The Arabian killifish ( Aphaniops dispar ) is a resilient freshwater species capable of surviving extreme environments, making it a valuable model for studying evolutionary adaptation in challenging environments. This study provides baseline genetic information of A. dispar populations from Saudi Arabian Wadis (Saul and Zaban) based on mitochondrial 12S rRNA gene sequencing. Our analyses of the 542 bp fragment revealed 58 polymorphic sites. Pairwise genetic distances (Kimura 2-parameter) indicated moderate divergence (0.00–0.12), with the highest differentiation observed between wadi saul populations. Analysis of molecular variance (AMOVA) showed significant genetic structuring, with 67.3% of variation attributed to regional differences. ABBA-BABA tests on genome-wide single nucleotide polymorphism (SNP) data revealed historical hybridization with Aphaniops fasciatus (D = 0.32, p < 0.001). While the 12S rRNA marker confirms broad phylogeographic patterns, these findings highlight the value of multi-faceted genomic approaches for understanding the evolution of A. dispar . To our knowledge, this is the first report of A. dispar on genetic diversity in the Asir region of Saudi Arabia.
This retrospective study characterizes the prevalence and spectrum of (BReast CAncer gene 1 and 2 ( BRCA1/2) mutations in Saudi breast and ovarian cancer patients referred for genetic testing, to define the population-specific mutational landscape. Comprehensive molecular characterization was performed on 145 blood samples from breast and ovarian cancer patients. Statistical analyses evaluated associations between mutation status and age, with significance thresholds set at p < 0.05. The overall prevalence of germline BRCA1/2 mutations was 19.3% (28/145 samples), with BRCA1 mutations accounting for 75% and BRCA2 for 25%. The most frequent mutation was BRCA1 p.(Ser1379Ter), detected in 39.3% of positive cases. Frameshift indels (71.4%) and stop-gained variants (10.7%) were predominant. Patients under 40 years showed a significantly higher mutation rate (32.9%, P=0.034). BRCA1 mutations were more common in ovarian cancer (71.4%), while BRCA2 variants were equally distributed between breast and ovarian cancers. This study reveals a prevalence of 19.3% for germline BRCA1/2 mutations in the tested population, with BRCA1 p.(Ser1379Ter) emerging as a recurrent variant, warranting further investigation as a possible founder mutation. The findings emphasize the importance of early genetic testing, particularly in younger high-risk individuals, and contribute to our understanding of BRCA1/2 mutational patterns in breast and ovarian cancers.
Municipal solid waste (MSW) management has significant impacts on humans, ecosystems, air quality, and land. Thermal treatment is the conventional method for converting waste energy into electricity. To address these issues and increase energy production, pyrolysis and gasification methods have undergone significant evolution. Use of refuse-derived fuel (RDF) is the correct path to get more resources back from MSW in developed countries. A pilot-scale bubbling fluidized bed gasifier capable of processing 5 to 15 kg/hr has been developed for the utilization of dry solid waste and other rice husk biomass. Various categories of solid waste materials were utilized in the gasification process, and the associated gas attributes, including gas quality, gas quantity, and calorific value, were examined during the tests. The characterization of the available RDF material shows that the sulfur content of the waste material was high, and to reduce it, rice husk has been mixed in different proportions. It was found that a 20% mixing of rice husk in pulverized RDF material yields promising results. The percentage of CO increased from 12.5% to 13.4%, and H 2 rose from 11.2% to 13.2% in a produced gas with the mixing of rice husk at a ratio of 20 to 80%. Mixing rice husk with a pulverized RDF material provides a promising solution for air gasification in a fluidized bed gasifier. GRAPHICAL ABSTRACT
