Structural damage identification using deep learning is often constrained by a reliance on single-data-type models and the challenges posed by non-stationary signals. This paper introduces a novel framework that integrates Variational Mode Decomposition (VMD) with multi-source data fusion to overcome these limitations. The proposed method processes raw, non-stationary acceleration and strain signals through VMD to generate stable intrinsic mode functions (IMFs), significantly enhancing their feature clarity for a subsequent convolutional neural network (CNN). A comparative analysis on the Va & uml;nersborg and KW51 bridge datasets confirms that VMD preprocessing consistently improves classification accuracy for both data types compared to using unprocessed signals. The core finding demonstrates that feature-level fusion of these VMD-processed signals is the optimal strategy, achieving peak accuracies of 98.4% and 96.6% on the Va & uml;nersborg and KW51 datasets, respectively. This approach not only surpassed the performance of acceleration-only models but also substantially bridged a critical performance gap by boosting the contribution of strain data by over 23% and improving strain-only model accuracy by 24.5%. While data-level and decision-level fusion showed context-dependent results, feature-level fusion proved uniquely robust and superior across both structures. The study conclusively establishes that the feature-level fusion of VMD-decomposed signals is a highly effective and reliable methodology for advanced structural health monitoring. To further support the effectiveness of VMD, it is additionally benchmarked against conventional baseline decomposition techniques Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD), and the results confirm that VMD consistently outperforms both methods under single-signal (data-only) evaluation as well as under feature-level fusion, demonstrating its superior decomposition capability and practical robustness for damage identification.

Structures may lose rigidity and exhibit a reduction in their fundamental natural frequency following a strong earthquake. Analysing the time–frequency representation of instrumented structure’s responses reveals key information about the changes in their frequency characteristics over time. The short‐time Fourier transform (STFT), Wigner–Ville distribution, reassigned smoothed pseudo‐Wigner–Ville distribution, wavelet transform, and Hilbert–Huang transform are among the well‐known time–frequency techniques that are commonly employed for building damage detection. This is the first application of synchro‐reassigning transform (SRT) for improved frequency‐shift detection of structural damage after an earthquake, with greater robustness and resolution compared to other time–frequency transforms. This study thoroughly examines its performance by analysing two synthetic signals and three real earthquake signals: two from the 1994 Northridge earthquake and one from the 1971 San Fernando earthquake. The simulation results show that the SRT method provides improved time–frequency resolution and fewer distortions and is less sensitive to noise compared to well‐established time–frequency techniques. Owing to these advantages, SRT could be more effective and promising in detecting frequency shifts for postearthquake structural assessment.

The global prevalence of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) has reached alarming levels, affecting nearly one-third of the world's population. This review analyzes current evidence on the intricate relationships between MASLD, insulin resistance, and type 2 diabetes mellitus (T2DM), with particular emphasis on gut microbiome interactions. As MASLD progresses from simple steatosis to Metabolic Dysfunction-Associated Steatohepatitis (MASH), it can lead to severe complications including fibrosis, cirrhosis, and hepatocellular carcinoma. The pathogenesis of MASLD is multifactorial, involving hepatic lipid accumulation, oxidative stress, inflammation, and dysregulation of the gut-liver axis. Insulin resistance is a central driver of disease progression, closely linked to obesity and metabolic syndrome. Recent research highlights how gut microbiome dysbiosis exacerbates MASLD through mechanisms such as increased intestinal permeability, systemic inflammation, and altered metabolic signaling. Identification of microbial signatures offers promise for novel diagnostic and therapeutic strategies. By integrating metabolic, inflammatory, and microbial perspectives, this review provides a comprehensive overview of MASLD pathogenesis and its association with obesity, insulin resistance, and T2DM.

Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things (IoMT). Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer. Therefore, this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention (ResMHCNN) blocks to classify medical images. We introduced three novel lightweight deep learning models (BT-Net, LCC-Net, and BC-Net) utilizing the ResMHCNN block as their backbone. These models were cross-validated and tested on three publicly available medical image datasets: a brain tumor dataset from Figshare consisting of T1-weighted magnetic resonance imaging slices of meningioma, glioma, and pituitary tumors; the LC25000 dataset, which includes microscopic images of lung and colon cancers; and the BreaKHis dataset, containing benign and malignant breast microscopic images. The lightweight models achieved accuracies of 96.9% for 3-class brain tumor classification using BT-Net, and 99.7% for 5-class lung and colon cancer classification using LCC-Net. For 2-class breast cancer classification, BC-Net achieved an accuracy of 96.7%. The parameter counts for the proposed lightweight models-LCC-Net, BC-Net, and BT-Net-are 0.528, 0.226, and 1.154 million, respectively. The presented lightweight models, featuring ResMHCNN blocks, may be effectively employed for accurate medical image classification. In the future, these models might be tested for viability in resource-constrained systems such as mobile devices and IoMT platforms.

Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth and yield from being captured. In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. While satellite imagery provides information on spatial data regarding crop health, weather data provides temporal insights, and the soil properties provide important fertility information. Fusing these heterogeneous data sources embeds an overall understanding of yield-determining factors in the model, decreasing the RMSE by 15% and improving R2 by 20% over single-source models. We further push the frontier of feature engineering by using Temporal Convolutional Networks (TCNs) and Graph Convolutional Networks (GCNs) to capture time series trends, geographic and topological information, and pest/disease incidence. TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. The attention mechanism enables more effective spatial feature encoding by focusing on critical image regions, while the HGNN captures interaction patterns that are complex between diverse data types. Finally, VAEs can generate robust feature representation. Such state-of-the-art architectures could then achieve an MAE improvement of 12%, while R2 for yield prediction improves by 25%. In this paper, the state of the art in yield prediction has been advanced due to the employment of multi-source data fusion, sophisticated feature engineering, and advanced neural network architectures. This provides a more accurate and reliable soybean yield forecast. Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process.

In the present research, we developed pyrimidine-based hybridized molecules with either imidazole or triazole to find effective anticancer drugs. The reaction was accomplished using a multicomponent reaction pathway. The synthetics were explored for their utility as an anticancer agent via human topoisomerase-II and tubulin inhibition. Among the synthetics, compounds 1B4, 1B5, and 1B6 were potent anticancer agents tested in five cancer cell lines compared to colchicine and etoposide employed as positive controls. These synthetics were found further devoid of any significant cytotoxicity towards normal cells, thus proving their selective anticancer nature. Further, these compounds inhibited both the tubulin and hTopoII as indicated by in vitro-based assay. The mechanistic insights were corroborated using molecular docking studies. Besides this, the molecules were found to portray their secondary anticancer cell death mechanism via apoptosis. They decreased the oxidative stress, induced apoptosis, and arrested the cell cycle arrest at the G2/M phase in cancer cells.

Pavement maintenance has become a critical priority in recent years. There has been a growing focus in research on advancing image-based pavement crack monitoring tools that utilize deep learning models to automate the detection of damage in civil infrastructure. Accurate automatic damage detection using deep learning models requires a comprehensive and extensive data source that can effectively capture anomalies in the photos. Nevertheless, these tools primarily rely on RGB/thermal images, which perform effectively in optimal lighting conditions but may experience diminished performance in challenging environments. For example, these RGB-based methods often struggle in challenging scenarios with low contrast, cluttered backgrounds, poor lighting, fog, or smoke obstruction inherent limitations of thermal images, such as edge blurring, low contrast, and local unevenness, can hinder the accuracy and robustness of some pavement crack detection methods. To improve crack detection accuracy, this research proposes a method based on RGB and thermal image fusion strategies such as early, intermediate, and late fusion. The comparative analysis demonstrated that the intermediate RGB-thermal fusion technique exhibited the highest performance, achieving F1 scores and mean intersection over union (MIoU) of 96.26% and 93.00%, followed by early fusion (F1: 95.36%, MIoU: 91.45%). The three fusion methods showed notably enhanced performance compared to segmentation models based on a single type, with the early and intermediate fusion methods demonstrating greater stability. The RGB-thermal fusions not only achieved a higher detection rate for damage but also excelled in distinguishing between different types of damage. It is evident that the integration of multimodal RGB-thermal fusion technologies significantly enhances the accuracy of asphalt pavement crack segmentation.

