Effects of Malnourishment on the immune system: a short review
Bibliographic record
Abstract
Malnutrition is the sweeping condition effecting the health of a children. The connection between immunity and dietary intake is very significant. Malnutrition effects the child’s growth and development. Now research study has shown that malnutrition has a higher impact on infants and preschool children due to their greater susceptibility. Malnutrition can cause by lack of food intake during the childhood period. Research have shown 45 percent children died before the age of 5 years. Malnutrition makes the child more susceptible to infection, disease. Undernourished children are potential risk for physical growth and mental abilities. PEM (Protein Energy Malnutrition) is found out as a crucial health problem in all over India. Nutritionally Acquired Immune Deficiency Syndromes are the name given to immunological disorders linked to malnutrition (NAIDS). Children and infants are especially vulnerable because of undernutrition, which stunts the development of their immune systems. The supplementation of good quality diet enrich with high calorie and protein, can help recovery Protein Energy Malnutrition or PEM. The effects of malnutrition on the immune system are outlined in this review article.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".