In the pursuit of effective, viable sources for potential health benefits, herbal medicines have gained renewed attention due to their increased safety margin. In this context, we evaluated the phytochemical, safety margin, and hepatoprotective effects of Maclura pomifera (Rafin.) fruit extracts (MPFE) in a thioacetamide (TAA)-induced liver cirrhosis animal model. Sprague-Dawley rats received three doses/week of 200 mg/kg TAA intraperitoneally and were treated daily with either normal saline, 50 mg/kg silymarin, 250 mg/kg MPFE, or 500 mg/kg MPFE. After two months, dissected livers and serum samples were evaluated for the level of pro-oxidants, apoptosis, inflammation, and vascular injury. The chemical profiling of MPFE showed increased total phenolic (434.7 µg GAE/mL) and flavonoid (358.0 µg rutin/mL) contents, with the methanolic extract being more efficient. MPFE mitigated TAA-hepatotoxicity and restored the macroscopic/microscopic alterations, comparable to those in silymarin-treated rats. MPFE attenuated TAA-mediated hepatic dysfunction, increased liver synthetic performance, improved total protein production, and reduced serum total bilirubin. MPFE strengthened liver tissue integrity, as evidenced by less cell infiltration, an organized serosa, fewer collagen deposits, less fibrosis around the central vein, and piecemeal necrosis in periportal areas. Moreover, MPFE (250mg/kg and 500mg/kg) suppressed the generation of proliferative protein β-catenin (a tissue fibrosis enhancer), the pro-apoptotic Bax protein, malondialdehyde, and the inflammatory cytokines (TNF-α and IL-10). Parallel with increasing the SOD by 17.14, 55.67, and CAT by 88.53, 159.45%, and GPx by 31.65, 77.5%, respectively, compared to the TAA control rats. The outcomes demonstrated increased MPFE tolerance and hepatoprotective effect of MPFE, attributing to its phytochemicals’ modulatory potential on inflammation, oxidative stress, and apoptosis.
This study integrates categorical principal component analysis (CATPCA) with nonparametric binary logistic spline regression to simultaneously address multicollinearity and predictor heterogeneity. The proposed CATPCA-based binary logistic spline regression approach is designed to handle heterogeneous, categorical, and correlated predictors. Optimal knot selection in the spline component is determined using the minimum generalized cross validation (GCV) criterion. Model performance is evaluated using deviance, accuracy, and the akaike information criterion (AIC). Simulation studies are conducted to compare models with and without CATPCA, followed by an application to real stunting-risk family data consisting of categorical predictors. Model parameters are estimated using maximum likelihood estimation based on CATPCA-derived component scores. Simulation results show that the CATPCA-based binary logistic spline model yields lower deviance and AIC values than the model without CATPCA, indicating improved model fit. Application to family stunting-risk data from South Sulawesi Province, Indonesia, identifies five principal components that capture both linear and nonlinear patterns of predictor influence on the response. The integrated CATPCA-based binary logistic spline regression model effectively explains the influence of heterogeneous and interdependent predictors on the response. The combination of spline-based nonparametric modeling and CATPCA-driven dimensionality reduction enhances the model’s ability to capture complex and detailed relationships between family-level determinants and stunting risk.
The swift emergence of multidrug resistance across diverse microorganisms presents a major clinical obstacle for healthcare providers managing infectious diseases. In recent years, studies have concentrated on developing metal-based nanomaterials with antibacterial, antiviral, and antifungal capabilities to address contagious diseases. Various metal nanomaterials, including gold, copper, silver, palladium, and metal oxides like titanium, zinc, and iron, have shown promising antimicrobial effects against multidrug-resistant pathogens. The interaction between nanoparticles and biological systems is significantly affected by their physicochemical characteristics, such as size, shape, surface charge, ligand coating, doping, pH stability, surface roughness, and crystalline structure. Upon interaction, these nanoparticles exert their antimicrobial effects through multiple mechanisms, including increased production of intracellular reactive oxygen species, damage to cell membranes, disruption of membrane potential, DNA impairment, and destabilization of biofilms through interactions with their components. A comprehensive understanding of how specific physicochemical properties of metal nanoparticles relate to their antimicrobial mechanisms remains limited. Therefore, this review article examines key aspects of various nanoparticle types commonly applied in health and medical fields, with a focus on antimicrobial chemotherapy. It explores critical factors employed by nanoparticles to achieve antibacterial effects, as well as the antifungal, antibacterial, and antiviral mechanisms of metal nanoparticles.
