Evaluation of the impact of <i>Shigella</i> virulence genes on the basis of clinical features observed in patients with shigellosis
Bibliographic record
Abstract
Abstract Shigella is still attributable to nearly 164,300 deaths annually, mostly in sub-Saharan Africa and South Asian young children, remaining as a major public health threat, especially in the developing countries. Our goal was to study the association between Shigella virulence genes and clinical features observed in shigellosis. Therefore, 61 S. flexneri strains were investigated, isolated from patients from a tertiary level facility in Bangladesh between 2009 to 2013. Subsequently, the presence of 140 MDa large virulence plasmid (p140), virulence ( ipaH, ial ), toxin ( set, sen ) and T3SS related genes ( virB, ipaBCD, ipgC, ipgB1, ipgA, icsB, ipgD, ipgE, ipgF, mxiH, mxiI, mxiK, mxiE, mxiC, spa15, spa47, spa32 and spa24 ) were evaluated. p140 was found in 79% (n=48) cases. ipaBCD was found in 90% (n=55) strains, while seven of them were missing p140. However, ial was found in 89% isolates, and ipgC and ipgE in 85% cases. The prevalence of the rest of the genes was less than 85%. These findings were then compared against the clinical features of each of the corresponding pathogens, and several statistically significant correlations were observed (all p<0.05). Briefly, the enterotoxin genes ( set, sen ) and another virulence gene ( ial ) were found significantly associated with several clinical features of shigellosis, including bloody mucoid stool, rectal straining, fever, and abdominal pain. Our findings reiterate that the diarrheal disease severity is significantly associated with the enterotoxin producing Shigella infection, also suggesting that the T3SS related virulence genes might be translocated elsewhere other than the 140 MDa large virulence plasmid.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".