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Wound healing efficacy of Couroupita Guianensis loaded carboxymethylcellulose film
Couroupita Guianensis extracts loaded carboxymethylcellulose film (CGELCF) was fabricated through a solution casting technique. Phytochemical screening confirmed the presence of alkaloids, proteins, flavonoids, saponins, carbohydrates, cardiac glycosides, and coumarins in CGE. The HPLC revealed that ellagic acid and gallic acid derivatives are abundant in CGE. FTIR spectrum confirmed the different functional group's presence in the film. The XRD, DSC, and TGA revealed the crystallinity, thermal stability, melting point (300 C), and degradation of the film. FESEM and EDX analysis revealed surface morphology and elemental compositions. The atomic wt% of C, O, Na, and P were found as follows 38.39, 49.34, 11.44, and 0.83. The zone of inhibition was found as 19 and 20 mm against Staphylococcus aureus and Escherichia coli respectively. The tensile strength and modulus of elasticity was found 7.35 MPa and 1.38 GPa respectively. In-vitro wound scratch revealed above 90 % of cell migrations for all the concentrations after 18 h. The in-vitro cytotoxicity revealed 90.83 % of cell viability and 9.17 % of toxicity. In-vivo studies revealed that wound closure and wound healing with the CGELCF mice was within 7 days and exhibited the rapid drug. The CGELCF may find usage as a bandage for wounds. 2025 -
Nanoscale synthesis of nickel oxide@carboxy methyl cellulose@nitrogen doped carbon nanotubes supported metal organic frameworks ternary composite for use symmetric supercapacitor
Metal-organic frameworks (MOFs) are a novel class of porous materials that combine organic linkers and inorganic metal ions. Supercapacitors use a large specific surface area, adjustable architecture, and tunable porosity and pore diameters to improve the electrochemical performances with metal sulfides. The main goal of this study was to make a nickel oxide ternary composite using a hydrothermal method with urea as a catalyst for electrochemical uses. We characterized these fabricated composite materials using analytical and morphological characterization for their confirmation. These results show that the composite electrode had a great specific capacitance of 464 F/g at 0.5 A/g in a 1 M KOH electrolyte when set up with three electrodes. The symmetric two-electrode system showed 52.83 F/g at 0.5 A/g with an excellent energy density of 13.14 Whkg?1 and a power density of 616 Wkg?1 via 1 M KOH electrolyte. The fabricated ternary composite electrode demonstrated cyclic stability, with an excellent retention rate of 89 % after 7000 cycles. Therefore, the fabricated ternary composite electrode materials have enormous potential for electrochemical storage properties. 2025 Elsevier B.V. -
Chitosan stabilized platinum nanoparticles: In vitro and in vivo screening for analgesic and anti-inflammatory applications
In this interdisciplinary research work, the chitosan stabilized platinum nanoparticles are synthesized through the wet chemical method, and the structural, surface morphological, and optical characterizations are done using X-ray crystallography, Raman spectroscopy, transmission electron microscopy, etc. The samples were tested in in vitro trials namely egg albumin denaturation assay and DPPH radical scavenging assays and showed significantly lower effective concentrations (EC50) such as 5.44 ?g/ml and 8.068 ?g/ml respectively. The in vitro experiments were followed by in vivo animal model for analgesic and anti-inflammatory behaviour at two doses of 25 mg/kg and 50 mg/kg utilizing the hot plate method and the carrageenan-induced paw edema model respectively. The in vivo hot plate model for analgesic effect demonstrated that the chitosan stabilized platinum nanoparticles perform exceptionally well and show >90 % analgesia (p < 0.01) by extending the reaction time in the hot plate methodindicating better analgesia. Carrageenan-induced paw edema model demonstrated the exceptional anti-inflammatory ability of chitosan-stabilized platinum nanoparticles. Despite being given at a comparatively lower dosage, chitosan stabilized platinum nanoparticles showed a considerable decrease in paw volume (4045 % edema inhibition) by the third hour of the anti-inflammatory experimentation (p < 0.01) outperforming the standard drug aspirin given at 100 mg/kg. 2025 Elsevier B.V. -
TiO2-sodium alginate core-shell nanosystem for higher antimicrobial wound healing application
