AIM: To evaluate the surgical outcomes and changes in the ocular surface microenvironment following radiofrequency minimally invasive treatment for conjunctivochalasis-induced epiphora.METHODS: Patients with epiphora primarily caused by conjunctivochalasis were enrolled. All patients had conjunctivochalasis of ≥grade II, and their symptoms showed no significant improvement after previous pharmacological treatment. All patients underwent radiofrequency minimally invasive correction of conjunctivochalasis, supplemented with artificial tears, anti-inflammatory therapy, and ocular surface repair treatment postoperatively. At 8 wk post-surgery, the ocular surface disease index(OSDI), eye redness, tear secretion, non-invasive tear break-up time, lipid layer thickness, tear ferning test, and conjunctival impression cytology were assessed to compare treatment efficacy and observe changes in the ocular surface microenvironment.RESULTS: A total of 43 cases(43 eyes)of conjunctivochalasis and with a main complaint of epiphora were included, including 23 males and 20 males, with a mean age of 64.69±3.36 years. The total effective rate of surgery was 91% at 8 wk postoperatively. Compared with preoperative values, the OSDI scores significantly decreased and the non-invasive tear break-up time was prolonged at 8 wk post-surgery(all P<0.05). No statistically significant differences were observed in lipid layer thickness or tear secretion at 8 wk postoperatively(all P>0.05). The normal rate of chloramphenicol taste test increased from 21% preoperatively to 63% postoperatively; the normal rate of eye redness increased from 40% to 70%; normal rate of tear ferning grading improved from 30% to 63%; and normal conjunctival impression cytology grading increased from 21% to 74%.CONCLUSION: Radiofrequency minimally invasive treatment is effective for conjunctivochalasis and is straightforward to perform. Patients with conjunctivochalasis often present with other ocular surface issues beyond conjunctivochalasis itself, such as insufficient tear secretion, reduced lipid layer thickness, and other dry eye-related problems. Therefore, a comprehensive approach emphasizing tear dynamics should be adopted during treatment.

ObjectiveTo investigate the contamination status of ochratoxin A (OTA) in commercially available food products in Shanghai, and to assess OTA exposure levels and the associated non-carcinogenic and carcinogenic risks among pregnant women by integrating dietary consumption data of this population. MethodsThe levels of OTA contamination in 1 520 food samples collected in Shanghai from 2022 to 2023 were determined using liquid chromatography-tandem mass spectrometry. An exposure assessment model was developed based on the dietary consumption levels of pregnant women from the 2016‒2017 Shanghai Pregnant Women Dietary Monitoring Survey to calculate the estimated daily intake (EDI) of OTA, the margin of exposure for non-carcinogenic toxicity (MOE₁), and the margin of exposure for carcinogenic toxicity (MOE₂). An MOE₁ greater than 200 and an MOE₂ greater than 10 000 indicate that the non-carcinogenic toxicity and carcinogenic toxicity resulting from exposure are negligible, respectively. For samples with OTA contamination levels below the limit of detection (LOD), which accounted for more than 80% of the samples, the OTA levels were assigned values of 0 and LOD, respectively, for subsequent calculations. ResultsThe detection rates of OTA in cereals, nuts, dried fruits, and alcohol samples collected in 2022 were 2.03%, 0, 0, and 0, respectively. The OTA detection rates in cereals, nuts, dried fruits, beans, and alcohol samples collected in 2023 were 2.50%, 0.39%, 2.47%, 1.67%, and 13.33%, respectively. For pregnant women in Shanghai in 2022, simulation results indicated that when assigning a value of 0 and the LOD, theP₅₀ values of EDI for dietary OTA exposure were 0.05 and 0.72 ng·(kg·d)^-1, respectively, and the P₉₅ values of EDI for dietary OTA exposure were 0.25 and 2.40 ng·(kg·d)^-1, respectively. For pregnant women in Shanghai in 2023, the P₅₀ values of EDI for dietary OTA exposure were 0.04 and 1.00 ng·(kg·d)^-1, respectively, and the P₉₅ values of EDI for dietary OTA exposure were 0.23 and 2.67 ng·(kg·d)^-1, respectively, both substantially below the tolerable daily intake (TDI) for OTA [17 ng·(kg·d)^-1]. The EDI for dietary OTA exposure in 100.0% of Shanghai pregnant women was lower than the TDI, indicating an overall low level of dietary OTA exposure among this population. For 100.0% of pregnant women, the MOE₁ for dietary OTA exposure exceeded 200. When assigned a value of 0, the MOE₂ for 100.0% of pregnant women in both 2022 and 2023 exceeded10 000. When assigned the LOD value, 72.3% and 81.8% of pregnant women in 2022 and 2023, respectively, had an MOE₂ exceeding 10 000. ConclusionFrom 2022 to 2023, samples of cereals, nuts, dried fruits, beans, and alcohol sold in Shanghai exhibited varying degrees of OTA contamination. The overall EDI of OTA exposure among pregnant women in Shanghai remained at a low level. The non-carcinogenic and carcinogenic risks associated with OTA exposure were generally low and at controllable levels.

