Objective To explore the shared mechanisms of genes related to three common chronic diseases non-alcoholic fatty liver disease(NAFLD),type 2 diabetes mellitus(T2DM)and atherosclerosis(AS)with ageing as well as potential therapeutic agents by using bioinformatics analysis,machine learning algorithms and molecular docking methods and techniques.Methods Ageing-related genes were collected and organized from AgeingAtlas,CellAge,GenAge,and MSigDB databases.After taking the intersection of genes related to NAFLD,T2DM and AS obtained from databases such as CTD,DisGeNET,GeneCards,OMIM,PharmGKB,and TTD and the gene sets obtained based on the GEO differential gene analysis,we obtained the set of related disease genes for these three common chronic diseases.The KEGG pathway enrichment analysis was performed on the ageing gene set and the three disease-related gene sets using the clusterProfiler package,and the intersection was taken.The enriched genes in the KEGG pathway were merged and imported into the STRING database;the PPI network was constructed.We analyzed the core sub-modules in the PPI network using the MCODE tool and calculated the essential values of Nim and Cim for each node and module.Meanwhile,three machine learning models were used to screen the feature genes:the Lasso regression model,the Boruta algorithm,and the random forest model.The HIT2.0 database was utilized to find the targeted TCM small molecules related to the key feature genes.Small molecules were evaluated and analyzed by ADMET using SwissADME and ADMETlab 3.0 online system.The molecular docking method was utilized to dock the key action targets and screen small molecules.Results A total of 1 325,616,78,and 597 genes related to ageing,NAFLD,T2DM,and AS were obtained.The KEGG pathway enrichment analysis results for ageing and the three diseases were taken to intersect to get two shared intersecting pathways containing 243 genes.The PPI network was constructed,and Cluster 2 had the highest Cim value among the three core submodules.According to the results of signature gene screening,combined with PPI network module analysis results,four signature genes related to ageing were found:CDK6,CDKN1A,MYC,and PTEN.These four targets have 94 potential TCM small molecule candidates,among which resveratrol(RSV)is a TCM small molecule common to these four targets.The ADMET evaluation showed that it had good drug-forming properties.The PTEN target had a high Nim value,and molecular docking of RSV with PTEN showed good binding stability.Conclusion A potential herbal small molecule,RSV,was identified from the perspective of ageing,which may prevent and treat three common chronic diseases,namely,NAFLD,T2DM and AS,by regulating the key gene PTEN.

Objective Jiedu Tongluo Tiaogan Formula(JTTF)is an effective formula for the clinical treatment of type 2 diabetes mellitus(T2DM).We used integrated pharmacology and in vitro experiments to explore the molecular mechanism of JTTF to improve insulin resistance(IR)in T2DM.Methods The drug targets of JTTF were obtained by identifying the key active ingredients of JTTF through UPLC-Q-TOF-MS.Multiple databases such as GeneCards,OMIM,and DrugBank were used to screen T2DM-IR related targets.Cytoscape software and String 11.0 database were used to construct the PPI network diagram of JTTF for T2DM-IR.GO and KEGG analyses were performed according to the Metascape platform to find the biological pathways related to the target proteins.AutoDock Tools software was used to simulate molecular docking.In vitro experiments were performed using palmitic acid(PA)-induced HepG2-IR cell model to detect the effect of JTTF on HepG2-IR.Results 28 effective active components of JTTF were screened.There were 857 gene targets of T2DM-IR,and 168 targets of drug-disease intersection.387 GO entries and 145 KEGG pathways were enriched.The molecular docking results showed that the main components of JTTF had good binding activities with PI3K and AKT-related proteins.The in vitro results showed that JTTF significantly alleviated PA-induced HepG2 cell injury,increased HepG2 glucose consumption,increased PI3K and AKT mRNA and protein expression,regulated the expression of GLUT2,GLUT4 and GSK3β,and improved cellular IR.Conclusion JTTF increases insulin sensitivity of HepG2-IR cells,promotes glucose uptake and intracellular glucose metabolism process,and its mechanism of action may be related to the up-regulation of PI3K/AKT signalling pathway.

