Type 2 diabetes is one of the most prevalent non-communicable diseases in Bangladesh. However, the correlates of type 2 diabetes among adults in Bangladesh remain unknown. We aimed to investigate the correlates of type 2 diabetes among the adults in Bangladesh. We conducted a cross-sectional study using data from the nationally representative 2011 Bangladesh Demographic and Health Survey. A random sample of 7,543 (3,823 women and 3,720 men) adults of age 35 years and older from both urban and rural areas, who participated in the survey was included. Diabetes was defined as having a fasting plasma blood glucose level of ≥ 7 mm/L or taking diabetes medication during the survey. Hypothesized factors, e.g., age, sex, education, place of residence, social status, body mass index, and hypertension were considered in the analyses. Multivariable logistic regression models were used to identify the important correlates of type 2 diabetes. Among the respondents, the overall prevalence of diabetes was 11 %, and the prevalence was slightly higher in women (11.2 %) than men (10.6 %). Respondents with the age group of 55–59 years had higher odds of having diabetes (odds ratios (OR) = 2.37, 95 % confidence interval (CI): 1.76–3.21) than the age group of 35–39 years. Moreover, respondents who had higher educational attainment (OR = 1.67, 95 % CI: 1.18–2.36) and higher social status (OR = 2.01, 95 % CI: 1.50–2.70) had higher odds of having diabetes than the respondents with no education and lower social status, respectively. We also found socioeconomic status, place of residence (rural or urban), regions of residence (different divisions), overweight and obesity, and hypertension as significant correlates of type 2 diabetes in Bangladesh. Our study shows that older age, higher socioeconomic status, higher educational attainment, hypertension, and obesity were found to be significant correlates of type 2 diabetes. Need-based policy program strategies including early diagnosis, awareness via mass media, and health education programs for changing lifestyles should be initiated for older age, wealthy, and/or higher educated individuals in Bangladesh. Moreover, area-specific longitudinal research is necessary to find out the underlying causes of regional variations.

Medical diagnoses have important implications for improving patient care, research, and policy. For a medical diagnosis, health professionals use different kinds of pathological methods to make decisions on medical reports in terms of the patients’ medical conditions. Recently, clinicians have been actively engaged in improving medical diagnoses. The use of artificial intelligence and machine learning in combination with clinical findings has further improved disease detection. In the modern era, with the advantage of computers and technologies, one can collect data and visualize many hidden outcomes such as dealing with missing data in medical research. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning (ML), data-driven algorithms can be utilized to validate existing methods and help researchers to make potential new decisions. The purpose of this study was to extract significant predictors for liver disease from the medical analysis of 615 humans using ML algorithms. Data visualizations were implemented to reveal significant findings such as missing values. Multiple imputations by chained equations (MICEs) were applied to generate missing data points, and principal component analysis (PCA) was used to reduce the dimensionality. Variable importance ranking using the Gini index was implemented to verify significant predictors obtained from the PCA. Training data ($n_{train}=399$) for learning and testing data ($n_{test}=216$) in the ML methods were used for predicting classifications. The study compared binary classifier machine learning algorithms (i.e., artificial neural network, random forest (RF), and support vector machine), which were utilized on a published liver disease data set to classify individuals with liver diseases, which will allow health professionals to make a better diagnosis. The synthetic minority oversampling technique was applied to oversample the minority class to regulate overfitting problems. The RF significantly contributed ($p<0.001$) to a higher accuracy score of 98.14% compared to the other methods. Thus, this suggests that ML methods predict liver disease by incorporating the risk factors, which may improve the inference-based diagnosis of patients.

