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Record W4403568813 · doi:10.1016/j.htct.2024.09.839

ANTITUMORAL POTENTIAL OF GREEN MICROALGAE EXTRACTS IN CHRONIC MYELOID LEUKEMIA (CML) IN VITRO MODEL

2024· article· en· W4403568813 on OpenAlex

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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHematology Transfusion and Cell Therapy · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer and biochemical research
Canadian institutionsCambrian CollegeLaurentian University
Fundersnot available
KeywordsMyeloid leukemiaMedicineIn vitroChronic myeloid leukaemiaCancer researchPharmacologyTraditional medicineBiologyBiochemistry

Abstract

fetched live from OpenAlex

Bioprospected microalgae collected from Northern Ontario, Canada water bodies have been shown to produce bioactive molecules, including carotenoids, polysaccharides, vitamins, and lipids with high pharmacological potential. Several studies in Canada with these microalgae have confirmed extracts with antioxidant, antimicrobial, and anticancer activity. Chronic myeloid leukemia (CML) is a clonal hematopoietic stem cell disorder and accounts for approximately 30% of the incidence of adult leukemias. Despite the use of tyrosine kinase inhibitors (TKIs) has improved the treatment and the prognosis of patients with CML therapeutic failures and adverse effects make treatment difficult, highlighting the need for the study of new molecules with antileukemic potential. This study aims to evaluate the in vitro antileukemic potential of different microalgae extracts in the K-562 cell line. Bioprospected microalgae strains S5, P981 ( Coccomyxa sp .), LL1 ( Scenedesmus sp .), LL2A, and CC ( Chlamydomonas sp .) were cultivated for 14 days in Bold's Basal Medium (BBM) with pH 7 under 16 hr:8 hr light-dark cycles, at 25°C, and were kept continuously agitated at 150 rpm. The microalgae were then harvested and methanol, ethanol, and aqueous extraction were performed on each strain. Tubes were placed under vacuum to evaporate the remaining solvent and the crude extract was weighted, DMSO was added to obtain a stock solution of microalgae extracts (ME) at 10 mg/mL. The CML in vitro model K-562 was plated in a 96-well plate and the microalgae extracts were added in a single dose (100 μg/mL) or in a concentration-response curve (200 μg/mL – 3.125 μg/mL) for 72 hours for evaluate the viability and the Half-maximal inhibitory concentration (IC 50 ) of the most cytotoxic ME, respectively by the MTT method. Fifteen extracts were produced, the ethanolic extracts from CC, LL2A, and P981 presented the highest inhibition percentages in the CML cell line, being 53.67 %, 54.69 %, and 47.09 %, respectively, and were chosen for the definition of the IC 50 . The extracts showed high antileukemic activity, being 80.22 μg/mL for CC, 91.93 μg/mL for LL2A, and 96.18 μg/mL for P918 ethanolic MEs. Previous studies from bioprospected microalgae collected from Northern Ontario have shown their antimicrobial, antiviral, antioxidant as well as antitumoral activity against ovarian and breast cancer cell lines. Ethanolic extracts possess compounds such as alkaloids, flavonoids, glycosides, terpenoids, tannins, saponins, and reducing sugars being sources of compounds with pharmaceutical properties. Our results demonstrated the antileukemic activity with an IC 50 of 80 – 96 μg/mL, corroborating with other studies in vitro . The initial results attested that the ethanolic extracts derived from green microalgae presented high antileukemic potential in the CML in vitro model, showing a promising source of new compounds with pharmacological activity. Further research is needed to visualize which compounds are present in the ethanolic ME that can confer its antileukemic activity and to better describe their mechanism of action in vitro and in vivo .

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 score0.017
Threshold uncertainty score0.471

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 head0.011
GPT teacher head0.272
Teacher spread0.260 · 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