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Record W1964642121 · doi:10.1021/jf802940x

New Method Development for Nanoparticle Extraction of Water-Soluble β-(1→3)-<scp>d</scp>-Glucan from Edible Mushrooms, <i>Sparassis crispa</i> and <i>Phellinus linteus</i>

2009· article· en· W1964642121 on OpenAlex
Hyuk-Gu Park, Youn Young Shim, Seung-oh Choi, Won-Mok Park

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2009
Typearticle
Languageen
FieldMedicine
TopicFungal Biology and Applications
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsPhellinus linteusGlucanChemistryFood scienceExtraction (chemistry)Medicinal fungiBotanyPolysaccharideChromatographyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Sparassis crispa and Phellinus linteus are edible/medicinal mushrooms that have remarkably high contents of beta-(1-->3)-D-glucan, which acts as a biological response modifier, but difficulty in cultivating the fruiting bodies and extraction of beta-D-glucan have restricted detailed studies. Therefore, a novel process for nanoparticle extraction of Sparan, the beta-D-glucan from Sparassis crispa, and Phellin, the beta-D-glucan from Phellinus linteus, has been investigated using insoluble tungsten carbide as a model for nanoknife technology. This is the first report showing that the nanoknife method results in high yields of Sparan (70.2%) and Phellin (65.2%) with an average particle size of 150 and 390 nm, respectively. The extracted Sparan with beta-(1-->3) linkages showed a remarkably high water solubility of 90% even after 10 min of incubation at room temperature. Therefore, it is likely that this nanoknife method could be used to produce beta-D-glucan for food, cosmetic, and pharmaceutical industries.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.264
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it