(Re)building the nervous system: A review of neuron–glia interactions from development to disease
Bibliographic record
Abstract
Neuron-glia interactions are fundamental to the development and function of the nervous system. During development, glia, including astrocytes, microglia, and oligodendrocytes, influence neuronal differentiation and migration, synapse formation and refinement, and myelination. In the mature brain, glia are crucial for maintaining neural homeostasis, modulating synaptic activity, and supporting metabolic functions. Neurons, inherently vulnerable to various stressors, rely on glia for protection and repair. However, glia, in their reactive state, can also promote neuronal damage, which contributes to neurodegenerative and neuropsychiatric diseases. Understanding the dual role of glia-as both protectors and potential aggressors-sheds light on their complex contributions to disease etiology and pathology. By appropriately modulating glial activity, it may be possible to mitigate neurodegeneration and restore neuronal function. In this review, which originated from the International Society for Neurochemistry (ISN) Advanced School in 2019 held in Montreal, Canada, we first describe the critical importance of glia in the development and maintenance of a healthy nervous system as well as their contributions to neuronal damage and neurological disorders. We then discuss potential strategies to modulate glial activity during disease to protect and promote a properly functioning nervous system. We propose that targeting glial cells presents a promising therapeutic avenue for rebuilding the nervous system.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".