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Effects of dietary pretreated anchovy waste on growth performance, meat composition, haematology and gut histology in quail

Spatial distribution and associated factors influencing 2024 Measles-Rubella vaccination campaign coverage among children aged 9–59 months in Mainland Tanzania: A multi-level mixed-effect analysis

Introduction Globally, measles remains a major cause of child mortality, and rubella is the leading cause of birth defects among all infectious diseases. In Mainland Tanzania, eliminating measles and rubella remains challenging due to geographical diversity, uneven healthcare facilities distribution, and socio-economic disparities across regions. Understanding spatial patterns and associated determinants of vaccination coverage is essential for improving campaign effectiveness. This study aimed to explore the spatial distribution and associated factors of measles-rubella campaign coverage among children aged 9–59 months in Mainland Tanzania. Methods A cross-sectional survey was conducted following the implementation of the February 2024 measles-rubella (MR) vaccination campaign to assess the spatial distribution and factors influencing MR vaccination coverage among children aged 9–59 months in Mainland Tanzania. Spatial autocorrelation was evaluated using Moran’s I to detect clustering, while Local Indicators of Spatial Association (LISA) was used to identify High-High and Low-Low clusters. Hotspot and cold-spot analyses were performed using Getis–Ord Gi* statistics at the 95% confidence level , consistent with standard epidemiological reporting practices, to identify statistically significant spatial clusters. To identify factors associated with the campaign coverage, we used a multivariable logistic regression model. Results The study included 16,703 children, of whom 81.5% received the MR vaccine during the campaign. Vaccination coverage varied notably between regions, with Tabora and Pwani having a low coverage rate of 58.8% (95% CI: 51.3%−65.9%) and 61.0% (95% CI: 45.0%−75.0%) respectively. Njombe and Mbeya demonstrated high MR vaccination coverage of 97.4% (95% CI: 90.5–99.3%) and 95.6% (95% CI: 90.9–97.9%), respectively. The household wealth quintile and place of residence, caregiver’s education, caregiver’s age, and their marital status were associated with receiving MR vaccination during the campaign among children aged 9–59 months in Mainland Tanzania. Spatial distribution revealed significant clustering of vaccination coverage (Moran’s I = 0.34, p < 0.01). The LISA identified two distinct categories of clusters: High-High Clusters (high-coverage) and Low-Low Clusters (low-coverage). High-High Clusters, which indicate regions with high MR vaccine rates surrounded by similar neighbors, are concentrated in regions such as Njombe and parts of Mbeya, whereas Low-Low Clusters, representing regions with low MR coverage, are found in areas like Tabora, Katavi, Dar es Salaam and Pwani. The Getis-Ord Gi* hotspot analysis shows significant clustering, with high-confidence hotspots in the southern highlands (Njombe and Mbeya) and notable cold spots in western Tanzania (Tabora and Katavi) and parts of eastern Tanzania. Conclusion This study demonstrates substantial spatial heterogeneity in measles–rubella vaccination coverage across Mainland Tanzania, with persistent geographic inequities driven by socio-economic and demographic factors. These findings demonstrate how integrating geospatial insights with equity-focused planning can support precision public health planning, enabling targeted interventions to close coverage gaps and accelerate progress toward measles and rubella elimination.

Adaptive self-supervised learning for real-time problem solving in autonomous systems

Prediction of factors contributing to Pain Intensity among low back pain patients: A comparative machine learning frameworks (Random Forest versus XGBoost)

