The mechanistic approach of arsenic mediated neurotoxicity-a concise review
Bibliographic record
Abstract
Arsenic is a naturally occurring element that may be found in both inorganic and organic forms throughout the environment. It is believed that inorganic arsenic are particularly detrimental to human health. Human exposure to inorganic arsenic is mostly caused by polluted drinking water, despite the fact that food and water are the main sources of inorganic arsenic exposure for people. In Bangladesh, West Bengal, China, Taiwan, Thailand, Ghana, Argentina, Chile, Mexico, Hungary, Canada, the United Kingdom, and some parts of the United States, widespread arsenic pollution of groundwater has been documented. However, if arsenic enters the neonate, it may pass the blood-brain barrier (BBB) and have an immediate impact on the central nervous system (CNS). Tight connections between capillary endothelial cells in the brain and epithelial cells in the choroid plexus make up the BBB, a structure designed to keep proteins and other tiny molecules from interacting with the cerebrospinal fluid. Additionally, as succinyl coA is present in complex II of the electron transport chain, arsenic prevents the synthesis of succinyl CoA, hinders the generation of ATP in cells, and completely shuts off the energy supply. Neurotransmitters, which are in charge of facilitating cell-to-cell communication in the brain, are impacted by arsenic-induced neurotoxicity. Arsenic serves to induce dopamine and serotonin levels while inversely regulating norepinephrine levels. The arsenic impact alters the amounts of Ɣ-aminobutyric acid (GABA), glutamate, and other biogenic amines. As a result of the arsenic threat, the levels of various inflammatory indicators, including IL-6, TNF-α, IL-1, and IFN-Ɣ, as well as mitochondrial apoptotic markers like bax, bak, bid, and bim change in neural tissues. The goal of this review paper is to provide an in-depth investigation of the mechanistic approach to arsenic-mediated neurotoxicity.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".