Additional file 1 of Unconjugated PLGA nanoparticles attenuate temperature-dependent β-amyloid aggregation and protect neurons against toxicity: implications for Alzheimer’s disease pathology
Bibliographic record
Abstract
Additional file 1: Figure S1. ThT kinetic assays showing aggregation of 2.5–20 µM Aβ1–42 over a 24 h incubation at 27 °C (A), 37 °C (B) and 40 °C (C). Figure S2. A–C ThT assays showing aggregation kinetics (A, D, G) and respective fluorescence images of 10 µM Aβ1–42 (B, E, H) and 10 µM Aβ42–1 (C, F, I) over 24 h incubation at 27 °C (A–C), 37 °C (D–F) and 40 °C (G–I). Note the absence of Aβ42–1 aggregation at any temperature over 24 h incubation. Figure S3. DLS analysis depicting a peak of ~ 100 nm diameter for unconjugated PLGA and its stability in phosphate buffer over 48 h at 27 °C (A, B), 37 °C (C, D) and 40 °C (E, F). DLS analysis depicting a peak of ~ 100 nm diameter for PLGA in phosphate buffer (G) and its stability at 37 °C over 48 h in culture medium (H, I). Note that PLGA nanoparticles are quite stable both in the phosphohate buffer as well as in culture medium over 48 h period. Figure S4. Histograms showing protection of mouse cultured neurons following co-treatment of 10 µM Aβ1–42 with 5 µM PEG-PLGA (A) or 100 nM PCL (B) over 24 h compared to neurons treated with 10 µM Aβ1–42 as detected using MTT assay. C–E Mouse brain section showing the site of fluoresence Aβ1-42 injection (arrow) using Hamilton syringe under anesthesia. The brain section shows nuclear labelling with DAPI (C), presence of fluoresence Aβ1–42 (D) and the merged image (E).
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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 | 0.002 | 0.024 |
| 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) | 0.890 | 0.185 |
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".