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Record W3179260933 · doi:10.3390/sym13071259

Simulated Breathing: Application of Molecular Dynamics Simulations to Pulmonary Lung Surfactant

2021· article· en· W3179260933 on OpenAlexaff
Maksymilian Dziura, Basel Mansour, Mitchell DiPasquale, P. Charukeshi Chandrasekera, James W. Gauld, Drew Marquardt

Bibliographic record

VenueSymmetry · 2021
Typearticle
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsPulmonary surfactantMonolayerMolecular dynamicsLungBiological systemComposition (language)BiophysicsFunction (biology)ChemistryChemical physicsLung functionComputational biologyCell biologyPhysicsBiologyStatistical physicsBiochemistryComputational chemistryMedicineInternal medicine

Abstract

fetched live from OpenAlex

In this review, we delve into the topic of the pulmonary surfactant (PS) system, which is present in the respiratory system. The total composition of the PS has been presented and explored, from the types of cells involved in its synthesis and secretion, down to the specific building blocks used, such as the various lipid and protein components. The lipid and protein composition varies across species and between individuals, but ultimately produces a PS monolayer with the same role. As such, the composition has been investigated for the ways in which it imposes function and confers peculiar biophysical characteristics to the system as a whole. Moreover, a couple of theories/models that are associated with the functions of PS have been addressed. Finally, molecular dynamic (MD) simulations of pulmonary surfactant have been emphasized to not only showcase various group’s findings, but also to demonstrate the validity and importance that MD simulations can have in future research exploring the PS monolayer system.

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)0.001
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 score0.793
Threshold uncertainty score0.486

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.363
Teacher spread0.344 · 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

Citations16
Published2021
Admission routes1
Has abstractyes

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