Objective: To investigate the current status and trends of depressive symptoms among middle school and college students in Hunan Province, and to explore the primary related factors of depressive symptoms, so as to provide a scientific basis for strengthening mental health among students. Methods: A total of 279 382 students in Hunan Province were selected through a stratified cluster random sampling method from 2021 to 2023. National Survey Questionnaire on Common Diseases and Health Influencing Factors among Students was adopted for the survey, and the Center for Epidemiological Studies Depression Scale was used to assess their depressive symptoms. The χ 2 test and trend χ 2 test were used to analyze depressive symptoms prevalence and trends, and multivariable Logistic regression was used to analyze the related factors of depressive symptoms. Results: The prevalence of depressive symptoms among students in Hunan Province from 2021 to 2023 were 19.66%, 20.17% and 21.47%, respectively, showing an upward trend ( χ 2 trend =9.07, P <0.01). In addition, the results of the multivariable Logistic regression analysis showed that students with healthy diet ( OR=0.43, 95%CI =0.40-0.45), adequate sleep ( OR=0.88, 95%CI =0.86-0.90), and acceptable screen time ( OR=0.61, 95%CI =0.60-0.62) had lower risks in depressive symptoms detection, while students with smoking ( OR= 1.95, 95%CI =1.88-2.02), secondhand smoke exposure ( OR=1.33, 95%CI =1.30-1.36) and Internet addiction ( OR= 4.19 , 95%CI =4.05-4.34) had higher risks in depressive symptoms detection, with differences in the degree of association among different genders, educational stages and urban rural groups ( OR=0.40-6.04, Z =-12.69-11.98) ( P <0.05). Conclusions There is an increasing trend of depressive symptoms among middle school and college students in Hunan Province from 2021 to 2023.Targeted depression prevention measures should be taken for students with different demographic characteristics to promote their mental health.

Mercury (Hg)-contaminated soil poses a significant threat to the environment and human health. Hg-resistant microorganisms have the ability to survive under the stress of inorganic and organic Hg and effectively reduce Hg levels and toxicity. Compared to physical and chemical remediation methods, microbial remediation technologies have garnered increasing attention in recent years due to their lower cost, remarkable efficacy, and minimal environmental impact. This paper systematically elucidates the molecular mechanisms of Hg resistance in microbes, with a focus on their potential applications in phytoremediation of Hg-contaminated soils through plant-microbe interactions. Furthermore, it highlights the critical role of microbes in enhancing the effectiveness of transgenic plants for Hg remediation, aiming to provide a theoretical foundation and scientific basis for the bioremediation of Hg-contaminated soils.
Mercury/toxicity* ; Biodegradation, Environmental ; Soil Pollutants/isolation & purification* ; Soil Microbiology ; Plants, Genetically Modified/metabolism* ; Bacteria/genetics*

