Improving prediction of maternal health risks using PCA features and TreeNet model.

Maternal healthcare is a critical aspect of public health that focuses on the well-being of pregnant women before, during, and after childbirth. It encompasses a range of services aimed at ensuring the optimal health of both the mother and the developing fetus. During pregnancy and in the postpartum period, the mother's health is susceptible to several complications and risks, and timely detection of such risks can play a vital role in women's safety. This study proposes an approach to predict risks associated with maternal health. The first step of the approach involves utilizing principal component analysis (PCA) to extract significant features from the dataset. Following that, this study employs a stacked ensemble voting classifier which combines one machine learning and one deep learning model to achieve high performance. The performance of the proposed approach is compared to six machine learning algorithms and one deep learning algorithm. Two scenarios are considered for the experiments: one utilizing all features and the other using PCA features. By utilizing PCA-based features, the proposed model achieves an accuracy of 98.25%, precision of 99.17%, recall of 99.16%, and an F1 score of 99.16%. The effectiveness of the proposed model is further confirmed by comparing it to existing state of-the-art approaches.

Attention 3D central difference convolutional dense network for hyperspectral image classification.

Hyperspectral Images (HSI) classification is a challenging task due to a large number of spatial-spectral bands of images with high inter-similarity, extra variability classes, and complex region relationships, including overlapping and nested regions. Classification becomes a complex problem in remote sensing images like HSIs. Convolutional Neural Networks (CNNs) have gained popularity in addressing this challenge by focusing on HSI data classification. However, the performance of 2D-CNN methods heavily relies on spatial information, while 3D-CNN methods offer an alternative approach by considering both spectral and spatial information. Nonetheless, the computational complexity of 3D-CNN methods increases significantly due to the large capacity size and spectral dimensions. These methods also face difficulties in manipulating information from local intrinsic detailed patterns of feature maps and low-rank frequency feature tuning. To overcome these challenges and improve HSI classification performance, we propose an innovative approach called the Attention 3D Central Difference Convolutional Dense Network (3D-CDC Attention DenseNet). Our 3D-CDC method leverages the manipulation of local intrinsic detailed patterns in the spatial-spectral features maps, utilizing pixel-wise concatenation and spatial attention mechanism within a dense strategy to incorporate low-rank frequency features and guide the feature tuning. Experimental results on benchmark datasets such as Pavia University, Houston 2018, and Indian Pines demonstrate the superiority of our method compared to other HSI classification methods, including state-of-the-art techniques. The proposed method achieved 97.93% overall accuracy on the Houston-2018, 99.89% on Pavia University, and 99.38% on the Indian Pines dataset with the 25 × 25 window size.

Exploring the path to polio eradication: insights from consecutive seroprevalence surveys among Pakistani children.

Introduction: After trivalent oral poliovirus vaccine (tOPV) cessation, Pakistan has maintained immunity to type 2 poliovirus by administering inactivated polio vaccine (IPV) in routine immunization, alongside monovalent OPV type 2 (mOPV2) and IPV in supplementary immunization activities (SIAs). This study assesses the change in poliovirus type 2 immunity after tOPV withdrawal and due to SIAs with mOPV2 and IPV among children aged 6-11 months. Methods: Three cross-sectional sequential serological surveys were conducted in 12 polio high-risk areas of Pakistan. 25 clusters from each geographical stratum were selected utilizing probability proportional to size. Results: Seroprevalence of type 2 poliovirus was 49%, with significant variation observed among surveyed areas; <30% in Pishin, >80% in Killa Abdullah, Mardan & Swabi, and Rawalpindi. SIAs with IPV improved immunity from 38 to 57% in Karachi and 60 to 88% in Khyber. SIAs with IPV following mOPV2 improved immunity from 62 to 65% in Killa Abdullah, and combined mOPV2 and IPV SIAs in Pishin improved immunity from 28 to 89%. Results also reflected that immunity rates for serotypes 1 and 3 were consistently above 90% during all three phases and across all geographical areas. Conclusion: The study findings highlight the importance of implementing effective vaccination strategies to prevent the re-emergence of poliovirus. Moreover, the results provide crucial information for policymakers working toward achieving global polio eradication.

Comparative Effect of Seed Coating and Biopriming of Bacillus aryabhattai Z-48 on Seedling Growth, Growth Promotion, and Suppression of Fusarium Wilt Disease of Tomato Plants.