Peripheral nerve injury (PNI) leads to significant loss of sensorimotor function and long-term disability, with limited options for complete recovery. In the current study, we investigated the neuroregenerative potential of Calotropis procera using a rodent model of sciatic nerve injury. A pharmacological screening approach was first employed to identify bioactive constituents and their predicted molecular targets. Based on these findings, a methanolic leaf extract of Calotropis procera leaves (CP-Ext) enriched in phenolic compounds was selected for further evaluation. Phytochemical analysis confirmed the presence of key constituents, including gallic acid, catechin, and quercetin. Functional assessment demonstrated that CP-Ext significantly improved recovery of sensory and motor function. In addition, treatment enhanced antioxidant defense mechanisms, as indicated by increased paraoxonase and arylesterase activities, and promoted early upregulation of the regeneration-associated protein S100β. These results indicate that CP-Ext facilitates nerve repair processes and supports its potential as a source for drug candidates for peripheral nerve regeneration.
The study aims to examine the phytochemical composition, antimicrobial, hemolytic, antioxidant, and potential anti-inflammatory properties of the aqueous leaf extract of Crossandra infundibuliformis (C. infundibuliformis). Fresh and shaded dried plant leaves were used for extraction using sterilized double distilled water. Qualitative and quantitative phytochemical analyses were conducted to estimate phenols, tannins, carbohydrates, and proteins. Gas chromatography mass spectroscopy (GC-MS) analysis was performed to identify bioactive compounds. Antimicrobial activity was examined against different bacteria ( Bacillus sp ., E. coli , and Klebsiella sp.). The antioxidant activity was determined using the 2,2-diphenyl-1-picrylhydrazyl (DPPH)+ radical scavenging activity. Further the haemolytic activity was evaluated to determine cytotoxicity. Selected GC-MS identified phytochemicals were further analyzed using Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) prediction and molecular docking studies against anti-inflammatory targets [Cyclooxygenase-1 (COX-1), Nuclear factor kappa-B (NF-κB), Tumor Necrosis Factor alpha (TNF-α)], with aspirin used as a reference drug. The phytochemical analysis exhibits the presence of phenols (2.8 ± 0.1 mg GAE/g), carbohydrates (28.8 ± 0.03 mg/g), and proteins (19.28 ± 0.09 mg/g). The analysis of GC- MS identified several bioactive compounds. The extract reveal greater antimicrobial activity against Klebsiella sp. compared to Bacillus sp. The DPPH + assay demonstrated dose dependent antioxidant activity with an IC₅₀ value of 43.43 µg/mL, comparison of standard ascorbic acid shown IC₅₀ value of 33.4 µg/mL. Further, the hemolytic activity remains below 45% at the highest tested concentration (10 mg/mL), indicating low cytotoxicity. Molecular docking results suggested favorable binding interactions of selected phytocompounds with COX-1, NF-κB, and TNF-α. Aqueous leaf extract of C. infundibuliformis having bioactive phytochemicals with antimicrobial, antioxidant, and potential anti-inflammatory activities, including with low hemolytic toxicity. These findings suggest that C. infundibuliformis is a promising and biocompatible therapeutic candidate. However, further experimental studies are needed to confirm its therapeutic efficacy.
