Curcumin Analogs Reduce Stress and Inflammation Indices in Experimental Models of Diabetes
Why this work is in the frame
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Bibliographic record
Abstract
Chronic inflammation and oxidative stress lead to a multitude of adverse cellular responses in target organs of chronic diabetic complications. Curcumin, a highly investigated phytochemical, has been shown to exhibit both anti-inflammatory and antioxidant activities. However, the clinical application of curcumin has been greatly limited due to a poor pharmacokinetic profile. To overcome these limitations, we have generated analogues of curcumin to enhance bioavailability and offer a preferable pharmacokinetic profile. Here, we explored the effects of two mono-carbonyl curcumin analogues, L2H21 and L50H46, in alleviating indices of inflammation and oxidative stress in cell culture and mouse model of diabetic complications. Our results show that L2H21 and L50H46 normalize inflammatory mediators (IL-6 and TNF-α), extracellular matrix proteins (FN and COL4α1), vasoactive factors (VEGF and ET-1) and a key transcriptional coactivator (p300) in cultured human retinal microvascular endothelial cells (HRECs) and dermal-derived microvascular endothelial cells (HMVECs) challenged with high levels of glucose. These curcumin analogues also reduced glucose-induced oxidative DNA damage as evidenced by 8-OHdG labeling. We further show that treatment of streptozotocin-induced diabetic mice with curcumin analogues prevents cardiac and renal dysfunction. The preservation of target tissue function was associated with normalization of pro-inflammatory cytokines and matrix proteins. Collectively, our results show that L2H21 and L50H46 offer the anti-inflammatory and antioxidant activities as has been reported for curcumin, and may provide a clinically applicable therapeutic option for the treatment of diabetic complications.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it