Iron, Zinc, and Copper Nutritional Status in Children Infected With <i>Helicobacter pylori</i>
Bibliographic record
Abstract
OBJECTIVE: : Helicobacter pylori colonizes the gastric mucosa of about half of the world's population and it has been related to extragastrointestinal diseases. The present study sought to evaluate the association between H pylori infection and iron, zinc, and copper nutritional status in symptomatic children. PATIENTS AND METHODS: : A cross-sectional study was carried out in 395 children (4-16 years) with upper gastrointestinal symptoms, who were tested for H pylori infection by the C-urea breath test. Iron status was determined by hemoglobin, serum ferritin, and serum transferrin receptors. Copper and zinc serum concentrations were also evaluated. Epidemiological data, dietary assessment, and anthropometric indicators were analyzed as potential confounding factors. RESULTS: : Prevalence of H pylori infection was 24.3%. Anemia and iron deficiency (ID) were found in 12.0% and 14.3% of the H pylori-positive and 8.9% and 11.0% of the H pylori-negative children, respectively. There was no association between H pylori infection and anemia (odds ratio = 1.54 [95% confidence interval [CI] 0.73%-3.24%]) or ID (odds ratio = 1.35 [95% CI 0.67-2.70]). Crude beta coefficients showed that H pylori has no significant effect on hemoglobin, serum ferritin, serum transferrin receptors, copper, and zinc concentrations. However, adjusted results suggested that H pylori-infected children had an increase of 9.74 microg/dL (95% CI 2.12-17.37 microg/dL) in copper concentrations. CONCLUSIONS: : This study revealed that H pylori infection was not associated with iron deficiency, anemia, or zinc concentrations; however, a positive relation with copper status was found after adjusting for confounding factors. The contribution of H pylori infection to higher copper concentrations needs to be confirmed by additional studies.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".