14. Pre-symptomatic detection of cytoplasmic TDP-43 accumulation using tissue-engineered skin model derived from C9ORF72-FALS 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 resulting in progressive paralysis and death. Current diagnosis of ALS is based on clinical assessment of related symptoms, which appear only 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, making ALS an incurable disease. Novel strategies for early diagnosis of ALS, to monitor disease progression and to assess response to existing and future treatments are urgently needed. Many neurological disorders, including ALS, are accompanied by skin changes that often precede the onset of neurological symptoms. We have developed a unique ALS tissue-engineered skin model (ALS-TES), derived from the cells of ALS patients, in order to study the earliest stages of ALS-related skin pathology. For each participant, two skin biopsies were collected using a 6-mm diameter punch biopsy. Tissue-engineered skin was then generated from isolated keratinocytes and fibroblasts, and examined by routine histochemistry and immunohistochemistry, as well as by confocal microscopy. The ALS-TES model presents a number of striking features including altered epidermal differentiation, abnormal dermo-epidermal junction, delamination, keratinocyte infiltration, collagen disorganization and cytoplasmic TDP-43 inclusions, which are not seen in skin models derived from healthy subjects. The same abnormal skin model changes were detected skin models derived from the cells of pre- symptomatic C9orf72-linked ALS patients carrying the GGGGCC DNA repeat expansion. Consequently, our ALS-TES skin model could represent a renewable source of human tissue to better understand the physiopathological mechanisms underlying this disease, including cytoplasmic TDP43 accumulation, and lead to better 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 | 0.001 |
| 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 | 0.000 |
| 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".