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Clusterbean (2n=14), also known as guar (Cyamopsis tetragonoloba (L.) Taub.) is relatively difficult to work in molecular biology because of the high polysaccharide such as galactomannan content of its endosperm and the high polyphenol content of its leaves. This study aimed to establish a robust genomic-DNA extraction protocol for clusterbean. The efficacy of DNA extraction protocol for a PCR quality genomic DNA extraction was found using DNA fingerprinting. This experiment involves extraction of DNA using a wash buffer containing Tris-HCl, PVP and addition of ↓- mercaptoethanol separately in each sample and extraction buffer containing CTAB, Tris-HCI, EDTA, NaCl and PVP followed by purification of DNA with RNAase, phenol : chloroform: isoamlyalcohol and finally precipitation of DNA by chilled isopropanol. The protocol is simple and has no special requisites. In terms of quantity (up to1009.5-2119.1 ng/μl) and quality (A260/280 = 1.76 to 1.87) the present method has advantages over many other plant DNA extraction protocols. This protocol can probably be extended also to other leguminous species mainly pulses. [ABSTRACT FROM AUTHOR]

Sphincter-preserving approaches to treat anal fistula do not jeopardize continence; however, healing rates are suboptimal. In this context, ligation of the intersphincteric fistula tract (LIFT) can be considered promising offering high success rates and a relatively simple procedure. This review aimed to investigate the outcomes of LIFT to treat anal fistula. We conducted a systematic review of the Pubmed, Web of Science, and Cochrane databases, to retrieve all relevant scientific original articles and scientific abstracts (Web of Science) related to the LIFT procedure for anal fistula between January 2007 and March 2013. The search yielded 24 original articles including 1,110 patients; these included one randomized controlled study, three case control studies, and 20 case series. Most studies included patients with trans-sphincteric or complex fistula, not amenable to fistulotomy. During a pooled mean 10.3 months of follow-up, the mean success, incontinence, intraoperative, and postoperative complication rates were 76.4, 0, 0, and 5.5 %, respectively. A sensitivity analysis showed that the impact on success in terms of follow-up duration, study size, and combining other procedures was limited. There was no association between pre-LIFT drainage seton and success of LIFT. Ligation of the intersphincteric fistula tract appears to be an effective and safe treatment for trans-sphincteric or complex anal fistula. Combining other procedures and a pre-LIFT drainage seton does not seem to confer any added benefit in terms of success. However, given the lack of prospective randomized trials, interpretation of these data must be cautious. Further trials are mandatory to identify predictive factors for success, and true effectiveness of the LIFT compared to other sphincter-preserving procedures to treat anal fistula. [ABSTRACT FROM AUTHOR]

Usage of internet and social media backgrounds tends in the use of sending, receiving and posting of negative, harmful, false or mean content about another individual which thus means Cyberbullying. Bullying over social media also works the same as threatening, calumny, and chastising the individual. Cyberbullying has led to a severe increase in mental health problems, especially among the young generation. It has resulted in lower self-esteem, increased suicidal ideation. Unless some measure against cyberbullying is taken, self-esteem and mental health issues will affect an entire generation of young adults. Many of the traditional machine learning models have been implemented in the past for the automatic detection of cyberbullying on social media. But these models have not considered all the necessary features that can be used to identify or classify a statement or post as bullying. In this paper, we proposed a model based on various features that should be considered while detecting cyberbullying and implement a few features with the help of a bidirectional deep learning model called BERT.