Effect of cadmium stress on seed germination and seedling morpho-physiological growth parameters of barnyard millet (Echinochloa frumentacea Link)

Cadmium (Cd) is a heavy metal, which is seen in the contaminated soils and severely affects the growth and development of plants in recent years. The study on the seed germination and morpho-physiological growth characteristics of barnyard millet (Echinochloa frumentacea) cultivar CO (KV) 2 treated with different concentrations (50, 100, 150, 200, and 250 mg/kg of soil) of Cd were evaluated at 15th, 30th, and 45th day of interval. The findings of this research demonstrate that the maximum dosage of Cd (250 mg/kg of soil) affects the germination percentage (65%) of barnyard millet. Seedling vigor index has been negatively influences a drop in germination percentage. Increasing concentrations of Cd reveals the growth of root and shoot length and the quantity of fresh and dry weight affected. The phytotoxicity percentage of roots and shoots also increases with increasing concentrations of Cd, whereas the tolerance index level decreases with increasing concentrations of Cd. In root and shoot, the relative growth index was reduced in higher concentration of Cd. The relative water content remains high in the initial stages of leaf development and declines when the leaf matures. From this study, it was found that the increase in the concentration of Cd leads to decrease the germination percentage and morpho-physiological growth parameters as compared to control

Designing of siRNAs for various target genes of Cucumber mosaic virus subgroup IB

119-125Cucumber mosaic virus (CMV) is a major production constraint in black pepper causing stunted disease. Resistant varieties are unavailable and control measures are not effective till now. Recently, RNA interference (RNAi) is the most promising strategy for combating virus infection in plants but the effectiveness depends on sequence specificity between the transgene and the targeting virus. This study was undertaken to design the most suitable region for double stranded RNA synthesis with maximum specificity and minimized off–targets for all the five genes of CMV from black pepper. A 400 bp off–target minimized region identified from each of the five genes was subjected to sequence polymorphism study with selected CMV subgroup IB strains and common ‘siRNAs’ were designed in silico. As 3b gene had the least variations (of 17%) with four common and potential siRNAs designed from this region in silico, a hairpin construct was assembled using this region in Agrobacterium that can be used for developing black pepper resistant to selected CMV subgroup IB strains

Diagnostic circulating biomarkers to detect vision-threatening diabetic retinopathy: Potential screening tool of the future?

With the increasing prevalence of diabetes in developing and developed countries, the socio-economic burden of diabetic retinopathy (DR), the leading complication of diabetes, is growing. Diabetic retinopathy (DR) is currently one of the leading causes of blindness in working-age adults worldwide. Robust methodologies exist to detect and monitor DR; however, these rely on specialist imaging techniques and qualified practitioners. This makes detecting and monitoring DR expensive and time-consuming, which is particularly problematic in developing countries where many patients will be remote and have little contact with specialist medical centres. Diabetic retinopathy (DR) is largely asymptomatic until late in the pathology. Therefore, early identification and stratification of vision-threatening DR (VTDR) is highly desirable and will ameliorate the global impact of this disease. A simple, reliable and more cost-effective test would greatly assist in decreasing the burden of DR around the world. Here, we evaluate and review data on circulating protein biomarkers, which have been verified in the context of DR. We also discuss the challenges and developments necessary to translate these promising data into clinically useful assays, to detect VTDR, and their potential integration into simple point-of-care testing devices

Deep Learning

Deep Learning was developed as a Machine learning approach to influence advanced input-output mappings. It had been for learning concerning multiple levels of illustration and abstraction to create sense of the information such as images, text and sound. Deep learning excels at distinguish patterns in unstructured information, that most of the people grasp as media like images, sound, video and text

Bioactive potential of selected actinobacterial strains against Mycobacterium tuberculosis and other clinical pathogens

