Correlation between coenzyme Q<sub>10</sub> content and the nutrient sensors in AKI induced by <i>Hemiscorpius lepturus</i> envenomation
Bibliographic record
Abstract
Introduction: Acute kidney injury (AKI) may have a negative effect on mitochondrial hemostasis and bioenergetics as well as coenzyme Q10 (CoQ10) content. PGC-1α, AMPK, sirtuin 1 (Sirt1), and Sirt3, as the key metabolic regulators under nutritional stress, stimulate energy production via mitochondrial biogenesis during AKI. However, no report is available on the relationship between CoQ10 level and nutrient sensors in the pathophysiology of AKI caused by Hemiscorpius lepturus scorpion envenomation. Methods: Three doses of venoms (1, 5, and 10 mg/kg) were administered by subcutaneous (SC) injection to male albino mice. The animals were sacrificed 1 day or 7 days after administration of venom and their kidneys were collected to analyze gene expression involved in AKI, nutrient sensors, and apoptosis signaling activation by real-time polymerase chain reaction (PCR) and the measurement of CoQ10 level using the High-performance liquid chromatography (HPLC) method. Results: The data indicated a significant decrease in CoQ10 level after the administration of venom in 5 and 10 mg/kg. In addition, 1 day after the treatment, a significant over-expression of Sirt1 (5 and 10 mg/kg) was observed compared with normal mice. Overexpression of Sirt3 occurred 1 day and 7 days after treatment only at the dose of 5.0 mg/kg of venom. Furthermore, over-expression of AMPK as an important mitochondrial energetic sensor happened 1 day and 7 days after the injection of venom (5 mg/kg) (P<0.01). The significant increase in the gene expression of caspase-9 and 3 after the injection of venom (5 and 10 mg/kg) confirmed the role of cell death signaling. Conclusion: The venom-induced energy-sensing pathways have a key role in gene expression of PGC-1α, AMPK, Sirt3, and CoQ10 content after venom-induced AKI.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".