Objective To investigate the relationship between the expression of serum long non coding RNA nuclear-enriched abundant transcript 1(lncRNA NEAT1)and microRNA-106a-5p(miR-106a-5p)and the severity and prognosis of sepsis patients.Methods A total of 185 patients with sepsis who were diagnosed and treated in this hospital from August 2021 to July 2024 were selected as the study group,and another 215 healthy volunteers who underwent physical examinations in this hospital during the same period were selected as the control group.The research group was divided into the mild group(n=92),the severe group(n=58)and the shock group(n=35)according to the severity of the disease.The research group was divided into the good prognosis group(n=121)and the poor prognosis group(n=64)according to the prognosis.The expres-sions of lncRNA NEAT1 and miR-106a-5p were detected by real-time fluorescence quantitative polymerase chain reaction,and the correlation of the expressions of lncRNA NEAT1 and miR-106a-5p in the study group was analyzed by Pearson correlation analysis.The binding sites of lncRNA NEAT1 and miR-106a-5p were an-alyzed by StarBase,and the influencing factors of poor prognosis in patients were analyzed by multivariate Lo-gistic regression.The receiver operating characteristic curve was used to analyze the diagnostic value of the combination of lncRNA NEAT1 and miR-106a-5p for poor prognosis in patients.Results The expression of lncRNA NEAT1 in the study group was higher than that in the control group,and the expression of miR-106a-5p was lower than that in the control group,the difference was statistically significant(P＜0.05).The expressions of lncRNA NEAT1 and miR-106a-5p in the study group were negatively correlated(r=-0.413,P＜0.001),and there were binding sites between the two.With the increase of the severity of sepsis,the ex-pression of lncRNA NEAT1 increased and the expression of miR-106a-5p decreased,and the difference was statistically significant(P＜0.05).The acute physiology and chronic health evaluation Ⅱ(APACHE Ⅱ)score,sequential organ failure assessment(SOFA)score,tumor necrosis factor-α,C-reactive protein,and ln-cRNA NEAT1 in the poor prognosis group were higher than those in the good prognosis group,while the ex-pression of miR-106a-5p was lower than that in the good prognosis group,the difference was statistically sig-nificant(P＜0.05).Elevated APACHE Ⅱ score,SOFA score and lncRNA NEAT1 were independent risk fac-tors for poor prognosis in patients,and miR-106a-5p was an independent protective factor(P＜0.05).The ar-ea under the curve of the combined diagnosis of the two was 0.918(95％CI:0.868-0.953),which was supe-rior to the separate diagnosis of each(Zcombined with lncRNA NEAT1=4.112,Zcombined with miR-106a-5p=4.023,P＜0.05).Con-clusion With the increase of disease severity in patients with sepsis,the expression of lncRNA NEAT1 in-creases and the expression of miR-106a-5p decreases.The combined diagnosis of the two for poor prognosis in patients has a high clinical value.

Objective Jiedu Tongluo Tiaogan Formula(JTTF)is an effective formula for the clinical treatment of type 2 diabetes mellitus(T2DM).We used integrated pharmacology and in vitro experiments to explore the molecular mechanism of JTTF to improve insulin resistance(IR)in T2DM.Methods The drug targets of JTTF were obtained by identifying the key active ingredients of JTTF through UPLC-Q-TOF-MS.Multiple databases such as GeneCards,OMIM,and DrugBank were used to screen T2DM-IR related targets.Cytoscape software and String 11.0 database were used to construct the PPI network diagram of JTTF for T2DM-IR.GO and KEGG analyses were performed according to the Metascape platform to find the biological pathways related to the target proteins.AutoDock Tools software was used to simulate molecular docking.In vitro experiments were performed using palmitic acid(PA)-induced HepG2-IR cell model to detect the effect of JTTF on HepG2-IR.Results 28 effective active components of JTTF were screened.There were 857 gene targets of T2DM-IR,and 168 targets of drug-disease intersection.387 GO entries and 145 KEGG pathways were enriched.The molecular docking results showed that the main components of JTTF had good binding activities with PI3K and AKT-related proteins.The in vitro results showed that JTTF significantly alleviated PA-induced HepG2 cell injury,increased HepG2 glucose consumption,increased PI3K and AKT mRNA and protein expression,regulated the expression of GLUT2,GLUT4 and GSK3β,and improved cellular IR.Conclusion JTTF increases insulin sensitivity of HepG2-IR cells,promotes glucose uptake and intracellular glucose metabolism process,and its mechanism of action may be related to the up-regulation of PI3K/AKT signalling pathway.

