Investigating the Role of Astrocyte-Neuron Lactate Shuttling in the Anterior Cingulate Cortex in Mouse Models of Inflammatory Pain
Bibliographic record
Abstract
Chronic pain is one of the most prevalent debilitating conditions, affecting an estimated 1 in 5 Canadians. Despite the current treatment options, many who suffer from the condition report inadequate pain management, demonstrating a need to improve our understanding of the molecular and cellular processes underlying the condition such that new therapies can be developed. Synaptic plasticity within the central nervous system has been repeatedly implicated in the functional changes to pain-associated neural networks in chronic pain. Increasing evidence shows that astrocytes contribute to synaptic plasticity and are also involved in the pathogenesis of chronic pain. The astrocyte-neuron lactate shuttle (ANLS), where astrocytes export lactate that neurons can take up and use for energy metabolism, plays a critical role in hippocampal synaptic plasticity and in animal models of long-term memory. This thesis investigated the possible contribution of the ANLS to pain-associated synaptic plasticity in the anterior cingulate cortex (ACC), an area of the brain associated with pain and emotion, and consistently shows altered activity in both clinical populations as well as in animal models of chronic pain. Following induction of chronic hindpaw inflammation, our results indicate the development of hypersensitive pain-like behavior and a temporal pattern of increased lactate levels in the ACC. Additionally, our preliminary findings suggest that inhibiting neuronal lactate uptake via monocarboxylate transporter 2 genetic knockdown may inhibit the expression of inflammation-induced pain-like behavior.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".