Lithium boosts neuronal bioenergetics in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Alzheimer's disease (AD) is characterized by the accumulations of amyloid beta and neurofibrillary tangles in brain tissue; however, AD is multifactorial and different etiopathogenic mechanisms involves that can affect mitochondrial function that are associated with AD. In the current study, we investigated the effect of lithium on mitochondrial function in AD. Method Neuronal cells were isolated separately from hippocampal of brain tissue of control mice (C57BL/6) and 3xTg model of AD. Mitochondrial oxygen consumption rate (OCR), mitochondrial Cytochrome C Oxidase (COX) activity, and total ATP activity were measured in control vs. AD neurons after one day and seven days dose‐dependent treatment with lithium. Result In the present study, short and long term lithium treatment significantly increased (p<0.05) mitochondrial OCR, COX, and total ATP level in 3xTg neurons. However, lithium had no effect on energy metabolism in control neurons. Together, these data indicate that lithium improves mitochondrial function under pathological states. Conclusion Overall, these results have important implications for the treatment of disorders in which brain energy regulation are compromised, including AD. Particularly, our results highlight a role for lithium in regulating bioenergetics in early stage AD and suggest that neuronal cells may be a crucial therapeutic target for preventing 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) | 0.002 | 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".