Objective To investigate the distribution characteristics of Oncomelania hupensis snails in Yunnan Province fol-lowing interruption of schistosomiasis transmission, so as to provide the evidence for assessing the risk of schistosomiasis transmission and scientifically formulating the schistosomiasis surveillance program. Methods According to the requirements of the National Schistosomiasis Surveillance Scheme (2020 Edition), O. hupensis snail surveillance data were collected from 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province from 2020 to 2024, including area of snail survey, area of snail habitats, area of re-emerging snail habitats, number of frames surveyed, number of frames with O. hupensis snails, number of O. hupensis snails captured, and number of living snails, and the occurrence of frames with snails and mean density of living snails were calculated. Changes in snail status over the 5-year period from 2020 to 2024 and the differences in snail distributions specified by epidemic intensity, environmental type, and vegetation type were analyzed. Results The areas of snail survey increased from 1 727.96 hm² in 2020 to 3 894.45 hm² in 2024 (peak) across 18 schistosomiasis-endemic counties (cities, districts) in Yunnan Province during the period from 2020 through 2024. The areas of snail habitats increased from 70.36 hm² in 2020 to a peak in 2023 (172.04 hm²), followed by a reduction to 132.36 hm² in 2024, and the areas of re-emerging snail habitats increased from 42.71 hm² in 2020 to a peak in 2022 (78.43 hm²), followed by a reduction to 40.21 hm² in 2024. The occurrence of frames with snails and mean density of living snails increased from 1.24% (3 025/244 404) and (0.033 2 ± 0.038 7) snails/0.1 m² in 2020 to peaks at 2.03% (6 231/307 563) and (0.066 9 ± 0.068 4) snails/0.1 m² in 2023, followed by reductions to 1.04% (5 829/559 941) and (0.032 6 ± 0.057 7) snails/0.1 m² in 2024, respectively. There was a significant difference in the occurrence of frames with snails over the 5-year study period (χ² = 1 962.95, P < 0.05), and the occurrence of frames with snails reduced by 48.71% in 2024 relative to in 2023 (χ² = 1 411.05, P < 0.005); however, there was no significant difference in the mean density of living snails over the 5 years (H = 5.310, P > 0.05). There were significant differences in the occurrence of frames with snails (χ² = 481.27, P < 0.05) and mean density of living snails (H = 6.872, P < 0.05) in schistosomiasis-endemic areas with different epidemic intensities. The occurrence of frames with snails (χ² = 25.32 and 38.70, both P values < 0.017) and mean density of living snails (Z = 28.55 and 49.96, both P values < 0.017) were higher in schistosomiasis transmission-interrupted and eliminated areas with snails than in schistosomiasis-eliminated areas without snails, and the occurrence of frames with snails (χ² = 453.54, P < 0.017) and mean density of living snails (Z = −56.97, P < 0.017) were higher in schistosomiasis-eliminated areas with snails than in schistosomiasis transmission-interrupted areas with snails. O. hupensis snails were mainly distributed in paddy fields, dry farmlands and ditches; however, the occurrence of frames with snails (13.40%, 424/3 164) and mean density of living snails [(0.252 8 ± 0.158 7) snails/0.1 m²] were higher in ponds/weirs than in other types of environments (both P values < 0.05). Rice, dry farmland crops and weeds were main vegetations in which O. hupensis snails were distributed, and the occurrence of frames with snails (2.29%, 7 111/310 140) and mean density of living snails [(0.072 3 ± 0.018 9) snails/0.1 m²] were higher in weeds than in other types of environments (both P values < 0.05). Conclusions O. hupensis snails have been effectively controlled in Yunnan Province following implementation of integrated schistosomiasis control measures; however, there are still risk factors for schistosomiasis transmission, including reduced attention to schistosomiasis control and snail re-emergence. Improved control efforts and surveillance system construction and timely identification of risk factors of snail status and timely management are recommended to ensure the achievement of the target of schistosomiasis elimination as scheduled.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

In order to explore the possible role and molecular mechanism of the combined action of leech and bear bile in liver and gallbladder diseases, this study first used network pharmacology methods to screen the components and targets of leech and bear bile, as well as the related target genes of liver and gallbladder diseases. The selected key genes were subjected to interaction network and GO/KEGG enrichment analysis. Then, using sodium oleate induced HepG2 cell lipid deposition model and DL-ethionine induced mouse fatty liver model, the activity of leech and bear bile alone and in combination in reducing liver fat was evaluated in vitro and in vivo, and the expression of key pathway related proteins suggested by network pharmacology was detected by Western blot. The results of network pharmacology analysis showed that the active ingredients of leech and bear bile have 295 intersecting targets with liver and gallbladder related diseases, involving more than 200 signaling pathways, including the PI3K/Akt signaling pathway and phospholipase D signaling pathway closely related to glucose and lipid metabolism. The results of in vitro validation experiments showed that both leech and bear bile, alone and in combination, can significantly inhibit the lipid deposition induced by sodium oleate in human liver cells, reduce the triacylglycerol level in cell culture supernatant, and inhibit the lipid content in liver cells. The observation results of Nile red staining confocal microscopy showed that the combination of leech and bear bile had better activity in reducing lipid deposition in liver cells compared to using them alone. In a mouse fatty liver model, the combination of leech and bear bile can better reduce elevated organ indices, blood lipids, and liver lipid levels, as well as lower the levels of serum liver injury biomarkers. The animals used in this experiment and related disposal meet the requirements of animal welfare. Before the experiment, it was reviewed and approved by IACUC, Institute of Materia Medica, Chinese Academy of Medical Sciences. The Western blot experiment results showed that the combination of leech and bear bile can significantly upregulate the expression levels of p-PI3K and p-Akt proteins, and increase the p-PI3K/PI3K and p-Akt/Akt ratios, which is consistent with the predicted results of network pharmacology. The combination of leech and bear bile has great potential for treating fatty liver disease, and activating the PI3K/Akt pathway may be one of the important mechanisms for reducing lipid deposition in liver cells.

