Composite superplastic aerogel scaffolds containing dopamine and bioactive glass-based fibers for skin and bone tissue regeneration

Multifunctional bioactive biomaterials with integrated bone and soft tissue regenerability hold great promise for the regeneration of trauma-affected skin and bone defects. The aim of this research was to fabricate aerogel scaffolds (GD-BF) by blending the appropriate proportions of short bioactive glass fiber (BGF), gelatin (Gel), and dopamine (DA). Electrospun polyvinyl pyrrolidone (PVP)-BGF fibers were converted into short BGF through calcination and homogenization. Microporous GD-BF scaffolds displayed good elastic deformation recovery and promoted neo-tissue formation. The DA could enable thermal crosslinking and enhance the mechanical properties and structural stability of the GD-BF scaffolds. The BGF-mediated release of therapeutic ions shorten hemostatic time (<30 s) in a rat tail amputation model and a rabbit artery injury model alongside inducing the regeneration of skin appendages (e.g., blood vessels, glands, etc.) in a full-thickness excisional defect model in rats (percentage wound closure: GD-BF2, 98 % vs. control group, 83 %) at day 14 in vitro. Taken together, these aerogel scaffolds may have significant promise for soft and hard tissue repair, which may also be worthy for the other related disciplines.

Quantification and polymeric characterization of microplastics in composts and their accumulation in lettuce

Organic fertilizers have become a vector for the transport of microplastics (MPs), which pose human health concerns through the food chain. This study aimed to quantify and characterize MPs in eight different compost samples of various raw materials and their subsequent translocation to lettuce (Lacuta sativa) grown on contaminated composts. The results revealed that the MP abundance ranged from 3810 to 16530 MP/kg. Municipal solid waste compost (MSWC) had highest abundance (16082 ± 632 MP/kg), followed by leaf compost (LC) and organic compost (OC) (6299 ± 1011 and 3680 ± 419 MP/kg, respectively). MPs of <100 μm in size were most dominant in MSWC and LC. Fragments and fibers were the prevalent shape types, with white/transparent colored MPs being more abundant. Polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) were the dominant polymers. MPs accumulation in the lettuce leaves was greatest in the lettuce plants grown on MSWC, followed by those grown on LC and OC, indicating that MSWC grown lettuce is not suitable for human consumption. The decrease in the growth (leaf length, number of leaves, leaf fresh and weights) and physiological (membrane stability index, relative water contents) parameters of lettuce was in line with the trend of MP accumulations. Hence, it is highly important to regulate the plastic contents in compost because it is a threat to ecosystems and human health.

Integrative metagenomic dissection of last-resort antibiotic resistance genes and mobile genetic elements in hospital wastewaters

Hospital wastewater is a critical source of antimicrobial resistance (AMR), which facilitates the proliferation and spread of clinically significant antimicrobial resistance genes (ARGs) and pathogenic bacteria. This study utilized metagenomic approaches, including advanced binning techniques, such as MetaBAT2, MaxBin2, and CONCOCT, which offer significant improvements in accuracy and completeness over traditional binning methods. These methods were used to comprehensively assess the dynamics and composition of resistomes and mobilomes in untreated wastewater samples taken from two general hospitals and one cancer hospital. This study revealed a diverse bacterial landscape, largely consisting of Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, with notable variations in microbial composition among hospitals. Analysis of the top 15 genera showed unique microbial pattern distribution in each hospital: Aeromonas was predominant in 1stHWTS (49.39 %), Acidovorax in the CAHWTS at 16.85 %, and Escherichia and Bacteroides in the 2ndHWTS at 11.44 % and 11.33 %, respectively. A total of 114 pathogenic bacteria were identified, with drug-resistant Aeromonas caviae and Escherichia coli being the most prevalent. The study identified 34 types and 1660 subtypes of ARGs, including important last-resort antibiotic resistance genes (LARGs), such as blaNDM, mcr, and tet(X). Using metagenomic binning, this study uncovered distinct patterns of host-resistance associations, particularly with Proteobacteria and Firmicutes. Network analysis highlighted the complex interactions among ARGs, mobile genetic elements (MGEs), and bacterial species, all contributing to the dissemination of AMR. These findings emphasize the intricate nature of AMR in hospital wastewater and the influence of hospital-specific factors on microbial resistance patterns. This study provides support for implementing integrated management strategies, including robust surveillance, advanced wastewater treatment, and strict antibiotic stewardship, to control the dissemination of AMR. Understanding the interplay among bacterial communities, ARGs, and MGEs is important for developing effective public health measures against AMR.