Two series of antibacterial agents, 1,2,3-triazole and aminopyrimidine benzimidazole hybrids, were designed, synthesized, and characterized by IR, NMR, Mass spectroscopy, and X-ray crystallography studies. The biological studies revealed that compounds 5a, 5b, 5c, 5d, 5e, 5f, 5g, 5h, 8d, 8e, 9d, 9e, 9f, 9h, 9j, and 9k exhibited significant antibacterial activity in vitro compared to the standard drug ciprofloxacin, against Gram-positive and Gram-negative bacterial strains. The study of hemotoxicity displayed a negligible toxicity profile for all the compounds. Furthermore, the mechanistic insights predicted via molecular docking studies on DNA gyrase revealed (Glide Scores) that compounds 5c and 5f possess better affinity within the active domain of DNA gyrase, which was further corroborated using molecular dynamics followed by direct DNA gyrase-based inhibition assays. Compound 5f was the most potent, while 5c showed an equipotent inhibition compared to a standard drug.

O 2 sensing by hypoxia-inducible factor (HIF) is a principal mechanism by which aerobic organisms adjust cellular energy metabolism and adapt to O 2 limitation. In this study, we show that H 2 S, a product of host and microbial metabolism, profoundly influences the threshold for HIF-dependent hypoxia-sensing by increasing intracellular O 2 . The dose-dependent destabilization of HIF by H 2 S is inversely correlated with sulfide quinone oxidoreductase, which oxidizes sulfide in the mitochondrion. Hypoxia sensors provide a quantitative estimate of the magnitude of H 2 S-induced perturbation. The O 2 concentration in cells grown in a 2% O 2 atmosphere is sensed as 5 or 15 % O 2 in the presence of 25 or 100 ppm H 2 S, respectively. Sustained exposure to H 2 S elicits the hallmarks of hyperoxia-associated cytotoxicity, including loss of Fe-S proteins in cellular and murine models. H 2 S thus emerges as a powerful regulator of O 2 sensing and signaling with possible implications for dysregulation in O 2 toxicity diseases. Significance Statement:The mitochondrial electron transport chain (ETC) accounts for ∼90% of whole body O 2 consumption. However, our understanding of how metabolites modify ETC flux and therefore, intracellular O 2 availability, are poor. In this study, we demonstrate that hydrogen sulfide (H 2 S), which is produced by host and gut microbes alike, increases intracellular O 2 by decreasing ETC flux, and destabilizes the principal hypoxia sensor, HIF-1α. The upshift in intracellular O 2 levels is quantitatively significant, such that 2% O 2 is sensed as 5-15% O 2 at varying H 2 S concentrations, with concomitant destabilization of Fe-S proteins, a signature of cellular hyperoxia. Our study identifies H 2 S as a HIF-1α regulator with important implications for the large class of mitochondrial diseases characterized by dysregulated O 2 metabolism.

In the present work, we have explored the importance of the imidazole ring and its importance in drug discovery, citing the key approvals in the present decade (2013-2024). The pharmacological attribution for the approved drugs revealed that out of 20 approved drugs, 45% of the approvals were made as anti-infectives, followed by approvals under the category of genetic and metabolic disorders, sexual endocrine disorders, anticancer, and to treat blood pressure, gastrointestinal disorders, and neurological conditions. Most approved drugs were dispensed through solid dosage forms (13) and thus had predominantly oral routes beside others. The metabolism pattern revealed that the drugs undergo metabolism via the involvement of multiple enzymes, where CYP3A4 and CYP3A5 were the core enzymes. The excretion pattern of these drugs revealed that the drugs are majorly excreted via the fecal route. The chemical analysis showed that pyrrolidine/pyrrole was the major heterocycle in the approved drugs, followed by the indole ring in the hybridization. Considering the substitution pattern, most drugs possessed amide, amines, and fluoro group as the functional substitution with the 2,4-substitution pattern seen in most approved drugs. Besides this, the three approved drugs were found to possess chiral centers and exhibit chirality. The article also expanded to cover the synthetic routes and metabolic routes for this versatile ring system and case studies for its utility to serve as bioisostere in drug discovery. Furthermore, this article also presents the receptor-ligand interactions of imidazole-based drugs with various target receptors. The present article is, therefore, put forth to assist medicinal chemists and chemists working in drug discovery of this versatile ring system.

