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. Kidneys were collected from mice treated with imipramine or chloroquine and then subjected to atmospheric pressure matrix-assisted laser desorption ionization-mass spectrometry imaging (AP-MALDI-MSI). We observed differential distributions of the drugs and their metabolites between male and female kidneys. Imipramine showed prominent distributions in the cortex and medulla in male and female kidneys, respectively. Desipramine, one of the metabolites of imipramine, showed significantly higher (*** p < 0.001) distributions in the medulla of the male kidney compared to that of the female kidney. Chloroquine and its metabolites were accumulated in the pelvis of both male and female kidneys. Interestingly, they showed a characteristic distribution in the medulla of the female kidney, while almost no distributions were observed in the same areas of the male kidney. For the first time, our study revealed that the distributions of imipramine, chloroquine, and their metabolites were different in male and female kidneys.

Machine learning models for prediction of double and triple burdens of non-communicable diseases in Bangladesh.

Increasing prevalence of non-communicable diseases (NCDs) has become the leading cause of death and disability in Bangladesh. Therefore, this study aimed to measure the prevalence of and risk factors for double and triple burden of NCDs (DBNCDs and TBNCDs), considering diabetes, hypertension, and overweight and obesity as well as establish a machine learning approach for predicting DBNCDs and TBNCDs. A total of 12,151 respondents from the 2017 to 2018 Bangladesh Demographic and Health Survey were included in this analysis, where 10%, 27.4%, and 24.3% of respondents had diabetes, hypertension, and overweight and obesity, respectively. Chi-square test and multilevel logistic regression (LR) analysis were applied to select factors associated with DBNCDs and TBNCDs. Furthermore, six classifiers including decision tree (DT), LR, naïve Bayes (NB), k-nearest neighbour (KNN), random forest (RF), and extreme gradient boosting (XGBoost) with three cross-validation protocols (K2, K5, and K10) were adopted to predict the status of DBNCDs and TBNCDs. The classification accuracy (ACC) and area under the curve (AUC) were computed for each protocol and repeated 10 times to make them more robust, and then the average ACC and AUC were computed. The prevalence of DBNCDs and TBNCDs was 14.3% and 2.3%, respectively. The findings of this study revealed that DBNCDs and TBNCDs were significantly influenced by age, sex, marital status, wealth index, education and geographic region. Compared to other classifiers, the RF-based classifier provides the highest ACC and AUC for both DBNCDs (ACC = 81.06% and AUC = 0.93) and TBNCDs (ACC = 88.61% and AUC = 0.97) for the K10 protocol. A combination of considered two-step factor selections and RF-based classifier can better predict the burden of NCDs. The findings of this study suggested that decision-makers might adopt suitable decisions to control and prevent the burden of NCDs using RF classifiers.

Predicting the risk of diabetic retinopathy using explainable machine learning algorithms.

BACKGROUND AND OBJECTIVE: Diabetic retinopathy (DR) is a global health concern among diabetic patients. The objective of this study was to propose an explainable machine learning (ML)-based system for predicting the risk of DR. MATERIALS AND METHODS: This study utilized publicly available cross-sectional data in a Chinese cohort of 6374 respondents. We employed boruta and least absolute shrinkage and selection operator (LASSO) based feature selection methods to identify the common predictors of DR. Using the identified predictors, we trained and optimized four widly applicable models (artificial neural network, support vector machine, random forest, and extreme gradient boosting (XGBoost) to predict patients with DR. Moreover, shapely additive explanation (SHAP) was adopted to show the contribution of each predictor of DR in the prediction. RESULTS: Combining Boruta and LASSO method revealed that community, TCTG, HDLC, BUN, FPG, HbAlc, weight, and duration were the most important predictors of DR. The XGBoost-based model outperformed the other models, with an accuracy of 90.01%, precision of 91.80%, recall of 97.91%, F1 score of 94.86%, and AUC of 0.850. Moreover, SHAP method showed that HbA1c, community, FPG, TCTG, duration, and UA1b were the influencing predictors of DR. CONCLUSION: The proposed integrating system will be helpful as a tool for selecting significant predictors, which can predict patients who are at high risk of DR at an early stage in China.