BACKGROUND:Reducing the rate of abnormal fertilization is an effective approach to improving the efficacy of in vitro fertilization and reducing patients'financial strain.However,the current research on abnormal fertilization has focused on exploring the types of prokaryotic nuclei and their generation mechanisms,as well as analyzing embryos formed by abnormal fertilization,chromosomal ploidy and utilization value.There is a lack of clinical prediction models for abnormal fertilization based on retrospective studies. OBJECTIVE:To construct a nomogram model to predict abnormal female factors in in vitro fertilization. METHODS:A total of 5 075 patients undergoing treatment for conventional in vitro fertilization at Nanxishan Hospital of Guangxi Zhuang Autonomous Region from March 2017 to March 2022 were retrospectively analyzed.The male confounders were calibrated on a 1:1 propensity score with a match tolerance of 0.02,and 1 672 cases were successfully matched.According to the Vienna Consensus,patients with≥60%normal fertilization capacity were included in the normal fertilization group(n=836)and those with<60%normal fertilization capacity were included in the abnormal fertilization group(n=836).The model and validation groups were obtained by random sampling at a ratio of 7:3.Factors related to the occurrence of abnormal fertilization following conventional in vitro fertilization in the model group were screened using univariate analysis and the best matching factors were selected using the Least Absolute Shrinkage and Selection Operator(LASSO)and included in a multifactorial forward stepwise Logistic regression to identify their independent influencing factors and plot a nomogram.Finally,the prediction model was validated for discrimination,accuracy and clinical application efficacy using receiver operating characteristic curves,calibration curves,clinical decision curves and clinical impact curves. RESULTS AND CONCLUSION:The univariate analysis indicated the factors influencing the occurrence of abnormal fertilization were age,controlled ovarian hyperstimulation protocol,number of assisted pregnancies,years of infertility,infertility factors,anti-mullerian hormone,sinus follicle count,basal luteinizing hormone,luteinizing hormone concentration on the human chorionic gonadotropin day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).LASSO regression further identified the best matching factors,including age,microstimulation protocol,number of assisted pregnancies,years of infertility,anti-mullerian hormone,luteinizing hormone level on human chorionic gonadotropin injection day,and estradiol level on human chorionic gonadotropin injection day(P<0.05).Multifactorial forward stepwise Logistic regression results showed that age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day were independent influencing factors for the occurrence of abnormal fertilization following conventional in vitro fertilization.The receiver operating characteristic curves showed an area under the curve of 0.761(0.746,0.777)for the model group and 0.767(0.733,0.801)for the validation group,indicating that the model has good discrimination.The mean absolute error of the calibration curve was 0.044,and the Hosmer-Lemeshow test indicated that there was no significant difference between the predicted probability of abnormal fertilization and the actual probability of abnormal fertilization(P>0.05),indicating the prediction model has good consistency and accuracy.The clinical decision curves and clinical impact curves showed that the model and validation groups had the maximum net clinical benefit at valve probability values of 0.00-0.52 and 0.00-0.48,respectively,and there was a good clinical application efficacy in this valve probability range.To conclude,the nomogram model has good discrimination and accuracy as well as clinical application efficacy for predicting the occurrence of abnormal fertilization in women undergoing conventional in vitro fertilization based on age,microstimulation protocol,number of assisted conceptions,years of infertility,anti-mullerian hormone,and estradiol level on human chorionic gonadotropin injection day.

Large-scale gene expression profiling generates or integrates massive data of gene expression under drug induction and employs artificial intelligence algorithms for pattern matching and association analysis. This approach facilitates the identification of complex relationships and functional networks between drugs, genes, and diseases, thereby significantly advancing drug research. Traditional Chinese medicine(TCM), with its characteristic multi-component, multi-target, and multi-pathway mechanisms, poses challenges to conventional methodologies in the comprehensive elucidation of its biological effects. The drug discovery strategy that combines large-scale gene expression profiling with artificial intelligence offers distinct advantages since it does not need the prior knowledge of specific drug targets or mechanisms. This article comprehensively reviews the innovative applications of large-scale gene expression profiling in TCM research as well as the recent advancements in the development of these technologies, the optimization of pattern matching algorithms, and the construction of related databases. In summary, the integration of large-scale gene expression profiling with artificial intelligence provides a powerful hypothesis-generating tool for the modern application and theoretical innovation of TCM.
Medicine, Chinese Traditional/methods* ; Gene Expression Profiling/methods* ; Humans ; Artificial Intelligence ; Drugs, Chinese Herbal/pharmacology* ; Algorithms ; Animals