This paper aims to investigate the detection of internal voids or decay anomalies in wood using Electrical Resistivity Tomography (ERT) and Tiny Machine Learning (TinyML). It proposes an anomaly detection method suitable for large-scale target detection, aiming to achieve multi-tree detection and automated detection of targets. The ERT tree detection model is simulated using COMSOL and MATLAB software. Based on the obtained boundary voltage data, a machine learning model is generated using NanoEdge AI Studio to simulate the detection of internal anomalies in trees. Finally, control circuits and control programs are designed, and the machine learning model is deployed on STM32G4 series microcontrollers. In the simulation detection using NanoEdge AI, 15 sets of anomalies are detected out of 16 sets of data for the multi-cavity model. The tree anomaly detection method based on Electrical Resistivity Tomography and Tiny Machine Learning can effectively detect anomalies in trees. TinyML conserves embedded system resources during multi-target detection, enabling low power consumption and real-time processing.

Chamoli 2021 disaster on 7th February 2021 was among the most devastating hazards in the history of Uttarakhand, India. This study investigates the potential of the Seismological Network Around Tehri Region (SNATR) to analyze the seismic precursors of the event, while simultaneously determining the early warning potential in the region. Seismic signals recorded at various stations of the Seismological Network Around Tehri Region (SNATR) revealed the dynamic properties of the event. It has been observed that the nucleation phase (04:51:18 UTC) of the event initiated after the P- and S-wave arrival at 04:31:41 UTC and 04:38:41 UTC, respectively, at the nearest seismic station to the source from a M6 earthquake which occurred in Davao del Sur, Philippines on 7th February 2021 at 04:31:41 UTC. The time–frequency analysis based on the Wavelet Synchrosqueezed Transform (WSST) reveals an increase in the signal energy from the earthquake and a surge post the initiation of the rock-ice avalanche. The analysis reveals the timing of four different phases of the Chamoli event with the highest dominant frequency (16–18) Hz due to the ice-rock fall, and the lowest frequency (2–5) Hz at the nucleation phase prior to the event and the range of seismic triggering frequency was (0.01–0.2) Hz. The velocity of the surface wave induced after the detachment phase was estimated to be about 2.71 km/s. We computed the time-difference based on different stations of the seismic network for estimating the early warning prospects of the seismic network and determined a warning period of approximately 10 and 14 min at distances of 13 and 19.7 km at the impacted sites of Rishiganga Power Corporation (RGPC) Ltd.’s 13.2 Mw Project and (Nation Thermal Power Corporation (NTPC), Tapovan Vishnugad 512 Mw Project), respectively, from the source at Raunthi peak.

ABSTRACTA complex microbial community in the gut may prevent the colonization of enteric pathogens such as Salmonella. Some individual or a combination of species in the gut may confer colonization resistance against Salmonella. To gain a better understanding of the colonization resistance against Salmonella enterica, we isolated a library of 1,300 bacterial strains from feral chicken gut microbiota which represented a total of 51 species. Using a co-culture assay, we screened the representative species from this library and identified 30 species that inhibited Salmonella enterica subspecies enterica serovar Typhimurium in vitro. To improve the Salmonella inhibition capacity, from a pool of fast-growing species, we formulated 66 bacterial blends, each of which composed of 10 species. Bacterial blends were more efficient in inhibiting Salmonella as compared to individual species. The blend that showed maximum inhibition (Mix10) also inhibited other serotypes of Salmonella frequently found in poultry. The in vivo effect of Mix10 was examined in a gnotobiotic and conventional chicken model. The Mix10 consortium significantly reduced Salmonella load at day 2 post-infection in gnotobiotic chicken model and decreased intestinal tissue damage and inflammation in both models. Cell-free supernatant of Mix10 did not show Salmonella inhibition, indicating that Mix10 inhibits Salmonella through either nutritional competition, competitive exclusion, or through reinforcement of host immunity. Out of 10 species, 3 species in Mix10 did not colonize, while 3 species constituted more than 70% of the community. Two of these species were previously uncultured bacteria. Our approach could be used as a high-throughput screening system to identify additional bacterial sub-communities that confer colonization resistance against enteric pathogens and its effect on the host.IMPORTANCESalmonella colonization in chicken and human infections originating from Salmonella-contaminated poultry is a significant problem. Poultry has been identified as the most common food linked to enteric pathogen outbreaks in the United States. Since multi-drug-resistant Salmonella often colonize chicken and cause human infections, methods to control Salmonella colonization in poultry are needed. The method we describe here could form the basis of developing gut microbiota-derived bacterial blends as a microbial ecosystem therapeutic against Salmonella.

