Identification, characterization and quantification of specific neuropeptides in rat spinal cord by liquid chromatography electrospray quadrupole ion trap mass spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
Substance P and CGRP play a central role in neuropathic pain development and maintenance. Additionally, dynorphin A is an endogenous ligand of opioid receptors implicated in the modulation of neurotransmitters including neuropeptides, such as substance P and CGRP. This manuscript proposes a method to characterize, identify and quantify substance P, CGRP and dynorphin A in rat spinal cord by HPLC-ESI/MS/MS. Rat spinal cords were collected and homogenized into a TFA solution. Samples were chromatographed using a microbore C(8) 100 x 1 mm column and a 19 min linear gradient (0:100 --> 40:60; ACN:0.2% formic acid in water) at a flow rate of 75 microL/min for a total run time of 32 min. The peptides were identified in rat spinal cord based on full-scan MS/MS spectra. Substance P, CGRP and dynorphin A were predominantly identified by the presence of specific b CID fragments. Extracted ion chromatogram (XIC) suggested selected mass transitions of 674 --> [600 + 254], 952 --> [1215 + 963] and 717 --> [944 + 630] for substance P, CGRP and dynorphin A can be used for isolation and quantitative analysis. A linear regression (weighted 1/x) was used and coefficients of correlations (r) ranging from 0.990 to 0.999 were observed. The precision (%CV) and accuracy (%NOM) observed were 10.9-14.4% and 8.9-14.2%, 8.8-13.0% and 91.0-110.2% and 97.2-107.3% and 91.8-97.3% for substance P, CGRP and dynorphin A respectively. Following the analysis of rat spinal cords, the mean endogenous concentrations were 110.7, 2541 and 779.4 pmol/g for substance P, CGRP and dynorphin A respectively. The results obtained show that the method provides adequate figures of merit to support targeted peptidomic studies aimed to determine neuropeptide regulation in animal neuropathic and chronic pain models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it