Spontaneous intracerebral hemorrhage (ICH) usually occurs in the basal ganglia and is highly lethal and disabling. The aim of this study was to evaluate the predictors of 30-day mortality in patients with severe spontaneous basal ganglia hemorrhage.This retrospective study included patients with severe basal ganglia intracerebral hemorrhage treated in the Third Affiliated Hospital of Soochow University from 2012 to 2018. Demographic, clinical, laboratory and neuroradiological data were collected. The short-term prognosis was evaluated and divided into death within 30-days and survival over 30-days. We studied the factors affecting the prognosis of patients with severe intracerebral hemorrhage, analyzed the parameters related to neutrophil-to-lymphocyte (NLR) at admission, and evaluated the predictive effect of NLR on 30-day mortality.A total of 105 patients was included in this retrospective study. The 30-day death group had a larger hematoma, a higher probability of ventricular hemorrhage, a higher ICH score and a lower Glasgow Coma Scale (GCS) score on admission. Meanwhile, the patients in the death group had higher White blood cells (WBC) counts, neutrophil counts, NLRs and C-reactive protein (CRP) levels. The risk factors for 30-day death were related to the ICH volume, GCS score, ICH score, WBC count, neutrophil count, NLR and CRP. The univariate receiver operating characteristic (ROC) curve of the risk factors showed that the NLR had the best prediction performance. Mathematical predictive models for ICH patients showed that the model with NLR had better prediction accuracy.The NLR is expected to be a potential biomarker for predicting the prognosis of patients with severe basal ganglia hemorrhage. As a de3) , reliabl3). Furthermore, the existing literatures roughly included patients with ICH into the study, and did not distinguish the location. It is well known that the location and volume of bleeding are important factors affecting the prognosis of patients.The ratio of the neutrophil count and lymphocyte count in the peripheral blood is known as the NLR, and it can dynamically monitor the body's immune ability and systemic inflammatory state . Its abi3), in order to investigate the predictive role of NLR in this highly fatal and disabling stroke.In the current study, we limited the bleeding location, hematoma volume, and admission status of patients, and only included patients with severe cerebral hemorrhage located in the basal ganglia  or up to 30-days (n = 11).Demographic information, medical histories, GCS and head CT scan results were retrieved from the EMR system. The ICH score was calculated according to the clinical data and CT results . All patt-tests. Categorical variables were expressed as percentages, and the chi-square test was used. To compare the diagnostic efficiency of different parameters, the receiver operating characteristic curve (ROC) and area under the curve (AUC) were analyzed. The diagnostic sensitivity, specificity and accuracy of each variable were calculated, and the optimal cutoff value was determined by the Youden index. Univariate and multivariate logistic regression analyses were performed to detect the correlation between the two variables, and odds ratios (ORs) were calculated to establish the logistic model. ROC and Youden index analyses were performed to establish the best mathematical model for the prediction of ICH patients. The ROCs of all parameters and models were compared by EmpowerStats software. All statistics were tested by a two-sided test, and p < 0.05 was considered statistically significant.SPSS software version 25.0 was used for the data analysis. Continuous normally distributed variables are presented as the mean ± SD and were analyzed with independent sample p = 0.006), a higher probability of ventricular hemorrhage , a higher ICH score on admission , and a lower GCS score  (p = 0.002), neutrophil count , NLR  and C-reactive protein level  in their peripheral blood samples, and the differences were significant  died within 30-days after onset. The patients were classified into different groups according to whether they died within 30-days after onset. The demographic data, clinical characteristics and laboratory parameters of the two groups at admission are shown in = 0.003) . Meanwhinificant .p = 0.062) but were related to the hematoma volume (p = 0.012), GCS score (p = 0.005), ICH score (p = 0.029), WBC count (p = 0.010), neutrophil count (p = 0.011), NLR (p = 0.001) and CRP level (p = 0.018)  . These r= 0.018) .To compare the diagnostic efficacy of risk indicators, we analyzed the univariate ROC curve of ICH volume, GCS score, ICH score, WBC count, neutrophil count, NLR and CRP level . The AUCp = 0.0270).We established a mathematical predictive model of ICH to further conduct multivariate logistic regression analyses. Death within 30-days was set as the dependent variable and ICH volume, GCS score, ICH score, WBC count, neutrophil count, and CRP level as the independent variables. The equation of ICH prediction Model 1 was −4.90143 + 0.01894 × (ICH volume) −0.22312 × (WBC) + 0.36191 ×  + 0.01277 × (CRP) + 0.74191 × (ICH Score) −0.38027 × (GCS score) . Then, tStroke is mainly divided into ICH and ischemic stroke. A study from bigdata observatory platform for stroke of China showed that the ICH group had the highest rate of in-hospital mortality . Meanwhile, the 1-year fatality and disability rates of patients with ICH were also higher than those of patients with ischemic stroke . Spontan3) and explored the relationship between NLR in peripheral blood routine at admission and 30-day mortality. Briefly, the NLR at admission is positively correlated with the 30-day mortality. To improve our understanding of this dangerous disease and improve the accuracy of disease prediction, we carried out univariate and multivariate logistic regression analyses and established mathematical predictive models. The univariate ROC analysis showed that the ICH volume, GCS, ICH score, WBC, neutrophil count and NLR could be used to predict the 30-day mortality in patients with severe basal ganglia ICH, and NLR was the most reliable. Further construction of the combined predictive model showed that compared with Model 1, predictive Model 2, which was composed of the NLR at admission, was superior to the single NLR and Model 1, and the specificity and accuracy were obviously improved. In conclusion, the NLR is expected to be a potential biomarker for predicting the prognosis of patients with severe basal ganglia hemorrhage.Inflammatory responses are widely involved in the process of a variety of diseases and can even be used as an independent risk factor for prognosis –28. AfteSome shortcomings were observed in this study. First, this was a retrospective study in which patients were selected from a single center; thus, the screening may be biased. Second, although inflammatory factors may be associated with the enlargement of hematoma and the aggravation of brain edema , 37, 38,Basal ganglia intracerebral hemorrhage is a type of stroke with high mortality and disability rate, and has a trend of youth, which brings heavy burden to society and families. Therefore, how to quickly, accurately and economically identify the risk factors of patients with cerebral hemorrhage is particularly important, which will help to provide reference for follow-up treatment. It is known that the neuroinflammatory reaction triggered by cerebral hemorrhage will lead to a series of reactions and affect the prognosis of patients . NeutropThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.This study was approved by the Ethics Committee of The Third Affiliated Hospital of Soochow University. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from the patient for anonymized information to be published in this study.JS was a major contributor to design the study, analyze the data, and draft the manuscript. YL and LW collected and organized the patients' data. WX was patient management. WG participated in the design of the study and coordination of the whole work. All authors read and approved the final manuscript.


