Distribution of acid-sensing ion channel subunits in human sensory neurons contrasts with that in rodents
Bibliographic record
Abstract
Abstract Acid-sensing ion channels (ASICs) play a critical role in nociception in human sensory neurons. Four genes (ASIC1, ASIC2, ASIC3, and ASIC4) encoding multiple subunits through alternative splicing have been identified in humans. Real time-PCR experiments showed strong expression of three subunits ASIC1, ASIC2, and ASIC3 in human dorsal root ganglia; however, their detailed expression pattern in different neuronal populations has not been investigated yet. In the current study, using an in situ hybridization approach (RNAscope), we examined the presence of ASIC1, ASIC2, and ASIC3 mRNA in three subpopulations of human dorsal root ganglia neurons. Our results revealed that ASIC1 and ASIC3 were present in the vast majority of dorsal root ganglia neurons, while ASIC2 was only expressed in less than half of dorsal root ganglia neurons. The distribution pattern of the three ASIC subunits was the same across the three populations of dorsal root ganglia neurons examined, including neurons expressing the REarranged during Transfection (RET) receptor tyrosine kinase, calcitonin gene-related peptide, and a subpopulation of nociceptors expressing Transient Receptor Potential Cation Channel Subfamily V Member 1. These results strongly contrast the expression pattern of Asics in mice since our previous study demonstrated differential distribution of Asics among the various subpopulation of dorsal root ganglia neurons. Given the distinct acid-sensitivity and activity dynamics among different ASIC channels, the expression differences between human and rodents should be taken under consideration when evaluating the translational potential and efficiency of drugs targeting ASICs in rodent studies.
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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) | 0.002 | 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".