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A series of semi-crystalline, wide band gap (WBG) photovoltaic polymers were synthesized with varying number and topology of fluorine substituents. To decrease intramolecular charge transfer and to modulate the resulting band gap of D-A type copolymers, electron-releasing alkoxy substituents were attached to electron-deficient benzothiadiazole (A) and electron-withdrawing fluorine atoms (0-4F) were substituted onto a 1,4-bis(thiophen-2-yl)benzene unit (D). Intra- and/or interchain noncovalent Coulombic interactions were also incorporated into the polymer backbone to promote planarity and crystalline intermolecular packing. The resulting optical band gap and the valence level were tuned to 1.93-2.15 eV and -5.37 to -5.67 eV, respectively, and strong interchain organization was observed by differential scanning calorimetry, high-resolution transmission electron microscopy and grazing incidence X-ray scattering measurements. The number of fluorine atoms and their position significantly influenced the photophysical, morphological and optoelectronic properties of bulk heterojunctions (BHJs) with these polymers. BHJ photovoltaic devices showed a high power conversion efficiency (PCE) of up to 9.8% with an open-circuit voltage of 0.94-1.03 V. To our knowledge, this PCE is one of the highest values for fullerene-based single BHJ devices with WBG polymers having a band gap of over 1.90 eV. A tandem solar cell was also demonstrated successfully to show a PCE of 10.3% by combining a diketopyrrolopyrrole-based low band gap polymer.

Abstract Introduction Nocardia cyriacigeorgica bacteraemia has been described in the setting of profound immunodeficiency in only two previous case reports. In both instances, diagnosis was rapidly facilitated by 16S rRNA gene sequencing of blood culture isolates. To the best of our knowledge, we believe that our case is the first presentation of N. cyriacigeorgica bacteraemia associated with acute cytomegalovirus disease in a kidney transplant recipient, which was then followed by severe and fatal pneumonia only seven days later. Case presentation We present the case of a 73-year-old Caucasian woman, a renal transplant recipient, with peripheral vascular disease, hypertension, osteoporosis and vascular dementia who was diagnosed with septicemia and pneumonia. In spite of appropriate anti-microbial therapy for nocardial sepsis, she developed severe pneumonia and acute renal failure. Conclusion This case illustrates a potential for disseminated nocardial infection to produce clinical syndromes that may be indistinguishable from acute cytomegalovirus disease. An atypical presentation (pneumonia and renal failure) of a rare disease (nocardial septicemia) in the setting of renal transplantation is discussed. This case illustrates that the possibility of severe cytomegalovirus disease should be considered in renal transplanted patients diagnosed with nocardial septicemia who subsequently develop severe sepsis, pneumonia, and renal failure. Molecular diagnosis should readily be available to assist with the prompt diagnosis and treatment of these infections in renal transplant patients.

Objective: Early detection of Parkinson's disease (PD) is essential for halting its progression, yet challenges remain in leveraging deep learning for accurate identification. This study aims to overcome these obstacles by introducing a hybrid deep learning approach that enhances PD detection through a combination of autoencoder (AE) and radial basis function neural network (RBFNN)., Methods: The proposed method analyzes the power spectral density (PSD) of preprocessed electroencephalography (EEG) signals, with artifacts removed, to assess energy distribution across EEG sub-bands. AEs are employed to extract features from reconstructed signals, which are subsequently classified by an RBFNN. The approach is validated on UC SanDiego's EEG dataset, consisting of 31 subjects and 93 minutes of recordings., Results: The hybrid model demonstrates promising performance, achieving a classification accuracy of 99%. The improved accuracy is attributed to advanced feature selection techniques, robust data preprocessing, and the integration of AEs with RBFNN, setting a new benchmark in PD detection frameworks., Conclusion: This study highlights the efficacy of the hybrid deep learning framework in detecting PD, particularly emphasizing the importance of using multiple EEG channels and advanced preprocessing techniques. The results underscore the potential of this approach for practical clinical applications, offering a reliable solution for early and accurate PD detection., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Magnetic resonance imaging (MRI) is a popular imaging tool that is valuable for the early detection and monitoring of malignancies because it does not involve radiation and is noninvasive. Metal-based contrast agents (CAs) are commonly used in clinical settings despite concerns about the toxicity of free metals. Therefore, finding alternative nontoxic imaging probes is vital. In this work, we have synthesized and effectively utilized sustainable biomass lignin-based all-organic nanoconjugates linked with nitroxide radicals as MRI CAs. Lignin grafted with poly(4-glycidyloxy-2,2,6,6-tetramethylpiperidine-1-oxyl) (LPGT) exhibits a longitudinal relaxivity of 0.54 mM -1 s -1 . LPGT shows exceptional characteristics, including resistance to reduction and nontoxicity toward living organisms. LPGT displays enhanced MRI contrast in the BALB/c mouse model for a duration exceeding 4.35 h. Our primary goal is to design MRI agents that are exceptionally effective sustainable biomass-derived materials and do not require the use of metals. Nicely, LPGT offers adequate contrast enhancement at 5-fold lower (0.020 mmol/kg) than the standard dose (0.1 mmol/kg), easing worries about toxic metal buildup. Consequently, LPGT shows promise as a feasible CA for metal-free MRI.

