Pre-symptomatic Detection of Cytoplasmic TDP-43 Accumulation Using Tissue-Engineered Skin Model Derived From C9ORF72-FALS Patients (P4.082)
Bibliographic record
Abstract
OBJECTIVE : Our objective is to reproduce the ALS-specific skin changes in a tissue-engineered skin derived from patients and use it to establish an early diagnosis of the disease. BACKGROUND : Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease affecting the brain and spinal cord’s motor neurons. It is impossible to predict the evolution of the disease since there is no known biomarkers and no early diagnosis methods available. DESIGN/METHODS : Tissue-engineered skin (TES) derived from ALS patients was developed as a new in vitro model to develop a diagnostic approach. Skin cells were isolated from a punch biopsy in order to generate TES as described. Standard and specialised colorations were used to characterize the TES. Histological slides were analysed using conventional microscopy. Protein expression was also confirmed by immunostaining and Western Blotting. RESULTS : Masson’s trichrome coloration revealed a number of structural abnormalities in ALS-TES including an undifferentiated epidermis, cohesive failure of the stratum corneum, abnormal dermo-epidermal junction, delamination and keratinocyte infiltration in FALS-linked C9ORF72 derived skins. This study also revealed TDP-43 mislocalization in the TES. These patients are still alive and have not yet developed any motor or dementia associated clinical 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".