Beneficial plant microbes can enhance the growth and quality of field crops. However, the benefits of microbes using cheap and efficient inoculation methods are still uncommon. Seed coating with biocontrol agents can reduce the amount of inocula along with having the potential for large-scale application. Hence, in this research work, the comparative potential of tomato seed coating and biopriming with Bacillus aryabhattai Z-48, harboring multiple plant-beneficial traits, to suppress Fusarium wilt disease along with its beneficial effect on seedling and plant growth promotion was analyzed. Among two bacterial strains, B. aryabhattai Z-48 was able to antagonize the mycelial growth of Fusarium oxysporum f.sp. lycopersici in vitro and its application as a seed coating superiorly benefited seedling traits like the germination percentage, vigor index, and seedling growth index along with a reduced germination time. The seed coating with B. aryabhattai Z-48 resulted in significant increases in the shoot length, root length, dry biomass, and total chlorophyll contents when compared with the bioprimed seeds with the same bacterial strain and non-inoculated control plants. The seed coating with B. aryabhattai Z-48 significantly reduced the disease index (>60%) compared with the pathogen control during pot trials. Additionally, the seed coating with B. aryabhattai Z-48 resulted in a significantly higher production of total phenolics, peroxidase, polyphenol oxidase, and phenylalanine ammonia lyase enzyme in tomato plants. The GC/MS-based non-targeted metabolic profiling indicated that the seed coating with B. aryabhattai Z-48 could cause large-scale metabolite perturbations in sugars, sugar alcohols, amino acids, and organic acids to increase the fitness of tomato plants against biotic stress. Our study indicates that a tomato seed coating with B. aryabhattai Z-48 can improve tomato growth and suppress Fusarium wilt disease effectively under conventional agricultural systems.

Endophytic Fungi-Mediated Defense Signaling in Maize: Unraveling the Role of WRKY36 in Regulating Immunity against Spodoptera frugiperda.

Seed priming with beneficial endophytic fungi is an emerging sustainable strategy for enhancing plant resistance against insect pests. This study examined the effects of Beauvaria bassiana Bb20091317 and Metarhizium rileyi MrCDTLJ1 fungal colonization on maize growth, defence signalling, benzoxazinoid levels and gene expression. The colonization did not adversely affect plant growth but reduced larval weights of Spodoptera frugiperda. Maize leaves treated with M. rileyi exhibited higher levels of jasmonic acid, jasmonoyl-Isoleucine, salicylic acid, and indole acetic acid compared to control. B. bassiana and M. rileyi accelerated phytohormone increase upon S. frugiperda herbivory. Gene expression analysis revealed modulation of benzoxazinoid biosynthesis genes. We further elucidated the immune regulatory role of the transcription factor zmWRKY36 using virus-induced gene silencing (VIGS) in maize. zmWRKY36 positively regulates maize immunity against S. frugiperda, likely by interacting with defense-related proteins. Transient overexpression of zmWRKY36 in tobacco-induced cell death, while silencing in maize reduced chitin-triggered reactive oxygen species burst, confirming its immune function. Overall, B. bassiana and M. rileyi successfully colonized maize, impacting larval growth, defense signalling, and zmWRKY36-mediated resistance. This sheds light on maize-endophyte-insect interactions for sustainable plant protection.

Quantification of quercetin from red onion (Allium cepa L.) powder via high-performance liquid chromatography-ultraviolet (HPLC-UV) and its effect on hyperuricemia in male healthy Wistar albino rats.

Onions (Allium cepa L.) contain various flavonols, including quercetin, kaempferol, anthocyanin, luteolin, and myricetin. Quercetin in onions is considered the primary bioactive component. To assess the impact of quercetin on hyperuricemia in healthy Wistar albino rats, this study used high-performance liquid chromatography with ultraviolet (HPLC-UV) to identify and measure quercetin in onion powder. Twenty-four 160 ± 10 g, six wistar albino male rats in each group were kept: NC (control sample, no onion powder), OT1, OT2, and OT3, which contained 11.13, 14.84, and 18.61 g/100 g onion powder, respectively. The treatment lasted 28 days, during which the last 7 days were for urine, feces, and blood collection. The results showed a trend of decreasing levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase, total bilirubin, total cholesterol, and low-density lipoprotein in rats fed OT1, OT2, and OT3 diets. Improvements were observed in feed, water, and nutrient intake, feed conversion ratio, feed efficiency ratio, nutrient digestibility, nitrogen balance, body weight, blood urea nitrogen, creatinine, and uric acid levels (p ≤ .05). In contrast, high-density lipoprotein, triglycerides, serum total protein, neutrophils, and lymphocytes did not change (p ≥ .05). White blood cells, red blood cell count, platelet count, hemoglobin, and monocytes showed an upward trend. Based on our calculations, we determined the optimal human dosage from the most effective amount of onion powder. By taking into account the ratio of human-to-rat surface area, we estimate that the equivalent human dose of onion is 181.04 grams with 204 mg of quercetin. Additionally, when factoring in the dry matter content, the recommended dose of onion is 29.19 grams with 220 mg of quercetin.