Background Predicting pain intensity in patients with low back pain (LBP) remains a complex task due to the biopsychosocial nature of pain. Pain intensity is shaped by multifaceted interactions among demographic, lifestyle, and clinical factors. Aim This study aimed to predict factors contributing to pain intensity in adults with lower back pain (LBP) using Random Forest (RF) and XGBoost models. It evaluated the association between lifestyle factors and lumbar spine MRI abnormalities, classifying pain intensity into strong (NRS 7–8) and very strong (NRS 9–10) categories among patients with lumbar disc disorders. Methods Cross-sectional study of 61 LBP patients (Numerical Rating Scale ≥ 7) at King Fahad Specialist Hospital, Saudi Arabia. Predictors included demographics (age, sex, body mass index), MRI findings (disc location, number of affected levels, pathology type), and lifestyle factors (exercise, sitting time). Random Forest (500 trees, 70/30 train-test split, 5-fold cross-validation) and XGBoost were compared. Results RF achieved accuracy = 0.579 (95% CI: 0.334–0.800), AUC = 0.607 (0.340–0.875), specificity = 0.917, and sensitivity = 0.000. The strongest predictors were number of affected disc levels (MDG = 0.62), L4–L5 disc location (MDA = 0.48), age (0.31), and exercise time (0.28). XGBoost achieved 66.67% accuracy but sensitivity of only 0.33, likely due to class imbalance (72.1% very strong pain). RF outperformed XGBoost in overall stability; XGBoost provided complementary feature-level insights via SHAP. These findings highlight the potential of machine learning as a decision-support tool for identifying pain-related risk factors in LBP. Implications RF demonstrated limited predictive utility in its current form, insufficient for clinical application. Future research should involve multi-center designs with larger sample sizes (n ≥ 200) and address class imbalance prior to considering clinical translation. Perspective This study demonstrates how integrating lumbar MRI findings with machine learning improves pain intensity prediction in low back pain, supporting more objective risk stratification and informed clinical decision-making.

Hybrid quantum-classical neural networks for low earth orbit satellite communications in 6G

CDCA8 regulates ATP5F1A protein stability and malignant phenotypes in wilms tumor cells: Prognostic implications and mechanistic insights

Background Wilms tumor (WT) is a prevalent pediatric renal malignancy, yet its molecular mechanisms remain poorly defined. Identifying key prognostic genes and understanding their functional roles is critical for improving clinical management. This study aimed to identify prognostic genes in WT and to investigate whether ATP5F1A functions as a candidate downstream effector in a CDCA8-associated regulatory axis. Methods Differentially expressed genes (DEGs) were identified by analyzing RNA-seq datasets (GSE11151, GSE73209), comparing WT tissues with normal kidney samples. Functional enrichment was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using Cytoscape. Prognostic significance was assessed through univariate and multivariate Cox regression analyses, and a risk prediction model was developed. External expression validation of candidate hub genes was performed using GSE11024 and GSE110696. CDCA8’s functional role in WT was validated by gene knockdown and overexpression experiments. Co-immunoprecipitation (Co-IP) and ubiquitination assays were conducted to explore the interaction between CDCA8 and ATP5F1A. Results A total of 987 DEGs were identified, with six hub genes—BUB1B, CDC45, CDCA8, KIF15, NDC80, and TOP2A—were identified. The exploratory risk score model stratified overall survival in the TARGET-WT cohort (HR = 2.973, Log-rank p = 0.000252), although time-dependent AUC values indicated modest discriminatory performance. CDCA8 was upregulated in WT tissues and associated with worse survival outcomes (HR = 2.03, p = 0.011). Functional assays suggested that CDCA8 knockdown suppressed malignant phenotypes in WiT49 cells. Mechanistically, CDCA8 knockdown reduced ATP5F1A protein levels without altering ATP5F1A mRNA expression, and this reduction was partially rescued by MG132. CDCA8 depletion was also associated with increased ATP5F1A ubiquitination, supporting a role for CDCA8 in maintaining ATP5F1A protein stability. Conclusions These findings suggest that CDCA8 may contribute to Wilms tumor cell phenotypes partly by maintaining ATP5F1A protein stability, providing preliminary evidence for a CDCA8-ATP5F1A regulatory axis in WT.

Exploring the role of pain-related fear and lifting biomechanics in predicting low back pain incidence using supervised machine learning

Abstract Greater lifting-specific pain-related fear has been associated with reduced lumbar spine motion during lifting, suggesting fear-driven protective movement strategies with potential negative consequences. However, the role of task-specific pain-related fear and lifting kinematics in the development of low back pain (LBP) remains unclear. This study aimed to develop and evaluate a supervised machine learning model to predict one-year LBP incidence and to identify the most important predictors. Baseline data from 156 healthy participants included pain-related fear, demographic, health, and lifestyle factors as well as lumbar spine range of motion (ROM) and whole-body lifting strategy during 15-kg lifting. LBP incidence was assessed using biweekly follow-up questionnaires. We trained and evaluated an Explainable Boosting Machine to predict one-year LBP incidence from baseline variables. The final model achieved an accuracy of 0.815 and an ROC AUC of 0.839 for out-of-sample predictions. The most important predictors were higher BMI, lower sleep quality, greater lifting-specific pain-related fear, and reduced lumbar spine ROM. This predictive performance suggests that a machine learning approach may help identify individuals at higher risk of developing LBP according to the applied criteria. These findings emphasize the role of lifting-specific pain-related fear and lifting kinematics in the development of LBP and highlight multifactorial contributors to LBP incidence.

