ObjectiveTo investigate the effects of jatrorrhizine on endogenous metabolites and metabolic pathways in the mouse model of ulcerative colitis. MethodsThirty male C57BL/6J mice were randomly divided into the normal group， the model group， the low-dose and high-dose jatrorrhizine groups （0.04， 0.16 g·kg^-1）， and the mesalazine group （0.52 g·kg^-1）The mouse model of ulcerative colitis was established with 3% dextran sulfate sodium （DSS） and treated with different doses of jatrorrhizine by gavage. The changes in body weight， colon length， disease activity index （DAI）， and colonic histopathology were analyzed to evaluate the therapeutic effects of jatrorrhizine. UPLC-Q-TOF/MS was employed to determine the serum and fecal levels of metabolites in mice. Metabolomics methods were used to screen the differential metabolites， on the basis of which the potential therapeutic mechanism of jatrorrhizine on DSS-induced ulcerative colitis in mice was investigated. ResultsAfter intervention with jatrorrhizine， the model mice showed significantly decreased DAI（P<0.05，P<0.01）， recovered colon length，（P<0.05，P<0.01） and alleviated histopathology of the colon. The metabolomics study screened out 13 differential metabolites in the serum and 8 differential metabolites in the feces. The pathway enrichment analysis predicted three potential metabolic pathways： Biosynthesis of unsaturated fatty acids， phenylalanine， tyrosine and tryptophan biosynthesis， and phenylalanine metabolism. ConclusionJatrorrhizine may treat ulcerative colitis by regulating the biosynthesis and metabolism of amino acids and the synthesis of unsaturated fatty acids.

Objective To establish a diagnostic model for depression in insomnia patients by mining polysomnography (PSG) data of insomnia patients with machine learning algorithms,and to provide a scientific basis for the diagnosis of depression in insomnia patients. Methods According to the inclusion and exclusion criteria,2162 insomnia inpatients and outpatients who attended the Inner Mongolia Autonomous Region Mental Health Center from January to December 2023 and underwent polysomnographic monitoring were included,and depression was diagnosed using the International Statistical Classification of Diseases and Related Health Problems,10th version (ICD-10). The general condition and PSG data of the patients were collected. Six algorithms—logistic regression (LR),Support vector machines (SVM),Random forest (RF),Adaptive Boosting (AdaBoost),Extreme Gradient Boosting (XGBoost) and Naive Bayes (NB)—were used to build the diagnostic model of depression in insomnia patients after the patients' general condition and PSG data were gathered. Results Among the enrolled patients with insomnia,40.1％ had comorbid depression. Among the six models,LR and RF exhibited the highest values of area under the curve (AUC) of receiver operating characteristic (ROC),at 0.825 and 0.823,respectively,indicating superior overall classification performance. Conclusion Logistic regression and random forest modeling have good diagnostic efficacy in the population of insomniacs with depression.

Objective: To describe the prevalence and the association of sleep quality trajectory, social jetlag and comorbid symptoms of anxiety and depression among college students, in order to provide a theoretical basis for improving the comorbid symptoms of anxiety and depression in college students. Methods: A questionnaire survey was conducted among 1 135 college students from two universities in Shangrao, Jiangxi Province and Hefei, Anhui Province from April to May 2019, and were followed up once every one year for a total of three times, with a valid sample size of 1 034 individuals after matching with the baseline survey. A selfassessment questionnaire was used to investigate the social jetlag of college students, the Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire 9 (PHQ-9) were used to evaluate anxiety and depression symptoms, respectively, while the Pittsburgh Sleep Quality Index (PSQI) was used to assess sleep quality. College students with GAD-7 score ≥5 and PHQ-9 score ≥5 were defined as having comorbid anxiety and depression symptoms. Latent class growth model (LCGM) was employed to analyze the sleep quality trajectory of college students, and binary Logistic regression was used to analyze the relationship between social jetlag, sleep quality trajectory and comorbid symptoms of anxiety and depression. Results: The detection rate of comorbid symptoms of anxiety and depression among college students was 16.9%, and the detection rate of social jetlag ≥2 h was 13.8%. The sleep quality showed an overall improvement trend, and the two trajectories were good sleep quality (81.6%) and poor sleep quality (18.4%). Binary Logistic regression model showed that poor sleep quality and social jetlag ≥2 h were positively correlated with comorbid symptoms of anxiety and depression (OR=5.94, 1.84, P<0.05). Conclusions Poor sleep quality and social jetlag ≥2 h in college students increase the risk of comorbid symptoms of anxiety and depression. Early screening and intervention of sleep quality and reduction of social jetlag are crucial for enhancing the mental health of college students.

