Distinct effects of <scp>SDC3</scp> and <scp>FGFRL1</scp> on selective neurodegeneration in <scp>AD</scp> and <scp>PD</scp>
Bibliographic record
Abstract
Abstract Alzheimer's disease (AD) and Parkinson's disease (PD) are age‐dependent neurodegenerative disorders. There is a profound neuronal loss in the basal forebrain cholinergic system in AD and severe dopaminergic deficiency within the nigrostriatal pathway in PD. Swedish APP (APP SWE ) and SNCA A53T mutations promote Aβ generation and α‐synuclein aggregation, respectively, and have been linked to the pathogenesis of AD and PD. However, the mechanisms underlying selective cholinergic and dopaminergic neurodegeneration in AD and PD are still unknown. We demonstrated that APP SWE mutation enhanced Aβ generation and increased cell susceptibility to Aβ oligomer in cholinergic SN56 cells, whereas SNCA A53T mutations promoted aggregates formation and potentiated mutant α‐synuclein oligomer‐induced cytotoxicity in MN9D cells. Furthermore, syndecan‐3 (SDC3) and fibroblast growth factor receptor‐like 1 (FGFRL1) genes were differentially expressed in SN56 and MN9D cells carrying APP SWE or SNCA A53T mutation. SDC3 and FGFRL1 proteins were preferentially expressed in the cholinergic nucleus and dopaminergic neurons of APP SWE and SNCA A53T mouse models, respectively. Finally, the knockdown of SDC3 and FGFRL1 attenuated oxidative stress‐induced cell death in SN56‐APP SWE and MN9D‐SNCAA53T cells. The results demonstrate that SDC3 and FGFRL1 mediated the specific effects of APP SWE and SNCA A53T on cholinergic and dopaminergic neurodegeneration in AD and PD, respectively. Our study suggests that SDC3 and FGFRL1 could be potential targets to alleviate the selective neurodegeneration in AD and PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".