Sathyabama Institute of Science and Technology

Non-invasive diagnostic monitoring techniques have become essential for treating lung cancer (LC), which continues to be the primary cause of cancer-related death worldwide. The new diagnostic biomarkers called tumour-educated platelets (TEPs) show strong prospects for providing vital information about tumor biology, tumor spread pathways, and treatment reaction patterns. Despite lacking a nucleus, platelets exhibit an active RNA profile that develops through interactions with tumor-derived compounds and the tumor microenvironments (TME). This review explains platelet-tumour interaction regulatory mechanisms while focusing on platelet contributions toward cancer development, immune system avoidance, and blood clot formation. The detection and classification of LC show promise through the analysis of RNA molecules extracted from platelets that encompass mRNAs and non-coding RNAs. RNA sequencing technology based on TEP demonstrates excellent diagnostic power by correctly identifying LC patients alongside their oncogenic alterations of EGFR, KRAS, and ALK. Treatment predictions have proven successful using platelet RNA profiles, specifically in immunotherapy and targeted therapy. Integrating next-generation sequencing with machine learning and artificial intelligence enhances TEP-based diagnostic tools, improving detection accuracy. Standardizing platelet extraction methods and vesicle purification from tumor material needs better development for effective and affordable clinical use. Future investigations should combine TEPs with circulating tumor DNA and exosomal RNA markers to enhance both earliest-stage LC diagnosis and patient-specific therapeutic approaches. TEPs introduce a groundbreaking technique in oncology since they can transform non-invasive medical diagnostics and therapeutic monitoring for cancer.

Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and poses a significant global health challenge due to its rising incidence and associated mortality. Recent advancements in understanding the cytosolic DNA sensing, the cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING) pathway have illuminated its critical role in the immune response to HCC. This narrative review deciphers the multifaceted involvement of cGAS-STING in HCC, mainly its function in detecting cytosolic DNA and initiating type I interferon (IFN-I) responses, which are pivotal for antitumor immunity. This immune response is crucial for combating pathogens and can play a role in tumor surveillance. In the context of HCC, the tumor microenvironment (TME) can exhibit immune resistance, which complicates the effectiveness of therapies like immune checkpoint blockade. However, activation of the cGAS-STING pathway has been shown to stimulate antitumor immune responses, enhancing the activity of dendritic cells and cytotoxic T lymphocytes. There is ongoing research into STING agonists as a treatment strategy for HCC, with some studies indicating promising results in prolonging survival and enhancing the immune response against tumors. By summarizing current knowledge and identifying research gaps, this review aims to provide a comprehensive overview of cGAS-STING signaling in HCC and its future directions, emphasizing its potential as a therapeutic target in the fight against HCC. Understanding these mechanisms could pave the way for innovative immunotherapeutic approaches that enhance the efficacy of existing treatments and improve patient prognosis.

Energy management in a microgrid is described as a control system with the required functionality to guarantee the generation and distribution systems supply energy at a lesser operation cost. But the output power was not at the required level. In addition, profit at the producer end was not improved. To address these problems, an Improved Meta-Heuristic Deming Ranked Firefly Optimization-based Bidding Strategy (IMHDRFOBS) model is introduced to increase the profit of power producers with lesser computational cost. The objective of this proposed approach is to regulate the energy management analysis during generation, demand and dispatch schedule. Consequently, the profit of power producers is boosted. The results are validated by considering a virtual power plant of capacity 250 kW comprising five PV systems, five MTs, and sixty PEVs was set up. The hourly adjustment range for the power load is set between 0.9 and 1.1 times the real data. The IMHDRFOBS model improves the energy management accuracy by 11% and 17%, increases the solar energy generation performance by 26% and 39% and reduces the operation cost by 29% and 37% when compared to conventional Energy Management System and Affinely adjustable robust bidding strategy respectively.The simulation results show the proposed model achieves higher energy management accuracy and generation of solar energy with lesser operation cost.

