Identification of <i>Salmonella</i> Serovars before and after Ultraviolet Light Irradiation by Fourier Transform Infrared (FT-IR) Spectroscopy and Chemometrics
Bibliographic record
Abstract
Vibrational properties and structural behavior upon ultraviolet (UV) irradiation are reported here for DNAs isolated from three serovars of Salmonella bacteria, respectively (S. enteritidis, S. infantis and S. typhimurium) by using Fourier transform infrared spectroscopy (FT-IR). The FT-IR absorbance spectra were recorded for bacterial DNAs, control and UV irradiated samples (30 min, 1 h, 1 h 30 min), respectively, and are presented in the wavenumber ranges from 800 to 1800 cm−1 and from 3000 to 4000 cm−1, respectively. Molecular specific structural information has been found based on DNA marker bands, which were monitored primarily in terms of intensity changes. As a general remark, a serovar’s dependent nonlinear behavior has been detected for some FT-IR band intensities corresponding to Salmonella DNAs, as a function of irradiation time. Particularly, alterations in DNA functional groups and molecular geometry have been observed. Chemometric analysis such as principal component analysis (PCA) and hierarchical clustering analysis (HCA) were successfully applied for the identification of inter- and intra- serovar differences between DNA spectra, before and after UV irradiation. The elucidation of the main spectral features characteristic to pathogenic bacterial DNAs offers further opportunity for bacteria identification and for establishing the mechanisms of infections, leading to the development of innovative diagnosis tools and more effective therapies. Particularly, DNA-damaging agents are most effective in cancer therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".