Endocrine Imbalance Associated With Proteome Changes in Diabetes
Bibliographic record
Abstract
ABSTRACT The dynamics of cellular metabolism involves rapid interactions between proteins and nucleic acids, proteins and proteins, and signaling. These involve the interactions with respect to the sulfur bond, noncovalent electrostatic interactions, protein structure stabilization and protein‐ligand binding, weak electrostatic interactions in proteins, oxygen radicals that initiate a change in conformation and a chain of events. We review a development in molecular medicine that is a very promising work in progress. We also review the current and future research methods involving mitochondria. Long‐term effects of diabetes include glycation of proteins, for example, glycohemoglobin (HbA1c), increased risk of cardiovascular diseases, atherosclerosis, retinopathy, nephropathy, and neurological dysfunctions. Tissues are exposed to significant quantities of highly reactive chemical species including nitric oxide •NO and reactive oxygen species ROS over months to years, to an extent generated by mitochondrial activities. The reactions of •NO can be broadly discussed with reference to three main processes which control their fate in biological systems: (1) diffusion and intra‐cellular consumption; (2) autooxidation to form nitrous anhydride N2O3; and (3) reaction with superoxide O2•‐ to form peroxynitrite ONOO–. Reactive nitrogen species produced by macrophages and neutrophils in the interstitial space, with emphasis on •NO, N2O3, ONOO–, and nitrogen dioxide radicals •NO2 generate protein and DNA damage. Serum thiol (‐SH) groups act as an important extracellular scavenger of peroxides and are therefore helpful in protecting the surrounding tissues. The events described here are a homeostatic endocrine imbalance that is associated with proteostasis. The advances we have seen in untangling this web of interactions are sure to continue at a breathtaking pace. J. Cell. Biochem. 118: 3569–3576, 2017. © 2017 Wiley Periodicals, Inc.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".