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Innovative Use of Endogenous Enzymes to Enhance Silage Chemical Quality of Corn Straw for Buffalo (Bubalusbubalis) Feed in Samosir Island
Full text linkOn Samosir Island, there is an annual shortage of buffalo feed during the dry season. However, this feed shortage has caused farmers to scramble to find substitute feed. Still, it has also caused stress in the buffalo, which has led to the emergence of Haemorrhagic septicemia (HS), as Samosir is an endemic area (HS). Beginning in 2024, corn cultivation began to be widely practiced on the island of Samosir, which generates a significant amount of waste such as corn straw. This research focuses on preparing feed for buffalo (Bubalus bubalis) using fermented corn straw, with the fermentation process utilizing endogenous enzymes derived from rumen fermentation (EERF). It was obtained from fermented 100- day buffalo rumen, where the buffalo rumen comes from buffalo that consume corn straw. This study used a completely randomized design, a 3×3factorial with three replications. Factor I was various doses of EERF(2%, 4%,6%),and Factor II was different fermentation times(5d,10d, 15d).Parameters that were observed in this study were chemical quality: Dry Matter (DM), Neutral Detergent Fiber (NDF), Acid Detergent Fiber (ADF), Crude Protein (CP), and pH. Previously, isolation on EERF was carried out to identify the dominant fiber-degrading colonies, as they produce enzymes. Analysis of the potential of corn straw for buffalo feed was conducted by calculating corn straw production from the corn harvest area on Samosir Island. The result of this study is that corn straw fermentation using endogenous enzymes improves the chemical quality of silage, such as DM increasing from 38.59 to 46.17, NDF from 46.23 to 40.48, ADF from 30.53 to 24.21, CP from 9.25 to 9.86, and pH from 5.15 to 4.76. Through this improvement in nutritional quality, the dietary needs of buffaloes are met. This corn straw is sufficient for 9,565,101 buffaloes. Since corn cultivation has become intensive, there is a tendency of HS cases to decrease, with only 6 instances of HS in 2024 and 13 cases up to June 2025, while in 2023 there were 202 cases
Genetic and Immunological Determinants of Atopic Dermatitis: A Systematic Review
No full textPurpose: The present work aimed to study the role of genetic and immunological factors in the development of atopic dermatitis (AD).
Material and Methods: A thorough systematic search of relevant information on AD presented in the PubMed, ResearchGate, Scopus, Web of Science, and Google Scholar databases for 2010-2023 was carried out. The total number of studies included was 50, with a primary focus on genetic association studies, epigenetic studies, and microbiome studies.
Results: The etiopathogenetic mechanisms of the pathogenesis remain under active investigation. It has been determined that the primary factors in the occurrence of AD pathology are the interaction between genetic abnormalities and environmental factors, including climatic factors (temperature, humidity), geographic location (urban vs. rural), air pollution (e.g., particulate matter, ozone), dietary influences (e.g., fat intake, allergens), and exposure to microbes (e.g., pets, infections). An imbalance of the normal intestinal microbiota is a significant predisposing factor. The pathogenetic basis of the disease is an inflammatory process with activation of the T-cell immune response and dysfunction of the genes encoding filaggrin, transglutaminase, and keratin. These disorders lead to increased permeability of the skin barrier and unhindered penetration of allergens. AD is a heterogeneous, multifaceted condition characterised by various endotypes, phenotypes, and clinical subtypes. It frequently commences in early childhood, during the maturation of the immune system and skin barrier. Typical symptoms encompass xerosis, erythema, and pruritus, with affected children exhibiting increased sensitivity to benign irritants, indicative of early immunological dysregulation.