A retrospective cross-sectional investigation assessed the effectiveness of sofosbuvir (SOF) and Daclatasvir (DCV) versus SOF and Ribavirin in treating Hepatitis C patients in Pakistan, highlighting the significant The Hepatitis C virus (HCV) health burden after the transition from interferon-based therapy. A retrospective study was performed at the Liver Clinic located at 250 Shadman, Lahore, Pakistan, spanning from January 2015 to December 2015. Two hundred patients, aged 20 to 75, were allocated to Group A, which received SOF + DCV combination for a duration of 3 months, and SOF and Ribavirin were given to Group B for six months. Various parameters, including direct and indirect bilirubin, total bilirubin, serum ALP and ALT levels, and hemoglobin (Hb) levels, were recorded. Analysis indicated that Group A exhibited superior outcomes, characterized by stable Hb levels, normalization of ALT; there was no elevation in blood bilirubin throughout the initial month of treatment compared to Group B. Both groups demonstrated dissimilar sustainable virological response rate (SVR) rates, with Group A at 72% and Group B at 94%. The research findings demonstrate that a 12-week treatment of SOF/ DCV is more efficacious in addressing hepatitis C than a 24-week treatment of SOF/Ribavirin in the studied population. Hemolytic anemia occurred in 50% of individuals with ribavirin, highlighting its related complications. This retrospective analysis demonstrates that the SOF/ DCV regimen administered over 12 weeks was better tolerated and exhibited more favorable biochemical and safety profiles than the SOF/Ribavirin regimen given over 24 weeks in the studied population. While both regimens resulted in comparable SVR rates, the combination of SOF and DCV exhibited a lower incidence of adverse effects.
Cerium oxide nanoparticles (CeO₂NPs) exhibit redox activity that may influence biological systems, while zinc plays an essential role in reproductive function. However, the combined effects of zinc-doped CeO₂NPs on early embryonic development remain unclear. This study evaluated the effects of pure and zinc-doped CeO₂NPs on in vitro fertilization (IVF) and early embryo development in mice, focusing on cleavage and blastocyst formation. A total of 144 superovulated SWR/J female mice were used to obtain 1833 oocytes for IVF. Fertilized oocytes were cultured in media supplemented with pure CeO₂NPs or Zn-doped CeO₂NPs (5%, 10%, and 20%) at concentrations of (0.01, 0.1, and 1 µg/ml). Embryo development was assessed based on cleavage and blastocyst rates. Cleavage was significantly influenced by nanoparticle formulation in a concentration-dependent manner. Pure CeO₂NPs at 0.1 µg/ml reduced cleavage rates compared with control group, whereas 5% Zn–CeO₂NPs improved cleavage under specific conditions. No consistent improvement was observed at higher zinc doping levels, and 20% Zn–CeO₂NPs showed reduced cleavage at 1 µg/ml. In contrast, blastocyst formation remained unaffected across all treatment groups. The effects of CeO₂NPs on early embryonic development are formulation-dependent rather than concentration-driven. 5% Zn–CeO₂NPs was associated with improved cleavage under specific conditions, whereas higher doping levels did not confer consistent benefit. The absence of differences at the blastocyst stage suggests that embryos successfully progressing through cleavage retain their developmental competence, highlighting the importance of nanoparticle design in assisted reproductive applications.
Industrial infrastructures often encounter durability issues with protective coatings, which can be potentially attributed to imbalance of nanoscale impermeability and uncontrolled opacity in coating formulations. This study aims to improve the protective performance of epoxy/polyamide coatings by strategically utilizing epoxy-functional silane (3-glycidoxypropyltrimethoxysilane, GLYMO) modified zinc oxide (ZnO) nanoparticles and micro titanium dioxide (TiO₂) pigments. The formulations of these hybrid coatings are prepared by the combination of ZnO nanoparticles (1–3 wt. %) with commercial TiO₂ pigment within an epoxy matrix. An ultrasonic process is utilized to ensure homogeneous nanoparticle dispersion, followed by micron-sized TiO₂. The coating is then applied to steel substrates by means of an automatic applicator, followed by curing at ambient temperature. The optimal nanoparticle level of 2.0wt.% ZnO in the formulation demonstrated performance enhancement. The enhancement was not only seen in mechanical properties, the anticorrosion properties analyzed in 3.5% NaCl solution also showed improvement in the when compared to the coating without nanoparticles up to 21 days of exposure which confirms the successful integration of nano and micro filler. The combination of nano and micro particles creates a microstructure that successfully elevates the mechanical as well as barrier properties. Such a hybrid coating formulation can be considered as a good option for protective coatings equivalent to marine environment that requires good corrosion resistance.
