Scientific Advances Research Articles

Italian guidelines for competitive sport eligibility (COCIS): what are the new indications in the updated 2023 version for the evaluation of athletes with arrhythmias?

The COCIS 2023 guidelines represent the latest update on competitive sports eligibility for athletes with heart disease, developed by the Italian Society of Sports Cardiology and associated medical societies. These updated guidelines reflect advancements in cardiology and sports medicine and introduce clear class of recommendations and levels of evidence for assessing athletes with heart disease. This document focuses on the differences between the 2023 and 2017 versions, particularly regarding athletes with arrhythmias. The guidelines integrate new scientific evidence, including modifications to criteria for specific arrhythmic conditions like Wolf-Parkinson-White (WPW) Syndrome, Brugada Syndrome, long QT syndrome (LQTS), and premature ventricular beats (PVBs). Updates on the return-to-play timing after successful catheter ablation are also included. Key updates include the revised arrhythmic risk thresholds for WPW syndrome; these guidelines also expand recommendations for asymptomatic pre-excitation cases. In Brugada Syndrome, eligibility remains dependent on the presence of malignant arrhythmias and genetic risk factors, with scoring systems to aid risk stratification. For LQTS, eligibility is reconsidered for asymptomatic individuals with a negative phenotype and a positive genotype, with beta-blocker use. Additionally, the management of PVBs is refined, with new criteria for further investigation and risk assessment. COCIS 2023 introduces a more nuanced, evidence-based approach to the eligibility of athletes with arrhythmias. The guidelines provide clinicians with detailed recommendations for managing a variety of arrhythmic conditions. As scientific research advances, these guidelines will continue to evolve, ensuring safe athletic participation for individuals with cardiovascular conditions.

Enhancing radiative heat transfer with meta-atomic displacement

Abstract Controlling and manipulating radiative heat transfer remains a pivotal challenge in both scientific inquiry and technological advancement, traditionally tackled through the precise geometric design of metastructures. However, geometrical optimization cannot break the inherent shackles of local modes within individual meta-atoms, which hinders sustained progress in radiative heat transfer. Here, we propose a comprehensive strategy based on interatomic displacement to achieve superior heat transfer performance while obviating the need for increasingly complex structural designs. This meta-atomic displacement strategy enables a shift from quasi-isolated localized resonances to extended nonlocal resonant modes induced by strong interactions among neighboring meta-atoms, resulting in a radiative heat conductance that surpasses other previously reported geometrical structures. Furthermore, this meta-atomic displacement strategy can be seamlessly applied to various metastructures, offering significant implications for advancing thermal science and next-generation energy devices.

Sandwich Miura-Ori Enabled Large Area, Super Resolution Tactile Skin for Human-Machine Interactions.

With substantial advances in materials science and electronics, flexible tactile sensors have emerged as a promising sector with extensive applications, notably in human-machine interactions. However, achieving large-area sensing with few sensing units at a low cost remains a challenge; the use of sensor arrays will complicate wiring and increase costs. To solve these issues, a sandwich Miura-ori (SMo)-enabled super-resolution tactile skin capable of resolving normal and shear forces is proposed, and a theoretical model that incorporates the impact of actual manufacturing process is also developed, enabling the model to be employed for different tactile skins following calibration. Using machine learning techniques, the proposed tactile skin can accurately localize touch inputs (average localization error of 1.89 mm) and estimate the external force (average estimation error of 8%). Furthermore, a curved SMo skin is designed and fabricated using the tessellation algorithm, then installed on a robotic arm to control the motion, demonstrating its potential in human-machine interactions. This research introduces a straightforward and cost-effective approach to the design and manufacturing of super-resolution tactile skins, and it also offers a valuable solution for future large-area tactile sensortechnologies.

