Rainwater harvesting for agriculture development using multi-influence factor and fuzzy overlay techniques.

Rainwater harvesting (RWH) is an essential technique for enhancing agricultural development, particularly in regions facing water scarcity or unreliable rainfall patterns. Water shortage, however, is one of the key causes of low crop production especially in mountainous regions like the Khyber Pakhtunkhwa province where most rainwater is lost by runoff. Therefore, rainwater harvesting could be a suitable to make better use of runoff and increase crop production. The study focuses on selecting suitable rainwater harvesting sites in District Karak to enhance agriculture by utilizing multi-influence factor (MIF) and fuzzy overlay techniques. We considered seven factors, i.e., land use land cover (LULC), slope, geology, soil, rainfall, lineament, drainage density, to create a ranking system to understand its application in site selection analysis. The results were combined into one overlay process to produce a rainwater harvesting suitability map. The weighted overlay analysis of the MIF model results reveals that 167.96 km2 area has a very high potential for rainwater harvesting, 874.17 km2 has a high potential, 1182.92 km2 has a moderate and 354.50 km2 has a poor potential for rainwater harvesting. The fuzzy overlay analysis revealed that 257.53 km2 has a very high potential for rainwater harvesting, 896.56 km2 area is classified as high, 1018.30 km2 moderate, and 407.7 km2 has poor potential for rainwater harvesting. The findings of this research work will help the policymakers and decision-makers construct various rainwater harvesting structures in the study area to overcome the water shortage problems.

Myogenesis and Analysis of Antimicrobial Potential of Silver Nanoparticles (AgNPs) against Pathogenic Bacteria.

The widespread and indiscriminate use of broad-spectrum antibiotics leads to microbial resistance, which causes major problems in the treatment of infectious diseases. However, advances in nanotechnology have opened up new domains for the synthesis and use of nanoparticles against multidrug-resistant pathogens. The traditional approaches for nanoparticle synthesis are not only expensive, laborious, and hazardous but also have various limitations. Therefore, new biological approaches are being designed to synthesize economical and environmentally friendly nanoparticles with enhanced antimicrobial activity. The current study focuses on the isolation, identification, and screening of metallotolerant fungal strains for the production of silver nanoparticles, using antimicrobial activity analysis and the characterization of biologically synthesized silver nanoparticles by X-ray diffraction (XRD) spectroscopy, energy-dispersive X-ray spectroscopy (EDX), and scanning electron microscopy (SEM). In total, 11 fungal isolates were isolated and screened for the synthesis of AgNPs, while the Penicillium notatum (K1) strain was found to be the most potent, demonstrating biosynthetic ability. The biologically synthesized silver nanoparticles showed excellent antibacterial activity against the bacteria Escherichia coli (ATCC10536), Bacillus subtilis, Staphylococcus aureus (ATCC9144), Pseudomonas aeruginosa (ATCC10145), Enterococcus faecalis, and Listeria innocua (ATCC13932). Furthermore, three major diffraction peaks in the XRD characterization, located at the 2θ values of 28.4, 34.8, 38.2, 44, 64, and 77°, confirmed the presence of AgNPs, while elemental composition analysis via EDX and spherical surface topology with a scanning electron microscope indicated that its pure crystalline nature was entirely composed of silver. Thus, the current study indicates the enhanced antibacterial capability of mycologically synthesized AgNPs, which could be used to counter multidrug-resistant pathogens.

Genome Investigation and Functional Annotation of Lactiplantibacillus plantarum YW11 Revealing Streptin and Ruminococcin-A as Potent Nutritive Bacteriocins against Gut Symbiotic Pathogens.

