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.

Dipsaci Radix is a commonly used Chinese herbal medicine in China, with triterpenoid saponins as the main active components. β-Amyrin synthase, a member of the oxidosqualene cyclase superfamily, plays a crucial role in the biosynthesis of oleanane-type triterpenoid saponins. Asperosaponin Ⅵ is an oleanane-type triterpenoid saponin. To explore the β-amyrin synthase genes involved in the biosynthesis of asperosaponin Ⅵ in Dipsacus asper, this study screened the candidate genes from the transcriptome data of D. asper. Two β-amyrin synthase genes, Da OSC1 and Da OSC2, were identified by phylogenetic analysis and correlation analysis. The coding sequences of Da OSC1 and Da OSC2 were 2 286 bp and 2 295 bp in length, encoding 761 and 764 amino acids,respectively. Multiple sequence alignments showed that Da OSC1 and Da OSC2 had three conserved motifs( DCTAE, QW, and MWCYCR) unique to the oxidosqualene cyclase family. Real-time quantitative PCR results showed that Da OSC1 and Da OSC2 had the highest expression levels in the roots. Compared with normal growth conditions, the low-temperature treatment significantly upregulated the expression of Da OSC1 and Da OSC2. Agrobacterium-mediated transient expression of Da OSC1 and Da OSC2 in Nicotiana benthamiana resulted in the production of β-amyrin, which suggested that Da OSC1 and Da OSC2 were able to catalyze the synthesis of β-amyrin. This study clarified the catalytic functions of two β-amyrin synthases in D. asper, analyzed their expression patterns in different tissue and at low temperatures. The findings provide a foundation for further studying the biosynthetic pathway and regulatory mechanism of asperosaponin Ⅵ in D. asper.
Intramolecular Transferases/chemistry* ; Phylogeny ; Plant Proteins/chemistry* ; Gene Expression Regulation, Plant ; Dipsacaceae/classification* ; Saponins/metabolism* ; Oleanolic Acid/metabolism*

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*

italic>Platycodon grandiflorum (Jacq.) A. DC is one of the most commonly used bulk medicinal herbs. It has important value in the fields of medicine, food and cosmetics, and its market demand is increasing year by year, and it has a good development prospect. In this study, based on 403 distribution records and 8 environmental variables, we used Maxent model to predict the potential distribution of P. grandiflorum under climate change. The results showed that the model simulation effect was the best when the Maxent parameter was FC (feature combination) = LQPH (L: linear features; Q: quadratic features; P: product features; H: hinge features) and RM (regularization multiplier) = 2.1, AUC (area under curve) = 0.901, and the model prediction showed that P. grandiflorum was widely distributed in 28 provinces of China under the current climate conditions, with a suitable area of 2 337 419.98 km². Under the influence of climate change in the future, the area of suitable habitat of P. grandiflorum showed a decreasing trend, and the distribution center as a whole showed a trend of northward and eastward shift. The distribution area of P. grandiflorum in the three main producing areas is affected by climate change, and the overall trend is that the future suitable distribution area of Chifeng in Inner Mongolia increases, while the future suitable distribution area of Zibo in Shandong and Taihe, Bozhou in Anhui decreases or even disappears under the SSP5-8.5 scenario (shared socioeconomic pathways). It is suggested to maintain and protect the ecological environment and germplasm resources of the reserved area suitable for the growth of P. grandiflorum, and increase the cultivation research in the expansion area of P. grandiflorum, so as to lay a foundation for the subsequent expansion of P. grandiflorum planting and to promote the protection and sustainable development of P. grandiflorum resources.

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.