Surface plasmon resonance (SPR) has gained attention as a promising method for effective label-free biosensing. Immunoglobulin (IgG) detection is very important to understand the past infection and immunity of any individual. Thus, this study aims to develop a SPR sensor with better sensitivity for detecting IgG. It emphasizes the utilization of a high-performance planar waveguide-based SPR sensor to detect IgG by analyzing a suitable sensor topology. The sensor configuration consists of five distinct layers: silver (Ag), silicon nitride ( Si_3N_4 ), black phosphorus (BP), an enzyme, and a sensing medium. Silver (Ag) stimulates surface plasmons, while Si3N4 and BP are utilized to enhance absorption capabilities and serve as the bio-molecular recognition element, respectively. The proposed sensor simulation employs the transfer matrix method and an angular interrogation scheme. To assess this proposed sensor’s impact, the sensing region is assessed while considering three layers: Ag, Ag-BP, and Ag–Si3N4. Initially, the thickness of the Ag layer is optimized by recording its transmittance and achieving a minimum transmittance of 0.0027 at a thickness of 50 nm. Subsequently, the performance parameters are assessed using four different structures with slight variations in the IgG samples. The results depict the maximum achieved sensitivities as follows: 192 ^∘ /RIU for conventional SPR, 203 ^∘ /RIU for BP-based SPR, 287 ^∘ /RIU for Si3N4-based SPR, and 352 ^∘ /RIU for the proposed structure. This comparative study demonstrates that the proposed SPR configuration significantly enhances sensitivity, quality factor, and detection accuracy performance.

This paper introduces an innovative hybrid wireless power transfer (H-WPT) scheme tailored for IIoT networks employing multiple relay nodes. The scheme allows relay nodes to dynamically select their power source for energy harvesting based on real-time channel conditions. Our analysis evaluates outage probability within decode-and-forward (DF) relaying and adaptive power splitting (APS) frameworks, while also considering the energy used by relay nodes for ACK signaling. A notable feature of the H-WPT scheme is its decentralized operation, enabling relay nodes to independently choose the optimal relay and power source using instantaneous channel gain. This approach conserves significant energy otherwise wasted in centralized control methods, where extensive information exchange is required. This conservation is particularly beneficial for energy-constrained sensor networks, significantly extending their operational lifetime. Numerical results demonstrate that the proposed hybrid approach significantly outperforms the traditional distance-based power source selection approach, without additional energy consumption or increased system complexity. The scheme’s efficient power management capabilities underscore its potential for practical applications in IIoT environments, where resource optimization is crucial.

The prevalence of cardiovascular diseases (CVDs) is constantly rising, making them a major health burden. In terms of global mortality and morbidity, they are still at the top. An alternate method of treating many illnesses, including CVDs, is the use of medicinal herbs. There is a current, unprecedented push to include herbal remedies into contemporary healthcare systems. The widespread conviction in their safety and the fact that they offer more effective treatment at a lower cost than conventional modern medicines are two of the main factors propelling this movement. However, there has not been enough testing of the purported safety of herbal remedies. As a result, people need to know that medical herbs can be toxic, have possibly fatal side effects, and can interact negatively with other drugs. Experimental evidence suggests that medicinal herbs may be useful in the treatment of cardiovascular diseases (CVDs) due to their ability to inhibit multiple risk factors for these conditions. So, in order to successfully use herbs in CVD therapy, there have been numerous initiatives to transition medicinal herb research from the lab to the clinic. Presented below are cardiovascular diseases (CVDs) and the variables that put people at risk for developing them. Next, we provide a synopsis of herbal medicine's role in the treatment of disease, with a focus on cardiovascular diseases. In addition, information is compiled and examined about the ethnopharmacological therapeutic possibilities and medicinal qualities against cardiovascular diseases of four commonly used plants: ginseng, gingko biloba, ganoderma lucidum, and gymnostemma pentaphyllum. The use of these four plants in the treatment of cardiovascular diseases (CVDs) including myocardial infarction, hypertension, peripheral vascular disorders, coronary heart disease, cardiomyopathies, and dyslipidemias has been well examined. We are also making an effort to describe the current in vitro and in vivo investigations that have attempted to examine the cellular and molecular underpinnings of the four plants' cardio-protective effects. Lastly, we highlighted the effectiveness, safety, and toxicity of these four medicinal herbs by reviewing and reporting the results of current clinical trials. GRAPHICAL ABSTRACT