1307-1311Marine actinobacteria produces diverse array of metabolites with novel chemical structures with potential bioactivities. Exploring the understudied ecosystems may increase the chance of getting novel actinobacteria and new metabolites.The present study explores the bioactive potential of actinobacteria isolated from the marine ecosystem of Andaman and Nicobar Islands, Bay of Bengal, against Mycobacterium tuberculosis and other clinical pathogens. The crude extracts from 15 marine actinobacterial strains were produced through agar surface fermentation using YEME agar and extracted using ethyl acetate. The crude extracts were tested against the standard strain M. tuberculosis H37Rv, clinical drug sensitive M. tuberculosis, and MDR M. tuberculosis strains by luciferase reporter phage (LRP) assay at 500 µg/ml concentration. The anti-microbial activity against other clinical pathogens, namely, Staphylococcus aureus, Escherichia coli, Salmonella paratyphi, Klebsiellapneumoniae, Pseudomonas aeruginosa, Candida albicans, and Cryptococcusneoformans and non-tubercular mycobacteria, M. smegmatis was studied by agar plug method. Among the 15 extracts that were tested for anti-tubercular activity, the crude ethyl acetate extract of the 14 actinobacterial strains showed anti-tubercular activity against at least one of the three M. tuberculosis strains. Exceptionally, the ethyl acetate extract of strain SACC 168 inhibited all three M. tuberculosis strains tested. In anti-microbial screening, the crude extracts of eight strains showed anti-microbial activity including six strains, which were active against the non-tuberculous mycobacteria. Further purification and characterization of the active molecule from the potential extracts will pave way for the potential natural product candidate for tuberculosis and other microbial infections

Artificial Intelligence-Driven Drug Discovery: Identifying Novel Compounds for Targeted Cancer Therapies

This study delves into the potential of artificial intelligence (AI) in revolutionizing drug discovery, specifically focusing on the identification of new compounds for targeted cancer therapies. Through the application of advanced machine learning algorithms, our methodology achieved impressive predictive accuracy, with an accuracy rate of 92.5%, an AUC-ROC of 0.94, and an AUC-PR of 0.91. The AI models successfully pinpointed 35 novel compounds predicted to demonstrate high efficacy against specific cancer targets, indicating promising prospects for advancements in cancer treatment. Examination of the molecular structures of these identified compounds unveiled positive characteristics, with 90% adhering to Lipinski's Rule of Five, indicating their suitability as potential drug candidates. Additionally, the average predicted half-life of 12 hours suggests advantageous pharmacokinetic properties, bolstering their potential viability. A comparative assessment highlighted the efficiency advantages of the AI-driven approach, revealing an 80% reduction in time and a 65% reduction in costs compared to traditional methods. Beyond its application in targeted cancer therapies, the success of our approach implies broader implications for the pharmaceutical research landscape, offering a more streamlined and accurate methodology. While these outcomes are promising, it is crucial to recognize limitations and stress the importance of sustained collaboration between computational and experimental researchers. Future directions encompass the refinement of models, incorporation of diverse datasets, and rigorous experimental validation. In summary, our study underscores the efficacy of AI-driven drug discovery in identifying new compounds for targeted cancer therapies. The identified compounds, characterized by favorable structural and pharmacokinetic attributes, present a promising avenue for overcoming challenges in current cancer treatments. These findings set the stage for ongoing exploration, collaborative initiatives, and advancements at the intersection of artificial intelligence and drug discover

A Requirement for Global Transcription Factor Lrp in Licensing Replication of Vibrio cholerae Chromosome 2

The human pathogen, Vibrio cholerae, belongs to the 10% of bacteria in which the genome is divided. Each of its two chromosomes, like bacterial chromosomes in general, replicates from a unique origin at fixed times in the cell cycle. Chr1 initiates first, and upon duplication of a site in Chr1, crtS, Chr2 replication initiates. Recent in vivo experiments demonstrate that crtS binds the Chr2-specific initiator RctB and promotes its initiator activity by remodeling it. Compared to the well-defined RctB binding sites in the Chr2 origin, crtS is an order of magnitude longer, suggesting that other factors can bind to it. We developed an in vivo screen to identify additional crtS-binding proteins and identified the global transcription factor, Lrp, as one such protein. Studies in vivo and in vitro indicate that Lrp binds to crtS and facilitates RctB binding to crtS. Chr2 replication is severely defective in the absence of Lrp, indicative of a critical role of the transcription factor in licensing Chr2 replication. Since Lrp responds to stresses such as nutrient limitation, its interaction with RctB presumably sensitizes Chr2 replication to the physiological state of the cell