DACH1 inhibits the proliferation and migration of papillary thyroid carcinoma.

DACH1 is an important component of the retinal determinate gene network (RDGN), which regulates the expression of target genes by directly binding or interacting with other factors. DACH1 shows inhibitory effects in most tumors, but its role in papillary thyroid carcinoma is unclear and warrants further investigation. We assessed the expression of DACH1 in different tissues and correlation with immune infiltration by The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMMER2.0 databases). The effects of DACH1 on the proliferation and migration of TPC-1 and Bcpap cells were assessed by cell viability assay, colony formation assay, wound healing assay, transwell migration assay, and flow cytometry. Finally, the effects of DACH1 on CXCL8, CXCL10, and CXCL12 expression in Nthy-ori-3-1, TPC-1 and Bcpap cells were assessed by enzyme-linked immunosorbent assay kit and real-time polymerase chain reaction, respectively. The results showed that DACH1 was differentially expressed in different tumors and tissues. Basal expression of DACH1 was lower in thyroid and papillary thyroid carcinoma than in other normal tissues and corresponding tumors, and positively correlated with CD8+ T cell infiltration. In Nthy-ori-3-1, TPC-1 and Bcpap cells, overexpression of DACH1 inhibited cell migration and proliferation, and the opposite results was obtained by knocking down DACH1 using small interfering RNA. We also demonstrated that DACH1 regulated chemokines CXCL8, CXCL10, and CXCL12, thereby modulating tumor immunity.

Serum glycated albumin as good biomarker for predicting type 2 diabetes: A retrospective cohort study of China National Diabetes and Metabolic Disorders Survey.

AIMS: Glycated albumin (GA) is a biomarker for short-term (2-3 weeks) glycaemic control. However, the predictive utility of GA for diabetes and prediabetes is largely uncharacterised. We aimed to investigate the relationships of baseline serum GA levels with incident diabetes and prediabetes. METHODS: This was a longitudinal cohort study involving 516 subjects without diabetes or prediabetes at baseline. Blood glucose levels were observed during follow-up. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using COX proportional hazard models. Receiver operating characteristic curves and areas under the curves (AUCs) were used to evaluate the discriminating abilities of glycaemic biomarkers and prediction models. RESULTS: During a 9-year follow-up, 51 individuals (9.88%) developed diabetes and 92 (17.83%) prediabetes. Unadjusted HRs (95% CI) for both diabetes and prediabetes increased proportionally with increasing GA levels in a dose-response manner. Multivariable-adjusted HRs (95% CI) for diabetes were significantly elevated from 1.0 (reference) to 5.58 (1.86-16.74). However, the trend was no longer significant for prediabetes after multivariable adjustment. AUCs for GA, fasting blood glucose (FBG) and 2-h postprandial blood glucose (2h-PBG) for predicting diabetes were 0.698, 0.655 and 0.725, respectively. The AUCs for GA had no significant differences compared with those for FBG (p = 0.376) and 2h-PBG (p = 0.552). Replacing FBG or 2h-PBG or both with GA in diabetes prediction models made no significant changes to the AUCs of the models. CONCLUSIONS: GA is of good prognostic utility in predicting diabetes. However, GA may not be a useful biomarker for predicting prediabetes.

Using noninvasive anthropometric indices to develop and validate a predictive model for metabolic syndrome in Chinese adults: a nationwide study.

PURPOSE: Metabolic syndrome (Mets) is a pathological condition that includes many abnormal metabolic components and requires a simple detection method for rapid use in a large population. The aim of the study was to develop a diagnostic model for Mets in a Chinese population with noninvasive anthropometric and demographic predictors. PATIENTS AND METHODS: Least absolute shrinkage and selection operator (LASSO) regression was used to screen predictors. A large sample from the China National Diabetes and Metabolic Disorders Survey (CNDMDS) was used to develop the model with logistic regression, and internal, internal-external and external validation were conducted to evaluate the model performance. A score calculator was developed to display the final model. RESULTS: We evaluated the discrimination and calibration of the model by receiver operator characteristic (ROC) curves and calibration curve analysis. The area under the ROC curves (AUCs) and the Brier score of the original model were 0.88 and 0.122, respectively. The mean AUCs and the mean Brier score of 10-fold cross validation were 0.879 and 0.122, respectively. The mean AUCs and the mean Brier score of internal-external validation were 0.878 and 0.121, respectively. The AUCs and Brier score of external validation were 0.862 and 0.133, respectively. CONCLUSIONS: The model developed in this study has good discrimination and calibration performance. Its stability was proved by internal validation, external validation and internal-external validation. Then, this model has been displayed by a calculator which can exhibit the specific predictive probability for easy use in Chinese population.

