Reactive Oxygen Species and Oxidative Stress in Obesity—Recent Findings and Empirical Approaches
Bibliographic record
Abstract
Objective High levels of reactive oxygen species (ROS) are intricately linked to obesity and associated pathologies, notably insulin resistance and type 2 diabetes. However, ROS are also thought to be important in intracellular signaling, which may paradoxically be required for insulin sensitivity. Many theories have been developed to explain this apparent paradox, which have broadened our understanding of these important small molecules. While many sites for intracellular ROS production have been described, mitochondrial generated ROS remain a major contributor in most cell types. Mitochondrial ROS generation is controlled by a number of factors described in this review. Moreover, these studies have established both a demand for novel sensitive approaches to measure ROS, as well as a need to standardize and review their suitability for different applications. Methods To properly assess levels of ROS and mitochondrial ROS in the development of obesity and its complications, a growing number of tools have been developed. This paper reviews many of the common methods for the investigation of ROS in mitochondria, cell, animal, and human models. Results Available approaches can be generally divided into those that measure ROS‐induced damage (e.g., DNA, lipid, and protein damage); those that measure antioxidant levels and redox ratios; and those that use novel biosensors and probes for a more direct measure of different forms of ROS (e.g., 2′,7′‐di‐chlorofluorescein (DCF), dihydroethidium (DHE) and its mitochondrial targeted form (MitoSOX), Amplex Red, roGFP, HyPer, mt‐cpYFP, ratiometric H2O2 probes, and their derivatives). Moreover, this review provides caveats and strengths for the use of these techniques in different models. Conclusions Advances in these techniques will undoubtedly advance the understanding of ROS in obesity and may help resolve unanswered questions in the field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".