The prognosis of gastric cancer (GC) is difficult to predict due to the disease's complex genetic and phenotypic characteristics. MUC16 has been reported to be involved in the progression of several tumors. In this study, we aimed to explore whether MUC16 mutation had any impact on the prognosis or treatments of GC patients. Additionally, this analysis uncovered possible critical pathways related with these systems. On the cBioPortal, we were able to locate the pertinent data of patients with MUC16 mutations. And then, GSEA analysis identified differences in mRNA levels between mutant and wild-type MUC16 patients in terms of biological function annotation and pathways. The KEGG and GO analyses were also performed using the differentially expressed genes (DEGs). There were 139 individuals with GC who had the MUC16 mutation, which accounts for 32 percent, and the remaining patients had the MUC16 wild type. Survival assays revealed that patients with the MUC16 mutation had longer overall survival and disease-free survival. GSEA analysis revealed that cell cycle, cysteine and methionine metabolism, Huntington's disease, one carbon pool by folate, pyrimidine metabolism, pyruvate metabolism, RNA degradation, spliceosome, and valine leucine and isoleucine degradation were distinctly enriched in patients with MUC16 mutation type. Moreover, we identified 323 DEGs. Among them, 162 genes were upregulated, and 161 genes were downregulated. GO and KEGG assays indicated DEGs as enriched in pancreatic secretion, neuroactive ligand-receptor interaction, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism. Overall, our data revealed that the MUC16 mutation in GC may affect the development of patients by altering several genes and pathways, indicating the importance of MUC16 mutation in the treatments of GC on an individual basis. Gastric cancer (GC) is the most common malignant tumor of the digestive system . DespiteGrowing studies have confirmed that gene mutations are very important in disease program . Chromoshttps://portal.gdc.cancer.gov/), an RNA-Seq dataset of GC was obtained, together with the clinical characteristics that corresponded to it. The website of the cBioPortal for Cancer Genomics was searched in order to collect the corresponding information associated to patients who had the MUC16 mutation .From the database of the Cancer Genome Atlas (TCGA)  of less than 0.25.MUC16 mutation and wild-type patients were compared using GSEA v3.0 to find differences in gene mRNA expressions of biological functions, which allowed us to better comprehend the effects of the MUC16 mutation on various biological function gene sets in the GC patient population . It was P value and the FDR were less than 0.05. The DEGs were applied for subsequent bioinformatics research.An R package called EdgeR, which is used for analyzing abnormal expressions of RNA-Seq count data, was applied in accordance with the user's guide in order to screen DEG between patients with the MUC16 mutation and those with the wild-type form of GC. The following criteria were used to identify DEGs: |fold change (FC)| greater than 2; both the http://org.Hs.eg.db” package to convert gene symbols into Entrez IDs, and then, we used the “cluster Profiler,” “ggplot2,” and “enrich plot” packages to do pathway enrichment analysis on the DEGs based on the GO database and KEGG. The assays were performed on the DEGs. After applying the FDR approach, the P values were recalculated, and significantly enriched pathways were determined to have an FDR of 0.25 or lower.We used the “t-test was applied to compare the MUC16 expressions between the MUC16 mutation and wild-type GC tissues. Kaplan-Meier plots and log rank tests were used for survival analysis. Controlling the FDR in edgeR and GSEA required an adjustment for multiple testing using the Benjamini-Hochberg method, respectively. All statistical analyses were performed in RStudio (version 3.6.8), and we considered a P value <0.05 to be statistically significant.The Student's We collected the information for 441 GC specimens and nontumor specimens RNA-Seq datasets from the TCGA database. These datasets included complete follow-up data. There were 139 individuals with GC who had the MUC16 mutation, which accounts for 32 percent, and the remaining patients had the MUC16 wild type . AmplifiP > 0.244, P = 4.380e − 3) and disease-free survival | more than 2.0 and ed above . The heaEnrichment studies for the GO and KEGG pathways were carried out in order to investigate the functional properties of the DEGs. In BP, the terms were mainly associated with epidermis development, keratinocyte differentiation, epidermal cell differentiation, skin development, and keratinization . In CC, The general outcome of advanced GC is quite poor, and only a few number of molecular targets have been demonstrated to be useful for GC , 23. In MUC16 is a unique glycoprotein, and its expression can be found in the cell membrane and a soluble form . As a biRegarding the mechanisms, we performed an analysis on the RNA-Seq dataset of GC that was retrieved from TCGA in order to determine the important pathways and genes that are linked with the MUC16 mutation. These analyses were involved in the application of bioinformatics. GSEA analysis in the present study suggested that cell cycle, cysteine and methionine metabolism, Huntington's disease, one carbon pool by folate, pyrimidine metabolism, pyruvate metabolism, RNA degradation, spliceosome and valine leucine, and isoleucine degradation were significantly enriched. Our findings revealed that the MUC16 mutation may suppress GC progression via regulating several pathways in cell cycle, RNA degradation, and metabolism.We identified the DEGs in order to conduct further research into the pathways and genes that were involved in the MUC16 mutation. There were a total of 323 genes that were found to be DEGs. GO assays revealed that 323 genes were involved in epidermis development, keratinocyte differentiation, epidermal cell differentiation, secretory granule lumen, cytoplasmic vesicle lumen, vesicle lumen, endopeptidase activity, and serine hydrolase activity. The results of KEGG assays revealed that the most distinctly enriched biological processes included pancreatic secretion, neuroactive ligand-receptor interaction, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism. Our findings provided evidence that the MUC16 mutation plays a role in the progression of cells and sheds light on a potentially effective therapeutic target in patients who carry the MUC16 mutation, which can be used to form tailored treatment recommendations.In conclusion, we demonstrated that the MUC16 mutation was related with the clinical outcomes of GC patients. Our research identified the probable pathways and key genes that were related to the MUC16 mutation in GC, which could potentially contribute to the development of therapeutic methods and predictive and prognostic tools for this particular subgroup of patients suffering from GC.


P = 0.032). We further found the loneliness group had a higher level of depression (P < 0.001) and anxiety (P < 0.001), but lower level of HRQoL (P < 0.001) than the non-loneliness group. We found there was a positive linear correlation between social support and HRQoL among the enrolled PLWHIVs . A structural equation model (SEM) was established to evaluate whether the loneliness played as a mediation role between social support and HRQoL. The model showed loneliness as a mediation from social support leading to a decrease of HRQoL. Our findings showed a potential psychological pathway from social support to HRQoL, suggesting the need for interventions focusing on social support may improve poor HRQoL lead by loneliness.This study focused on the mental health of people living with HIV(PLWHIV) and explored their relationship between loneliness and perceived social support, health related quality of life (HRQoL) with a method of structural equation model. We collected clinical and psychological data from consecutively enrolled PLWHIV. A total of 201 PLWHIVs were enrolled and measured with self-reporting survey instruments of UCLA Loneliness Scale, Self-Rating Depression Scale, Self-Rating Anxiety Scale, Social Support Ratio Scale and Short Form Health Survey-36. The levels of loneliness, depression, anxiety, perceived social support and HRQoL were assessed. PLWHIV enrolled were divided into two groups of loneliness and non-loneliness based on their UCLA Loneliness Scale scores. Multivariable analysis indicated that being married is a protective factor associated with loneliness (OR = 0.226;  With the widespread use of effective antiretroviral therapy (ART), great progress has been made in increasing the life expectancy of people living with HIV (PLWHIV) . HIV infHowever, the specific mechanism of how perceived social support leading to disorientating HRQoL still remains unknown. Recent studies , 16 indiTherefore, our study aimed to investigate the potential mediation mechanism of loneliness between social support and HRQoL in PLWHIV. The risk factors associated with loneliness in PLWHIV and the relationship between loneliness and depression, anxiety, HRQoL were also evaluated. The results of this study will help to understand the role that loneliness played in mental health perspectives and reveal a potential intervention target to improve HRQoL of PLWHIV.Participants were recruited from a cohort regularly followed up in Nanfang Hospital, Southern Medical University with a method of randomized sampling. Eligible patients were (1) adults (≥18 years of age), (2) those confirmed to have HIV infection, and (3) had no underlying medical conditions that can interfere with comprehension of questionnaire content. Signed informed consent was obtained from all patients enrolled. All the patients completed the questionnaire surveys in a quiet room without any interference or disruptions. The institutional review board of the Nanfang Hospital has approved the study. A total of 201 patients were enrolled in our study and divided into loneliness and non-loneliness groups based on a UCLA Loneliness Scale cutoff value of 44 points . There wCD4+/CD8+ T lymphocyte counts were determined with a flow cytometer. HIV RNA was detected by polymerase chain reaction. Demographic and epidemiological information including age, gender, marriage, education, income, fasting, short sleep duration, fitness, alcohol consumption and binge drinking were also collected. Education level was defined as follows: primary, if patients received < 9 years of education, and senior, if patients received more than 9 years of education. Income level was defined as follows: high, if the income was more than 240,000 RMB per year, and low, if the income was < 240,000 RMB per year.Loneliness was measured with the UCLA Loneliness Scale Version 3, which consists of 20 questions with answer choices of “Never” (1 point) to “Often” (4 points), with total scores ranging from 20 to 80 . The higDepression was measured with the Self-Rating Depression Scale (SDS), which was designed by Duke University psychiatrist William W. K. Zung, MD, to assess the level of depression in patients diagnosed with depressive disorder . This quAnxiety was measured with the Self-Rating Anxiety Scale (SAS) . The SASSocial support was assessed with the Social Support Ratio Scale (SSRS), which was designed by Xiao . Social Health-related quality of life (HRQoL) was assessed using the MOS  short form health survey (SF-36) . The SF-t-test were used to determine whether the results were significantly different. Univariate and multivariate logistic regression analysis to determine factors related to loneliness among the PLWHIV. Simple linear regression was also used to determine the relationship between UCLA loneliness scale scores and SAS scores, SDS scores, and SF-36 scores which was performed by Pearson's correlation. The significance level was set as P < 0.05 (two-tailed). Data analysis and quality control procedures were performed using SPSS 26.0 . To further determine the latent mechanism of loneliness in the association between social support and HRQoL, we performed a path analysis with AMOS 25.0 version module (IBM). This was presented by a structural equation model . Standardized regression weights (β coefficients) were reported with their P-values. CMIN (chi-squared test), CMIN/DF, GFI (Goodness of Fit Index), NFI (Normed Fit Index), IFI , TLI (Tucker-Lewis's index), CFI (Comparative fit index) and RMSEA (Root Mean Squared Error of Approximation) were used to assess the model fit. CMIN/DF between 1 and 3, GFI, NFI, IFI, TLI, and CFI of > 0.95, and RMSEA < 0.05 indicate good model fit. HOELTER > 200 indicated an adequate sample size.In our study, we used the mean ± standard deviation, and categorical variables were used to express variables when appropriate. The chi-square test and P = 0.020).A total of 201 patients were enrolled in this study, 94 of whom were determined to have loneliness and 107 of whom were not. The demographic and clinical characteristics are shown in P = 0.024). However, in the multivariate analysis, we found that being married  and regularly fasting  were independent factors related to loneliness , as shown in P < 0.001) (P < 0.001) in the non-loneliness group (P < 0.001) in the non-loneliness group  . The pross group , while tss group . Furtherss group , S2.2 = 0.335, P < 0.001) and anxiety  were significantly positively associated with the level of loneliness, as shown in To further evaluated the relationship between loneliness and anxiety or depression in PLWHIV, we conducted correlation analysis. Levels of depression  and the similar relationship between its two dimensions of mental health  and physical health  were also determined , subjective support  and utilization of support  were positively and significantly with SF-36 scale scores on mental health  rather than the other two dimensions  and mental health , as shown in  and mental health   , D.Physical aspects of HRQoL, including physical functioning, physical role functioning, bodily pain, and general health perceptions, were found significantly lower in PLWHIV with loneliness, and the psychological aspects of HRQoL, including vitality, social role