Alzheimer's disease (AD) is the leading cause of dementia, accounts for 60 to 80 percent cases. Two main factors called β-amyloid (Aβ) plaques and tangles are prime suspects in damaging and killing nerve cells. However, oxidative stress, the process which produces free radicals in cells, is believed to promote its progression to the extent that it may responsible for the cognitive and functional decline observed in AD. As of today there are few FDA approved drugs in the market for treatment, but their cholinergic adverse effect, potentially distressing toxicity and limited targets in AD pathology limits their use. Therefore, it is crucial to find an effective compounds to combat AD. We choose 45 plant-derived natural compounds that have antioxidant properties to slow down disease progression by quenching free redicals or promoting endogenous antioxidant capacity. However, we performed molecular docking studies to investigate the binding interactions between natural compounds and 13 various anti-Alzheimer drug targets. Three known Cholinesterase inhibitors (Donepezil, Galantamine and Rivastigmine) were taken as reference drugs over natural compounds for comparison and drug-likeness studies. Few of these compounds showed good inhibitory activity besides anti-oxidant activity. Most of these compounds followed pharmacokinetics properties that make them potentially promising drug candidates for the treatment of Alzheimer's disease. Graphical Abstract : Pharmacokinatics and Molecular docking studies of 45 natural antioxidant compounds with most known Alzheimer asscociated targets. Administration (FDA) have approved two medications-cholinesterase inhibitors and Memantine. Over the past decade, much of the research on Alzheimer disease (AD) has focused on oxidative stress mechanisms and its importance in disease pathogenesis. The net effect of oxygen radicals is damaging, such damage present in AD includes advanced glycation end products [2], nitration [3], lipid peroxidation adduction products [4-5] as well as carbonyl-modified neurofilament protein and free carbonyls [6-7]. Significantly, this damage involves all neurons at risk to death in AD, not just those containing neurofibrillary tangles.Nature has gifted us lots of natural remedies in the form of fruits, leaves, bark, vegetables and nuts, etc. The various ranges of bioactive nutrients present in these natural products play a vital role in prevention and cure of various neurodegenerative diseases, such as AD,Parkinson's disease and other neuronal dysfunctions. Previous studies suggested that the naturally occurring phytochemicals, such as polyphenolic antioxidants found in fruits, vegetables, herbs and nuts, may potentially hinder neurodegeneration, and improve memory and cognitive functions.

Cardiorenal syndromes constellate primary dysfunction of either heart or kidney whereby one organ dysfunction leads to the dysfunction of another. The role of several microRNAs (miRNAs) has been implicated in number of diseases, including hypertension, heart failure, and kidney diseases. Wide range of miRNAs has been identified as ideal candidate biomarkers due to their stable expression. Current study was aimed to identify crucial miRNAs and their target genes associated with cardiorenal syndrome and to explore their interaction analysis. Three differentially expressed microRNAs (DEMs), namely, hsa-miR-4476, hsa-miR-345-3p, and hsa-miR-371a-5p, were obtained from GSE89699 and GSE87885 microRNA data sets, using R/GEO2R tools. Furthermore, literature mining resulted in the retrieval of 15 miRNAs from scientific research and review articles. The miRNAs-gene networks were constructed using miRNet (a Web platform of miRNA-centric network visual analytics). CytoHubba (Cytoscape plugin) was adopted to identify the modules and the top-ranked nodes in the network based on Degree centrality, Closeness centrality, Betweenness centrality, and Stress centrality. The overlapped miRNAs were further used in pathway enrichment analysis. We found that hsa-miR-21-5p was common in 8 pathways out of the top 10. Based on the degree, 5 miRNAs, namely, hsa-mir-122-5p, hsa-mir-222-3p, hsa-mir-21-5p, hsa-mir-146a-5p, and hsa-mir-29b-3p, are considered as key influencing nodes in a network. We suggest that the identified miRNAs and their target genes may have pathological relevance in cardiorenal syndrome (CRS) and may emerge as potential diagnostic biomarkers.

The information on the genotype–phenotype relationship in Turner Syndrome (TS) is inadequate because very few specific candidate genes are linked to its clinical features. We used the microarray data of TS to identify the key regulatory genes implicated with TS through a network approach. The causative factors of two common co-morbidities, Type 2 Diabetes Mellitus (T2DM) and Recurrent Miscarriages (RM), in the Turner population, are expected to be different from that of the general population. Through microarray analysis, we identified nine signature genes of T2DM and three signature genes of RM in TS. The power-law distribution analysis showed that the TS network carries scale-free hierarchical fractal attributes. Through local-community-paradigm (LCP) estimation we find that a strong LCP is also maintained which means that networks are dynamic and heterogeneous. We identified nine key regulators which serve as the backbone of the TS network. Furthermore, we recognized eight interologs functional in seven different organisms from lower to higher levels. Overall, these results offer few key regulators and essential genes that we envisage have potential as therapeutic targets for the TS in the future and the animal models studied here may prove useful in the validation of such targets.

Spina Bifida (SB) is a congenital spinal cord malformation. Efforts to discern the key regulators (KRs) of the SB protein-protein interaction (PPI) network are requisite for developing its successful interventions. The architecture of the SB network, constructed from 117 manually curated genes was found to self-organize into a scale-free fractal state having a weak hierarchical organization. We identified three modules/motifs consisting of ten KRs, namely, TNIP1, TNF, TRAF1, TNRC6B, KMT2C, KMT2D, NCOA3, TRDMT1, DICER1, and HDAC1. These KRs serve as the backbone of the network, they propagate signals through the different hierarchical levels of the network to conserve the network’s stability while maintaining low popularity in the network. We also observed that the SB network exhibits a rich-club organization, the formation of which is attributed to our key regulators also except for TNIP1 and TRDMT1. The KRs that were found to ally with each other and emerge in the same motif, open up a new dimension of research of studying these KRs together. Owing to the multiple etiology and mechanisms of SB, a combination of several biomarkers is expected to have higher diagnostic accuracy for SB as compared to using a single biomarker. So, if all the KRs present in a single module/motif are targetted together, they can serve as biomarkers for the diagnosis of SB. Our study puts forward some novel SB-related genes that need further experimental validation to be considered as reliable future biomarkers and therapeutic targets.

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