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.

Debates persist regarding the efficacy and safety of azvudine, particularly its real-world outcomes. This study involved patients aged ≥60 years who were admitted to 25 hospitals in mainland China with confirmed SARS-CoV-2 infection between December 1, 2022, and February 28, 2023. Efficacy outcomes were all-cause mortality during hospitalization, the proportion of patients discharged with recovery, time to nucleic acid-negative conversion (T _NANC), time to symptom improvement (T _SI), and time of hospital stay (T _HS). Safety was also assessed. Among the 5884 participants identified, 1999 received azvudine, and 1999 matched controls were included after exclusion and propensity score matching. Azvudine recipients exhibited lower all-cause mortality compared with controls in the overall population (13.3% vs. 17.1%, RR, 0.78; 95% CI, 0.67-0.90; P = 0.001) and in the severe subgroup (25.7% vs. 33.7%; RR, 0.76; 95% CI, 0.66-0.88; P < 0.001). A higher proportion of patients discharged with recovery, and a shorter T _NANC were associated with azvudine recipients, especially in the severe subgroup. The incidence of adverse events in azvudine recipients was comparable to that in the control group (2.3% vs. 1.7%, P = 0.170). In conclusion, azvudine showed efficacy and safety in older patients hospitalized with COVID-19 during the SARS-CoV-2 omicron wave in China.

Homeostasis and energy and substance metabolism reprogramming shape various tumor microenvironment to sustain cancer stemness, self-plasticity and treatment resistance. Aiming at them, a lipid-based pharmaceutical loaded with CaO₂ and glucose oxidase (GOx) (LipoCaO₂/GOx, LCG) has been obtained to disrupt calcium homeostasis and interfere with glycometabolism. The loaded GOx can decompose glucose into H₂O₂ and gluconic acid, thus competing with anaerobic glycolysis to hamper lactic acid (LA) secretion. The obtained gluconic acid further deprives CaO₂ to produce H₂O₂ and release Ca²⁺, disrupting Ca²⁺ homeostasis, which synergizes with GOx-mediated glycometabolism interference to deplete glutathione (GSH) and yield reactive oxygen species (ROS). Systematical experiments reveal that these sequential multifaceted events unlocked by Ca²⁺ homeostasis disruption and glycometabolism interference, ROS production and LA inhibition, successfully enhance cancer immunogenic deaths of breast cancer cells, hamper regulatory T cells (Tregs) infiltration and promote CD8⁺ T recruitment, which receives a considerably-inhibited outcome against breast cancer progression. Collectively, this calcium homeostasis disruption glycometabolism interference strategy effectively combines ion interference therapy with starvation therapy to eventually evoke an effective anti-tumor immune environment, which represents in the field of biomedical research.

OBJECTIVE To establish a high-performance liquid chromatography method for the simultaneous determination of 14 components including geniposide, mangiferin, baicalin, berberine hydrochloride, wogonoside, baicalein, aloe-emodin, rhein, wogonin, emodin, praeruptorin A, chrysophanol, physcion and praeruptorin B in Qingfei Yihuo tablets. METHODS Titank C18 (250 mm×4.6 mm, 5 μm) column was used; 0.1% phosphoric acid solution(A)-acetonitrile(B) was used as the mobile phase with gradient elution; detection wavelengths: geniposide at 238 nm, aloe-emodin, rhein, emodin, chrysophanol, physcion and mangiferin at 254 nm, berberine hydrochloride at 265 nm, baicalin, wogonoside, baicalein, and wogonin at 280 nm, praeruptorin A and praeruptorin B at 321 nm. The flow rate was 1.0 mL·min−1 and the column temperature was 35 ℃; the injection volume was 10 μL. RESULTS The linear ranges of geniposide, mangiferin, baicalin, berberine hydrochloride, wogonoside, baicalein,aloe-emodin, rhein, wogonin, emodin, praeruptorin A, chrysophanol, physcion and, praeruptorin B were 4.96−223.17, 0.84−42.22, 18.76−938.16, 4.46−223.17, 4.86−243.10, 1.59−79.32, 0.76−38.17, 1.03−51.49, 1.59−79.40, 1.21−60.72, 1.80−90.06, 0.91−45.48, 1.04−51.83 and 0.86−43.23 μg·mL−1, with r all ≥ 0.999 9. The RSDs of instrument precision, stability and reproducibility tests were <3%, and the average recoveries in sample(n=6) were >90%, with the RSDs <3%. CONCLUSION The method is simple and reproducible and can provide a scientific basis for improving the quality standard of Qingfei Yihuo tablets.

