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Record W4396976742 · doi:10.3390/separations11050155

Coordination Ion Spray for Analysis of the Growth Hormones Releasing Peptides in Urine—An Application Study

2024· article· en· W4396976742 on OpenAlexaff
А. З. Темердашев, Elina Gashimova, A. A. Azaryan, Yu‐Qi Feng, Sanka N. Atapattu

Bibliographic record

VenueSeparations · 2024
Typearticle
Languageen
FieldChemistry
TopicMass Spectrometry Techniques and Applications
Canadian institutionsCanAm Bioresearch (Canada)
FundersRussian Science FoundationNational Natural Science Foundation of China
KeywordsUrineChemistryHormoneGrowth hormoneChromatographyBiologyBiochemistry

Abstract

fetched live from OpenAlex

In this article, a comparison of ionization techniques is provided and discussed. Conventional liquid chromatography with an electrospray ionization source shows higher robustness and repeatability in comparison with liquid chromatography coupled with a coordination ion spray (CIS-MS) source using silver nitrate as the dopant. However, the higher sensitivity and possibility to collect more data in untargeted applications mean CIS-MS is emerging as an instrument used in specific applications. During this research, the limit of detection (LOD) for GHRP-2 and GHRP-6 was established at 0.2 ng/mL, and the lower limit of quantification (LLOQ) was 0.5 ng/mL for CIS-MS. For conventional ESI-MS combined with solid-phase extraction on weak cation exchange columns, the limit of detection was found to be 1 ng/mL, and the lower limit of quantification was 2 ng/mL.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.019
GPT teacher head0.330
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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