functioning, emotional role functioning, also had the same relative relationships .P < 0.001). Loneliness was associated with both anxiety  and depression . Together with perceived social support , both loneliness  and anxiety  were predictable for HRQoL. Moreover, anxiety was related to depression  although depression is not a direct predictor for anxiety . The model reflected a good model fit: . The HOELTER was 254 which indicated an adequate sample size.We established a conceptual psychosocial model and confirmed it by Structural Equation Model (SEM) . The perIn our study, we performed a cross-sectional study design with PLWHIV consecutively enrolled to manage psychological measurement and to collect information. The PLWHIV were divided into two groups based on their UCLA Loneliness Scale scores. We found the levels of anxiety and depression were significantly different in the two groups. Moreover, there was a correlation observed between perceived social support and HRQoL of PLWHIV. Furthermore, an association between loneliness and HRQoL of PLWHIV was also observed. Based on the findings above, we proposed a hypothesis model where loneliness played a mediation role between social support and HRQoL, which was confirmed by a method of structural equation model in the present study.We found the incidence of loneliness is 47% in PLWHIV enrolled in our study. This is much higher than that in normal populations, ranging from 9.2% in Southeast Asia to 14.4% in the Eastern Mediterranean region, as reported before . For PLWAll those findings indicated a potential relationship between PLWHIV's received care and support from society and their quality of life. Thus, we assessed the association between perceived social support and HRQoL along with its two dimensions. A negative correlation between levels of social support and HRQoL was determined. The impact of social support on mental health was especially more pronounced. The results were consistent with a previous study evaluating Chinese PLWHIV . MoreoveWe evaluated whether there was a difference in HRQoL between the loneliness and non-loneliness groups. The results showed that there was a negative correlation between loneliness and HRQoL levels. The higher the levels of loneliness, the worse the physical and mental quality of life. The non-loneliness group had better performance than the loneliness group in physical functioning, role physical, bodily pain, general health, vitality, social functioning, role emotional, and mental health. A study  reportedBased on our study, we proposed a hypothesis that loneliness may play a mediation role between perceived social support and poor HRQoL among PLWHIV. A structural equation model was adopted to describe the correlation between psychological variables. We found there was a direct and indirect influence of social support on HRQoL where loneliness acted mediately. A high level of social support predicts a low level of loneliness, which predicts a poor HRQoL in turn. In addition, the findings of the associations between loneliness, anxiety, and depression were also included in the model. However, in our model, together with loneliness, anxiety also predicts a poor level of HRQoL, in which depression did not take a mediating role. This is different from some formerly published studies emphasizing the role of depression , 45. It The reasons for the development of loneliness varied for the young and elderly populations in China. In our study, we focused on the impact of social support on the loneliness of a young population with an average age of 27 years. For children infected with HIV, the factors associated with their loneliness may be stigma, discrimination, and a lack of social support . HoweverStudies have reported that the number of Chinese patients infected with HIV is increasing, especially among young people . All patIn our study, the prevalence of loneliness was 47% in this population of PLWHIV. Being married is an independent protective factor associated with loneliness in PLWHIV. There was a negative direct and indirect impact of perceived social support on HRQoL where loneliness played a mediation role in this SEM. Besides, PLWHIV with loneliness also had higher levels of anxiety and depression. In the model, together with loneliness, anxiety can predict poor HRQoL. Interventions focused on reducing loneliness or just improving social support for PLWHIV simultaneously may be effective in improving HRQoL among the population.There are some limitations in our study. Its cross-sectional design precludes inferences about causality and limitations exist with interpreting the findings. Although the sample size was sufficient to complete this study by statistical tests, we expect a larger sample size to maintain the stability of the study results. Next, a longitudinal cohort study to further determine the causality of social support and loneliness on HRQoL needs to be performed.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by Ethics Committee of Nanfang Hospital, Southern Medical University. The patients/participants provided their written informed consent to participate in this study.ZQ finished the data analysis and drafted the manuscript. BL finished data collection. GL and HC censored the process. JH, TY, and XX assisted data analyzing. SC and JP designed the study. All authors contributed to the article and approved the submitted version.


A stable and motivated CDC workforce is critical for Chinese public health system improvement in the post-pandemic period of COVID-19. Meanwhile, the lack of career development prospects, low income, low status and the widespread and increasingly serious job burnout of employees CDC staff is a complex and difficult problem for the government. Therefore, this study explored the relationship between CDC personnel subjective socioeconomic status and turnover intention using a sample of 2,502 grass-roots CDC personnel who were administered with a subjective socioeconomic status scale, turnover intention scale, job burnout scale, and payment questionnaire. The results showed that: (1) subjective socioeconomic status had a significant association with job burnout and turnover intention; (2) all three dimensions of job burnout played a mediating role in the relationship between subjective socioeconomic status and turnover intention; (3) expected salary change played a moderating role between subjective socioeconomic status and turnover intention. The effect was stronger for workers with low expected salary change, which means due to the multidimensional comparative and complex mechanism of salary change, which had limited effect on turnover intention. These findings provide a basis for the relationship between turnover intention and socioeconomic status of grass-roots CDC personnel, and also provide ideas for reducing job burnout and staff turnover. The COVID-19 pandemic has made more and more people to realize the importance of a robust public health system to respond appropriately to emerging and traditional health challenges . ChineseTurnover has been a central topic for economists, psychologists, and management scholars , 5. TurnThe subjective socioeconomic status of CDC personnel refers to the individual's perception and identification of their position in the social and economic structure , and is In the late 1980's, Pines and Aronson defined job burnout as a state of physical, emotional and mental exhaustion , 16. TheMoreover, employees are often more focused on relative salary differences than absolute differences, and we called it expected salary change, which is the difference between expected income and actual income. A meta-analysis of 203 studies revealed that discrepancy between actual pay and deserved pay is the primary determinant of turnover intention and actual turnover . HoweverTo sum up, our study intends to select district and county-level CDC staff, a group with serious attrition problem, to investigate the impact of grass-roots CDC staff subjective socioeconomic status on turnover intention, the mediating role of job burnout between them, and the moderating role of expected salary change. This is of great significance for promoting the subjective socioeconomic status of grass-roots workers and decreasing their turnover intention.In previous studies, scholars paid more attention to objective economic status, including social identity, occupational status, income level, education level and other factors, which can be used as a basis to divide individuals into higher and lower social classes . While, Since the outbreak of COVID-19, the work content and intensity of grassroots CDC personnel have greatly increased, but their professional effort is severely disproportionate to the reward received. As a matter of fact, grass-roots CDC staff are facing serious self-development stress, such as poor salaries, limited development prospects and low social status, which will affect their cognition of subjective socioeconomic status results and have the intention to leave. Therefore, there is an urgent need to investigate the current situation of Chinese CDC staff subjective socioeconomic status and the possible impact on turnover intention.Previous research studies revealed that people with different income levels and occupational status have significantly different turnover tendencies, and had a predictive effect on turnover intention. Based on these, we established our first hypothesis:Hypothesis 1: Subjective socioeconomic status can significantly negatively predict turnover intention.Job burnout refers to the emotional exhaustion caused by pressure from work or other reasons. According to current research, job burnout of CDC personnel is harmful in three aspects. The first is the impact on personal physical and mental health, such as depression and metabolic syndrome. Secondly, it will reduce work efficiency and cause tension in interpersonal relationship. Last but not least, long-term job burnout will have a huge impact on the organization of the individual, increase the turnover rate of CDC personnel, resulting in the instability of the CDC team . There iThere are not many research studies that directly study the relationship between subjective socioeconomic status and burnout. However, many studies have shown that education level, job title and income, which are important indicators of social and economic status, can have impact on job burnout. Subjective socioeconomic status is a comprehensive assessment of the subjective perception of an individual on the basis of a simple assessment of his or her social and economic status. We proposal that it is necessary to study the influence of CDC personnel subjective socioeconomic status on job burnout, and whether it will have an impact on turnover intention. Based on this, we established our second hypothesis:Hypothesis 2: Job burnout plays a mediating role in subjective socioeconomic status and turnover Intention.One of the key factors that determines turnover intention is job satisfaction, and the least satisfied in CDC staff of all is the salary . Pay satHypothesis 3: Expected salary change plays a moderating role in the direct path of the mediating path.In summary, this study constructed a model . Among the valid samples, there were 928 men (37.1%) and 1,574 women (62.9%). The average age of participants was 40.85 years (SD = 9.67), 2,043 (81.7%) were married, and 459 (18.3%) were unmarried; 864 (33.8%) had a college degree, 1,549 (61.9%) had a bachelor degree, and 107 (4.3%) had a master's degree; 249 (10.0%) Senior title, 928 (37.1%) had an intermediate title and 1,325 (53.0%) had a junior and below title. The years of working experience ranged from 1 to 43 years, including 936 (37.4%) with 1–9 years, 634 (25.3%) with 10–19 years, 627 (25.1%) with 20–29 years, and 305 (12.2%) with over and 30 years of working experience in CDC. The average number of years of working experience was 14.93 years (SD = 11.08).CSSS is translated from the Mac Arthur Scale of Subjective Social Status for adults developed by Adler et al. . The SubJob burnout is a kind of long-term reaction caused by the individual's inability to cope with the constant pressure at work, which is manifested as emotional exhaustion, depersonalization and personal accomplishment . The jobThe Turnover Intention Scale was developed by Tao et al. and has been widely used in sociological field . It consThe payment questionnaire is designed on the basis of literature review , includiSPSS version 25.0  was used for the statistical analysis. Descriptive statistics were produced for all variables, while the PROCESS macro for SPSS (Model 4) was applied to examine the mediating effect of job burnout. Finally, the PROCESS macro for SPSS (Model 5) was used to examine the moderated mediating effect of expected salary change on the direct path .As all the survey data were from the CDC staff self-reports, there may be common method deviation. Therefore, the Harman single factor test was used to test the deviation of variables. The results showed that the eigenvalues of 6 factors were >1, and the explanatory power of the first factor was <40% of the critical value . Therefore, common method bias did not affect the data results.r = −0.19, p < 0.001), depersonalization  and turnover intention , and was positively correlated with personal accomplishment. Emotional exhaustion and depersonalization were positively correlated with turnover intention , and personal accomplishment was negatively correlated with turnover intention . In addition, expected salary change was positively correlated with turnover intention . A t-test was conducted to assess whether there were gender differences between the following variables. The results showed that there were significant gender differences in depersonalization, personal accomplishment, expected salary change and turnover intention . Compared with male CDC workers, female workers had higher burnout, expected salary change and turnover intention. The ANOVA on educational level shown that the higher degree workers had higher burnout and turnover intention.The descriptive statistical results are shown in t = 9.69, p < 0.001; β = −0.16, t = −3.99, p < 0.001), which in turn was positively