The Role of <i>Hemophilus influenzae</i> Infection and Its Relationship With Colorectal Cancer
Bibliographic record
Abstract
Background: Hemophilus influenzae is a gram-negative coccobacillus. Non-typeable H. influenzae infection is a significant cause of disease that activates the inflammatory pathway involving the nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (NLRP3) inflammasome. A gain-of-function mutation in NLRP3 results in cryopyrin-associated periodic syndromes characterized by inflammatory conditions in the lungs, skin, joints, and eyes but not in the gut. This leads to homeostasis of the gut microbiota, which reduces inflammation and may have protective effect against colorectal cancer (CRC). This study aimed to evaluate the correlation between H. influenzae infection and the incidence of CRC. Methods: A retrospective study was conducted from 2010 to 2019 using a HIPAA-compliant national database. ICD-10, ICD-9, CPT, and National Drug Codes were used to identify patients with or without a history of H. influenzae infection. Standard statistical methods were used to analyze the outcomes. Results: The query was analyzed and matched, resulting in 13,610 patients in both groups. The incidence of CRC was 167 and 446 in the H. influenzae and control groups, respectively. The difference was statistically significant with P < 2.2 ×10 -16 and an odds ratio of 0.41 (95% confidence interval: 0.36 - 0.47). Additionally, the groups were further evaluated and matched by treatment, which resulted in a statistically significant decrease in CRC incidence in the H. influenzae group. Conclusion: This study showed a statistically significant correlation between H. influenzae and the reduced incidence of CRC. This reduction in CRC in patients with a history of H. influenzae infection suggests a potential link to the NLRP3 inflammasome, which should be further studied. World J Oncol. 2023;14(3):188-194 doi: https://doi.org/10.14740/wjon1584
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".