Timely diagnosis of brain tumors using MRI and its potential impact on patient survival are critical issues addressed in this study. Traditional DL models often lack transparency, leading to skepticism among medical experts owing to their "black box" nature. This study addresses this gap by presenting an innovative approach for brain tumor detection. It utilizes a customized Convolutional Neural Network (CNN) model empowered by three advanced explainable artificial intelligence (XAI) techniques: Shapley Additive Explana-tions (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Gradient-weighted Class Activation Mapping (Grad-CAM). The study utilized the BR35H dataset, which includes 3060 brain MRI images encompassing both tumorous and non-tumorous cases. The proposed model achieved a remarkable training accuracy of 100 % and validation accuracy of 98.67 %. Precision, recall, and F1 score metrics demonstrated exceptional performance at 98.50 %, confirming the accuracy of the model in tumor detection. Detailed result analysis, including a confusion matrix, comparison with existing models, and generalizability tests on other datasets, establishes the superiority of the proposed approach and sets a new benchmark for accuracy. By integrating a customized CNN model with XAI techniques, this research enhances trust in AI-driven medical diagnostics and offers a promising pathway for early tumor detection and potentially life-saving interventions., 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 article., (© 2024 The Authors.)

The exploration of mechanical motion in molecular crystals under external stimuli is of great interest because of its potential applications in diverse fields, such as electronics, actuation, or sensing. Understanding the underlying processes, including phase transitions and structural changes, is crucial for exploiting the dynamic nature of these crystals. Here, we present a novel organic compound, PT-BTD , consisting of five interconnected aromatic units and two peripheral alkyl chains, which forms crystals that undergo a drastic anisotropic expansion (33% in the length of one of its dimensions) upon thermal stimulation, resulting in a pronounced deformation of their crystal shape. Remarkably, the transformation occurs while maintaining the single-crystal nature, which has allowed us to follow the crystal-to-crystal transformation by single-crystal analysis of the initial and expanded polymorphs, providing valuable insights into the underlying mechanisms of this unique thermosalient behavior. At the molecular level, this transformation is associated with subtle, coordinated conformational changes, including slight rotations of the five interconnected aromatic units in its structure and increased dynamism in one of its peripheral alkyl chains as the temperature rises, leading to the displacement of the molecules. In situ polarized optical microscopy reveals that this transformation occurs as a rapidly advancing front, indicative of a martensitic phase transition. The results of this study highlight the crucial role of a soft and flexible structural configuration combined with a highly compact but loosely bound supramolecular structure in the design of thermoelastic materials.

Objective: Brain tumors are a leading global cause of mortality, often leading to reduced life expectancy and challenging recovery. Early detection significantly improves survival rates. This paper introduces an efficient deep learning model to expedite brain tumor detection through timely and accurate identification using magnetic resonance imaging images., Methods: Our approach leverages deep transfer learning with six transfer learning algorithms: VGG16, ResNet50, MobileNetV2, DenseNet201, EfficientNetB3, and InceptionV3. We optimize data preprocessing, upsample data through augmentation, and train the models using two optimizers: Adam and AdaMax. We perform three experiments with binary and multi-class datasets, fine-tuning parameters to reduce overfitting. Model effectiveness is analyzed using various performance scores with and without cross-validation., Results: With smaller datasets, the models achieve 100% accuracy in both training and testing without cross-validation. After applying cross-validation, the framework records an outstanding accuracy of 99.96% with a receiver operating characteristic of 100% on average across five tests. For larger datasets, accuracy ranges from 96.34% to 98.20% across different models. The methodology also demonstrates a small computation time, contributing to its reliability and speed., Conclusion: The study establishes a new standard for brain tumor classification, surpassing existing methods in accuracy and efficiency. Our deep learning approach, incorporating advanced transfer learning algorithms and optimized data processing, provides a robust and rapid solution for brain tumor detection., (© The Author(s) 2024.)