Wounds that are not properly managed can cause complications. Prompt and proper care is essential, to prevent microbial infection. Growing interest in metal oxide nanoparticles (NPs) for innovative wound treatments targeting healing and microbial infections. In this research, sodium alginate-coated titanium dioxide (TiSA) NPs are synthesized through a green co-precipitation method, combining inorganic TiO2 (Titanium dioxide) and SA (sodium alginate). Analysis via XRD and TEM revealed that the resulting TiSA NPs possessed an anatase phase and polygonal structure, respectively. Biomedical investigations demonstrated that TiSA NPs exhibited enhanced antimicrobial activity compared to the positive control, as well as its counterparts, and showed higher wound healing capabilities compared to TiO2 NPs. The antimicrobial effectiveness of TiSA NPs relied on various physicochemical factors, including small particle size, an altered band gap, and the presence of oxygen vacancies, resulting in microbial cell death. Moreover, TiSA NPs treatment demonstrated higher wound healing activity (98 1.09 %) compared to its counterparts after 24 h of incubation. Assessment of cytotoxicity on healthy fibroblast cells (L929) revealed that TiSA NPs exhibited lower toxicity compared to TiO2 NPs. These findings support the potential of TiSA NPs as promising agents for antimicrobial activity and wound healing. 2025 Elsevier B.V. -
Bougainvillea glabra-mediated synthesis of Zr?O and chitosan-coated zirconium oxide nanoparticles: Multifunctional antibacterial and anticancer agents with enhanced biocompatibility
The effectiveness and safety of nanomaterials (NMs) are essential for their use in healthcare. This study focuses on creating NPs with multifunctional antibacterial and anticancer properties to combat bacterial infections and cancer disease more effectively than traditional antibiotics. This study investigates the synthesis of Zr3O and chitosan (ch) coated zirconium oxide nanoparticles (chZrO NPs) using Bougainvillea glabra (B. glabra) plant extract through a green, one-pot precipitation method. The synthesized NPs were analyzed using various techniques. Their antibacterial properties are attributed to the production of reactive oxygen species (ROS), influenced by their size, large surface area, oxygen vacancies, ion release, and diffusion capabilities. The chZrO NPs showed superior antibacterial activity compared to Zr3O and chitosan alone, with effective inhibition against both Gram-positive bacteria (S. aureus and B. subtilis) and Gram-negative bacteria (E. coli and P. aeruginosa). Additionally, anticancer studies of chZrO NPs demonstrated significant activity against colon cancer HCT116 cells with C50 values of 4.98 ?g/mL compared to chitosan and Zr3O with 9.62, 6.69 ?g/mL, while biocompatibility tests on L929 cells confirmed their safety showing 93 % cell viability compared to ch and Zr3O. These findings suggest that chZrO NPs are promising candidates for future use in clinical and healthcare applications. 2025 Elsevier B.V. -
Fractional MooreGibsonThomson thermoelastic analysis of nonlocal nanobeams under moving heat source with machine learning-assisted predictive modeling
This study presents a comprehensive investigation of thermoelastic wave propagation in nonlocal nanobeams subjected to a moving heat source within the framework of fractional MooreGibsonThomson (MGT) heat conduction theory. The model incorporates nonlocal elasticity to capture size-dependent mechanical behavior and employs a fractional-order formulation to account for thermal memory and finite-speed heat propagation. The coupled governing equations are derived and solved analytically using Laplace transform techniques to obtain the temperature, displacement, and stress distributions. A detailed parametric analysis is performed to examine the effects of fractional order, nonlocal parameter, thermal relaxation time, and source velocity on the thermoelastic response. The results reveal significant modifications in wave attenuation, temperature evolution, and stress distribution due to the combined influence of nonlocality and fractional thermal effects, particularly under moving thermal loads. To enhance computational efficiency and enable rapid prediction of system responses, a machine learning-based surrogate framework is developed using an artificial neural network (ANN). The network is trained on data generated from the present analytical model and is shown to accurately predict thermoelastic fields across a wide range of governing parameters. The ANN predictions exhibit excellent agreement with analytical results, demonstrating its capability as a reliable reduced-order modeling tool. The proposed hybrid analyticalcomputational approach provides new insights into thermoelastic behavior at the nanoscale and offers an efficient predictive framework for heat transfer applications involving moving thermal loads. This study is motivated by the need to address unresolved challenges in modeling thermoelastic behavior at the nanoscale, particularly the simultaneous incorporation of fractional heat conduction, nonlocal elasticity, and moving thermal loads within a unified framework. 2026 Published by Elsevier Ltd. -
Non-Fourier thermal transport analysis in the human eye using a dual-phase-lag bioheat framework under environmental exposure