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. The isolated MCC, synthesized Na-CMC, and corresponding hydrogels were characterized by Fourier transform infrared (FTIR), X-ray diffraction (XRD), scanning electronic microscopy (SEM), and energy dispersive spectroscopy (EDS) for functional groups, crystallinity, surface morphology, and composite elemental composition, respectively. Pseudo-first-order, pseudo-second-order, and Elovich models were used to investigate the adsorption kinetics. The pseudo-second-order one is favorable for both hydrogels. Freundlich, Langmuir, and Temkin adsorption isotherm models were investigated. MCCH follows the Freundlich model (R2 = 0.9967), and Na-CMCH follows the Langmuir isotherm model (R2 = 0.9974). The methylene blue (MB) dye adsorption capacities of ionic (Na-CMCH) and nonionic (MCCH) hydrogels in different contact times (up to 600 min), initial concentrations (10-50 ppm), and temperatures (298-318 K) were investigated and compared. The maximum adsorption capacity of MCCH and Na-CMCH was 23.73 and 196.46 mg/g, respectively, and the removal efficiency of MB was determined to be 26.93% for MCCH and 58.73% for Na-CMCH. The Na-CMCH efficiently removed the MB from aqueous solutions as well as spiked industrial wastewater. The Na-CMCH also remarkably efficiently reduced priority metal ions from an industrial effluent. An effort has been made to utilize inexpensive, readily available, and environmentally friendly waste materials (jute sticks) to synthesize valuable adsorbent materials.

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. After that, protein-protein interaction networks were constructed using STRING and then Cytoscape was used to determine hub genes, significant modules, and their associated genes. Simultaneously, three ML-based techniques such as support vector machine (SVM), least absolute shrinkage and selection operator-logistic regression (LASSO-LR), and partial least squares-discriminant analysis (PLS-DA) were implemented to determine the discriminative genes of HCC from common DEGs. Moreover, metadata of hub genes were formed by listing all hub genes from existing studies to incorporate other findings in our analysis. Finally, seven key candidate genes (ASPM, CCNB1, CDK1, DLGAP5, KIF20 A, MT1X, and TOP2A) were identified by intersecting common genes among hub genes, significant modules genes, discriminative genes from SVM, LASSO-LR, and PLS-DA, and meta hub genes from existing studies. Another three independent test datasets were also used to validate these seven key candidate genes using AUC, computed from ROC.

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. MATERIALS AND METHODS: The HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN. RESULTS: The overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN. CONCLUSIONS: The proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment.

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. The study identified 187 alleles at 18 SSR loci. The amplified allele frequency per microsatellite locus was 10.4 and the highest allele per locus was 17 in SSR primer pair phi026. SSR primer set p-umc1292, phi074 and phi090 showed the lowest 6 alleles per genotype per locus. The locus phi026 showed the highest degree of gene diversity (0.92), and the locus p-umc1292 had the lowest of gene diversity (0.77) with a mean value of 0.862 among the microsatellites. At each site, the most prevalent allele varied between 0.14 (bnlg371) and 0.36. (p-umc1292). At any given locus, an average of 0.22 out of the 22 selected maize inbred lines had a common major allele. The average value of the polymorphic information content (PIC) was 0.85, within the range of 0.74 at the lowest to 0.92 at the highest. The higher PIC values of phi026 and nc013 established them to be the best markers for maize inbred lines. The UPGMA clustering generated seven distinct groups having 12.5% of similarity coefficient. The results revealed that inbred lines E10, E27, E19, E34, E35, E4, E43, E28, E11, E21, E17, E38, E25, E34, E14, E16, E39 and E3 were more diversified. These lines are promising to be used as parent materials for hybrid maize development in the future.

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). Then, five ML-based techniques, including logistic regression, naïve Bayes, J48, multilayer perceptron, and random forest (RF) were employed to predict T2D. This study utilized two publicly available datasets, derived from the National Health and Nutrition Examination Survey, 2009-2010 and 2011-2012. About 4922 respondents with 387 T2D patients were included in 2009-2010 dataset, whereas 4936 respondents with 373 T2D patients were included in 2011-2012. This study identified six risk factors (age, education, marital status, SBP, smoking, and BMI) for 2009-2010 and nine risk factors (age, race, marital status, SBP, DBP, direct cholesterol, physical activity, smoking, and BMI) for 2011-2012. RF-based classifier obtained 95.9% accuracy, 95.7% sensitivity, 95.3% F-measure, and 0.946 area under the curve.

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. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

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. It was a household survey that included people aged 15 years or older and covered all 30 states and 2 Union Territories (UTs) of India. A logistic regression model was used to determine the correlates of smoke-free status of homes and workplaces. Additionally, the Pearson correlation was used to explore the relationship between smoke-free status and the proportion of participants exposed to SHS both at homes and in the workplaces. RESULTS: The overall prevalence of smoke-free status in the home and workplace was 62.8% and 51.7%, respectively. Results of multivariate analysis (Logistic regression) illustrated that indicators like tobacco smoking status, place of residence, region, education, occupation, wealth quintile, and knowledge status about children's illness were significantly associated with the respondent's intention to live in a completely smoke-free environment both at home and in the workplace in India. This study revealed that SHS exposure was significantly negatively associated with a smoke-free status. CONCLUSION: This study will help the policymakers to promote efficient policies for improving smoke-free status and to ensure a better environment both at home and in the workplace in India.