Enhancing the sustainability of cultural identity in science curricula through artificial intelligence as an innovative educational approach

This study investigates the role of AI as an innovative educational approach in promoting the sustainability of cultural identity within science curricula. To achieve this aim, a qualitative research design was employed, using semi-structured interviews and classroom observations as primary data collection methods. The study sample consisted of forty-seven science teachers from the Al-Qassim and Al-Ahsa regions in Saudi Arabia, selected purposively based on their experience with technology-enhanced instruction. The collected data were analyzed using systematic qualitative procedures involving open and axial coding, which facilitated the emergence of key categories related to AI integration, cultural relevance, and instructional practices. Accordingly, the analytical process is best understood as an inductive thematic qualitative approach rather than a full grounded theory methodology, as it does not include theoretical sampling, data saturation, or iterative cycles of data collection. This clarification ensures methodological transparency and alignment between design and analysis. Findings derived from both teachers’ perceptions and classroom observations indicate that AI is not perceived merely as a tool for knowledge transmission, but rather as a supportive instructional mechanism that enables the contextualization of scientific concepts within local cultural settings. In addition, AI-supported instructional practices appear to foster more interactive and collaborative classroom environments, while also contributing to more responsive approaches to assessment. However, it is important to emphasize that these findings are based on teachers’ reported perceptions and observed instructional practices rather than direct measurement of student learning outcomes. Despite this limitation, classroom observations suggest that integrating culturally relevant examples, analyzing cultural texts, and designing collaborative, culturally responsive learning activities may enhance student engagement and strengthen their connection to cultural identity within science learning contexts. Overall, the study highlights the potential of AI to bridge scientific knowledge and local cultural heritage in science education. It contributes to the growing body of literature on AI in education by emphasizing its role in fostering culturally responsive pedagogy and enhancing meaningful learning experiences. The study further underscores the importance of providing teachers with targeted professional development in AI integration, developing culturally responsive instructional materials, and designing assessment practices that address both conceptual understanding and cultural dimensions of learning.

Dynamic rough set learning for reliable early warning in industrial time-series systems

Abstract Industrial early-warning systems require models that can detect transitional risk states before failure while also explaining uncertainty in the resulting decisions. Existing machine-learning and deep-learning approaches can achieve strong predictive performance, but they often provide limited information about whether a time-series window is certainly normal, certainly critical or only ambiguously classified. This paper addresses this problem by proposing a dynamic rough set learning framework for uncertainty-aware early warning in industrial time-series systems. The proposed method converts multivariate sensor trajectories into sliding-window decision systems and constructs time-dependent neighborhood rough approximations for evolving decision classes. A rough early-warning index is introduced to quantify boundary-region expansion over time, and a dynamic dependency-based feature-reduction procedure is developed to retain informative window descriptors while preserving rough decision ability. Unlike purely black-box classifiers, the framework provides class predictions together with support gaps, local uncertainty scores, boundary-region information, deferred decisions and warning rates. The framework is evaluated on the NASA C-MAPSS FD001 turbofan degradation benchmark. The full dynamic rough set model achieves a test accuracy of 0.8647, balanced accuracy of 0.7570 and Macro-F1 score of 0.7895. Random Forest and XGBoost obtain stronger pure classification scores, with Macro-F1 values of 0.8715 and 0.8611, respectively. However, the proposed method supplies additional rough uncertainty outputs that are useful for inspecting transitional and ambiguous windows. Dynamic rough feature reduction decreases the feature set from 118 to 40 features while retaining a test accuracy of 0.8459 and Macro-F1 score of 0.7564. Robustness analysis further shows that the model is stable under moderate Gaussian feature noise, whereas high missingness mainly affects the warning class. These findings indicate that the proposed framework is most suitable for early-warning settings where interpretability, uncertainty qualification and inspection prioritization are as important as raw classification accuracy.

