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:To summarize the clinical characteristics of elderly patients with stage Ⅰ diffuse large B-cell lymphoma (DLBCL) and analyze the factors associated with prognosis.Methods:A case series study was conducted by retrospectively collecting clinical data from patients aged over 60 years with newly diagnosed stage Ⅰ DLBCL across 20 medical centers in Jiangsu Province, China, between June 2010 and April 2023. The involved site, classification and treatment plan were summarized. The primary endpoints were progression-free survival (PFS) and overall survival (OS). Statistical analyses were performed using the Kaplan-Meier method, and Cox regression model.Results:The study included 255 patients with a median age of 69 years, of whom 130 (51.0%) were male, 66 (25.9%) were aged ≥75 years and 26 (10.1%) had a high Charlson Comorbidity Index (CCI) score of ≥2. Extranodal involvement was observed in 163 (63.9%) patients, with the stomach (37.4%, 61/163), intestine (19.0%, 31/163), testes (11.0%, 18/163), and breast (7.4%, 12/163) being the most frequently affected sites. The non-germinal center B-cell (non-GCB) subtype was prevalent in 63.7% of patients (142/223), with no significant difference between the nodal and extranodal groups ( P=0.681). Furthermore, 73.9% (184/249) and 11.7% (29/249) of patients received the R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisone) and R-miniCHOP regimen, respectively. The overall 3-year PFS rate was 81.5%, and the 3-year OS rate was 85.6%. Patients aged ≥75 years ( HR=2.910, 95% CI 1.565-5.408, P=0.001) and/or with a CCI score ≥2 ( HR=2.324, 95% CI 1.141-4.732, P=0.020) had a significantly poorer PFS. Incorporating age ≥75 years and CCI score ≥2 into the stage-modified international prognostic index (sm-IPI) can better stratify the prognosis of elderly patients with stage Ⅰ DLBCL. The 3-year PFS rate was 48.7% in the high-risk group versus 85.7% in the low-risk group ( P<0.001). Conclusions:Our findings show that the elderly patients with stage Ⅰ DLBCL were predominantly characterized by extranodal involvement (particularly in the stomach and intestinal tract) and non-GCB subtype. Age ≥75 years and CCI ≥2 were identified as independent prognostic factors. The newly established sm-IPI-75-CCI incorporating these factors demonstrated superior prognostic discrimination compared to conventional risk assessment systems.

AIM To establish the quantitative models for gallic acid,mononucleoside,loganin,resveratrol,and rhein in Yishen Xiezhuo Mixture.METHODS HPLC was adopted in the content determination of various effective components,after which the near-infrared spectroscopy(NIRS)data were collected in 128 batches of samples and pretreatment was conducted,competitive adaptive reweighting sampling(CARS)algorithm was used for screening wavelength,partial least square method(PLS)regression analysis was performed.RESULTS There were no significant differences between the predicted values obtained by PLS models and measured values obtained by HPLC for various effective components(P＞0.05).CONCLUSION The quantitative models established by NIRS combined with chemometrics display good predictive performance,which can be used for the rapid determination of effective components in Yishen Xiezhuo Mixture,and provide a reference for the rapid monitoring of other traditional Chinese medicine preparations in production processes.

High mobility group box protein 1(HMGB1)is one of the most widely expressed protein member in the HMGs family,which is well known for its involvement in the body inflammatory response.Previous researches have found that it plays a significant role in cell migration,immune identification and neuroprotection.Spinal cord injury is a disease that causes severe damage to the nervous system,and neural circuits are disrupted after a spinal cord injury,which leads to many conditions including ischemia and hypoxia,inflammatory responses,demyelinating lesions,and glial scar formation that are detrimental to nerve regeneration and repair,making it one of the most difficult diseases to treat in the modern spinal surgery field.HMGB1 is upregulated after spinal cord injury,thereby regulating neuroinflam-matory responses,and participating in the neuronal apoptosis,promoting neuronal regeneration,and inducing neural stem cell differentiation and migration,which plays an important role in the process of neural function recovery.This paper summarizes the structure and function of HMGB1,as well as its role in spinal cord injury,in order to provide direction for founding therapeutic target for neurological function recovery after spinal cord injury.

Objective:To evaluate the clinical efficacy of"Shibao Decoction"in the management of late-onset hypogonadism(LOH)caused by deficiency of kidney essence.Methods:Sixty male patients with late-onset hypogonadism of kidney essence defi-ciency type were randomly assigned to the treatment group and the control group,each with 30 cases.The patients in treatment group were treated with oral Shibao Decoction,while the control group was treated with oral Testosterone Undecanoate Capsules.The patients in both groups were treated for 12 weeks.The PADAM symptom score,TCM syndrome score,serum total testosterone(TT),serum free testosterone(FT),sex hormone binding globulin(SHBG),body mass index(BMI),total skeletal muscle mass index(SMI),appendicular skeletal muscle mass index(ASMI),FBG,FINS,and insulin resistance index(HOMA-IR)levels were compared be-tween the two groups.Results:After treatment,PADAM scores for each item and TCM symptoms score decreased,TT and FT in-creased in both groups,all with statistically significant differences from those of pre-treatment(P＜0.05).The level of SHBG in the control group decreased(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).After treatment,SMI and ASMI increased in both groups significantly(P＜0.05).BMI decreased in the control group(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).The level of FINS decreased in the control group(P＜0.05),which had not changed significantly in the treatment group(P＞0.05).FPG had not changed significantly in both groups(P＞0.05),and the insulin resist-ance index(HOMA-IR)had significantly improved in both groups,all with statistically significant differences from those of pre-treat-ment(P＜0.05).After treatment,the total effective rates of PADAM score and TCM syndrome score in the treatment group were 73.3％and 86.6％respectively,and the total effective rates in the control group were 66.7％and 76.6％respectively.The total ef-fective rates of the two scores in the treatment group were slightly higher than those in the control group(P＞0.05).There was no sig-nificant difference in the indicators between the two groups after treatment,and the treatment group is generally comparable with the control group in the therapeutic effects(P＞0.05).And no adverse reactions occurred during treatment in both groups.Conclu-sion:The"Shibao Decoction"has a remarkable therapeutic effect on late-onset hypogonadism caused by deficiency of kidney essence and has good safety.It can be used as an alternative to testosterone undecanoate and is worthy of clinical promotion and application.

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.