All nutrient-rich feed and food environments, as well as animal and human mucosae, include lactic acid bacteria known as Lactobacillus plantarum. This study reveals an advanced analysis to study the interaction of probiotics with the gastrointestinal environment, irritable bowel disease, and immune responses along with the analysis of the secondary metabolites' characteristics of Lp YW11. Whole genome sequencing of Lp YW11 revealed 2297 genes and 1078 functional categories of which 223 relate to carbohydrate metabolism, 21 against stress response, and the remaining 834 are involved in different cellular and metabolic pathways. Moreover, it was found that Lp YW11 consists of carbohydrate-active enzymes, which mainly contribute to 37 glycoside hydrolase and 28 glycosyltransferase enzyme coding genes. The probiotics obtained from the BACTIBASE database (streptin and Ruminococcin-A bacteriocins) were docked with virulent proteins (cdt, spvB, stxB, and ymt) of Salmonella, Shigella, Campylobacter, and Yersinia, respectively. These bacteria are the main pathogenic gut microbes that play a key role in causing various gastrointestinal diseases. The molecular docking, dynamics, and immune simulation analysis in this study predicted streptin and Ruminococcin-A as potent nutritive bacteriocins against gut symbiotic pathogens.

Purification of Galacto-oligosaccharide (GOS) by fermentation with Kluyveromyces lactis and Interaction between GOS and casein under simulated acidic fermentation conditions.

In the enzymatic synthesis of galacto-oligosaccharide (GOS), the primary by-products include glucose, galactose and unreacted lactose. This This study was aimed to provide a method to to purify GOS by yeat fermentation and explore the interaction between GOS and CAS with a view for expanding the prospects of GOS application in the food industry. The crude GOS(25.70 g/L) was purified in this study using the fermentation method with Kluyveromyces lactis CICC 1773. Optimal conditions for purification with the yeast were 75 g/L of the yeast inoculation rate and 50 g/L of the initial crude GOS concentration for 12 h of incubation. After removing ethanol produced by yeast by low-temperature distillation, GOS content could reach 90.17%. A study of the interaction between GOS and casein (CAS) in a simulated acidic fermentation system by D-(+)-gluconic acid δ-lactone (GDL) showed that the GOS/CAS complexes with higher GOS concentrations, e.g., 4% and 6% (w/v), was more viscoelastic with higher water-holding capacity, but decreased hardness, elasticity, and cohesiveness at 6% (w/v) of GOS. The addition of GOS to CAS suspension significantly caused (p<0.05) decreased particle sizes of the formed GOS/CAS complexes, and the suspension system became more stable. FT-IR spectra confirmed the existence of different forms of molecular interactions between CAS and GOS, e.g., hydrogen bonding and hydrophobic interaction, and the change of secondary structure after CAS binding to GOS.

Functional Annotation of Lactiplantibacillus plantarum 13-3 as a Potential Starter Probiotic Involved in the Food Safety of Fermented Products.

The important role of Lactiplantibacillus plantarum strains in improving the human mucosal and systemic immunity, preventing non-steroidal anti-provocative drug-induced reduction in T-regulatory cells, and as probiotic starter cultures in food processing has motivated in-depth molecular and genomic research of these strains. The current study, building on this research concept, reveals the importance of Lactiplantibacillus plantarum 13-3 as a potential probiotic and bacteriocin-producing strain that helps in improving the condition of the human digestive system and thus enhances the immunity of the living beings via various extracellular proteins and exopolysaccharides. We have assessed the stability and quality of the L. plantarum 13-3 genome through de novo assembly and annotation through FAST-QC and RAST, respectively. The probiotic-producing components, secondary metabolites, phage prediction sites, pathogenicity and carbohydrate-producing enzymes in the genome of L. plantarum 13-3 have also been analyzed computationally. This study reveals that L. plantarum 13-3 is nonpathogenic with 218 subsystems and 32,918 qualities and five classes of sugars with several important functions. Two phage hit sites have been identified in the strain. Cyclic lactone autoinducer, terpenes, T3PKS, and RiPP-like gene clusters have also been identified in the strain evidencing its role in food processing. Combined, the non-pathogenicity and the food-processing ability of this strain have rendered this strain industrially important. The subsystem and qualities characterization provides a starting point to investigate the strain's healthcare-related applications as well.

Evaluation and Proteomic Analysis of Lead Adsorption by Lactic Acid Bacteria.