Objective To explore the shared mechanisms of genes related to three common chronic diseases non-alcoholic fatty liver disease(NAFLD),type 2 diabetes mellitus(T2DM)and atherosclerosis(AS)with ageing as well as potential therapeutic agents by using bioinformatics analysis,machine learning algorithms and molecular docking methods and techniques.Methods Ageing-related genes were collected and organized from AgeingAtlas,CellAge,GenAge,and MSigDB databases.After taking the intersection of genes related to NAFLD,T2DM and AS obtained from databases such as CTD,DisGeNET,GeneCards,OMIM,PharmGKB,and TTD and the gene sets obtained based on the GEO differential gene analysis,we obtained the set of related disease genes for these three common chronic diseases.The KEGG pathway enrichment analysis was performed on the ageing gene set and the three disease-related gene sets using the clusterProfiler package,and the intersection was taken.The enriched genes in the KEGG pathway were merged and imported into the STRING database;the PPI network was constructed.We analyzed the core sub-modules in the PPI network using the MCODE tool and calculated the essential values of Nim and Cim for each node and module.Meanwhile,three machine learning models were used to screen the feature genes:the Lasso regression model,the Boruta algorithm,and the random forest model.The HIT2.0 database was utilized to find the targeted TCM small molecules related to the key feature genes.Small molecules were evaluated and analyzed by ADMET using SwissADME and ADMETlab 3.0 online system.The molecular docking method was utilized to dock the key action targets and screen small molecules.Results A total of 1 325,616,78,and 597 genes related to ageing,NAFLD,T2DM,and AS were obtained.The KEGG pathway enrichment analysis results for ageing and the three diseases were taken to intersect to get two shared intersecting pathways containing 243 genes.The PPI network was constructed,and Cluster 2 had the highest Cim value among the three core submodules.According to the results of signature gene screening,combined with PPI network module analysis results,four signature genes related to ageing were found:CDK6,CDKN1A,MYC,and PTEN.These four targets have 94 potential TCM small molecule candidates,among which resveratrol(RSV)is a TCM small molecule common to these four targets.The ADMET evaluation showed that it had good drug-forming properties.The PTEN target had a high Nim value,and molecular docking of RSV with PTEN showed good binding stability.Conclusion A potential herbal small molecule,RSV,was identified from the perspective of ageing,which may prevent and treat three common chronic diseases,namely,NAFLD,T2DM and AS,by regulating the key gene PTEN.

Objective To identify potential biomarkers associated with LN,with the goal of improving early diagnosis,disease monitoring,and the development of more precise treatment strategies.Methods Gene expression data were downloaded from the Gene Expression Omnibus(GEO)database for datasets GSE22221,GSE112943,GSE99967,and GSE32591.Intersecting genes were obtained through the application of weighted gene co-expression network analysis(WGCNA)and linear models for microarray data(LIMMA).Subsequently,biological function and pathway analyses were conducted on these intersecting genes using Gene Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG).Next,protein-protein interaction(PPI)network analysis was performed,and hub genes highly associated with LN were identified using the CytoHubba algorithm,support vector machine(SVM),and random forest(RF)methods.Receiver operating characteristic(ROC)analysis was performed,and three potential biomarkers were validated using the GSE72798 dataset.Results The green-yellow module(P=7.4e-40)and the cyan module(P=1.5e-14)were identified through WGCNA analysis.A total of 193 differentially expressed genes were identified using LIMMA,with 113 intersecting genes related to LN being identified.GO and KEGG analyses indicated that these genes were mainly enriched in viral or bacterial defense,type I interferon signaling pathway,neutrophil-mediated immunity,and Toll-like receptor signaling.MX1,IFI44,and STAT1 were identified as hub genes using CytoHubba,SVM,and RF methods,with AUC values of 0.874,0.879,and 0.833,respectively.Validation using the GSE72798 dataset demonstrated that the expression of MX1,IFI44,and STAT1 was significantly higher in LN patients compared to healthy individuals(P＜0.001 for all).Conclusion MX1,IFI44,and STAT1 play crucial roles in the pathogenesis of LN and may serve as important biomarkers and potential therapeutic targets for LN.

