Leveraging machine learning to evaluate factors influencing vitamin D insufficiency in SLE patients: A case study from southern Bangladesh.

Vitamin D insufficiency appears to be prevalent in SLE patients. Multiple factors potentially contribute to lower vitamin D levels, including limited sun exposure, the use of sunscreen, darker skin complexion, aging, obesity, specific medical conditions, and certain medications. The study aims to assess the risk factors associated with low vitamin D levels in SLE patients in the southern part of Bangladesh, a region noted for a high prevalence of SLE. The research additionally investigates the possible correlation between vitamin D and the SLEDAI score, seeking to understand the potential benefits of vitamin D in enhancing disease outcomes for SLE patients. The study incorporates a dataset consisting of 50 patients from the southern part of Bangladesh and evaluates their clinical and demographic data. An initial exploratory data analysis is conducted to gain insights into the data, which includes calculating means and standard deviations, performing correlation analysis, and generating heat maps. Relevant inferential statistical tests, such as the Student's t-test, are also employed. In the machine learning part of the analysis, this study utilizes supervised learning algorithms, specifically Linear Regression (LR) and Random Forest (RF). To optimize the hyperparameters of the RF model and mitigate the risk of overfitting given the small dataset, a 3-Fold cross-validation strategy is implemented. The study also calculates bootstrapped confidence intervals to provide robust uncertainty estimates and further validate the approach. A comprehensive feature importance analysis is carried out using RF feature importance, permutation-based feature importance, and SHAP values. The LR model yields an RMSE of 4.83 (CI: 2.70, 6.76) and MAE of 3.86 (CI: 2.06, 5.86), whereas the RF model achieves better results, with an RMSE of 2.98 (CI: 2.16, 3.76) and MAE of 2.68 (CI: 1.83,3.52). Both models identify Hb, CRP, ESR, and age as significant contributors to vitamin D level predictions. Despite the lack of a significant association between SLEDAI and vitamin D in the statistical analysis, the machine learning models suggest a potential nonlinear dependency of vitamin D on SLEDAI. These findings highlight the importance of these factors in managing vitamin D levels in SLE patients. The study concludes that there is a high prevalence of vitamin D insufficiency in SLE patients. Although a direct linear correlation between the SLEDAI score and vitamin D levels is not observed, machine learning models suggest the possibility of a nonlinear relationship. Furthermore, factors such as Hb, CRP, ESR, and age are identified as more significant in predicting vitamin D levels. Thus, the study suggests that monitoring these factors may be advantageous in managing vitamin D levels in SLE patients. Given the immunological nature of SLE, the potential role of vitamin D in SLE disease activity could be substantial. Therefore, it underscores the need for further large-scale studies to corroborate this hypothesis.

Effect of environmental and socio-economic factors on the spreading of COVID-19 at 70 cities/provinces.

The main goal of this article is to demonstrate the impact of environmental and socio-economic factors on the spreading of COVID-19. In this research, data has been collected from 70 cities/provinces of different countries around the world that are affected by COVID-19. In this research, environmental data such as temperatures, humidity, air quality and population density and socio-economic data such as GDP (PPP) per capita, per capita health expenditure, life expectancy and total test in each of these cities/provinces are considered. This data has been analyzed using statistical models such as Poisson and negative binomial models. It is found that a negative binomial regression model is the best fit for our data. Our results reveal higher population density to be an important factor for the quick spread of COVID-19 as maintenance of social distancing requirements are more difficult in urban areas. Moreover, GDP (PPP) and PM2.5 are linked with fewer cases of COVID-19 whereas PM10, and total number of tests are strongly associated with the increase of COVID-19 case counts.

DNA Repair Mechanism Gene, XRCC1A ( Arg194Trp) but not XRCC3 ( Thr241Met) Polymorphism Increased the Risk of Breast Cancer in Premenopausal Females: A Case-Control Study in Northeastern Region of India.

