Stem Cell-Derived Neurons as Cellular Models of Sporadic Alzheimer’s Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) occurs as either an autosomal dominant inherited disease or sporadically. While familial mutant genes can be expressed in cells or in animal models to assess dysregulated functions, sporadic AD cannot be replicated in models given our lack of understanding of causality. Furthermore, the study of sporadic forms of AD is difficult given the inaccessibility of brain tissues in living individuals and the manifestation of symptoms years after the onset of disease. Here, the objective was to assess if induced pluripotent stem cell-derived neurons from well-ascertained sporadic AD individuals could represent potential cellular models to determine the underlying molecular mechanisms of disease. We used cryopreserved peripheral blood mononuclear cells from three well-ascertained sporadic AD and three non-cognitively impaired (NCI) individuals of the CIMA-Q cohort to obtain iPSC-derived neurons. Microtubule associated protein 2 was decreased in AD neurons, whereas expression of AD-associated amyloid precursor protein, tau, and amyloid-β peptide was similar in AD and NCI individuals. RNA sequencing identified several upregulated and downregulated mRNAs in AD relative to NCI neurons. Of these, complement Factor H (CFH), signal regulatory protein beta1 (SIRPB1), and insulin like growth factor binding protein 5 (IGFBP5) were previously associated with AD. In addition, several transcription factors not previously associated with AD, but involved in neuronal proliferation and differentiation were differentially expressed. The results identify novel avenues for the study of the underlying causes of sporadic AD and support the establishment of additional lines to identify mechanisms of disease in sporadic AD individuals.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".