Heavy metals are a growing threat to human health due to the resulting damage to the ecology; the removal of heavy metals by lactic acid bacteria (LAB) has been a focus of many studies. In this study, 10 LAB strains were evaluated for their ability to absorb and tolerate lead. Lactobacillus plantarum YW11 was found to possess the strongest ability of lead absorbing and tolerance, with the rate of absorption as high as 99.9% and the minimum inhibitory concentration of lead on YW11 higher than 1000 mg/L. Based on the isobaric tags for relative and absolute quantitation (iTRAQ) proteomics analysis of YW11, a total of 2009 proteins were identified both in the lead-treated strain and the control without the lead treatment. Among these proteins, 44 different proteins were identified. The abundance of 25 proteins increased significantly, and 19 proteins decreased significantly in the treatment group. These significantly differential abundant proteins are involved in the biological processes of amino acid and lipid metabolism, energy metabolism, cell wall biosynthesis, and substance transport. This study contributed further understanding of the molecular mechanism of L. plantarum in the binding and removal of lead to explore its potential application in counteracting heavy metal pollution of environment, food, and other fields.

Review of reinforcement learning applications in segmentation, chemotherapy, and radiotherapy of cancer.

Owing to early diagnosis and treatment of cancer as a prerequisite in recent times, the role of machine learning has been increased substantially. The mathematically powerful and optimized solutions for the detection and cure of cancer are constantly being explored and novel models based upon standard algorithms are also being developed. Leveraging one such solution is Reinforcement Learning (RL), which is a semi-supervised type of learning. The paper presents a detailed discussion on the various RL techniques, algorithms, and open issues, in addition to the review of literature for diagnosis and treatment of cancer. A smaller number of publications for diagnosis and treatment of cancer have been reported before 2011 but now after the success of Deep Learning (DL) and the advent of Deep Reinforcement Learning (DRL), the publications have grown in number from 2017 onwards. The scope of RL for cancer diagnosis and treatment is also demystified and provides the research community with the insights of how to formulate RL problem as a Cancer diagnostic problem. RL has been found successful for landmark detection in medical images and optimal control of drugs and radiations.

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges.

Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the accurate identification and analysis of diseases, leading to severe consequences such as patient misdiagnosis or mortality. Various types of noise, including Gaussian, Rician, and Salt-pepper noise, can corrupt the area of interest, limiting the precision and accuracy of algorithms. Denoising algorithms have shown the potential in improving the quality of medical images by removing noise and other artifacts that obscure essential information. Deep learning has emerged as a powerful tool for image analysis and has demonstrated promising results in denoising different medical images such as MRIs, CT scans, PET scans, etc. This review paper provides a comprehensive overview of state-of-the-art deep learning algorithms used for denoising medical images. A total of 120 relevant papers were reviewed, and after screening with specific inclusion and exclusion criteria, 104 papers were selected for analysis. This study aims to provide a thorough understanding for researchers in the field of intelligent denoising by presenting an extensive survey of current techniques and highlighting significant challenges that remain to be addressed. The findings of this review are expected to contribute to the development of intelligent models that enable timely and accurate diagnoses of medical disorders. It was found that 40% of the researchers used models based on Deep convolutional neural networks to denoise the images, followed by encoder-decoder (18%) and other artificial intelligence-based techniques (15%) (Like DIP, etc.). Generative adversarial network was used by 12%, transformer-based approaches (13%) and multilayer perceptron was used by 2% of the researchers. Moreover, Gaussian noise was present in 35% of the images, followed by speckle noise (16%), poisson noise (14%), artifacts (10%), rician noise (7%), Salt-pepper noise (6%), Impulse noise (3%) and other types of noise (9%). While the progress in developing novel models for the denoising of medical images is evident, significant work remains to be done in creating standardized denoising models that perform well across a wide spectrum of medical images. Overall, this review highlights the importance of denoising medical images and provides a comprehensive understanding of the current state-of-the-art deep learning algorithms in this field.

