Bacterial Identification by Protein Mass Mapping Combined with an Experimentally Derived Protein Mass Database
Why this work is in the frame
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Bibliographic record
Abstract
A protein mass mapping approach using mass spectrometry (MS) combined with an experimentally derived protein mass database is presented for rapid and effective identification of bacterial species. A prototype mass database from the protein extracts of nine bacterial species has been created by off-line high-performance liquid chromatography (HPLC) matrix-assisted laser desorption/ionization (MALDI) MS, in which the microbiological parameter of bacterial growth time is considered. A numerical method using a statistical weight factor algorithm is devised for matching the protein masses of an unknown bacterial sample against the database. The sum of these weight factors produces a corresponding summed weight factor score for each bacterial species listed in the database, and the database species producing the highest score represents the identity of the respective unknown bacterium. The applicability and reliability of this protein mass mapping approach has been tested with seven bacterial species in a single-blind study by both direct MALDI MS and HPLC electrospray ionization MS methods, and identification results with 100% accuracy are obtained. Our studies have demonstrated that the protein mass database can be rapidly established and readily adopted with relatively less dependency on experimental factors. Furthermore, it is shown that a number of proteins can be detected using a protein sample amount equivalent to an extract of less than 1000 cells, demonstrating that this protein mass mapping approach can potentially be highly sensitive for rapid bacterial identification.
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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) | 0.000 | 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) | 0.000 | 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