Genome-wide analysis and identification of Carotenoid Cleavage Oxygenase (CCO) gene family in coffee (coffee arabica) under abiotic stress

The coffee industry holds importance, providing livelihoods for millions of farmers globally and playing a vital role in the economies of coffee-producing countries. Environmental conditions such as drought and temperature fluctuations can adversely affect the quality and yield of coffee crops.Carotenoid cleavage oxygenases (CCO) enzymes are essential for coffee plants as they help break down carotenoids contributing to growth and stress resistance. However, knowledge about the CCO gene family in Coffee arabica was limited. In this study identified 21 CCO genes in Coffee arabica (C. arabica) revealing two subfamilies carotenoid cleavage dioxygenases (CCDs) and 9-cis-epoxy carotenoid dioxygenases (NCED) through phylogenic analysis. These subfamilies exhibited distribution patterns in terms of gene structure, domains, and motifs. The 21 CaCCO genes, comprising 5 NCED and 16 CCD genes were found across chromosomes. Promoter sequencing analysis revealed cis-elements that likely interact with plant stress-responsive, growth-related, and phytohormones, like auxin and abscisic acid. A comprehensive genome-wide comparison, between C. arabica and A. thaliana was conducted to understand the characteristics of CCO genes. RTqPCR data indicated that CaNCED5, CaNCED6, CaNCED12, and CaNCED20 are target genes involved in the growth of drought coffee plants leading to increased crop yield, in a conditions, with limited water availability. This reveals the role of coffee CCOs in responding to abiotic stress and identifies potential genes useful for breeding stress-resistant coffee varieties.

Multi-Resolution Wavelet Fractal Analysis and Subtask Training for Enhancing Few-Shot Noisy Brainwave Recognition

The integration of healthcare monitoring with Internet of Things (IoT) networks radically transforms the management and monitoring of human well-being. Portable and lightweight electroencephalography (EEG) systems with fewer electrodes have improved convenience and flexibility while retaining adequate accuracy. However, challenges emerge when dealing with real-time EEG data from IoT devices due to the presence of noisy samples, which impedes improvements in brainwave detection accuracy. Moreover, high inter-subject variability and substantial variability in EEG signals present difficulties for conventional data augmentation and subtask learning techniques, leading to poor generalizability. To address these issues, we present a novel framework for enhancing EEG-based recognition through multi-resolution data analysis, capturing features at different scales using wavelet fractals. The original data can be expanded many times after continuous wavelet transform (CWT) and recombination, alleviating insufficient training samples. In the transfer stage of deep learning (DL) models, we adopt a subtask learning approach to train the recognition model to generalize efficiently. This incorporates wavelets at various scales instead of exclusively considering average prediction performance across scales and paradigms. Through extensive experiments, we demonstrate that our proposed DL-based method excels at extracting features from small-scale and noisy EEG data. This significantly improves healthcare monitoring performance by mitigating the impact of noise introduced by the external environment.

Advances in biodegradable materials: Degradation mechanisms, mechanical properties, and biocompatibility for orthopedic applications

Mg-based and Zn-based biodegradable materials have the potential to become the next-generation implant materials to treat bone diseases, because of their desired degradation and mechanical properties. This article reviews the status of these implant materials. The required properties of biodegradable materials such as biodegradability, mechanical properties, and biocompatibility for performance evaluation were briefly discussed. The influence of fabrication techniques, microstructure, alloying elements, and post-processing techniques on the properties of Mg and Zn-based materials was addressed. The degradation mechanism by dissolution, oxidation, and interaction with human body cells was discussed. The biocompatibility of Mg and Zn-based biodegradable materials was analyzed. The significance of in vitro and in vivo biocompatibility testing was highlighted, emphasizing the superiority of in vivo results over cell line studies. This article identifies the many Mg and Zn-based biodegradable materials and summarizes the key findings.

Mesoporous Silicon with Strontium-Powered Poly(Lactic-Co-Glycolic acid)/Gelatin-Based Dressings Facilitate Skin Tissue Repair

Purpose

Functional inorganic nanomaterials (NMs) are widely exploited as bioactive materials and drug depots. The lack of a stable form of application of NMs at the site of skin injury, may impede the removal of the debridement, elevate pH, induce tissue toxicity, and limit their use in skin repair. This necessitates the advent of innovative wound dressings that overcome the above limitations. The overarching objective of this study was to exploit strontium-doped mesoporous silicon particles (PSiSr) to impart multifunctionality to poly(lactic-co-glycolic acid)/gelatin (PG)-based fibrous dressings (PG@PSiSr) for excisional wound management.

Methods

Mesoporous silicon particles (PSi) and PSiSr were synthesized using a chemo-synthetic approach. Both PSi and PSiSr were incorporated into PG fibers using electrospinning. A series of structure, morphology, pore size distribution, and cumulative pH studies on the PG@PSi and PG@PSiSr membranes were performed. Cytocompatibility, hemocompatibility, transwell migration, scratch wound healing, and delineated angiogenic properties of these composite dressings were tested in vitro. The biocompatibility of composite dressings in vivo was assessed by a subcutaneous implantation model of rats, while their potential for wound healing was discerned by implantation in a full-thickness excisional defect model of rats.