related to CDC staff turnover intention . And subjective socioeconomic status was positively associated with personal accomplishment , which in turn was negatively related to CDC staff turnover intention . In all three dimensions, the negative direct association between subjective socioeconomic status and turnover intention perpetration remained significant . Therefore, Hypothesis 1, 2 were supported. Job burnout partially mediated the relationship between subjective socioeconomic status and turnover intention ; DE: indirect effect = −0.03, SE = 0.01, 95% CI = ; PA: indirect effect = −0.01, SE = 0.002, 95% CI = ]. The mediation effect of EE, DE, PA accounts for 46.54, 13.25, 2.71% of the total effect of subjective socioeconomic status on turnover intention.Model 4 of the PROCESS macro was used to investigate the predictive effect of CDC staff subjective socioeconomic status on turnover intention, and the mediating role of job burnout t . Since tt =2.01, p < 0.05). Therefore, we plotted predicted subjective socioeconomic status against turnover intention, separately for low- and high-levels of expected salary change (M ± 1SD). Simple slope tests showed that for CDC workers with high expected salary change, subjective socioeconomic status significantly predicted turnover intention, βsimple = −0.20, t = 11.69, p < 0.001. However, for CDC workers with low expected salary change, subjective socioeconomic status conflict significantly predicted turnover intention but to a much weaker extent, βsimple = −0.31, t = 11.11, p < 0.001 (To test the moderated mediation model, we used Model 5 of the SPSS PROCESS macro compiled by Hayes . After c < 0.001 . The resThe results showed that subjective socioeconomic status had a significant negative predictive effect on turnover intention, so Research Hypothesis 1 was verified. It has confirmed a relationship between the subjective socioeconomic status and turnover intention of the Chinese grass-roots CDC staff, and expanded the application of social comparison theory in the Chinese context. At the same time, it is call on the government to pay attention to CDC personnel not only in the level of oral propaganda, but also to think about how to solve their practical difficulties, such as raising their salary, implement the epidemic prevention subsidy and optimize their career path . On the This study found that job burnout played a mediating role between the subjective socioeconomic status and turnover intention of grass-roots CDC personnel, so Research Hypothesis 2 was verified. COVID-19 pandemic has been going on for 2 years, and the long hours of high-intensity work and great mental pressure have placed a huge burden on the physical and mental health of CDC personnel. In the case of serious inequality between pay and income, it is easy to cause job burnout. To some extent, it fills a research gap by confirming the mediating mechanism between CDC staff subjective socioeconomic status and turnover intention. Low subjective socioeconomic status will aggravate job burnout, and then affect the turnover tendency of CDC personnel. Our study emphasizing the important role of personal subjective feelings in turnover intention, improving their compensation and treatment, and paying more attention to their mental health should become the important contents of the CDC system improvement, which is the core of solving the problem of staff turnover.With the development of social economy, we are faced with more and more public health problems, and the work scope and pressure of disease control personnel are getting bigger and bigger . HoweverThe results of the moderating effect analysis showed that expected salary change played a significant moderating role in the path of subjective socioeconomic status to turnover intention, which verified Hypothesis 3. The results showed that the grass-roots CDC workers, who have higher expected salary change, will have higher degree of turnover intention . It may The current research has the following crucial theoretical and practical contributions. First, it is emphasized that more attention should be paid to the current situation of low social status and low salary of CDC personnel, which reflect that the problem of appreciating medical treatment but neglecting prevention is still outstanding. Second, during the fight against COVID-19, CDC staff are overworked, facing great work pressure and risk of infection, and the detection rate of job burnout is high. Relevant departments should provide appropriate care and support. The degree of job burnout can be alleviated by improving staff's self-identification with their professional value and social status and strengthening organizational support, and the talent team of disease prevention and control system should be expanded to relieve the working pressure of existing CDC staff. Finally, this study revealed that expected salary change has a complex role in subjective economic status, highlighting that raising wages alone will do little to reduce attrition, it should be considered from multiple dimensions such as fairness of salary distribution, career development prospect and social status.Several limitations need to be considered when interpreting the findings. First, our cross-sectional data limit causal inferences. Previous studies have also indicated that job burnout can contribute to subjective economic status. Therefore, it is necessary to use longitudinal designs to obtain stronger empirical evidence of causal evidence in future research. This survey adopts the method of network non-probabilistic sampling, and the sample representativeness is limited to some extent. However, the conclusions and findings of this study still have certain reference value. Finally, although this study tested the moderating role of expected salary change between subjective economic status and turnover intention, future research needs to conduct a more in-depth study on more multi-level and multi-angle salary demand, to let CDC personnel get equal income with their efforts.In summary, this study is an important step forward in understanding how subjective socioeconomic status relates to the turnover intention of Chinese grass-roots CDC personnel. It has very important significance for the Chinese grass-roots CDC personnel who want to improve their socioeconomic status, and provides reference basis for the government to formulate corresponding measures to alleviate the loss of CDC personnel and reduce job burnout. First, it reveals the relationship between subjective socioeconomic status and burnout of grass-roots CDC personnel in the critical period of modernization improvement of disease prevention and control. Second, it shows that job burnout serves as a mediating role between subjective socioeconomic status and turnover intention, which highlights the important role of occupational mental health in socioeconomic status and talent force stability. Moreover, the relationship between subjective socioeconomic status and turnover intention is moderated by expected salary change, and the relationship appears to be stronger for CDC workers with low expected salary change than for those with high expected salary change. Finally, we hope that the government and society have more understanding and attention to the CDC personnel. Only understanding can bring understanding and empathy, and further bring participation, support, cooperation and construction.The data that support the findings of this study are available from the corresponding author upon reasonable request.The studies involving human participants were reviewed and approved by Institutional Review Board of Shandong University. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.SL was the principal investigator. YS and GL collected and analyzed the data under the supervision of SL. SL and YS designed the study, contributed to materials and analysis tools, and contributed to the writing of the manuscript. SL and CZ provided guidance to the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: There are several possible links that have been used to claim that osteoporosis and peripheral artery disease (PAD) are associated; however, the solid evidence is not sufficient. This study aimed to use the Taiwan National Health Insurance Research Database (NHIRD) to determine if osteoporosis is associated with peripheral artery disease (PAD). Method: NHIRD records from 23 million patients were collected to recruit two matched cohort groups: 64,562 patients with and 64,562 patients without osteoporosis. To compare the crude hazard ratio (HR) and the incidence rate ratio between the two cohort groups for PAD, the Cox model was used. Result: With an adjusted HR of 1.18 , the osteoporosis cohort group had a significantly greater risk for PAD than the group without osteoporosis. The cumulative incidence of PAD in the cohort group was also statistically higher than it was in the group without osteoporosis  over the 10-year follow-up period. In addition, the osteoporotic patients with ischemic stroke, chronic obstructive pulmonary disease (COPD), and congestive heart failure (CHF) had a significantly increased risk of PAD based on subgroup analysis. Conclusions: There was a positive association between osteoporosis and the development of PAD, as patients with osteoporosis had an increased incidence of PAD over time. Osteopenia (OR: 1.15) and osteoporosis (OR: 1.8) were found to be independent risk factors for the presence of PAD in women [Peripheral artery disease (PAD) has frequently been investigated to determine its prevalence, pathophysiology, frequency and occurrence, management, and prevention in previous literature. Despite a decline in mortality rates, PAD remains highly prevalent due to the aging population, with a prevalence of approximately 10 to 20% ,4,5. Betin women . AlthougThis study used the National Health Insurance Research Database (NHIRD), which is a large, computerized database that represents more than 99% of the Taiwanese population  . The datThis research was a retrospective, case-compared, database cohort study for the evaluation of the different risks of developing PAD in an osteoporosis group and in a nonosteoporosis group. If patients were (1) older than 50 years of age and (2) had been diagnosed with osteoporosis (ICD-9-CM Code: 733.0) by a physician from 1 January 2006 to 31 December 2008, they were included in this study. If a patient had at least three different medical claims issued in an out-patient setting or at least one claim in an in-patient setting, they were defined as having osteoporosis. BMD measurement is the gold standard for osteoporosis diagnosis in the spine and hip and measures how different the mineral and calcium levels are in a particular area of the bone. A value of 2.5 or more in the standard deviation (SD) indicates osteoporosis. As shown in The incidence of PAD was the primary outcome measurement. At least three different medical statements issued in an out-patient setting or at least one claim in an in-patient setting was the definition for diagnosing PAD . During the first year of the follow up, patients with PAD were excluded from the overall risk calculation to avoid protopathic bias. t-test. For the hazard ratios (aHRs) with 95% confidence intervals (CIs) and for the estimated incidences of the case and comparison groups, multivariate-adjusted Cox proportional hazard regression was calculated. A forest plot was applied to demonstrate the differences between the two cohorts in the subgroup analysis. Kaplan–Meier curve analyses were used to evaluate the cumulative incidences of patients with PAD, and the log-rank test was applied to determine the significance. All statistical analyses used the two-tailed p < 0.05 and were conducted with SAS version 9.4 . The method used for feature selection was implemented using R software .The basic characteristics are presented as N (%) for the categorical variables according to the chi-square test, and the continuous variables are defined as the mean ± standard deviation (mean ± SD) according to a p = 0.06).p < 0.001).As shown in p < 0.001). For patients who were older in age , DM, RA, and CKD showed an increased risk of PAD compared to those who did not have these comorbidities. Regarding anti-osteoporosis drug usage, there was no significant difference in the risk of PAD. p < 0.001, log-rank) for the osteoporosis and nonosteoporosis cohorts, respectively.Osteoporosis and atherosclerosis are two chronic degenerative diseases that share several biochemical pathways and risk factors. A previous study proved an association between osteoporosis and coronary artery disease, carotid atherosclerosis, and ischemic stroke ,15,16. Hp < 0.001). However, after adjusting for confounding factors, the correlation was much weaker  [The Rancho Bernardo Study  found a  < 0.05) . This in < 0.05) , and theAs for the interconnections between osteoporosis and PAD, many possible pathogenesis pathways have been discussed . First, This study has many advantages. A large sample size, a sufficient follow-up duration of up to ten years, and proper, well-balanced random matching between two cohort groups make this study valuable. However, this study has three limitations. First, due to the many physicians, specialists, and medical staff inputting data into the administrative database, possible coding errors and other coding problems may have occurred. The accuracy and coding were not verified, even though the definition of PAD and osteoporosis was based on the coding definition. However, to the best of our ability, we added two restrictions: the diagnosis (1) must have been made during two ambulatory care visits or during one or more in-patient visits and (2) needed to have been made by certain specialists. These case definitions for chronic health conditions have been widely used in the literature based on administrative data . Second,Patients with osteoporosis had a significant increased risk of developing PAD compared to patients without osteoporosis. A positive association between osteoporosis and the subsequent development of PAD could be observed. Patients with osteoporosis are recommended to receive a personalized risk screening for PAD to prevent the complications of PAD.


