.81 and AUC of 0.961 in the external test set. The accuracy of models using the SGD optimizer reduced by 29.16% as compared to those using the Adam optimizer (χ² = 81.325, P < 0.001), and the accuracy of models using the cross entropy loss function reduced by 0.80% as compared to the Group 4 model (χ² = 3.147, P > 0.05), while the accuracy of models using the multi-step learning rate decay strategy increased by 0.65% as compared to the Group 4 model (χ² = 0.208, P > 0.05). In addition, the model with the baseline + Mixup + Gaussianblurring data augmentation approach and hyperparameters of Adam optimizer, focal loss function and multi-step learning rate decay strategy showed the highest performance, with an accuracy of 91.03%, precision of 91.97%, recall of 88.11%, F1 score of 90.00%, Youden’s index of 0.82 and AUC values of 0.969 in external test set, respectively. Conclusions The intelligent recognition model of O. hupensis robertsoni based on EfficientNet-B4 model is accurate for identification of O. hupensis robertsoni and Tricula snails in Yunnan Province.

Objective To evaluate the clinical value of optic nerve sheath diameter(ONSD)measured on thin-slice CT scan in the diagnosis and prognostic assessment of cerebral venous sinus thrombosis(CVST).Methods The clinical data of patients with CVST,who were admitted to the First Affiliated Hospital of Nanjing Medical University of China to receive treatment from January 1,2016 to December 31,2022,were retrospectively analyzed.The difference in ONSD was compared between CVST patients and normal population,the postoperative changes in ONSD was analyzed.Results A total of 49 patients with CVST(CVST group)and 49 normal persons having no brain disorders(control group)were enrolled in this study.In CVST group,the preoperative ONSD was(5.33±0.50)mm,which was significantly higher than(4.40±0.40)mm in control group(P＜0.001),the postoperative ONSD remarkably decreased to(4.98±0.59)mm(P＜0.01).The difference value between postoperative ONSD and preoperative ONSD in the patients receiving pure anticoagulation treatment was not statistically significant different from that in the patients receiving endovascular treatment[(-0.43±0.22)mm vs.(-0.40±0.42)mm,P=0.84].The preoperative ONSD in the patients having intracranial hemorrhage and in the patients having no intracranial hemorrhage was(5.26±0.51)mm and(5.41±0.49)mm respectively(P=0.31),and the difference value between postoperative ONSD and preoperative ONSD was(-0.39±0.40)mm and(-0.45±0.25)mm respectively(P=0.66).At the three-month follow-up visit,the difference in ONSD between the patients having a good prognosis(mRS score being 0-2 points)and the patients having a poor prognosis was not statistically significant(P＞0.05).Conclusion ONSD that is measured on plain head CT scan can be used as a response indicator of elevated intracranial pressure in CVST patients,which can be used to monitor the changes in intracranial pressure before and after treatment,but its value in assessing the curative efficacy of different therapeutic methods needs to be further explored.

Oxidative Phosphorylation ; Acetylglucosamine ; Uridine Diphosphate N-Acetylglucosamine/metabolism*

OBJECTIVE: To explore the effect and mechanism of schisandrin B (Sch B) in the treatment of cerebral ischemia in rats. METHODS: The cerebral ischemia models were induced by middle cerebral artery occlusion (MCAO) and reperfusion. Sprague-Dawley rats were divided into 6 groups using a random number table, including sham, MCAO, MCAO+Sch B (50 mg/kg), MCAO+Sch B (100 mg/kg), MCAO+Sch B (100 mg/kg)+LY294002, and MCAO+Sch B (100 mg/kg)+wortmannin groups. The effects of Sch B on pathological indicators, including neurological deficit scores, cerebral infarct volume, and brain edema, were subsequently studied. Tissue apoptosis was identified by terminal transferase-mediated dUTP nick end-labeling (TUNEL) staining. The protein expressions involved in apoptosis, inflammation response and oxidative stress were examined by immunofluorescent staining, biochemical analysis and Western blot analysis, respectively. The effect of Sch B on phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling was also explored. RESULTS: Sch B treatment decreased neurological deficit scores, cerebral water content, and infarct volume in MCAO rats (P<0.05 or P<0.01). Neuronal nuclei and TUNEL staining indicated that Sch B also reduced apoptosis in brain tissues, as well as the Bax/Bcl-2 ratio and caspase-3 expression (P<0.01). Sch B regulated the production of myeloperoxidase, malondialdehyde, nitric oxide and superoxide dismutase, as well as the release of cytokine interleukin (IL)-1 β and IL-18, in MCAO rats (P<0.05 or P<0.01). Sch B promoted the phosphorylation of PI3K and AKT. Blocking the PI3K/AKT signaling pathway with LY294002 or wortmannin reduced the protective effect of Sch B against cerebral ischemia (P<0.05 or P<0.01). CONCLUSIONS Sch B reduced apoptosis, inflammatory response, and oxidative stress of MCAO rats by modulating the PI3K/AKT pathway. Sch B had a potential for treating cerebral ischemia.