The pathogenesis of cardiovascular diseases (CVD) is complex, and dynamic imbalances in protein acylation modification are significantly associated with the development of CVD. In recent years, most studies on exercise-regulated protein acylation modifications to improve cardiovascular function have focused on acetylation and lactylation. Protein acylation modifications are usually affected by exercise intensity. High-intensity exercise directly affects oxidative stress and cellular energy supply, such as changes in ATP and NAD⁺ levels; moderate-intensity exercise is often accompanied by improvements in aerobic metabolism, such as fatty acid β-oxidation and TCA cycle, which modulate mitochondrial biogenesis. The above processes may affect the acylation status of relevant regulatory enzymes and functional proteins, thereby altering their function and activity and triggering signaling cascades to adapt to exercise’s metabolic demands and stresses. Exercise regulates the levels of acylation modifications of H3K9, H3K14, H3K18, and H3K23, which are involved in regulating the transcriptional expression of genes involved in oxidative stress, glycolysis, inflammation, and hypertrophic response by altering chromatin structure and function. Exercise can regulate the acylation modification of non-histone-specific sites in the cardiovascular system involved in mitochondrial function, glycolipid metabolism, fibrosis, protein synthesis, and other biological processes, and participates in the regulation of protein activity and function by altering the stability, localization, and interaction of proteins, and ultimately works together to achieve the improvement of cardiovascular phenotypes and biological functions. Exercise affects acyl donor concentration, acyltransferase, and deacetylase expression and activity by influencing acyl donor concentration, acyltransferase, and deacetylase. Exercise regulates the abundance of acyl donors such as acetyl coenzyme A, propionyl coenzyme A, butyryl coenzyme A, succinyl coenzyme A, and lactoyl coenzyme A by promoting glucose and lipid metabolism and improving intestinal bacterial flora, which in turn affects protein acylation modification, accelerates oxidative decarboxylation of pyruvic acid in the body, and activates the energy-sensing molecule, adenosine monophosphate-activated protein kinase (AMPK), to improve cardiovascular function. Exercise may affect protein acylation modifications in the cardiovascular system by regulating the activity and expression of adenoviral E1A binding protein of 300 kDa (p300)/cyclic adenosine monophosphate response element-binding protein (CBP), general control nonderepressible 5-related N-acetyltransferases (GNAT), and alanyl-transfer t-RNA synthetase (AARS), which in turn improves cardiovascular function. The relationship between exercise and cardiovascular deacetylases has attracted much attention, with SIRT1 and SIRT3 of the silence information regulator (SIRT) family of proteins being the most studied. Exercise may exert transient or long-term stable cardiovascular protective benefits by promoting the enzymatic activity and expression of SIRT1, SIRT3, and HDAC2, inhibiting the enzymatic activity and expression of HDAC4, and mediating the deacylation of metabolic regulation-related enzymes, cytokines, and molecules of signaling pathways. This review introduces the role of protein acylation modification on CVD and the effect of exercise-mediated protein acylation modification on CVD. Based on the existing studies, it analyzes the possible mechanisms of exercise-regulated protein acylation modification to improve CVD from the perspectives of acylation modification donors, acyltransferases, and deacetylases. Deciphering the regulation of cardiovascular protein acylation and modification by exercise and exploring the essential clues to improve cardiovascular disease can enrich the theoretical basis for exercise to promote cardiovascular health. However, it is also significant for developing new cardiovascular disease prevention and treatment targets.