This study analyzed the anemia status and change trend of 219 835 pregnant women in eight provinces from 2016 to 2020 in the Maternal and Newborn Health Monitoring Program(MNHMP). The results showed that from 2016 to 2020, the anemia rate of pregnant women in eight provinces was 41.27%, and the rates of mild, moderate and severe anemia were 28.56%, 12.59% and 0.12% respectively; the anemia rates in eastern, central and western regions were 41.87%, 36.09% and 44.63% respectively, and the anemia rates in urban and rural areas were 39.87% and 42.23%. From 2016 to 2020, the anemia rate of pregnant women decreased from 44.93% to 38.22%, with an average annual decline of 3.86% (95%CI:-5.84%, -1.85%). The anemia rate among pregnant women of the eastern region (AAPC=-6.16%, 95%CI:-9.79%, -2.38%) fell faster than that among pregnant women of the central region (AAPC=0.71%, 95%CI:-6.59%, 8.57%) and western region (AAPC=-1.53%, 95%CI:-5.19%, 2.28%). From 2016 to 2020, the moderate anemia rate in pregnant women decreased from 14.98% to 10.74%, with an average annual decline of 8.72% (95%CI:-12.90%, -4.34%), with a statistically significant difference (P<0.05); AAPC for mild and severe anemia in pregnant women was 1.56% (95%CI: 3.44%, 0.36%) and 18.86% (95%CI: 39.88%, 9.52%), respectively, without statistically significant difference (P>0.05).
Infant, Newborn ; Female ; Humans ; Pregnancy ; Pregnant Women ; Prevalence ; Anemia/epidemiology* ; China/epidemiology* ; Family ; Rural Population

Objective: To explore the efficacy and safety of real-world eribulin in the treatment of metastatic breast cancer. Methods: From December 2019 to December 2020, patients with advanced breast cancer were selected from Beijing Chaoyang District Sanhuan Cancer Hospital, Shandong Cancer Hospital, Peking University Cancer Hospital, Baotou Cancer Hospital, Shengjing Hospital Affiliated to China Medical University, and Cancer Hospital of Chinese Academy of Medical Sciences. Kaplan-Meier method and Log rank test were used for survival analysis, and Cox regression model was used for multivariate analysis. Results: The median progression-free survival (PFS) of 77 patients was 5 months, the objective response rate (ORR) was 33.8%, and the disease control rate (DCR) was 71.4%. The ORR of patients with triple-negative breast cancer was 23.1%, and the DCR was 57.7%; the ORR of patients with Luminal breast cancer was 40.0%, and the DCR was 77.8%; the ORR of patients with HER-2 overexpression breast cancer was 33.3%, and the DCR was 83.3%. ORR of 50.0% and DCR of 66.7% for patients treated with eribulin as first to second line treatment, ORR of 29.4% and DCR of 76.5% for patients treated with third to fourth line and ORR of 28.6% and DCR of 71.4% for patients treated with five to eleven line. The ORR of patients in the eribulin monotherapy group was 40.0% and the DCR was 66.0%; the ORR of patients in the combination chemotherapy or targeted therapy group was 22.2% and the DCR was 81.5%. Patients with a history of treatment with paclitaxel, docetaxel, or albumin paclitaxel during the adjuvant phase or after recurrent metastasis had an ORR of 32.9% and a DCR of 69.9% when treated with eribulin. The treatment efficacy is an independent prognostic factor affecting patient survival (P<0.001). The main adverse reactions in the whole group of patients were Grade Ⅲ-Ⅳ neutrophil decline [29.9% (23/77)], and other adverse reactions were Grade Ⅲ-Ⅳ fatigue [5.2% (4/77)], Grade Ⅲ-Ⅳ peripheral nerve abnormality [2.6% (2/77)] and Grade Ⅲ-Ⅳ alopecia [2.6% (2/77)]. Conclusions: Eribulin still has good antitumor activity against various molecular subtypes of breast cancer and advanced breast cancer that has failed multiple lines of chemotherapy, and the adverse effects can be controlled, so it has a good clinical application value.
Breast Neoplasms/pathology* ; Female ; Furans/adverse effects* ; Humans ; Ketones/adverse effects* ; Paclitaxel/adverse effects* ; Treatment Outcome ; Triple Negative Breast Neoplasms/drug therapy*

