Deep Learning De-Noising Improves CT Perfusion Image Quality in the Setting of Lower Contrast Dosing: A Feasibility Study.

BACKGROUND AND PURPOSE: Considering recent iodinated contrast media (ICM) shortages, this study compared reduced ICM and standard dose CTP acquisitions, and the impact of deep learning (DL)-denoising on CTP image quality in preclinical and clinical studies. MATERIALS AND METHODS: Twelve swine underwent 9 CTP exams each, performed at combinations of 3 different X-ray (37, 67, and 127mAs) and ICM doses (10, 15, and 20mL). Clinical CTP acquisitions performed before and during the ICM shortage and protocol change (from 40 mL to 30 mL) were retrospectively included. Eleven patients with reduced ICM dose and 11 propensity-score-matched controls with standard ICM dose were included. A Residual Encoder-Decoder Convolutional-Neural-Network (RED-CNN) was trained for CTP denoising using K-space-Weighted Image Average (KWIA) filtered CTP images as the target. The standard, RED-CNN denoised, and KWIA noise-filtered images for animal and human studies were compared for quantitative SNR and qualitative image evaluation. RESULTS: The SNR of animal CTP images decreased with reductions in ICM and mAs doses. Contrast dose reduction had a greater effect on SNR than mAs reduction. Noise-filtering by KWIA and RED-CNN denoising progressively improved SNR of CTP maps, with RED-CNN resulting in the highest SNR. The SNR of clinical CTP images was generally lower with reduced ICM dose, which was improved by KWIA and RED-CNN denoising (p<0.05). Qualitative readings consistently rated RED-CNN denoised CTP as best quality, followed by KWIA and then standard CTP images. CONCLUSIONS: DL-denoising can improve image quality for low ICM CTP protocols, and could approximate standard ICM dose CTP, in addition to potentially improving image quality for low mAs acquisitions. ABBREVIATIONS: ICM=iodinated contrast media; DL=deep learning; KWIA=k-space weighted image average; LCD=low-contrast dose; SCD=standard contrast dose; RED-CNN=Residual Encoder-Decoder Convolutional Neural Network; PSNR=Peak Signal to Noise Ratio; RMSE=Root Mean Squared Error; SSIM=Structural Similarity Index.

Effectual seizure detection using MBBF-GPSO with CNN network.

EEG is the most common test for diagnosing a seizure, where it presents information about the electrical activity of the brain. Automatic Seizure detection is one of the challenging tasks due to limitations of conventional methods with regard to inefficient feature selection, increased computational complexity and time and less accuracy. The situation calls for a practical framework to achieve better performance for detecting the seizure effectively. Hence, this study proposes modified Blackman bandpass filter-greedy particle swarm optimization (MBBF-GPSO) with convolutional neural network (CNN) for effective seizure detection. In this case, unwanted signals (noise) is eliminated by MBBF as it possess better ability in stopband attenuation, and, only the optimized features are selected using GPSO. For enhancing the efficacy of obtaining optimal solutions in GPSO, the time and frequency domain is extracted to complement it. Through this process, an optimized features are attained by MBBF-GPSO. Then, the CNN layer is employed for obtaining the productive classification output using the objective function. Here, CNN is employed due to its ability in automatically learning distinct features for individual class. Such advantages of the proposed system have made it explore better performance in seizure detection that is confirmed through performance and comparative analysis.

Biological Potential and Therapeutic Effectiveness of Phytoproduct 'Fargesin' in Medicine: Focus on the Potential of an Active Phytochemical of Magnolia fargesii.

