ArticleProteome science2008
Data mining of plasma peptide chromatograms for biomarkers of air contaminant exposures.
Article in Proteome science, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 4 citations in OpenAlex.
- Current status and future perspectives of mass spectrometry imaging.International journal of molecular sciences · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterrogation of chromatographic data for biomarker discovery becomes a tedious task due to stochastic variability in retention times arising from solvent and column performance. The difficulty is further compounded when the effects of exposure (e.g. to environmental contaminants) and biological variability result in varying numbers and intensities of peaks among chromatograms.
resultsWe developed a software tool to correct the stochastic time shifts in chromatographic data through iterative selection of landmark peaks and isometric interpolation to improve alignment of all chromatographic peaks. To illustrate application of the tool, plasma peptides from Fischer rats exposed for 4 h to clean air or Ottawa urban particles (EHC-93) were separated by HPLC with autofluorescence detection, and the retention time shifts between chromatograms were corrected (dewarped). Both dewarped and non-dewarped datasets were then mined for models containing peptide peaks that best discriminate among the treatment groups using ClinproToolstrade mark. In general, models generated by dewarped datasets were able to better classify test sample chromatograms into either clean air or EHC-93 exposure groups, and 0 or 24 h post-recovery time groups. Peak areas of peptides in a model that produced the best discrimination of treatment groups were analyzed by two-way ANOVA with exposure (clean air, EHC-93) and recovery time (0 h, 24 h) as factors. Statistically significant (p < 0.05) time-dependent and exposure-dependent increases and decreases were noted establishing these as biomarker candidates for further validation.
conclusionOur software tool provides a simple and portable approach for alignment of chromatograms with complex, bi-directional retention time shifts prior to data mining. Reliable biomarker discovery can be achieved through chromatographic dewarping using our software followed by pattern recognition by commercial data mining applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.