Exploring the Anti-inflammatory Potential of Blue-Green Algae: Formulation and Evaluation of Spirulina Ointment
Bibliographic record
Abstract
Blue-green algae, also known as cyanobacteria, are a diverse and ancient group of photosynthetic microorganisms that have been of great interest to scientists due to their nutritional, medicinal, and industrial applications. These microbes, some of the oldest organisms on our planet, are currently being discovered as a rich reservoir of bioactive compounds with applications ranging from nutrition to drug discovery. Spirulina and other cyanobacterial genera, in specific, have exhibited strong anti-inflammatory, antioxidant, and immunomodulatory activities and are potential drugs for topical and systemic therapy. Bioactives like phycocyanin, polysaccharides, and carotenoids are key players in exerting these properties and have been effectively added to ointments for better delivery and efficacy. Cyanobacteria exhibit significant utility in promoting human health and possess extensive applications in the field of cosmeceuticals due to their photoprotective properties and skin-regenerative capabilities. Furthermore, they are employed in bioremediation, biofuel generation, and nutraceutical synthesis, thereby constituting a vital component of sustainable biotechnological innovations. Despite these advantages, challenges such as the occurrence of cyanotoxins like microcystins, variability in bioactive compound content, and constraints associated with cultivation underscore the imperative for additional research and standardization efforts. The current investigation aimed to examine the anti-inflammatory properties of the blue-green algae Spirulina, alongside the formulation and evaluation of Spirulina-based ointments. This review endeavors to highlight recent advancements in the anti-inflammatory potential of blue-green algae, with particular focus on the formulation of topical ointments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".