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Record W2319350211 · doi:10.1139/w2012-112

Copper stress and filamentous fungus<i>Humicola lutea</i>103 — ultrastructural changes and activities of key metabolic enzymes

2012· article· en· W2319350211 on OpenAlexvenueno aff
Ekaterina Ts. Krumova, Stoyanka Stoitsova, Tsvetelina Paunova‐Krasteva, Svetlana B. Pashova, Maria Angelova

Bibliographic record

VenueCanadian Journal of Microbiology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsPentose phosphate pathwayIsocitrate dehydrogenaseMalate dehydrogenaseCitric acid cycleBiochemistryBiologyDehydrogenaseEnzymeIsocitrate lyaseHexokinaseGlycolysisMetabolic pathwayMitochondrionEffluxMalate synthaseGlyoxylate cycle

Abstract

fetched live from OpenAlex

Humicola lutea 103 is a copper-tolerant fungal strain able to grow in the presence of 300 μg·mL(-1) Cu(2+) under submerged cultivation. To prevent the consequences of copper overload, microorganisms have evolved molecular mechanisms that regulate its uptake, intracellular traffic, storage, and efflux. In spite of this avoidance strategy, high heavy-metal concentrations caused distinct and widespread ultrastructural alterations in H. lutea. The mitochondria were the first and main target of the toxic action. The effect of copper on activities of the key enzymes (hexokinase, glucose-6-phosphate dehydrogenase, malate dehydrogenase, and isocitrate dehydrogenase) included in the 3 main metabolic pathways, glycolysis, pentose phosphate pathway, and tricarboxylic acid cycle, was investigated. High metal concentrations exhibited a dramatic negative effect on hexokinase, while the other 3 enzymes showed a significant and dose-dependent stimulation. On the basis of the present and previous results we concluded that the copper-induced oxidative stress plays an important role in the fungal tolerance to high Cu (2+) concentrations.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.420
Threshold uncertainty score0.989

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 head0.185
Teacher spread0.180 · 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

Citations20
Published2012
Admission routes1
Has abstractyes

Explore more

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