Objective To investigate the awareness of malaria-related knowledge, the use of mosquito nets and their influencing factors among residents in Banlao Township, Cangyuan County, Yunnan Province.. Methods In August 2020, 19 settlement sites in Banlao Township, Cangyuan County, Lincang City, Yunnan Province were selected as study areas, and permanent residents at ages of 10 years and older were enrolled for a questionnaire survey, including residents’ demographics, family economic status, malaria control knowledge and use of mosquito nets. In addition, the factors affecting the use of mosquito nets in the night prior to the survey were identified using multivariate logistic regression analysis. Results A total of 320 questionnaires were allocated, and all were recovered (a 100% recovery rate). There were 316 valid questionnaires, with an effective recovery rate of 98.75%. The 316 respondents included 152 men and 164 women and 250 Chinese respondents and 66 foreign respondents. The awareness of clinical syndromes of malaria was significantly higher among Chinese residents (71.60%) than among foreign residents (50.00%) (χ² = 11.03, P < 0.01), and the proportions of Chinese and foreign residents sleeping under mosquito nets were 46.00% and 69.70% on the night prior to the survey, respectively (χ² = 11.73, P < 0.01). Multivariate logistic regression analysis identified ethnicity group and type of residence as factors affecting the use of mosquito nets in the night prior to the survey. Conclusions The awareness of malaria control knowledge, the coverage and the use of mosquito nets were low among residents in Banlao Township, Cangyuan County, Yunnan Province. Targeted health education is recommended to improve the awareness of malaria control knowledge and self-protection ability. In addition, improving the allocation of long-lasting mosquito nets and health education pertaining to their uses and increasing the proportion of using mosquito nets correctly is needed to prevent re-establishment of imported malaria.

In order to investigate the molecular mechanism of silk/threonine protein kinase (STK)-mediated blue light response in the algal Chlamydomonas reinhardtii, phenotype identification and transcriptome analysis were conducted for C. reinhardtii STK mutant strain crstk11 (with an AphvIII box reverse insertion in stk11 gene coding region) under blue light stress. Phenotypic examination showed that under normal light (white light), there was a slight difference in growth and pigment contents between the wild-type strain CC5325 and the mutant strain crstk11. Blue light inhibited the growth and chlorophyll synthesis in crstk11 cells, but significantly promoted the accumulation of carotenoids in crstk11. Transcriptome analysis showed that 860 differential expression genes (DEG) (559 up-regulated and 301 down-regulated) were detected in mutant (STK4) vs. wild type (WT4) upon treatment under high intensity blue light for 4 days. After being treated under high intensity blue light for 8 days, a total of 1 088 DEGs (468 upregulated and 620 downregulated) were obtained in STK8 vs. WT8. KEGG enrichment analysis revealed that compared to CC5325, the crstk11 blue light responsive genes were mainly involved in catalytic activity of intracellular photosynthesis, carbon metabolism, and pigment synthesis. Among them, upregulated genes included psaA, psaB, and psaC, psbA, psbB, psbC, psbD, psbH, and L, petA, petB, and petD, as well as genes encoding ATP synthase α, β and c subunits. Downregulated genes included petF and petJ. The present study uncovered that the protein kinase CrSTK11 of C. reinhardtii may participate in the blue light response of algal cells by mediating photosynthesis as well as pigment and carbon metabolism, providing new knowledge for in-depth analysis of the mechanism of light stress resistance in the algae.
Chlamydomonas reinhardtii/genetics* ; Photosynthesis/genetics* ; Plants/metabolism* ; Protein Kinases ; Threonine/metabolism* ; Carbon/metabolism* ; Serine/metabolism*

