Additional file 1 of Neuronal SNCA transcription during Lewy body formation
Bibliographic record
Abstract
Additional file 1: Fig. S1. Method of outlining the region of interest and capturing the positive signals in RNAscope. Raw data image of the SN section in a case of Lewy body disease (A). Lookup Table strength of the cell-specific marker and DAPI channels are increased enough to make the level of autofluorescence of neuromelanin and/or lipofuscin pigments visible (B). Then, the cell body borders are traced by their signals as total cell area (C). DAPI channel is selected and the nuclear edge is outlined as the nucleus area (D). Phosphorylated-α-syn immunostaining channel is represented in green and selected. The edge of LB is drawn as LB area (E). Subsequently, returning the LUT parameters to the default settings, positive signals corresponding to SNCA transcripts above the threshold within the region of interest are captured by the NIS-Elements software (F). Red displays SNCA transcripts, magenta shows RBFOX3 transcripts, and blue exhibits DAPI. Scale bar represents 10 μm. Fig. S2. Method of outlining the region of interest and capturing synuclein-immunoreactive neurites in HALO. The SN, region of interest, is demarcated by a yellow line (A). A magnified view of the boxed area in A is presented in (B), with the captured area highlighted in red (C). Fig. S3. Immunohistochemistry for SYN-1 and 5G4 α-synuclein (α-syn) antibodies. SYN-1 antibody cross-reacts with the physiological monomeric α-syn and shows a synaptic pattern in both cases of controls (A, C, E, G) and LBD (B, D, F, H) in addition to revealing Lewy body and related pathology in the diseased substantia nigra (SN, D) and putamen (F). In contrast, the 5G4 antibody does not label the physiological synaptic staining in the SN and putamen in cases of control (C, G) nor of LBD (D, H) and highlights only the disease associated α-syn immunoreactivity in LBD (D, H). Immunostaining for SYN-1 (A, B, E, F) and 5G4 (C, D, G, H) anti-α-syn antibodies in the SN (A–D) and putamen (E–H). The scale bars represent 50 μm for each image.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.878 | 0.218 |
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".