Brazilian kefir fraction mitigates the Alzheimer-like phenotype in Drosophila melanogaster with β-amyloid overexpression model
Bibliographic record
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative condition and the primary form of dementia among elderly people. The amyloidogenic hypothesis is the main theory that explains this phenomenon and describes the extracellular accumulation of amyloid beta (Aβ) peptides. Model organisms such as Drosophila melanogaster have been utilized to improve the understanding of this disease and its treatment. This study evaluated the effects of peptide and metabolic fractions of Brazilian kefir on a strain of D. melanogaster that expresses human Aβ peptide 1-42 in the eye. The parameters assessed included ommatidial organization, vacuole area, retinal thickness, and Aβ peptide quantification. The present study revealed that the fractions, particularly the peptidic fraction, significantly reduced the vacuole area and increased the retina thickness in treated flies, indicating an improvement in neurodegeneration phenotype. The peptidic fraction was also found to alter Aβ aggregation dynamics, inhibiting Aβ fibril formation, as revealed by dynamic light scattering. This study demonstrated that kefir fractions, particularly the peptidic fraction < 10 kDa, have the potential to regulate Aβ aggregation and alleviate neurodegeneration in a Drosophila melanogaster AD-like model. These findings suggest that kefir fractions could be viable for the bioprospection of novel drug prototypes for AD treatment, providing valuable insights into strategies targeting Aβ aggregation and neurodegeneration in AD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".