Results

The PG@PSiSr membranes can afford the sustained release of silicon ions (Si4+) and strontium ions (Sr2+) for up to 192 h as well as remarkably promote human umbilical vein endothelial cells (HUVECs) and NIH-3T3 fibroblasts migration. The PG@PSiSr membranes also showed better cytocompatibility, hemocompatibility, and significant formation of tubule-like networks of HUVECs in vitro. Moreover, PG@PSiSr membranes also facilitated the infiltration of host cells and promoted the deposition of collagen while reducing the accumulation of inflammatory cells in a subcutaneous implantation model in rats as assessed for up to day 14. Further evaluation of membranes transplanted in a full-thickness excisional wound model in rats showed rapid wound closure (PG@SiSr vs control, 96.1% vs 71.7%), re-epithelialization, and less inflammatory response alongside skin appendages formation (eg, blood vessels, glands, hair follicles, etc.).

Conclusion

To sum up, we successfully fabricated PSiSr particles and prepared PG@PSiSr dressings using electrospinning. The PSiSr-mediated release of therapeutic ions, such as Si4+ and Sr2+, may improve the functionality of PLGA/Gel dressings for an effective wound repair, which may also have implications for the other soft tissue repair disciplines.

Droplet Microfluidics Powered Hydrogel Microparticles for Stem Cell-Mediated Biomedical Applications

Stem cell-related therapeutic technologies have garnered significant attention of the research community for their multi-faceted applications. To promote the therapeutic effects of stem cells, the strategies for cell microencapsulation in hydrogel microparticles have been widely explored, as the hydrogel microparticles have the potential to facilitate oxygen diffusion and nutrient transport alongside their ability to promote crucial cell-cell and cell-matrix interactions. Despite their significant promise, there is an acute shortage of automated, standardized, and reproducible platforms to further stem cell-related research. Microfluidics offers an intriguing platform to produce stem cell-laden hydrogel microparticles (SCHMs) owing to its ability to manipulate the fluids at the micrometer scale as well as precisely control the structure and composition of microparticles. In this review, the typical biomaterials and crosslinking methods for microfluidic encapsulation of stem cells as well as the progress in droplet-based microfluidics for the fabrication of SCHMs are outlined. Moreover, the important biomedical applications of SCHMs are highlighted, including regenerative medicine, tissue engineering, scale-up production of stem cells, and microenvironmental simulation for fundamental cell studies. Overall, microfluidics holds tremendous potential for enabling the production of diverse hydrogel microparticles and is worthy for various stem cell-related biomedical applications.

Exploring the mechanism of action of spirooxindoles as a class of CDK2 inhibitors: a structure-based computational approach

Cyclin-dependent kinase 2 (CDK2) regulates cell cycle checkpoints in the synthesis and mitosis phases and plays a pivotal role in cancerous cell proliferation. The activation of CDK2, influenced by various protein signaling pathways, initiates the phosphorylation process. Due to its crucial role in carcinogenesis, CDK2 is a druggable hotspot target to suppress cancer cell proliferation. In this context, several studies have identified spirooxindoles as an effective class of CDK2 inhibitors. In the present study, three spirooxindoles (SOI1, SOI2, and SOI3) were studied to understand their inhibitory mechanism against CDK2 through a structure-based approach. Molecular docking and molecular dynamics (MD) simulations were performed to explore their interactions with CDK2 at the molecular level. The calculated binding free energy for the spirooxindole-based CDK2 inhibitors aligned well with experimental results regarding CDK2 inhibition. Energy decomposition (ED) analysis identified key binding residues, including I10, G11, T14, R36, F82, K89, L134, P155, T158, Y159, and T160, in the CDK2 active site and T-loop phosphorylation. Molecular mechanics (MM) energy was identified as the primary contributor to stabilizing inhibitor binding in the CDK2 protein structure. Furthermore, the analysis of binding affinity revealed that the inhibitor SOI1 binds more strongly to CDK2 compared to the other inhibitors under investigation. It demonstrated a robust interaction with the crucial residue T160 in the T-loop phosphorylation site, responsible for kinase activation. These insights into the inhibitory mechanism are anticipated to contribute to the development of potential CDK2 inhibitors using the spirooxindole scaffold.