This paper proposes a novel approach for resource allocation in Non Orthogonal Multiple Access Cognitive Radio Networks, named Enabled Stackelberg Pairing and Allocation (CRN-NOMA-ESPA) technique. This study comprehensively addresses the major resource allocation challenges of spectral efficiency, user fairness, and interference management in the presence of a heterogeneous ultra-dense network. The proposed ESPA technique exploits a two-step optimization process: a matching-based user-channel assignment followed by a two-level Stackelberg game-theoretic power allocation. The performance is evaluated based on different benchmarks such as system sum rate, fairness index, outage probability, energy efficiency, and interference to primary users. Extensive simulation results verify that proposed CRN-NOMA-ESPA achieve up to 23.6% higher system sum rate, 12.6% better fairness, and 2.1% of magnitude lower outage probability compared with state of art techniques includes Orthogonal Frequency-Division Multiple Access (OFDMA), Non-Continuous OFDM (NC-OFDM), Non-Cooperative Game-Based OFDMA (NC-Game-OFDMA), Energy-Efficient Stackelberg Pairing and Power Allocation (ES-PPA), Radio Access Network Pairing and Power Allocation (RAN-PPA), Orthogonal Multiple Access (OMA), Full-Duplex Non-Orthogonal Multiple Access (F-NOMA), Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA), and Optimal Power Allocation in Non-Orthogonal Multiple Access (OPA-NOMA) at the cost of low energy efficiency and highly controlled interference to primary users.

Antimicrobial resistance (AMR) represents one of the most significant global health challenges of the twenty-first century. The emergence of drug-resistant pathogens, particularly multidrug-resistant (MDR) strains, threatens to undermine decades of progress in medicine, rendering infections once easily treatable with antibiotics increasingly difficult to manage. In addition, Climatic change and surrounding environmental conditions have led to a pragmatic shift to evolve through, adapt to new environments and surface new pathogenic emerging infectious diseases by existing pathogens. These pathogens acquire certain traits by various gene transfer mechanisms exhibiting wide host specificity and thereby influence surfacing epidemics or pandemics upon appropriate favourable conditions. Efforts to combat diseases are primarily focused on developing countries by medical sectors. This review summarizes various microbial disease resistance mechanisms, with emphasis on quorum sensing as one the important mechanism of resistance to drugs. In addition, advances in quorum sensing inhibitors/quenchers involving small molecules, bio-actives from microbial sources, natural compounds and quenching enzymes inhibiting microbial infections were discussed.

Hospital plastic waste poses significant challenges due to its volume, hazardous nature, and environmental persistence. This review consolidates recent advancements in the pyrolysis of hospital plastic waste, evaluating its feasibility as a sustainable solution within healthcare waste management systems. Findings reveal that pyrolysis offers high oil yields when optimized for key operational parameters – specifically temperatures between 400 and 500 °C, moderate heating rates, and residence times tailored to specific plastic types. The review identifies polypropylene and polyethylene as the most suitable hospital-derived plastics for pyrolysis, though the presence of contaminants, such as PVC or biological residues, can significantly hinder process efficiency and environmental compliance. Emerging studies demonstrate that upgrading pyrolysis oil quality and utilizing byproducts like char and syngas can improve the overall economic and ecological performance of the process. Furthermore, integration of pyrolysis into hospital waste management systems is technically feasible and scalable, especially when supported by pre-sorting protocols and decentralized processing units. The review concludes that pyrolysis, when appropriately managed and regulated, can contribute to a circular economy by converting hazardous plastic waste into energy resources while minimizing environmental impact. These findings support the implementation of pyrolysis as a green and economically viable technology, encouraging policy frameworks and infrastructure investment to promote its adoption in healthcare facilities.

Degradation of proteostasis, mitochondrial function, and cellular stress resistance results in a build-up of damaged proteins, oxidative insult, and chronic inflammation, characteristic of aging. CHIP is essential for maintaining protein quality control and cellular homeostasis by having dual E3 ubiquitin ligase and co-chaperone activities. CHIP facilitates proteostasis by maintaining proteostasis in misfolded, aggregated proteins by promoting their degradation. Mitochondrial dysfunction, oxidative imbalance, and cellular senescence are caused by its age-associated decline and contribute to neurodegenerative, cardiovascular, and oncogenic disease pathogenesis. Examples of recent pharmacological and gene-based strategies to correct CHIP and restore stress resilience have been made. This review examines the multiple facets of the aging role of CHIP and its potential as an aging disease therapy target.