Flos Magnoliae is one of the important medicinal plants in different traditional medicine, including Chinese herbal medicine. Lignans and neolignans, including tetrahydrofurofuran, tetrahydrofuran, and aryltetralin, are present in the Flos Magnoliae species. A wide range of pharmacological activity of Flos Magnoliae has been reported in medicine. Fargesin has been isolated from Magnolia fargesii and is a lignan-class phytochemical. Fargesin has numerous pharmacological activities in medicine, including its effectiveness on lipid and glucose metabolism, oxidative stress, myocardial apoptosis, etc. In the present work, we have summarized the detailed scientific information of fargesin concerning its medicinal properties and pharmacological activities. Numerous biological and chemical aspects of fargesin are discussed here, including the detailed pharmacological activities and analytical aspects of fargesin. In this review, we have also compiled analytical data on fargesin based on available scientific literature. Ethnopharmacological information on fargesin was gathered by a literature survey on Pubmed, Science Direct, Google, and Scopus using the terms fargesin, Flos Magnoliae, phytochemical, and herbal medicine. The present review paper compiled the scientific data on fargesin in medicine for its pharmacological activities and analytical aspects in a very concise manner with proper citations. The present work signified the biological importance of fargesin in medicine due to its significant impact on bone disorders, lung injury, colon cancer, atherosclerosis, neurological disorders, ischemia, sars-cov-2, allergy, lipid and glucose metabolism, melanin synthesis, and different classes of enzymes. Furthermore, fargesin also has anti-inflammatory, antihypertensive, antiprotozoal, antimycobacterial, and antifeedant activity. However, analytical methods used for the separation, identification and isolation of fargesin in different biological and non-biological samples were also covered in the present review. The present work revealed the pharmacological activities and analytical aspects of fargesin in medicine and other allied health sectors.

Stacked hybridization to enhance the performance of artificial neural networks (ANN) for prediction of water quality index in the Bagh river basin, India.

Water quality assessment is paramount for environmental monitoring and resource management, particularly in regions experiencing rapid urbanization and industrialization. This study introduces Artificial Neural Networks (ANN) and its hybrid machine learning models, namely ANN-RF (Random Forest), ANN-SVM (Support Vector Machine), ANN-RSS (Random Subspace), ANN-M5P (M5 Pruned), and ANN-AR (Additive Regression) for water quality assessment in the rapidly urbanizing and industrializing Bagh River Basin, India. The Relief algorithm was employed to select the most influential water quality input parameters, including Nitrate (NO3-), Magnesium (Mg2+), Sulphate (SO42-), Calcium (Ca2+), and Potassium (K+). The comparative analysis of developed ANN and its hybrid models was carried out using statistical indicators (i.e., Nash-Sutcliffe Efficiency (NSE), Pearson Correlation Coefficient (PCC), Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Root Square Error (RRSE), Relative Absolute Error (RAE), and Mean Bias Error (MBE)) and graphical representations (i.e., Taylor diagram). Results indicate that the integration of support vector machine (SVM) with ANN significantly improves performance, yielding impressive statistical indicators: NSE (0.879), R2 (0.904), MAE (22.349), and MBE (12.548). The methodology outlined in this study can serve as a template for enhancing the predictive capabilities of ANN models in various other environmental and ecological applications, contributing to sustainable development and safeguarding natural resources.

Lung microbiome in lung cancer: A new horizon in cancer study.

Lung cancer (LC) is the second most prevalent cancer worldwide and a leading cause of cancer-related deaths. Recent technological advancements have revealed that the lung microbiome, previously thought to be sterile, is host to various microorganisms. The association between the lung microbiome and LC initiation, progression, and metastasis is complex and contradictory. However, disruption in the homeostasis of microbiome compositions correlated with the increased risk of LC. This review summarises current knowledge on the most recent developments and trends in lung cancer-related microbiota or microbial components. This manuscript aims to provide information on this rapidly evolving field while giving context to the general role of the lung microbiome in LC. In addition, this review briefly discussed the causative association of lung microbiome with LC. We will review the mechanisms of how lung microbiota influences carcinogenesis, focusing on microbiota dysbiosis. Moreover, we will also discuss the host-microbiome interaction as host-microbiota plays a crucial role in stimulating and regulating the immune response. Finally, we provide information on the diagnostic role of the microbiome in LC. It aims to offer an overview of the lung microbiome as a predictive and diagnostic biomarker in LC.

Indian herb Tinospora cordifolia and Tinospora species: Phytochemical and therapeutic application.

