The protective effects of insulin-like growth factor-1 on neurochemical phenotypes of dorsal root ganglion neurons with BDE-209-induced neurotoxicity <i>in vitro</i>
Bibliographic record
Abstract
Polybrominated diphenyl ethers (PBDEs) exist extensively in the environment as contaminants, in which 2,2',3,3',4,4',5,5',6,6'-decabrominated diphenyl ether (BDE-209) is the most abundant PBDE found in human samples. BDE-209 has been shown to cause neurotoxicity of primary sensory neurons with few effective therapeutic options available. Here, cultured dorsal root ganglion (DRG) neurons were used to determine the therapeutic effects of insulin-like growth factor-1 (IGF-1) on BDE-209-induced neurotoxicity. The results showed that IGF-1 promoted neurite outgrowth and cell viability of DRG neurons with BDE-209-induced neurotoxicity. IGF-1 inhibited oxidative stress and apoptotic cell death caused by BDE-209 exposure. IGF-1 could reverse the decrease in growth-associated protein-43 (GAP-43) and calcitonin gene-related peptide (CGRP), but not neurofilament-200 (NF-200), expression resulting from BDE-209 exposure. The effects of IGF-1 could be blocked by the extracellular signal-regulated protein kinase (ERK1/2) inhibitor PD98059 and the phosphatidylinositol 3-kinase (PI3K) inhibitor LY294002, either alone or in combination. IGF-1 may play an important role in neuroprotective effects on DRG neurons with BDE-209-induced neurotoxicity through inhibiting oxidative stress and apoptosis and regulating GAP-43 and CGRP expression of DRG neurons. Both ERK1/2 and PI3K/Akt signaling pathways were involved in the effects of IGF-1. Thus, IGF-1 might be one of the therapeutic agents on BDE-209-induced neurotoxicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".