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 N,N-dimethylaniline. Characterization was done by 1H-NMR, 13C-NMR, and FTIR spectroscopy. Differential scanning calorimetry (DSC) reveals that phase transition for this molecule is exothermic. The optical band gap is estimated from the absorption cutoff point using UV-Visible spectroscopy. Thermal gravimetric analysis (TGA) addresses the thermal stability of the molecule and is found to be at ∼250 °C. The structure of the synthesized molecule is analogous to that of methyl orange and contains three azo groups. These three azo groups help accept more than two protons and provide two pK a values when diprotic acid or a mixture of acids is used in different titrations. Specifically, when a polybasic acid is in strong base titration, the pK a values were found to be 3.5 and 9.1. Moreover, for strong base and (strong + weak) acid mixture titration, the pK a values are found to be 9.2 and 3.3. Furthermore, the pK a values are found to be 8.6 and 2.8 for (strong and weak) base mixture and (strong and weak) acid mixture titration, respectively. Owing to its increased proton accepting capacity, it can be found in the two pH ranges of 2.1-3.8 for orange color and 8.2-9.8 for yellow color, thus indicating a unique property as a universal indicator for acid-base titration. The dissociation constant of this dye is found to be 3.4 × 10-6, determined in a mixed aqueous solution of 10 wt% ethanol, and a linear relationship between pK a and pH is observed in this solvent system.

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. MATERIALS AND METHODS: The data utilized in this research was derived from the latest nationally representative Bangladesh Demographic and Health Survey, 2017-18, and included a total of 2327 respondents. The concentration index (C-index) and concentration curve were used to investigate the socioeconomic inequality in LBW among the singleton newborn babies. Additionally, an adjusted binary logistic regression model was utilized for calculating adjusted odds ratio and p-value (<0.05) to identify the significant determinants of LBW. RESULTS: The overall prevalence of LBW among singleton births in Bangladesh was 14.27%. We observed that LBW rates were inequitably distributed across the socioeconomic groups (C-index: -0.096, 95% confidence interval: [-0.175, -0.016], P = 0.029), with a higher concentration of LBW infants among mothers living in the lowest wealth quintile (poorest). Regression analysis revealed that maternal age, region, maternal education level, wealth index, height, age at 1st birth, and the child's aliveness (alive or died) at the time of the survey were significantly associated determinants of LBW in Bangladesh. CONCLUSION: In this study, socioeconomic disparity in the prevalence of singleton LBW was evident in Bangladesh. Incidence of LBW might be reduced by improving the socioeconomic status of poor families, paying special attention to mothers who have no education and live in low-income households in the eastern divisions (e.g., Sylhet, Chittagong). Governments, agencies, and non-governmental organizations should address the multifaceted issues and implement preventive programs and policies in Bangladesh to reduce LBW.

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. Therefore, the present study aimed to identify key candidate genes for IgAN using machine learning (ML) and statistics-based bioinformatics models. First, differentially expressed genes (DEGs) were identified using limma, and then enrichment analysis was performed on DEGs using DAVID. Protein-protein interaction (PPI) was constructed using STRING and Cytoscape was used to determine hub genes based on connectivity and hub modules based on MCODE scores and their associated genes from DEGs. Furthermore, ML-based algorithms, namely support vector machine (SVM), least absolute shrinkage and selection operator (LASSO), and partial least square discriminant analysis (PLS-DA) were applied to identify the discriminative genes of IgAN from DEGs. Finally, the key candidate genes (FOS, JUN, EGR1, FOSB, and DUSP1) were identified as overlapping genes among the selected hub genes, hub module genes, and discriminative genes from SVM, LASSO, and PLS-DA, respectively which can be used for the diagnosis and treatment of IgAN.

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. Overall, about 80% of women consumed IFA supplements in any quantity. About 76% of women received antenatal care at least once, only 8% received ≥180 IFA tablets, and 6% had user adherence-adjusted coverage of antenatal IFA supplementation. Multivariable analysis showed a linear relationship between the number of antenatal care (ANC) visits and the number of IFA supplements consumed, which was modified by the timing of the first ANC visit. Women's education, free IFA, and advice on IFA were also associated with higher IFA consumption. Interventions targeting at least eight ANC contacts, starting early in pregnancy, providing advice on the importance of IFA, and providing IFA supplements in higher quantity at ANC contacts are likely to increase effective coverage of antenatal IFA supplementation.

