Spectrophotometric determination of biofilm formation by Mycobacterium avium ssp. paratuberculosis in aqueous extract of schmutzdecke for clarifying untreated water in water treatment operations
Bibliographic record
Abstract
Mycobacterium avium ssp.paratuberculosis (Map) causes Johne's disease in ruminants, and implicated in the aetiology of human Crohn's disease.The survival of Map in the environment and its ability to multiply inside a host has been reported, however, unknown by which mechanism.Biofilm formation by some species of mycobacteria is noted as a means of survival and host adaptation, with such knowledge lacking for Map.In this work, biofilm formation by 3 isolates of Map, 2 from the environment and 1 from a Crohn's disease patient, was determined using spectrophotometric analysis.Since Map is fastidious and persists in water with or without nutrients, aqueous extract of schmutzdecke was employed to ascertain the impact of a complex microcosm of nutritional composition on biofilm formation by a fastidious slow growing Mycobacterium.Since cells must adhere onto a suitable surface in order to initiate biofilm formation, adherence assay was carried out firstly on 2 surfaces i.e. aluminium and stainless steel plates on which all the 3 Map isolates adhered, but greatly on the aluminium compared to the stainless steel.Secondly, biofilm formation by the isolates was determined on polyvinyl chloride (PVC) plates, and all were positive for biofilm formation.The extent of biofilm formation as influenced by distilled water (DW, control) alongside aqueous extracts of filtered and unfiltered schmutzdecke (FAES and UAES) was also determined statistically at P<0.05.The order of significance was 0.0004, 0.0307 and 0.0487 for DW, FAES and UAES respectively.This study showed that Map could form biofilm under conditions that its immediate environment provides, and could serve as a mechanism for its survival and thrive in the environment and host.
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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 | 0.001 | 0.001 |
| 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".