FLASH-PCR as a Simple and Efficient Method for Detection of Brucella spp. Infection
Bibliographic record
Abstract
Background: Brucella spp. are Gram-positive, rod-shaped, and spore-forming bacilli. Brucella abortus and B. melitensis are the main causes of brucellosis. Objectives: The aim of the study was to establish a rapid and simple molecular method for the detection of this disease. Methods: Forty-five Brucella spp. were isolated from blood samples using the BACTEC Fluorescent 9050 system and were detected by anti-IgM or IgG Brucella specific antigen. DNA extraction was conducted on all samples. Fluorescent amplification based specific hybridization (FLASH-PCR) test was utilized to detect the 351-bp fragment from eryD gene, which was specific for Brucella spp. Results: A 351-bp fragment resulted from PCR reaction and showed the accuracy of designed primers. This fragment was successfully amplified in the FLASH-PCR reaction. In this study, we have positive and negative samples and a standard method. In addition, we calculated the sensitivity and specificity of this method as 100%. Conclusions: Results of the study was proved that the FLASH-PCR method was a rapid, sensitive, and safe method for the detection of Brucella genome in whole blood samples of patient harbored brucellosis and is recommended for routine usage.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".