Neurodegenerative Disease-Related Proteins within the Epidermal Layer of the Human Skin
Bibliographic record
Abstract
There is increasing evidence suggesting that amyloidogenic proteins might form deposits in non-neuronal tissues in neurodegenerative disorders such as Alzheimer's or Parkinson's diseases. However, the detection of these aggregation-prone proteins within the human skin has been controversial. Using immunohistochemistry (IHC) and mass spectrometry tissue imaging (MALDI-MSI), fresh frozen human skin samples were analyzed for the expression and localization of neurodegenerative disease-related proteins. While α-synuclein was detected throughout the epidermal layer of the auricular samples (IHC and MALDI-MSI), tau and Aβ34 were also localized to the epidermal layer (IHC). In addition to Aβ peptides of varying length (e.g., Aβ40, Aβ42, Aβ34), we also were able to detect inflammatory markers within the same sample sets (e.g., thymosin β-4, psoriasin). While previous literature has described α-synuclein in the nucleus of neurons (e.g., Parkinson's disease), our current detection of α-synuclein in the nucleus of skin cells is novel. Imaging of α-synuclein or tau revealed that their presence was similar between the young and old samples in our present study. Future work may reveal differences relevant for diagnosis between these proteins at the molecular level (e.g., age-dependent post-translational modifications). Our novel detection of Aβ34 in human skin suggests that, just like in the brain, it may represent a stable intermediate of the Aβ40 and Aβ42 degradation pathway.
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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) | 0.002 | 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".