Generating a Peptide Library Using the Repeats of Amino Acid Scaffolds Created by Sliding the Framework of a 7-mer Human Chemerin Segment and Discovery of Potent Antibacterial and Antimycobacterial Peptides.

The quest for new approaches for generating novel bioactive designer proteins/peptides has continued with their success in various biomedical applications. Previously, we designed a 14-mer α-helical peptide with antimicrobial and antimycobacterial activities by employing a tandem repeat of the 7-mer, "KVLGRLV" human chemerin segment. Herein, we devised a new method of "sliding framework" with this segment to create amino acid scaffolds of varying sizes and sequences and explored the design of a peptide library with antibacterial and antimycobacterial activities.

Hybrid of Deep Feature Extraction and Machine Learning Ensembles for Imbalanced Skin Cancer Datasets.

Skin cancer remains one of the most common and deadly forms of cancer, necessitating accurate and early diagnosis to improve patient outcomes. In order to improve classification performance on unbalanced datasets, this study proposes a distinctive approach for classifying skin cancer that utilises both machine learning (ML) and deep learning (DL) methods. We extract features from three different DL models (DenseNet201, Xception, Mobilenet) and concatenate them to create an extensive feature set.

Coagulopathy in acute liver failure.

Acute liver failure (ALF) is a rare but rapidly progressing syndrome, marked by severe liver dysfunction and altered mental status. While definitions of ALF vary across different guidelines, with timelines ranging from 4 to 26 weeks between jaundice onset and encephalopathy, the key defining features remain encephalopathy and coagulopathy. Elevated coagulation markers, particularly prothrombin time and international normalized ratio, have traditionally been associated with bleeding risks.

Inhibiting de novo lipogenesis identifies a therapeutic vulnerability in therapy-resistant colorectal cancer.

A significant clinical challenge in patients with colorectal cancer (CRC), which adversely impacts patient survival, is the development of therapy resistance leading to a relapse. Therapy resistance and relapse in CRC is associated with the formation of lipid droplets (LD) by stimulating de novo lipogenesis (DNL). However, the molecular mechanisms underlying the increase in DNL and the susceptibility to DNL-targeted therapies remain unclear.

Molecular modeling of cellulose tosylate immobilized α-amylases: An in silico case study through MD simulation and refinement.

The use of enzymes as catalysts in industrial processes has been studied, and they offer more ecologically friendly options for chemical reactions. In the current work, we investigated the potential of molecular modeling to solve the ordinarily difficult problem of identifying the amino acids involved in the covalent mode of immobilization by in silico investigations. The immobilized α-Amylase on Cellulose tosylate (henceforth referred to as Celltos) shows extra peaks of OH and NH, CN, SO, C-O-C, and CS.