Clinical investigations are increasingly focusing on natural materials with medical benefits because, in contrast to medicines, they have extremely few adverse effects. Tinospora species of the Menispermaceae family has many bioactive principles for plant nutraceuticals. A thorough assessment of the existing literature revealed that Indian Tinospora species are an important group of medicinal herbs used for a variety of pharmacological activities. While, Tinospora cordifolia is widely recognized as a significant herb in the Indian System of Medicines (ISM) due to its bioactive components and has been used in the treatment of diabetes, cancer, urinary problems, fever, jaundice, helminthiasis, leprosy, dysentery, skin diseases, and many more. Using the search phrases "phytochemistry," "traditional uses," and "pharmacological evaluation of Indian Tinospora species," appropriate articles were carefully extracted from the MEDLINE/PubMed, Scopus, and WOS databases. Around 180 articles, related to the India Tinospora species, were selected from a pool of 200 papers published between 1991 and 2023. T. cordifolia has received a lot of scientific attention because of its diverse therapeutic characteristics in treating various diseases. Our present study in this review encompasses 1.) Phytochemistry, traditional uses and pharmacological potential of T. cordifolia as well as other Indian Tinospora species. 2.) Safety and toxicity study and available marketed formulation of T. cordifolia for the treatment of various diseases. The chemical constitution and pharmacological characteristics of other Tinospora species must also be investigated, indicating a need for further scientific research.

A Critical Review on the Role of Probiotics in Lung Cancer Biology and Prognosis.

Lung cancer remains the leading cause of cancer-related deaths worldwide. According to the American Cancer Society (ACS), it ranks as the second most prevalent type of cancer globally. Recent findings have highlighted bidirectional gut-lung interactions, known as the gut-lung axis, in the pathophysiology of lung cancer. Probiotics are live microorganisms that boost host immunity when consumed adequately. The immunoregulatory mechanisms of probiotics are thought to operate through the generation of various metabolites that impact both the gut and distant organs (e.g., the lungs) through blood. Several randomized controlled trials have highlighted the pivotal role of probiotics in gut health especially for the prevention and treatment of malignancies, with a specific emphasis on lung cancer. Current research indicates that probiotic supplementation positively affects patients, leading to a suppression in cancer symptoms and a shortened disease course. While clinical trials validate the therapeutic benefits of probiotics, their precise mechanism of action remains unclear. This narrative review aims to provide a comprehensive overview of the present landscape of probiotics in the management of lung cancer.

Non-coding RNA mediated regulation of PI3K/Akt pathway in hepatocellular carcinoma: Therapeutic perspectives.

Hepatocellular carcinoma (HCC) is among the primary reasons for fatalities caused by cancer globally, highlighting the need for comprehensive knowledge of its molecular aetiology to develop successful treatment approaches. The PI3K/Akt system is essential in the course of HCC, rendering it an intriguing candidate for treatment. Non-coding RNAs (ncRNAs), such as long ncRNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs), are important mediators of the PI3K/Akt network in HCC. The article delves into the complex regulatory functions of ncRNAs in influencing the PI3K/Akt system in HCC. The study explores how lncRNAs, miRNAs, and circRNAs impact the expression as well as the function of the PI3K/Akt network, either supporting or preventing HCC growth. Additionally, treatment strategies focusing on ncRNAs in HCC are examined, such as antisense oligonucleotide-based methods, RNA interference, and small molecule inhibitor technologies. Emphasizing the necessity of ensuring safety and effectiveness in clinical settings, limitations, and future approaches in using ncRNAs as therapies for HCC are underlined. The present study offers useful insights into the complex regulation system of ncRNAs and the PI3K/Akt cascade in HCC, suggesting possible opportunities for developing innovative treatment approaches to address this lethal tumor.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration.

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott's index of agreement (WI), and Legates-McCabe's index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

Synthesis, antifungal evaluation, two-dimensional quantitative structure-activity relationship and molecular docking studies of isoxazole derivatives as potential fungicides.

