Bibliographic record
Abstract
AIMS: Hypersensitivity pneumonitis of machinists associated with metalworking fluids (MWF) was recently linked to Mycobacterium immunogenum. In addition to Mycobacterium, impacts of continuous and massive contact to other micro-organisms, such as Pseudomonas, were little studied. This report intended to quantify and characterize the microbial load of 44 in-use MWF. METHODS AND RESULTS: The main biodiversity of MWF was assessed using cultural methods, quantitative PCR (qPCR) and denaturing gradient gel electrophoresis (DGGE). Total bacteria concentrations ranged from undetectable to 10(9) 16S rRNA gene copies per millilitre. Concentrations obtained by qPCR were up to five orders of magnitude higher than by culture, suggesting that MWF contamination is generally underestimated. Two samples showed high concentrations of Myco. immunogenum (1.55 x 10(7) and 3.49 x 10(5) 16S rRNA gene copies per millilitre). The overall biodiversity was low, as observed by culture and DGGE, and was comparable to data found in the literature. Pseudomonas pseudoalcaligenes was by far the main bacteria found in MWF samples (33 out of 44), followed by Ochrobactrum anthropi (32 out of 44). There was no significant relationship between the biodiversity profiles and the kind of MWF or equipment used, making it difficult to predict which micro-organisms will colonize each particular MWF. CONCLUSIONS: Very high concentrations of bacteria were found in most MWF studied and limited biodiversities were observed. Many species of micro-organisms were retrieved from MWF samples, but they were mostly colonized by Pseudomonas pseudoalcaligenes and Ochrobactrum anthropi. SIGNIFICANCE AND IMPACT OF THE STUDY: The major micro-organisms observed or recovered in this study from in-use MWF were present in very high concentrations, and thus further studies are needed to confirm their role in workers' respiratory disorders or health-related problems.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".