Conclusion: AD substantially lowers quality of life and presents mental health risks, especially in young patients. The early onset underscores the necessity for swift action to facilitate immunological development and protect child health
The Role of Lifestyle Behaviors in Early Childhood Obesity: Insights from Pre-School Populations
No full textAim: Childhood obesity is an escalating global health concern. Identifying modifiable risk factors is crucial to inform effective prevention strategies. This study explored lifestyle behaviors, including chrono-nutrition and sedentary behaviors, associated with overweight/obesity among Saudi pre-school children.
Methods: This cross-sectional study of 450 children aged 3-6 years from 20 pre-schools assessed chrono-nutrition, sedentary, and sleep behaviors through questionnaires filled by parents/guardians. Height, weight, and skinfold thickness were measured. BMI was calculated using International Obesity Task Force classifications for children aged 2-18.
Results: Overweight/obesity prevalence in preschoolers was 22.67%. Weight, skinfold, and body fat percentage were significantly higher among the overweight/obese group (p<0.001). A significant association (p=0.009) was observed between sleeping time and BMI. However, insignificant associations were observed between BMI and chrono-nutrition or physical activity. Logistic regression analysis revealed that evening (OR=0.142, 95%CI: 0.024-0.834, p=0.031) and irregular screen time (OR=0.162, 95%CI: 0.036-0.730, p=0.018) as well as more than two hours of napping (OR=0.268, 95%CI: 0.073-0.987, p=0.048) were associated with lower odds of overweight or obesity status.
Conclusions: Selected lifestyle behaviors exhibited significant associations with lower overweight/obesity among preschoolers. Future studies on pre-school children's lifestyle behaviors are warranted to enhance preventive health education and health promotion among young children
Alpha Diversity Analysis of Microbiota Dysbiosis in Normal and Colorectal Cancer of Mice Feces
Full text linkBackground: Colorectal cancer development is influenced by both environmental and genetic factors, with the gut microbiota playing a significant role. This research investigates how alterations in gut microbiota are associated with the incidence, progression, prognosis, and early detection of CRC.
Methods: An experimental laboratory study was carried out using Sprague Dawley rats that were induced with azoxymethane (AOM) and Dextran Sodium Sulfate (DSS). The thirty rats were divided into three groups: normal, cancer-induced, and treatment. The fecal microbiota profiles were examined through Next Generation Sequencing (NGS), and the data were analyzed for alpha diversity, highlighting the dynamics of the microbial community.
Results: The cancer-induced group (K2 Plus) exhibited the highest microbial diversity across Shannon, Simpson, Chao1, and PD Whole Tree indices, while the treatment group (P2 Plus) demonstrated the lowest.
Conclusion: These findings suggest that the increase in diversity observed in cancer-induced mice reflects disruption of community stability and blooming of pathobionts. Conversely, treatment with Lactococcus lactis D4 reduced diversity, potentially by selectively suppressing pro-inflammatory or pathogenic taxa, indicating a beneficial probiotic effect in mitigating dysbiosis associated with colorectal cancer
Bayesian Estimation for Factor Analysis Model in Geriatric Medicine
Full text linkBayesian factor analysis has gained prominence in statistical MODELING, particularly in handling parameter uncertainty and small sample sizes. This study presents a Metropolis- Hastings within Gibbs sampling algorithm for estimating a factor analysis model, incorporating Cauchy priors for factor loadings and log-normal priors for residual errors. Unlike traditional approaches, the proposed methodology effectively addresses heavy-tailed distributions in factor loadings and captures the skewness in residual variances. A geriatric dataset comprising 25 items related to locomotive function is used to illustrate the implementation of this Bayesian framework. Model fit is assessed using standard fit indices such as Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), and Standardized Root Mean Square Residual (SRMR). The results demonstrate that incorporating non-conjugate priors improves model flexibility and enhances interpretability in factor structure identification. The findings suggest that Cauchy and log-normal priors outperform conventional normal priors in capturing latent structures, providing a robust alternative for Bayesian factor analysis in geriatric research
