Bibliographic record
Abstract
Despite disorders with different etiology, Alzheimer's disease (AD) and Down syndrome (DS) share many neuropathological features, such as brain accumulation of amyloid-β (Aβ) and Tau protein.Thus, important clues of synapse-disruptive mechanisms implicated in AD likely contribute to DS neuronal anomalies and brain pathology.Recently, Tau has been demonstrated to be the final executor of Aβ neurotoxicity, as Tau hyperphosphorylation and accumulation, as well as imbalance of isoform ratio, have been shown to trigger neuronal malfunction and neurodegeneration in AD.Moreover, deletion of Tau gene has been shown to block the Aβ detrimental effects on AD brain, thus arising Tau reduction as a promising therapeutic approach.Antisense oligonucleotides (ASOs) are small synthetic strings of nucleotides that regulate the RNA levels of the protein of interest while they have been proven to be safe for both animal and human use.Thus, this work was based on Tau reduction through ASOs as a promising innovative therapeutic intervention against Tau-driven neuronal malfunction in AD and DS.For that, we designed and monitored the efficiency of 50 novel ASOs against total Tau or selectively 4R-Tau in cell lines, primary neurons, and DS mouse model.Our in vitro and in vivo studies have identified a set of novel and highly efficient ASOs for reducing Tau or modulating 4R/3R isoform ratio as assessed by different types of molecular and cellular, neurostructural, synaptic and behavioral analysis.Altogether, these data provide the first in vitro and in vivo evidence of the beneficial use of ASOs against Tau-related neuronal malfunction in DS, supporting ASOs as an innovative RNA-based therapeutic approach in neurodevelopment pathologies of the brain.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.784 | 0.606 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".