Objective To establish a diagnostic model for depression in insomnia patients by mining polysomnography (PSG) data of insomnia patients with machine learning algorithms,and to provide a scientific basis for the diagnosis of depression in insomnia patients. Methods According to the inclusion and exclusion criteria,2162 insomnia inpatients and outpatients who attended the Inner Mongolia Autonomous Region Mental Health Center from January to December 2023 and underwent polysomnographic monitoring were included,and depression was diagnosed using the International Statistical Classification of Diseases and Related Health Problems,10th version (ICD-10). The general condition and PSG data of the patients were collected. Six algorithms—logistic regression (LR),Support vector machines (SVM),Random forest (RF),Adaptive Boosting (AdaBoost),Extreme Gradient Boosting (XGBoost) and Naive Bayes (NB)—were used to build the diagnostic model of depression in insomnia patients after the patients' general condition and PSG data were gathered. Results Among the enrolled patients with insomnia,40.1％ had comorbid depression. Among the six models,LR and RF exhibited the highest values of area under the curve (AUC) of receiver operating characteristic (ROC),at 0.825 and 0.823,respectively,indicating superior overall classification performance. Conclusion Logistic regression and random forest modeling have good diagnostic efficacy in the population of insomniacs with depression.

Objective: To describe the prevalence and association of sleep quality and anxiety-depression co-morbid symptoms among nursing students, in order to provide a reference basis for promoting the development of nursing students mental health. Methods: Using a prospective study design, baseline survey was conducted in January 2019 among a random cluster sample of 1 716 individuals in three medical universities in Hefei, Anhui Province, and a follow-up survey was conducted in October 2019, with a valid number of 1 573 individuals after matching with the baseline survey. The Pittsburgh Sleep Quality Index (PSQI) was used to assess nursing students sleep quality, and the Depression Anxiety Stress Scale (DASS-21) to assess the anxiety-depression comorbid symptoms. Results: The detection rates of anxiety-depression co-morbidities among nursing students at baseline and follow-up survey were 16.9% and 18.2%, respectively, and the detection rates of poor sleep quality among nursing students at baseline and follow-up survey were 10.1% and 10.3%, respectively. The results of the binary Logistic regression model showed that baseline PSQI score were positively associated with the risk of anxiety-depression co-morbid symptoms among nursing students at baseline ( OR=1.49, 95%CI =1.40-1.59) and after nine months of follow-up ( OR=1.22, 95%CI =1.16-1.28). Furthermore, the influence of baseline sleep quality on the risk of anxiety-depression co-morbid symptoms were mainly concentrated in the five dimensions of sleep time, sleep efficiency, sleep disorders, hypnotic drugs and daytime dysfunction, and such effects of sleep time, sleep disorders and daytime dysfunction still existed in the follow-up investigation. Conclusion Poor sleep quality of nursing students can increase the risk of anxiety-depression co-morbidities. Improving sleep quality of nursing students has a positive effect on improving their mental health.

