Evaluation of in vitro and invivo anti-inflammatory activities of Parthenium camphora

The present investigation was carried out to evaluate the anti-inflammatory potential of solvent extracts of Parthenium camphora (Family: Compositae), a non-useful and waste weed growing through waste sides. The anti-inflammatory activities were assessed through in vitro and in vivo procedures, the results were found to be very surprising and promising. Aqueous and Ethanolic solvent extracts of Parthenium camphora were found to have significant anti-inflammatory activity at doses 100 and 120 mg/Kg during in vitro anti-inflammatory assay. The ethanolic fractions of the plant causes significant reduction in inflammation i.e. 92 % (120 mg/kg) followed by aqueous extract i.e. 85 % (120 mg/kg) compared to standard anti-inflammatory drug, Diclofenac Sodium i.e. 87 % (10 mg/kg). The values of reduction in paw volume, 0.10 ± 0.05, 0.14 ± 0.05 and 0.16 ± 0.05 were found significantly of ethanol extract, aqueous extract and Diclofenac sodium, respectively at 4 h after carrageenan administration. Ethanolic extracts showed potent anti-inflammatory activity in comparison to aqueous extracts. The extracts showed higher anti-inflammatory potential as the dose varies. Thus results showed that extracts showed significant anti-inflammatory activity in dose-dependent manner. The extracts exhibited membrane stabilization effect by inhibiting hypotonicity induced lysis of erythrocyte membrane. The erythrocyte membrane is analogous to the lysosomal membrane, and its stabilization implies that the extract may as well stabilize lysosomal membrane. Stabilization of lysosomal membrane is important in limiting the inflammatory response by preventing the release of lysosomal constituents of activated neutrophils such as bacterial enzymes and proteases which cause further tissue inflammation and damage. From the above study it was concluded that the ethanolic extract of Parthenium camphora has significant membrane stabilization property compared to the aqueous extract of the same plant and it was comparable to the standard drug Diclofenac Sodium

Phenotype Enhancement Screen of a Regulatory spx Mutant Unveils a Role for the ytpQ Gene in the Control of Iron Homeostasis

Spx is a global regulator of genes that are induced by disulfide stress in Bacillus subtilis. The regulon that it governs is comprised of over 120 genes based on microarray analysis, although it is not known how many of these are under direct Spx control. Most of the Spx-regulated genes (SRGs) are of unknown function, but many encode products that are conserved in low %GC Gram-positive bacteria. Using a gene-disruption library of B. subtilis genomic mutations, the SRGs were screened for phenotypes related to Spx-controlled activities, such as poor growth in minimal medium and sensitivity to methyglyoxal, but nearly all of the SRG mutations showed little if any phenotype. To uncover SRG function, the mutations were rescreened in an spx mutant background to determine which mutant SRG allele would enhance the spx mutant phenotype. One of the SRGs, ytpQ was the site of a mutation that, when combined with an spx null mutation, elevated the severity of the Spx mutant phenotype, as shown by reduced growth in a minimal medium and by hypersensitivity to methyglyoxal. The ytpQ mutant showed elevated oxidative protein damage when exposed to methylglyoxal, and reduced growth rate in liquid culture. Proteomic and transcriptomic data indicated that the ytpQ mutation caused the derepression of the Fur and PerR regulons of B. subtilis. Our study suggests that the ytpQ gene, encoding a conserved DUF1444 protein, functions directly or indirectly in iron homeostasis. The ytpQ mutant phenotype mimics that of a fur mutation, suggesting a condition of low cellular iron. In vitro transcription analysis indicated that Spx stimulates transcription from the ytpPQR operon within which the ytpQ gene resides. The work uncovers a link between Spx and control of iron homeostasis

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Stroke genetics informs drug discovery and risk prediction across ancestries

Previous genome-wide association studies (GWASs) of stroke — the second leading cause of death worldwide — were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries

