Pomegranate Juice Improves Iron Status and Ameliorates Iron Deficiency Induced Cellular Changes in Saccharomyces cerevisiae
Bibliographic record
Abstract
Background: Iron Deficiency Anemia (IDA) is most prevalent form of anemia affecting around 2 billion people world-wide. Ayurveda, an Indian system of medicine, describes pomegranate (Punica granatum) fruits as a Rasayana and a dietary supplement for managing a condition called Pandu, which is akin to IDA. Rasayanas are methods to maintain homeostasis by improving digestion, metabolism and absorption of nutrients and elimination of waste. Yeast (Saccharomyces cerevisiae) has been a well-accepted model organism to study iron metabolism. Materials & Methods: In the current study we developed ‘anemic yeast’ by culturing yeast cells in iron-free medium with bathophenanthroline disulfonate (BPS). The effect of pomegranate juice (PJ) on reversing the ‘IDA like’ condition in yeast was studied. Results: Culturing iron deficient (ID) cells in the presence of 10% PJ supplemented medium (IDP), improved iron status by at least 7 fold (p<0.0001) and reversed mitochondrial degeneration induced by iron deficiency. Percentage of healthy reticulate mitochondria in IDP cells was >30% higher (p<0.0001) than that in the ID cells grown in iron deficient medium (IDD) and at least 14% more than that in ID cells grown in 10% PJ-equivalent iron substituted media. Interestingly, PJ substitution improved the functional ferrous (Fe2+) form as well as the bio-assimilated heme form of iron, but not the ferric (Fe3+) storage form in ID cells. Conclusion: Yeast model can be useful as a quick screen to identify potential nutritional supplements. Pomegranate’s potential role as a nutritional supplement in IDA management and as a hematinic is worthy of further research
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".