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. Worldwide, millions of people are at risk due to the current pandemic of coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2). Despite the lack of clinically authorized antiviral medications and vaccines for COVID-19, clinical trials of many recognized antiviral agents, their combination, and vaccine development in patients with confirmed COVID-19 are still ongoing. This discovery gave us a chance to get immune to this disease worldwide and end the pandemic. However, the unexpected capacity of mutation of the SARS-CoV-2 virus makes it difficult, like the recent SAS-CoV-2 Omicron variant. Therefore, there is a great necessity to spread the vaccination programs and prevent the spread of this dreadful epidemic by identifying and isolating afflicted patients. Furthermore, several COVID-19 tests are thought to be expensive, time-consuming, and require the use of adequately qualified persons to be carried out efficiently. In addition, we also conversed about how the various COVID-19 testing methods can be implemented for the first time in a developing country and their cost-effectiveness, accuracy, human resources requirements, and laboratory facilities.

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. The risk factors associated with low birth weight were investigated using binary logistic regression. Two machine learning-based classifiers (logistic regression and decision tree) were adopted to characterize and predict low birth weight. The model performances were evaluated by accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve. RESULTS: The average percentage of low birth weight in Bangladesh was 16.2%. The respondent's region, education, wealth index, height, twin child, and alive child were statistically significant risk factors for low birth weight babies. The logistic regression-based classifier performed 87.6% accuracy and 0.59 area under the curve for holdout (90:10) cross-validation, whereas the decision tree performed 85.4% accuracy and 0.55 area under the curve. CONCLUSIONS: Logistic regression-based classifier provided the most accurate classification of low birth weight babies and has the highest accuracy. This study's findings indicate the necessity for an efficient, cost-effective, and integrated complementary approach to reduce and correctly predict low birth weight babies in Bangladesh.

Kernel Water Relations and Kernel Filling Traits in Maize (Zea mays 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. In this context, two field experiments in two consecutive years were conducted with five levels of water regimes: control (D1), and four water deficit treatments [V10 to V13 (D2); V13 to V17 (D3); V17 to blister stage (D4); blisters to physiological maturity (D5)], on three maize hybrids (Pioneer 30B80, NK 40, and Suwan 4452) in Expt. 1. Expt. 2 had four water regimes: control (D1), three water deficit treatments [V10 to anthesis (D2); anthesis to milk stage (D3); milk to physiological maturity (D4)], and two maize hybrids (NK 40 and Suwan 4452). Water deficit imposed at different stages significantly reduced maximum kernel water content (MKWC), kernel filling duration (KFD), final kernel weight (FKW), and kernel weight ear-1 while it increased kernel water loss rate (KWLR), kernel filling rate (KFR), and stem weight depletion (SWD) across maize hybrids in both experiments. The lowest MKWC under water deficit was at D3 in both experiments, indicating that lower KFR results in lowest FKW in maize. Findings indicate that the MKWC (R 2 = 0.85 and 0.41) and KFR (R 2 = 0.62 and 0.37) were positively related to FKW in Expt. 1 and 2, respectively. The KFD was reduced by 5, 7, 7, and 11 days under water deficit at D3, D4 in Expt. 2 and D4, D5 in Expt. 1 as compared to control, respectively. Water deficit at D5 in Expt. 1 and D4 in Expt. 2 increased KWLR, KFR, and SWD. In Expt. 2, lower canopy temperature and electrical conductivity indicated cell membrane stability across water regimes in NK 40. Hybrid NK 40 under water deficit had significantly higher cellular adaptation by increasing the number of xylem vessel while reducing vessel diameter in leaf mid-rib and attached leaf blade. These physiological adjustments improved efficient transport of water from root to the shoot, which in addition to higher kernel water content, MKWC, KFD, KFR, and stem reserve mobilization capacity, rendered NK 40 to be better adapted to water-deficit conditions under tropical environments.

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

[This corrects the article DOI: 10.1371/journal.pone.0245923.].

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. The leave-one-out cross-validation protocol is executed and compared ML-based techniques performance using accuracy and area under the curve (AUC). RESULTS: The data utilized in this work consists of 133 respondents having 73 DN patients with an average age of 69.6±10.2 years and 54.2% of DN patients are female. Our findings illustrate that PCA combined with SVM-RBF classifier yields 88.7% accuracy and 0.91 AUC at 0.96 PCA cutoff. CONCLUSIONS: This study also suggests that PCA combined with SVM-RBF classifier may correctly classify DN patients with the highest accuracy when compared to the models published in the existing research. Prospective studies are warranted to further validate the applicability of our model in clinical settings.