Objective: To investigate the prevalence and association of hyperuricemia (HUA) and hypertriglyceridemic Waist (HTW) phenotype in children and adolescents aged 6-17 years in Inner Mongolia, providing a basis for the prevention and treatment of hyperuricemia in adolescents in Inner Mongolia. Methods: A total of 2 175 students of primary, junior high, and senior high school students from eight counties (districts) in Inner Mongolia were chosen and received a questionnaire survey, physical examination, and laboratory test by used a multi stage stratified random sampling approach. The association between the HTW phenotype and HUA was analyzed using binary Logistic regression. Results: The prevalence of the HTW phenotype was 2.1%, with boys(2.5%) higher than that of girls(1.6%) ( χ 2=14.50, P<0.05). The average SUA level of the participants was 308.00 (259.00, 371.00) mmol/L, with a statistically significant sex difference(Z=-9.87, P<0.05). The prevalence of HUA was 21.1%. The frequency of HUA in the HTW phenotype(44.4%) was higher than in other phenotypes, followed by enlarged waist (EW) phenotype. After controlling for associated variables, the EW phenotypes (OR=1.76，95%CI=1.26-2.47) and HTW phenotypes (OR=2.25, 95%CI=1.12-4.52) were associated with higher risk for HUA(P<0.05). Conclusion In Inner Mongolia, the prevalence of HUA in children and adolescents aged 6-17 years is high, and there shows a positive association between the HTW phenotype and hyperuricemia. For the prevention of hyperuricemia, more attention should be paid to children and adolescents with HTW phenotype.

Animals ; CRISPR-Cas Systems ; Gene Editing ; Humans

Hypoxia-inducible factor-1 (HIF-1) is a key transcription factor on hypoxia responses in mammalian tissues. HIF-1 plays as a positive factor in solid tumor and leads to hypoxia-driven responses that enhance its downstream gene expression for tumor growth and survival. LXY6099 was obtained by the structural modification and optimization of manassantin A (MA) as a high potent HIF-1 inhibitor. Antitumor activity of LXY6099 was observed in this study. LXY6099 with an IC50 value of 2.46 x 10(-10) mol x L(-1) showed more sensitive inhibition activity to HIF-1 than that of MA detected by reporter gene assay (> 100 folds). It showed strong inhibition on the growth of human solid tumor cell lines. Furthermore, LXY6099 exhibited significant antitumor activity against established human tumor xenografts in nu/nu mice with treatment of MX-1 breast cancer. Thus, LXY6099 as a novel HIF-1 inhibitor could be further developed into anti-cancer agents.

Objective To study the responsible genes of psoriasis vulgaris on chromosome 1q21 in Chinese Han population.Methods Thirty-six families with psoriasis vulgaris,including 92 patients and 98 normal relatives,aged from 12 to 81 years with an average age at 44 years,were enrolled in this study.Blood samples were obtained from all the participants and subjected to DNA extraction.A genome scan was performed with eight microsatellites distributing over chromosome 1q21-1q23.1.Evidence for linkage disequilibrium was assessed with extended transmission disequilibrium test(ETDT)program and Genehunter software.Results Three short tandem repeat markers were found to be associated with psoriasis vulgaris.With Genehunter,evidence for linkage disequilibrium between D1S2345 and psoriasis was found with the NPL value being 1.735(P=0.0329).Moreover,ETDT revealed that the 97-bp allele of D1S2346 and 283-bp allele of D1S484 were preferentially delivered to affected descendants(P＜0.05).Conclusion Chromosome 1q21 contains genes associated with psoriasis vulgaris in Chinese Han population.

Objective To investigate the association of SPRR2E gene and psoriasis vulgaris by sequencing the DNA of Chinese Han psoriatic families. Methods DNA was extracted from the peripheral blood cells of thirty-two Chinese psoriatic families. The sequence of the SPRR2E encoding region was measured by ABI377 DNA Sequencer. The linkage disequilibrium was assessed by extended transmission disequilibrium test (ETDT) and GENEHUNTER software. Results An A/G polymorphism at nucleotide 156 of the SPRR2E encoding region was identified. There were three genotypes, including AA, GG and AG. Although the single nucleotide polymorphism (SNP) did not change the encoding of amino acid, the single nucleotide polymorphism locus was associated with psoriasis vulgaris by the ETDT analyzing. The A-allele was found to be transmitted more frequently than that of the G-allele. GENEHUNTER analysis was concordant with the results of the ETDT. Conclusion A single nucleotide polymorphism (SNP) in SPRR2E gene encoding region is associated with psoriasis vulgaris in Han Chinese population.