Long non-coding RNAs (LncRNAs) are abnormally expressed in a variety of tumors and participate in the occurrence and development of tumors. However, the expression and function of many LncRNAs in tumors have not been fully clarified. In this paper, 113 normal breast tissues and 1 109breast cancer tissues were analyzed in TCGT database. LncRNA AL133467. 1 was found to be lowly expressed in breast cancer tissues and negatively correlated with poor prognosis of breast cancer patients. The expression of AL133467. 1 in breast cancer cells was significantly lower than that in normal breast epithelial cells. We overexpressed AL133467. 1 in relatively low-expression breast cancer cells SKBR3and BT474, and cell count and plate colony-formation experiments showed that overexpression ofAL133467. 1 could significantly inhibit the proliferation and colony formation of breast cancer cells (P< 0. 01). Cell scratch and Transwell assays showed that the migration and invasion ability of breast cancer cells overexpressing AL133467. 1 was significantly reduced compared with the control group (P<0. 01). MiRDB database showed that AL133467. 1 had binding sites with miR-661. miR-661 could bind the transducer of ErbB2, 2 (ErbB2, 2, TOB2). qRT-PCR showed that miR-661 was highly expressed inbreast cancer cells and positively correlated with poor prognosis of breast cancer patients (P < 0. 001). Luciferase reporter assays showed that AL133467. 1 had specific binding to miR-661 (P < 0. 01). AL133467. 1 overexpression could inhibit the expression of miR-661 in breast cancer cells (P<0. 0001). Transfection of miR-661 mimics eliminated the inhibitory effect of overexpression of AL133467. 1 on breast cancer cells (P < 0. 001). In addition, qRT-PCR and Western blotting results showed that overexpression of AL133467. 1 up-regulated TOB2 mRNA (P < 0. 0001) and protein levels. But whenmiR-661 mimics were transfected, TOB2 mRNA (P < 0. 0001) and protein levels were significantly inhibited. In conclusion, as a competitive endogenous RNA of miR-661. AL133467. 1 promotes the expression of TOB2, thereby inhibiting the proliferation and invasion of breast cancer cells.

Objective: To develop a model incorporating radiomic features and clinical factors to accurately predict acute ischemic stroke (AIS) outcomes. Materials and Methods: Data from 522 AIS patients (382 male [73.2%]; mean age ± standard deviation, 58.9 ± 11.5 years) were randomly divided into the training (n = 311) and validation cohorts (n = 211). According to the modified Rankin Scale (mRS) at 6 months after hospital discharge, prognosis was dichotomized into good (mRS ≤ 2) and poor (mRS > 2); 1310 radiomics features were extracted from diffusion-weighted imaging and apparent diffusion coefficient maps. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator logistic regression method were implemented to select the features and establish a radiomics model. Univariable and multivariable logistic regression analyses were performed to identify the clinical factors and construct a clinical model. Ultimately, a multivariable logistic regression analysis incorporating independent clinical factors and radiomics score was implemented to establish the final combined prediction model using a backward step-down selection procedure, and a clinical-radiomics nomogram was developed. The models were evaluated using calibration, receiver operating characteristic (ROC), and decision curve analyses. Results: Age, sex, stroke history, diabetes, baseline mRS, baseline National Institutes of Health Stroke Scale score, and radiomics score were independent predictors of AIS outcomes. The area under the ROC curve of the clinical-radiomics model was 0.868 (95% confidence interval, 0.825–0.910) in the training cohort and 0.890 (0.844–0.936) in the validation cohort, which was significantly larger than that of the clinical or radiomics models. The clinical radiomics nomogram was well calibrated (p > 0.05). The decision curve analysis indicated its clinical usefulness. Conclusion The clinical-radiomics model outperformed individual clinical or radiomics models and achieved satisfactory performance in predicting AIS outcomes.