Heart Disease Prediction using an Ensemble Learning Method: A Study at King Abdullah Hospital in Bisha, Saudi Arabia
Full text linkThe detection of diseases is essential to improving healthcare outcomes and saving lives. Thanks to technological advancements in medicine, machine learning has become a valuable tool for predicting future patient health outcomes. Despite the abundance of available patient data, accurately predicting cardiac disease has become increasingly challenging. In response, we developed an innovative ensemble learning approach (ELA) that combines three powerful machine learning (ML) techniques. Our ELA provides reliable predictions of cardiac disease that surpass those of the individual classification algorithms, resulting in higher accuracy. Our research yields a new combination of classification algorithms that significantly increases the prediction accuracy. We tested our model on a regional dataset collected from King Abdullah Hospital in Bisha, Saudi Arabia. We obtained the best results false negatives (FN ) of 8, true positives (TP) of 70, true negatives (TN) of 72, false positives (FP) of 6, accuracy of 0.9113, sensitivity of 0.8839, specificity of 0.95, PPV of 0.9389, NPV of 0.8878, AUC of 0.9569, F1 of 0.9133 Kappa of 0.8220, MCC of 0.8277 with an ELA comprising logistic regression (LR), extra trees (ET) and support vector machine (SVM) with radial basis function (RBF) kernel. With our ELA, medical professionals can detect cardiac disease and provide timely interventions to prevent potentially life-threatening health issues
A Flexible Extension of the Log-Logistic Distribution with Application to Cancer Data
Full text linkThis article introduces the Type II Half Logistic Topp-Leone-G (TIIHLTL-G) family, which unifies the structural properties of the Type II Half Logistic-(G TIIHL-G) and Topp-Leone-G (TL-G) family of distributions. The novelty of the TIIHLTL-G family lies in its enhanced shape flexibility and ability to model various skewness and kurtosis patterns beyond those captured by existing extensions. The statistical features of the new TIIHLTL-G family have been thoroughly investigated, including the probability-weighted moment, hazard function, moments, order statistics, quantile function, and survival function. Parameters are estimated using classical techniques, with maximum likelihood estimation performing best overall. Application to two real cancer datasets demonstrates the superiority of the proposed model over competing distributions, including the Log-logistic and related variants, with lower AIC, and BIC confirming its improved goodness-of-fit and predictive accuracy
Beyond the Cox Model: A Comparative Parametric Survival Modelling of Time to First Birth Among Married Women
Full text linkBackground: Data on time-to-first birth typically involves censoring, as not all individuals in the survey experience their first birth by the survey date. Traditional analyses often rely on the semi-parametric Cox proportional hazards model; however, violations of the proportional hazards (PH) assumption necessitate more flexible modelling approaches.
Objectives: This study aimed to compare the performance of multiple parametric survival models against the Cox model in estimating time-to-first birth among currently married women in Bangladesh and to identify key predictors of time-to-first birth.
Methods: Data were drawn from the 2022 Bangladesh Demographic and Health Survey (BDHS), encompassing 17,146 currently married women aged 15–49 years. Survival analyses were conducted using the Kaplan–Meier estimator, log-rank tests, Cox regression, and five parametric models: Exponential, Weibull, Log-normal, Gompertz, and Generalised Gamma. Model fit was assessed using AIC and BIC, and log-likelihood statistics.
Results: The mean time-to-first birth after marriage was 40.12 ± 0.50 months, with a median of 26 months, indicating a right-skewed distribution caused by some women experiencing notably delayed first births. The Cox model failed PH assumption tests, highlighting its inadequacy. Among parametric models, the Generalized Gamma model provided the best fit, effectively capturing complex hazard structures. Key predictors of the time-to-first birth included age at first marriage, women's and husbands' education, contraceptive use, administrative division, living arrangement with spouse, and media exposure.