Understanding how heat propagates inside the human eye is important for preventing thermal damage during environmental exposure, laser treatments, and biomedical procedures, particularly in hot climates where ocular tissues are vulnerable to temperature rise. Conventional bioheat models based on Fourier heat conduction assume instantaneous heat transfer and may therefore fail to capture delayed thermal responses occurring in heterogeneous biological tissues. The aim of this study is to develop and analytically investigate a dual-phase-lag bioheat model capable of accurately predicting intraocular temperature evolution under combined environmental and physiological thermal loading. Motivated by the need for a more realistic and computationally efficient framework for ocular thermal safety assessmentaligned with Saudi Arabias Vision 2030 goals in healthcare innovation and preventive medicinethis study develops a dual-phase-lag (DPL) bioheat model to analyze heat transport in a multilayer human eye under combined environmental and physiological loading. Closed-form analytical solutions are obtained using normal-mode analysis for all six ocular layers while accounting for convection, evaporation, blood perfusion, and tissue porosity. Results show that the DPL model predicts lower and smoother temperature distributions compared with Fourier and LordShulman models, indicating more physiologically realistic thermal behavior. Ambient temperature and evaporation primarily control heating in anterior eye regions, whereas perfusion and tissue porosity dominate thermal regulation in deeper layers. Sensitivity analysis and thermal-safety maps identify critical combinations of exposure conditions that may increase thermal risk. A surrogate-based reduced-order model is further developed and validated, enabling rapid prediction of intraocular temperature with high accuracy. The study demonstrates that incorporating non-Fourier thermal effects significantly improves prediction of ocular temperature dynamics and provides a practical framework for thermal safety assessment, ophthalmic treatment planning, and climate-adaptive healthcare technologies. 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Hybrid fractional thermoelasticmachine learning (KNN, CNN and SVM classifier) framework for heat and mass transfer: A computational mechanics approach
The main goal of this study is to create a single fractional thermoelasticmachine learning framework that can accurately model how heat and stress move through skin tissue over time and automatically sort thermal regimes into safe and dangerous ones. The proposed method combines the AtanganaBaleanu fractional operator with the CattaneoVernotte heat flux law and data-driven classifiers (KNN, SVM, and CNN), and Laplace Transforms techniques to derive generalized thermoelastic formulations capable of capturing finite-speed thermal propagation, memory effects, and nonlocal stress relaxation. This connects strict analytical modeling with smart thermal safety prediction. Closed-form expressions for temperature, displacement, dilation, and stress fields are obtained in the Laplace domain and numerically inverted to evaluate transient responses under thermal shock. All fractional thermoelastic simulations and Laplace inversions were executed in MATLAB R2023a, whereas the machine-learning models (KNN, SVM, CNN) were implemented in Python 3.10 using scikit-learn and TensorFlow. To extend the predictive capacity of the analytical models, simulation-derived datasets are used to train three machine learning classifiersK-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Comparative analyses through confusion matrices, dispersion maps, ROC curves, residual maps, and bar charts demonstrate that CNN achieves superior nonlinear feature extraction and generalization, SVM provides stable global decision boundaries, and KNN efficiently identifies localized thermalmechanical anomalies. The AB fractional model is shown to suppress temperature overshoot and reduce stress concentration relative to CV, offering safer predictions for biological tissues. The combined fractionalML framework enables rapid classification of safe and risky heating regimes, with potential applications in hyperthermia therapy, burn injury prevention, dermatological laser treatments, and thermal hotspot detection in engineered composites. This study establishes a unified pathway where fractional thermoelastic modeling, deep learning, and classical machine learning synergistically addresses complex biomedical and material thermal interactions. A synthetic dataset generated from fractional ABCV thermoelastic simulations was used for training the ML classifiers. 2026 Elsevier Ltd -
Machine learning-enhanced heat and mass transfer study of elliptic motion in piezoelectric thermoelastic plates using Green-Naghdi III and three-phase-lag theories
