An outbreak of classical swine fever in pigs in Bangladesh, 2015
Bibliographic record
Abstract
Abstract In a group of 22 healthy pigs aged between 4 and 6 months, 2 pigs became ill with high fever, complete anorexia, cough and abnormal swaying movements on 22 June 2015. One of them died on June 24 and the second died on July 3. Shortly after, the remaining pigs also fell ill and died from the same illness by 10 August 2015. We investigated the aetiology, epidemiological and clinical features of the outbreak. We recorded the clinical signs and symptoms for each pig with the date of onset of illness. Veterinarians conductedpost‐mortemexaminations on the 12 dead pigs, they collected tissue samples from the dead pigs and placed them in a tube containing 1 mL of nucleic acid extraction buffer (lysis buffer). We tested all the tissue samples by real‐time reverse transcription polymerase chain reaction (rRT‐PCR) to detect classical swine fever virus (CSFV) because the animals’ symptoms matched those of this disease. We also conducted a phylogentic analysis of the nucleotide sequence of the E2 gene segment ofCSFVdetected in a lung tissue sample. The attack rate (22/22) and the case fatality were 100%. The predominant symptoms of the disease included high fever, cough, diarrhoea and swaying movements of the hind legs prior to death. Of the 12 pigs tissue samples tested, all had evidence of the presence ofCSFV RNAbyrRT‐PCR. The phylogenetic analysis indicated that the virus belongs to genotype 2.2, which is closely related toCSFVgenotype 2.2 reported in India. Our investigation suggests thatCSFis circulating in pigs, posing a risk for communities in Bangladesh that rely on pigs for economic income and dietary protein. Future research could focus on estimating the disease and economic burden ofCSFVin pig rearing areas to determine if interventions might be warranted or cost‐effective.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".