BACKGROUND: Sheath blight and bakanae disease, prominent among emerging rice ailments, exert a profound impact on rice productivity, causing severe impediments to crop yield. Excessive use of older fungicides may lead to the development of resistance in the pathogen. Indeed, a pressing and immediate need exists for novel, low-toxicity and highly selective fungicides that can effectively combat resistant fungal strains. RESULTS: A series of 20 isoxazole derivatives were synthesized using alkoxy/halo acetophenones and N,N-dimethylformamidedimethylacetal. These compounds were characterized by various spectroscopic techniques, namely 1H nuclear magnetic resonance (NMR), 13C NMR and liquid chromatography-high-resolution mass spectrometry, and were evaluated for their fungicidal activity against Rhizoctonia solani and Fusarium fujikuroi. Compound 5n (5-(2-chlorophenyl) isoxazole) exhibited highest activity (effective dose for 50% inhibition [ED50] = 4.43 µg mL-1) against R. solani, while 5p (5-(2,4-dichloro-2-hydroxylphenyl) isoxazole) exhibited highest activity (ED50 = 6.7 µg mL-1) against F. fujikuroi. Two-dimensional quantitative structural-activity relationship (QSAR) analysis, particularly multiple linear regression (MLR) (Model 1), highlighted chi6chain and DistTopo as the key descriptors influencing fungicidal activity. Molecular docking studies revealed the potential of these isoxazole derivatives as novel fungicides targeting sterol 14α-demethylase enzyme, suggesting their importance as crucial intermediates for the development of novel and effective fungicides. CONCLUSION: All test compounds were effective in inhibiting both fungi, according to the QSAR model, with various descriptors, such as structural, molecular shape analysis, electronic and thermodynamic, playing an important role. Molecular docking studies confirmed that these compounds can potentially replace commercially available fungicides and help control fungal pathogens in rice crops effectively. © 2024 Society of Chemical Industry.

Exploring the role of the ocular surface in the lung-eye axis: Insights into respiratory disease pathogenesis.

Chronic respiratory diseases (CRDs) represent a significant proportion of global health burden, with a wide spectrum of varying, heterogenic conditions largely affecting the pulmonary system. Recent advances in immunology and respiratory biology have highlighted the systemic impact of these diseases, notably through the elucidation of the lung-eye axis. The current review focusses on understanding the pivotal role of the lung-eye axis in the pathogenesis and progression of chronic respiratory infections and diseases. Existing literature published on the immunological crosstalk between the eye and the lung has been reviewed. The various roles of the ocular microbiome in lung health are also explored, examining the eye as a gateway for respiratory virus transmission, and assessing the impact of environmental irritants on both ocular and respiratory systems. This novel concept emphasizes a bidirectional relationship between respiratory and ocular health, suggesting that respiratory diseases may influence ocular conditions and vice versa, whereby this conception provides a comprehensive framework for understanding the intricate axis connecting both respiratory and ocular health. These aspects underscore the need for an integrative approach in the management of chronic respiratory diseases. Future research should further elucidate the in-depth molecular mechanisms affecting this axis which would pave the path for novel diagnostics and effective therapeutic strategies.

Review on the impact of cell phone radiation effects on green plants.

The aim of this review is to assess the impact of cell phone radiation effects on green plants. Rapid progress in networking and communication systems has introduced frequency- and amplitude-modulated technologies to the world with higher allowed bands and greater speed by using high-powered radio generators, which facilitate high definition connectivity, rapid transfer of larger data files, and quick multiple accesses. These cause frequent exposure of cellular radiation to the biological world from a number of sources. Key factors like a range of frequencies, time durations, power densities, and electric fields were found to have differential impacts on the growth and development of green plants. As far as the effects on green plants are concerned in this review, alterations in their morphological characteristics like overall growth, canopy density, and pigmentation to physiological variations like chlorophyll fluorescence and change in membrane potential etc. have been found to be affected by cellular radiation. On the other hand, elevated oxidative status of the cell, macromolecular damage, and lipid peroxidation have been found frequently. On the chromosomal level, micronuclei formation, spindle detachments, and increased mitotic indexes etc. have been noticed. Transcription factors were found to be overexpressed in many cases due to the cellular radiation impact, which shows effects at the molecular level.

Diagnostic evaluation of nested PCR and microscopy for Cryptosporidiosis in goats: can COWP gene based qRT-PCR be useful in assessment of active Cryptosporidial infections?

The present research delved into the transmission patterns, diagnostic methods, molecular traits, and phylogenetic analysis of Cryptosporidium species. The research was undertaken to enhance comprehension of the epidemiology and the potential for zoonotic transmission. A total of 80 goat-kid samples were tested, 7 were confirmed positive by mZN microscopy and 12 by nested-PCR. By PCR, 18SSUrRNA, HSP70, and GP60 amplicons were tested for Cryptosporidium. The restriction enzymes viz., SspI, VspI and MboII were used to genotype 12 Cryptosporidium positive samples by which C. parvum and C. bovis mixed infections were detected. Quantitative reverse transcription real-time PCR was used to transcriptionally screen the COWP-subunit genes to assess the severity of the infection in goat-kids, which showed upregulation of COWP6 and COWP4, while COWP9 and COWP3 genes were downregulated. A silent mutation was found at the codon CCA→CCC, which is being reported for the first time in goat field isolates. Phylogenetic and sequencing analyses confirmed the presence of the anthropozoonotic IIe subtype.