The sorghum aphid, Melanaphis sorghi Theobald, 1904 (Hemiptera: Aphididae), is one of the main pests of sorghum ( Sorghum bicolor L. Moench, Poaceae) in Mexico. Its control relies primarily on synthetic insecticides, although more sustainable alternatives using plant-derived extracts and compounds have been explored. In this study, we evaluated the insecticidal activity of Dodonaea viscosa leaves using a sequential approach. Initially, a crude acetone extract of Dodonaea viscosa (EADv) was prepared and subsequently fractionated (DvF1–DvF5) based on chemical composition; from the most active fraction, we isolated a pure diterpene, hautriwaic acid. Results showed that EADv achieved 76% mortality at 2,500 Parts per million (ppm) after 72 h. Among the fractions, DvF3 exhibited the strongest activity with 92% mortality and a median lethal concentration (LC 50 ) of 414.9 ppm at 72 h. The hautriwaic acid isolated from DvF3 was even more effective, causing 96% mortality at 50 ppm and an LC 50 of 43.79 ppm at 72 h. These findings demonstrate that D. viscosa contains compounds with significant insecticidal activity against M. sorghi , highlighting hautriwaic acid as a potent insecticidal agent at low concentrations, making it a promising alternative for integrated pest management in sorghum.
Serum albumin (SA) is a highly water-soluble plasma protein essential for maintaining physiological homeostasis. It plays a crucial role in supporting liver and kidney function and regulating plasma osmolality. Reduced SA levels are frequently linked to liver failure and chronic hepatitis, emphasizing the urgent need for low-cost, accurate, and rapid methods for SA analysis. In this study, we have developed a fluorescent probe, triphenylamine derivative substituted with rhodanine-3-acetic acid (mRA),that exhibits a robust fluorescence response upon binding to bovine serum albumin (BSA). mRA is a bifunctional molecule containing both electron-rich and electron-deficient moieties, enabling strong intramolecular charge transfer (ICT)-mediated emission characteristics. mRA displays negligible fluorescence in aqueous solution. While it bind to BSA binding pocket, it restricts the intramolecular rotation, resulting in a significant increase in fluorescence intensity. A BSA concentration-dependent fluorescence enhancement was observed in the range of 0.01 to 300 μg/mL, with a detection limit as low as 8 ng/mL. This proof-of-concept was validated using BSA-spiked samples. Additionally, demonstrate that mRA can monitor BSA consumption/degradation in cell culture. The observed fluorescence recovery supports the effectiveness of this detection strategy as a viable alternative to conventional techniques. Moreover, this approach holds strong potential for the development of point-of-care diagnostic tools for detecting liver diseases.
Tissue-associated microbial communities contribute to plant adaptation in extreme environments, yet the compartment-specific organization of tissue-associated bacterial communities in desert halophytes remains insufficiently understood. This study investigated how plant tissue type may influence the tissue-associated bacterial phytobiome of Salvadora persica growing under saline desert conditions along the western coast of Saudi Arabia. Leaf and root tissues were collected and analyzed using high throughput 16S rRNA gene amplicon sequencing. After quality filtering, non-chimeric reads were processed using DADA2 for amplicon sequence variant (ASV) inference, and microbial diversity and community structure were assessed using alpha diversity indices, rarefaction analysis, beta diversity metrics, principal coordinates analysis (PCoA), and UniFrac-based clustering. Sequencing produced 68,817–108,461 high-quality non-chimeric reads per sample. Leaf tissue-associated bacterial communities were highly conserved and taxonomically simplified (10–22 ASVs; Shannon index 0.89–1.01; Faith’s PD 1.92–4.02), dominated mainly by Cyanobacteriota and Pseudomonadota. Root tissue-associated bacterial communities exhibited substantially higher richness and phylogenetic complexity (286–508 ASVs; Faith’s PD 31.03–51.05), with one root sample showing dominance of Bacillota and Bacteroidota and enrichment of anaerobic genera, while other root samples were enriched in Cyanobacteriota and Pseudomonadota alongside diverse low-abundance soil-associated phyla. Beta diversity analyses revealed compartment-associated patterns, where ordination and phylogenetic clustering suggested separation between leaf- and root-associated communities; however, permutational multivariate analysis of variance (PERMANOVA) did not detect statistically significant differences between compartments based on Bray–Curtis and unweighted UniFrac distances. Overall, the data indicates trends consistent with compartment differentiation in diversity and composition between leaf- and root-associated bacterial communities in S. persica , supporting compartment-associated structuring of tissue-associated bacterial assemblages under saline desert conditions and providing a foundation for future functional and metagenomic investigations.