Uncovering PPAR-γ agonists: An integrated computational approach driven by machine learning

Peroxisome proliferator-activated receptor gamma (PPAR-γ) serves as a nuclear receptor with a pivotal function in governing diverse facets of metabolic processes. In diabetes, the prime physiological role of PPAR-γ is to enhance insulin sensitivity and regulate glucose metabolism. Although PPAR-γ agonists such as Thiazolidinediones are effective in addressing diabetes complications, it is vital to be mindful that they are associated with substantial side effects that could potentially give rise to health challenges. The recent surge in the discovery of selective modulators of PPAR-γ inspired us to formulate an integrated computational strategy by leveraging the promising capabilities of both machine learning and in silico drug design approaches. In pursuit of our objectives, the initial stage of our work involved constructing an advanced machine learning classification model, which was trained utilizing chemical information and physicochemical descriptors obtained from known PPAR-γ modulators. The subsequent application of machine learning-based virtual screening, using a library of 31,750 compounds, allowed us to identify 68 compounds having suitable characteristics for further investigation. A total of four compounds were identified and the most favorable configurations were complemented with docking scores ranging from -8.0 to -9.1 kcal/mol. Additionally, the compounds engaged in hydrogen bond interactions with essential conserved residues including His323, Leu330, Phe363, His449 and Tyr473 that describe the ligand binding site. The stability indices investigated herein for instance root-mean-square fluctuations in the backbone atoms indicated higher mobility in the region of orthosteric site in the presence of agonist with the deviation peaks in the range of 0.07-0.69 nm, signifying moderate conformational changes. The deviations at global level revealed that the average values lie in the range of 0.25-0.32 nm. In conclusion, our identified hits particularly, CHEMBL-3185642 and CHEMBL-3554847 presented outstanding results and highlighted the stable conformation within the orthosteric site of PPAR-γ to positively modulate the activity.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

Composite Aerogel Scaffolds Containing Flexible Silica Nanofiber and Tricalcium Phosphate Enable Skin Regeneration

Poor hemostatic ability and less vascularization at the injury site could hinder wound healing as well as adversely affect the quality of life (QOL). An ideal wound dressing should exhibit certain characteristics: (a) good hemostatic ability, (b) rapid wound healing, and (c) skin appendage formation. This necessitates the advent of innovative dressings to facilitate skin regeneration. Therapeutic ions, such as silicon ions (Si4+) and calcium ions (Ca2+), have been shown to assist in wound repair. The Si4+ released from silica (SiO2) can upregulate the expression of proteins, including the vascular endothelial growth factor (VEGF) and alpha smooth muscle actin (α-SMA), which is conducive to vascularization; Ca2+ released from tricalcium phosphate (TCP) can promote the coagulation alongside upregulating the expression of cell migration and cell differentiation related proteins, thereby facilitating the wound repair. The overarching objective of this study was to exploit short SiO2 nanofibers along with the TCP to prepare TCPx@SSF aerogels and assess their wound healing ability. Short SiO2 nanofibers were prepared by electrospinning and blended with varying proportions of TCP to afford TCPx@SSF aerogel scaffolds. The TCPx@SSF aerogels exhibited good cytocompatibility in a subcutaneous implantation model and manifested a rapid hemostatic effect (hemostatic time 75 s) in a liver trauma model in the rabbit. These aerogel scaffolds also promoted skin regeneration and exhibited rapid wound closure, epithelial tissue regeneration, and collagen deposition. Taken together, TCPx@SSF aerogels may be valuable for wound healing.

Temporal changes in the levels of virus and betasatellite DNA in B. tabaci feeding on CLCuD affected cotton during the growing season

Cotton, a key source of income for Pakistan, has suffered significantly by cotton leaf curl disease (CLCuD) since 1990. This disease is caused by a complex of phylogenetically-related begomovirus (genus Begomovirus, family Geminiviridae) species and a specific betasatellite (genus Betasatellite, family Tolecusatellitidae), cotton leaf curl Multan betasatellite. Additionally, another DNA satellite called alphasatellite (family Alphasatellitidae), is also frequently associated. All these virus components are vectored by a single species of whitefly (Bemisia tabaci). While many factors affect cotton productivity, including cotton variety, sowing time, and environmental cues such as temperature, humidity, and rainfall, CLCuD is a major biotic constraint. Although the understanding of begomoviruses transmission by whiteflies has advanced significantly over the past three decades, however, the in-field seasonal dynamics of the viruses in the insect vector remained an enigma. This study aimed to assess the levels of virus and betasatellite in whiteflies collected from cotton plants throughout the cotton growing season from 2014 to 2016. Notably, begomovirus levels showed no consistent pattern, with minimal variations, ranging from 0.0017 to 0.0074 ng.μg-1 of the genomic DNA in 2014, 0.0356 to 0.113 ng.μg-1 of the genomic DNA in 2015, and 0.0517 to 0.0791 ng.μg-1 of the genomic DNA in 2016. However, betasatellite levels exhibited a distinct pattern. During 2014 and 2015, it steadily increased throughout the sampling period (May to September). While 2016 showed a similar trend from the start of sampling (July) to September but a decline in October (end of sampling). Such a study has not been conducted previously, and could potentially provide valuable insights about the epidemiology of the virus complex causing CLCuD and possible means of controlling losses due to it.