Rayleigh-type surface waves in piezoelectric (PE) solids are pivotal for acoustic sensors, microelectromechanical systems (MEMS), and non-destructive evaluation. However, classical thermoelastic models fail under high heat flux due to the assumption of infinite thermal signal speeds, which limits their accuracy in coupled thermo-mechanical systems. To capture finite-speed and memory-dependent thermal effects, the Rayleigh wave propagation in a transversely isotropic (TI) PE half-space using generalized theories (such as Green-Naghdi type III (GN-III) and three-phase-lag (TPL)) is studied in this paper. The analytical formulation under varied electrical and thermal boundary conditions has been obtained. Secular equations are derived to characterize phase velocity, attenuation, and specific energy loss. A regression-based machine learning (ML) surrogate model is trained by using an analytical dataset to provide rapid predictions of wave parameters. Additionally, a confusion matrix classifier is applied to identify boundary conditions from simulated wave response features. The results demonstrated that the phase velocity increases with inclination angle and stabilizes with wave number, whereas attenuation and specific loss vary strongly by boundary condition (e.g., minimal in shorted-isothermal cases). The ML surrogate successfully reconstructed analytical predictions with minimal residual error, and the confusion matrix demonstrates accurate classification performance and validates the diagnostic potential of the framework. The novelty of this paper lies in integrating dual thermoelastic theories with machine learning, merging mechanics, heat transfer, and intelligent computing. These findings enable enhanced SAW sensor designs for precise gas/chemical detection, low-loss NDE tools for aerospace composite defect identification, and real-time diagnostics in biomedical ultrasonics for clearer imaging and efficient energy harvesting. 2026 Elsevier Ltd -
Hybrid fractional thermoelasticmachine learning framework for heat and mass transfer in skin tissue: Enhanced simulations using AtanganaBaleanu, CattaneoVernotte models, and KNNSVM classifiers
This study presents a hybrid computational framework that couples advanced fractional thermoelastic modeling with machine-learning-based safety classification for heat and mass transfer in skin tissue. The classical CattaneoVernotte (CV) non-Fourier heat conduction law is extended through the AtanganaBaleanu (AB) fractional operator to capture memory-driven thermal responses, finite thermal wave propagation, and nonlocal biological effects more accurately than traditional Fourier-type formulations. Closed-form expressions are derived using Laplace transforms and inverted numerically to obtain transient temperature, displacement, dilation, and stress fields within the tissue. The AB fractional model demonstrates smoother thermal evolution, reduced overshoot, and lower stress concentrations relative to the CV model, reflecting improved biomedical safety margins during rapid thermal exposure. To enable real-time risk assessment, synthetic datasets generated from the thermoelastic simulations are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The ML models reliably distinguish safe and risky thermal regimes, with SVM offering superior generalization and KNN capturing localized variations. The novelty of this work lies in directly integrating fractional physics-based modeling with machine-learning classification for thermal safety diagnosticsestablishing a unified paradigm for predictive biomedical heat transfer. The framework advances thermal therapy planning, burn-injury prevention, implant design, and smart clinical monitoring. While the current study is based on idealized geometry and simulated data, future extensions will incorporate in-vivo tissue characteristics, complex skin layers, and deep learning models to further enhance clinical applicability. 2025 Elsevier Ltd -
An improved LSTM based thermal prediction and control algorithm for battery management system in hybrid electric vehicles
Effective thermal management of lithium-ion batteries is critical for ensuring safety, longevity, and optimal performance in Hybrid Electric Vehicles (HEV). This research proposes an improved Long Short-Term Memory (LSTM) based thermal prediction and control algorithm for Battery Management Systems (BMS) to enhance temperature regulation accuracy and computational efficiency. The proposed model integrates an optimized LSTM network with attention mechanisms to capture long-term dependencies in thermal dynamics while reducing prediction latency. A multi-physics-based thermal model is employed to generate high-fidelity training data, accounting for electrochemical-thermal coupling effects. The algorithm incorporates adaptive learning rates and dropout regularization to mitigate overfitting and improve generalization under varying load conditions. A model predictive control framework is designed to leverage real-time LSTM predictions for proactive cooling strategy optimization, minimizing energy consumption while maintaining safe operating temperatures. The proposed model reached RMSE of Heat generation rate of 1.08 W/mA3, Entropy coefficient Error of 0.024 mV/K, Thermal conductivity of 0.626 w/mK, Latency of 28 ms, Cooling energy Consumption of 314.61 kWh and Temperature deviation of 3.34 AC. The proposed solution offers a computationally efficient, scalable framework for next-generation BMS, enhancing battery reliability and vehicle efficiency. 2025 Elsevier Ltd -
