Therapeutic Applications of Human and Bovine Colostrum in the Treatment of Gastrointestinal Diseases and Distinctive Cancer Types: The Current Evidence
Bibliographic record
Abstract
The incidence of gastrointestinal disorders (GID) and cancers is escalating all over the world. Limited consumption of colostrum by newborns not only weakens the immune system but also predisposes infants to microbial infections. Colostrum is nature's perfect food, sometimes referred to as the 'elixir of life'. Breast-fed infants have a lower incidence of GI tract infections than infants fed formula or cow's milk. As per WHO statistics, cancer is the most prevalent disease globally and causes 9.6 million deaths worldwide. The current strategies for treating cancer include chemotherapy, radiation, and surgery. However, chemotherapy and radiation exposure are usually associated with serious long-term side effects and deterioration in the quality of life (QOL) of patients. Furthermore, the hospitalization and medication costs for treating cancers are exorbitant and impose high economic burden on healthcare systems. People are desperately looking for cost-effective and affordable alternative therapies for treating GID and cancers. Therefore, there is an urgent need for clinically evaluating the anticancer compounds isolated from plants and animals. Such therapies would not only be economical and have fewer side effects, but also help to improve the QOL of cancer patients. Recently, bovine colostrum (BC) has caught the attention of many investigators to explore its anticancer potential in humans. BC impregnated dressings are highly effective in treating chronic wounds and diabetic foot ulcer. BC is rich in lactoferrin, a glycoprotein with strong antioxidant, anti-inflammatory, anti-cancer, and anti-microbial properties. Intravaginal application of BC tablets is effective in causing the regression of low-grade cervical intraepithelial neoplasia. The underlying mechanisms of BC at cellular, genetic, and molecular levels remain to be ascertained. Oral BC supplement is well-tolerated, but some people may experience problems such as flatulence and nausea. Well-designed, randomized, placebo-controlled, clinical trials are needed to access the therapeutic potential, long-term safety, and optimal doses of BC products. This review is aimed to highlight the anticancer potential of BC and its components, and the therapeutic applications of BC supplements in treating gastrointestinal diseases in children and adults. We also discuss the health promotion benefits and therapeutic potential of BC nutraceuticals in reducing the incidence of non-communicable diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".