The spatiotemporal evolution and influencing factors of carbon emissions in the Yellow River Basin based on nighttime light data

To achieve sustainable ecological development in the contemporary global economy and technology, addressing carbon emissions is imperative. The issue of carbon emissions and their impact on the environment has been the subject of intense scrutiny and debate among academicians worldwide. This investigation investigates the Yellow River Basin in China, a region with substantial industrial development. It analyses the fluctuations in carbon emissions and their influencing factors from 2010 to 2022 using nighttime light data. The spatial clustering features of carbon emissions in the prefecture-level communities of the Yellow River Basin from 2005 to 2022 are verified by the spatial autocorrelation analysis. The Gini coefficient is employed to examine regional disparities in carbon emissions in three distinct ways: total difference, intra-regional difference, and inter-regional difference. Ultimately, the GTWR model is implemented to evaluate the variables that influence carbon emissions within the Yellow River Basin. The results suggest that the Yellow River Basin is characterized by substantial spatial clustering. Shandong Province and Lvliang City are home to high-high clustering cities, while Gansu Province, Shaanxi Province, and Sichuan Province are home to low-low clustering cities. Carbon emissions are increasing annually. In comparison to the Upper, Middle, and Lower Yellow River regions, the disparities in carbon emissions between the Middle and Lower Yellow River regions are somewhat lesser. Intra-regional differences follow the trend of Upper Yellow River > Middle Yellow River > Lower Yellow River. Economic development, industrial structure, scientific advancement, and education level consistently positively impact carbon emissions in the Yellow River Basin. However, financial development has a sustained inhibiting effect on carbon emissions, and infrastructure development initially promotes but eventually inhibits carbon emissions.

Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study.

Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant problem. Prior research has used clustering techniques on rehabilitation data to identify movement patterns and forecast recovery outcomes. Nonetheless, these initiatives have not yet used force or motion datasets obtained outside a clinical setting, thereby limiting the capacity for therapeutic decisions. Biomechanical data analysis has demonstrated considerable potential in bridging these gaps and improving clinical decision-making in rehabilitation settings. This study presents a comprehensive clustering analysis of multidimensional movement datasets captured using a novel home exercise device, the "Slider". The aim is to identify clinically relevant movement patterns and provide answers to open research questions for the first time to inform personalized rehabilitation protocols, predict individual recovery trajectories, and assess the risks of potential postoperative complications. High-dimensional, time-dependent, bilateral knee kinetic datasets were independently analyzed from 32 participants using four unsupervised clustering techniques: k-means, hierarchical clustering, partition around medoids, and CLARA (Clustering Large Applications). The data comprised force, laser-measured distance, and optical tracker coordinates from lower limb activities. The optimal clusters identified through the unsupervised clustering methods were further evaluated and compared using silhouette analysis to quantify their performance. Key determinants of cluster membership were assessed, including demographic factors (eg, gender, BMI, and age) and pain levels, by using a logistic regression model with analysis of covariance adjustment. Three distinct, time-varying movement patterns or clusters were identified for each knee. Hierarchical clustering performed best for the right knee datasets (with an average silhouette score of 0.637), while CLARA was the most effective for the left knee datasets (with an average silhouette score of 0.598). Key predictors of the movement cluster membership were discovered for both knees. BMI was the most influential determinant of cluster membership for the right knee, where higher BMI decreased the odds of cluster-2 membership (odds ratio [OR] 0.95, 95% CI 0.94-0.96; P<.001) but increased the odds for cluster-3 assignment relative to cluster 1 (OR 1.05, 95% CI 1.03-1.06; P<.001). For the left knee, all predictors of cluster-2 membership were significant (.001≤P≤.008), whereas only BMI (P=.81) could not predict the likelihood of an individual belonging to cluster 3 compared to cluster 1. Gender was the strongest determinant for the left knee, with male participants significantly likely to belong to cluster 3 (OR 3.52, 95% CI 2.91-4.27; P<.001). These kinetic patterns offer significant insights for creating personalized rehabilitation procedures, potentially improving patient outcomes. These findings underscore the efficacy of unsupervised clustering techniques in the analysis of biomechanical data for clinical rehabilitation applications.

Recent advances in mesoporous silica nanoparticle: synthesis, drug loading, release mechanisms, and diverse applications

Despite significant advancements in pharmaceutical sciences, conventional drug delivery system remains limited by issues like poor permeability, toxicity, suboptimal efficacy, and inadequate targeting. These challenges pose substantial barrier to effective treatment for complex conditions like cancer, heart problems, chronic pain management, etc. Mesoporous silica nanoparticle (MSN), with their remarkable structural tunability and multifunctionality, have emerged as a transformative solution in the realm of drug delivery system. This review delves into the state-of-the-art synthesis methods of MSNs including physical, chemical, top down and bottom-up approaches with particular attention to the widely used Sol-Gel process. We also explore innovative drug loading strategies and controlled release mechanisms, underscoring how factors such as pore size, particle shape, and surface charge influence therapeutic outcomes. Furthermore, we highlight the burgeoning applications of MSNs across multiple domains, ranging from anticancer therapy and gene delivery to emerging fields such as precision agriculture and environmental remediation. Recent studies demonstrate the versatility of MSNs in addressing both biomedical and ecological challenges, making them an indispensable tool in modern science. By synthesizing Collectively, this review aims to provide a comprehensive resource for researchers and practitioners, fostering continued innovation in the design and application of MSN-based nanotechnology.