Conclusion: This study underscores the importance of using flexible parametric models—such as the Generalised Gamma model—when dealing with time-to-event data where the proportional hazards assumption is violated. This approach provides more reliable effect estimates and improves the interpretability of covariate influences on fertility timing. Findings underscore the importance of the identified predictors in designing reproductive health policies and interventions aimed at delaying early childbearing
An RP-based Resampling Method for the Logistic Distribution and Its Application
Full text linkThis paper proposes a representative point-based bootstrap (RP-bootstrap) to improve confidence interval estimation for the logistic distribution. The method replaces the traditional empirical distribution with a smoothed approximation constructed from statistically optimal representative points (RPs), leading to a more stable resampling distribution. We integrate the RP-bootstrap with the bootstrap-t, percentile, and BCa methods to construct intervals for the location and scale parameters. Its performance is compared to the classical nonparametric bootstrap via comprehensive Monte Carlo simulations and two real-data applications. The results show that the RP-bootstrap delivers noticeable improved finite-sample performance, particularly for small samples where standard bootstrapping often under-covers. It achieves recognizably higher coverage probabilities while maintaining shorter or comparable expected interval lengths. These improvements are strongest for the bootstrap-t interval and are consistent for both parameters, though more marked for the location. In conclusion, the RP-bootstrap is a computationally efficient and reliable alternative for logistic inference, offering enhanced accuracy, especially in small-sample scenarios.
Purpose: Construction of confidence intervals under small sample size is frequently encountered in statistical inference, such as estimating some treatment effect in medical research with limited number of patients. Traditional nonparametric bootstrap methods often suffer from undercoverage in such settings. To address this limitation, we propose the RP-bootstrap—a resampling procedure that draws samples from an approximated distribution formed by representative points (RPs) of the logistic distribution.
Methods: The RP-bootstrap is developed for constructing confidence intervals for the mean and variance of the logistic distribution. The algorithm generates weighted samples from the estimated RPs. The RP-bootstrap method is applied to construction of different types of confidence intervals (CIs) like the bootstrap-t, percentile, and {\rm BCa} CIs. Its performance and comparison with the traditional nonparametric bootstrap are evaluated through Monte Carlo simulation and real-data application.
Results: Based on the Monte Carlo study under a set of small sample sizes, the RP-bootstrap achieves noticeable higher empirical coverage probability and competitive or shorter expected interval lengths compared with the nonparametric bootstrap. The improvements are much noticeable for small sample sizes like n<30 and for the bootstrap-t confidence intervals, where the nonparametric bootstrap frequently shows undercoverage of the true population parameter.
Contribution: This study demonstrates that representative points provide a stable and efficient alternative to resampling methods from logistic models. The RP-bootstrap offers a practical method for reliable small-sample inference and yields confidence intervals with improved accuracy and reduced variability relative to the traditional nonparametric bootstrap method
A New Family of Generalized Distributions, with Applications and Benchmarking against Machine Learning Models
Full text linkIn this study, we introduce a new family of generalized distributions using the Lomax tangent generalized transformation. We derive the general formulas for its cumulative distribution function (CDF) and probability density function (PDF). As a specific sub-model, we construct the new generalized Lomax tangent transformed exponential (NGLTGE) distribution by using the exponential distribution as the baseline. We investigate the model’s key mathematical properties and conduct a Monte Carlo simulation, which confirms that the estimators exhibit good asymptotic behavior. A group acceptance sampling plan is also designed to demonstrate its utility in quality control. The NGLTGE model is then applied to real-world datasets from cryptocurrency, COVID-19, and breast cancer, where it consistently provides a superior statistical fit compared to related distributions. Finally, we apply the NGLTGE distribution within a machine learning framework using a PyTorch maximum likelihood estimation. The model’s predictive performance is found to be competitive with, and in some cases superior to, state-of-the-art machine learning density estimators like the Log-Gaussian Mixture Model (Log-GMM) and Masked Autoregressive Flow (MAF), especially for data with heavy tails. This work positions the NGLTGE distribution as a valuable, interpretable, and scalable model for both classic statistical and modern data science applications