Depressive symptoms are common in individuals with advanced cancer. Objectives. This study sought to analyze the relationship between physical and functional status and depressive symptoms, and to assess the role of mental adjustment across these variables in people with advanced cancer. Methods. A prospective, cross-sectional design was adopted. Data were collected from 748 participants with advanced cancer at 15 tertiary hospitals in Spain. Participants completed self-report measures: Brief Symptom Inventory (BSI), Mini-Mental Adjustment to Cancer (Mini-MAC) scale, and the European Organization for Research and Treatment of Cancer (EORTC) questionnaire. Results. Depression was present in 44.3% of the participants and was more common among women, patients <65 years old, non-partnered, and those with recurrent cancer. Results revealed a negative correlation with functional status, and functional status was negatively associated with depressive symptoms. Mental adjustment affected functional status and depression. Patients having a positive attitude displayed fewer depressive symptoms, while the presence of negative attitudes increased depressive symptoms in this population. Conclusions. Functional status and mental adjustment are key factors in the presence of depressive symptoms among people with advanced cancer. Assessment of functional status and mental adjustment should be considered when planning treatment and rehabilitation in this population. Patients with advanced cancer often experience varying degrees of depression for a host of reasons, including as a reaction to the cancer diagnosis, symptoms secondary to the tumor itself, treatment, and uncertainty surrounding the risk of disease progression ,2,3. EstSeveral psychosocial factors, including age, sex, type of tumor, and disease progression, can contribute to the development of depression in individuals affected by cancer ,11. In aPhysical and functional status are particularly significant in advanced cancer patients, considering the extent of cancer symptoms, comorbidity with other conditions, the ease with which a person can perform daily activities, and how much help is needed for basic self-care ,15. FuncIn recent years, there has been a growing interest in understanding and addressing the challenges associated with coping with cancer. Mental adjustment refers to an individual’s cognitive and behavioral reactions to receiving a diagnosis of cancer . Active Identifying factors associated with depression can provide a reference for intervention and treatment. To the best of the authors’ knowledge, there are no studies that analyze the role of mental adjustment across physical and functional status and depressive symptoms in a large sample of Spanish patients with advanced cancer. The study objective was to examine the relationship across physical and functional status and depressive symptoms and evaluate the role of mental adjustment on these variables in advanced cancer. The hypothesis is that functional status, mental adjustment, and depression are interconnected, and that mental adjustment (as in negative and positive attitudes) play a significant role in the relationship between functional status and depression.This is a multicenter, prospective, cross-sectional study. Cases of advanced cancer were consecutively recruited from 15 medical oncology departments of different hospitals in Spain between February 2020 and June 2022. Patients were selected at their first visit to the medical oncologist who explained the diagnosis, stage, incurable disease status, and systemic antineoplastic treatment options. Eligible candidates were over 18 years of age with histologically confirmed, advanced cancer who were not eligible for surgery or other therapies with curative intent. Patients with physical conditions, comorbidity, and/or age that represented a contraindication in the opinion of the attending oncologist to receive antineoplastic treatment; who had received cancer treatment in the previous 2 years for another advanced cancer; or with any underlying personal, family and sociological, geographical, and/or medical condition that could hinder the patient’s ability to participate in the study were excluded. A total of 857 patients were enrolled; 837 were eligible, and 20 were excluded , as shown in the flow diagram, www.neoetic.es).This research was conducted in accordance with current ethical principles and had the prior approval of the Ethics Review Committees of each institution and of the Spanish Agency of Medicines and Health Products . The study involved completing several questionnaires and collecting clinical data from the interview and medical history. Data collection procedures were similar in all hospitals and patients’ data were obtained from the institutions where they received treatment. Those who agreed to participated signed the consent form, were given instructions on how to fill in the written questionnaires, completed it at home, and handed them to the auxiliary staff at the following visit. All participants provided informed consent before inclusion. Data were collected and updated by the medical oncologist, through a web-based platform  were completed at home during the interval between the first visit to the oncologist and the beginning of systemic treatment. Clinical variables pertaining to cancer such as primary tumor site, histology, recurrent cancer (yes/no), anti-neoplastic treatment, and outcomes were gathered by the medical oncologist from the medical records. Depression was assessed using the Brief Symptom Inventory (BSI) . The queCoping strategies for cancer were assessed using the Mini-Mental Adjustment to Cancer (Mini-MAC) . It contSymptoms and functional status were probed using the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ-C30) .2) was computed to assess effect size of continuous variables. Eta-squared ranged from 0 to 1, with η2~0.01, η2~0.06, and η2 > 0.14 for a small, medium, and large effect size, respectively [2 value , goodness-of-fit index, Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Normed Fit Index (NFI) (>0.95 indicating an excellent fit), and root mean square of approximation  [p < 0.05 for all tests. Statistical analyses were performed using the IBM-SPSS 23.0 statistical and AMOS 23.0 software package for Windows PC. Descriptive statistics were performed and both means (M) and standard deviations (SD) were calculated for demographic and clinical characteristics. ANOVAs were used to examine differences in depressive symptom (BSI score) as a function of demographic and clinical variables. Eta squared  . BilaterData from 837 participants  were included in the analysis after excluding missing data (n = 10). Most of the participants were men (54%); 78% were married; 52% completed junior high school; and 51% were retired or unemployed. The most common tumors were bronchopulmonary (32%), colorectal (15%), pancreatic (10%), breast (7%), and gastric (6%). Adenocarcinoma histology was the most prevalent (63%) and most cancers were diagnosed in stage IV (81%). The most frequent treatment was chemotherapy alone or combined with other treatment modalities (80%). Estimated survival was <12 months for 27% of the sample. p = 0.001, partial eta-squared = 0.021), age , marital status , and recurrent cancer ; no significant differences were found for the remaining variables , marital status , symptom scale score , and negative attitude , whereas they correlated negatively with age , functional scale , and positive emotion . No significant correlations were detected between cognitive avoidance and depressive symptoms . As displayed in β = −0.76, p < 0.01); functional scale score was directly and positively associated with positive attitude , and negatively associated with negative attitude  and depression . Negative attitude was positively associated with depression  and positive attitude was negatively associated with depression ; the more symptoms patients exhibit, the worse their functional status and the more depressive symptoms increase; furthermore, positive attitude and negative attitude mediated in the association between functional status and depressive symptoms—The model exhibited excellent fit to the data . They experience every change as a sign that their situation is deteriorating, resulting in them feeling worse and experiencing more depressive symptoms. In contrast, those having a more active and positive coping style, attempting to manage their situation, searching for alternatives and solutions, and maintaining their expectations as well as their confidence in being able to adapt to the challenges of the disease (positive attitude) present fewer symptoms of depression. Interventions that seek to enhance functional status and coping strategies could, ultimately, lessen symptoms of depression.