Depression Predicts Chronic Pain Interference in Racially Diverse, Income-Disadvantaged Patients.

BACKGROUND: Chronic pain is one of the most common reason adults seek medical care in the United States, with prevalence estimates ranging from 11% to 40%. Mindfulness meditation has been associated with significant improvements in pain, depression, physical and mental health, sleep, and overall quality of life. Group medical visits are increasingly common and are effective at treating myriad illnesses, including chronic pain. Integrative Medical Group Visits (IMGV) combine mindfulness techniques, evidence based integrative medicine, and medical group visits and can be used as adjuncts to medications, particularly in diverse underserved populations with limited access to non-pharmacological therapies. OBJECTIVE AND DESIGN: The objective of the present study was to use a blended analytical approach of machine learning and regression analyses to evaluate the potential relationship between depression and chronic pain in data from a randomized clinical trial of IMGV in diverse, income-disadvantaged patients suffering from chronic pain and depression. METHODS: The analytical approach used machine learning to assess the predictive relationship between depression and pain and identify and select key mediators, which were then assessed with regression analyses. It was hypothesized that depression would predict the pain outcomes of average pain, pain severity, and pain interference. RESULTS: Our analyses identified and characterized a predictive relationship between depression and chronic pain interference. This prediction was mediated by high perceived stress, low pain self-efficacy, and poor sleep quality, potential targets for attenuating the adverse effects of depression on functional outcomes. CONCLUSIONS: In the context of the associated clinical trial and similar interventions, these insights may inform future treatment optimization, targeting, and application efforts in racialized, income-disadvantaged populations, demographics often neglected in studies of chronic pain.

The phenotype and related gene expressions of macrophages in adipose tissue of T2D mice following MSCs infusion.

BACKGROUND: Infusion of mesenchymal stem cells (MSCs) induces polarization of M2 macrophages in adipose tissue of type 2 diabetes (T2D) mice. Studies have shown that M2 macrophages were divided into four sub-phenotypes (M2a, M2b, M2c and M2d) with different functions, and manuscripts have also confirmed that macrophages co-cultured with MSCs were not matched with known four phenotype macrophages. Therefore, our study explored the phenotype and related gene expressions of macrophages in the adipose tissue of T2D mice with/without MSCs infusion. METHODS: We induced a T2D mouse model by using high-fat diets and streptozotocin (STZ) injection. The mice were divided into three groups: the control group, the T2D group, and the MSCs group. MSCs were systemically injected once a week for 6 weeks. The phenotype of macrophages in adipose tissue was detected via flow cytometric analysis. We also investigated the gene expression of macrophages in different groups via SMART-RNA-sequencing and quantitative real-time reverse transcriptase polymerase chain reaction (qRT-PCR). RESULTS: The present study found that the macrophages of adipose tissue in the MSCs group were polarized to the M2 phenotype mixed with four sub-phenotypes. Besides, M2a and M2c held a dominant position, while M2b and M2d (tumor-associated macrophages, TAMs) exhibited a decreasing trend after infusion of MSCs. Moreover, the MSCs group did not appear to express higher levels of tumor-associated, inflammation-associated, or fibrosis-associated genes in comparison to the T2D group. CONCLUSION: The present results unveiled that the macrophage phenotype was inclined to be present in a hybridity state of four M2 sub-phenotypes and the genes related to tumor-promoting, pro-inflammation and pro-fibrosis were not increased after MSCs injection.

Epidemiological Data Mining for Assisting with Foodborne Outbreak Investigation.

