Search Results (1,270)

The plastic pieces of synthetic polymers, which were previously regarded as primary pollutants of the environment, are increasingly being discovered as internal pollutants of the human body. This review provides a comprehensive overview of the available evidence on human exposure, tissue distribution, and associated biological effects of micro- and nanoplastics. Ingesting contaminated food and water is the major exposure pathway, with inhalation and dermal contact being secondary routes. Various organ systems have been identified as containing polymer particles through the use of advanced analytical methods, including blood, liver, lungs, placenta, breast milk, and brain tissue. Experimental animal studies suggest associations with tissue injury, metabolic illness, and neurotoxicity. Polyethylene, polypropylene, polystyrene, and polyethylene terephthalate are the most frequently found polymers in human samples. New clinical findings indicate potential health implications, though current human evidence remains largely associative rather than causal: a cardiovascular study observed more than a two-fold rise in mortality among patients with polymer-containing arterial plaques, and recent evidence demonstrates over-accumulation of polymers in brain tissue, raising questions about neuroinflammatory processes. Detection technologies have advanced substantially, with deep learning-based polymer classification achieving 95–99% accuracy and ultrasensitive electrochemical and surface plasmon resonance biosensors reaching detection limits approaching 10⁻¹¹ M. Despite these advances, critical issues remain, including lack of standardized analytical procedures, absence of chronic exposure models for humans, and insufficient longitudinal epidemiological data. To address these gaps, physiologically relevant experimental systems including organoids and organ-on-chip platforms will be required, in addition to well-designed prospective cohort studies. Full article

Glutamate excitotoxicity is a critical pathological mechanism underlying neuronal death in ischemic stroke, epilepsy, and neurodegenerative diseases. Caveolin-1 (Cav-1), a structural protein of caveolae membrane microdomains, has emerged as a potential modulator of neuronal survival, yet its precise mechanisms in excitotoxicity remain incompletely understood. In this study, we investigated the role of Cav-1 in regulating glutamate-induced calcium dysregulation, reactive oxygen species (ROS) generation, and mitochondrial dysfunction in primary hippocampal neurons. Using Cav-1 overexpression (Cav-1OE) and Cav-1 knockdown (Cav-1KD) approaches, we demonstrate that Cav-1OE significantly attenuates glutamate-stimulated intracellular Ca²⁺ elevation, reduces ROS generation, and prevents mitochondrial membrane potential (Ψ_m) depolarization. Further investigation revealed that Cav-1OE reduces, while Cav-1KD enhances, calcium responses mediated by NMDA, AMPA, and KA receptors. These findings establish that Cav-1 functionally attenuates excitotoxic signaling by negatively regulating ionotropic glutamate receptor-mediated Ca²⁺ influx. Full article

Spatial studies play a significant role in navigating the actions, experiences, and interactions happening in a specific place and context. The intersection of spatiality and theology will expand the scope of relating the biblical elements to contemporary relevant issues. Genesis 6–9, often termed the flood narrative, is considered a major biblical evidence in terms of environmental vulnerability and divine faith. By incorporating geocriticism alongside the other spatial theories of Tuan, Bachelard, and Tally, this study offers a critical, exegetical textual analysis of Genesis 6–9 to understand the dynamics of mobility, spatial agency, and re-habitation as shown in the narrative. The paper’s primary argument is that the ark functions as an affective space and an architectural structure of protection, belonging, and preservation during and after the flood. Secondly, it focuses on how the flood narrative negotiates between the real, imagined and textual spaces of spatial re-creation and how it resonates with contemporary environmental concerns by interpreting it as an instance of environmentally induced displacement while retaining its theological significance. Full article

Ischemic Heart Disease (IHD) is the leading cause of cardiovascular mortality worldwide. Early detection of ischemic changes using electrocardiogram (ECG) signals is vital for timely intervention and enhanced clinical outcomes. However, the diagnosis of IHD varies significantly between men and women. Women often present with atypical symptoms, and their cardiovascular risk is frequently underestimated, which leads to delayed diagnosis. Also, existing approaches face challenges in subtle early-stage abnormalities, single-lead ECG presentation, and the limited interpretability of deep learning models. These cause significant challenges to the accurate diagnosis of IHD. To address these, this study proposes a gender-aware framework, Gender-Aware Morphology Encoder Network (GAMENet), for early ischemic heart disease detection using 12-lead ECG signals with clinical metadata. A novel GAMENet is developed using the PTB-XL database. The Adaptive Morphology Deviation Encoder (AMDE) through Morphology Segment Extraction (MSEG-R) using R-Peak anchoring, isolates clinically relevant waveform components (P-wave, QRS complex, ST-segment, and T-wave) from the preprocessed ECG signals. The feature vector of morphology features is passed through dense layers with dropout regularization and a SoftMax classifier. Statistical and comparative analysis ensures that the proposed framework enables accurate IHD classification and improved interpretability. Full article

