Abilities of the mCP Agar Method and CRENAME Alpha Toxin-Specific Real-Time PCR Assay To Detect Clostridium perfringens Spores in Drinking Water
Bibliographic record
Abstract
We first determined the analytical specificity and ubiquity (i.e., the ability to detect all or most strains) of a Clostridium perfringens-specific real-time PCR (rtPCR) assay based on the cpa gene (cpa rtPCR) by using a bacterial strain panel composed of C. perfringens and non-C. perfringens Clostridium strains. All non-C. perfringens Clostridium strains tested negative, whereas all C. perfringens strains tested positive with the cpa rtPCR, for an analytical specificity and ubiquity of 100%. The cpa rtPCR assay was then used to confirm the identity of 116 putative C. perfringens isolates recovered after filtration of water samples and culture on mCP agar. Colonies presenting discordant results between the phenotype on mCP agar and cpa rtPCR were identified by sequencing the 16S rRNA and cpa genes. Four mCP(-)/rtPCR(+) colonies were identified as C. perfringens, whereas 3 mCP(+)/rtPCR(-) colonies were identified as non-C. perfringens. The cpa rtPCR was negative with all 51 non-C. perfringens strains and positive with 64 of 65 C. perfringens strains. Finally, we compared mCP agar and a CRENAME (concentration and recovery of microbial particles, extraction of nucleic acids, and molecular enrichment) procedure plus cpa rtPCR (CRENAME + cpa rtPCR) for their abilities to detect C. perfringens spores in drinking water. CRENAME + cpa rtPCR detected as few as one C. perfringens CFU per 100 ml of drinking water sample in less than 5 h, whereas mCP agar took at least 25 h to deliver results. CRENAME + cpa rtPCR also allows the simultaneous and sensitive detection of Escherichia coli and C. perfringens from the same potable water sample. In itself, it could be used to assess the public health risk posed by drinking water potentially contaminated with pathogens more resistant to disinfection.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".