WRKY is a superfamily of plant-specific transcription factors, playing a critical regulatory role in multiple biological processes such as plant growth and development, metabolism, and responses to biotic and abiotic stresses. Although WRKY genes have been characterized in a variety of higher plants, little is known about them in eukaryotic algae, which are close to higher plants in evolution. To fully characterize algal WRKY family members, we carried out multiple sequence alignment, phylogenetic analysis, and conserved domain prediction to identify the WRKY genes in the genomes of 30 algal species. A total of 24 WRKY members were identified in Chlorophyta, whereas no WRKY member was detected in Rhodophyta, Glaucophyta, or Bacillariophyta. The 24 WRKY members were classified into Ⅰ, Ⅱa, Ⅱb and R groups, with a conserved heptapeptide domain WRKYGQ(E/A/H/N)K and a zinc finger motif C-X4-5-C-X22-23-H-X-H. Haematococcus pluvialis, a high producer of natural astaxanthin, contained two WRKY members (HaeWRKY-1 and HaeWRKY-2). Furthermore, the coding sequences of HaeWRKY-1 and HaeWRKY-2 genes were cloned and then inserted into prokaryotic expression vector. The recombinant vectors were induced to express in Escherichia coli BL21(DE3) cells and the fusion proteins were purified by Ni-NTA affinity chromatography. HaeWRKY-1 had significantly higher expression level than HaeWRKY-2 in H. pluvialis cultured under normal conditions. High light stress significantly up-regulated the expression of HaeWRKY-1 while down-regulated that of HaeWRKY-2. The promoters of HaeWRKY genes contained multiple cis-elements responsive to light, ethylene, ABA, and stresses. Particularly, the promoter of HaeWRKY-2 contained no W-box specific for WRKY binding. However, the W-box was detected in the promoters of HaeWRKY-1 and the key enzyme genes HaeBKT (β-carotene ketolase) and HaePSY (phytoene synthase) responsible for astaxanthin biosynthesis. Considering these findings and the research progress in the related fields, we hypothesized that the low expression of HaeWRKY-2 under high light stress may lead to the up-regulation of HaeWRKY-1 expression. HaeWRKY-1 may then up-regulate the expression of the key genes (HaeBKT, HaePSY, etc.) for astaxanthin biosynthesis, consequently promoting astaxanthin enrichment in algal cells. The findings provide new insights into further analysis of the regulatory mechanism of astaxanthin biosynthesis and high light stress response of H. pluvialis.
Eukaryota ; Gene Expression Regulation, Plant ; Phylogeny ; Plant Proteins/metabolism* ; Plants/metabolism* ; Stress, Physiological/genetics* ; Transcription Factors/metabolism*

Objective:To investigate the complication and compliance of different supine time and the degree of obedience in children with leukemia after intrathecal chemotherapy.Methods:A total of 553 children with leukemia after intrathecal chemotherapy from April 1, 2017 to March 28, 2019 in Beijing Children′s Hospital Affiliated to Capital Medical University were selected. Children who received intrathecal injection from April 1, 2017 to December 31, 2017 were selected as control group(274 cases), from January 1, 2018 to March 28, 2019 were selected as research group(279 cases). The children in research group were supine 2 hours but control group were supine 4 hours after intrathecal. The complications that occurred after injection and within one week after injection and compliance were observed.Results:Finally, 457 children included in this study, there were 235 cases in research group and 222 cases in control group. The number of cases of limb numbness and local pressure reddening in the research group and the control group were 18, 20 and 31, 34, respectively, with statistically significant differences between the two groups( χ2 values were 4.74, 5.07, P<0.05). After intrathecal injection, the patients' compliance with the time of supine removal was completely acceptable to the research group, relatively acceptable, acceptable, and required efforts to adhere to the number of cases were 65, 83, 42, 45, respectively, while the control group were 34, 50, 76, 62, respectively, the differences were significant ( χ2 value was 30.04, P<0.05). Conclusions:Supine for 2 hours after intrathecal injection can reduce the incidence of complications and improve compliance, which is safe and feasible. So, supine for 2 hours after intrathecal injection is recommended.