Through the advancement of the contemporary web and the rapid adoption of social media platforms such as YouTube, Twitter, and Facebook, for example, life has become much easier when dealing with certain highly personal problems. The far-reaching consequences of online harassment require immediate preventative steps to safeguard psychological wellness and scholarly achievement via detection at an earlier stage. This piece of writing aims to eliminate online harassment and create a criticism-free online environment. In the paper, we have used a variety of attributes to evaluate a large number of Bengali comments. We communicate cleansed data utilizing machine learning (ML) methods and natural language processing techniques, which must be followed using term frequency and reverse document frequency (TF-IDF) with a count vectorizer. In addition, we used tokenization with padding to feed our deep learning (DL) models. Using mathematical visualization and natural language processing, online bullying could be detected quickly. Multi-layer Perceptron (MLP), K-Nearest Neighbors (K-NN), Extreme Gradient Boosting (XGBoost), Adaptive Boosting Classifier (AdaBoost), Logistic Regression Classifier (LR), Random Forest Classifier (RF), Bagging Classifier, Stochastic Gradient Descent (SGD), Voting Classifier, and Stacking are employed in the research we conducted. We expanded our investigation to include different DL frameworks. Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Convolutional-Long Short-Term Memory (C-LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) are all implemented. A large amount of data is required to precisely recognize harassing behavior. To rapidly recognize internet harassment written material, we combined two sets of data, producing 94,000 Bengali comments from different points of view. After understanding the ML and DL models, we can see that a hybrid model (MLP+SGD+LR) performed more effectively when compared to other models, its evaluation accuracy is 99.34%, precision is 99.34%, recall rate is 99.33%, and F1 score is 99.34% on multi-label class. For the binary classification model, we got 99.41% of accuracy., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Mohi Uddin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Inflammatory bowel disease (IBD) is a chronic idiopathic inflammatory disorder of the gastrointestinal tract with relapsing and remitting course. Recurrent abdominal pain and discomfort in association with abnormal defecation in the absence of structural abnormality of the gut is the key feature of irritable bowel syndrome (IBS). Faecal biomarker may be used a precise tool in the differentiation of IBD and IBS. The aim of this study was to measure faecal calprotectin (FC) in patients with IBD and IBS and compare between them. This was a cross-sectional study done in the department of Gastroenterology, BSMMU, Bangladesh from May 2017 to August 2018. IBD patients were diagnosed on the basis of compatible history, clinical examination, laboratory, radiological and endoscopic findings. IBS patients were selected by using the Rome IV criteria. Quantitative faecal calprotectin ELISA (BUHLMANN Quantum Blue) test was done and compared between IBD and IBS patients. In this study, ninety (90) patients were enrolled, 45 patients with IBD and 45 patients with IBS. Mean age of the IBD patients was 32.24±9.76 years and IBS patients was 33.80±9.70 years. There were 28(62.2%) male and 17(37.8%) female patients with IBD and 30(66.7%) male and 15(33.3%) female patients with IBS. We found faecal calprotectin (FC) level was 445.68±237.35μg/gm in IBD patients and 39.16±17.31μg/gm in IBS patients. There was a significant difference of faecal calprotectin level between IBD and IBS patients (p-value <0.001). The sensitivity and specificity of faecal calprotectin to differentiate IBD from IBS was 91.1% and 86.7% respectively. The test accuracy was 88.9%. Area under ROC was 0.959 (95% CI, 0.909 to 1.0). This study showed that faecal calprotectin appears to be clinically useful, non-invasive, rapid and reliable marker to differentiate IBD from IBS.

Growth hormone-releasing hormone antagonists (GHRHAnt) have been associated with antitumor and antioxidative activities. The present study investigates for the first time the effects of those compounds towards pro-inflammatory cytokine expression in a murine model of cecal ligation and puncture (CLP) - induced sepsis. The results indicate that GHRHAnt JV-1-36 significantly suppressed IL-1α, IL-6, and pSTAT3 activation in septic lungs. Moreover, GHRHAnt treatment reduced bronchoalveolar lavage fluid (BALF) protein concentration, suggesting a protective effect of that compound in sepsis-induced lung edema. Based on those findings, it is suggested that GHRHAnt may represent an exciting new therapeutic possibility in sepsis-induced endotoxemia and lung injury., Competing Interests: Declaration of competing interest The authors declare no conflict of interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Diabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient ML model to diagnose diabetes accurately., Methods: In this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly., Results: The performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing., Conclusions: This excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs., Competing Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

