Mercury-induced toxicity of rat cortical neurons is mediated through N-methyl-D-Aspartate receptors
Bibliographic record
Abstract
BACKGROUND: Mercury is a well-known neurotoxin implicated in a wide range of neurological or psychiatric disorders including autism spectrum disorders, Alzheimer's disease, Parkinson's disease, epilepsy, depression, mood disorders and tremor. Mercury-induced neuronal degeneration is thought to invoke glutamate-mediated excitotoxicity, however, the underlying mechanisms remain poorly understood. Here, we examine the effects of various mercury concentrations (including pathological levels present in human plasma or cerebrospinal fluid) on cultured, rat cortical neurons. RESULTS: We found that inorganic mercuric chloride (HgCl₂--at 0.025 to 25 μM) not only caused neuronal degeneration but also perturbed neuronal excitability. Whole-cell patch-clamp recordings of pyramidal neurons revealed that HgCl₂ not only enhanced the amplitude and frequency of synaptic, inward currents, but also increased spontaneous synaptic potentials followed by sustained membrane depolarization. HgCl₂ also triggered sustained, 2-5 fold rises in intracellular calcium concentration ([Ca²⁺]i). The observed increases in neuronal activity and [Ca²⁺]i were substantially reduced by the application of MK 801, a non-competitive antagonist of N-Methyl-D-Aspartate (NMDA) receptors. Importantly, our study further shows that a pre incubation or co-application of MK 801 prevents HgCl₂-induced reduction of cell viability and a disruption of β-tubulin. CONCLUSIONS: Collectively, our data show that HgCl₂-induced toxic effects on central neurons are triggered by an over-activation of NMDA receptors, leading to cytoskeleton instability.
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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 | 0.000 |
| 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".