Visible Light-Promoted Regioselective Benzannulation of Vinyl Sulfoxonium Ylides with Ynoates.

Herein, we report a highly regioselective [4 + 2]-annulation of vinyl sulfoxonium ylides with ynoates under light-mediated conditions. The reaction proceeds through the new dienyl sulfoxonium ylide, which undergoes photolysis under blue light irradiation to give highly substituted naphthalene scaffolds. The method presented here operates at room temperature and does not require the addition of an external photosensitizer. The in situ-generated dienyl sulfoxonium ylide absorbs light and acts as a photosensitizer for the formation of arenes. The synthetic potential of these benzannulations was further illustrated by various synthetic transformations and a scale-up reaction. Moreover, control experiments and quantum chemical calculations reveal the mechanistic details of the developed reaction.

Evaluate effect of 126 pre-processing methods on various artificial intelligence models accuracy versus normal mode to predict groundwater level (case study: Hamedan-Bahar Plain, Iran).

The estimation of groundwater levels is crucial and an important step in ensuring sustainable management of water resources. In this paper, selected piezometers of the Hamedan-Bahar plain located in west of Iran. The main objective of this study is to compare effect of various pre-processing methods on input data for different artificial intelligence (AI) models to predict groundwater levels (GWLs). The observed GWL, evaporation, precipitation, and temperature were used as input variables in the AI algorithms. Firstly, 126 method of data pre-processing was done by python programming which are classified into three classes: 1- statistical methods, 2- wavelet transform methods and 3- decomposition methods; later, various pre-processed data used by four types of widely used AI models with different kernels, which includes: Support Vector Machine (SVR), Artificial Neural Network (ANN), Long-Short Term memory (LSTM), and Pelican Optimization Algorithm (POA) - Artificial Neural Network (POA-ANN) are classified into three classes: 1- machine learning (SVR and ANN), 2- deep learning (LSTM) and 3- hybrid-ML (POA-ANN) models, to predict groundwater levels (GWLs). Akaike Information Criterion (AIC) were used to evaluate and validate the predictive accuracy of algorithms. According to the results, based on summation (train and test phases) of AIC value of 1778 models, average of AIC values for ML, DL, hybrid-ML classes, was decreased to -25.3%, -29.6% and -57.8%, respectively. Therefore, the results showed that all data pre-processing methods do not lead to improvement of prediction accuracy, and they should be selected very carefully by trial and error. In conclusion, wavelet-ANN model with daubechies 13 and 25 neurons (db13_ANN_25) is the best model to predict GWL that has -204.9 value for AIC which has grown by 5.23% (-194.7) compared to the state without any pre-processing method (ANN_Relu_25).

Zirconium oxide nano-catalyzed synthesis of pharmacologically important bis(indolyl)methanes: a highly efficient and mild approach.

We report herein a highly efficient and mild approach for synthesizing pharmacologically active bis(indolyl)methanes 3a-z, utilizing ZrO2 nanoparticles as a catalyst. The method involves a condensation reaction between indole and diverse aromatic aldehydes in acetonitrile under mild conditions. The ZrO2 nano-catalyst prepared via a co-precipitation method demonstrates exceptional efficacy, leading to favourable yields of the target bis(indolyl)methanes 3a-z. The versatility of this methodology is highlighted through substrate screening, showcasing its applicability to various aromatic aldehydes.

Size-induced exchange bias in single-phase CoO nanoparticles.

