Phyto-assisted synthesis of Silver nanoparticles using Tinospora cordifolia leaf extract and their antibacterial activity: An ecofriendly approach

To meet the increasing demands for commercial nanoparticles new eco-friendly methods of synthesis are being discovered. Plant mediated synthesis of nanoparticles offers single step, easy extracellular synthesis of nanoparticles. We report the synthesis of antibacterial Silver nanoparticles using leaf extract of the medicinal plant, Tinospora cordifolia. The leaf extract was prepared by boiling chopped leaves of Tinospora cordifolia in deionized water for 10 min and filtering the mixture with Whatman filter paper No.1. The filtrate was used as a reducing agent and stabilising agent for AgNO3. On adding 1 mM solution of Silver nitrate to the leaf extract and stirring at 75 °C for 25 min, a change in colour from yellow-brown to brown-black specified the production of Silver nanoparticles. The formation of Silver nanoparticles was monitored by UV-visible spectroscopy and further characterization of the synthesized Silver nanoparticles was done by XRD studies. The antibacterial studies were performed on Gram negative and Gram positive pathogens, Salmonella typhi, Pseudomonas aeruginosa, Enterobacter aerogenes and Staphylococcus aureus, by agar well diffusion method, on Mueller Hinton agar medium. The Silver nanoparticles synthesized from Tinospora cordifolia leaf extract were found to have antimicrobial activity against these Gram negative and Gram positive pathogenic bacteria

A systematic review and meta-analysis of CD22 CAR T-cells alone or in combination with CD19 CAR T-cells

Chimeric antigen receptor (CAR) T-cells are an emerging therapy for the treatment of relapsed/refractory B-cell malignancies. While CD19 CAR-T cells have been FDA-approved, CAR T-cells targeting CD22, as well as dual-targeting CD19/CD22 CAR T-cells, are currently being evaluated in clinical trials. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of CD22-targeting CAR T-cell therapies. We searched MEDLINE, EMBASE, Web of Science, and the Cochrane Central Register of Controlled Trials from inception to March 3rd 2022 for full-length articles and conference abstracts of clinical trials employing CD22-targeting CAR T-cells in acute lymphocytic leukemia (ALL) and non-Hodgkin’s lymphoma (NHL). The primary outcome was best complete response (bCR). A DerSimonian and Laird random-effects model with arcsine transformation was used to pool outcome proportions. From 1068 references screened, 100 were included, representing 30 early phase studies with 637 patients, investigating CD22 or CD19/CD22 CAR T-cells. CD22 CAR T-cells had a bCR of 68% [95% CI, 53-81%] in ALL (n= 116), and 64% [95% CI, 46-81%] in NHL (n= 28) with 74% and 96% of patients having received anti-CD19 CAR T-cells previously in ALL and NHL studies respectively. CD19/CD22 CAR T-cells had a bCR rate of 90% [95% CI, 84-95%] in ALL (n= 297) and 47% [95% CI, 34-61%] in NHL (n= 137). The estimated incidence of total and severe (grade ≥3) CRS were 87% [95% CI, 80-92%] and 6% [95% CI, 3-9%] respectively. ICANS and severe ICANS had an estimated incidence of 16% [95% CI, 9-25%] and 3% [95% CI, 1-5%] respectively. Early phase trials of CD22 and CD19/CD22 CAR T-cells show high remission rates in ALL and NHL. Severe CRS or ICANS were (1)rare and dual-targeting did not increase toxicity. Variability in CAR construct, dose, and patient factors amongst studies limits comparisons, with long-term outcomes yet to be reported.Systematic review registrationhttps://www.crd.york.ac.uk/prospero, identifier CRD42020193027

Panoramic Human Structure Maintenance based on Invariant Features of Video Frames

[[abstract]]Panoramic photography is becoming a very popular and commonly available feature in the mobile handheld devices nowadays. In traditional panoramic photography, the human structure often becomes messy if the human changes position in the scene or during the combination step of the human structure and natural background. In this paper, we present an effective method in panorama creation to maintain the main structure of human in the panorama. In the proposed method, we use an automatic method of feature matching, and the energy map of seam carving is used to avoid the overlapping of human with the natural background. The contributions of this proposal include automated panoramic creation method and it solves the human ghost generation problem in panorama by maintaining the structure of human by energy map. Experimental results prove that the proposed system can be effectively used to compose panoramic photographs and maintain human structure in panorama.[[incitationindex]]SCI[[booktype]]電子

Assessing the Completeness of Reporting in Preclinical Oncolytic Virus Therapy Studies

Irreproducibility of preclinical findings could be a significant barrier to the “bench-to-bedside” development of oncolytic viruses (OVs). A contributing factor is the incomplete and non-transparent reporting of study methodology and design. Using the NIH Principles and Guidelines for Reporting Preclinical Research, a core set of seven recommendations, we evaluated the completeness of reporting of preclinical OV studies. We also developed an evidence map identifying the current trends in OV research. A systematic search of MEDLINE and Embase identified all relevant articles published over an 18 month period. We screened 1,554 articles, and 236 met our a priori-defined inclusion criteria. Adenovirus (43%) was the most commonly used viral platform. Frequently investigated cancers included colorectal (14%), skin (12%), and breast (11%). Xenograft implantation (61%) in mice (96%) was the most common animal model. The use of preclinical reporting guidelines was listed in 0.4% of articles. Biological and technical replicates were completely reported in 1% of studies, statistics in 49%, randomization in 1%, blinding in 2%, sample size estimation in 0%, and inclusion/exclusion criteria in 0%. Overall, completeness of reporting in the preclinical OV therapy literature is poor. This may hinder efforts to interpret, replicate, and ultimately translate promising preclinical OV findings

Detection of simple and complex de novo mutations with multiple reference sequences.

The characterization of de novo mutations in regions of high sequence and structural diversity from whole-genome sequencing data remains highly challenging. Complex structural variants tend to arise in regions of high repetitiveness and low complexity, challenging both de novo assembly, in which short reads do not capture the long-range context required for resolution, and mapping approaches, in which improper alignment of reads to a reference genome that is highly diverged from that of the sample can lead to false or partial calls. Long-read technologies can potentially solve such problems but are currently unfeasible to use at scale. Here we present Corticall, a graph-based method that combines the advantages of multiple technologies and prior data sources to detect arbitrary classes of genetic variant. We construct multisample, colored de Bruijn graphs from short-read data for all samples, align long-read-derived haplotypes and multiple reference data sources to restore graph connectivity information, and call variants using graph path-finding algorithms and a model for simultaneous alignment and recombination. We validate and evaluate the approach using extensive simulations and use it to characterize the rate and spectrum of de novo mutation events in 119 progeny from four Plasmodium falciparum experimental crosses, using long-read data on the parents to inform reconstructions of the progeny and to detect several known and novel nonallelic homologous recombination events

Thick Line Segment Detection with Fast Directional Tracking

International audienceThis paper introduces a fully discrete framework for a new straight line detector in gray-level images, where line segments are enriched with a thickness parameter intended to provide a quality criterion on the extracted feature. This study is based on a previous work on interactive line detection in gray-level images. At first, a better estimation of the segment thickness and orientation is achieved through two main improvements: adaptive directional scans and control of assigned thickness. Then, these advances are exploited for a complete unsupervised detection of all the line segments in an image. The new thick line detector is left available in an online demonstration