Potential protein phycocyanin: an overview on its properties, extraction, and utilization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cyanobacteria capable of oxygenic photosynthesis uses multiple pigments to efficiently convert light radiation energy to chemical energy in the form of ATP (Adenosine Tri-phosphate). A crucial pigment protein from its photosynthetic machinery called phycocyanin has been extensively studied for its immense application in fluorescent probe, food additives, nutraceutical applications, and silver-nanoparticles production. The present review provides a narrative amalgamation of the said applications and technologies to grow, extract, and purify the phycocyanin from various algal cultures. The expression of phycocyanin protein gene at the molecular level has been discussed for its potential to enhance the yield and stability. Furthermore, advantageous methods for the extraction and purification of phycocyanin from possible host cyanobacterial strains (Spirulina platensis, Cyanidium caldarium, Synechococcus vulcanus, Gracilaria chilensis, Polysiphonia urceolata, Thermosynechcoccus vulcanus, and Galdieria sulphuraria) such as physical, chemical and enzymatic methods are also compared to find the most efficient strategy in subsequent scientific applications. The comparative study also expects to ways to achieve sustainable development goal 3 i.e. good health & wellbeing.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it