The tuning of exchange bias (EB) in nanoparticles has garnered significant attention due to its diverse range of applications. Here, we demonstrate EB in single-phase CoO nanoparticles, where two magnetic phases naturally emerge as the crystallite size decreases from 34.6 ± 0.8 to 10.8 ± 0.9 nm. The Néel temperature (TN) associated with antiferromagnetic ordering decreases monotonically with the reduction in crystallite size, highlighting the significant influence of size effects. The 34.6 nm nanoparticles exhibit magnetization irreversibility between zero-field cooled (ZFC) and field-cooled (FC) states belowTN. With further reduction in size this irreversibility appears well aboveTN, resulting in the absence of true paramagnetic regime which indicates the occurnace of an additional magnetic phase. The frequency-dependent ac-susceptibility in 10.8 nm nanoparticles suggests slow dynamics of disordered surface spins aboveTN, coinciding with the establishment of long-range order in the core. The thermoremanent magnetization (TRM) and iso-thermoremanent magnetization (IRM) curves suggest a core-shell structure: the core is antiferromagnetic, and the shell consists of disordered surface spins causing ferromagnetic interaction. Hence, the EB in these CoO nanoparticles results from the exchange coupling between an antiferromagnetic core and a disordered shell that exhibits unconventional surface spin characteristics.

Weight gain in infants with congenital heart disease; breastfeeding alone versus supplemental spoon-feeding of expressed breast milk: an open-label, pilot, randomised control trial.

OBJECTIVES: Infants with congenital heart disease and increased pulmonary blood flow frequently suffer from feeding difficulties and growth failure. Providing expressed breast milk by spoon has been hypothesised to decrease energy expenditure in these infants as compared to breastfeeding. This study assessed the effect of supplemental feeding of expressed breast milk on weight gain in infants with unoperated congenital heart disease. METHOD: This was a prospective open-label randomised control trial. In total, 50 infants with post tricuspid left to right shunt were enrolled in the study. In the intervention group, apart from breastfeeding, a minimum predetermined volume of expressed breast milk was targeted to be given by spoon. 30-50 kcal/kg/day was given by expressed breast milk by spoon-feeding. In the control group, the infants were given at least 8 feeds per 24 hours by direct breastfeeding. Both groups were followed up for 1 month and assessed for weight gain. RESULT: Despite a high rate of protocol breach in both groups (30% overall), infants in the intervention group had better weight gain at one-month follow-up compared to those in the control group, 780 ± 300 versus 530 ± 250 gm (p = 0.01). CONCLUSION: In infants with left to right shunts, supplemental feeding of expressed breast milk by spoon along with breastfeeding resulted in significantly higher average weight gain at 30 days compared to the control group who received breastfeeding alone. Future studies with larger sample sizes and longer follow-ups need to be done to confirm the findings of this study.

Integrated meta-analysis and transcriptomics pinpoint genomic loci and novel candidate genes associated with submergence tolerance in rice.

BACKGROUND: Due to rising costs, water shortages, and labour shortages, farmers across the globe now prefer a direct seeding approach. However, submergence stress remains a major bottleneck limiting the success of this approach in rice cultivation. The merger of accumulated rice genetic resources provides an opportunity to detect key genomic loci and candidate genes that influence the flooding tolerance of rice. RESULTS: In the present study, a whole-genome meta-analysis was conducted on 120 quantitative trait loci (QTL) obtained from 16 independent QTL studies reported from 2004 to 2023. These QTL were confined to 18 meta-QTL (MQTL), and ten MQTL were successfully validated by independent genome-wide association studies from diverse natural populations. The mean confidence interval (CI) of the identified MQTL was 3.44 times narrower than the mean CI of the initial QTL. Moreover, four core MQTL loci with genetic distance less than 2 cM were obtained. By combining differentially expressed genes (DEG) from two transcriptome datasets with 858 candidate genes identified in the core MQTL regions, we found 38 common differentially expressed candidate genes (DECGs). In silico expression analysis of these DECGs led to the identification of 21 genes with high expression in embryo and coleoptile under submerged conditions. These DECGs encode proteins with known functions involved in submergence tolerance including WRKY, F-box, zinc fingers, glycosyltransferase, protein kinase, cytochrome P450, PP2C, hypoxia-responsive family, and DUF domain. By haplotype analysis, the 21 DECGs demonstrated distinct genetic differentiation and substantial genetic distance mainly between indica and japonica subspecies. Further, the MQTL7.1 was successfully validated using flanked marker S2329 on a set of genotypes with phenotypic variation. CONCLUSION: This study provides a new perspective on understanding the genetic basis of submergence tolerance in rice. The identified MQTL and novel candidate genes lay the foundation for marker-assisted breeding/engineering of flooding-tolerant cultivars conducive to direct seeding.