Localized delivery and retention of hydrogen sulfide causing regional lipid accumulation in mouse adipose tissues in vivo
Bibliographic record
Abstract
Abstract Therapeutical application of hydrogen sulfide (H2S) is limited due to the lack of delivery routes for specific organs and the rapid and wide dispersal of H2S in vivo. While H2S shows adipogenic effects in vitro, its in vivo impacts on obesity remain unclear. This study applies a H2S-slow-releasing hydrogel (H2S gel) to deliver H2S locally in subcutaneous adipose tissue and examines local lipid accumulation in mice. H2S is released from H2S gels within 6 h and lasts for 72 h, elevating H2S levels in local adipose tissue but not in the plasma. Localized H2S gel delivery causes significant lipid accumulation and larger lipid droplet diameter in mouse adipose tissues. The expressions of sterol regulatory element-binding protein, peroxisome proliferator-activated receptor-γ, adiponectin, and perilipin are all upregulated by H2S gel injections. Local delivery and retention of H2S in adipose tissues increase lipid accumulation more in wild-type than in cystathionine-γ-lyase knockout mice. This study confirms the feasibility of selectively delivering H2S via injectable hydrogels and their effectiveness in regulating targeted tissue functions. Furthermore, this work deepens our understanding of the role of H2S in obesity development under physiological conditions and offers a practical implementation strategy for H2S-based therapeutic interventions.
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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 | 0.000 |
| 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".