Background: Morphological evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer is gaining momentum as an immunological biomarker. This experiment evaluates the role of TILs in distant tumors as a measure of abscopal effect from cryoablation of breast cancer. Methods: BALB/c mice underwent bilateral orthotopic transplant with 4T1-12B (triple-negative) cells. At 2 weeks, left tumors were treated by either resection (standard of care group) or cryoablation (intervention group) followed by resection of the distant right tumors 1 week posttreatment. TIL scores were calculated from hematoxylin and eosin-stained sections and phenotyped for cytotoxic T-lymphocyte (CTL) markers by immunofluorescence. Primarily resected tumors served as baseline (T_baseline), whereas resected distant right-sided served as the readout for abscopal effect (Abs_Res or Abs_Cryo). Mice were monitored for tumor recurrence and metastasis. Results: The Abs_Cryo had a significant mean (SD) increase in stromal (2.8 [1.1]%; p = 0.015) and invasive margin TILs (50 [12]%; p = 0.02) compared with T_Baseline (1.0 [0]% and 31 [4.9]%, respectively). CTL phenotyping revealed a significant increase in mean (SD) CD8⁺ T cells (15.7 [12.1]; p = 0.02) and granzyme B (4.8 [3.6]; p = 0.048) for the Abs_Cryo compared with T_Baseline (5.2 [4.7] and 2.4 [0.9], respectively). Posttreatment, the cryoablation group had no recurrence or metastasis, whereas the resected group showed local recurrence and lung metastasis in 40% of the mice. Postprocedure increase in TIL score of distant tumors was associated with decrease in tumor relapse (p = 0.02). Conclusions: Cryoablation induced a robust tumor-specific TIL response compared with resection, suggesting an abscopal effect leading to the prevention of cancer recurrence and metastasis.

This article is concerned with making predictive inference on the basis of a doubly censored sample from a two-parameter Rayleigh life model. We derive the predictive distributions for a single future response, the ith future response, and several future responses. We use the Bayesian approach in conjunction with an improper flat prior for the location parameter and an independent proper conjugate prior for the scale parameter to derive the predictive distributions. We conclude with a numerical example in which the effect of the hyperparameters on the mean and standard deviation of the predictive density is assessed.

The purpose of our study is to understand the role of the RALBP1 gene in oxidative stress (OS), mitochondrial dysfunction and cognition in Alzheimer’s disease (AD) pathogenesis. The RALPB1 gene encodes the 76 kDa protein RLIP76 (Rlip). Rlip functions as a stress-responsive/protective transporter of glutathione conjugates (GS-E) and xenobiotic toxins. We hypothesized that Rlip may play an important role in maintaining cognitive function. The aim of this study is to determine whether Rlip deficiency in mice is associated with AD-like cognitive and mitochondrial dysfunction. Brain tissue obtained from cohorts of wildtype (WT) and Rlip^+/− mice were analyzed for OS markers, expression of genes that regulate mitochondrial fission/fusion, and synaptic integrity. We also examined mitochondrial ultrastructure in brains obtained from these mice and further analyzed the impact of Rlip deficiency on gene networks of AD, aging, stress response, mitochondrial function, and CREB signaling. Our studies revealed a significant increase in the levels of OS markers and alterations in the expression of genes and proteins involved in mitochondrial biogenesis, dynamics and synapses in brain tissues from these mice. Furthermore, we compared the cognitive function of WT and Rlip^+/− mice. Behavioral, basic motor and sensory function tests in Rlip^+/− mice revealed cognitive decline, similar to AD. Gene network analysis indicated dysregulation of stress-activated gene expression, mitochondrial function and CREB signaling genes in the Rlip^+/− mouse brain. Our results suggest that Rlip deficiency-associated increases in OS and mitochondrial dysfunction could contribute to the development or progression of OS-related AD processes.

Background: Cervical cancer is the second most common cancer among women resulting in nearly 500,000 cases annually. Screening leads to better treatment and survival time. However, human papillomavirus (HPV) exposure, screening, and treatment vary among races and ethnicities in the United States. The purpose of this study is to examine disparities in characteristics of cervical cancer and survival of cases between White Hispanic (WH) and White non-Hispanic (WNH) women in the United States. Methods: We used a stratified random sampling method to select cervical cancer patient records from nine states; a simple random sampling method to extract the demographic and disease characteristics data within states from the Surveillance Epidemiology and End Results (SEER) database. We used statistical probability distribution methods for discrete and continuous data. The chi-square test and independent samples t -test were used to evaluate statistically significant differences. Furthermore, the Cox Proportional Regression and the Kaplan–Meier survival estimators were used to compare WH and WNH population survival times in the United States. Results: The samples of WNH and WH women included 4,000 cervical cancer cases from 1973–2009. There were statistically significant differences between ethnicities: marital status ( p < 0.001); primary site of cancer ( p < 0.001); lymph node involvement ( p < 0.001); grading and differentiation ( p < 0.0001); and tumor behavior ( p < 0.001). The mean age of diagnosis for both groups showed no statistical differences. However, the mean survival time for WNH was 221.7 (standard deviation [SD] = 118.1) months and for WH was 190.3 (SD = 120.3), which differed significantly ( p < 0.001). Conclusions: Clear disparities exist in risk factors, cervical cancer characteristics, and survival time between WH and WNH women.