Noval soliton solution, sensitivity and stability analysis to the fractional gKdV-ZK equation.

This work examines the fractional generalized Korteweg-de-Vries-Zakharov-Kuznetsov equation (gKdV-ZKe) by utilizing three well-known analytical methods, the modified [Formula: see text]-expansion method, [Formula: see text]-expansion method and the Kudryashov method. The gKdV-ZK equation is a nonlinear model describing the influence of magnetic field on weak ion-acoustic waves in plasma made up of cool and hot electrons. The kink, singular, anti-kink, periodic, and bright soliton solutions are observed. The effect of the fractional parameter on wave shapes have been analyzed by displaying various graphs for fractional-order values of [Formula: see text]. In addition, we utilize the Hamiltonian property to observe the stability of the attained solution and Galilean transformation for sensitivity analysis. The suggested methods can also be utilized to evaluate the nonlinear models that are being developed in a variety of scientific and technological fields, such as plasma physics. Findings show the effectiveness simplicity, and generalizability of the chosen computational approach, even when applied to complex models.

White blood cells classification using multi-fold pre-processing and optimized CNN model.

White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.

Contradiction in text review and apps rating: prediction using textual features and transfer learning.

Mobile app stores, such as Google Play, have become famous platforms for practically all types of software and services for mobile phone users. Users may browse and download apps via app stores, which also help developers monitor their apps by allowing users to rate and review them. App reviews may contain the user's experience, bug details, requests for additional features, or a textual rating of the app. These ratings can be frequently biased due to inadequate votes. However, there are significant discrepancies between the numerical ratings and the user reviews. This study uses a transfer learning approach to predict the numerical ratings of Google apps. It benefits from user-provided numeric ratings of apps as the training data and provides authentic ratings of mobile apps by analyzing users' reviews. A transfer learning-based model ELMo is proposed for this purpose which is based on the word vector feature representation technique. The performance of the proposed model is compared with three other transfer learning and five machine learning models. The dataset is scrapped from the Google Play store which extracts the data from 14 different categories of apps. First, biased and unbiased user rating is segregated using TextBlob analysis to formulate the ground truth, and then classifiers prediction accuracy is evaluated. Results demonstrate that the ELMo classifier has a high potential to predict authentic numeric ratings with user actual reviews.

Widespread incomplete lineage sorting and introgression shaped adaptive radiation in the Gossypium genus.

Cotton (Gossypium) stands as a crucial economic crop, serving as the primary source of natural fiber for the textile sector. However, the evolutionary mechanisms driving speciation within the Gossypium genus remain unresolved. In this investigation, we leveraged 25 Gossypium genomes and introduced four novel assemblies-G. harknessii, G. gossypioides, G. trilobum, and G. klotzschianum (Gklo)-to delve into the speciation history of this genus. Notably, we encountered intricate phylogenies potentially stemming from introgression. These complexities are further compounded by incomplete lineage sorting (ILS), a factor likely to have been instrumental in shaping the swift diversification of cotton. Our focus subsequently shifted to the rapid radiation episode during a concise period in Gossypium evolution. For a recently diverged lineage comprising G. davidsonii, Gklo, and G. raimondii, we constructed a finely detailed ILS map. Intriguingly, this analysis revealed the non-random distribution of ILS regions across the reference Gklo genome. Moreover, we identified signs of robust natural selection influencing specific ILS regions. Noteworthy variations pertaining to speciation emerged between the closely related sister species Gklo and G. davidsonii. Approximately 15.74% of speciation structural variation genes and 12.04% of speciation-associated genes were estimated to intersect with ILS signatures. These findings enrich our understanding of the role of ILS in adaptive radiation, shedding fresh light on the intricate speciation history of the Gossypium genus.

