MicroRNA dynamics, PTEN/PI3K/AKT signaling, and their relationship to breast cancer: prospects for pharmaceuticals and natural product application.

Background: Globally, Breast Cancer (BC) is the most frequent cancer in women and has a major negative impact on the physical and emotional well-being of its patients as well as one of the most common cancers to be diagnosed. Numerous studies have been published to identify various molecular pathways, including PI3K/AKT/PTEN. Moreover, growing evidence suggests that miRNAs have been found to play a vital role in the growth and carcinogenesis of tumors.

Numerical analysis and device modelling of a lead-free SrPI/SrSbI double absorber solar cell for enhanced efficiency.

Halide perovskites are the most promising options for extremely efficient solar absorbers in the field of photovoltaic (PV) technology because of their remarkable optical qualities, increased efficiency, lightweight design, and affordability. This work examines the analysis of a dual-absorber solar device that uses SrSbI as the bottom absorber layer and SrPI as the top absorber layer of an inorganic perovskite through the SCAPS-1D platform. The device architecture includes ZnSe as the electron transport layer (ETL), while the active layer consists of SrPI and SrSbI with precise bandgap values.

Elucidating Gender-Specific Distribution of Imipramine, Chloroquine, and Their Metabolites in Mice Kidney Tissues through AP-MALDI-MSI.

Knowledge of gender-specific drug distributions in different organs are of great importance for personalized medicine and reducing toxicity. However, such drug distributions have not been well studied. In this study, we investigated potential differences in the distribution of imipramine and chloroquine, as well as their metabolites, between male and female kidneys.

Jute Stick-Derived Cellulose-Based Hydrogel: Synthesis, Characterization, and Methylene Blue Removal from Aqueous Solution.

In this work, microcrystalline cellulose (MCC) was isolated from jute sticks and sodium carboxymethyl cellulose (Na-CMC) was synthesized from the isolated MCC. Na-CMC is an anionic derivative of microcrystalline cellulose. The microcrystalline cellulose-based hydrogel (MCCH) and Na-CMC-based hydrogel (Na-CMCH) were prepared by using epichlorohydrin (ECH) as a crosslinker by a chemical crosslinking method.

Gene Expression and Metadata Based Identification of Key Genes for Hepatocellular Carcinoma Using Machine Learning and Statistical Models.

Biomarkers associated with hepatocellular carcinoma (HCC) are of great importance to better understand biological response mechanisms to internal or external intervention. The study aimed to identify key candidate genes for HCC using machine learning (ML) and statistics-based bioinformatics models. Differentially expressed genes (DEGs) were identified using limma and then selected their common genes among DEGs identified from four datasets.

Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.

Background And Objectives: Hypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.

Microsatellite marker-based genetic diversity assessment among exotic and native maize inbred lines of Bangladesh.

Hybrid development is basically dependent on the variability among available genetic resources. Polymorphism among the maize inbreds is essentially needed for maize hybridization. This study aimed at the assessment of diversity among 22 maize inbreds by 18 microsatellite markers.

Identification of the risk factors of type 2 diabetes and its prediction using machine learning techniques.

This study identified the risk factors for type 2 diabetes (T2D) and proposed a machine learning (ML) technique for predicting T2D. The risk factors for T2D were identified by multiple logistic regression (MLR) using p-value (p<0.05).

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning.

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC.

Smoke-free status of homes and workplaces among Indian people: Evidence from Global Adult Tobacco SurveyData-2016/2017.

Aims: This study aimed to determine the impact of correlates on tobacco control/smoke-free status of homes and workplace among Indian people. To assess the magnitude of the problem, the relationship between smoke-free status and secondhand smoke (SHS) exposure was also explored.

Methods: Data was extracted from the Global Adult Tobacco Survey Data (GATS)-2017.

Synthesis, characterization and application of a novel polyazo dye as a universal acid-base indicator.

A novel organic polyazo dye is synthesized by the diazotization of aromatic aniline, followed by coupling it with sulfanilic acid and ,-dimethylaniline. Characterization was done by H-NMR, C-NMR, and FTIR spectroscopy. Differential scanning calorimetry (DSC) reveals that phase transition for this molecule is exothermic.

Socioeconomic inequality in the prevalence of low birth weight and its associated determinants in Bangladesh.

