Blastocystis is a zoonotic intestinal parasite that can infect a variety of animals,including humans,and can cause symptoms such as diarrhea,abdominal pain,and bloating in both humans and animals.Although blastocystosis is widespread in the world,it has not attracted enough atten-tion,and has been listed as one of the neglected diseases by the World Health Organization.Some subtypes of Blastocystis in cattle belong to zoonotic subtypes,which have the potential risk of cross-species transmission to humans and pose a potential threat to human public health security.Therefore,understanding the prevalence,subtype distribution and diagnostic methods of Blasto-cystis in cattle is of great significance for the prevention and control of blastocystosis.Based on this,this article reviewed the subtype classification,diagnostic methods,epidemic situation and public health significance of Blastocystis in cattle,which provided an important reference for the prevention and control of blastocystosis.

Blastocystis is a zoonotic intestinal parasite that can infect a variety of animals,including humans,and can cause symptoms such as diarrhea,abdominal pain,and bloating in both humans and animals.Although blastocystosis is widespread in the world,it has not attracted enough atten-tion,and has been listed as one of the neglected diseases by the World Health Organization.Some subtypes of Blastocystis in cattle belong to zoonotic subtypes,which have the potential risk of cross-species transmission to humans and pose a potential threat to human public health security.Therefore,understanding the prevalence,subtype distribution and diagnostic methods of Blasto-cystis in cattle is of great significance for the prevention and control of blastocystosis.Based on this,this article reviewed the subtype classification,diagnostic methods,epidemic situation and public health significance of Blastocystis in cattle,which provided an important reference for the prevention and control of blastocystosis.

With the vigorous development of computers and internet, the construction of the information management system for Occupational Health Technical Service (OHTS) institutions in China has achieved impressive progress. But for the management of OHTS institutions, there are relatively few systems that can fully explore and utilize OHTS information. Base on this background, in light of the actual situation of the OHTS institution in Henan Province, an OHTS Information Management System was developed under the Java Spring Boot framework, with a MySQL database and a B/S multi-tier architecture. The platform integrates a vertical three-level network of ″provincial-municipal-county/district″ and a horizontal network involving health commissions, disease prevention and control bureaus, Centers for Disease Control and Prevention (occupational disease prevention and treatment institutes), and OHTS institutions. The system includes five core modules: dynamic management of institutional and personnel qualifications, full-process project supervision (including five categories of technical services such as pre-evaluation and control-effectiveness evaluation), multidimensional decision analysis (including eight statistical indicators of institutional distribution, equipment allocation, and occupational hazard factors), rapid generation and automated submission of various reports, and early warning and intelligent supervision. The system has been implemented in 61 OHTS institutions in Henan Province, improving the ″off-site supervision rate″ of supervision department and promoting the standardization and digital transformation of occupational health services.

Objective: To systematically evaluate the influencing factors for medication compliance in patients with comorbidities of chronic diseases, so as to provide the evidence for improving medication compliance. Methods: Literature on influencing factors for medication compliance in patients with comorbidities of chronic diseases were retrived from CNKI, Wanfang Data, VIP, SinoMed, PubMed, Web of Science, Cochrane Library and Embase from inception to January 20, 2024. After independent literature screening, data extraction, and quality assessment by two researchers, a meta-analysis was performed using RevMan 5.4 and Stata 16.0 softwares. Literature were excluded one by one for sensitivity analysis. Publication bias was assessed using Egger's test. Results: Initially, 7 365 relevant articles were retrieved, and 35 of them were finally included, with a total sample size of about 150 000 individuals. There were 30 cross-sectional studies and 5 cohort studies; and 11 high-quality studies and 24 medium-quality studies. The meta-analysis showed that the demographic factors of lower level of education (OR=2.148, 95%CI: 1.711-2.696), lower economic income (OR=1.897, 95%CI: 1.589-2.264), male (OR=0.877, 95%CI: 0.782-0.985), living alone (OR=2.833, 95%CI: 1.756-4.569) and unmarried (OR=2.784, 95%CI: 1.251-6.196); the medication treatment factors of polypharmacy (OR=1.794, 95%CI: 1.190-2.706), potentially inappropriate medication (OR=2.988, 95%CI: 1.527-5.847), low frequency of daily medication (OR=0.533, 95%CI: 0.376-0.754) and adverse drug reactions (OR=3.319, 95%CI: 1.967-5.602); the disease factors of long course of disease (OR=2.118, 95%CI: 1.643-2.730), more comorbidities (OR=1.667, 95%CI: 1.143-2.431) and cognitive impairment (OR=2.007, 95%CI: 1.401-2.874); and the psychosocial factors of poor belief in taking medication (OR=1.251, 95%CI: 1.011-1.547), poor self-rated health (OR=1.990, 95%CI: 1.571-2.522) and being guided by healthcare professionals (OR=0.151, 95%CI: 0.062-0.368) were the influencing factors for medication compliance in patients with chronic comorbidities. Conclusion The medication compliance in patients with comorbidities of chronic diseases is associated with demographic factors, pharmacological factors, disease factors and psychosocial factors, mainly including living alone, adverse drug reactions, course of disease, number of comorbidities and medication beliefs.