An Amiable Design of Cobalt Single Atoms as the Active Sites for Oxygen Evolution Reaction in Desalinated Seawater.

Green fuel from water splitting is hardcore for future generations, and the limited source of fresh water (<1%) is a bottleneck. Seawater cannot be used directly as a feedstock in current electrolyzer techniques. Until now single atom catalysts were reported by many synthetic strategies using notorious chemicals and harsh conditions. A cobalt single-atom (CoSA) intruding cobalt oxide ultrasmall nanoparticle (Co3 O4 USNP)-intercalated porous carbon (PC) (CoSA-Co3 O4 @PC) electrocatalyst was synthesized from the waste orange peel as a single feedstock (solvent/template). The extended X-ray absorption fine structure spectroscopy (EXAFS) and theoretical fitting reveal a clear picture of the coordination environment of the CoSA sites (CoSA-Co3 O4 and CoSA-N4 in PC). To impede the direct seawater corrosion and chlorine evolution the seawater has been desalinated (Dseawater) with minimal cost and the obtained PC is used as an adsorbent in this process. CoSA-Co3 O4 @PC shows high oxygen evolution reaction (OER) activity in transitional metal impurity-free (TMIF) 1 M KOH and alkaline Dseawater. CoSA-Co3 O4 @PC exhibits mass activity that is 15 times higher than the commercial RuO2 . Theoretical interpretations suggest that the optimized CoSA sites in Co3 O4 USNPs reduce the energy barrier for alkaline water dissociation and simultaneously trigger an excellent OER followed by an adsorbate evolution mechanism (AEM).

Determinants of immunization in polio super high-risk union councils of Pakistan.

BACKGROUND: The current polio epidemiology in Pakistan poses a unique challenge for global eradication as the country is affected by ongoing endemic poliovirus transmission. Across the country, 40 union councils (UCs) which serve as core reservoirs for poliovirus with continuous incidences of polio cases are categorized as super-high-risk union councils (SHRUCs). METHODOLOGY: A cross-sectional survey was conducted in 39 SHRUCs using a two-stage stratified cluster sampling technique. 6,976 children aged 12-23 months were covered. A structured questionnaire was used for data collection. Data were analyzed using STATA version 17. RESULTS: Based on both vaccination records and recall, 48.3% of children were fully-, 35.4 % were partially-, and 16.3% were non-vaccinated in the SHRUC districts. A child is considered fully vaccinated when h/she completed vaccination for BCG, OPV0, OPV 1-3, Penta 1-3, PCV 1-3, IPV, and MCV1. Vaccination cards were seen for over half of the children in the SHRUC districts of Khyber Pakhtunkhwa (KP) and the majority of the SHRUC districts in Sindh, except for the SHRUC district of Malir the districts of Balochistan. Results for polio vacancies show that 60.9% of children from the SHRUC districts were vaccinated with at least three doses of OPV and one dose of IPV, while 20.4% were vaccinated with any OPV doses or IPV and 18.7% of children did not receive any polio vaccines. The dropout rate between vaccine visits was higher than the WHO-recommended cutoff point of 10% for all vaccine doses in the SHRUC districts. The likelihood of being fully vaccinated was higher among the children of educated parents. Full vaccination was found significant among the children of any SHRUC districts compared to district Killa Abdullah. CONCLUSION: Context-specific strategies with more focus on community engagement and targeted mobilization, along with robust monitoring mechanisms, would help address the underlying challenges of under-immunization in the SHRUCs.

Skeletal age evaluation using hand X-rays to determine growth problems.

A common clinical method for identifying anomalies in bone growth in infants and newborns is skeletal age estimation with X-ray images. Children's bone abnormalities can result from several conditions including wounds, infections, or tumors. One of the most frequent reasons for bone issues is that most youngsters are affected by the slow displacement of bones caused by pressure applied to the growth plates as youngsters develop. The growth plate can be harmed by a lack of blood supply, separation from other parts of the bone, or slight misalignment. Problems with the growth plate prevent bones from developing, cause joint distortion, and may cause permanent joint injury. A significant discrepancy between the chronological and assessed ages may indicate a growth problem because determining bone age represents the real level of growth. Therefore, skeletal age estimation is performed to look for endocrine disorders, genetic problems, and growth anomalies. To address the bone age assessment challenge, this study uses the Radiological Society of North America's Pediatric Bone Age Challenge dataset which contains 12,600 radiological images of the left hand of a patient that includes the gender and bone age information. A bone age evaluation system based on the hand skeleton guidelines is proposed in this study for the detection of hand bone maturation. The proposed approach is based on a customized convolutional neural network. For the calculation of the skeletal age, different data augmentation techniques are used; these techniques not only increase the dataset size but also impact the training of the model. The performance of the model is assessed against the Visual Geometry Group (VGG) model. Results demonstrate that the customized convolutional neural network (CNN) model outperforms the VGG model with 97% accuracy.