Talaromyces marneffei (TM) is an opportunistic fungus that is predominantly prevalent among patients who are HIV-positive in South-East Asia. However, few studies focused on the clinical features, laboratory findings, and prognosis across varying immune states.n = 18), HIV negative but with immunocompromised conditions , and immunocompetent patients (n = 25).A total of 54 patients with TM infection in Xiangya Hospital of Central South University from January 1, 2006 to October 31, 2021 were retrospectively analyzed. Clinical profiles were compared across the different immune statuses by HIV-positive . The median age was 50, and patients with HIV were much younger compared to the other two groups. The most common symptom at presentation was fever (79.6%), followed by cough (70.4%), weight loss (61.1%), and expectoration (53.7%). The patients with HIV were more likely to develop into a subtype of disseminated TM affecting multiple organs including lymph node, liver, skin, and spleen, thus, resulting in higher hospital mortality compared to the other two groups. Patients without HIV but with immunocompromised conditions presented similar hospital mortality rates compared to immunocompetent patients, while experiencing longer days of hospitalization to recover from the diseases. Additionally, in this study, the pathogen culture easily confirmed the patients with HIV. However, mNGS presented as a promising tool to confirm TM infection in those suspicious patients without HIV.In summary, patients with HIV were more likely to develop into disseminated TM, resulting in higher mortality compared to those patients without HIV. Additionally, mNGS presented as an important supplementary tool to confirm TM infection in patients without HIV, particularly in those with immunocompromised diseases. Talaromyces marneffei (TM) is an opportunistic and highly invasive fungus endemic, that is predominantly restricted to Southeast and Eastern Asia , but in recent years, more and more TM infection has been diagnosed in patients without HIV, and even in immunocompetent individuals , 10. HowWe retrospectively extracted and analyzed the clinical profile data on patients with TM infections in Xiangya Hospital of Central South University from January 2006 to October 2021. This study was approved by the Institutional Review Boards (IRBs) in Xiangya Hospital, Central South University (No. 202104005). Clinical profiles including demographics, comorbidities, clinical symptoms, laboratory and imaging findings, and prognosis were obtained from the electronic record. The TM infection was diagnosed by experienced doctors, combining suspicious clinical manifestations with one of the following evidence –13:a. Pathogen culture: Using standard culture techniques to isolate TM from clinical specimens. The crucial evidence of the positive TM was its thermal dimorphism, that is, the form of mycelium at 25°C coupling with brick red pigment and the form of yeast at 37°C.b. After Wright staining, Giemsa staining, or periodic acid Schiff staining of cytology and histopathology specimens, the special yeast form and diaphragm of TM are directly observed with the microscope.c. Genetic methods: Genetic methods including mNGS (in the current study) and other genetic tests illustrate that TM is positive.Imaging and physical exam findings, positive culture site, and histopathology were commonly used to define sites of infection. If patients had only one involved lesion or organs without systemic symptoms or physical signs, that would be defined as focalized TM infection. When the existence of TM is confirmed in blood or bone marrow, or more than one viscus with systemic symptoms, that would be defined as disseminated TM infection. Immune status was divided into three groups as following criteria: (1) HIV group was defined as patients accompanied with HIV infection; (2) Non-HIV with IC group was defined as those patients without HIV, but with immunocompromised conditions as any of the following situation: progressed malignancy, chemotherapy, solid organ or a stem-cell transplant, or immunosuppressive medications including corticosteroids, biologic immunosuppressants, etc.; (3) the immunocompetent group was classified as those patients without HIV or absence of an immunocompromised condition.P < 0.05 was considered statistically significant.All data in the current study were analyzed with SPSS 26.0 software . Continuous variables with skewed distribution were presented as medians and interquartile ranges and compared by the Kruskal-Wallis H test. Categorical variables were expressed as frequencies and percentages (%) and analyzed by the Chi-square test or Fisher's exact test. A value of P = 0.02). The most common symptom at presentation was fever (79.6%), followed by cough (70.4%), lymphadenopathy (70.4%), weight loss (61.1%), expectoration (53.7%), skin lesion (50%), gastrointestinal symptoms (33.3%), splenomegaly (31.5%), hepatomegaly (35.2%), bone destruction . The patients with HIV were more likely to develop into a subtype of disseminated TM, affecting multiple organs including lymph node, liver, skin and spleen, and so on  Patients with HIV had worst immunity and more possibility to disseminate into blood compared to the other two groups, thus, greatly increasing the chance of obtaining a positive culture result from the blood samples; (2) the sensitivity of positive TM by pathogen culture could be affected by the complicated history of medication and multiple pathogen infections in those patients without HIV but with immunocompromised diseases. To further confirm the 9 patients who were diagnosed with mNGS but without positive colonies, their clinical characteristics, treatment procedure, and prognosis coincided with TM infection . Thus, iOur research had a few limitations. Firstly, this study was a single-center and retrospective study, so we involved a very small part of patients with TM infection in Hunan. There was a relatively small proportion of immunocompromised patients without HIV, thus, introducing outcome bias. A large cohort study is needed to further affirm our conclusions. Secondly, the accurate identification of TM should combine colonies (culture positive) and PCR sequencing, however, the PCR test was not applied as a conventional test in most hospitals including ours.In summary, patients with HIV were more likely to develop into disseminated TM and resulting in higher mortality compared to those patients without HIV. Additionally, mNGS presented as an important supplementary tool to confirm TM infection in patients without HIV, particularly in those patients with immunocompromised diseases.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Institutional Review Boards (IRBs) in Xiangya Hospital, Central South University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.DL and HL were involved in the design of the study, collection of research data, data collation and analysis, and writing of the manuscript. YZha and PP were involved in the design of the study and review of the article, funding support and other aspects of supervision. YZhu and QC were involved in the collection of research data and writing of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Natural Science Foundation of China (Nos. 82100037 and 81770080), National Key R&D Program of China (No. 2016YFC1304204), National Science Foundation for Post-doctoral Scientists of China (No. 2021TQ0375), Hunan Outstanding Postdoctoral Innovative Talents Program (No. 2021RC2018), Key R&D Program of Hunan Province (No. 2022SK2038), and Youth Foundation of Xiangya Hospital (No. 2020Q06).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level. EoE is diagnosed in the setting of symptoms of esophageal dysfunction and histopathological demonstration of marked esophageal eosinophilia5. Endoscopic features associated with EoE include edema, rings, exudates, furrows, and strictures6. While the presence of these morphological changes is not required for diagnosis, they are supportive and prompt the biopsies necessary for histopathological confirmation5. However, the endoscopic features of EoE may be missed, either because physicians are not familiar with them or the morphologic changes are too subtle8.The incidence of eosinophilic esophagitis (EoE) has risen significantly in the past decade and has become a significant cause of dysphagia and food impaction10 but is still not used as a standard tool in many settings11. For enhanced detection of EoE, Artificial Intelligence (AI) with deep learning (DL) could be an additional diagnostic option. In general, the application of AI and machine learning (ML) in gastrointestinal (GI) endoscopy has made significant progress in the past few years, especially in the domain of image and pattern recognition13. Clinical studies have applied AI in benign and malignant disorders with excellent results, including Helicobacter pylori diagnosis, esophageal and gastric cancer, as well as colorectal polyp detection19. For the diagnosis of EoE, however, there has been only one study to date in which endoscopic images of EoE were assessed using a convolutional neural network (CNN)20. EoE was distinguished from the normal esophagus and candidal esophagitis with promising results. In deep learning, CNN architectures use basic convolution modules and complement them with sigmoidal activation functions and pooling operations21. In the image-understanding domain, numerous CNN architectures for different tasks have been implemented, allowing for deep networks with 100 layers or more.The EoE Endoscopic Reference Score (EREFS), based on the endoscopic features described above, has improved the recognition, reporting, and classification of EoEWith this background, the aims of this study were to develop and then externally validate a deep learning-based AI model to detect EoE and quantify EREFS and assess the ability to recognize endoscopic images of EoE and report EREFS of human endoscopists as compared to the AI model.This was a 3-phase study in which an AI model was trained to detect EoE on endoscopic white light images. In the first phase, the AI model was trained and validated with an internal data set (InD). In the second phase, the performance of the AI model was tested on an external data set (ExD) from a separate hospital; in this phase, the benefit of using the EREFS scores in the AI model was studied. In the third phase, the performance of the AI model was compared with human endoscopists with different levels of experience.The pathology reports archived in the laboratory information system  of the Institute of Pathology and Molecular Diagnostics of the University Hospital of Augsburg, Germany, were screened for the german terms “Ösophagus” and “eosinophile Ösophagitis”. The corresponding endoscopic reports and white light images of patients identified within a 10-year period between 06/2010 and 05/2020 were extracted from the endoscopy database ) of the University Hospital of Augsburg, Germany, by two board-certified gastroenterologists. Endoscopic images were selected for AI training according to the following criteria:5Images from patients with active EoE (≥ 15 eosinophils/HPF) who were diagnosed as per consensus guidelinesImages from patients with an endoscopically normal-appearing esophagus who also had normal esophageal biopsies(1) Inclusion criteria:Images with other visible pathologies, such as reflux esophagitis, candida esophagitis, mass, or other findingsImages with visible stricture formation and stenosisPoor quality images with blurring, inadequate focus, excessive bubbles, blood or mucus covering the mucosa(2) Exclusion criteria:All images of the InD were taken with an Olympus gastroscope  at the University Hospital Augsburg, Germany.10. Images with obvious strictures were excluded  because it was assumed that the additional benefit of AI support in patients with stricture formation or stenosis is limited, and the actual challenge lies in the identification of EoE patients with more subtle endoscopic features, who are probably in an earlier phase of the disease.The images were assessed for the EREFS by two board-certified gastroenterologists. EREFS were reported using the standard scoring system, including edema 0–1 point, rings 0–3 points, exudates 0–2 points, and furrows 0–2 pointsIn addition to the main binary classification branch , a specific auxiliary branch for each of the EREFS categories was included in the training phase of the AI system. In other words, two AI models were trained, one with (AI-EoE-EREFS) and a second without the auxiliary EREFS categories (AI-EoE).22. The models were pretrained on a non-medical dataset (ImageNet23) to learn basic abstract visual features. The final classification layer of the neural network was then adjusted to enable a binary classification—the detection and classification of EoE. The threshold probability was set to 0.5. Before training, InD images were cropped to exclude black borders and resized for consistency across the dataset, after which data augmentation, including scaling and shifting of images, was applied. The intention of data augmentation was to enable the algorithm to be more robust to slight variations in the input images. During training, the model’s parameters were optimized to minimize the cross-entropy loss with label smoothing, achieve a global binary prediction, and accurately classify the particular EREFS features. The models were trained for 6000 iterations with a batch size of 48 and a sampling strategy such that both classes are equally represented in each batch. The initial learning and weight-decay for the Stochastic Gradient Descent algorithm were set to 0.01 and 5e−4. Over the course of training, the learning rate was decayed with a cosine annealing schedule. All models were implemented in the PyTorch Deep-Learning framework.The