Morphological and Mechanical Abnormalities Detected in Tissue-Engineered Reconstructed Skin Equivalents Derived From ALS Patients (P1.078)
Bibliographic record
Abstract
OBJECTIVE: To develop articifial skin model, derived from ALS patients, to facilitate the identification of biomarkers for early diagnosis and disease progression. BACKGROUND: It has been repeatedly noted, but never as yet fully explained, that many neurological conditions are accompanied by skin changes, which frequently appear before the onset of neurological symptoms. For instance, there is a growing body of literature on apparently unique skin changes occuring in ALS patients. While these interesting findings reveal that it is possible to detect changes in biopsied skin samples collected from ALS patients, it is diffucult to fing any significant corrélation between these skin changes and the disease due the limited size of the biopsies and lack of validation. DESIGN/METHODS: We developed a unique tissue-engineered reconstructed skin model derived from ALS patients. A systematic approach will be use to determine how each of the structural, biochemical, molecular and histopathological findings (alone or in combination with one another) can be predictable for ALS or can be use as biomarkers. RESULTS: So far our preliminary results show that it is possible to detect a number of pathological features associated with ALS using this in vitro reconstructed skin model. Of particular interest, TDP-43 misexpression and mislocalisation have been detected in our skin model derived from patients. CONCLUSIONS: Our reconstructed skin equivalents could represent a renewable source of human tissue, derived from patient’s own cells, to better understand the physiophatological mechanisms underlying these diseases and hopefully to identify and validate specific disease biomarkers Study Supported by: ALS Society of Canada
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".