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Record W4297229704 · doi:10.3390/molecules27196264

Structural Characterization of Cis– and Trans–Pt(NH3)2Cl2 Conjugations with Chitosan Nanoparticles

2022· article· en· W4297229704 on OpenAlexafffund
P. Chanphai, Gervais Bérubé, Heidar‐Ali Tajmir‐Riahi

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldMedicine
TopicMetal complexes synthesis and properties
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChitosanChemistryPolymerDrug deliveryNanoparticleAdductNuclear chemistryStereochemistryNanotechnologyOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

The conjugation of chitosan 15 and 100 KD with anticancer drugs cis– and trans–Pt (NH3)2Cl2 (abbreviated cis–Pt and trans–Pt) were studied at pH 5–6. Using multiple spectroscopic methods and thermodynamic analysis to characterize the nature of drug–chitosan interactions and the potential application of chitosan nanoparticles in drug delivery. Analysis showed that both hydrophobic and hydrophilic contacts are involved in drug–polymer interactions, while chitosan size and charge play a major role in the stability of drug–polymer complexes. The overall binding constants are Kch–15–cis–Pt = 1.44 (±0.6) × 105 M−1, Kch–100–cis–Pt = 1.89 (±0.9) × 105 M−1 and Kch–15–trans–Pt = 9.84 (±0.5) × 104 M−1, and Kch–100–trans–Pt = 1.15 (±0.6) × 105 M−1. More stable complexes were formed with cis–Pt than with trans–Pt–chitosan adducts, while stronger binding was observed for chitosan 100 in comparison to chitosan 15 KD. This study indicates that polymer chitosan 100 is a stronger drug carrier than chitosan 15 KD in vitro.

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 score0.158
Threshold uncertainty score0.210

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 head0.022
GPT teacher head0.219
Teacher spread0.198 · 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

Citations2
Published2022
Admission routes2
Has abstractyes

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