training of both AI models was based on a CNN with a ResNet architectureTo internally validate the models, we performed five repeated runs of five-fold cross-validation. In five-fold cross-validation, the dataset is split into five disjoint subsets. Four of the five folds are used as training data for the algorithm. The remaining one is the held-out validation set. The procedure is repeated such that each fold was in the role of the validation set once. We did not perform hyperparameter optimization or early stopping techniques on the validation set but trained our algorithms for a fixed number of iterations. The cross-validation scheme is repeated five times with randomized subset compositions and seeds for the random number generators from 0 to 4.After constructing the AI models, we evaluated their performance on an independent and externally acquired test set (ExD). ExD comprised a total of 200 WL images, including 100 WL images from EoE patients with active disease (≥ 15 eos/hpf) diagnosed per consensus guidelines and 100 WL images of normal esophagus in patients without any visible, histologic, or known esophageal pathology. The test set was provided by the University of North Carolina, Chapel Hill (UNC), with patients who underwent endoscopy between August, 2020, and January, 2021. Both AI algorithms had never seen the ExD images before the evaluation. The evaluation and analyses for these images were performed in a blinded fashion, with the code of EoE vs. normal only revealed after the results of AI-EoE and AI-EoE-EREFS had been finalized and transmitted to the UNC. Exemplary images are shown in Figs. Endoscopy beginners (n = 2)Senior fellows (n = 2)Consultant endoscopists (n = 2)To better understand the performance of AI-EoE and the impact of EREFS on the diagnostic accuracy, ExD images were evaluated by six endoscopists who were rated according to their level of experience, including:Endoscopists were asked to assess the images for the presence of EoE according to the following process:Assessment of all 200 ExD images (1–200) according to the clinical impression of the endoscopist after looking at the images without explicit use of EREFS.10; they were also shown 30 representative endoscopic white light images of EoE with the corresponding EREFS scores. Following this training phase, an additional assessment of the second 100 images (101–200) using the EREF score was performed. The assessment of the first 100 images was done for adjusting the individual performance of the endoscopists. The assessment of the second 100 images was done to quantify the enhancement of diagnosis having the EREFS explicitly in mind. Each group contained one endoscopist from each experience level.Assessment of the first 100 ExD images (1–100) according to the clinical impression of the endoscopist. After this, the endoscopists were asked to review the initial description of the EREFS criteria by Hirano et al.The sensitivity, specificity, accuracy, the area under the ROC curve (AUC), and the harmonic mean (F1) between sensitivity and precision on the ExD images were used to measure the performance of the models, AI-EoE and AI-EoE-EREFS, trained without and with the additional EREFS branches, respectively. These statistics are calculated from the true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) produced by the algorithm.Statistical significance between groups was determined with the McNemar test.By testing multiple models, we investigated whether the inclusion of the EREFS criteria led to an improvement in the performance of AI-EoE.The performance of the human endoscopists on the same data set (ExD) was also evaluated using the same parameters described above.Ethics approval was granted by the Institutional Review Board of the University Hospital Augsburg , as well as by the Institutional Review Board of UNC . All methods used in this study were carried out in accordance with the declaration of Helsinki and in accordance with relevant guidelines and regulations. All images used in this study were obtained from endoscopic procedures for which patients had provided their informed consent. For patients under 16 years, parents or legally authorized representatives provided informed consent.A total of 401 images of EoE from 61 patients and 871 images of a normal esophagus from 393 patients were used to internally train and validate the AI models. The baseline characteristics of patients are shown in Table In the internal validation, the mean scores and standard deviations achieved with the AI-EoE model for sensitivity, specificity, accuracy and harmonic mean (F1) were: 0.857 (0.016), 0.959 (0.007), 0.927 (0.003), 0.881 (0.005).The results for the AI-EoE-EREFS algorithm for the respective metrics were: 0.866 (0.006), 0.957 (0.007), 0.928 (0.005), 0.884 (0.005).The AUC values for AI-EoE and AI-EoE-EREFS were 0.947 and 0.954, respectively Fig. .Figure 3There was no significant difference between AI-EoE and AI-EoE-EREFS in the internal validation using cross-validation.The overall sensitivity, specificity, accuracy, and F1 of AI-EoE were 0.93 for all measures Table . The AUCThe sensitivity, specificity, accuracy, and F1 of AI-EoE-EREFS were 0.96, 0.94, 0.95, and 0.95, respectively Table . The AUCWhile the AI-EoE-EREFS was numerically superior to AI-EoE, the performance did not differ significantly.Six endoscopists (three per group) assessed the ExD images as follows:The endoscopy beginner, senior fellow and consultant endoscopist had an overall sensitivity, specificity, accuracy, and F1 of 0.56, 0.97, 0.77 and 0.70; 0.87, 0.97, 0.92 and 0.92, as well as 0.96, 0.99, 0.97 and 0.97, respectively. Accuracy and F1 differed by + 10% and + 22%, respectively, for the beginner, − 2% and − 3%, respectively, for the senior fellow and + 1% and + 1%, respectively, for the consultant endoscopist regarding the first 100 images and the second 100 images.On the first 100 images, the endoscopy beginner, senior fellow and consultant endoscopist had a sensitivity, specificity, accuracy, and F1 of 0.40, 0.46, 0.43 and 0.41; 0.60, 0.92, 0.76 and 0.71, as well as 0.88, 0.68, 0.78 and 0.80, respectively. After education and information on the EREFS scores, the endoscopy beginner, senior fellow and consultant endoscopist improved their performance with a sensitivity, specificity, accuracy, and F1 of 0.58, 0.96, 0.77, and 0.72; 0.66, 0.98, 0.82, and 0.79, as well as 0.98, 0.52, 0.75, and 0.80, respectively. Therefore, the accuracy and F1 differed by + 79% and + 76%, respectively, for the beginner, + 8% and + 11%, respectively, for the senior fellow and − 4% and 0%, respectively, for the consultant endoscopist regarding the first 100 images and the second 100 images.The overall performance of the AI models with and without auxiliary EREFS was better than the performance of the beginners, senior fellows, and consultant endoscopists. Using the McNemar test, we found a statistically significant difference in the accuracy and sensitivity between AI and beginners. The AI-EoE-EREFS algorithm also shows statistically significant improvements in sensitivity over the senior fellow group. The specificity did not improve significantly for both models. In the comparison between AI and consultant endoscopists, no significant difference could be found. The ROC curve comparing human endoscopists with AI is shown in Fig. 25. The suspicion of EoE during routine EGD and based on the endoscopic image alone is challenging8, and the presence of EoE must be suspected either clinically or macroscopically to prompt taking esophageal biopsies. A study from Denmark illustrates that even in dysphagia patients, adequate biopsy sampling is often not performed26. With the advancement of AI and ML, assistance can be offered to endoscopists in various detection and characterization of pathologies. A first paper on the use of AI in the detection of EOE demonstrated excellent sensitivity and specificity on an internally validated image dataset20. Guimarães et al. were able to show an overall accuracy of 0.91 in their study. Interestingly, the CNN model was also able to distinguish esophageal candidiasis, which has white plaques representing an important differential diagnosis of EoE during EGD. This shows the enormous potential of AI and especially deep learning.EoE is becoming increasingly important due to its rising incidence, but endoscopists may still have difficulty detecting and diagnosing EoE during routine EGD, leading to diagnostic delay27. On external endoscopic WL images, our AI model, which was trained not only with a binary classification but also with the auxiliary EREFS data, produced an overall sensitivity, specificity, and accuracy of 0.96, 0.94, and 0.95 in the detection of EoE. It must be stated that the results of the external validation were even better than the internal cross-validation because the internal validation was done without additional hyperparameter tuning.Our study sought to demonstrate the robustness of a trained CNN model by additionally evaluating and validating its performance on externally acquired data. Testing on external data is essential because AI models should work for and generalize towards new data to avoid overfitting bias28. The visualizations in combination with the improved metrics hint that augmenting the network with EREFS is beneficial, although we could not prove this result with statistical significance in this study.Interestingly, the addition of the auxiliary EREFS categories (AI-EoE-EREFS) improved the performance of the AI model. Grad-Cam visualizations (Gradient-weighted Class Activation Mapping) point to the inclusion of EREFS, forcing the model to detect features in the input that more cleanly align with the human understanding of the image Fig. 28. The v33. For endoscopists, being aware of the EREFS criteria may lower the threshold of biopsy sampling in the esophagus, which could subsequently lead to higher sensitivity for EoE during routine EGD. An essential aspect of this may also be the experience of the physician performing EGD. We divided the participating endoscopists into two groups to investigate whether the EREFS categories also affected their performance. Group 1 assessed all ExD images based on their clinical impression and experience. Group 2 was not reminded of the EREFS criteria and was also asked to assess the first 100 images based on their clinical impression. After this, physicians in group 2 were educated on the EREFS criteria and then asked to evaluate the second batch of ExD images. Similar to the AI model, the EREFS criteria improved the beginner endoscopists' performance as well as the senior fellows’ performance in this process. In our experiment, the performance improved for the beginners using EREFS, adjusted to the baseline performance of the individual for accuracy and F1, by 69 and 54 percentage points. Even senior fellows profited, given the improvement in accuracy and F1 by 10 and 14 percentage points, respectively. These results suggest that the training of endoscopists with the EREFS scores, especially beginners, can improve their diagnostic accuracy for eosinophilic esophagitis considerably. Eluri et al. showed that the proportion of patients with a normal esophagus decreased significantly after the introduction of the first EoE guidelines, suggesting improved recognition of endoscopic findings with the EREFS criteria7. The overall performance of the endoscopy beginners and senior fellows was well below the more experienced consultant endoscopists. Comparing the overall results for all data of the group of beginners, senior fellows, and consultant endoscopists with the AI results, AI performs best, irrespective of whether the EREFS criteria were used or not. However, AI models may be particularly suitable for the support of endoscopists with lesser experience. In addition, the training and education of beginners may improve with the help of an AI model.The EREFS system was developed and validated to improve and standardize the reporting of endoscopic features of EoE, but also to aid with diagnosis and monitoring, both in practice and in trialsAs with most studies showing the construction and validation of endoscopic AI models, the major limitation of this study is the amount and diversity of data available for training and validation. In addition, the number of endoscopists who underwent evaluation of the dataset was small, thereby limiting the validity of the comparison to the human endoscopists. Also, the fact that data was collected retrospectively may have influenced the quality of the proposed algorithm. Nevertheless, the excellent results on the test data set may show that the generalizability of the AI model was sufficient, at least on the images provided by the external partners. Another limitation is that only a two-class differentiation, EoE vs. normal esophagus, was done. This may not depict the real-life situation sufficiently, where other pathologies, such as reflux esophagitis, candida esophagitis, lymphocytic esophagitis, lichen planus, and others, must be considered. Finally, even though our first step was to use static endoscopic images, video images, or even real-time assessment during EGD will be necessary to assess for this AI tool to be used in clinical situations. In real-life, blurry images, bubble formation, and even blood and mucus may influence the quality of vision, making it difficult to detect and diagnose pathologies such as EoE.In conclusion, our study showed that an AI model can be developed to identify EoE using endoscopic still images and validated from an external data set with excellent performance. Additional training with the EREFS scores improves the diagnostic ability of human endoscopists as well as the AI model. Future directions will incorporate video so that this tool can ultimately be used at the point of care.


