Социально-экономическое и политическое развитие западноадыгского социума в последней четверти XV-первой половине xvi вв
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Neurons are highly sensitive to metabolic and oxidative injury, but endogenous astrocyte mechanisms have a critical capacity to provide protection from these stresses. We previously reported that the master regulator PGC-1α (peroxisome proliferator-activated receptor gamma coactivator-1α) is necessary for retinal astrocytes to mount effective injury responses, with particular regard to oxidative stress. Yet, this pathway has not been well studied in glia. PGC-1α is a transcriptional co-activator that is dysregulated in a variety of neurodegenerative diseases. It functions as a master regulator of cellular bioenergetics, with the ability to regulate tissue specific responses. A key inducer of PGC-1α signaling is adenosine monophosphate-activated kinase (AMPK). Thus, the AMPK-PGC-1α signaling axis coordinates metabolic and oxidative damage responses in the central nervous system (CNS). Here we report that AMPK selectively regulates expression of GCLM (glutamate cysteine ligase modulatory subunit) in astrocytes, but not neurons, through PGC-1α activation. Glutamate cysteine ligase (GCL) is the rate limiting enzyme in the biosynthesis of glutathione (GSH); a critical antioxidant and detoxifying peptide in the CNS. Through this mechanism we describe PGC-1α-dependent induction of GSH synthesis and antioxidant activity in astrocytes, and in the rodent retina in vivo. Furthermore, we demonstrate that therapeutic agonism of this pathway with the AMP mimetic, AICAR, rescues GSH levels in vivo, while reducing RGC death and astrocyte reactivity, following retinal ischemia/reperfusion injury. This mechanism presents a novel strategy for enhancing protective astrocyte antioxidant capacity in the CNS.
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.
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 | 0.013 | 0.014 |
| Meta-epidemiology (narrow) | 0.015 | 0.016 |
| Meta-epidemiology (broad) | 0.014 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.014 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.049 | 0.036 |
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