Objective To explore the impact of sluggish cognitive tempo(SCT)on the efficacy of atomoxetine in the treatment of attention-deficit/hyperactivity disorder(ADHD).Methods A prospective study was conducted.Pediatric patients aged 6-12 years with a diagnosis of ADHD were selected from the Department of Psychiatry,Xijing Hospital,Air Force Military Medical University from May 2020 to May 2023.All the patients were treated with atomoxetine for 8 weeks.ADHD symptoms and SCT symptoms were assessed by Swanson,Nolan,and Pelham-Ⅳrating scale(SNAP-Ⅳ)and SCT rating scale at baseline(before treatment),4 weeks and 8 weeks after treatment.Pearson correlation analysis was used to analyze the correlation between SCT and SNAP-Ⅳscores.Results A total of 61 pediatric patients were enrolled,including 10 cases of inattentive type and 51 cases of mixed type.Baseline SCT score was positively correlated with baseline SNAP-Ⅳscore(r=0.490,P＜0.001)and reduction rate of SNAP-Ⅳscore at week 8(r=0.670,P＜0.001).The total SCT score at week 8 was positively correlated with total SNAP-Ⅳscore at baseline(r=0.320,P=0.012).The baseline SCT score of patients with mixed type was positively correlated with reduction rate of SNAP-Ⅳscore at week 8(r=0.578,P＜0.001).Conclusions The severity of SCT symptoms is positively correlated with the SNAP-Ⅳreduction rate 8 weeks after atomoxetine treatment,which could be a predictor of the efficacy of atomoxetine.

Objective To construct and identify an organoid model of human placental site trophoblastic tumor(PSTT).Methods The tumor cells were obtained by digesting and separating the PSTT tissues and then embedded in Matrigel.The organoids were cultured in the specific organoid medium.The histological morphology of the organoid model was observed by HE staining and the expression levels of the PSTT specific markers[human placental prolactin(HPL),human leukocyte antigen-G(HLA-G)and placental alkaline phosphatase(PLAP)]were detected by immunohistochemistry and immunofluorescence,so as to evaluate the consistency between the organoid model and the PSTT tissue.Meanwhile,the morphology and forming efficiency of the constructed model were observed under a microscope after primary culture,passage generation and cryopreservation to evaluate its potential application as an organoid model in basic and clinical translational research of PSTT.Results The constructed organoid model could proliferate stably,growing from small microspheres into compact solid spheres or spheres with follicle-like structures,and could passage after fully grown in 7-10 days.The cell state remained stable after passage,frozen storage and recovery.HE staining showed that the morphology of the cells in the organoids was similar to that of the primary PSTT tumor cells,and immunofluorescence staining showed that the organoids highly expressed HLA-G and lowly expressed β-HCG,indicating that the constructed organoid model mainly contained intermediate trophoblast.Conclusion The adult-derived PSTT organoid(ADPO)models were successfully established.

Artemisia argyi is a perennial herbaceous herb of the Artemisia family, with leaves for medical use. However, the germplasm of A. argyi is seriously unclear and mixed during production, and it is urgent to protect new varieties of A. argyi. The distinctness, uniformity, and stability(DUS) testing of the new varieties of plants is the basis for the protection of new varieties of plants, and the development of the DUS testing guidelines is the technical basis for DUS testing. To develop the DUS testing guidelines of A. argyi, A. argyi of 100 germplasm was used as the research objects, and their agronomic and medicinal quality characters were observed and measured during six growth stages, and each character was graded and described. A total of 53 test characters were determined, including 19 characters that must be tested; there were four plant characters, two rhizome characters, five stem characters, three branching characters, 29 leaf characters, three floral characters, five medicinal quality characters, and two other characters. It also involved 16 quality characters, 22 quantitative characters, and 15 pseudo-quantitative characters. Seven grouping characters were determined from 53 characters, including "emergence period" "plant-plant type" "branching-primary branching site" "stem-color" "middle leaf-number of leaf splits" "budding period", and "plant-height". By searching for standard characters, 16 standard varieties were ultimately determined. The preparation of this guideline was of great significance for the review and protection of new A. argyi varieties, the protection of breeders' rights, and the promotion of the development of A. argyi industry.
Artemisia/chemistry* ; Plant Leaves/chemistry* ; Quality Control ; Plants, Medicinal/classification* ; Guidelines as Topic

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