Early detection of structural abnormalities and cytoplasmic accumulation of TDP-43 in tissue-engineered skins derived from ALS patients
Bibliographic record
Abstract
Amyotrophic lateral sclerosis (ALS) is an adult-onset disease characterized by the selective degeneration of motor neurons in the brain and spinal cord progressively leading to paralysis and death. Current diagnosis of ALS is based on clinical assessment of related symptoms. The clinical manifestations observed in ALS appear relatively late in the disease course after degeneration of a significant number of motor neurons. As a result, the identification and development of disease-modifying therapies is difficult. Therefore, novel strategies for early diagnosis of neurodegeneration, to monitor disease progression and to assess response to existing and future treatments are urgently needed. Factually, many neurological disorders, including ALS, are accompanied by skin changes that often precede the onset of neurological symptoms. Aiming to generate an innovative human-based model to facilitate the identification of predictive biomarkers associated with the disease, we developed a unique ALS tissue-engineered skin model (ALS-TES) derived from patient's own cells. The ALS-TES presents a number of striking features including altered epidermal differentiation, abnormal dermo-epidermal junction, delamination, keratinocyte infiltration, collagen disorganization and cytoplasmic TDP-43 inclusions. Remarkably, these abnormal skin defects, uniquely seen in the ALS-derived skins, were detected in pre-symtomatic C9orf72-linked ALS patients carrying the GGGGCC DNA repeat expansion. Consequently, our ALS skin model could represent a renewable source of human tissue, quickly and easily accessible to better understand the physiophatological mechanisms underlying this disease, to facilitate the identification of disease-specific biomarkers, and to develop innovative tools for early diagnosis and disease monitoring.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".