Random forest modelling of neuropathological features identifies microglial activation as an accurate pathological classifier of C9orf72-related amyotrophic lateral sclerosis
Bibliographic record
Abstract
Abstract Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are regarded as two ends of a pathogenetic spectrum, termed ALS-frontotemporal spectrum disorder (ALS-FTSD). However, it is currently difficult to predict where on the spectrum an individual will lie, especially for patients with C9orf72 hexanucleotide repeat expansions (HRE), a mutation associated with both ALS and FTD. It has been shown that both inflammation and protein misfolding influence aspects of ALS and ALS-FTSD disease pathogenesis, such as the manifestation or severity of motor or cognitive symptoms. Previous studies have highlighted markers which may influence C9orf72 -associated disease presentation in a targeted fashion, though there has yet to be a systematic and quantitative assessment of common immunohistochemical markers to investigate the significance of these pathways in an unbiased manner. Here we report the first extensive digital pathological assessment with random forest modelling of pathological markers often used in neuropathology practice. This study profiles glial activation and protein misfolding in a cohort of deeply clinically profiled post-mortem tissue from patients with a C9orf72 HRE, who either met the criteria for a diagnosis of ALS or ALS-FTSD. We show that microglial immunohistochemical staining features, both morphological and spatial, are the best independent classifiers of disease status and that clinicopathological associations exist between microglial activation status and cognitive dysfunction in ALS-FTSD patients with C9orf72 HRE. Furthermore, we show that spatially resolved changes in FUS staining are also an accurate predictor of disease status, implying that liquid-liquid phase shift of this aggregation-prone RNA-binding protein may be important in ALS caused by a C9orf72 HRE. Our findings provide further support to the hypothesis of dysfunctional immune regulation and proteostasis in the pathogenesis of C9orf72 ALS and provide a framework for digital analysis of commonly used neuropathological stains as a tool to enrich our understanding of clinicopathological associations between cohorts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".