Amyotrophic Lateral Sclerosis, a Multisystem Pathology: Insights into the Role of TNF<i>α</i>
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is considered a multifactorial, multisystem disease in which inflammation and the immune system play important roles in development and progression. The pleiotropic cytokine TNFαis one of the major players governing the inflammation in the central nervous system and peripheral districts such as the neuromuscular and immune system. Changes in TNFαlevels are reported in blood, cerebrospinal fluid, and nerve tissues of ALS patients and animal models. However, whether they play a detrimental or protective role on the disease progression is still not clear. Our group and others have recently reported opposite involvements of TNFR1 and TNFR2 in motor neuron death. TNFR2 mediates TNFαtoxic effects on these neurons presumably through the activation of MAP kinase-related pathways. On the other hand, TNFR2 regulates the function and proliferation of regulatory T cells (Treg) whose expression is inversely correlated with the disease progression rate in ALS patients. In addition, TNFαis considered a procachectic factor with a direct catabolic effect on skeletal muscles, causing wasting. We review and discuss the role of TNFαin ALS in the light of its multisystem nature.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".