Association of care fragmentation with financial and clinical outcomes of transcatheter aortic valve replacement in the US: A retrospective cohort study

Purpose Care fragmentation (CF), defined as readmission to a non-index facility, has historically been linked with adverse clinical and financial outcomes for a variety of cardiac procedures. However, its impact on patients undergoing transcatheter aortic valve replacement (TAVR) remains understudied. Methods This retrospective study investigated the prevalence and impact of CF on outcomes of patients undergoing TAVR. All adults (≥18 years) undergoing an isolated TAVR procedure, who were readmitted within 90 days, were tabulated using the Nationwide Readmissions Database from 2016 to 2021. A 90-day readmission window was chosen in accordance with prior literature on readmission. Patients treated at a non-index facility were categorized into the CF cohort. Multivariable logistic models were developed to characterize the association of care fragmentation status with perioperative complications and resource utilization; candidate variables were selected using elastic net regularization to minimize variable collinearity. Results Of an estimated 55,891 patients who were readmitted within 90 days, 37.4% were treated at a non-index facility. While TAVR utilization more than doubled, both the readmission and care fragmentation rates fell during the study period. Following adjustment, CF status was associated with respiratory (AOR [Adjusted Odds Ratio] 1.20, 95% CI [Confidence Interval] 1.11–1.29, p < 0.001), gastrointestinal (AOR 1.21, 95% CI 1.05–1.38, p = 0.008), and infectious complications (AOR 1.17, 95% CI 1.10–1.25, p < 0.001) at readmission. Furthermore, CF was linked with greater risk of non-home discharge (AOR: 1.13 95% CI [1.03–1.25], P = 0.01). Conclusions As TAVR utilization continues to expand nationwide, further efforts to enhance care coordination and streamline information sharing are warranted to mitigate the burden of care fragmentation.

Optimal capacitor value calculation for self excited induction generators using hybrid grey wolf differential evolution optimization with experimental validation

Continuation of beta-blockers during prolonged dobutamine infusion in heart transplant–prioritised patients: A competing-risk analysis

Background Patients awaiting heart transplantation (HT) frequently require prolonged continuous dobutamine infusion. Whether oral beta-blocker (BB) therapy should be maintained during this period remains controversial and poorly studied, particularly in the context of prolonged concomitant use. The impact of BB continuation on waiting-list outcomes in this specific scenario is unknown. Methods We conducted a retrospective single-centre cohort study including adult patients listed for HT who received ≥ 30 consecutive days of concomitant oral BB therapy and continuous intravenous dobutamine between January 2020 and December 2023. The index date was defined as the first day after completion of this 30-day period, identifying patients who had already demonstrated sustained haemodynamic tolerance to the combination. Patients were classified according to BB continuation or suspension after the index date. Competing-risk analyses were performed using inverse probability of treatment weighting (IPTW), Aalen–Johansen cumulative incidence functions, and Fine–Gray regression. Robustness was assessed through nonparametric bootstrapping (1,500 replications). Results Among 53 eligible patients (mean age 50.8 ± 11.9 years; 64% male), 29 underwent HT and 16 died before transplantation. After IPTW adjustment, BB continuation was associated with a higher cumulative incidence of HT at ≥ 270 days (61.1% vs 14.3%; p < 0.001) and a lower cumulative incidence of pre-transplant death (13.3% vs 68.7%; p < 0.001). Fine–Gray regression confirmed these associations: HT sHR 8.79 (95% bootstrap CI 2.66–54.31) and pre-transplant mortality sHR 0.077 (95% bootstrap CI 0.014–0.192). Results were consistent across four pre-specified sensitivity analyses. Conclusions This study evaluates outcomes in patients sustaining oral beta-blocker therapy during prolonged dobutamine infusion while awaiting heart transplantation, a clinical scenario with limited available data. BB continuation was associated with a higher probability of transplantation, with consistent direction across pre-specified sensitivity analyses. Given the small sample size and the potential for residual confounding by indication, these findings should be interpreted with caution and are best viewed as hypothesis-generating. They may help inform future research, particularly prospective studies in this setting.

