A Bird’s-Eye View of the Multiple Biochemical Mechanisms that Propel Pathology of Alzheimer’s Disease: Recent Advances and Mechanistic Perspectives on How to Halt the Disease Progression Targeting Multiple Pathways
Bibliographic record
Abstract
Neurons consume the highest amount of oxygen, depend on oxidative metabolism for energy, and survive for the lifetime of an individual. Therefore, neurons are vulnerable to death caused by oxidative-stress, accumulation of damaged and dysfunctional proteins and organelles. There is an exponential increase in the number of patients diagnosed with neurodegenerative diseases such as Alzheimer's (AD) as the number of elderly increases exponentially. Development of AD pathology is a complex phenomenon characterized by neuronal death, accumulation of extracellular amyloid-β plaques and neurofibrillary tangles, and most importantly loss of memory and cognition. These pathologies are most likely caused by mechanisms including oxidative stress, mitochondrial dysfunction/stress, accumulation of misfolded proteins, and defective organelles due to impaired proteasome and autophagy mechanisms. Currently, there are no effective treatments to halt the progression of this disease. In order to treat this complex disease with multiple biochemical pathways involved, a complex treatment regimen targeting different mechanisms should be investigated. Furthermore, as AD is a progressive disease-causing morbidity over many years, any chemo-modulator for treatment must be used over long period of time. Therefore, treatments must be safe and non-interfering with other processes. Ideally, a treatment like medicinal food or a supplement that can be taken regularly without any side effect capable of reducing oxidative stress, stabilizing mitochondria, activating autophagy or proteasome, and increasing energy levels of neurons would be the best solution. This review summarizes progress in research on different mechanisms of AD development and some of the potential therapeutic development strategies targeting the aforementioned pathologies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".