Genome wide analysis of carotenoid cleavage oxygenases (CCO) gene family in Arachis hypogaea (peanut) under biotic stress

Carotenoid cleavage oxygenases (CCOs) enzymes play a vital role in plant growth and development through the synthesis of apocarotenoids and their derivative. These chemicals are necessary for flower and fruit coloration, as well as the manufacture of plant hormones such as abscisic acid (ABA) and strigolactones, which control a variety of physiological processes. The CCOs gene family has not been characterized in Arachis hypogaea. Genome mining of A. hypogaea identifies 24 AhCCO gene members. The AhCCO gene family was divided into two subgroups based on the recent study of the Arabidopsis thaliana CCO gene family classification system. Twenty-three AhCCO genes, constituting 95.8% of the total, were regulated by 29 miRNAs, underscoring the significance of microRNAs (miRNAs) in governing gene expression in peanuts. AhCCD19 is the only gene that lacks a miRNA target site. The physicochemical characteristics of CCO genes and their molecular weights and isoelectric points were studied further. The genes were then characterized regarding chromosomal distribution, structure, and promoter cis-elements. Light, stress development, drought stress, and hormone responsiveness were discovered to be associated with AhCCO genes, which can be utilized in developing more resilient crops. The investigation also showed the cellular location of the encoded proteins and discovered that the peanut carotenoid oxygenase gene family's expansion was most likely the result of tandem, segmental, and whole-genome duplication events. The localization expresses the abundance of genes mostly in the cytoplasm and chloroplast. Expression analysis shows that AhCCD7 and AhCCD14 genes show the maximum expression in the apical meristem, lateral leaf, and pentafoliate leaf development, while AhNCED9 and AhNCED13 express in response to Aspergillus flavus resistance. This knowledge throws light on the evolutionary history of the AhCCO gene family and may help researchers better understand the molecular processes behind gene duplication occurrences in plants. An integrated synteny study was used to find orthologous carotenoid oxygenase genes in A. hypogaea, whereas Arabidopsis thaliana and Beta vulgaris were used as references for the functional characterization of peanut CCO genes. These studies provide a foundation for future research on the regulation and functions of this gene family. This information provides valuable insights into the genetic regulation of AhCCO genes. This technology could create molecular markers for breeding programs to develop new peanut lines.

Coupling of phycoremediation and phytoremediation technologies to treat tannery effluents with rainwater dilutions

From tannery effluent (TE) severely polluted with heavy metals (HMs viz., Cr, Cu, Cd, and Pb), hydrophytic phytoextraction remains a challenge as transplanted plants succumb to death on facing acclimatization shock. Current study was aimed at diluting TE with harvested rainwater (HR) for improving HM phytoextraction potential of Phragmites australis (a hydrophyte) assisted with phycoremediation of coupled algae (viz., Oedogonium sp. and Pithophora sp.). The TE:HR dilutions (TEDs) 0, 25, 50, 75, and 100% (v/v) included three sets: set-1 included algae only, set-2 included P. australis only and set-3 included P. australis coupled with combined algal inoculum. Results showed that P. australis assisted with HR dilution and combined algal inoculum showed significantly greater uptake of HMs from each of the TEDs than respective control treatments. Combined algal application in the TEDs proved phycoremediation assistants based on their bioaccumulation factor (BF). The dry biomass of P. australis in TEDs applied with phycoremediation assistants remained greater than uninoculated ones. Overall, HM translocation factor (TF) of P. australis for Cr, Cu, Cd, and Pb remained ≥ 1. The study concludes that HM phytoextraction is substantially increased when concentrated TE is diluted with HR and assisted with phycoremediation of HM tolerant algae.

Genetics aspect of vitamin C (Ascorbic Acid) biosynthesis and signaling pathways in fruits and vegetables crops