Academic Research Consulting &amp; Services

The Academic Research Consulting and Services (ARCS) initiative represents a strategic evolution in academic library functions, enhancing users' information literacy skills amidst the complexities of contemporary data landscapes. As the nature of information has transformed with advancements in data science and artificial intelligence, the Libraries have adapted their services to support the diverse research needs of users. This article explores the inception and development of ARCS, a unified team designed to streamline research consulting services across campus, facilitating interdisciplinary collaboration and enhancing the research lifecycle from ideation to publication. The transition of ARCS to a departmental structure within the Libraries signifies a commitment to ongoing service enhancement and collaborative partnerships, aimed at amplifying research endeavors and advancing innovative practices in librarianship and campus research support.

New perspectives on green and sustainable wet cleaning systems for art conservation.

The field of cultural heritage conservation science has seen significant advancements over recent decades, particularly through the application of soft matter and colloid science. Gels, nanostructured fluids, nanoparticles, and other advanced functional materials have been developed to address challenges in cleaning, consolidation, and protection of art. More recently, the focus has shifted toward "green" materials and sustainable practices, aligning with broader trends in science and technology. This emphasis on sustainability has revealed the immense potential for cross-disciplinary exchange between conservation science and fields like drug delivery, the food industry, tissue engineering, and more. A clear example of this synergy is seen in the cleaning of artworks, where bio-derived surfactants and biomaterials are increasingly incorporated into microemulsions and gels. These innovations not only enhance cleaning efficacy but also align conservation practices with sustainable principles, drawing parallels to research in cosmetics, pharmaceuticals, and detergents. The examples and materials discussed in this contribution illustrate how advancements in art conservation science can foster mutual technological transfer with other industries. By leveraging the central role of soft matter and colloids, these collaborations produce sustainable solutions that can address critical societal, environmental, and economic challenges.

RELEVANCE OF ICT-RELATED INSTRUCTIONAL MATERIALS IN THE TEACHING OF ISLAMIC STUDIES

Islamic Education is as old as Islam itself because right from inception, it emphasizes the need to acquire education regardless of distance or source once such knowledge is considered lawful and a person is not expected to perform any religious functions unless he is fully aware of its legal injunctions. Hence, it is not a matter of coincidence that Islam and Islamic education go together. Islamic Education is a vast and continuous process for all ages. As an old institution, Islamic Education has passed through ages and stages from the Prophetic era to date in terms of development. On the other hand, through advancements in science and technology, modern man has turned the world into a global village and through advancement is Information and Communication Technology, information is disseminated far and wide within the shortest period. Since Islam does not oppose any modern inventions that are beneficial to mankind and since information and communication technological inventions can be used for either good or bad, this paper intends to identify the relevance of ICT-related instructional materials in the teaching of Islamic Studies. The paper however recommends among others, that Islamic studies teachers need to equip themselves with the knowledge of information and Communication technology which acts as the most powerful tools of instruction.

Biological AIE Molecules: Innovations in Synthetic Design and AI-Driven Discovery.

Biological aggregation -induced emission (AIE) molecules offer significant advantages over synthetic organic fluorophores, particularly in biocompatibility, environmental sustainability, and emission properties in biological systems. Derived from biomolecules such as peptides, proteins, and nucleic acids, biological AIE molecules hold great promise for applications in biosensing, bioimaging, and target drug delivery. This review explores the design principles, mechanistic insights, and functional properties of biological AIE molecules whiles highlighting the role of artificial intelligence (AI) in accelerating their discovery and optimization. AI-driven approaches, including machine learning and computational modeling, are transforming the identification and synthesis of AIE molecules by enabling precise structural modifications and enhanced fluorescence efficiency. These advancements are paving the way for the integration of AIE molecules in next-generation smart biomedical devices, personalized medicine and sustainable technological applications. Emerging trends, including hybrid biomaterials, Ai-guided molecular engineering, and advanced imaging techniques, are expanding the scope of biological AIE molecules in healthcare and environmental monitoring. The synergy between AI and biological AIE molecules is unlocking new frontiers in biomedical technology, enabling transformative advancements in material science and healthcare applications, and shaping the future of fluorescence- based diagnostics and therapeutics.