Qualitative and quantitative analysis of 2-(2-phenylethyl) chromones in sodium chloride(NaCl)-treated suspension cells of Aquilaria sinensis was conducted by UPLC-Q-Exactive-MS and UPLC-QQQ-MS/MS. Both analyses were performed on a Waters T3 column(2.1 mm×50 mm, 1.8 μm) with 0.1% formic acid aqueous solution(A)-acetonitrile(B) as mobile phases at gradient elution. MS data were collected by electrospray ionization in positive ion mode. Forty-seven phenylethylchromones was identified from NaCl-treated suspension cell samples of A. sinensis using UPLC-Q-Exactive-MS, including 22 flindersia-type 2-(2-phenylethyl) chromones and their glycosides, 10 5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones and 15 mono-epoxy or diepoxy-5,6,7,8-tetrahydro-2-(2-phenylethyl) chromones. Additionally, 25 phenylethylchromones were quantitated by UPLC-QQQ-MS/MS. Overall, the rapid and efficient qualitative and quantitative analysis of phenylethylchromones in NaCl-treated suspension cells of A. sinensis by two LC-MS techniques, provides an important reference for the yield of phenylethylchromones in Aquilariae Lignum Resinatum using in vitro culture and other biotechnologies.
Chromones ; Sodium Chloride ; Chromatography, Liquid ; Flavonoids ; Tandem Mass Spectrometry ; Thymelaeaceae

The postmortem interval (PMI) estimation is a key and difficult point in the practice of forensic medicine, and forensic scientists at home and abroad have been searching for objective, quantifiable and accurate methods of PMI estimation. With the development and combination of high-throughput sequencing technology and artificial intelligence technology, the establishment of PMI model based on the succession of the microbial community on corpses has become a research focus in the field of forensic medicine. This paper reviews the technical methods, research applications and influencing factors of microbial community in PMI estimation explored by using high-throughput sequencing technology, to provide a reference for the related research on the use of microbial community to estimate PMI.
Humans ; Postmortem Changes ; Artificial Intelligence ; Autopsy ; Cadaver ; Microbiota

On February 2022, WHO released the evidence-based guideline on control and elimination of human schistosomiasis, with aims to guide the elimination of schistosomiasis as a public health problem in disease-endemic countries by 2030 and promote the interruption of schistosomiasis transmission across the world. Based on the One Health concept, six evidence-based recommendations were proposed in this guideline. This article aims to analyze the feasibility of key aspects of this guideline in Chinese national schistosomiasis control program and illustrate the significance to guide the future actions for Chinese national schistosomiasis control program. Currently, the One Health concept has been embodied in the Chinese national schistosomiasis control program. Based on this new WHO guideline, the following recommendations are proposed for the national schistosomiasis control program of China: (1) improving the systematic framework building, facilitating the agreement of the cross-sectoral consensus, and building a high-level leadership group; (2) optimizing the current human and livestock treatments in the national schistosomiasis control program of China; (3) developing highly sensitive and specific diagnostics and the framework for verifying elimination of schistosomiasis; (4) accelerating the progress towards elimination of schistosomiasis and other parasitic diseases through integrating the national control programs for other parasitic diseases.
China/epidemiology* ; Disease Eradication ; Humans ; Public Health ; Schistosomiasis/prevention & control* ; World Health Organization

Objective To evaluate the risk of hearing loss of assembly workers in an automobile manufacturing factory. Methods An 8-hour equivalent sound level monitoring was carried out for assembly posts in an automobile factory. The risk of noise-induced hearing loss of assembly workers was measured using the method specified in ISO 1999:2013(E). The risk of noise-induced hearing loss was assessed in a graded manner according to the Guidelines for the Management of Occupational Disease Hazards from Noise. The results were statistically analyzed using Pearson correlation analysis. Results The average 8-hour equivalent sound level of the assembly work post in this automobile manufacturing factory was 89.5 dB (A). At 4000 Hz, the hearing loss N₅₀ (dB) of assembly workers reached the maximum. The longer the exposure time, the higher the risk of high-frequency standard hearing threshold shift. The risk of high-frequency standard hearing threshold shift was at a relatively high level at 30 years of work, while the risk of noise deafness reached a higher level after 40 years of work. Conclusion The 8-hour equivalent sound level (L_EX,8h) of assembly workers in the automobile factory exceeds the occupational exposure limit. With the increase of exposure years, the risk of high-frequency standard hearing threshold shift and noise deafness increases.

Since the global pandemic of coronavirus disease 2019 (COVID-19) in late 2019, artificial intelligence technology has shown increasing values in the research and control of tropical infectious diseases. The introduction of artificial intelligence technology has shown remarkable effectiveness to reduce the diagnosis and treatment burdens, reduce missing diagnosis and misdiagnosis, improve the surveillance and forecast ability and enhance the medicine and vaccine development efficiency. This paper summarizes the current applications of artificial intelligence in tropical infectious disease control and research and discusses the important values of artificial intelligence in disease diagnosis and treatment, disease surveillance and forecast, vaccine and drug discovery, medical and public health services and global health governance. However, artificial intelligence technology suffers from problems of single and inaccurate diagnosis, poor disease surveillance and forecast ability in open environments, limited capability of intelligent system services, big data management and model interpretability. Hereby, we propose suggestions with aims to improve multimodal intelligent diagnosis of multiple tropical infectious diseases, emphasize intelligent surveillance and forecast of vectors and high-risk populations in open environments, accelerate the research and development of intelligent management system, strengthen ethical security, big data management and model interpretability.