The ever-evolving global landscape of communication, driven by Information Technology advancements, underscores the importance of emotion detection in natural language processing. However, challenges persist in interpreting emotions within linguistically diverse contexts, notably in low-resource languages like Bengali, compounded by the emergence of Banglish. To address this gap, we present "Bengali & Banglish," an extensive dataset comprising 80,098 labelled samples across six emotion classes. Our dataset fills a void in fine-grained emotion classification for Bengali and pioneers in emotion detection in Banglish. We achieve significant performance metrics through meticulous annotation and rigorous evaluation, including a weighted F1 score of 71.30% for Bengali and 64.59% for Banglish using BanglaBERT. Also, our dataset facilitates Bengali-to-Banglish Machine Translation, contributing to the advancement of language processing models. Furthermore, our dataset demonstrates a high Cohen's Kappa score of 93.5%, affirming the reliability and consistency of our annotations. This research underscores the importance of linguistic diversity in NLP and provides a valuable resource for enhancing Emotion Detection capabilities in Bengali and Banglish across digital platforms., (© 2024 The Author(s).)

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.

Background: Contagious and economically devastating, peste des petits ruminants (PPR) is a viral disease affecting goats and sheep, causing significant losses in livestock productivity and posing a threat to food security and rural livelihoods worldwide., Objectives: This study was conducted to assess the status of goat farmer's knowledge, attitude and practice (KAP) about PPR disease at Sylhet district of Bangladesh., Methods: A comprehensive cross-sectional survey, conducted over 11 weeks, targeted 130 goat owners. Following a pilot study with 20 farmers, a set of 17 validated questions on PPR KAP was validated. Data collection was performed through face-to-face interviews by a trained team using KOBO Toolbox, with interpretation of responses based on established thresholds for knowledge (>65%), attitude (>75%) and practice (>70%)., Results: Overall, 67.7% of participants demonstrated a good level of knowledge about PPR disease. Males exhibited 1.42 times higher odds of knowledge compared to females (odds ratio = 1.42). The middle age group (31-45 years) showed significantly higher knowledge levels (83.3%, p < 0.001). Within this age group, positive practice levels were also notably higher (54.8%). Those with a higher secondary education background exhibited the most positive practice levels (>80%). Participants whose additional income came from non-governmental organization employment showed a higher positive practice level (71.4%), 1.46 times higher than other income sources., Conclusion: Strategic interventions should prioritize female farmers, educational empowerment and collaboration with non-governmental organizations to bolster livestock health and rural livelihoods in Bangladesh as part of national PPR control strategy to fulfil the goals of Office International des Epizooties/World Organization for Animal Health (OIE/WOAH) and Food and Agriculture Organization (FAO) PPR eradication by 2030., (© 2024 The Author(s). Veterinary Medicine and Science published by John Wiley & Sons Ltd.)

Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. Here we utilized a bioinformatics approach to identify genetic commonalities between these two diseases. To this end, we examined microarray datasets from GEO (Gene Expression Omnibus) to identify differentially expressed genes, common genes, and hub genes between the selected two diseases. Our analyses identified potential therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). These therapeutic molecules may have the potential for simultaneous treatment of these diseases., (© 2024 The Authors. FEBS Open Bio published by John Wiley & Sons Ltd on behalf of Federation of European Biochemical Societies.)

Data-driven approaches are often utilized to model and generate vibrotactile feedback and sounds for rigid stylus-based interaction. Nevertheless, in prior research, these two modalities were typically addressed separately due to challenges related to synchronization and design complexity. To this end, we introduce a novel multimodal multitask deep learning framework. In this paper, we developed a comprehensive end-to-end data-driven system that encompasses the capture of contact acceleration signals and sound data from various texture surfaces. This framework introduces novel encoder-decoder networks for modeling and rendering vibrotactile feedback through an actuator while routing sound to headphones. The proposed encoder-decoder networks incorporate stacked transformers with convolutional layers to capture both local variability and overall trends within the data. To the best of our knowledge, this is the first attempt to apply transformer-based data-driven approach for modeling and rendering of vibrotactile signals as well as sounds during tool-surface interactions. In numerical evaluations, the proposed framework demonstrates a lower RMS error compared to state-of-the-art models for both vibrotactile signals and sound data. Additionally, subjective similarity evaluation also confirm the superiority of proposed method over state-of-the-art., (© 2024. The Author(s).)