Objective: To conduct a scoping review on prognostic prediction models for patients with comorbidity of chronic diseases, and understand modeling methods, predictive factors and predictive effect of the models, so as to provide the reference for prognostic evaluation on patients with comorbidity of chronic diseases. Methods: Literature on prognostic prediction models for patients with comorbidity of chronic diseases was collected through SinoMed, CNKI, Wanfang Data, VIP, PubMed, Embase, Cochrane Library and Web of Science published from the time of their establishment to November 1, 2023. The quality of literature was assessed using prediction model risk of bias assessment tool (PROBAST), then modeling methods, predictive factors and predictive effects were reviewed. Results: Totally 2 130 publications were retrieved, and nine publications were finally enrolled, with an overall high risk of bias. Thirteen models were involved, with three established using machine learning methods and ten established using logistic regression. The prediction results of four models were death, with main predictive factors being age, gender, body mass index (BMI), Barthel index and pressure ulcers; the prediction results of nine models were rehospitalization, with main predictive factors being age, BMI, hospitalization frequency, duration of hospital stay and hospitalization costs. Eleven models reported the area under the receiver operating characteristic curve (AUC), ranging from 0.663 to 0.991 6; two models reported the C-index, ranging from 0.64 to 0.70. Eight models performed internal validation, one model performed external validation, and four models did not reported verification methods. Conclusions The prognostic prediction models for patients with comorbidity of chronic diseases are established by logistic regression and machine learning methods with common nursing evaluation indicators, and perform well. Laboratory indicators should be considered to add in the models to further improve the predictive effects.

Objective:To summarize the best evidence of medication adherence interventions for patients with multiple chronic conditions.Methods:According to the "6S" evidence model, literature on medication adherence in patients with multiple chronic conditions was retrieved from BMJ Best Clinical Practice, UpToDate, Medlive, National Institute for Health and Clinical Excellence, Cochrane Library, Embase, PubMed, Web of Science, China Biology Medicine disc, China National Knowledge Infrastructure, WanFang data and so on. The search period was from establishing the database to August 30, 2023.Results:A total of 16 articles were included, including three guidelines, four expert consensus, seven systematic reviews, and two meta-analyses. Twenty-seven pieces of evidence were summarized from six aspects of compliance assessment, educational intervention, behavioral intervention, optimized treatment program, technical reminder intervention, and social-psychological-emotional intervention.Conclusions:The best evidence of medication adherence interventions for patients with multiple chronic conditions summarized provides a reference for medical and nursing staff to develop medication adherence interventions.

Background and purpose:Leptomeningeal metastasis is a form of central nervous system metastasis of melanoma.High mobility group A2(HMGA2)has been proven to play an important role in the occurrence and development of various tumors,but its biological functions in leptomeningeal metastatic melanoma cells remain unclear.On the basis of building mouse models of central nervous system metastasis of melanoma,this study investigated the differences in cell migration and cell proliferation among leptomeningeal metastatic melanoma cells,primary site melanoma cells and brain parenchymal metastatic melanoma cells,and further clarified the effects of differentially expressed gene HMGA2 on cell migration and proliferation of leptomeningeal metastatic melanoma cells.Methods:B16 mouse melanoma cells(B16-parental cells,B16-Par)stably expressing tdTomato and luciferase were generated by lentiviral infection.Subsequently,B16 specific brain parenchymal metastatic cells(B16-brain metastatic cells,B16-BrM)and B16 specific leptomeningeal metastatic cells(B16-leptomeningeal metastatic cells,B16-LM)were collected after adaptive screening of metastatic sites in vivo.The differences in migration and proliferation among B16-Par,B16-BrM and B16-LM were assessed by wound healing assay and cell counting kit-8(CCK-8).RNA sequencing(RNA-seq)was used to analyze differential gene expression in B16-Par,B16-BrM and B16-LM,and HMGA2 gene specifically upregulated in B16-LM was screened out.The results were verified by real-time fluorescence quantitative polymerase chain reaction(RTFQ-PCR)and Western blot.Gene ontology(GO)analysis was performed for genes which were upregulated in B16-LM specifically.siRNA was used to interfere with the expression of HMGA2 gene in B16-LM,and the knock-down effect was verified by RTFQ-PCR and Western blot.The effects of knocking down HMGA2 on cell migration and proliferation were detected by wound healing assay and CCK-8 assay.Using GSE174401 data in Gene Expression Omnibus(GEO),the specificity of HMGA2 gene expression in leptomeningeal metastatic melanoma cells from patients was verified.Results:Compared with Par cells,tumor cells screened by the brain environment were more likely to colonize the central nervous system.B16-LM had stronger migration and proliferation abilities,and upregulated the expression of HMGA2 gene.GO analysis revealed that HMGA2 was associated with many biological processes such as angiogenesis and cell proliferation.When the expression of HMGA2 gene was knocked down,the migration and proliferation of B16-LM could be inhibited.HMGA2 was upregulated in leptomeningeal metastatic melanoma cells from patients.Conclusion:Leptomeningeal metastatic melanoma cells had relatively unique cellular characteristics,which promoted cell migration and proliferation by upregulating HMGA2 gene expression.