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic.

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (IoT) provides data transmission without human intervention and thus mitigates infection chances. A road map is discussed in this study regarding the role of IoT applications to combat COVID-19. In addition, a real-time solution is provided to identify and monitor COVID-19 patients. The proposed framework comprises data collection using IoT-based devices, a health or quarantine center, a data warehouse for artificial intelligence (AI)-based analysis, and healthcare professionals to provide treatment. The efficacy of several machine learning models is also analyzed for the prediction of the severity level of COVID-19 patients using real-time IoT data and a dataset named 'COVID Symptoms Checker'. The proposed ensemble model combines random forest and extra tree classifiers using a soft voting criterion and achieves superior results with a 0.922 accuracy score. The use of IoT applications is found to support medical professionals in investigating the features of the contagious disease and support managing the COVID pandemic more efficiently.

Insect multimeric G-quadruplexes fold into antiparallel structures of different compactness and stability in K+ and Na+ solutions.

Human telomere sequences (TTAGGG)n fold into G-quadruplexes with different conformations in K+ and Na+ solutions, which are highlighted for their potential as antitumor drug targets. Moreover, human multimeric G-quadruplexes have been broadly studied potentially for screening ligands with higher selectivity than monomeric G-quadruplexes. Most insects have telomeres consisting of pentanucleotide (TTAGG) repeats, which fold into an antiparallel structured G-quadruplex with a two-layer G-planar in a K+ solution. However, the structure of insect telomeric G-quadruplexes in Na+ solutions and their higher-order structures have not been explored. The quinoline derivative BMPQ-1 has been reported to bind human multimeric G-quadruplex. This study compared the stability and compactness of insect monomeric and multimeric G-quadruplex structures in K+ and Na+ solutions and further validated the interaction between BMPQ-1 and insect multimeric G-quadruplexes. Circular dichroism (CD) spectral scanning analysis revealed that although the insect telomeric G-quadruplex folds into an antiparallel structure in both K+ and Na+ solutions, all the insect telomeric G-quadruplexes are more stable in Na+ solutions. Fluorescence resonance energy transfer (FRET) analysis indicated insect telomeric G-quadruplexes have a more compact structure in Na+ solutions. BMPQ-1 exhibited higher selectivity for insect multimeric G-quadruplex Bom37 than monomeric G-quadruplex Bom17, and had a different binding pattern to Bom37 G-quadruplex in K+ and Na+ solutions. Finally, BMPQ-1 was found to have a significant inhibitory effect on the proliferation of pest cells. This study contributes to our comprehensive understanding of insect telomeric G-quadruplexes.

Breast cancer detection employing stacked ensemble model with convolutional features.

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI.

The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.

Effects of different levels of dried onion powder on nutrient digestibility, biochemical parameters, and nitrogen balance in Wistar albino rats with induced hyperuricemia.