Recent advances of silver nanoparticle-based polymer nanocomposites for biomedical applications.

Silver nanoparticle-polymer nanocomposites (AgNP-PNCs) represent a transformative advancement in biomedical material science, integrating the potent antimicrobial properties of AgNPs with the structural versatility of polymer matrices. This synergy enables enhanced infection control, mechanical stability, and controlled drug delivery, making these nanocomposites highly suitable for applications such as wound healing, medical coatings, tissue engineering, and biosensors. Recent progress in synthesis and functionalization has led to greater control over particle morphology, dispersion, and stability, optimizing AgNP-PNCs for clinical and translational applications. However, challenges related to cytotoxicity, long-term stability, immune response, and scalability persist, necessitating systematic improvements in surface functionalization, hybridization strategies, and biocompatibility assessments. This review critically evaluates the latest advancements in AgNP-PNC development, focusing on their functionalization techniques, regulatory considerations, and emerging strategies to overcome biomedical challenges. Additionally, it discusses preclinical and translational aspects, including commercialization barriers and regulatory frameworks such as FDA and EMA guidelines, ensuring a comprehensive outlook on their clinical feasibility. By bridging the gap between innovation and practical application, this review investigates the transformative potential of AgNP-PNCs in advancing next-generation biomedical materials.

Medicinal Plants and Traditional Uses and Modern Applications

Medicinal plants have played a pivotal role in the development of traditional healing systems throughout history. Across diverse cultures, these plants have been used to treat a broad spectrum of diseases, ranging from minor ailments such as colds and digestive issues to more severe conditions like infections, cancer, and chronic inflammatory disorders. The use of medicinal plants is rooted in centuries-old knowledge, passed down through generations, that often combines both empirical observations and spiritual beliefs. In traditional medicine systems, these plants are typically utilized in the form of decoctions, infusions, poultices, or extracts, with specific dosage instructions determined by experienced practitioners. Despite their historical significance, modern pharmacological research has only recently begun to rigorously explore and validate the therapeutic potential of many of these plants, uncovering a wealth of bioactive compounds that contribute to their medicinal effects. This paper aims to examine the traditional uses of medicinal plants from a global perspective, highlighting specific plants that have shown consistent efficacy in the treatment of common and complex health conditions. Furthermore, it investigates how contemporary scientific advancements are reshaping the understanding and applications of these plants, from the isolation of active compounds to the formulation of plant-based pharmaceuticals. The paper also emphasizes the growing role of medicinal plants in modern healthcare, including their use in complementary and alternative medicine (CAM), the development of herbal supplements, and their integration into biotechnological innovations. Moreover, the challenges faced in the standardization, quality control, and regulation of plant-based medicines are explored, along with the growing demand for sustainable harvesting practices to ensure the conservation of valuable plant species. In the face of rising healthcare costs and growing concerns about the safety and efficacy of synthetic pharmaceuticals, there is renewed interest in the therapeutic properties of medicinal plants. The combination of traditional wisdom and modern scientific validation presents a unique opportunity for the future of healthcare. This paper aims to bridge the gap between traditional knowledge and contemporary biomedical research, providing a holistic view of the role medicinal plants play in both ancient healing practices and their potential to shape future medical therapies.

A New Semiparametric Power-Law Regression Model With Long-Term Survival, Change-Point Detection and Regularization.

Kidney cancer, a potentially life-threatening malignancy affecting the kidneys, demands early detection and proactive intervention to enhance prognosis and survival. Advancements in medical and health sciences and the emergence of novel treatments are expected to lead to a favorable response in a subset of patients. This, in turn, is anticipated to enhance overall survival and disease-free survival rates. Cure fraction models have become essential for estimating the proportion of individuals considered cured and free from adverse events. This article presents a novel piecewise power-law cure fraction model with a piecewise decreasing hazard function, deviating from the traditional piecewise constant hazard assumption. By analyzing real medical data, we evaluate various factors to explain the survival of individuals. Consistently, positive outcomes are observed, affirming the significant potential of our approach. Furthermore, we use a local influence analysis to detect potentially influential individuals and perform a postdeletion analysis to analyze their impact on our inferences.