This paper discusses a new bivariate double uniform smoothing transformation (DUS)-Weibull distribution which has marginals are DUS-Weibull distribution. We use the Farlie-gumbel morgenstern copula to construct a new bivariate distribution. The main statistical properties of the new bivariate distribution are introduced. Two estimation methods are introduced to estimate the bivariate distribution parameters. Monte-Carlo simulation study is constructed to compare two estimation methods and study the behavior of the estimators. Finally, real data is used to show the validity of a new bivariate DUS-Weibull distribution.
This study aimed to systematically evaluate the factors affecting traffic congestion at urban intersections and rank them based on congestion levels. Three multi-criteria decision-making (MCDM) techniques—Content validity method (CVM), best-worst method (BWM), and technique for order preference by similarity to ideal solutions (TOPSIS)—were used in an integrated framework. First, 15 potential congestion-related criteria were identified through literature and expert input. Using CVM, 8 key criteria were selected based on field-measured traffic data, official accident records obtained from relevant authorities, and structured on-site observational measurements. Their optimal weights were then calculated via a linear optimization model within the BWM. These weights were fed into TOPSIS for estimation and ranking of junction congestion. The examination also showed the Karayolları intersection to be the most congested (score: 0.711) and Toplu Konut Boulevard Intersection to be the least congested (score: 0.140). The findings provide essential information for decision-makers, aiding initiatives to enhance urban traffic flow by pinpointing crucial crossings and suggesting alternative geometric configurations. In addition, a sensitivity analysis across different weight conditions has confirmed the reliability and practical usefulness of the model. This proposed approach serves also as a tactical tool to prioritize interventions and improve the efficacy of transportation networks in urban environments.
Surface passivation has showed to be a successful strategy to enhance the overall performance of the dye-sensitized solar cells (DSSCs) and reduce the charge recombination kinetics. Machine learning (ML), however, has recently made a significant impact on the field of energy by determining secreted patterns and classifying correlations between desired output values and input objects. Organic passivating materials, compared to other passivating materials, play a significant role in reducing the charge recombination rate while enhancing the performance. Herein, an organic passivating compound of 2-aminoterephthalic acid (2ATA) is applied to passivate SnO 2 photoanodes for the first time, and the observed impact on the photovoltaic properties of DSSCs. The SnO 2 photoanode was synthesized using a facile modified-solvothermal technique. The prepared sample was studied by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier transform infrared spectrometer (FTIR), UV–vis spectrophotometer (UV-Vis), and current density- voltage (J-V). The XRD pattern confirmed the tetragonal rutile phase of SnO 2 structure, with pure and amorphous in nature. The FE-SEM exhibited platelet morphology of pure SnO 2 with more agglomeration. The surface engineering of SnO 2 photoanodes by 2ATA passivation, as confirmed by FTIR, UV-Vis spectra, and finally improving device performance. It was found that passivated SnO 2 enhanced overall performance compared to bare SnO 2 photoanodes and achieved the highest efficiency of ∼ 0.64%. The ML algorithms, XGBoost regression, and random forest (RF) regression were analyzed to predict the efficiency. The three different architectures of the artificial neural network (ANN) algorithm with 100, 1000, and 2000 epochs were also analyzed with different metrics to accurately predict the efficiency and finally compared with this experimental SnO 2 based DSSCs work.
