A mechanistic overview of spinal cord injury, oxidative DNA damage repair and neuroprotective therapies
Bibliographic record
Abstract
Despite substantial development in medical treatment strategies scientists are struggling to find a cure against spinal cord injury (SCI) which causes long term disability and paralysis. The prime rationale behind it is the enlargement of primary lesion due to an initial trauma to the spinal cord which spreads to the neighbouring spinal tissues It begins from the time of traumatic event happened and extends to hours and even days. It further causes series of biological and functional alterations such as inflammation, excitotoxicity and ischemia, and promotes secondary lesion to the cord which worsens the life of individuals affected by SCI. Oxidative DNA damage is a stern consequence of oxidative stress linked with secondary injury causes oxidative base alterations and strand breaks, which provokes cell death in neurons. It is implausible to stop primary damage however it is credible to halt the secondary lesion and improve the quality of the patient's life to some extent. Therefore it is crucial to understand the hidden perspectives of cell and molecular biology affecting the pathophysiology of SCI. Thus the focus of the review is to connect the missing links and shed light on the oxidative DNA damages and the functional repair mechanisms, as a consequence of the injury in neurons. The review will also probe the significance of neuroprotective strategies in the present scenario. HIGHLIGHTSSpinal cord injury, a pernicious condition, causes excitotoxicity and ischemia, ultimately leading to cell death.Oxidative DNA damage is a consequence of oxidative stress linked with secondary injury, provoking cell death in neurons.Base excision repair (BER) is one of the major repair pathways that plays a crucial role in repairing oxidative DNA damages.Neuroprotective therapies curbing SCI and boosting BER include the usage of pharmacological drugs and other approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".