Marfan Syndrome: Using genetic maps to lead the way to new diagnostics and treatments
Bibliographic record
Abstract
Marfan Syndrome is a heritable disorder of connective tissue caused by a mutated extracellular matrix glycoprotein protein, affecting 1 in 5,000 people worldwide. This protein is responsible for support and elasticity meaning that people affected by this disorder manifest with weakened tendons, ligaments and other connective tissues. Patients exhibit a wide variety of symptoms including, scoliosis, abnormally slender digits, vision problems and enlarged blood vessels. Marfan’s follows an autosomal dominant pattern of inheritance and has a penetrance of 100%, meaning that anyone inheriting the gene will be affected by the disease. This study focuses on the developments in the field of DNA mapping and how these advancements have improved the diagnostic tools and treatments for this disease. After exploring the methodology of DNA mapping, the LOD scores for Marfan Syndrome are discussed and compared in order to conclude which chromosome carried the mutation; it was found that chromosome 15 carries. Additionally, the results compare and contrast different genetic markers and identifies a link between markers D15529 and D15545. Although this technology is fairly recent and has thus not been studied as extensively as traditional methods, the information gathered in this research illustrates the methodology of DNA mapping and how; by understanding the gene expression and mutation at a biochemical level, diagnostics and treatments for patients can be tailored specifically to the disease and not just management of the symptoms.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".