Introduction: Lumpy skin disease (LSD) is a highly contagious vector-borne viral disease of cattle. LSD has emerged in Bangladesh in 2019, causing significant economic losses due to its high morbidity and mortality. This research was designed to isolate, identify, and assess the immunogenicity of LSD virus (LSDV) using nodular tissue samples obtained from affected cattle during the 2019-20 outbreak across nine districts of Bangladesh., Methods: To determine the presence of LSDV in nodular tissues, we initially used iiPCR and PCR, followed by histopathological examination. 151 were positive via iiPCR and PCR among the 180 collected samples. The PCR positive 151 samples were then inoculated into 10-day-old embryonated chicken eggs via the CAM route to isolate LSDV, confirmed through PCR. Subsequently, partial sequencing and phylogenetic analysis of the P32 gene were performed to determine the origin of the circulating LSDV strain. The immunogenicity of selected LSDV strains was assessed through an ELISA test., Results: The PCR results revealed a distinct positive band at 192 bp in both the nodular tissue samples and the LSDV isolated from chicken embryo inoculations. Microscopic analysis of the nodular lesions revealed thickening of the epidermis, ballooning degeneration of keratinocytes, and proliferation of follicular epithelia. Additionally, mononuclear infiltration was observed at the demarcation line between infected and healthy tissue, with necrosis of muscular tissues beneath the epidermis. The LSDV isolate from Bangladesh exhibited a close genetic relationship with LSDV strains isolated from neighboring and other regional countries including India, Myanmar, and Mongolia. This observation strongly suggests the possibility of a transboundary spread of the LSD outbreak in Bangladesh during 2019-2020. The results of the immunogenicity test showed that the serum antibody titer remained at a protective level for up to 18 months following secondary immunization with inactivated LSDV antigen. This finding suggests that the inactivated LSDV antigen could be a potential vaccine candidate to protect cattle in Bangladesh against LSDV., Conclusion: In conclusion, our research successfully isolated, identified, and characterized LSDV in cattle nodular tissues from the 2019-20 outbreak in Bangladesh. Furthermore, it provided insights into the probable origin of the circulating strain and investigated a potential vaccine candidate to protect cattle in the region from LSDV., Competing Interests: The authors extend their heartfelt thanks to the Managing Director of FnF Pharmaceuticals Ltd., Bangladesh, for providing financial support that enabled the execution of this research. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Uddin, Hossain, Rahman, Siddique, Kafi, Hossain, Chakraborty, Rahman, Khasruzzaman, Ward and Islam.)

Chronic kidney diseases (CKDs) are a significant public health issue with potential for severe complications such as hypertension, anemia, and renal failure. Timely diagnosis is crucial for effective management. Leveraging machine learning within healthcare offers promising advancements in predictive diagnostics. In this paper, we developed a machine learning-based kidney diseases prediction (ML-CKDP) model with dual objectives: to enhance dataset preprocessing for CKD classification and to develop a web-based application for CKD prediction. The proposed model involves a comprehensive data preprocessing protocol, converting categorical variables to numerical values, imputing missing data, and normalizing via Min-Max scaling. Feature selection is executed using a variety of techniques including Correlation, Chi-Square, Variance Threshold, Recursive Feature Elimination, Sequential Forward Selection, Lasso Regression, and Ridge Regression to refine the datasets. The model employs seven classifiers: Random Forest (RF), AdaBoost (AdaB), Gradient Boosting (GB), XgBoost (XgB), Naive Bayes (NB), Support Vector Machine (SVM), and Decision Tree (DT), to predict CKDs. The effectiveness of the models is assessed by measuring their accuracy, analyzing confusion matrix statistics, and calculating the Area Under the Curve (AUC) specifically for the classification of positive cases. Random Forest (RF) and AdaBoost (AdaB) achieve a 100% accuracy rate, evident across various validation methods including data splits of 70:30, 80:20, and K-Fold set to 10 and 15. RF and AdaB consistently reach perfect AUC scores of 100% across multiple datasets, under different splitting ratios. Moreover, Naive Bayes (NB) stands out for its efficiency, recording the lowest training and testing times across all datasets and split ratios. Additionally, we present a real-time web-based application to operationalize the model, enhancing accessibility for healthcare practitioners and stakeholders. Web app link: https://rajib-research-kedney-diseases-prediction.onrender.com/., Competing Interests: The authors declare that they have no conflict of interest and no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)