Differentiation of the metabolic profile of actinobacteria isolated from the soil of the caatinga biome by paper spray mass spectrometry
Bibliographic record
Abstract
Paper spray mass spectrometry (PS-MS) is an ambient ionization technique that allows for rapid and direct mass spectrometry analysis for a wide range of chemical compounds due to its portability, little to no sample preparation, and cost-effective materials. As applications with this technique continue to expand, the identification and discrimination of bacteria at the strain level remain a promising avenue for researchers. Although studies in the past demonstrated the applicability of PS-MS to discriminate bacteria at the strain level, no one has reported the strain-level differentiation of actinobacteria without using solvent for PS-MS. Hence, this study demonstrates that optimization of PS-MS permits the investigation and differentiation of the metabolic profiles of actinobacteria without the need for solvents, diminishing the potential for sample contamination and consequently increasing the versatility of this technique. In doing so, strains of actinobacteria (CAAT P5-21, CAAT P5-16, CAAT 8-25, CAAT P8-92, and CAAT P11-13) were grown and transferred to produce a crude growth medium. The supernatant was used for the PS-MS analyses using a Thermo Scientific LTQ mass spectrometer. Multivariate statistical analysis, including principal component analysis (PCA) and hierarchal cluster analysis (HCA), was employed to chemically distinguish the strains of bacteria. As a result, each strain of actinobacteria could be visually differentiated based on their metabolic profile. These findings demonstrate the practicability of using a liquid medium as an alternative to many other organic solvents when analyzing bacteria, making PS-MS a crucial addition to a microbiologist's research toolkit.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".