Sus scrofa), an ancient breed in China, has been known for its high adaptability to harsh environments and for its meat quality. To understand the underlying mechanisms facilitating to shape these unique features, in this study, 16S rRNA sequencing using pigs feces and subsequent bacterial functional prediction were performed. Also, the gut bacteria of two other breeds of pigs, Barkshire and Landrace, were examined for comparison. It was revealed that the structure of bacterial community in Tibetan pigs appeared to be more complex; the relative abundances of dominant bacterial families varied inversely with those of the other pigs, and the proportion of Firmicutes in Tibetan pigs was lower, but Bacteroides, Fibrobacterota, Lachnospiraceae, Oscillospiraceae, and Ruminococcaceae were higher. Bacterial functional prediction revealed that the dominant flora in the Tibetan pigs was more correlated with functions regulating the hosts’ immune and inflammatory responses, such as NOD-like_receptor_signaling_pathway and vitamin metabolism. In addition, in Tibetan pigs, the taxonomic relationships in the gut bacteria on day 350 were closer than those on earlier stages. Furthermore, gender played a role in the composition and function of bacterial inhabitants in the gut; for boars, they were more correlated to drug resistance and xenobiotics metabolism of the host compared to the sows. In sum, our preliminary study on the gut bacterial composition of the Tibetan pigs provided an insight into the underlying host–microorganism interactions, emphasizing the role of intestinal bacteria in the context of modulating the host’s immune system and host development.Gut bacterial community plays a key role in maintaining host health. The Tibetan pig ( Gut microflora plays a vital role in maintaining the health of hosts . PreviouPrevotella copri was a factor for host obesity and fat accumulation  used were a cross between hybrid sows (S. scrofa) and purebred boar (S. scrofa); the parental sows were a progeny of Tibetan pigs crossed with other black pig breeds, including Beijing black pigs and hybrid Tibetan pigs. These second-generation of Tibetan pigs employed in this study could be degenerated into wild type, accelerating farrowing rates, which was in line with actual production needs. Currently, most Tibetan pigs circulating in the market are this second-generation Tibetan pig.In this study, the Tibetan pigs . In this study, three levels of comparisons were performed, including pigs of different breeds, Tibetan pigs of different growth stages, and Tibetan pigs of different sexes. Fresh feces of Landrace pigs were collected under housing conditions, while those of Berkshire pigs and Tibetan pigs were collected under semi-grazing conditions. They are fed all-natural cornmeal and wheat bran. In addition, Berkshire pigs and Tibetan pigs can eat freely on the mountain. For the comparison of gut bacteria between breeds, pigs of different breeds were raised to about 350 days of age, and their feces were collected. The growth of pigs are usually differentiated into phases of lactation, weaning, and fattening. Naturally, the lactation and growing time of Tibetan pigs are relatively long; accordingly, three time points, days 7, 60, and 350, were selected for evaluating the gut bacterial composition in Tibetan pigs. In addition, the feces of Tibetan boars, pregnant sows, and suckling sows were collected and detected. Six parallel samples in each group were set up to eliminate individual differences. Separate stool samples were collected in individual sterilized containers after rectal stimulation and then immediately transported to laboratory on dry ice, and stored at −80°C.The fecal samples were subjected to DNA isolation with PowerSoil DNA Isolation Kit  following the manufacturer’s instruction. Purity and quality of the genomic DNA were checked by a NanoDrop spectrophotometer .16S rDNA V3–V4 (338–806) was used to design primers as follows: 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTA CNNGGGTATCTAAT-3′)  amplification. The variable region of TAAT-3′) . PCR wasTAAT-3′) . PCR conTAAT-3′) . After tSequencing reactions were carried out at Allwegene Company in Beijing, China using Illumina Miseq PE300 platform .The off-machine data were divided into samples according to the barcode sequence through the QIIME (v1.8.0), and Pear (v 0.9.6) was used to filter and splice the data . Ambiguo16S rRNA gene cluster survey along the samples accompanied by its relative abundance in each of the samples. R was used for heatmap visualization. The top 15 correlated pathways in Tibetan pigs at different growth stages were presented in this study. To determine the correlation between functional pathways and gender, the comparison was visualized with a histogram.QIIME1 (v1.8.0) was used to generate rarefaction curves, α indices , and β diversity. The VennDiagram package of R (v3.6.0) was used for making Venn diagrams , which at-test and one-way ANOVA are also performed to calculate the significance of data.Metastats analysis was performed to determine the significant differences between groups. Kruskal–Wallis tests were conducted to calculate the differences between the means of independent data in multiple groups, and Wilcoxon tests were carried out to compare the differences between two paired groups. Besides, 16S rRNA sequencing generated 4,297,102 raw reads in total. Good_coverage index of the 48 samples in this study was greater than 99%. As shown in the curves of Rarefaction   . Landrac50 days) , which sBoxplot and PCoA based on the unweighted UniFrac distance of MetaOTUs showed clear differences between the community structures of the three breeds of pigs, and the Landrace pigs samples are most similar to each other . The anaFirmicutes and Bacteroides were revealed to be the two dominant phyla inhabited in the guts of the three breeds of pigs, yet accounting to varied proportions; Firmicutes in Tibetan pigs accounted for 74%, and Firmicutes in Barkshire and Landrace pigs accounted for 81 and 91.5%, respectively . However, Verrucobacteria and Desulfobacterota were higher in Berkshire pigs (p < 0.05 and p < 0.01), and Cyanobacteria and Actinobacteriota in Landrace pigs were higher than the other two breeds (p < 0.05).Nine phyla were screened out of the three gut bacterial communities after the flora data were subjected to Kruskal–Wallis tests . Firmicuectively , while Bely 4.5% . Moreoveo breeds    .Compared to previous research , the TibAs expected, the structure of gut community in the Tibetan pigs was more complex than the other two breeds. Previous studies had demonstrated that the gut microbiota played an important role on the adaptability of yak to harsh environments of the Qinghai-Tibet Plateau . TibetanFirmicutes colonized in the gut of Tibetan pigs was significantly lower, whereas Bacteroides was much higher than in the other two breeds. The roles of intestinal Firmicutes and Bacteroides on host’s obesity were initially established in 2005, an observation based on the investigation of the microbiota of genetically modified, lean, and wild-type mice, which demonstrated that the colonized Bacteroidetes was 50% lower in obese animals than their counterpart in lean mice  responsible for the protection of intestinal mucosae  was shown to be capable of inhibiting inflammatory responses in the animal’s intestinal tract, playing an important role in maintaining intestinal health . Written informed consent was obtained from the owners for the participation of their animals in this study.W-TY, C-FW, G-LY, and C-WS designed the research. HN, X-ZF, DZ, and H-LC performed the research. H-BH, Y-LJ, NW, J-ZW, XC, and YZ analyzed the data. W-TY, C-FW, and G-LY supported the research. HN and W-TY wrote the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.