Study of chaos in RayleighBard convection of a micropolar fluid
The paper considers the micropolar fluid (MPF) in a RayleighBard situation and investigates regular and periodic convection, and chaos in the fluid for a wide range of values of the scaled Rayleigh number. The fourth-order scaled Lorenz model that governs weakly non-linear convection is an energy-conserving model whose bounded solution remains within the finiteness of a three-ellipsoid. All the characteristics of the classical Lorenz model are seen in the generalized one. The scaling of the equations is done in such a way that the classical Lorenz model can be obtained as a limiting case of the generalized Lorenz model. The scaled versions of the critical Rayleigh number and the HopfRayleigh number are quantified to determine the onset-of-regular-convection and chaos. Chaotic and periodic regimes appear alternately as the scaled Rayleigh number, r, increases. It is well known from existing literature that the effect of micron-sized particles in the Newtonian fluid is to delay the onset-of-regular-convection. In the paper, it has been found that the suspended particles delay the onset of chaos and the appearance of periodic motion when compared to that in the case of a Newtonian fluid. 2025 Elsevier Ltd -
Molecular and electronic structure of 5-coordinated [Fe(CO)?(X?)] complexes: A quantum chemical study
Quantum mechanical DFT calculations were performed on Fe(CO)5 and for the axial and equatorial isomers of 5-coordinated [Fe(CO)4(X2)] (where X = N, P, As, and Sb) complexes. The equatorially substituted complexes of 5-coordinated [Fe(CO)4(X2)] are more stable than the axially substituted complexes, as seen from their energy values. These complexes were further studied to understand their bonding nature using results from Natural population analysis (NPA) and energy decomposition analysis (EDA) calculations. The Wiberg bond indices (WBI) analysis provides the bond index of the bond between Fe and X2. The Frontier molecular orbital (FMO) studies show these complexes have a HOMO-LUMO energy gap in the values ranging from 1.99 to 5.04 eV, which is less than that of [Fe(CO)5]. From the Natural bond order (NBO) analysis, the contribution of the X?-atom is smaller in the ? bond formation compared to X? in P2, As2, and Sb2. Similar contributions are seen with the carbonyl group (in the Fe[sbnd]C bond), although the contribution of the carbon atom is larger than that of the X atom. 2025 Elsevier B.V. -
Multimodal artificial intelligence for early cancer detection via liquid biopsy, imaging, and clinical records
Tumours are diverse and multiscale, making it difficult for modern medicine to diagnose early cancer. Using structured clinical data, radiologic imaging features, and liquid samples, this research presents a multimodal AI framework for the early and reliable detection of cancer. The proposed approach surpasses single-modality approaches by integrating signals from various domains, including cancer genetic, anatomical, and physiological data. Using attention-based fusion, representation learning, and better preprocessing, we developed a prediction model that fine-tuned the weights of different modes. The results of the experiments demonstrated that it outperformed unimodal models on all datasets in terms of sensitivity, specificity, and generalisation. The framework has potential for screening purposes because of its ability to detect cancer at an early stage. Clinical confidence and interpretability were both boosted by the results of explainability tests, which revealed substantial feature contributions. The suggested multimodal framework outperformed unimodal baselines across all assessment cohorts with an AUC of 0.94, sensitivity of 0.91, and specificity of 0.88. Experimental results confirm multimodal fusion's clinically interpretable early cancer detection and precision oncology decision assistance. Copyright 2026. Published by Elsevier B.V. -
Comprehensive investigation on mechanical properties of mango seed shell short fiber-reinforced epoxy based polymer composites