Histopathological changes of the Eustachian tube mucosa following balloon Eustachian tuboplasty in children: insights from standard endovascular balloon application

Abstract Balloon Eustachian tuboplasty (BET) has emerged as an established intervention for obstructive Eustachian tube dysfunction; however, the high cost of proprietary balloon systems limits widespread accessibility. Standard endovascular balloons represent a low-cost alternative, yet their tissue-level effects remain insufficiently characterized, particularly in pediatric populations. This study aimed to evaluate histopathological changes of the Eustachian tube (ET) mucosa before and after BET using a standard endovascular balloon. Twenty-two Eustachian tubes (ETs) from eleven children with bilateral obstructive dysfunction refractory to medical and prior surgical management underwent BET. Mucosal biopsies were obtained immediately before and after dilation in all cases. In addition, follow-up biopsies at 16 weeks were obtained from two ETs in a single patient to explore short-term histopathological changes. Specimens were independently analyzed by two blinded pathologists using light microscopy, focusing on epithelial integrity, inflammatory infiltration, lymphoid tissue architecture, and fibrosis. Pre-dilation specimens demonstrated epithelial inflammation with dense submucosal lymphocytic infiltration. Immediately following dilation, mucosal thinning, partial epithelial disruption, and compression of lymphoid tissue were observed, while the basal epithelial layer remained largely preserved. At 16 weeks, limited follow-up specimens demonstrated partial re-epithelialization with restoration toward pseudostratified ciliated columnar epithelium and reduction in inflammatory infiltrates, without histological evidence of tissue necrosis or marked fibrosis in the sampled specimens. BET using a standard endovascular balloon was associated with immediate epithelial and submucosal injury-related histopathological alterations while preserving the basal epithelial layer. Limited follow-up histological observations from a single case demonstrated partial re-epithelialization with reduced inflammatory infiltrates. However, these findings should be considered preliminary and do not permit definitive conclusions regarding reparative response, clinical tolerability, safety, feasibility, or long-term tissue remodeling, which require validation in larger prospective longitudinal studies.

Validation of the Arab Mental Health Continuum Short Form (AMHC-SF) in Oman: Psychometric properties and factorial structure

Background The Mental Health Continuum–Short Form (MHC–SF) is a widely used instrument designed to assess three dimensions of positive mental health: emotional, social, and psychological well-being. However, there is limited evidence regarding its validity within Arab populations. Method This study aimed to validate the Arabic version of the MHC–SF (AMHC–SF) among a community-based sample in Oman. A total of 1,101 participants aged 18–65 years (M = 31.49, SD = 9.69) completed the questionnaire through an online survey. Confirmatory factor analyses (CFA) were performed to examine factorial validity, while internal consistency reliability was assessed using Cronbach’s alpha and McDonald’s omega coefficients. Discriminant validity was examined through correlations with anxiety scales. Results Confirmatory factor analysis supported the superiority of the bifactor model over the original three-factor structure, demonstrating excellent model fit (CFI = 0.99, TLI = 0.98, RMSEA = 0.032) and substantial general factor loadings (0.64–0.76). Reliability estimates for the total scale and subscales were excellent, with Cronbach’s alpha and McDonald’s omega coefficients ranging from 0.86 to 0.95. The AMHC-SF also showed apparent discriminant validity, as higher well-being scores were associated with lower anxiety levels. Conclusion The findings support the factorial validity, reliability, and discriminant validity of the AMHC–SF, confirming its cross-cultural applicability within the Arab context. The scale represents a brief, psychometrically sound instrument for assessing positive mental health among adults in Oman and other Arabic-speaking populations.

Epicardial adipose tissue thickness is associated with circulating biomarkers in individuals without known cardiac disease across a broad adiposity spectrum

Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection

Background Distinguishing individuals with cognitive decline (CD), including early Alzheimer’s disease, from cognitively normal (CN) individuals is essential for improving diagnostic accuracy and enabling timely intervention. Positron emission tomography (PET) captures metabolic brain alterations associated with CD, but its broader application is often limited by cost and radiation exposure. To enhance the clinical utility of PET while addressing data limitations, we propose a data-efficient framework that integrates complementary multi-scale PET representations at voxel-level and region-level. Methods Voxel-level features were extracted using convolutional neural networks (CNN) or principal component analysis networks (PCANet) from [¹⁸F]FDG PET imaging. Region-level features were derived from standardized uptake value ratio measurements across predefined brain regions and processed using a deep neural network (DNN). These voxel- and region-level information are integrated through direct concatenation. For the final prediction, different machine learning models and ensemble technique were applied. The models were trained and validated using 5-fold cross-validation on PET scans from 252 participants in the Alzheimer’s Disease Neuroimaging Initiative, comprising 118 CN and 134 CD subjects. Additional correlation analysis and disease classification comparison with the Mini-Mental State Examination (MMSE) were also performed. Results In 5-fold cross-validation, CNN, PCANet, and DNN models achieved classification accuracies of 0.69 ± 0.04, 0.69 ± 0.06, and 0.82 ± 0.06, respectively. The integrated DNN-CNN model using direct concatenation yielded the highest accuracy (0.87 ± 0.05), with a 6.33% improvement in accuracy and reduced standard deviation relative to the DNN-only model. Overall, there were an increase of 14.22% in Recall (0.77 to 0.88) and an increase of 7.92% in F1-Score (0.82 to 0.88). Moreover, the predicted probability of CD showed a significant correlation with MMSE scores, and the model achieved higher accuracy, recall, and F1-score than MMSE-based classification. Conclusion Combining complementary voxel-level and region-level PET representations with deep learning improved classification performance over single-representation models, particularly by enhancing sensitivity to cognitive decline. These findings support the potential utility of multi-scale FDG-PET representations for machine learning-based cognitive decline detection.