Diseases caused by the consumption of food are a significant but avoidable public health issue, and identifying the source of contamination is a key step in an outbreak investigation to prevent foodborne illnesses. Historical foodborne outbreaks provide rich data on critical attributes such as outbreak factors, food vehicles, and etiologies, and an improved understanding of the relationships between these attributes could provide insights for developing effective food safety interventions. The purpose of this study was to identify hidden patterns underlying the relations between the critical attributes involved in historical foodborne outbreaks through data mining approaches. A statistical analysis was used to identify the associations between outbreak factors and food sources, and the factors that were strongly significant were selected as predictive factors for food vehicles. A multinomial prediction model was built based on factors selected for predicting "simple" foods (beef, dairy, and vegetables) as sources of outbreaks. In addition, the relations between the food vehicles and common etiologies were investigated through text mining approaches (support vector machines, logistic regression, random forest, and naïve Bayes). A support vector machine model was identified as the optimal model to predict etiologies from the occurrence of food vehicles. Association rules also indicated the specific food vehicles that have strong relations to the etiologies. Meanwhile, a food ingredient network describing the relationships between foods and ingredients was constructed and used with Monte Carlo simulation to predict possible ingredients from foods that cause an outbreak. The simulated results were confirmed with foods and ingredients that are already known to cause historical foodborne outbreaks. The method could provide insights into the prediction of the possible ingredient sources of contamination when given the name of a food. The results could provide insights into the early identification of food sources of contamination and assist in future outbreak investigations. The data-driven approach will provide a new perspective and strategies for discovering hidden knowledge from massive data.

A Novel Foodborne Illness Detection and Web Application Tool Based on Social Media.

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak.

[Diagnostic value of MRI in testicular tumor].

OBJECTIVE: To study the MRI manifestation of testicular tumor and the value of MRI in the diagnosis of the disease. METHODS: We retrospectively analyzed 23 cases of pathologically confirmed testicular tumor, and observed the morphological characteristics, signals and surrounding conditions of the tumor using plain and enhanced MRI scanning. RESULTS: Of the 23 cases, seminoma was identified in 7, mixed germinoma in 3, teratoma in 3, endodermal sinus tumor in 2, epidermoid in 1, Leydig cell tumor in 1, leucoma in 1, nonspecific inflammatory mass in 3, and tuberculosis in 2. MRI revealed the precise locations and specific characteristics of CONCLUSION: Based on MRI findings and clinical manifestation, most testicular tumors can be diagnosed correctly.

Crowdsourcing and machine learning approaches for extracting entities indicating potential foodborne outbreaks from social media.

Foodborne outbreaks are a serious but preventable threat to public health that often lead to illness, loss of life, significant economic loss, and the erosion of consumer confidence. Understanding how consumers respond when interacting with foods, as well as extracting information from posts on social media may provide new means of reducing the risks and curtailing the outbreaks. In recent years, Twitter has been employed as a new tool for identifying unreported foodborne illnesses. However, there is a huge gap between the identification of sporadic illnesses and the early detection of a potential outbreak. In this work, the dual-task BERTweet model was developed to identify unreported foodborne illnesses and extract foodborne-illness-related entities from Twitter. Unlike previous methods, our model leveraged the mutually beneficial relationships between the two tasks. The results showed that the F1-score of relevance prediction was 0.87, and the F1-score of entity extraction was 0.61. Key elements such as time, location, and food detected from sentences indicating foodborne illnesses were used to analyze potential foodborne outbreaks in massive historical tweets. A case study on tweets indicating foodborne illnesses showed that the discovered trend is consistent with the true outbreaks that occurred during the same period.

BF175 inhibits endometrial carcinoma through SREBP-regulated metabolic pathways in vitro.

Elevated lipogenesis is an important metabolic hallmark of rapidly proliferating tumor such as endometrial carcinoma (EC). The sterol regulatory element-binding protein 1 (SREBP1) is a master regulator of lipogenesis and involved in EC proliferation. BF175 is a novel chemical inhibitor of SREBP pathway, and has shown potent anti-lipogenic effects. However, the effect of BF175 on EC cells are yet to be determined. In the present study, we found that BF175 decreased cell viability, colony formation and migratory capacity, inducing autophagy and mitochondrial related apoptosis in EC cell line AN3CA. Z-VAD-FMK partially attenuated the effect of BF175 on AN3CA. In addition, BF175 significantly downregulated SREBPs and their downstream genes. The levels of free fatty acids and total cholesterol were also inhibited. Microarray analysis suggested BF175 treatment obviously affected lipid metabolic pathways in EC. Taken together, we validated BF175 exhibited anti-tumor activity by targeting SREBP-dependent lipogenesis and inducing apoptosis which mitochondrial pathway involved in, suggesting that it's potential as a novel therapeutic reagent for EC.