Objective: To observe the effects of herb-partitioned moxibustion and ginger-partitioned moxibustion on the growth of colon tumors in rats with colitis-associated colon cancer (CACC), and explore the mechanism of moxibustion intervening CACC through the purinergic receptor P2X ligand-gated ion channel 7 (P2X7R)/signal transducer and activator of transcription 3 (STAT3)/vascular endothelial growth factor (VEGF) pathway. Methods: A total of 26 male Sprague-Dawley rats were selected. According to the random number table method, 6 rats were selected as the normal group. The remaining 20 rats were injected intraperitoneally with azoxymethane (AOM) combined with oral dextran sodium sulfate (DSS) to prepare the CACC model. After the model was successfully established, 2 rats were randomly selected for model identification. The remaining 18 rats which were successfully modeled were randomly divided into a model group, a herb-partitioned moxibustion group and a ginger-partitioned moxibustion group, with 6 rats in each group. Moxibustion intervention was performed in the herb-partitioned moxibustion group and the ginger-partitioned moxibustion group at Qihai (CV 6) and bilateral Tianshu (ST 25). Moxibustion was performed twice at each point each time, once a day, at a 1-day interval after 6 consecutive interventions, for a total of 30 interventions. After intervention, the colon tumor load, pathological change and histopathological score were observed. Immunohistochemistry was used to detect the expressions of VEGF, P2X7R, phospho-STAT3 (p-STAT3), and nuclear factor-kappa B p65 (NF-κB p65) proteins in rat colon tissue. Western blot was used to detect the levels of p-STAT3 and NF-κB p65 proteins in rat colon tissue. Results: Compared with the normal group, the colon tumor load and histopathological score in the model group were significantly increased (both P<0.001), and different grades of dysplasia were observed in colon tissue from the model group, reaching the degree of adenocarcinoma; the expression level of P2X7R protein in colon tissue was significantly decreased (P<0.001), and the expression levels of p-STAT3, NF-κB p65 and VEGF proteins were significantly increased (all P<0.001) in the model group. Compared with the model group, the colon tumor load, colon histopathological score and the levels of p-STAT3, NF-κB p65 and VEGF proteins in colon tissue were significantly decreased (all P<0.05) in the herb-partitioned moxibustion group and the ginger-partitioned moxibustion group while the expression levels of P2X7R protein in colon tissue were significantly increased (both P<0.05). Conclusion: Both herb-partitioned moxibustion and ginger-partitioned moxibustion can reduce the colon tumor load in CACC rats and delay the progression of colon adenomas. The mechanism may be mediated by the P2X7R/STAT3 pathway to inhibit STAT3 phosphorylation, thereby reducing VEGF protein expression.

During the high-temperature and rainy season from June to October in 2017-2019,serious southern blight broke out in the Cynanchum stauntonii planting area in Tuanfeng county,Hubei province,which had a great impact on the yield and quality of medicinal materials. In this study,the pathogen of C. stauntonii was isolated,purified,and identified,and the pathogenicity was tested according to Koch's postulates. Meanwhile,the biological characteristics of the pathogen were analyzed. On this basis,the effective fungicides were screened in laboratory. Finally,the pathogen( BQ-1) was identified as Athelia rolfsii( Deuteromycotina,Basidiomycota,anamorph: Sclerotium rolfsii). The optimum growth conditions for BQ-1 were 25-30 ℃,p H 5-8,and alternating light and dark.The effective chemical fungicides were lime-sulphur-synthelic-solution( LSSS) and flusilazole,and the effective botanical fungicide was osthole. BQ-1 was highly homologous to the pathogen HS-1 of peanut southern blight,with the similarity of 18 S r DNA and TEF sequences at 99. 09%. The southern blight in C. stauntonii might be resulted from that in peanut. In the production of C. stauntonii,the following measures should be taken: avoiding rotation or neighboring with peanut,draining water from June to October to reduce humidity,and reasonably applying fungicides.
Basidiomycota ; Cynanchum ; Fungicides, Industrial/pharmacology* ; Humidity