18<sup>th</sup> International Conference of Biochemistry & Molecular Biology (18<sup>th</sup> ICBMB)
Bibliographic record
Abstract
Multiple Sclerosis (MS) is one of the most common neurological disorders affecting the central nervous system.Globally, there are over 2.5 million cases of MS, with the Middle Eastern and North African nations classified as low-to moderaterisk region for the disease.MS is characterized by chronic inflammation, demyelination, and neurodegeneration, leading to a wide range of clinical symptoms.Neurotrophic factors, such as long non-coding RNAs (lncRNAs), play crucial roles in the development, survival, and maintenance of neurons.Using real-time PCR, we quantified the expression levels of neurotrophic lncRNAs, focusing on those previously implicated in neuroprotection, synaptic plasticity, and cognitive impairment.Our findings revealed distinct expression profiles of these lncRNAs across different MS subtypes, suggesting their potential involvement in disease pathogenesis and progression.In conclusion, our investigation utilizing real-time PCR highlights the significance of neurotrophic lncRNAs in the context of MS.Further exploration of these lncRNAs and their functional roles may contribute to the development of novel therapeutic strategies for MS patients, ultimately improving their quality of life.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.256 | 0.145 |
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".