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 study the correlations of neutrophil-lymphocyte ratio(NLR)and platelet-lymphocyte ratio(PLR)with arte-riovenous fistula(AVF)stenosis in hemodialysis(HD)patients.Methods:Data were collected from 625 patients who underwent arterio-venous fistula hemodialysis at the Department of Nephrology,Affiliated Hospital of North Sichuan Medical College between January 2021 and June 2022.Of these,395 eligible patients with complete information were selected as subjects of study.The 245 patients with AVF stenosis were designated as group 1 and the 150 patients with-out AVF stenosis were designated as group 2.The routine biochemi-cal parameters and complete blood count were recorded for all pa-tients.Results:①Compared with patients in group 2,those in group 1 showed significantly higher NLR(5.07(4.00,6.66)vs.3.46(2.63,4.15),P<0.001),PLR(169.52(127.56,227.11)vs.125.66(89.31,165.31),P<0.001),and C-reactive protein(Hs-CRP)(1.90(0.80,2.99)vs.0.82(0.42,1.27),P<0.001).②Multivariate logistic regression analysis,which was corrected for age,sex,body mass index,AVF anastomosis,puncture method,and diabetes,showed that NLR(OR=2.195,95%CI=1.674～2.878,P<0.001),PLR(OR=1.008,95%CI=1.002～1.012,P=0.007),and Hs-CRP(OR=2.170,95%CI=1.607～2.751,P<0.001)were independent risk factors for AVF ste-nosis in HD patients.③Receiver operating characteristic curve analysis showed that the area under the NLR curve(0.799,95%CI=0.756～0.838,P<0.001),PLR(0.694,95%CI=0.646～0.740,P<0.001),and Hs-CRP(0.717,95%CI=0.670～0.761,P<0.001)could be used to predict AVF stenosis.Their optimal critical values for prediction were 4.08,122.49,and 1.62,respectively.Their combina-tion showed improved prediction effect(AUC 0.870,95%CI=0.833～0.901,P<0.001),high sensitivity(79.18%),and high specificity(81.33%).Conclusion:NLR,PLR,and Hs-CRP were independent risk factors and predictors of AVF stenosis,and their combination has higher predictive value.

Liquiritigenin(LG)is a flavone of pharmacological importance,however,its application potential is severely limited due to its poor water solubility.LG could be disassociated slightly in water to form phenolate anion,therefore,better solubilization effect is expected by inclusion with cationic cyclodextrins(CCDs).In this work,four kinds of CCDs modified with amino groups at the primary face were synthesized,and their solid inclusion complexes with LG were successfully prepared by preparing their saturated solutions.The formation of the solid inclusion complexes was confirmed by scanning electron microscopy(SEM)and powder X-ray diffraction(PXRD),and their supramolecular binding behavior in solution was studied using multiple techniques.A 1∶1 inclusion stoichiometry of inclusion complexation was defined using Job plot by ultraviolet-visible(UV-vis)spectroscopy,and their binding stability constants(Ks)were determined as 2862.77,3494.70,6521.85 and 9599.48 L/mol using UV-vis spectroscopic titration,far more superior to that of nativeβ-CD(Ks=236.79 L/mol).This indicated that the amino side chains on CCDs could actively participate in the inclusion complexation through anion-cation interactions,significantly strengthening the host-guest binding between CCDs and LG.The inclusion modes were further elucidated based on proton and two-dimensional rotating-frame overhauser enhancement spectroscopy(2D-ROESY)nuclear magnetic resonance(NMR)experiments and molecular docking.Water solubility of LG was dramatically promoted up to 4.9 mg/mL,which was 70-fold higher than that of native LG.This study could draw inspiration for the binding and solubilization of phenols such as flavones by design of cationic macrocyclic molecules.

Haloacetic acids(HAAs),as a class of disinfection byproducts in drinking water,pose potential threats to human health,so the rapid,accurate and simultaneous detection of HAAs is of great significance for ensuring drinking water safety.Aiming at the challenges in HAAs detection and risk analysis,a novel method for synchronous rapid detection of seven kinds of HAAs in drinking water based on in situ derivatization technology and headspace gas chromatography was developed in this study.Through single-factor optimization experiments,the optimal reaction parameters for in situ derivatization were determined,including the type and dosage of salting-out agent,the acidity of reaction system,the amount of phase transfer catalyst,the dosage of derivatization agent,and the extraction solvent volume.Methodologic validation showed that the seven kinds of HAAs exhibited excellent linear relationships within their respective detection concentration ranges(R2>0.998).The method detection limits(MDLs)ranged from 0.04 to 0.33 μg/L,and the limits of quantification(LOQs)were between 0.14 and 1.34 μg/L.For real water samples,the average spiked recoveries of the seven HAAs ranged from 90.9%to 107.7%,with relative standard deviation(RSDs)between 1.55%and 6.49%,and the HAAs contents in all tested samples were below the limits specified in the Standards for Drinking Water Quality(GB 5749-2022)of China.This method was featured with simple operation,fast analysis speed,high sensitivity,and good accuracy,providing an efficient and reliable technical support for routine monitoring of HAAs contaminants in drinking water and showing promising application value for widespread promotion.

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.