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Record W3036936698 · doi:10.1002/jbio.202000109

Comparative study on the inactivation of <scp>MS2</scp> and <scp>M13</scp> bacteriophages using energetic femtosecond lasers

2020· review· en· W3036936698 on OpenAlexafffund
Aziz Berchtikou, Esen Sokullu, Sharifun Nahar, Peter Tijssen, Marc A. Gauthier, T. Ozaki

Bibliographic record

VenueJournal of Biophotonics · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicListeria monocytogenes in Food Safety
Canadian institutionsInstitut National de la Recherche Scientifique
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaserFemtosecondCapsidIrradiationBiophysicsMetastabilityChemistryMaterials scienceBiologyOpticsVirusPhysicsVirology

Abstract

fetched live from OpenAlex

Femtosecond (fs) laser irradiation techniques are emerging tools for inactivating viruses that do not involve ionizing radiation. In this work, the inactivation of two bacteriophages representing protective capsids with different geometric constraints, that is, the near-spherical MS2 (with a diameter of 27 nm) and the filamentous M13 (with a length of 880 nm) is compared using energetic visible and near-infrared fs laser pulses with various energies, pulse durations, and exposure times. Intriguingly, the results show that inactivation using 400 nm lasers is substantially more efficient for MS2 compared to M13. In contrast, using 800 nm lasers, M13 was slightly more efficiently inactivated. For both viruses, the genome was exposed to a harmful environment upon fs-laser irradiation. However, in addition to the protection of the genome, the metastable capsids differ in many properties required for stepwise cell entry that may explain their dissimilar behavior after (partial) disassembly. For MS2, the dominant mechanism of fs-laser inactivation was the aggregation of the viral capsid proteins, whereas aggregation did not affect M13 inactivation, suggesting that the dominant mechanism of M13 inactivation was related to breaking of secondary protein links.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.816
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.134
GPT teacher head0.367
Teacher spread0.233 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations5
Published2020
Admission routes2
Has abstractyes

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