The mechanical properties of discarded Mango Seed Shell Fiber (MSF)-reinforced epoxy composites are studied in this work. MSF, which was obtained through agricultural wastes, was added to the epoxy matrix in varying weight fractions viz., 5%, 10%, 15%, 20%, and 25% using the hand lay-up method. The outcome shows that the best mechanical performance is reached at the 15% MSF content, i.e., the tensile strength of 29.35?MPa and tensile modulus of 758?MPa, an improvement of 24% in comparison with the unreinforced (neat) epoxy. The modulus and flexural strength were 2962?MPa and 48.13?MPa for 15% MSF content which was 68% and 42% more than neat epoxy. The highest impact strength of 75.93?J/m for 15% MSF which corresponds to 148% higher than the neat epoxy, and the hardness was between 47 RHN and 56 RHN and was maximum for 10% MSF. The novelty of the current study lies in the utilization of mango seed shell fiber, which is an underutilized agro-waste product that has been utilized systematically as a reinforcing element in epoxy composite and the determination of optimal fiber loading by thorough mechanical testing is accomplished in the present work. The results provide the base of mechanical performance data of MSF-reinforced eco-composites and confirm its opportunities as sustainable and cost-efficient reinforcement for lightweight and environmental-friendly structural applications. 2026 Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license. http://creativecommons.org/licenses/by-nc-nd/4.0/ -
Appraisal of prolyl 4-hydroxylase alpha subunit gene polymorphisms in Spondyloepimetaphyseal dysplasia of Handigodu type (SEMDHG)
Background: The Handigodu variant of Spondyloepimetaphyseal Dysplasia (SEMDHG) is a severe, progressive osteoarthritic disorder characterized by chronic pain and joint degeneration. Clinically, the disorder presents in three distinct phenotypic forms, each exhibiting varying degrees of stature reduction and disease severity. Urine analysis of affected individuals reveals an elevated peptide-bound proline to 4-hydroxyproline ratio relative to controls, suggesting disruptions in collagen metabolism. Given the critical role of prolyl 4-hydroxylase enzymes in stabilizing collagen structure, this study undertook a comprehensive sequence analysis of all three isoforms of prolyl 4-hydroxylase in both affected and unaffected individuals to elucidate potential molecular underpinnings of the disorder. Method: The entire exonic regions and 2000 base pairs upstream of the translation start sites of the P4HA1, P4HA2, and P4HA3 genes were sequenced in a cohort of 300 individuals, comprising 166 affected and 134 unaffected individuals. Results: Sequence analysis of the ? (I), ? (II), and ? (III) subunit genes identified three novel SNPs and a 39-bp deletion variant, in addition to ten previously reported SNPs catalogued in dbSNP. The SNP rs28384495 in P4HA1, the 39-bp deletion variant, and a novel mutation (SNP3) in P4HA3 exhibited significantly different allele frequencies between patients and controls. Genotype association analysis revealed that SNPs in P4HA1 and P4HA3 were associated with Type 2 and Type 3 HD under various genetic models. Notably, all Type 2 HD patients were heterozygous for the 39-bp deletion, whereas all Type 3 HD patients were homozygous for the variant. Haplotype analysis corroborated the findings of the genotype association analysis. Conclusion: This study is the first to account an association between the P4H gene and disease. Further research is needed to evaluate the functional implications of the identified mutations. 2024 -
An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes
Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function. 2025 -
Impact of plastic contaminants on marine ecosystems and advancement in the detection of micro/nano plastics: A review
Micro/nanoplastics pollute all levels of the food web, beginning from aquatic algae, invertebrates, and other fish, through bioaccumulation or even physical and chemical damages augmenting degradation of the marine ecosystem. Besides plastic litter, other toxic chemicals employed in the manufacture of plastics also destroy stable ecosystems. Micro/nanoplastics are toxic to marine organisms through induction of blockage of ingestion, oxidative stress, and reproductive effects. Bivalves such as oysters accumulate microplastics in tissues, which decreases filtration rates. Polystyrene nanoplastics induce endocrine disturbance and neurotoxicity in fish. Seabirds suffer from gut inflammation ("plasticosis"), and zooplankton suffers from decreased feeding rates, which impacts trophic transfer. This review identifies some of the recent developments in electrochemical detection techniques, with a focus on electrochemical sensors and surface-enhanced Raman spectroscopy (SERS). Electrochemical sensors like CdS/CeO? heterojunction-based sensors have been able to detect 0.38 ng/mL of polystyrene nanoplastics. Biochar-modified electrodes and nanoporous gold sensors have also become more sensitive to trace detection levels (?0.44 nM) for microplastics. SERS-based techniques, for instance, membranes with Ag nanoparticles on anodic aluminium oxide (AAO) and metalphenolic