Vitamin C, also known as ascorbic acid, is an essential nutrient that plays a critical role in many physiological processes in plants and animals. In humans, vitamin C is an antioxidant, reducing agent, and cofactor in diverse chemical processes. The established role of vitamin C as an antioxidant in plants is well recognized. It neutralizes reactive oxygen species (ROS) that can cause damage to cells. Also, it plays an important role in recycling other antioxidants, such as vitamin E, which helps maintain the overall balance of the plant's antioxidant system. However, unlike plants, humans cannot synthesize ascorbic acid or vitamin C in their bodies due to the absence of an enzyme called gulonolactone oxidase. This is why humans need to obtain vitamin C through their diet. Different fruits and vegetables contain varying levels of vitamin C. The biosynthesis of vitamin C in plants occurs primarily in the chloroplasts and the endoplasmic reticulum (ER). The biosynthesis of vitamin C is a complex process regulated by various factors such as light, temperature, and plant hormones. Recent research has identified several key genes that regulate vitamin C biosynthesis, including the GLDH and GLDH genes. The expression of these genes is known to be regulated by various factors such as light, temperature, and plant hormones. Recent studies highlight vitamin C's crucial role in regulating plant stress response pathways, encompassing drought, high salinity, and oxidative stress. The key enzymes in vitamin C biosynthesis are L-galactose dehydrogenase (GLDH) and L-galactono-1, 4-lactone dehydrogenase (GLDH). Genetic studies reveal key genes like GLDH and GLDH in Vitamin C biosynthesis, offering potential for crop improvement. Genetic variations influence nutritional content through their impact on vitamin C levels. Investigating the roles of genes in stress responses provides insights for developing resilient techniques in crop growth. Some fruits and vegetables, such as oranges, lemons, and grapefruits, along with strawberries and kiwi, are rich in vitamin C. Guava. Papaya provides a boost of vitamin C and dietary fiber. At the same time, red and yellow bell peppers, broccoli, pineapple, mangoes, and kale are additional sources of this essential nutrient, promoting overall health. In this review, we will discuss a brief history of Vitamin C and its signaling and biosynthesis pathway and summarize the regulation of its content in various fruits and vegetables.

Genome-wide identification and in-silico expression analysis of CCO gene family in sunflower (Helianthus annnus) against abiotic stress

Carotenoid cleavage oxygenases (CCOs) enzymes play an important role in plant growth and development by producing a wide array of apocarotenoids and their derivatives. These compounds are vital for colouring flowers and fruits and synthesizing plant hormones such as abscisic acid and strigolactones. Despite their importance, the gene family responsible for CCO enzymes in sunflowers has not been identified. In this study, we identify the CCO genes of the sunflower plant to fill this knowledge gap. Phylogenetic and synteny analysis indicated that the Helianthus annnus CCO (HaCCO) genes were conserved in different plant species and they could be divided into three subgroups based on their conserved domains. Analysis using MEME tool and multiple sequence alignment identified conserved motifs in the HaCCO gene sequence. Cis-regulatory elements (CREs) analysis of the HaCCO genes indicated the presence of various responsive elements related to plant hormones, development, and responses to both biotic and abiotic stresses. This implies that these genes may respond to plant hormones, developmental cues, and drought stress, offering potential applications in the development of more resistant crops. Genes belonging to the 9-cis-epoxy carotenoid dioxygenases (NCED) subgroups predominantly exhibited chloroplast localization, whereas the genes found in other groups are primarily localized in the cytoplasm. These 21 identified HaCCOs were regulated by 60 miRNAs, indicating the crucial role of microRNAs in gene regulation in sunflowers. Gene expression analysis under drought stress revealed significant up-regulation of HaNCED16 and HaNCED19, genes that are pivotal in ABA hormone biosynthesis. During organ-specific gene expression analysis, HaCCD12 and HaCCD20 genes exhibit higher activity in leaves, indicating a potential role in leaf pigmentation. This study provides a foundation for future research on the regulation and functions of the CCO gene family in sunflower and beyond. There is potential for developing molecular markers that could be employed in breeding programs to create new sunflower lines resistant to biotic and abiotic stresses.

Genome-wide analysis of NPR1-like genes in citrus species and expression analysis in response to citrus canker (Xanthomonas axonopodis pv. citri)

Citrus fruits, revered for their nutritional value, face significant threats from diseases like citrus canker, particularly impacting global citrus cultivation, notably in Pakistan. This study delves into the critical role of NPR1-like genes, the true receptors for salicylic acid (SA), in the defense mechanisms of citrus against Xanthomonas axonopodis pv. citri (Xcc). By conducting a comprehensive genome-wide analysis and phylogenetic study, the evolutionary dynamics of Citrus limon genes across diverse citrus cultivars are elucidated. Structural predictions unveil conserved domains, such as the BTB domain and ankyrin repeat domains, crucial for the defense mechanism. Motif analysis reveals essential conserved patterns, while cis-regulatory elements indicate their involvement in transcription, growth, response to phytohormones, and stress. The predominantly cytoplasmic and nuclear localization of NPR1-like genes underscores their pivotal role in conferring resistance to various citrus species. Analysis of the Ks/Ka ratio indicates a purifying selection of NPR1-like genes, emphasizing their importance in different species. Synteny and chromosomal mapping provide insights into duplication events and orthologous links among citrus species. Notably, Xac infection stimulates the expression of NPR1-like genes, revealing their responsiveness to pathogenic challenges. Interestingly, qRT-PCR profiling post-Xac infection reveals cultivar-specific alterations in expression within susceptible and resistant citrus varieties. Beyond genetic factors, physiological parameters like peroxidase, total soluble protein, and secondary metabolites respond to SA-dependent PR genes, influencing plant characteristics. Examining the impact of defense genes (NPR1) and plant characteristics on disease resistance in citrus, this study marks the inaugural investigation into the correlation between NPR1-associated genes and various plant traits in both susceptible and resistant citrus varieties to citrus bacterial canker.

