A Follow‐Up Investigation: In Vitro Effects of Kefir‐Derived Biomolecules on β‐Amyloid Aggregation
Bibliographic record
Abstract
BACKGROUND: Kefir is a probiotic-rich fermented milk beverage composed of a symbiotic consortium of bacteria and yeasts. Emerging evidence has shown its neuroprotective potential, including that of its derived metabolites and fractions, in mitigating β-amyloid (Aβ42)-induced neurotoxicity in cultured neuronal cells and neurodegeneration in Drosophila melanogaster models for Alzheimer's disease (AD). Building on these findings, we explored the in vitro effects of kefir-derived fractions and synthetic peptides on Aβ42 aggregation and disaggregation. METHOD: Two kefir fractions, Ethyl Acetate (EtOAc) and <10kDa, and two kefir-derived peptides (KDPs) identified in our prior research were tested. For the preventive assay, Aβ42 (10 µM) was co-incubated with kefir fractions (0.25 mg/mL) or KDPs (1, 10 and 100 µM) for 24 hours, with fluorescence readings (Thioflavin T) taken hourly. For the treatment assay, Aβ42 was incubated alone for 48 hours to induce aggregation, followed by treatment with fractions or KDPs, with fluorescence readings taken after an additional 48-hour incubation. All experiments were performed in 96-well plates, with samples in quintuplicate. Statistical analysis was conducted using one-way ANOVA. RESULT: Fluorescence intensity measurements revealed that, in the preventive assay, all treatments significantly reduced Aβ42 aggregation compared to the untreated control (p<0.0001). In the treatment assay, significant disruption of Aβ42 aggregation was observed with KDP-1 (p=0.0055) and KDP-2 (p<0.0001). CONCLUSION: This study highlights the potential ability of kefir fractions and synthetic peptides to prevent and disrupt Aβ42 aggregation in vitro, supporting their therapeutic promise in neurodegenerative disorders. Further studies should explore their mechanisms of action and efficacy in vivo.
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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 | 0.001 |
| 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".