Genotypic Profiling, Functional Analysis, Cholesterol-Lowering Ability, and Angiotensin I-Converting Enzyme (ACE) Inhibitory Activity of Probiotic Lactiplantibacillus plantarum K25 via Different Approaches.

Due to its alleged health advantages, several uses in biotechnology and food safety, the well-known probiotic strain Lactiplantibacillus plantarum K25 has drawn interest. This in-depth investigation explores the genetic diversity, makeup, and security characteristics of the microbial genome of L. plantarum K25, providing insightful knowledge about its genotypic profile and functional characteristics. Utilizing cutting-edge bioinformatics techniques like comparative genomics, pan-genomics, and genotypic profiling was carried out to reveal the strain's multidimensional potential in various fields. The results not only add to our understanding of the genetic makeup of L. plantarum K25 but also show off its acceptability in various fields, notably in biotechnology and food safety. The explanation of evolutionary links, which highlights L. plantarum K25's aptitude as a probiotic, is one notable finding from this research. Its safety profile, which is emphasized by the absence of genes linked to antibiotic resistance, is crucial and supports its status as a promising probiotic option.

Metabolomics analysis of potential functional metabolites in synbiotic ice cream made with probiotic Saccharomyces cerevisiae var. boulardii CNCM I-745 and prebiotic inulin.

Probiotic lactic acid bacteria have been widely studied, but much less was focused on probiotic yeasts in food systems. In this study, probiotic Saccharomyces cerevisiae var. boulardii CNCM I-745 was employed to prepare ice cream added with and without inulin (1%, w/v). Metabolomics analysis on the effect of inulin showed 84 and 147 differentially expressed metabolites identified in the ice cream samples from day 1 and day 30 of storage (-18 °C), respectively. Various potential functional metabolites were found, including citric acid, ornithine, D-glucuronic acid, sennoside A, stachyose, maltotetraose, maltopentaose, maltohexaose, maltoheptaose, cis-aconitic acid, gamma-aminobutyric acid, L-threonine, L-glutamic acid, tryptophan, benzoic acid, and trehalose. Higher expression of these metabolites suggested their possible roles through relevant metabolic pathways in improving survivability of the probiotic yeast and functionality of ice cream. This study provides further understanding on the metabolic characteristics of probiotic yeast that potentially affect the functionality of ice cream.

Revolutionizing the probiotic functionality, biochemical activity, antibiotic resistance and specialty genes of Pediococcus acidilactici BCB1H via in-vitro and in-silico approaches.

This study presents a comprehensive genomic exploration, biochemical characterization, and the identification of antibiotic resistance and specialty genes of Pediococcus acidilactici BCB1H strain. The functional characterization, genetic makeup, biological activities, and other considerable parameters have been investigated in this study with a prime focus on antibiotic resistance and specialty gene profiles. The results of this study revealed the unique susceptibility patterns for antibiotic resistance and specialty genes. BCB1H had good in vitro probiotic properties, which survived well in simulated artificial gastrointestinal fluid, and exhibited acid and bile salt resistance. BCB1H didn't produce hemolysis and had certain antibiotic sensitivity, making it a relatively safe LAB strain. Simultaneously, it had good self-coagulation characteristics and antioxidant activity. The EPS produced by BCB1H also had certain antioxidant activity and hypoglycemic function. Moreover, the genome with a 42.4 % GC content and a size of roughly 1.92 million base pairs was analyzed in the genomic investigations. The genome annotation identified 192 subsystems and 1,895 genes, offering light on the metabolic pathways and functional categories found in BCB1H. The identification of specialty genes linked to the metabolism of carbohydrates, stress response, pathogenicity, and amino acids highlighted the strain's versatility and possible uses. This study establishes the groundwork for future investigations by highlighting the significance of using multiple strains to investigate genetic diversity and experimental validation of predicted genes. The results provide a roadmap for utilizing P. acidilactici BCB1H's genetic traits for industrial and medical applications, opening the door to real-world uses in industries including food technology and medicine.

Identification and mapping of groundwater recharge zones using multi influencing factor and analytical hierarchy process.

The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones.

