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Record W4415522833 · doi:10.1021/acsomega.5c07322

Chemodiversity of Cyanobacteria from Brazil Investigated by Metabolomics and Bioassays

2025· article· en· W4415522833 on OpenAlexafffund
Francisco Henrique da Silva, Leonardo Santos de Jesus, Michael J. J. Recchia, Kleyton J. G. de Morais, Sandra Regina Soares, Helori Vanni Domingos, Hannah Cavanagh, Frederico J. Gueiros‐Filho, José Ângelo Lauletta Lindoso, Letícia V. Costa‐Lotufo, Roger G. Linington, Roberto G. S. Berlinck, Camila Manoel Crnkovic

Bibliographic record

VenueACS Omega · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsCyanobacteriaBioassayMetabolomicsMetabolomeBacteria

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Investigations on cyanobacterial secondary metabolites in Brazil have been scarce, despite the country’s significant biodiversity. Herein, we report the results of a screening of cyanobacterial cultures using both bioassays and untargeted metabolomics. Nineteen cyanobacterial strains collected at various locations in Brazil were cultured. Cultures were extracted and prefractionated. Fractions were evaluated for antibacterial, cytotoxic, and antileishmanial activities. The same fractions were analyzed by UHPLC–HRMS–MS/MS. Results from bioassays and LC–MS were integrated using metabolomics tools such as NP Analyst and GNPS Molecular Networking, allowing for feature prioritization. Cyanobacteria belonging to genera Calothrix and Phormidium presented high-priority molecular features associated with observed biological activities, indicating that such strains are producers of potentially novel and bioactive metabolites.

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.

How this classification was reachedexpand

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 score0.052
Threshold uncertainty score0.540

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 head0.005
GPT teacher head0.222
Teacher spread0.217 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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