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Vascular endothelial growth factor receptor-2 (VEGFR2) plays a pivotal role in promoting angiogenesis and contributing to the growth and progression of renal cancer. Hence, the current investigation was undertaken with the aim of identifying safe and potent phytochemicals from Aeginetia indica whole plant extract (AiWE) that can efficiently suppress the overexpression of VEGFR2. HPLC analysis identified and quantified 11 polyphenols in considerable amounts in AiWE. All the compounds showed good binding energies with VEGFR2 in the molecular docking study, except catechin hydrate and rutin hydrate. However, among the polyphenols, myricetin exhibited an almost similar hydrogen bonding pattern with the active site of VEGFR2. The all-atom molecular dynamic simulation revealed that myricetin showed a very stable interaction with the active site of VEGFR2 throughout the simulation. Based on these results, it is suggested that myricetin may inhibit angiogenesis by suppressing the VEGFR2 signaling, thereby impeding the growth and progression of renal cancer., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2025 The Authors.)

Food contamination by heavy metals is a concerning issue worldwide. The presence of elevated levels of heavy metals in commonly consumed rice has emerged as a critical issue in ensuring food safety. This research encompassed the collection of 44 rice samples, representing seven distinct varieties, through a randomized sampling approach across various retail markets within Dhaka city. The investigation of heavy metal content (including Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Cd, Hg, and Pb) within the rice samples was carried out employing Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The findings disclosed substantial disparities in heavy metal concentrations among the different rice varieties. Notably, the concentrations of Cr (0.99 mg/kg), Fe (8.35 mg/kg), Pb (0.49 mg/kg), and Co (0.02 mg/kg) were observed to exceed the established maximum permissible limits. This contamination of the rice varieties may stem from either natural processes or human activities. Utilizing these metal concentrations, this study employed Monte-Carlo simulation to calculate health risk and probabilistic health risk. The Target Hazard Quotients (THQs) for Cr, Fe, and As exceeded the threshold of 1 in each rice variety, particularly in Amon and Lal Biroi in Mymensingh, Najirshail in Bogra, 28 in Sherpur and Kushtia, Miniket in Bogra and Rajshahi, and lastly, paijam in Rangpur and Tangail. Furthermore, the Hazard Index (HI) exceeded 1 in all rice varieties, implying that the consumption of these selected rice grains may have a substantial impact on overall food quality and its potential health consequences. Both carcinogenic risk and probabilistic risk for As, Ni, Cd, Cr, and Pb were found to surpass the threshold levels and safe limits, respectively. This suggests that individuals who have regularly consumed these rice varieties may face a heightened probability of developing cancer in the future, as predicted. According to sensitivity analysis, metal concentration and food ingestion rate (FIR) are the most relevant components that contribute to the significant impact of carcinogenic health hazards. Finally, the study concluded that heavy metal intake from food poses a risk to human health, necessitating the effective monitoring based on each geographic location and identify the source of this heavy metal to limit its exposure., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

For the first time, different pollution indices and a receptor model have been used to quantify eco-environmental and health risk assessments as well as identify the sources of potentially toxic elements in soil along the Barapukuria Coal Mine (BCM). Individual indices include enrichment and contamination factors showing the soil samples are moderately to highly contaminated by arsenic, cobalt, chromium, copper, lead, and zinc and heavily contaminated by sulfur. According to the geo-accumulation index, there is significant pollution with arsenic (1.24 ± 0.38), lead (1.49 ± 0.58), cobalt (1.49 ± 0.58), and sulfur (1.63 ± 0.38). Modified hazard quotient and ecological risk factor values also suggest low to moderate environmental risk hazards from the same elements. The nemerow pollution index, pollution load index, nemerow risk index, ecological risk index, and toxic risk index of soil range from 1.65 to 3.03, 0.82-1.23, 11-26, 77-165, and 6.82-11.76 suggest low toxic risk and moderate pollution, among other synergistic indices. Health risk assessment indicates that iron poses lower cancer risk for children than adults, while both face unacceptable cancer risks from inhaling chromium, cobalt, or arsenic. Principal component and phylogenetic cluster analysis extracted by the multiple linear regression with the absolute principal component score (APCS-MLR) model refer to the fact that manganese, iron, titanium, and nickel have originated from geogenic sources, while coal mine effluents enrich elements like arsenic, chromium, zinc, lead, uranium, sulfur, thorium, and zinc and phosphorous sourced from agriculture. In addition, geogenic and anthropogenic sources, including mine and agriculture activities, could potentially pollute the soil and ecosystem. The findings are crucial for regional and national planners in devising strategies to mitigate potentially toxic element pollution in soil along coal mine areas., Competing Interests: We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere. We know of no conflict of interest associated with this publication, and there has been no significant financial support for this work that could have influenced the outcome. As corresponding authors, we confirm that the manuscript has been read and approved by all the names of the authors., (© 2024 The Authors.)