Mechanistic investigation of Chai Gui Zexie Decoction in treating non-small cell lung cancer: an integrated metabolomics and network pharmacology approach

Correction for Stöhr et al., Distinct synthetic Aβ prion strains producing different amyloid deposits in bigenic mice

MSPFormer: An enhanced multi-scale and semantic-preserving transformer for sheep ownership identification in precision livestock farming

Overgrazing is a major driver of grassland degradation on the Qinghai–Tibet Plateau, posing significant challenges for sustainable livestock management. To mitigate this issue, intelligent technologies that can effectively recognize and manage different herders’ sheep flocks are essential for achieving balanced grass–livestock management and reducing overgrazing. This study aims to develop a robust and efficient model for intelligent sheep ownership recognition by leveraging sheep back color features, facilitating scientific grazing management, and supporting herder conflict resolution. A dedicated dataset was constructed, capturing diverse color distributions under variable lighting and complex backgrounds. We propose MSPFormer, a multi-scale and semantic-preserving Transformer model, which builds upon Mask2Former by integrating three key modules: (1) an Atrous Spatial Pyramid Pooling (ASPP) module between the Pixel Decoder and Transformer Decoder to enhance multi-scale feature extraction; (2) a Dynamic Prompt Attention (DPA) module to improve semantic consistency; and (3) a Content-Aware ReAssembly of Features (CARAFE) upsampling module after the Transformer Decoder to optimize spatial detail recovery. Experimental results show that MSPFormer achieves superior segmentation performance, increasing the mIoU from 82.46% to 83.81% (a relative improvement of approximately 1.35%), the mF1-score from 90.08% to 90.92% (an improvement of approximately 0.84%), the mPrecision from 89.50% to 91.52% (an improvement of approximately 2.02%), and the mRecall from 90.74% to 90.92% (an improvement of approximately 0.18%). Validation on the public VOC2012 dataset and several small-sample subsets further demonstrates the model’s strong generalization capability and stability. This study provides an effective and intelligent solution for sheep ownership recognition, contributing to sustainable grazing management and conflict resolution among herders on the Qinghai–Tibet Plateau. Future work will focus on lightweighting the model through pruning and knowledge distillation to facilitate practical deployment.

Novel nano-reinforced chitosan derivatives for adsorption of heavy metal ions from aqueous media

Abstract A novel NH 2 -MIL-53(Al)@CS-EDTA hybrid nanocomposite was synthesized by functionalizing chitosan with EDTA and integrating it with an Al-based MOF for heavy metal removal. Structural and morphological characterizations were performed using FT-IR, XRD, SEM-EDX, N2 adsorption-desorption isotherms, point of zero charge (PZC), and thermogravimetric analysis (TGA). Batch experiments showed maximum adsorption capacities of 31.8, 28.1, and 21 mg·g − 1 for Cu 2+ , Fe 2+ , and Ni 2+ at pH 6. The adsorption data were best fitted by the Langmuir isotherm and the pseudo-second-order kinetic model under the investigated experimental conditions. The observed adsorption behavior is proposed to involve coordination and electrostatic interactions between the metal ions and the abundant functional groups (–OH, –NH₂, and –COOH) of the composite. The biopolymer MOF composite’s reusability was also evaluated, and even after five consecutive adsorption-desorption cycles, > 90% efficiency was found, indicating acceptable stability over repeated cycles. The novelty of this study lies in the synergistic integration of biopolymer, chelating agent, and MOF into a single, reusable adsorbent with improved properties.