Ensuring functional correctness in digital circuitry is arguably the most labor-intensive stage of hardware creation, routinely accounting for upwards of 70% of a project’s total resource allocation. While traditional coverage-driven verification (CDV) attempts to validate every operational state, reaching full coverage closure via manual intervention or constrained–random techniques requires significant engineering time and domain knowledge. To overcome this bottleneck, this study introduces an automated testing architecture that leverages the Advantage Actor–Critic (A2C) Reinforcement Learning (RL) algorithm. This agent intelligently navigates functional coverage closure across five diverse hardware designs: an Advanced Peripheral Bus Universal Asynchronous Receiver-Transmitter (APB UART), an Serial Peripheral Interface (SPI) Memory unit, a synchronous First-In First-Out (FIFO) queue, an APB RAM, and an Advanced High-performance Bus (AHB) Slave interface. By interfacing QuestaSim 2024.1 with a Python-based intelligent agent via a SystemVerilog DPI-C socket, the system dynamically produces test vectors informed by real-time coverage metrics. Based on evaluations across five distinct random seeds, the methodology successfully attains 95.1% to 100% coverage across all testbenches, with three designs achieving 100% and two reaching 95–98%. Notably, the RL-guided system achieved target coverage using approximately 35% fewer simulation cycles than an unguided random baseline, and 22% fewer cycles compared to a traditional constrained–random setup utilizing expert-defined rules. Ultimately, this framework bypasses the necessity for manual constraint formulation and seamlessly scales to novel hardware environments with negligible setup overhead. Full article

Welding aluminum (Al) to titanium (Ti) is particularly challenging because of the large differences in their melting points and the tendency to form cavities and brittle intermetallic compounds. Such issues can be mitigated in friction stir welding (FSW) by understanding the underlying mechanisms of microstructural evolution and tensile fracture behavior. In the present study, FSW was carried out on commercially pure Al and commercially pure Ti. X-ray micro-computed tomography results show that the distribution of Ti fragments depends on their morphology, with fine particles (volume 10³–10⁴ µm³) being distributed homogeneously, while large flakes (10⁷–10⁹ µm³) are concentrated near the joint interface. A three-dimensional analysis of Ti fragment distribution was performed to clarify material flow and particle dispersion within the weld nugget. EDS (Energy-Dispersive Spectroscopy) and EPMA (Electron Probe Microanalysis) composition mapping confirmed the formation of AlTi and Al₃Ti intermetallic phases, with Al₃Ti as the dominant phase (consistent with its lower Gibbs free energy of formation). Because Al is the primary element in the matrix and undergoes the highest degree of deformation, its microstructural evolution in Al was examined using Electron Backscatter Diffraction (EBSD). Grain refinement in Al was attributed to continuous dynamic recrystallization (CDRX). Mechanical mixing and intermetallic formation increased the hardness of the weld, while the tensile response corresponded to a joint efficiency of approximately 77%, alone with an 11% improvement in elongation over base Al. The study further establishes a correlation among Ti particle distribution, local microstructural evolution, and the tensile response of the joint. Fractographic analysis indicates a bimodal fracture mechanism, and failure occurred away from the joint interface, indicating a strong joint. To interpret this behavior, a spring-based model was proposed to relate the fracture location and tensile deformation to the spatial variation in microstructure across the welded zones. This approach provides a conceptual framework that is extendable to other dissimilar material systems with spatially varying microstructures. Full article