The variety of microorganisms represents the most prevalent sources utilized within diverse industries and research fields. Enzymes with microorganisms are applied in the use of industrial biotechnology. Since the dawn of civilization, there are techniques like extraction and fermentation that used plant or bacterial enzymes as well as other byproducts. Enzymes, the natural catalysts, are intricately involved in many aspects of life. Enzymes pose remarkable specificity for their substrate, which implies that these metabolic cycles in a living cell need to be executed by a team working in collaboration. The major sources of these enzymes are yeast, some fungi and bacteria. Just like all living forms, microbes interact with their environment in which they must live in order to survive. A large number of microorganisms that are capable of producing great varieties of enzymes are important in the production of bread, cheese, yogurt, beer, and many other foods. One of the most widely used lipolytic enzyme is lipase from various sources including food and dairy industry, leather, detergent, pulp and paper, bioenergy and even pharma. With the latest innovation in biotechnology, the need for organisms that produce different commercially important lipases which other strains of lipases do is increasing. Lipases produced from microbial cells have a major industrial significance because of their property versatility and ease of mass production. This review seeks to clarify the sources of microorganisms, lipase production and purification processes, as well as the environmental and industrial uses of lipase enzymes.

Technology significantly influences patient care, positioning medicine prescription as a vital area of research. Ontology, a growing discipline in the semantic web, enables hierarchical domain representation, allowing finer data access. Deep Learning (DL) supports pattern recognition in Electronic Health Records (EHR), which include patient demographics and diagnosis histories. Prescribing medications with minimal adverse effects is crucial, especially for patients requiring multiple drugs, as interactions can result in more complex conditions. This paper introduces an integrated approach that combines Ontology with DL neural networks to improve prescription accuracy. We propose NexusOpti, a model featuring an Enhanced Gated Recurrent Unit (E-GRU) layer. To understand drug–disease interactions, hierarchical data are extracted from the International Classification of Diseases (ICD) Ontology and Anatomical Therapeutic Chemical (ATC) Ontology. These structured data are processed using a self-attention mechanism to enhance recommendation precision. This integration not only addresses data security concerns but also improves the accuracy of medicine recommendations. The model was evaluated using key metrics such as hit ratio and normalized discounted cumulative gain (NDCG). Evaluation results show that NexusOpti outperforms existing methods, achieving a 13% improvement in performance. These findings highlight the model’s effectiveness in advancing personalized, safer, and data-driven medication prescriptions.

Background Proliferative diabetic retinopathy (PDR) is a serious vision-threatening complication of diabetes. Chronic kidney disease (CKD), measured by estimated glomerular filtration rate (eGFR), shares similar pathophysiological mechanisms with diabetic retinopathy, including inflammation, oxidative stress, and vascular dysfunction. However, the strength of the association between eGFR and PDR remains unclear. This review evaluates the association between reduced eGFR and the risk of PDR in individuals with diabetes. Methods A comprehensive literature search was conducted in PubMed, Embase, and Web of Science, from inception to October 2024. Observational studies reporting both eGFR values and PDR status were included. Study quality was assessed using the Newcastle–Ottawa Scale. Pooled standardized mean differences (SMD) were calculated using a fixed-effects model when heterogeneity was low (I² ≤ 50%). Subgroup analyses based on eGFR estimation method Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), sensitivity analyses, and meta-regression for diabetes duration and HbA1c were conducted. Publication bias was evaluated using funnel plots and Egger’s test. Results A total of 11 studies were included, comprising 602 patients with PDR and 5,475 individuals without diabetic retinopathy. The pooled SMD for eGFR between PDR and non-PDR groups was − 0.43 (95% CI − 0.52 to − 0.34; P < 0.0001), indicating significantly lower eGFR in PDR patients. Heterogeneity was moderate (I² = 42.3%). Subgroup analysis showed an SMD of − 0.58 (95% CI − 1.02 to − 0.14; I² = 0%) using the MDRD formula and − 0.43 (95% CI − 0.58 to − 0.28; I² = 80.4%) with the CKD-EPI formula. Meta-regression revealed a significant negative association between diabetes duration and PDR proportion (P = 0.0155), but no association with HbA1c (P = 0.7798). The prediction interval ranged from − 0.53 to − 0.33. Funnel plot asymmetry suggested potential publication bias (P < 0.05). Conclusions This systematic review and meta-analysis found a significant association between reduced eGFR and PDR in patients with diabetes, with consistent findings across studies and eGFR estimation methods. Though heterogeneity suggests caution in interpretation. Additional prospective using standardized methodologies are needed to clarify causality and enhance risk prediction.