Assessing the efficacy of cinnamon compounds against H. pylori through molecular docking, MD Simulations and ADMET analyses

Antibiotics are the drugs that are used for the management of microbial diseases. However, these conventional synthetic drugs can harmfully affect the human health. Since phytochemicals are extracted from natural sources and, are hence relatively safer for human health, they are the enticing alternatives in this regard. Cinnamon is also one of those plants which is being employed as herbal medication for centuries against certain microbial infections due its significant therapeutic effectiveness. A well-known pathogenic bacterium called H. pylori causes a wide range of illnesses in human body. This pathogen's pathogenicity is determined by certain virulent proteins. In this study, some of such proteins, which included virB4, virB8, and virB9 were selected to evaluate the therapeutic efficiency of cinnamon compounds. These proteins were identified in different isolates of H. pylori. The structural modelling of all these proteins were performed initially in order to proceed them for molecular docking analysis. While, the docking studies illustrated that one of the cinnamon compounds, cinnamyl acetate, showed significant binding interactions with virB4 and virB9. However, benzyl benzoate which is another cinnamon compound, docked well with virB8. Afterwards, the MD simulations were incorporated to explore the interaction motions and structural stability of all the docked complexes. In this regard, the resultant maps of Bfactor, eigenvalues and elastic network model, among other factors ensured the structural stabilities of all the respective complexes. After these crucial estimations, benzyl benzoate and cinnamyl acetate underwent the ADMET investigation to assess their pharmacokinetic characteristics. SwissADME and ADMETLab 2.0 server were employed for this investigation. The compiled findings these servers revealed that both, benzyl benzoate and cinnamyl acetate, exhibited a significant level of pharmacokinetic and drug-likeness conformity.

Genome-wide analysis of plant specific YABBY transcription factor gene family in carrot (Dacus carota) and its comparison with Arabidopsis

YABBY gene family is a plant-specific transcription factor with DNA binding domain involved in various functions i.e. regulation of style, length of flowers, and polarity development of lateral organs in flowering plants. Computational methods were utilized to identify members of the YABBY gene family, with Carrot (Daucus carota) 's genome as a foundational reference. The structure of genes, location of the chromosomes, protein motifs and phylogenetic investigation, syntony and transcriptomic analysis, and miRNA targets were analyzed to unmask the hidden structural and functional characteristics YABBY gene family in Carrots. In the following research, it has been concluded that 11 specific YABBY genes irregularly dispersed on all 9 chromosomes and proteins assembled into five subgroups i.e. AtINO, AtCRC, AtYAB5, AtAFO, and AtYAB2, which were created on the well-known classification of Arabidopsis. The wide ranges of YABBY genes in carrots were dispersed due to segmental duplication, which was detected as prevalent when equated to tandem duplication. Transcriptomic analysis showed that one of the DcYABBY genes was highly expressed during anthocyanin pigmentation in carrot taproots. The cis-regulatory elements (CREs) analysis unveiled elements that particularly respond to light, cell cycle regulation, drought induce ability, ABA hormone, seed, and meristem expression. Furthermore, a relative study among Carrot and Arabidopsis genes of the YABBY family indicated 5 sub-families sharing common characteristics. The comprehensive evaluation of YABBY genes in the genome provides a direction for the cloning and understanding of their functional properties in carrots. Our investigations revealed genome-wide distribution and role of YABBY genes in the carrots with best-fit comparison to Arabidopsis thaliana.

Methacrylated gelatin and platelet-rich plasma based hydrogels promote regeneration of critical-sized bone defects

Physiological repair of large-sized bone defects requires instructive scaffolds with appropriate mechanical properties, biocompatibility, biodegradability, vasculogenic ability and osteo-inductivity. The objective of this study was to fabricate in situ injectable hydrogels using platelet-rich plasma (PRP)-loaded gelatin methacrylate (GM) and employ them for the regeneration of large-sized bone defects. We performed various biological assays as well as assessed the mechanical properties of GM@PRP hydrogels alongside evaluating the release kinetics of growth factors (GFs) from hydrogels. The GM@PRP hydrogels manifested sufficient mechanical properties to support the filling of the tissue defects. For biofunction assay, the GM@PRP hydrogels significantly improved cell migration and angiogenesis. Especially, transcriptome RNA sequencing of human umbilical vein endothelial cells and bone marrow-derived stem cells were performed to delineate vascularization and biomineralization abilities of GM@PRP hydrogels. The GM@PRP hydrogels were subcutaneously implanted in rats for up to 4 weeks for preliminary biocompatibility followed by their transplantation into a tibial defect model for up to 8 weeks in rats. Tibial defects treated with GM@PRP hydrogels manifested significant bone regeneration as well as angiogenesis, biomineralization, and collagen deposition. Based on the biocompatibility and biological function of GM@PRP hydrogels, a new strategy is provided for the regenerative repair of large-size bone defects.

