Eukaryotic translation initiation is an intricate process involving at least 11 formally classified eIFs (eukaryotic initiation factors), which, together with the ribosome, comprise one of the largest molecular machines in the cell. Studying such huge macromolecular complexes presents many challenges which cannot readily be overcome by traditional molecular and structural methods. Increasingly, novel quantitative techniques are being used to further dissect such complex assembly pathways. One area of methodology involves the labelling of ribosomal subunits and/or eIFs with fluorophores and the use of techniques such as FRET (Förster resonance energy transfer) and FA (fluorescence anisotropy). The applicability of such techniques in such a complex system has been greatly enhanced by recent methodological developments. In the present mini-review, we introduce these quantitative fluorescence methods and discuss the impact they are beginning to have on the field.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1042/BST0381587 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Single-Cell Multi-Omics Profiling of Immune Cells Isolated from Atherosclerotic Plaques in Male ApoE Knockout Mice Exposed to Arsenic.
Environ Health Perspect
January 2025
Division of Experimental Medicine, Department of Medicine, McGill University, Montréal, Canada.
Background: Millions worldwide are exposed to elevated levels of arsenic that significantly increase their risk of developing atherosclerosis, a pathology primarily driven by immune cells. While the impact of arsenic on immune cell populations in atherosclerotic plaques has been broadly characterized, cellular heterogeneity is a substantial barrier to in-depth examinations of the cellular dynamics for varying immune cell populations.
Objectives: This study aimed to conduct single-cell multi-omics profiling of atherosclerotic plaques in apolipoprotein E knockout () mice to elucidate transcriptomic and epigenetic changes in immune cells induced by arsenic exposure.
Identification of the subventricular tegmental nucleus as brainstem reward center.
Science
January 2025
Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary.
Rewards are essential for motivation, decision-making, memory, and mental health. We identified the subventricular tegmental nucleus (SVTg) as a brainstem reward center. In mice, reward and its prediction activate the SVTg, and SVTg stimulation leads to place preference, reduced anxiety, and accumbal dopamine release.
View Article and Find Full Text PDFThe tumour histopathology "glossary" for AI developers.
PLoS Comput Biol
January 2025
Centre for Evolution and Cancer, Institute of Cancer Research, London, United Kingdom.
The applications of artificial intelligence (AI) and deep learning (DL) are leading to significant advances in cancer research, particularly in analysing histopathology images for prognostic and treatment-predictive insights. However, effective translation of these computational methods requires computational researchers to have at least a basic understanding of histopathology. In this work, we aim to bridge that gap by introducing essential histopathology concepts to support AI developers in their research.
View Article and Find Full Text PDFPharmacogenetics: Opportunities for the Research Program and Other Large Data Sets to Advance the Field.
Annu Rev Pharmacol Toxicol
January 2025
Clinical and Translational Science Institute, Colleges of Medicine and Pharmacy, The Ohio State University, Columbus, Ohio, USA.
Pharmacogenetic variation is common and an established driver of response for many drugs. There has been tremendous progress in pharmacogenetics knowledge over the last 30 years and in clinical implementation of that knowledge over the last 15 years. But there have also been many examples where translation has stalled because of the lack of available data sets for discovery or validation research.
View Article and Find Full Text PDFNoninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.
Transl Vis Sci Technol
January 2025
School of Optometry and Vision Science, University of New South Wales, Sydney, Australia.
Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.
Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!