Non-communicable diseases, such as cardiovascular disease, cancer, chronic respiratory diseases, and diabetes, are responsible for approximately 71% of all deaths worldwide. Stroke, a cerebrovascular disorder, is one of the leading contributors to this burden among the top three causes of death. Early recognition of symptoms can encourage a balanced lifestyle and provide essential information for stroke prediction. To identify a stroke patient and risk factors, machine learning (ML) is a key tool for physicians. Due to different data measurement scales and their probability distributional assumptions, ML-based algorithms struggle to detect risk factors. Furthermore, when dealing with risk factors with high-dimensional features, learning algorithms struggle with complexity. In this study, rigorous statistical tests are used to identify risk factors, and PCA-FA (Integration of Principal Components and Factors) and FPCA (Factor Based PCA) approaches are proposed for projecting suitable feature representations for improving learning algorithm performances. The study dataset consists of different clinical, lifestyle, and genetic attributes, allowing for a comprehensive analysis of potential risk factors associated with stroke, which contains 5110 patient records. Using significant test ( P -value <0.05), chi-square and independent sample t -test identified age, heart&#95;disease, hypertension, work&#95;type, ever&#95;married, bmi, and smoking&#95;status as risk factors for stroke. To develop the predicting model with proposed feature extraction techniques, random forests approach provides the best results when utilizing the PCA-FA method. The best accuracy rate for this approach is 92.55%, while the AUC score is 98.15%. The prediction accuracy has increased from 2.19% to 19.03% compared to the existing work. Additionally, the prediction results is robustified and reproducible with a stacking ensemble-based classification algorithm. We also developed a web-based application to help doctors diagnose stroke risk based on the findings of this study, which could be used as an additional tool to help doctors diagnose., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Diabetes mellitus, a chronic metabolic disorder, continues to be a major public health issue around the world. It is estimated that one in every two diabetics is undiagnosed. Early diagnosis and management of diabetes can also prevent or delay the onset of complications. With the help of a variety of machine learning and deep learning models, stacking algorithms, and other techniques, our study's goal is to detect diseases early. In this study, we propose two stacking-based models for diabetes disease classification using a combination of the PIMA Indian diabetes dataset, simulated data, and additional data collected from a local healthcare facility. We use both the classical and deep neural network stacking ensemble methods to combine the predictions of multiple classification models and improve classification accuracy and robustness. In the evaluation protocol, we used both the train-test and cross-validation (CV) techniques to validate our proposed model. The highest accuracy is obtained by stacking ensemble with three NN architectures, resulting in an accuracy of 95.50 %, precision of 94 %, recall of 97 %, and f1-score of 96 % using 5-fold CV on simulation study. The stacked accuracy obtained from ML algorithms for the Pima Indian Diabetes dataset is 75.03 % using the train-test split protocol, while the accuracy obtained from the CV protocol is 77.10 % on the stacked model. The range of performance scores that outperformed the CV protocol 2.23 %-12 %. Our proposed method achieves a high accuracy range from 92 % to 95 %, precision, recall, and F1-score ranges from 88 % to 96 % using classical and deep neural network (NN)-based stacking method on the primary dataset. The proposed dataset and ensemble method could be useful in the early detection and treatment of diabetes, as well as in the advancement of machine learning and data analysis techniques in the healthcare industry., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The study aims to investigate the effects of different drying methods on the changes in functional properties, physicochemical composition, bioactive compounds, antioxidant activity, sensory attributes, and microstructural quality of the banana flours. Two local banana cultivars, Mehersagar and Sabri, were dried to produce flour using four distinct drying methods: freeze drying (FD), cabinet drying (CD), microwave oven drying (MOD), and forced air oven drying (FOD). The functional properties of the developed banana flours were observed where the findings were as water holding capacity (0.93 ± 0.06-2.74 ± 0.04 g water/g dry sample), oil absorption capacity (0.87 ± 0.06-2.22 ± 0.10 g oil/g dry sample), swelling capacity (4.62 ± 0.02-5.05 ± 0.03 g paste/g dry sample), bulk density (0.54 ± 0.04-0.81 ± 0.02 g/ml), tapped density (0.62 ± 0.04-0.93 ± 0.03 g/ml) and Carr's Index (9.38 ± 0.47-13.58 ± 0.43%). Freeze-dried Mehersagar cultivar's flour showed the leading functional properties with good flowability and cohesiveness. The physicochemical parameters of the flours also revealed significant differences (p < 0.05) in lightness (L*) (50.51 ± 1.49-72.21 ± 1.05), moisture content (3.96 ± 0.09-7.74 ± 0.13%), protein (2.72 ± 0.07-3.93 ± 0.06%), crude fat (0.11 ± 0.01-0.36 ± 0.04%), crude fiber (0.64 ± 0.03-1.22 ± 0.03%), carbohydrate (84.15 ± 0.24-88.26 ± 0.15%) and energy content (354.25 ± 0.57-370.02 ± 0.39 kcal/g). Total flavonoid content (21.44 ± 0.04-34.34 ± 0.03 mgQE/100g) and phenolic content (29.91 ± 0.01-71.46 ± 0.03 mgGAE/100g) was observed, while the highest retention of bioactive compounds was exhibited in Mehersagar cultivar's flour. In terms of appearance, fineness, taste, flavor, color, and overall acceptability, the dried banana flour of both the cultivars obtained from freeze-dried scored overall acceptability 8.04 ± 0.02 and 7.92 ± 0.17, respectively. The scanning electron microscopy analysis of the microstructure of flour granules from each sample revealed a diverse morphological configuration in particle size and shape. According to the findings of this study, the freeze-drying technology is superior to others, and the Mehersagar banana cultivar is more satisfactory in terms of quality characteristics. Moreover, the quality parameters of banana flour may facilitate the formulation of different flour-based gluten-free baked products and food supplements., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)