Objective To assess the causality between 14 malignant tumors and hypertension.Methods Publicly available datasets from genome-wide association study were used,from which independent genetic variants strongly associated with hypertension and 14 malignant tumors were extracted as instrumental variables for bidirectional Mendelian randomization(MR)analysis,including random effect inverse variance weighted(IVW),simple mode,weighted median,weighted mode and MR-Egger to evaluate the causal effect.Sensitivity analysis was used to test the validity and robustness of the analytical results,and multivariate MR method was used to further control for the effects of confounding factors.Results In the MR analysis of malignant melanoma and hypertension,the study included a total of 11 single nucleotide polymorphisms(SNPs)strongly associated with malignant melanoma.After Bonferroni correction,the IVW-based results showed a causal relationship between malignant melanoma and hypertension(OR=1.67,95％CI:1.27-2.21,P＜0.001).Cochran's Q test,Mendelian randomization pleiotropy residual sum and outlier test and MR-Egger intercept test showed that there were no outliers and no horizontal pleiotropy among the instrumental variables,and the sensitivity analysis of the leave-one-out method showed that there was no single SNP that had a significant impact on the overall results.In the analysis of hypertension and leukemia,the preliminary analysis results showed that there may be a relationship between the two,but after adjusting for confounders,the effect of hypertension on the risk of leukemia was no longer significant.Conclusion Malignant melanoma may be a risk factor in the development of hypertension.

Objective: To systematically evaluate risk factors for cardiometabolic multimorbidity (CMM), so as to provide the evidence for formulating CMM prevention and control strategies. Methods: Publications pertaining to the risk factors for CMM were retrieved from databases, including SinoMed, CNKI, Wanfang Data, VIP, PubMed and Cochrane Library from inception to March 31, 2023. Meta-analysis was performed using the software RevMan 5.4 and Stata 16.0, and sensitivity analysis was performed using the leave-one-out method. The publication bias was evaluated using Egger's test. Results: Totally 494 publications were screened, and 20 publications were included in the final analysis, including 13 cohort studies (covering 1 940 000 participants) and 7 cross-sectional studies (covering 13 000 000 participants). Meta-analysis revealed that female (OR=1.54, 95%CI: 1.40-1.71), middle age (OR=3.80, 95%CI: 3.33-4.34), elderly (OR=2.82, 95%CI: 1.48-5.37), urban resident (OR=1.41, 95%CI: 1.27-1.57), higher education level (OR=2.01, 95%CI: 1.35-3.01), higher economic level (OR=1.21, 95%CI: 1.16-1.25), overweight (OR=1.92, 95%CI: 1.64-2.26), obesity (OR=3.01, 95%CI: 2.30-3.93), central obesity (OR=1.70, 95%CI: 1.12-2.56), smoking (OR=1.27, 95%CI: 1.07-1.51), alcohol consumption (OR=1.27, 95%CI: 1.01-1.59), irregular diet (OR=1.10, 95%CI: 1.02-1.18), insufficient intake of vegetables and fruits (OR=1.12, 95%CI: 1.07-1.17), lack of sleep at night (OR=1.17, 95%CI: 1.08-1.27), and depression (OR=1.50, 95%CI: 1.33-1.69) were risk factors for CMM. Sensitivity analysis of effects of central obesity and alcohol consumption were not robust. No publication bias was examined by Egger's test. Conclusions Female, middle age, elderly, urban resident, higher education level, higher economic level, overweight, obesity, central obesity, smoking, alcohol consumption, irregular diet, insufficient intake of vegetables and fruits, lack of sleep at night and depression are risk factors for CMM.