Thyroid tumors rank among the most frequently occurring endocrine cancers because early detection helps doctors deliver effective treatments that lead to better patient results. Ultrasound imaging enables the detection of thyroid nodules, yet medical professionals struggle to differentiate between benign and malignant nodules through their diagnostic tests. This study introduces a new medical framework that enables thyroid nodule diagnosis through ultrasound imaging. The proposed model combines advanced segmentation with feature extraction, classification, and reasoning components to create a complete system. The specialized segmentation method shows accurate results when it detects nodule boundaries, which leads to better analysis of specific regions. The Hybrid Vision Transformer (HVT) operates to capture detailed textural information together with complete environmental patterns, which boosts its ability to classify different elements. The proposed framework incorporates a Large Language Model (LLM), specifically Med-PaLM, to provide context-aware clinical reasoning and interpretation. The structured evaluation process uses Thyroid Imaging Reporting and Data System (TI-RADS)-based feature scoring to compare model results with designated clinical standards. The diagnostic process is enhanced through the use of a language model, which delivers contextual understanding and produces valuable information from features that have been extracted. The proposed model achieves excellent performance with accuracy at 98.5%, precision at 98.7%, recall at 98.4%, and F1-score at 98.5%, which demonstrates its capacity for accurate and equivalent performance across different classifications. The experimental results demonstrate that the model achieves better results than existing methods. The combination of multimodal data with clinical reasoning improves both the accuracy and the user experience of the system. The proposed framework provides an efficient, interpretable, and scalable solution for thyroid nodule diagnosis. Full article

In recent years, artificial intelligence (AI) has been increasingly used in criminal justice systems across the world. To achieve objectives set out through Sustainable Development Goals (SDGs), adoption of technology is inevitable and undeniable. The press release dated 25 February 2025 from India’s Ministry of Law and Justice, quoting Prime Minister of India Narendra Modi to make a “justice system that will be fully future-ready”, confirmed that the Indian law enforcement agencies are integrating AI into policing and law enforcement to enhance crime detection, criminal investigation, etc. It is intended to enhance their capabilities in solving criminal cases and delivering justice speedily and more efficiently. However, the usage of AI tools in such contexts presents a double-edged sword, as evidenced by their application in a number of cases across the world like Christopher Gatlin, Nijeer Parks, the Harm Assessment Risk Tool (HART), and in India during the 2020 Delhi riots cases. As reported by the Washington Post, in Christopher Gatlin’s case it was found that the police arrested him on the basis of the facial recognition programme matching his face with the captured video footage. He spent 17 months in jail before his release by the court, observing that the police failed to conduct fair investigation. A similar incident was reported by NJ.com and CNN Business. In the investigations following the 2020 Delhi riots, Delhi Police effected over 1900 arrests in 758 riot-related cases, relying predominantly on AI-driven facial recognition matches. Subsequent court scrutiny in decided cases raised questions about reliability, leading to widespread acquittals and discharges of the accused in 82% of decided cases as of early 2025. In certain cases, AI-driven solutions have failed, leading to criminal prosecutions of innocent people based on AI-generated evidence. This study examines the reliability, validity, and ethics of AI technology in the criminal justice system in India’s unique socio-legal and political environment. The researchers analyse three interrelated axes. First, a comprehensive review of the international algorithmic policing literature to identify successes and failures. In addition, cases of AI-assisted investigations during the Delhi riots show how facial recognition systems and other AI techniques were used for inquiry. Finally, stakeholders’ perspectives, including a preliminary survey of 27 legal experts showing strong consensus on classifying AI-FRT outputs strictly as corroborative evidence and highlighting BSA insufficiencies for addressing opacity and explainability, help identify practical, procedural, and normative fault lines. Researchers noted that while AI has the potential to revolutionise resource-constrained investigative agencies, its unquestioning and uncritical adoption risks amplify pre-existing biases, undermine presumptions of innocence, and shift the burden of refuting algorithmic inference onto the accused. Independent algorithmic audits, transparent documentation of error rates and confidence thresholds, statutory guidelines on AI tool use and admissibility, and sustained capacity-building throughout the justice delivery chain are needed to integrate it into the Indian criminal justice system. Without such measures, the very tools designed and introduced to enhance accuracy threaten to undermine the fundamental norms of the criminal justice system such as fairness and due process. This fills a gap in doctrinal analysis of AI-specific evidentiary admissibility in non-Western contexts like India. This study aims to propose policy reforms, enhance judicial discourse, and promote a more circumspect trajectory for AI adoption in Indian law enforcement by mapping the potential and risks of algorithmic evidence in a non-Western legal order. Full article

