Bibliographic record
Abstract
Amyotropic lateral sclerosis (ALS) is a devastating disease that can produce upper and lower motor neuron deficits secondary to degeneration of spinal cord neurons and brain stem motor nuclei. Since before Lou Gehrig's epic struggle, researchers have worked to understand the pathophysiology of ALS, with the hope of translating scientific insight into clinical treat-ment paradigms. Currently, most ALS patients die within 5 years of diagnosis, although a modest improvement in survival with administration of riluzole, an inhibitor of presynaptic glutamate release, has offered some hope. To date the majority of bench research has focused on the anterior horn cells in the spinal cord, based on the premise that the ultimate fate of these patients is due to focal cellular deficits in neuronal function. However, recent findings by Clement and colleagues published in Science may lead researchers to look at ALS and other neurodegenerative diseases differently. In this important collaborative effort, scientists from the University of California San Diego, McGill University, Harvard University, and Boston University provide strong evidence that defects in nonneuronal cells may be responsible for the clinical sequelae of ALS (Science 302:113–117, 2003).Figure: Henry Louis Gehrig (1903–1941), American professional baseball player.ALS is commonly studied in mice with super oxide dismutase (SOD) mutations that exert a toxic effect leading to neuronal cell death and ALS-like disease. By generating chimeric mice with SOD mutations, Clement and colleagues demonstrated that decreasing the number of neuronal and nonneuronal cells containing the toxic SOD mutation led to a significant amelioration of the disease. This clinical improvement correlated with decreased axonal degeneration, astrocytosis, and microgliosis. The authors then focused on chimeric mice without wild-type neuronal cells in the spinal cord in order to evaluate the relative protective effect of various cell populations. In a series of elegant experiments, the authors found that mutant neuronal cell survival was affected by surrounding nonneuronal cells. The number of wild-type nonneuronal cells seemed to correlate with neuronal survival and concordant improvement in clinical outcome. Conversely, wild- type neuronal cells that were surrounded by mutant nonneuronal cells had increased cell death with the development of end-stage disease, more so than in mice expressing high levels of mutant neuronal SOD. These results suggest that the ability of mutant SOD to cause ALS in mice is mediated more by its effects in nonneuronal cells than neuronal cells. Furthermore, wild-type nonneuronal cells appear to rescue or ameliorate SOD mediated neuronal cell death and hence slow the progression of ALS in these mice. The authors conclude from their study that motor neuron death in ALS could be a result of damage to multiple types of adjacent cells such as inter-neurons, astrocytes, and microglia. The findings of Clement and colleagues have important implications in the treatment of patients with ALS and other neurodegenerative disease. Translating these results into an effective treatment will depend upon more specifically defining the nonneuronal cells that are most protective. Ultimately, pharmacologic agents, stem cell therapies, and gene therapy strategies may be more successful in treating ALS patients by targeting these nonneuronal cell populations. RENE SANCHEZ-MEJIA M.D. ANDREW T. PARSA, M.D., PH.D. TRANSLATIONAL RESEARCH
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".