Background Hernias are a major health concern in India, with varying incidence and prevalence influenced by socio-demographic factors. Despite global advances in hernia management, regional disparities are evident within India. Method This analysis utilized data from the Global Burden of Disease (GBD) Study 2021 to examine inguinal, femoral, and abdominal hernias across India from 1990 to 2021, categorized by ICD-10 codes. Key metrics analyzed included prevalence, incidence, mortality, and Disability-Adjusted Life Years (DALYs), with age-standardized rates (ASRs). The Estimated Annual Percentage Change (EAPC) for incidence and mortality, Spearman correlation for assessing the relationship between Socio-Demographic Index (SDI) and hernia metrics, and ARIMA models for future trend projections were employed. Result Between 1990 and 2021, the age-standardized incidence rate (ASIR) of hernias in India decreased from 143.85 to 137.05 per 100,000, a reduction of 4.72%, despite a 46% increase in the absolute number of hernia cases due to population growth. Mortality rates significantly declined by 57.05%. DALYs also decreased from 98.01 to 43.51 per 100,000. Projections for 2031 indicate stabilization of incidence rates and an increase in prevalence. Conclusions Significant improvements in hernia management in India have been achieved over three decades, driven by advances in healthcare and socio-demographic progress. However, the rising number of cases and expected increase in prevalence highlight the need for enhanced healthcare strategies and resource allocation to manage the hernia burden effectively.

This work analyzes the impact of viscous dissipation and variable thermal conductivity on second-grade nanofluid. Boundary conditions are used for the analysis of heat and mass transmission. Stream functions and similarity variables are utilized to reduce the complexity of the governed PDEs (partial differential equations) and altered into ODEs (ordinary differential equations). The mechanism can be analyzed and solved more easily due to this modification. In order to efficiently handle boundary value issues by turning them into initial value problems, the method of shooting is employed to achieve numerical solutions for the physical phenomena under the Newton–Raphson scheme and Keller-box approach. The conclusions of physical attributes on temperature, velocity, and mass transportation are graphically represented using these methods. These parameters include heat production, variable thermal conductivity, second-order fluid properties, the Eckert number, Brownian motion, Prandtl number, thermophoresis, and the Lewis number. This study found that the temperature and velocity sketches improve as the estimations of the variable thermal conductivity parameter rises. The temperature profile drops and the velocity sketch rises as the second-grade fluid parameter escalates. Eckert number variations are greater in the temperature and concentration profiles. Furthermore, the velocity profile of the second-grade nanofluid decreases with increasing Prandtl numbers. Higher temperature-dependent density signifies the greatest fluid temperature and concentration values. Greater Brownian motion results in improved mass and heat transmission magnitudes. When the Prandtl number rises, the Nusselt number, skin friction coefficient, and Sherwood number drop, but enhances when the Lewis number rises.

Pure Bi2O3/CuO (PBCuO) and Fe-doped Bi2O3/CuO (FBCuO) nanocomposites were synthesized via the hydrothermal process for photocatalytic applications. The structural, morphological, optical, electrical and surface properties are considered using ‎field emission scanning electron microscopy, X-ray diffraction, current-voltage (I-V) ‎measurements, photoluminescence spectroscopy, Brunauer–Emmett–Teller surface area ‎analysis and Raman spectroscopy. ‎The photocatalytic performance of the nanocomposites is assessed by the Rhodamine B degradation under sunlight. The results showed that the PBCuO and FBCuO samples exhibited degradation efficiencies of 85.17% and 93.10%, respectively, with corresponding reaction rate constants of 0.0076 and 0.01 min⁻¹ within 110 min under sunlight. FBCuO demonstrated superior photocatalytic efficiency due to its reduced crystalline size (31.66 nm), mixed morphology, enhanced surface area (88.90 m²/g), increased defects, decreased band gap (2.43 eV) and improved conductivity. Effect of scavengers on the FBCuO sample is also carried out. These findings suggest the potential application of Fe-doped Bi2O3/CuO nanocomposites for environmental remediation.

A first principles study is used to investigate the physical features of double perovskites oxides (DPOs) X2MoSeO6 (X = Na, Li) using PBEsol-GGA potential in CASTEP. Both materials are found to have stable cubic structure which is established by imaginary values of formation enthalpy (ΔH), and values in stability range of tolerance and octahedral factors. This calculated ΔH value is -6.13 eV for Na2MoSeO6 and − 6.39 eV for Li2MoSeO6. Their mechanical stability is validated through Born stability criteria. The semiconducting nature is seen for Li2MoSeO6 and Na2MoSeO6 with bandgap (Eg) values of 2.679 eV and 2.821 eV, correspondingly that make these DPOs optimal for renewable energy and photonic applications. Optical analysis reveals that both DPOs have optical absorption values of 125,571 cm− 1 at 6 eV Li2MoSeO6 for and 160,071 cm− 1 at 7.75 eV for Na2MoSeO6. Both have reflectivity (R) below 0.3, demonstrating their strong capability to absorb light within this wavelength range. These results establish Li2MoSeO6 and Na2MoSeO6 efficient materials for solar cells and optoelectronic uses.