Green synthesis of KGM-capped CeO₂ nanoparticles was successfully achieved through a simple coprecipitation method using Konjac Glucomannan (KGM) as a biopolymeric capping and stabilizing agent. The reaction conditions were optimized by varying pH (9–11) and temperature (30–70 °C) to evaluate their influence on nanoparticle formation and photocatalytic performance. The synthesized KGM–CeO₂ nanoparticles were comprehensively characterized using FTIR, UV–Vis spectroscopy, XRD, SEM–EDS, TEM, DLS, and ZP analysis to investigate their structural, optical, morphological, and surface properties. The characterization results confirmed the successful formation of porous sponge-like branched CeO₂ nanostructures with irregular morphology. XRD analysis revealed the crystalline nature of the nanoparticles with an average crystallite size of approximately 7.7 nm, while DLS analysis showed an average hydrodynamic particle size of 29.7 nm with a biomodal particle size distribution. The positive zeta potential value (+16.75 mV) confirmed good colloidal stability and reduced agglomeration due to effective capping by KGM. The synthesized nanoparticles also exhibited favorable optical properties with band gap values suitable for photocatalytic applications. The adsorption and photocatalytic degradation performance of the KGM–CeO₂ nanoparticles was investigated against synthetic textile dyes, including Naphthol Blue Black (NBB), Methyl Orange (MO), and a mixed NBB–MO dye system under acidic conditions. Using an adsorbent dosage of 50 mg and dye concentrations of 100 mg/L, the material achieved degradation efficiencies of approximately 99% for NBB, 91% for MO, and 52% for the mixed dye system under UV irradiation for 120 min. Adsorption kinetic studies indicated that the pseudo-second-order model provided the best fit, suggesting that chemisorption is the dominant adsorption mechanism involving multifunctional surface interactions. These findings are particularly relevant for industrial wastewater treatment, since actual textile effluents typically contain complex mixtures of dyes and organic contaminants rather than single dye pollutants. The mixed dye experiments, therefore, provide a more realistic simulation of industrial wastewater conditions. Overall, the synthesized KGM–CeO₂ nanoparticles demonstrate excellent potential as an eco-friendly, cost-effective, and sustainable multifunctional material for adsorption-assisted photocatalytic treatment of dye-contaminated wastewater. Further optimization of operational conditions and catalyst surface properties may enhance its efficiency in multicomponent wastewater systems. Full article

This study investigates the effect of waste cement dust (CD) and rock wool (RW) inorganic fiber on the tribological performance of brake friction composite materials. Five formulations were fabricated by varying CD from 65 to 45 wt.% and RW from 5 to 25 wt.% and evaluated for tribological properties on a Chase friction testing machine in accordance with IS 2742 test procedures. The results show that composites containing higher CD and lower RW exhibited higher coefficients of friction, lower friction variability, and improved fade resistance. In contrast, composites containing higher RW and lower CD showed improved recovery characteristics and substantially enhanced wear resistance. The performance coefficient of friction decreased from about 0.521 to 0.442 as the formulation shifted from CD-rich to RW-rich compositions, while the variability coefficient increased from about 0.364 to 0.516. The highest wear was recorded for the composite containing 65 wt.% CD and 5 wt.% RW inorganic fiber, whereas the lowest friction fluctuations were obtained for the composite containing 55 wt.% CD and 15 wt.% RW inorganic fiber. Finally, a simple ranking process-based decision-making technique was employed to evaluate the overall performance of all the composites, suggesting 55 wt.% CD as the optimal content. These findings confirm the potential of waste CD as a viable functional constituent in brake friction composites when combined with RW inorganic fiber in an optimized manner. Full article

Heat shock protein 90 (HSP90) is a molecular chaperone essential for maintaining the stability of many oncogenic client proteins. Although several HSP90 inhibitors (HSP90i) have entered clinical trials, their use has been limited by toxicity and resistance, underscoring the need for improved therapeutic strategies. In this study, we assessed the therapeutic potential of a new HSP90i, SP11, in T-cell acute lymphoblastic leukemia (T-ALL) in vitro and in the DLA mouse model in vivo, using single-cell transcriptomic profiling. Single-cell RNA sequencing showed that SP11 treatment reduces key oncogenic drivers, including MYC, BCL2, and stemness-related genes, consistent with impaired leukemic survival programs. In the DLA mouse model, SP11-mediated HSP90 inhibition was associated with alterations in the tumor microenvironment, including increased immune cell representation and enrichment of cytokine- and antigen-presentation-related transcriptional pathways. Despite these antitumor effects, a distinct subpopulation of cells continued to express or re-express MYC and BCL2, suggesting the development of early adaptive resistance. Consistent with these findings, an SP11-resistant MOLT4 cell line maintained high levels of MYC and BCL2 at both the transcript and protein levels, maintained CD44 expression, and exhibited altered inflammatory cytokine signaling. Functional studies confirmed that pharmacological inhibition of BCL2 notably increased SP11 sensitivity, supporting a rational combination strategy. Collectively, our results show that SP11 may exert both tumor-intrinsic and immune-modulating effects and reveal transcriptionally defined adaptive cellular states linked to resistance. This study provides mechanistic in sights into responses to HSP90 inhibition and supports combination approaches for improving therapeutic outcomes in T-ALL. Full article

