The Pivotal Neuroinflammatory, Therapeutic and Neuroprotective Role of Alpha-Mangostin
Bibliographic record
Abstract
Alpha-mangostin (α-MG), one of the key xanthone derivatives, displays a multiplicity of curative qualities like antiinfective, anti-malarial, anticarcinogenic, antidiabetic actions as well as antioxidant properties. Furthermore, it has proven to have neuroprotective, hepatoprotective, and cardioprotective features, of which the anticarcinogenic action is the most auspicious. Additionally, several in vitro and in vivo analyses confirm that α-MG partakes in the major stages of tumor growth: initiation, promotion, and progression. Nevertheless, α-MG services as an inhibitory agent that modulate various enzymes involved in the metabolic activation and excretion of carcinogens, resistance to oxidative damage, and attenuation of inflammatory response. Numerous studies have also implicated α-MG in central nervous system (CNS) disorders, among which neuroinflammatory disorders and brain cancer are cardinal. Based on these initial studies on α-MG, we reviewed the pivotal role of α-MG in neuroinflammatory disorders. J Neurol Res. 2017;7(4-5):67-79 doi: https://doi.org/10.14740/jnr455w
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".