Whole Genome Analysis of Tibetan Kefir-Derived Lactiplantibacillus Plantarum 12-3 Elucidates Its Genomic Architecture, Antimicrobial and Drug Resistance, Potential Probiotic Functionality and Safety.

BACKGROUND: Lactiplantibacillus plantarum 12-3 holds great promise as a probiotic bacterial strain, yet its full potential remains untapped. This study aimed to better understand this potential therapeutic strain by exploring its genomic landscape, genetic diversity, CRISPR-Cas mechanism, genotype, and mechanistic perspectives for probiotic functionality and safety applications. METHODS: L. plantarum 12-3 was isolated from Tibetan kefir grains and, subsequently, Illumina and Single Molecule Real-Time (SMRT) technologies were used to extract and sequence genomic DNA from this organism. After performing pan-genomic and phylogenetic analysis, Average Nucleotide Identity (ANI) was used to confirm the taxonomic identity of the strain. Antibiotic resistance gene analysis was conducted using the Comprehensive Antibiotic Resistance Database (CARD). Antimicrobial susceptibility testing, and virulence gene identification were also included in our genomic analysis to evaluate food safety. Prophage, genomic islands, insertion sequences, and CRISPR-Cas sequence analyses were also carried out to gain insight into genetic components and defensive mechanisms within the bacterial genome. RESULTS: The 3.4 Mb genome of L. plantarum 12-3, was assembled with 99.1% completeness and low contamination. A total of 3234 genes with normal length and intergenic spacing were found using gene prediction tools. Pan-genomic studies demonstrated gene diversity and provided functional annotation, whereas phylogenetic analysis verified taxonomic identity. Our food safety study revealed a profile of antibiotic resistance that is favorable for use as a probiotic. Analysis of insertional sequences, genomic islands, and prophage within the genome provided information regarding genetic components and their possible effects on evolution. CONCLUSIONS: Pivotal genetic elements uncovered in this study play a crucial role in bacterial defense mechanisms and offer intriguing prospects for future genome engineering efforts. Moreover, our findings suggest further in vitro and in vivo studies are warranted to validate the functional attributes and probiotic potential of L. plantarum 12-3. Expanding the scope of the research to encompass a broader range of L. plantarum 12-3 strains and comparative analyses with other probiotic species would enhance our understanding of this organism's genetic diversity and functional properties.

Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions.

Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorithms can accurately label them as cancerous or non-cancerous. Although many studies have investigated the application of deep learning for diagnosing various diseases, there is a lack of research focusing on the evolution, limitations, and gaps of intelligent algorithms in conjunction with WSI for cervical cancer. This paper provides a comprehensive overview of the state-of-the-art deep learning algorithms used for the timely and precise analysis of cervical WSI images. A total of 115 relevant papers were reviewed, and 37 were selected after screening with specific inclusion and exclusion criteria. Methodological aspects including deep learning techniques, data sources, architectures, and classification techniques employed by the selected studies were analyzed. The review presents the most popular techniques and current trends in deep learning-based cervical classification systems, and categorizes the evolution of the domain based on deep learning techniques, citing an in-depth analysis of various models developed over time. The paper advocates for the implementation of transfer supervised learning when utilizing deep learning models such as ResNet, VGG19, and EfficientNet, and builds a solid foundation for applying relevant techniques in different fields. Although some progress has been made in developing novel models for the diagnosis of cervical cancer, substantial work remains to be done in creating standardized benchmark databases of WSI images for the research community. This paper serves as a comprehensive guide for understanding the fundamental concepts, benefits, and challenges related to various deep learning models on WSI, including their application for cervical system classification. Additionally, it provides valuable insights into future research directions in this area.

Investigation of VEGF (rs 699947) polymorphism in the progression of Rheumatoid Arthritis (RA) and in-silico nanoparticle drug delivery of potential phytochemicals to cure RA.