Geolocation accuracy improvement for NovaSAR-1 imagery acquired through TLE orbit

NovaSAR-1 is a small S-band Synthetic Aperture Radar (SAR) mission mainly for land and maritime applications. Since SAR has side-looking geometry, thus slant and ground range SAR products have geometric distortions. To correct these geometric distortions, orthorectification needs to be done using SAR Rigorous Sensor Model (RSM) algorithm and Digital Elevation Model (DEM). NovaSAR-1 provides the satellite state vectors either through a Global Positioning System (GPS) or through a Two-Line Element (TLE) orbit source. It is observed that, after orthorectification, it shows poor geolocation accuracy (200 m to 9000 m) for the TLE orbit source. In this paper, geolocation accuracy improvement has been done for NovaSAR-1 ground range imagery acquired through TLE orbit source by generating orthorectified products using SAR Rigorous Sensor Model (RSM) algorithm and Ground control point (GCP). After orthorectification, geolocation accuracy of the NovaSAR-1 imagery acquired through TLE orbit source is reduced to less than 10 m

Drug utilization in clinical conditions: an update on its essentials

Drug utilization studies may be defined as studies of the marketing, distribution, prescription and use of drugs in a society, with special emphasis on the resulting medical, social and economic consequences. Drug utilization studies can provide highly valuable information, at a reasonable price, on the costs and effects (harmful and beneficial) of drugs. Such studies make available much useful information including indirect data on morbidity, the pharmaceutical component of the treatment cost of an illness, therapeutic compliance, the incidence of adverse reactions, the effectiveness of drug consumption and the choice of comparators. This information can be of great use in the subsequent elaboration of pharmacoeconomy studies, or in the selection of problematic areas in which these studies may be applied. Pharmacoeconomy studies, in turn, can be used to discover the economic repercussions of inappropriate prescribing and to quantify the cost effectiveness of various therapeutic interventions.The use of drug utilization studies in conjunction with pharmacoeconornic analysis can result in more cost effective utilization of medicines and a better utilization of pharmacoeconomy methods, both of which contribute to a more rational use of drugs

Fully Unsupervised Machine Translation Using Context-Aware Word Translation and Denoising Autoencoder

Learning machine translation by using only monolingual data sets is a complex task as there are many possible ways to connect or associate target sentences with source sentences. The monolingual word embeddings are linearly mapped on a common shared space through robust learning or adversarial training in an unsupervised way, but these learning techniques have fundamental limitations in translating sentences. In this paper, a simple yet effective method has been proposed for fully unsupervised machine translation that is based on cross-lingual sense to word embedding instead of cross-lingual word embedding and language model. We have utilized word sense disambiguation to incorporate the source language context in order to select the sense of a word more appropriately. A language model for considering target language context in lexical choices and denoising autoencoder for language insertion, deletion, and reordering are integrated. The proposed approach eliminates the problem of noisy target language context due to erroneous word translations. This work takes into account the challenge of homonyms and polysemous words in the case of morphologically rich languages. The experiments performed on English-Hindi and Hindi-English using different evaluation metrics show an improvement of +3 points in BLEU and METEOR-Hindi over the baseline system

Study of association between Vitamin D levels and HbA1C levels in children with diabetes mellitus type 1.

Aims and Objectives: To evaluate the association between diabetes mellitus type 1(T1DM) and vitamin D deficiency, to compare vitamin D deficiency and glycemic control in paediatric patients with diabetes. Materials and Methods: It’s a cross-sectional study done at civil hospital, Ahmedabad over a period of 10 months from October 2016 to July 2017. A total of 45 patients having DM type 1 in age groups between 1 to 12 years of age were included in the study. Vitamin D and HbA1C levels were done in all the patients. Patients with malnutrition, liver disease and end stage renal disease were excluded from the study. Results: The incidence of vitamin D deficiency in 75.5% among patients with T1DM. 59% of the vitamin D deficient group showed poor control with HbA1C levels >9%