Hypoxia, characterized by reduced oxygen levels, plays a pivotal role in cancer progression, profoundly influencing tumor behavior and therapeutic responses. A hallmark of solid tumors, hypoxia drives significant metabolic adaptations in cancer cells, primarily mediated by hypoxia-inducible factor-1α (HIF-1α), a key transcription factor activated in low-oxygen conditions. This hypoxic environment promotes epithelial-mesenchymal transition (EMT), enhancing cancer cell migration, metastasis, and the development of cancer stem cell-like properties, which contribute to therapy resistance. Moreover, hypoxia modulates the expression of circular RNAs (circRNAs), leading to their accumulation in the tumor microenvironment. These hypoxia-responsive circRNAs regulate gene expression and cellular processes critical for cancer progression, making them promising candidates for diagnostic and prognostic biomarkers in various cancers. This review delves into the intricate interplay between hypoxic circRNAs, microRNAs, and RNA-binding proteins, emphasizing their role as molecular sponges that modulate gene expression and signaling pathways involved in cell proliferation, apoptosis, and metastasis. It also explores the relationship between circRNAs and the tumor microenvironment, particularly how hypoxia influences their expression and functional dynamics. Additionally, the review highlights the potential of circRNAs as diagnostic and prognostic tools, as well as their therapeutic applications in innovative cancer treatments. By consolidating current knowledge, this review underscores the critical role of circRNAs in cancer biology and paves the way for future research aimed at harnessing their unique properties for clinical advancements. Specifically, this review examines the biogenesis, expression patterns, and mechanistic actions of hypoxic circRNAs, focusing on their ability to act as molecular sponges for microRNAs and their interactions with RNA-binding proteins. These interactions impact key signaling pathways related to tumor growth, metastasis, and drug resistance, offering new insights into the complex regulatory networks governed by circRNAs under hypoxic stress.

Investigating effective nanomaterials for the detection of hydroxyurea anticancer drugs is essential for promoting human health and safeguarding environmental integrity. This research utilized first-principles estimations for examining the adhesion and electronic characteristics of hydroxyurea (HU) on both pristine and Si-decorated innovative two-dimensional boron nitride allotrope, known as Irida analogous (Ir-BNNS). Analyzing the adsorption energy revealed that the HU molecule has a significant interaction (Ead = −1.27 eV) with the Si@Ir-BNNS, whereas it has weak interaction P-Ir-BN. Moreover, the analysis of the electron density distributions was conducted to investigate the microcosmic interaction mechanism between HU and Ir-BNNS. The Si@Ir-BNNS was highly sensitive to HU due to the observable alterations in the electrical conductance and magnetism. At ambient temperature, the Si@Ir-BNNS had a recovery time of 5.96 ms towards HU molecules. The DFT estimations can be conducive to exploring the applications of Si@Ir-BNNS in effectively sensing HU.

This article addresses the pressing question of how advanced analytical tools, specifically artificial intelligence (AI)-driven sentiment analysis, can be effectively integrated into psychiatric care to enhance patient outcomes. Utilizing specific search phrases like “AI-driven sentiment analysis,” “psychiatric care,” and “patient outcomes,” a comprehensive survey of English-language publications from the years 2014–2024 was performed. This examination encompassed multiple databases such as PubMed, PsycINFO, Google Scholar, and IEEE Xplore. Through a comprehensive analysis of qualitative case studies and quantitative metrics, the study uncovered that the implementation of sentiment analysis significantly improves clinicians’ ability to monitor and respond to patient emotions, leading to more tailored treatment plans and increased patient engagement. Key findings indicated that sentiment analysis improves early mood disorder detection, personalizes treatments, enhances patient-provider communication, and boosts treatment adherence, leading to better mental health outcomes. The significance of these findings lies in their potential to revolutionize psychiatric care by providing healthcare professionals with real-time insights into patient feelings and responses, thereby facilitating more proactive and empathetic care strategies. Furthermore, this study highlights the broader implications for healthcare systems, suggesting that the incorporation of sentiment analysis can lead to a paradigm shift in how mental health services are delivered, ultimately enhancing the efficacy and quality of care. By addressing barriers to new technology adoption and demonstrating its practical benefits, this research contributes vital knowledge to the ongoing discourse on optimizing healthcare delivery through innovative solutions in psychiatric settings.