X-ray repair cross complementary group gene is one of the most studied candidate gene involved in different types of cancers. Studies have shown that X-ray repair cross complementary genes are significantly associated with increased risk of breast cancer in females. Moreover, studies have revealed that X-ray repair cross complementary gene polymorphism significantly varies between and within different ethnic groups globally. The present case-control study was aimed to investigate the association of X-ray repair cross complementary 1A (Arg194Trp) and X-ray repair cross complementary 3 (Thr241Met) polymorphism with the risk of breast cancer in females from northeastern region of India. The present case-control study includes histopathologically confirmed and newly diagnosed 464 cases with breast cancer and 534 apparently healthy neighborhood community controls. Information on sociodemographic factors and putative risk factors were collected from each study participant by conducting face-to-face interviews. Genotyping of X-ray repair cross complementary 1A (Arg194Trp) and X-ray repair cross complementary 3 (Thr241Met) was carried out by polymerase chain reaction-restriction fragment length polymorphism. For statistical analysis, both univariate and multivariate logistic regression analyses were performed. We also performed stratified analysis to find out the association of X-ray repair cross complementary genes with the risk of breast cancer stratified based on menstrual status. This study revealed that tryptophan allele (R/W-W/W genotype) in X-ray repair cross complementary 1A (Arg194Trp) gene significantly increased the risk of breast cancer (adjusted odds ratio = 1.44, 95% confidence interval = 1.06-1.97, P < .05 for R/W-W/W genotype). Moreover, it was found that tryptophan allele (W/W genotype) at codon 194 of X-ray repair cross complementary 1A (Arg194Trp) gene significantly increased the risk of breast cancer in premenopausal females (crude odds ratio = 1.66, 95% confidence interval = 1.11-2.46, P < .05 for R/W-W/W genotype). The present study did not reveal any significant association of X-ray repair cross complementary 3 (Thr241Met) polymorphism with the risk of breast cancer. The present study has explored that X-ray repair cross complementary 1A (Arg194Trp) gene polymorphism is significantly associated with the increased risk of breast cancer in premenopausal females from northeastern region of India which may be beneficial for prognostic purposes.

Cryptococcus neoformans var. grubii Infection in HIV-Seronegative Patients from Northeast India: Report of Two Cases with Review of Literature.

Cryptococcus neoformans infection can occur in a wide range of hosts ranging from those who are severely immunosuppressed to those who are apparently immunocompetent. Two apparently immunocompetent HIV-seronegative patients with cryptococcal meningitis and multiple skin lesions, both due to C. neoformans var. grubii, are reported. Pregnancy was found as an associated factor in cryptococcal meningitis in a 20-year-old female patient from Arunachal Pradesh. Multiple skin lesions were the presenting feature of an 18-year-old male patient from Dibrugarh, eastern Assam. The organism was identified both phenotypically and by sequencing of ITS1 and ITS2 regions of rRNA gene. The cases are reported because of rarity of this infection in non-HIV-infected patients.

TLR2∆22 (-196-174) significantly increases the risk of breast cancer in females carrying proline allele at codon 72 of TP53 gene: a case-control study from four ethnic groups of North Eastern region of India.

Breast cancer (BC) is the second most common cancer in women. In the North Eastern Region (NER) of India, BC is emerging as an important concern as evidenced by the data available from population and hospital-based cancer registries. Studies on genetic susceptibility to BC are important to understand the increase in the incidence of BC in NER. The present case control study was conducted to investigate the association between tumour suppressor gene TP53 codon 72 polymorphism and innate immune pathway gene TLR2∆22 (-196-174) polymorphism with BC in females of NER of India for the identification of novel biomarker of BC. Four hundred sixty-two histopathologically confirmed BC cases from four states of NER of India, and 770 healthy controls were included by organizing community surveys from the neighbourhood of cases. In our study, no significant association between TP53 codon 72 polymorphisms and the risk of BC was found. However, our study has shown that TP53 codon 72 polymorphism is an important effect modifier. In the present study it was found that females carrying 22 base-pair deletion in the promoter region of their TLR2 gene had two times (AOR= 2.18, 95 % CI 1.13-4.21, p=0.019 in dominant model; AOR= 2.17, 95 % CI 1.09-4.34, p=0.027 in co-dominant model) increased risk of BC whwn they also carry proline allele at codon 72 of their TP53 gene.