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.

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.

Objective To investigate the curative effect of suture anchor(SA)repair combined with open reduction and internal fixation(ORIF)on patients with ankle joint fracture(AF)combined with deltoid ligament injury(DLI).Methods A total of 138 patients with AF combined with DLI admitted to our hospital from January 2020 to September 2022 were selected and divided into the control group(69 cases)and the observation group(69 cases)according to the random number table method.Patients in the control group were treated with ORIF,while patients in the observation group were treated with SA repair on the basis of the control group.The clinical efficacy,American Orthopedic Foot and Ankle Society(AOFAS)score,visual analogue scale(VAS)score,talus inclination angle,medial malleolar space of affected side,bone metabolic indexes[serum bone gla protein(BGP),β-collagen degradation product(β-CTX)]levels and the incidence of complications before and 3 months after treatment were compared between the two groups.Results The total effective rate in the observation group was higher than that in the control group,and the difference was statistically significant(P<0.05).Compared with before treatment,the talus inclination angle,medial malleolar space of affected side,VAS score,β-CTX level 3 months after treatment of patients in the two groups were reduced,while the AOFAS score and BGP level were increased,and the differences were statistically significant(P<0.05).After treatment,the AOFAS score and BGP level in the observation group were higher than those in the control group,while the talus inclination angle and medial malleolar space of affected side were smaller than those in the control group,and the VAS score and β-CTX level were lower than those in the control group,with statistically significant differences(P<0.05).The total incidence of complications in the observation group was lower than that in the control group,and the difference was statistically significant(P<0.05).Conclusion SA repair has a definite therapeutic effect on AF combined with DLI,which can improve patients' symptoms and promote the recovery of ankle joint function and bone metabolism.

Here,5 important pathogens carried by ticks in Maanshan City,Anhui Province,China were identified.In to-tal,642 ticks were collected from 13 villages around Maanshan City and identified by morphological and mitochondrial COI genes.The 16S rRNA gene of Francisella tularensis,ssrA gene of Bartonella,16S rRNA,ompA and ompB genes of Rickett-sia,16S rRNA and gltA genes of Anaplasma,and groEL and rpoB genes of Coxiella were sequenced.Reference sequences were retrieved from a public database.Phylogenetic trees were constructed with MEG A1 1.0 software.In total,36 Rickettsiae isolates were detected in 640 Haemaphysalis longicornis ticks,which included 20 isolates of Rickettsia heilongjian-gensis,16 of Candidatus Rickettsia jingxinensis,2 of Ana-plasma bovis,and 186 of Coxiella-like endosymbiont.R.hei-longjiangensis HY2 detected in this study and Anhui B8 strain,Ca.R.jingxinensis QL3 and those from Shanxi Prov-ince and Jiangsu Province,A.bovis JX4 and those from Shanxi Province were clustered on the same branch.Overall,17 ticks had combined infections and none of the 5 bacteria were detected in two Amblyomma testudinarium ticks.This is the first report of Ca.R.jingxinensis detected in H.longicornis ticks from Anhui Province.It is recommended that the two types of Rickettsia that cause spotted fever and A.bovis should be reported to local health authorities to initiate appropriate prevention and control measures.

Purpose/Significance To explore the prospect and challenges of applying artificial intelligence(AI)technology to the training of general practitioners,and to promote the improvement of the treatment level of general practitioners and the intelligent develop-ment of community hospitals.Method/Process The paper analyzes the application status of AI in the medical field,expounds 3 directions of AI application in the field of general medicine,and puts forward countermeasures for challenges.Result/Conclusion Strategies should be adopted to support the open sharing of desensitized clinical medical data,gather experts in the field to build knowledge platforms,and encourage general practitioners to cooperate with research and development teams,so as to promote the application of intelligent systems in general practitioner training and promote the improvement of the quality of primary medical care in China.