A big problem in healthcare around the world is neurological illnesses. There is a huge healthcare and financial burden on society worldwide due to the dramatically increased risk of chronic sickness and diseases linked with posed lifestyle changes. Fine treatment for sick illnesses with few known adverse effects is the goal of research. A number of functional food studies have been launched in the last few decades in an effort to identify meals with enhanced therapeutic activity and reduced adverse effects. As a result, research into nutraceutical therapy for illness prevention and various extraction procedures for disorders has been underway. Progressive memory loss characterises Alzheimer's disease (AD), a neurodegenerative disorder. The pharmaceutical options available today are expensive, come with unwanted side effects, and are in short supply. Scientists and researchers have noticed that nutraceuticals have a big impact. The anti-Alzheimer's efficacy of nutraceuticals was examined in a number of clinical and preclinical investigations. The study of new therapeutic targets, such as the pathophysiological mechanisms and unique cascades, has resulted from the growing understanding of the AD pathogenesis. Therefore, the most effective and well-known nutraceuticals will be showcased in the present development, together with brief mechanisms involving antioxidants, autophagy control, anti-inflammatory, mitochondrial homeostasis, and more. Nutraceuticals have real-world impacts, and getting your hands on phytochemicals and other vital bioactive ingredients from therapeutically active foods is a top priority. Because of this, the term "functional foods" has been muddied and replaced with similar ones such as "pharmafoods," "medifoods," "vita foods," or "medicinal foods." Nutraceuticals are in high demand to counteract neurological interventions, and there is an urgent need to stick to healthy options. Nutraceuticals may play a preventative role in neurological therapies due to the demonstrated correlation between dietary patterns and lifestyle factors and neurodegeneration. Examining high-quality clinical trials is the focus of the present study, which touches on several important neurological topics. In light of nutraceuticals' promise as multi-targeted therapy for Alzheimer's disease, it is critical to assess them as promising lead molecules for the development of new drugs. Prospective studies should, according to the authors' understanding, take into account blood-brain barrier permeability alteration, bioavailability, and features of randomised clinical trials.

The devised method passed the ICH Q2 (R1) validation test, and the findings imply it might be utilised for both the regular monitoring of pharmaceutical formulation and raw material quality. Results for Nebivolol and Hydrochlorothiazide were consistently linear across concentration ranges of 4-24 and 10-60 μg/mL, respectively, according to the linearity study. The proposed approach was determined to be appropriate based on precision data and minimal relative standard deviation (RSD). Our lab confirmed the LOD and LOQ values for cilnidipine and lisinopril dihydrate. The method's accuracy within the given range is demonstrated by the low relative standard deviation (RSD) values. We found that the suggested method was linear, sensitive, accurate, and exact when it came to estimating hydrochlorothiazide and nebivolol in bulk and in pharmaceutical formulations. This conclusion was reached when the validation inquiry was finished and the results were discovered.

Rice, a staple food for a significant portion of the global population, faces persistent threats from various pathogens and pests, necessitating the development of resilient crop varieties. Deployment of resistance genes in rice is the best practice to manage diseases and reduce environmental damage by reducing the application of agro-chemicals. Genome editing technologies, such as CRISPR-Cas, have revolutionized the field of molecular biology, offering precise and efficient tools for targeted modifications within the rice genome. This study delves into the application of these tools to engineer novel alleles of resistance genes in rice, aiming to enhance the plant’s innate ability to combat evolving threats. By harnessing the power of genome editing, researchers can introduce tailored genetic modifications that bolster the plant’s defense mechanisms without compromising its essential characteristics. In this study, we synthesize recent advancements in genome editing methodologies applicable to rice and discuss the ethical considerations and regulatory frameworks surrounding the creation of genetically modified crops. Additionally, it explores potential challenges and future prospects for deploying edited rice varieties in agricultural landscapes. In summary, this study highlights the promise of genome editing in reshaping the genetic landscape of rice to confront emerging challenges, contributing to global food security and sustainable agriculture practices.