Introduction: Onions (Allium cepa L.) are excellent sources of bioactive compounds and phytochemicals such as allicin, quercetin, fisetin, and other sulfurous compounds. Therefore, our study aimed to investigate the effects of dried onion powder on growth performance, nitrogen balance, and biochemical parameters in Wistar albino rats with induced hyperuricemia. Methods: A total of 24 rats were randomly divided into four groups, with six in each group: HU (positive control) and HOT1, HOT2, and HOT3 groups, which received a diet containing onion powder at concentrations of 11.13, 14.84, and 18.61 g/100 g, respectively. Hyperuricemia was induced in rats by administering a new formulation intraperitoneally (250 mg/kg potassium oxonate) and orally (40 mg/kg potassium bromate) daily for 14 days. After confirmation of hyperuricemia induction, rats were fed with onion-treated diets with various concentrations of quercetin for 21 days. Results: Significant decreases (p ≤ 0.05) in serum uric acid, alanine aminotransferase, aspartate aminotransferase, alkaline phosphatase, total bilirubin, total cholesterol, and low-density lipoprotein were observed. An increasing trend (p ≤ 0.05) in the levels of hemoglobin (Hb), white blood cell (WBC), red blood cell (RBC), and platelet count was observed. An improvement in the levels of serum high-density lipoprotein, triglycerides, blood urea nitrogen, serum creatinine, serum total protein and neutrophils, lymphocytes, and monocytes was observed. A positive progress (p ≤ 0.05) was observed in growth performance and nutrient digestibility. Conclusion: In conclusion, a significantly lower uric acid level was observed in rats fed with HOT2 diet. Based on the ratio of the surface area (human/rat), the best recommended dose of onion for the incidence and prevention of hyperuricemia is 189.95 g, corresponding to the dose of 204 mg/day of quercetin in humans.

Decoding the guardians of cotton resilience: A comprehensive exploration of the ßCA genes and its role in Verticillium dahliae resistance.

Plant Carbonic anhydrases (Cas) have been shown to be stress-responsive enzymes that may play a role in adapting to adverse conditions. Cotton is a significant economic crop in China, with upland cotton (Gossypium hirsutum) being the most widely cultivated species. We conducted genome-wide identification of the ßCA gene in six cotton species and preliminary analysis of the ßCA gene in upland cotton. In total, 73 ßCA genes from six cotton species were identified, with phylogenetic analysis dividing them into five subgroups. GHßCA proteins were predominantly localized in the chloroplast and cytoplasm. The genes exhibited conserved motifs, with motifs 1, 2, and 3 being prominent. GHßCA genes were unevenly distributed across chromosomes and were associated with stress-responsive cis-regulatory elements, including those responding to light, MeJA, salicylic acid, abscisic acid, cell cycle regulation, and defence/stress. Expression analysis indicated that GHßCA6, GHßCA7, GHßCA10, GHßCA15, and GHßCA16 were highly expressed under various abiotic stress conditions, whereas GHßCA3, GHßCA9, GHßCA10, and GHßCA18 had higher expression patterns under Verticillium dahliae infection at different time intervals. In Gossypium thurberi, GthßCA1, GthßCA2, and GthßCA4 showed elevated expression across stress conditions and tissues. Silencing GHßCA10 through VIGS increased Verticillium wilt severity and reduced lignin deposition compared to non-silenced plants. GHßCA10 is crucial for cotton's defense against Verticillium dahliae. Further research is needed to understand the underlying mechanisms and develop strategies to enhance resistance against Verticillium wilt.

Transcriptomic analysis reveals the beneficial effects of salt priming on enhancing defense responses in upland cotton under successive salt stress.

Priming-mediated stress tolerance in plants stimulates defense mechanisms and enables plants to cope with future stresses. Seed priming has been proven effective for tolerance against abiotic stresses; however, underlying genetic mechanisms are still unknown. We aimed to assess upland cotton genotypes and their transcriptional behaviors under salt priming and successive induced salt stress. We pre-selected 16 genotypes based on previous studies and performed morpho-physiological characterization, from which we selected three genotypes, representing different tolerance levels, for transcriptomic analysis. We subjected these genotypes to four different treatments: salt priming (P0), salt priming with salinity dose at 3-true-leaf stage (PD), salinity dose at 3-true-leaf stage without salt priming (0D), and control (CK). Although the three genotypes displayed distinct expression patterns, we identified common differentially expressed genes (DEGs) under PD enriched in pathways related to transferase activity, terpene synthase activity, lipid biosynthesis, and regulation of acquired resistance, indicating the beneficial role of salt priming in enhancing salt stress resistance. Moreover, the number of unique DEGs associated with G. hirsutum purpurascens was significantly higher compared to other genotypes. Coexpression network analysis identified 16 hub genes involved in cell wall biogenesis, glucan metabolic processes, and ribosomal RNA binding. Functional characterization of XTH6 (XYLOGLUCAN ENDOTRANSGLUCOSYLASE/HYDROLASE) using virus-induced gene silencing revealed that suppressing its expression improves plant growth under salt stress. Overall, findings provide insights into the regulation of candidate genes in response to salt stress and the beneficial effects of salt priming on enhancing defense responses in upland cotton.