The prediction of chemical toxicity is crucial for applications in drug discovery, environmental safety, and regulatory assessments. This study aims to evaluate the performance of advanced deep learning architectures, TabNet and TabTransformer, in comparison to traditional machine learning methods, for predicting the toxicity of chemical compounds across 12 toxicological endpoints. The dataset consisted of 12,228 training and 3057 test samples, each characterized by 801 molecular descriptors representing chemical and structural features. Traditional machine learning models, including XGBoost, CatBoost, SVM, and a voting classifier, were paired with feature selection techniques such as principal component analysis (PCA), recursive feature elimination (RFE), and mutual information (MI). Advanced architectures, TabNet and TabTransformer, were trained directly on the full feature set without dimensionality reduction. Model performance was assessed using accuracy, F1‐score, AUC‐ROC, AUPR, and Matthews correlation coefficient (MCC), alongside SHAP analysis to interpret feature importance and enhance model transparency under class imbalance conditions. Cross‐validation and test set evaluations ensured robust comparisons across all models and toxicological endpoints. TabNet and TabTransformer consistently outperformed traditional classifiers, achieving AUC‐ROC values up to 96% for endpoints such as SR.ARE and SR.p53. TabTransformer showed the highest performance on complex labels, benefiting from self‐attention mechanisms that captured intricate feature relationships, while TabNet achieved competitive outcomes with an efficient, dynamic feature selection. In addition to standard metrics, we reported AUPR and MCC to better evaluate model performance under class imbalance, with both models maintaining high scores across endpoints. Although traditional classifiers, particularly the voting classifier, performed well when combined with feature selection—achieving up to 94% AUC‐ROC on SR.p53—they lagged behind the deep learning models in generalizability and feature interaction modeling. SHAP analysis further highlighted the interpretability of the proposed architectures by identifying influential descriptors such as VSAEstate6 and MoRSEE8. This study highlights the superiority of TabNet and TabTransformer in predicting chemical toxicity while ensuring interpretability through SHAP analysis. These models offer a promising alternative to traditional in vitro and in vivo approaches, paving the way for cost‐effective and ethical toxicity assessments.

The development of advanced electrocatalysts for oxygen evolution reaction (OER) is essential for improving effectiveness of electrocatalytic water splitting (EWS). Perovskite-type oxides acquired attention for their outstanding electrocatalytic capabilities in OER performance. This research involved the preparation of reduced graphene oxide (rGO) based perovskite CoSnO3 material via basic hydrothermal technique to improve OER efficiency. The produced composite was tested by multiple analytical methods to evaluate its structural, surface area and compositional properties. CoSnO3/rGO catalyst demonstrated a remarkable overpotential (η) of 209 mV at 10 mA cm⁻², along with Tafel slope (36 mV dec⁻¹), showcasing enhanced OER performance. Electrochemical surface area (ECSA) of CoSnO3/rGO catalyst was obtained to be 642.5 cm², with enhanced cyclic durability of 35 h and least charge transfer resistance (Rct) of 0.88 Ω. The outcomes indicated that incorporating rGO resulted in an increased surface area (SA), which enhanced conductivity and significantly improved the OER activity of the catalysts. The noteworthy electrochemical characteristics of CoSnO3/rGO composite render it a superior material for applications in electrical and various other domains in the future. Graphical Abstract

While advancements have been made in cancer treatment, achieving effective localized therapy remains a significant challenge. Major obstacles include the inefficiency of drug delivery methods and the side effects linked to traditional chemotherapeutics. In this study, we present an innovative delivery system designed to transport doxorubicin (DOX) directly to the lungs. This system employs PVA-stabilized DOX-loaded MXene, aiming to improve targeted delivery and drug efficacy while minimizing toxicity. Our approach represents a promising advancement in the optimization of cancer therapeutics. Using in silico and computational methods, we evaluated the interactions between PVA, DOX, and MXene. Characterization techniques demonstrated that the synthesized PVA@Mxene/DOX exhibited favorable physicochemical properties. We assessed the anticancer potential of PVA@Mxene/DOX through the MTT assay, in vitro migration assay, and apoptosis assay. The findings revealed that the developed anticancer PVA@Mxene/DOX displayed a layered structure with controlled release kinetics. Notably, it significantly reduced cancer cell growth (P < 0.05), induced apoptosis in cancer cells, and inhibited their migration. These results suggest that PVA@Mxene/DOX holds promise as an effective anticancer agent to enhance lung cancer treatment and improve patient care.