Evaluation of chalcones as new glycogen phosphorylase inhibitors - an in-vitro and in-silico approach

Diabetes mellitus (DM) remains one of the pivotal diseases that have drawn the attention of researchers recently and during the last few decades. Due to its devastating symptoms, attempts to develop new drugs with mild side effects have resulted in a number of drugs that are functioning through various mechanisms. Among these, Glycogen phosphorylase (GP) inhibitors emerged as a new strategy for combating DM. GP is an enzyme that regulates blood glucose levels; it catalyses the breakdown of glycogen to glucose-1-phosphate in the liver and tissues with high and fluctuating energy demands. In the present research, we evaluate the possibility of type 2 diabetes therapy with the help of chalcones which are known to have antidiabetic activities. For this purpose, 29 chalcones were modelled, synthesised and investigated for their inhibitory activity against GP using in-vitro methods. Compounds 1, 2, and 3 were found to be the most potent compounds with IC50 values 26.6, 57.1 and 75.6 µM respectively. The observed results were further validated using in-silico methods. Molecular docking simulation revealed interaction patterns that explain the structure-activity relationships of the compounds with GP. Molecular dynamic (MD) simulation demonstrated a stable complex formation between compound 1 and GP through lower value and uniformity in root mean square deviation (RMSD) of the complex and root mean square fluctuation (RMSF) of the protein Cα.

NIMEQ-SACNet: A novel self-attention precision medicine model for vision-threatening diabetic retinopathy using image data

In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.

The least sample size essential for detecting changes in clustering solutions of streaming datasets

The clustering analysis approach treats multivariate data tuples as objects and groups them into clusters based on their similarities or dissimilarities within the dataset. However, in modern world, a significant volume of data is continuously generated from diverse sources over time. In these dynamic scenarios, the data is not static but continually evolves. Consequently, the interesting patterns and inherent subgroups within the datasets also change and develop over time. The researchers have paid special attention to monitoring changes in cluster solutions of evolving streams. For this matter, several algorithms have been proposed in the literature. However, to date, no study has examined the effect of variability in cluster sizes on the evolution of cluster solutions. Moreover, no guidance is available on determining the impact of cluster sizes on the type of changes they experience in the streams. In the present simulation study using artificial datasets, the evolution of clusters is examined concerning the variability in cluster sizes. The findings are substantial because tracing and monitoring the changes in clustering solutions have a wide range of applications in every field of research. This study determines the minimum sample size required in the clustering of time-stamped datasets.

A deep learning-based theoretical protocol to identify potentially isoform-selective PI3Kα inhibitors

Phosphoinositide 3-kinase alpha (PI3Kα) is one of the most frequently dysregulated kinases known for their pivotal role in many oncogenic diseases. While the side effects linked to existing drugs against PI3Kα-induced cancers provide an avenue for further research, the significant structural conservation among PI3Ks makes it extremely difficult to develop new isoform-selective PI3Kα inhibitors. Embracing this challenge, we herein designed a hybrid protocol by integrating machine learning (ML) with in silico drug-designing strategies. A deep learning classification model was developed and trained on the physicochemical descriptors data of known PI3Kα inhibitors and used as a screening filter for a database of small molecules. This approach led us to the prediction of 662 compounds showcasing appropriate features to be considered as PI3Kα inhibitors. Subsequently, a multiphase molecular docking was applied to further characterize the predicted hits in terms of their binding affinities and binding modes in the targeted cavity of the PI3Kα. As a result, a total of 12 compounds were identified whereas the best poses highlighted the efficiency of these ligands in maintaining interactions with the crucial residues of the protein to be targeted for the inhibition of associated activity. Notably, potential activity of compound 12 in counteracting PI3Kα function was found in a previous in vitro study. Following the drug-likeness and pharmacokinetic characterizations, six compounds (compounds 1, 2, 3, 6, 7, and 11) with suitable ADME-T profiles and promising bioavailability were selected. The mechanistic studies in dynamic mode further endorsed the potential of identified hits in blocking the ATP-binding site of the receptor with higher binding affinities than the native inhibitor, alpelisib (BYL-719), particularly the compounds 1, 2, and 11. These outcomes support the reliability of the developed classification model and the devised computational strategy for identifying new isoform-selective drug candidates for PI3Kα inhibition.