Mutation in the VEGF gene disturbs the production of chondrocytes and angiogenesis which are essential for cartilage health. Cytokines and chemokines produced by auto-activation of B-cells degrade cartilage. Bruton's Tyrosine Kinase (BTK) plays a crucial role in the activation of these B-cells. VEGF has a central part in angiogenesis, in the recruitment of endothelial cells, and is involved in mechanisms that result in tumour formation. The objective of this research is to investigate the potential role of VEGF polymorphism in the development of Rheumatoid Arthritis (RA) and the screening of potential natural, synthetic BTK inhibitor compounds as possible in-silico chemotherapeutic agents to control auto-activation of B-cells and cartilage degrading cytokines. In this study, it had been shown that allele A frequency was significantly higher than that of allele C in RA-positive patients as compared to controls. Hence it depicts that allele A of VEGF (rs699947) can increase the risk of RA while allele C has a protective role. The phytochemicals which showed maximum binding affinity at the inhibitory site of BTK include beta boswellic acid, tanshinone, and baicalin. These phytochemicals as BTK inhibitor give insights to use them as anti-arthritic compounds by nanoparticle drug delivery mechanism.

Comparative genomics of food-derived probiotic Lactiplantibacillus plantarum K25 reveals its hidden potential, compactness, and efficiency.

This study aimed to investigate the intricate genetic makeup of the Lactiplantibacillus plantarum K25 strain by conducting a comprehensive analysis of comparative genomics. The results of our study demonstrate that the genome exhibits a high-level efficiency and compactness, comprising a total of 3,199 genes that encode proteins and a GC content of 43.38%. The present study elucidates the evolutionary lineage of Lactiplantibacillus plantarum strains through an analysis of the degree of gene order conservation and synteny across a range of strains, thereby underscoring their closely interrelated evolutionary trajectories. The identification of various genetic components in the K25 strain, such as bacteriocin gene clusters and prophage regions, highlights its potential utility in diverse domains, such as biotechnology and medicine. The distinctive genetic elements possess the potential to unveil innovative therapeutic and biotechnological remedies in future. This study provides a comprehensive analysis of the L. plantarum K25 strain, revealing its remarkable genomic potential and presenting novel prospects for utilizing its unique genetic features in diverse scientific fields. The present study contributes to the existing literature on Lactiplantibacillus plantarum and sets the stage for prospective investigations and practical implementations that leverage the exceptional genetic characteristics of this adap organism.

Particle Nanoarchitectonics for Nanomedicine and Nanotherapeutic Drugs with Special Emphasis on Nasal Drugs and Aging.

Aging is a multifunctional physiological manifestation. The nasal cavity is considered a major site for easy and cost-effective drug and vaccine administration, due to high permeability, low enzymatic activity, and the presence of a high number of immunocompetent cells. This review article primarily focuses on aging genetics, physical parameters, and the use of nanoparticles as delivery systems of drugs and vaccines via the nasal cavity. Studies have identified various genes involved in centenarian and average-aged people. VEGF is a key mediator involved in angiogenesis. Different therapeutic approaches induce vascular function and angiogenesis. FOLR1 gene codes for folate receptor alpha protein that helps in regulating the transport of vitamin B folate, 5-methyltetrahydrofolate and folate analogs inside the cell. This gene also aids in slowing the aging process down by cellular regeneration and promotes healthy aging by reducing aging symptoms. It has been found through the literature that GATA 6, Yamanaka factors, and FOLR1 work in synchronization to induce healthy and delayed aging. The role and applications of genes including CBS, CISD, SIRT 1, and SIRT 6 play a significant role in aging.

Evaluating the influence of Aloe barbadensis extracts on edema induced changes in C-reactive protein and interleukin-6 in albino rats through in vivo and in silico approaches.