In this work, we fabricated dendritic mesoporous silica nanoparticles (DMSNs) functionalized with amino acids (leucine, serine, and ornithine) for removal of methylene blue (MB) from aqueous solutions. Structural characterization showed mesoporous architecture, with TEM showing almost uniform particle size of approximately 210 nm for DMSNs. Dynamic light scattering (DLS) analysis showed that the nanoparticles had hydrodynamic diameters of 232 nm for leucine-modified DMSNs (LE-DMSN), 247 nm for serine-modified DMSNs (SE-DMSN), and 256 nm for ornithine-modified DMSNs (OR-DMSN). FT-IR spectroscopy showed the appearance of characteristic amide carbonyl stretching at ∼1690 cm⁻ 1 . Batch adsorption investigations showed pH 10 as optimal for maximum MB removal efficiency. The adsorption data exhibited agreement with the Langmuir isotherm model, suggesting monolayer adsorption on homogeneous binding sites. Kinetic studies showed that the adsorption process followed pseudo-second-order kinetics with rate constants ( k 2 ) ranging from 0.03 to 0.15 g·mg⁻ 1 ·min⁻ 1 and calculated equilibrium capacities ( q e ) between 4.34 and 7.23 mg·g⁻ 1 within a contact time of 120 minutes. From the first to the third regeneration cycle, the removal efficiency of LE-DMSN decreased from ∼83% to ∼60%, while the efficiencies of OR-DMSN and SE-DMSN decreased from ∼64%/63% to ∼46%.
In the modern world, nanotechnology has gained incredible attention due to its virtue for the development of smart materials, innovative devices, and enriched technological applications. Nanoparticles (NPs) are now widely employed across diverse sectors, including industry, agriculture, medicine, cosmetics, food processing, and environmental remediation, owing to their unique physicochemical properties. Despite the demonstrated benefits of NPs in various applications, accumulating evidence indicates that their widespread use may be associated to substantial risks to both human health and the environment. Due to their extremely small size, these NPs can interact with biological systems in unique ways such as molecular and cellular levels, raising significant concerns related to toxicity, bioaccumulation, and long-term exposure. Keeping the facts in the field of nanoscience, the aims of this review to provide a comprehensive overview of the uptake, translocation, and accumulation of NPs in humans, with particular emphasis on exposure via the food chain. As NPs enter agricultural systems and food products, they may ultimately reach consumers, making dietary intake via exposure route. In addition to ingestion, humans are also exposed to NPs through inhalation of airborne particles and dermal absorption from consumer products, occupational settings, and environmental contamination. A deep understanding of these NPs exposure routes is crucial for assessing potential health risks. Furthermore, the review also emphasized the toxicological impacts of NPs on both plants and human health. Moreover, in plants, NPs can influence growth, physiology, nutrient uptake, and metabolic processes, which may impact on food quality and safety. Similarly, in humans, nanoparticle exposure has been linked to oxidative stress, inflammation, and potential organ damage. Overall, this article underscores the need for systematic evaluation, regulation, and responsible use of nanomaterials to ensure environmental integrity and human health safety. GRAPHICAL ABSTRACT
Precise and reliable Solar Radiation (SR) prediction is an integral part of the thermal systems for renewable energy production. A lightweight convolutional neural network (CNN) is designed to generate complex features and a hybrid optimization algorithm combining the artificial hummingbird algorithm (AHA) and Cheetah optimizer (CO), named as AHA-CO, is introduced for Feature selection (FS). The CNN model comprising three convolution layers interlaced by max pooling layers is used to transform inputs to complex features and the AHA-CO is employed to retain only the most informative features without compromising prediction performance and avoid getting stuck in local minima. The developed method is evaluated against 12 individual and three hybrid metaheuristic methods, demonstrating the superior performance of the AHA-CO. Subsequently, the CNN features selected by the AHA-CO are used as input to five regression models. The effectiveness of the SR prediction is assessed on a publicly available dataset. The support vector regression (SVR) model performed the best, forming the CNN-AHA-CO-SVR framework. The results show that the developed framework attained superior performance with 0.0365 mean absolute error, 0.0074 mean squared error, and 0.9251 coefficient of determination, highlighting its robustness and suitability for accurate SR prediction.