Aluminum–lithium (Al–Li) alloys are widely used in aerospace applications because of their high strength-to-weight ratio and reduced density. However, their corrosion behavior can be significantly affected by thermomechanical processing and exposure to chloride-containing environments. In the present study, the corrosion behavior of AA8090-T3 Al–Li alloy was investigated in 3.5 wt.% NaCl solution under simulated marine conditions. The specimens were extracted from a plate and subsequently subjected to annealing and rolling treatments using a specially designed wedge-shaped geometry to generate a continuous strain gradient, enabling the evaluation of deformation-dependent corrosion behavior across different deformation zones. The corrosion behavior was evaluated using potentiodynamic polarization, immersion testing, and surface characterization techniques. The results revealed significant variations in corrosion behavior with thermomechanical condition and deformation zone. The T3 temper-rolled specimen exhibited superior corrosion resistance compared to the annealed and rolled conditions. The lowest corrosion rate of 0.003 mpy was observed for the highly deformed T3 temper-rolled condition, whereas annealed specimens showed higher corrosion susceptibility associated with localized corrosion attack and precipitate-related galvanic activity. Surface characterization confirmed the formation of aluminum hydroxide- and copper oxide-based corrosion products. The study demonstrates the effectiveness of the wedge-shaped rolling methodology for evaluating zone-dependent corrosion behavior in thermomechanically processed AA8090 alloy. Full article

A series of 1,2,3-triazole-linked-thiazolidine-2,4-dione hybrids (SDP1–SDP15) were designed, synthesized, and evaluated for their antidiabetic potential. All structures were characterized by FT-IR and NMR spectroscopy (¹H and ¹³C). All derivatives exhibited significant in vitro inhibition of α-glucosidase (IC₅₀: 24.17–46.41 µg/mL) and α-amylase (23.25–50.66 µg/mL), comparable to the standard drug acarbose (IC₅₀: 25.18 and 32.53 µg/mL) and superior to the reference drug pioglitazone (IC₅₀: 84.24 and 79.74 µg/mL) for α-glucosidase and α-amylase, respectively. Molecule SDP8 emerged as the most potent with an IC50 of 24.17 and 23.25 µg/mL for α-glucosidase and α-amylase, respectively. Further, SDP8 exhibited a higher docking score of −10.7 kcal/mol and −10.4 kcal/mol against α-glucosidase and α-amylase than pioglitazone (−8.1 kcal/mol and −7.7 kcal/mol, respectively), suggesting that interaction with these two enzymes may be the cause for its antidiabetic activity. Furthermore, DFT analysis revealed favorable electronic properties with a low HOMO-LUMO energy gap, whereas ADMET predictions revealed moderate drug-like characteristics with some limitations, such as poor solubility, relatively high lipophilicity, and partial noncompliance with drug-likeness regulations. Overall, these results highlight triazole-linked thiazolidinedione hybrids as promising candidates for further development in T2DM, with SDP8 serving as a preliminary lead requiring additional optimization and validation. Full article

Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are persistent pollutants resistant to conventional treatment processes and pose significant environmental risks. The aim of this study was to comparatively evaluate the metatranscriptomic responses of activated sludge bacterial communities to PFOS and PFOA exposure at environmentally relevant (150 ng/L) and elevated (1050 ng/L) concentrations. Activated sludge from the Zeekoegat Wastewater Treatment Plant (Pretoria, South Africa) was used under aerobic conditions for 45 days. Taxonomic profiling revealed a Proteobacteria-dominated community with distinct pollutant-specific shifts. Under PFOA exposure, Pseudomonas dominated at low concentration, while Achromobacter and Burkholderia increased at higher levels. Under PFOS exposure, Kerstersia dominated at low concentration, whereas Comamonas, Sphingopyxis, and Polaromonas were enriched at higher concentration. Functional analysis revealed increased abundance of stress-response and metabolic pathways, including ABC transporters, chaperonins (GroEL), and β-oxidation. Overall, the results indicate a dose-dependent microbial adaptation, with pollutant type driving functional responses. These findings highlight pollutant-specific microbial responses and adaptation under PFAS exposure in activated sludge systems. These findings highlight pollutant-specific microbial strategies and the potential of activated sludge microbiomes in PFAS transformation processes. Full article