State funding for cannabis research: an analysis of funding mechanisms and levels

BackgroundThis paper examines cannabis research funding across U.S. states that have legalized cannabis for medical or adult (non-medical) use. It specifically looks at state legislative efforts to fund cannabis research, and the amount and mechanisms used for funding distribution.MethodsWe reviewed the text of legalization measures within states allowing medical or adult use (non-medical) cannabis for statutory language relating to research or scientific funding. When statutory language on research or scientific funding was not readily available or unclear in the legislative text, we reviewed state government websites or reference materials, or contacted state officials directly.ResultsOverall, we found that 17 out of 38 states that have passed either medical or adult use laws have legislation that specify a funding mechanism for cannabis research. Of the 17 states that have legislation directing funding to research, only 12 have allocated funding to date. Of those states that have allocated funding, six states distributed funds directly to an academic institution and five moved funding first through state agencies. One state – California – distributed research funding to both an academic institution and through the state cannabis regulatory agency. The amount of funding varies significantly across the states.ConclusionStates have much to gain from scientific advancements in the cannabis field, especially as they navigate a preponderance of public policy issues without a federal structure to lean upon. However, with less than half of states that have legalized cannabis use in some form providing funding for research, there is a missed opportunity for states to increase understanding of the risks and benefits of cannabis use within their state. There is also a missed opportunity for researchers and cannabis regulators to collaborate on informing policy options and developing future evidence-informed cannabis regulations. There is a need for more states to consider adopting mechanisms to support cannabis research.

An update on current type 2 diabetes mellitus (T2DM) druggable targets and drugs targeting them.

Type 2 diabetes mellitus (T2DM) is characterized by hyperglycemia and affects millions of people globally. Even after advancement and development in medical science, it is a big task to achieve victory over type 2 diabetes mellitus (T2DM). T2DM can be a reason for fatal events like stroke, cardiac failure, nephropathy, and retinopathy. Many advanced antidiabetic drugs have been introduced in the market in the past two decades, leading researchers to hunt for new target proteins and their potential modulators that can help develop newer antidiabetic drugs. This review article comprises a broad literature of the latest developments in the management of T2DM concerning new target proteins, their inhibitors, or drugs from the clinical arena employed for the successful management of symptoms of T2DM using mono, dual, or triple combination medication therapy. The review categorizes antidiabetic drugs into three general classes that include conventional drug targets, currently explored targets, and upcoming emerging targets. The review aims to merge information on the medicines affecting these targets, their mechanisms, followed by the chemical structures, and recent advancements.

ECLIPSE OF THE CONCEALED SUBJECT IN CHARAKA SAMHITA’S INDRIYA STHANA

Indriya Sthana provides a deep dive into the signs of life, not just focusing on the determinants or symptoms of death. Charaka explores near-death indicators that can be perceived through visual and auditory senses. Charaka's aim is to offer guidance to physicians when it may be best not to treat a patient. The term Arishta is used in both positive and negative contexts. ‘Indra’ symbolises ‘Prana’, or ‘life force’. Charaka Samhita stands out as the most crucial of the Chikitsa Granthas. The core principles of Indriya Sthana are highly enlightening. Arishta is the term used to describe symptoms that arise from untreatable defects spreading throughout the body. These symptoms indicate impending death due to a decrease in Agni. Recent scientific advancements now allow for the validation of predicted time of death, previously determined by seasoned experts. The signs and symptoms observed in this section and other chapters manifest without identifiable causes. The subsequent sections of Charaka Samhita outline various signs and symptoms resulting from Dosha imbalances.

Scientific Advances in STEM: Synergies to Achieve Success (Volume 3)

“Scientific Advances in STEM: Synergies to Achieve Success, Volume 3” emphasizes the importance of interdisciplinary collaboration in Science, Technology, Engineering, and Mathematics (STEM) for driving innovation and societal benefits [...]

