A Guide for Future Therapeutics Based upon the Function of Enzymes and Proteins in Human Pathologic Metabolic Processes
Bibliographic record
Abstract
Abstract: The investigation updates information on enzymes and proteins related to their classification, functions, properties, and role in human pathology. Enzymes are any of a group of complex or conjugated proteins that are produced by living cells and act as catalysts in specific biochemical reactions. The three types of enzymes, metabolic, digestive and food-based, play key roles in the treatment of all the major sources of morbidity and mortality including cancer, dementia, diabetes, cardiac disease, and obesity. The ability to accurately target metabolic pathways and pathologic pathways allow adaptations by changing the expression of specific enzymes implicated in the pathogenesis or prevention of diseases. This overview provides a summary to guide the development of enzyme-based therapeutics. The changes of expression and activity of lipid metabolising enzymes are directly regulated by oncogenic signals. Hyper activation of the Poly (ADP-ribose) polymerase [PARP] pathway may be exploited to selectively kill cancer cells. Amyloid beta (Aβ) peptides play a major role in the pathogenesis of Alzheimer's disease (AD). Sphingolipid metabolites play important roles in the regulation of glucose metabolism. In diabetes and insulin resistance, sphingosine kinase 1 (SPK1) is the key enzyme in the sphingolipid metabolic pathway. SPK1 gene therapy may represent a novel approach to wound healing related to diabetes. Several P450s enzymes modulate important steps in the pathogenesis of ischemic heart disease (IHD). The homologous sirtuin (Sirt) family of proteins have beneficial effects in metabolism and aging-related diseases in mammalian systems. These proteins play an important role in maintaining neuronal health during aging.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".