networks with luminescence, have facilitated detection of polystyrene, polyethylene, and polypropylene nanoplastics in environmental matrices, with detection limits of 0.1 ?g/mL for 500 nm polystyrene and 1 ?g/mL for smaller plastic mimics. Although portable Raman spectrometers are sufficient for larger particulates, they need SERS enhancement for detecting oceanic matrix-bound nanoparticles. This article presents a critical overview of recent progress in the application of electrochemical sensors, Raman spectroscopy, and commercially available hardware to investigate their extended applications. Challenges and future directions for improved real-time monitoring with improved sensitivity and selectivity are also presented along with interference mitigation. 2025 The Author(s) -
Engineered biocorona on microplastics as a toxicity mitigation strategy in marine environment: Experiments with a marine crustacean Artemia salina
The marine environment has become a major sink for microplastics (MPs) wastes. When MPs interact with biological macromolecules, the biocorona forms on their surface, which can alter their biological reactivity and toxicity. In this study, we investigated the impact of biocorona formation on the toxicity of aminated (NH2) and carboxylated (COOH) polystyrene MPs towards the marine crustacean Artemia salina. Biocoronated MPs were prepared using cell-free extracts (CFEs) from microalgae Chlorella sp. (phytoplankton) and the brine shrimp Artemia salina (zooplankton). The results revealed that biocorona formation effectively reduced the toxicity of MPs. Pristine NH2-MPs exhibited higher reactive oxygen species production (ROS) (182%) compared to COOH-MPs (162%) in Artemia salina. Notably, NH2-MPs coronated with brine shrimp CFE exhibited a substantial reduction in ROS production (127%) than those coronated with algal CFE, with COOH-MPs showing a similar trend (120%). Biocorona formation also significantly decreased malondialdehyde (MDA) levels and antioxidant activity compared to pristine MPs. Molecular docking and dynamics simulations demonstrated a strong binding between polystyrene and acetylcholinesterase (AChE). In vitro studies indicated that pristine NH2-MPs exhibited more reduction in AChE activity (84%) compared to COOH-MPs (95%). However, no significant reduction in AChE activity was observed upon exposure to MPs coronated with either algal or brine shrimp cell-free extracts. Independent action modeling indicated an antagonistic interaction for MPs coronated with both the CFEs. Pearson correlation and cluster heatmap analysis suggested that the toxicity reduction in Artemia salina might be driven by decreased oxidative stress followed by the corona formation. Overall, this study provides valuable insights into the potential of biomolecules from phytoplankton and zooplankton to reduce MPs toxicity in Artemia salina, while highlighting their role in modulating the toxicity of other marine pollutants. 2024 The Author(s) -
Recent developments in melamine detection: Applications of gold and silver nanostructures in colorimetric and fluorometric assays
The purity of milk, traditionally regarded as a symbol of health and nourishment, has been undermined by the alarming issue of melamine (MLM) adulteration. This nitrogen-rich compound is illicitly introduced to falsely enhance protein content, posing significant health risks. Traditional detection methods are often labor-intensive, time-consuming, or require expensive equipment. In response, researchers have developed colorimetric detection techniques to efficiently screen milk for MLM contamination. These methods are particularly promising due to their ease of preparation, rapid detection, high sensitivity, and capability for naked-eye detection. Furthermore, the unique optical properties of advanced nanomaterials have facilitated fluorometric detection, wherein the presence of contaminants induces detectable changes in fluorescence intensity or wavelength. This study offers an in-depth review of recent advancements in colorimetric and fluorometric probes based on silver (Ag) and gold (Au) nanostructures, exploring their application in food analysis. It delves into the underlying sensing mechanisms of these probes, showcasing their efficacy in detecting food contaminants. Despite the numerous advantages of Ag and Au nanostructure-based probes, challenges remain, particularly in addressing the complexity of food matrices, achieving simultaneous detection of multiple analytes, and mitigating interference from testing conditions. Additionally, this review highlights the emergence of immunoassay-based sensors, noting that many commercially available MLM testing kits utilize ELISA and LFIA platforms. For the first time, a comprehensive list of MLM testing devices and assay kits is presented, accompanied by key findings from recent studies and recommendations for future research directions. 2024 The Author(s)