Timber Harvesting in Mountainous Regions: A Comprehensive Review

Mountain ecosystems play a crucial role in providing ecosystem services, with some of the most important being carbon sequestration, biodiversity conservation, land protection, and water source preservation. Additionally, timber harvesting in these regions presents significant environmental, economic, and social challenges. This study provides a comprehensive bibliometric and systematic analysis of publications on timber harvesting in mountainous areas, examining the current state, global trends, key contributors, and the impact of forestry operations. A total of 357 publications on timber harvesting in mountainous areas have been identified, spanning from 1983 to 2024. These publications predominantly originate from the USA, Canada, Australia, and China, with additional contributions from European institutions. The research is published in leading forestry, ecology, and environmental science journals, highlighting its global impact. This study provides an in-depth bibliometric and systematic analysis, assessing research trends, key contributors, and their influence on scientific advancements in sustainable forestry and ecological conservation. These articles belong to the scientific fields of Environmental Science and Ecology, Forestry, Zoology, and Biodiversity Conservation, among others. They have been published in numerous journals, with the most frequently cited ones being Forest Ecology and Management, Journal of Wildlife Management, and Forests. The most frequently used keywords include dynamics, management, and timber harvest. The analysis of publications on timber harvesting in mountainous areas highlights the widespread use of primary harvesting methods, the negative effects of logging activities on soil, forest regeneration processes, and wildlife populations, as well as the role of advanced technologies in improving harvesting efficiency. While sustainable management practices, such as selective cutting and low-impact harvesting techniques, can mitigate some negative effects, concerns remain regarding soil erosion, habitat alteration, and carbon emissions. This analysis underscores the need for flexible forest management strategies that balance economic efficiency with ecological sustainability. Future research should focus on innovative harvesting techniques, adaptation measures to terrain and climate conditions, and the long-term impact of forestry activities on mountain ecosystems.

Evaluation of the preservative efficacy of green‐synthesized ZnO Nanoparticles using Cucumis sativus in cream formulations

AbstractDue to concerns about the safety of preservatives in cosmetic formulations, there is an urgent need to explore eco‐friendly alternatives that do not pose a risk to consumers. Zinc oxide nanoparticles (ZnO NPs) are promising preservatives for pharmaceutical and cosmetic products. In this study, ZnO NPs were synthesized using Cucumis sativus (cucumber) extract as a stabilizing agent and zinc acetate dihydrate as a precursor. Polyphenols, terpenoids, and flavonoids in cucumber extract act as capping agents and facilitate the reduction of metal ions to form ZnO NPs. Synthesized ZnO NPs were characterized using various techniques, including powder x‐ray diffraction, x‐ray fluorescence, scanning electron microscopy, Fourier transform infrared spectroscopy, photoluminescence and spectroscopy UV–Vis. The effectiveness of ZnO NPs as a preservative in cream formulation was evaluated along with their antioxidant and antibacterial properties against Gram‐positive and Gram‐negative bacteria. This study introduces a new green synthesis method to produce ZnO NPs using C. sativus extract. This eco‐friendly and cost‐effective approach is a sustainable alternative for nanoparticle synthesis. It is worth noting that this is the first time that zinc oxide nanoparticles are used as a natural preservative in a moisturizing cream formulation. The resulting cosmetic formulation represents a unique advancement in cosmetic science that meets consumer demands for safe and eco‐friendly skin care products while utilizing the preservative properties of ZnO NPs. This research has significant potential to change the cosmetics industry by providing natural and sustainable alternatives to traditional preservatives.

Potential of Nano-particles in Mitigating Abiotic Stress in Crops

Climate change, triggered by anthropogenic activities and other inexorable factors, has led to a surge in abiotic stresses, compromising agricultural productivity and exacerbating environmental degradation (Shahzad et al., 2018). Empirical evidence suggests that abiotic stresses significantly impair crop yields, underscoring the need for innovative solutions (Khan et al., 2018). Recent advances in plant sciences have elucidated the complex mechanisms underlying abiotic stress-induced damage to crop plants. Conversely, breakthroughs in plant physiology, genetics, and applied biology have paved the way for the development of stress-tolerant crop varieties. Furthermore, the nascent field of nanotechnology has emerged as a promising tool for enhancing plant resilience to abiotic stresses through the strategic application of nanoparticles (NPs) (Moisala et al., 2003). Studies have demonstrated that NPs can positively impact plant performance under stress conditions (Yadav et al., 2020). Moreover, the use of nano-scale agrochemicals, including nano-formulated pesticides, herbicides, and fertilizers, has garnered significant attention as a potential means of augmenting plant growth and productivity (Abdel Latef et al., 2017).