Objective: To find out the incidence of complications of Endoscopic Variceal Band Ligation, when done under Propofol sedation. Methods: This was a retrospective observational study, which was conducted in the Gastroenterology department, PAF Hospital. From 1st Feb 2019 to 30th June 2023. Patients who underwent Endoscopic Variceal Band Ligation were included in the study. Anesthesia was provided using Propofol and Midazolam. Endoscopic variceal band ligation was carried out by a consultant gastroenterologist. Incidence of complications during the procedure and within 48 hours was noted. Results: Out of 385 patients included in the study, re-bleeding was reported in 8 patients (2.1%), bacteremia in 8 patients (2.1%), hypotension in 77 patients (20%), and hypoxia in 56 patients (14.5%). The rest of the complications such as perforation, Mallory-Weis tear, cardiac arrhythmias, and myocardial infarction were not observed in any patient. Conclusion: Propofol is a safe drug for deep sedation during Endoscopic Variceal Band Ligation associated with reduced complication risk.
N7-methylguanosine (m7G) is one of the most important epigenetic modifications found in rRNA, mRNA, and tRNA, and performs a promising role in gene expression regulation. Owing to its significance, well-equipped traditional laboratory-based techniques have been performed for the identification of N7-methylguanosine (m7G). Consequently, these approaches were found to be time-consuming and cost-ineffective. To move on from these traditional approaches to predict N7-methylguanosine sites with high precision, the concept of artificial intelligence has been adopted. In this study, an intelligent computational model called N7-methylguanosine-Long short-term memory (m7G-LSTM) is introduced for the prediction of N7-methylguanosine sites. One-hot encoding and word2vec feature schemes are used to express the biological sequences while the LSTM and CNN algorithms have been employed for classification. The proposed “m7G-LSTM” model obtained an accuracy value of 95.95%, a specificity value of 95.94%, a sensitivity value of 95.97%, and Matthew’s correlation coefficient (MCC) value of 0.919. The proposed predictive m7G-LSTM model has significantly achieved better outcomes than previous models in terms of all evaluation parameters. The proposed m7G-LSTM computational system aims to support the drug industry and help researchers in the fields of bioinformatics to enhance innovation for the prediction of the behavior of N7-methylguanosine sites.
In eukaryotic cells, Piwi-interacting RNAs (piRNAs) are the type of short chain non-coding RNA molecules, which interconnect with PIWI proteins. It performs various cellular and genetic functions such as gene-specific protein translation, expression regulation, maintenance, and formulation of germ cells. Seeing the prominent contribution of piRNA in eukaryotic organism cells, many attempts were made to identify it computationally, however, unsatisfactory results were obtained. So, it is requisite to extend the concept of a computational tool in such a way that accurately represents piRNA. In this regard, intelligent and high discriminative deep learning i.e., the convolutional neural network based sequential-computational model known as "piRNA-CNN" is carried out for the prediction of piRNA. RNA sequences are mathematically expressed using the natural language processing method namely: word2vec in order to get prominent, relevant, and high variated numerical descriptors. The proposed "piRNA-CNN" model yields an accuracy of 93.83% for the first-layer in which the provided query RNA molecule is predicted as non-piRNA or piRNA. In case of the piRNA, the proposed model identified the query as mRNA deadenylation or without deadenylation in the second layer, and achieved 91.19% of accuracy. The obtained outcomes authenticated that the piRNA-CNN model exposed substantial results matched to the current tools stated in the literature, so far. It is further expected that the suggested predictive tool will assist scientists and researchers to design improved computational tools.
Pseudouridine is the most prevalent RNA modification and has been found in both eukaryotes and prokaryotes. Currently, pseudouridine has been demonstrated in several kinds of RNAs, such as small nuclear RNA, rRNA, tRNA, mRNA, and small nucleolar RNA. Therefore, its significance to academic research and drug development is understandable. Through biochemical experiments, the pseudouridine site identification has produced good outcomes, but these lab exploratory methods and biochemical processes are expensive and time consuming. Therefore, it is important to introduce efficient methods for identification of pseudouridine sites. In this study, an intelligent method for pseudouridine sites using the deep-learning approach was developed. The proposed prediction model is called iPseU-CNN (identifying pseudouridine by convolutional neural networks). The existing methods used handcrafted features and machine-learning approaches to identify pseudouridine sites. However, the proposed predictor extracts the features of the pseudouridine sites automatically using a convolution neural network model. The iPseU-CNN model yields better outcomes than the current state-of-the-art models in all evaluation parameters. It is thus highly projected that the iPseU-CNN predictor will become a helpful tool for academic research on pseudouridine site prediction of RNA, as well as in drug discovery.
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