Background And Objective: Low birth weight (LBW) is a major risk factor of child mortality and morbidity during infancy (0-3 years) and early childhood (3-8 years) in low and lower-middle-income countries, including Bangladesh. LBW is a vital public health concern in Bangladesh. The objective of the research was to investigate the socioeconomic inequality in the prevalence of LBW among singleton births and identify the significantly associated determinants of singleton LBW in Bangladesh.

Identification of key candidate genes for IgA nephropathy using machine learning and statistics based bioinformatics models.

Immunoglobulin-A-nephropathy (IgAN) is a kidney disease caused by the accumulation of IgAN deposits in the kidneys, which causes inflammation and damage to the kidney tissues. Various bioinformatics analysis-based approaches are widely used to predict novel candidate genes and pathways associated with IgAN. However, there is still some scope to clearly explore the molecular mechanisms and causes of IgAN development and progression.

Iron and Folic Acid Supplementation in Pregnancy: Findings from the Baseline Assessment of a Maternal Nutrition Service Programme in Bangladesh.

Effective coverage of antenatal iron and folic acid (IFA) supplementation is important to prevent adverse maternal and newborn health outcomes. We interviewed 2572 women from two rural districts in Bangladesh who had a live birth in the preceding six months. We analysed the number of IFA tablets received and consumed during pregnancy and examined the factors influencing IFA consumption by multiple linear regression and user adherence-adjusted effective coverage of IFA (consuming ≥180 IFA tablets) by Poisson regression.

COVID-19 diagnostic methods in developing countries.

COVID-19 has become one of the few leading causes of death and has evolved into a pandemic that disrupts everyone's routine, and balanced way of life worldwide, and will continue to do so. To bring an end to this pandemic, scientists had put their all effort into discovering the vaccine for SARS-CoV-2 infection. For their dedication, now, we have a handful of COVID-19 vaccines.

Predicting risks of low birth weight in Bangladesh with machine learning.

Background And Objective: Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of low birth weight and predict low birth weight babies based on machine learning algorithms.

Materials And Methods: Low birth weight data has been taken from the Bangladesh Demographic and Health Survey, 2017-18, which had 2351 respondents.

Kernel Water Relations and Kernel Filling Traits in Maize ( L.) Are Influenced by Water-Deficit Condition in a Tropical Environment.

Water deficit is a major limiting condition for adaptation of maize in tropical environments. The aims of the current observations were to evaluate the kernel water relations for determining kernel developmental progress, rate, and duration of kernel filling, stem reserve mobilization in maize. In addition, canopy temperature, cell membrane stability, and anatomical adaptation under prolonged periods of pre- and post-anthesis water deficit in different hybrids was quantified to support observations related to kernel filling dynamics.

Correction: Prevalence of unintended pregnancy and its associated factors: Evidence from six south Asian countries.

[This corrects the article DOI: 10.1371/journal.pone.

Risk prediction of diabetic nephropathy using machine learning techniques: A pilot study with secondary data.

Aims: This research work presented a comparative study of machine learning (ML), including two objectives: (i) determination of the risk factors of diabetic nephropathy (DN) based on principal component analysis (PCA) via different cutoffs; (ii) prediction of DN patients using ML-based techniques.

Methods: The combination of PCA and ML-based techniques has been implemented to select the best features at different PCA cutoff values and choose the optimal PCA cutoff in which ML-based techniques give the highest accuracy. These optimum features are fed into six ML-based techniques: linear discriminant analysis, support vector machine (SVM), logistic regression, K-nearest neighborhood, naïve Bayes, and artificial neural network.

Investigation of the effect of different process variables on color and physical properties of viscose and cotton knitted fabrics.

The aim of this study was to investigate and compare the effect of process variables on the color and physical characteristics of viscose and cotton knitted fabrics. The effect of dye concentration, salt concentration, soda ash, dyeing time, dyeing temperature, material to liquor ratio, different dye class, fabric GSM, washing time, washing temperature, and enzyme concentration were investigated in terms of color strength (K/S value), color fastness, and pilling resistance. The K/S value of the colored fabric was calculated using UV visible spectrophotometer SF 650 TM and the pilling resistance of the enzyme-treated fabrics was tested by an ICI pilling tester.

Investigate the risk factors of stunting, wasting, and underweight among under-five Bangladeshi children and its prediction based on machine learning approach.

Aims: Malnutrition is a major health issue among Bangladeshi under-five (U5) children. Children are malnourished if the calories and proteins they take through their diet are not sufficient for their growth and maintenance. The goal of the research was to use machine learning (ML) algorithms to detect the risk factors of malnutrition (stunted, wasted, and underweight) as well as their prediction.