The current study investigated the in-vivo and in-silico anti-inflammatory effect of Aloe barbadensis in edema induced rat and its blood biomarkers. 60 albino rats (160-200 g) were divided into 4 groups. The 1st group (control) comprised of 6 rats that were treated with saline. The 2nd group (standard) comprised of 6 rats that were treated with diclofenac. The 3rd and 4th experimental groups consisted of 48 rats, treated with A. barbadensis gel ethanolic and aqueous extracts respectively at doses of 50, 100, 200 and 400 mg/kg. According to paw sizes, groups III and IV showed 51% and 46% inhibition respectively at the 5th hour, as compared to group II with 61% inhibition. Correlation was negative between biomarkers in group III, while, positive in group IV. Blood samples were collected; C-reactive protein and interleukin-6 were measured using commercially available ELISA kits. Similarly, biomarkers showed significant effect in dose-dependent manner. In molecular docking, for CRP both ligands aloe emodin and emodin showed -7.5 kcal/mol binding energy as compared to diclofenac with -7.0 kcal/mol. For IL-1beta, both ligands showed -4.7 kcal/mol binding energy as compared to diclofenac -4.4 kcal/mol. Hence, we concluded that A. barbadensis extracts can be used as an effective drug for managing inflammation.

Integrated genome based evaluation of safety and probiotic characteristics of Lactiplantibacillus plantarum YW11 isolated from Tibetan kefir.

The comparative genomic analysis of Lactiplantibacillus plantarum YW11 (L. plantarum YW11) isolated from Tibetan kefir involves comparison of the complete genome sequences of the isolated strain with other closely related L. plantarum strains. This type of analysis can be used to identify the genetic diversity among strains and to explore the genetic characteristics of the YW11 strain. The genome of L. plantarum YW11 was found to be composed of a circular single chromosome of 4,597,470 bp with a G + C content of 43.2%. A total of 4,278 open reading frames (ORFs) were identified in the genome and the coding density was found to be 87.8%. A comparative genomic analysis was conducted using two other L. plantarum strains, L. plantarum C11 and L. plantarum LMG21703. Genomic comparison revealed that L. plantarum YW11 shared 72.7 and 75.2% of gene content with L. plantarum C11 and L. plantarum LMG21703, respectively. Most of the genes shared between the three L. plantarum strains were involved in carbohydrate metabolism, energy production and conversion, amino acid metabolism, and transcription. In this analysis, 10 previously sequenced entire genomes of the species were compared using an in-silico technique to discover genomic divergence in genes linked with carbohydrate intake and their potential adaptations to distinct human intestinal environments. The subspecies pan-genome was open, which correlated with its extraordinary capacity to colonize several environments. Phylogenetic analysis revealed that the novel genomes were homogenously grouped among subspecies of l Lactiplantibacillus. L. plantarum was resistant to cefoxitin, erythromycin, and metronidazole, inhibited pathogens including Listeria monocytogenes, Clostridium difficile, Vibrio cholera, and others, and had excellent aerotolerance, which is useful for industrial operations. The comparative genomic analysis of L. plantarum YW11 isolated from Tibetan kefir can provide insights into the genetic characteristics of the strain, which can be used to further understand its role in the production of kefir.

Enhancement of shelf-life of food items via immobilized enzyme nanoparticles on varied supports. A sustainable approach towards food safety and sustainability.

This study was designed to extend the shelf life of fruits and vegetables through a novel technique based on utilization of microbially driven enzyme glucose oxidase and casting a fine layer of hydrogen peroxide on the food item that protected the fruit from decay. The produced nanoparticles (ZnO, Ag) were ligated with Glucose Oxidize (GOx) purified from Aspergillus niger. Post ligation studies revealed that ligated enzymes display relatively enhanced activity. Four types of sprays were prepared in order to compare their effectiveness. Glucose oxidase/silver nanoparticles (GOx/AgNPs), glucose oxidase/zinc oxide nanoparticles (GOx/ZnONPs), AgNPs and ZnONPs sprays were applied to guava fruit samples as post-harvest therapeutic agents for a period of 15 days. Fruit quality parameters such as total suspended solids (TSS), pH, weight loss, DPPH free radical capturing performance and firmness confirms that usage of the bioconjugates especially that of GOx/ZnONP was curiously active to maintain the physical appearance of fruit well along with no such deterioration in chemical composition of fruit. Consequently, enzymes ligated on the surface of nanoparticles (ZnONP) are exceptional for extension of post-harvest shelf life of fruits such as guava.