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Record W4306842654 · doi:10.26685/urncst.373

The Use of Mannose-Grafted and Lipopeptide-Conjugated PE Liposomes in the Delivery of Docetaxel for the Treatment of Glioblastoma Multiforme: A Research Protocol

2022· article· en· W4306842654 on OpenAlexaff
Cheshta Gupta, Medha Radhamma Krishnan

Bibliographic record

VenueUndergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNanoparticle-Based Drug Delivery
Canadian institutionsWestern University
Fundersnot available
KeywordsLiposomeDrug deliveryBlood–brain barrierDocetaxelPhosphatidylethanolamineTargeted drug deliveryPharmacologyCalceinEndocytosisChemistryDrugMedicineCancerCellBiochemistryPhosphatidylcholineInternal medicinePhospholipidCentral nervous system

Abstract

fetched live from OpenAlex

Introduction: One of the biggest obstacles in delivering anti-cancer drugs to brain tumours is the penetration of the blood-brain barrier. Docetaxel is a promising drug used for glioblastoma multiforme that works by promoting mitotic arrest and cell death of tumorous cells, yet it encounters this obstacle presented by the selectivity of the blood-brain barrier. Due to the barrier’s highly selective nature and the imprecision of current cancer treatments, the use of nanoparticles in drug delivery has been an area of significant interest. To address these issues, we propose using mannose and lipopeptide-grafted phosphatidylethanolamine liposomes as a drug delivery mechanism to effectively eliminate the obstacle of penetrating the blood-brain barrier in the treatment of glioblastomas. The truncated fibroblast growth factor and GALA lipopeptides increase the precision of the chemotherapeutic agent in targeting the tumour cells. Simultaneously, the mannose allows the nanoparticle to be recognized by sugar receptors on the blood-brain barrier, enabling it to pass through. This novel drug delivery system broadens the variety and increases the effectiveness of anti-tumoral drugs used in the treatment of brain cancer. Methods: The lipopeptides are prepared through pyridyl disulfide reactions. The phosphatidylethanolamine liposomes are prepared using standard thin-film hydration in which the lipopeptides, docetaxel, and calcein (to track the drug delivery) are incorporated into the liposomal lumen. Mannose is then grafted onto the liposomal surfaces through the covalent coupling of p-aminophenyl-D-glycosides to phosphatidylethanolamine liposomes. The synthesized liposomes would be administered intravenously alongside radiation. Statistical analyses will be conducted to measure the growth of the tumour and the accuracy of drug delivery. Discussion: The tumour cells should display a greater level of fluorescence, indicating a more accurate administration of the drug. It is expected that the patients will respond favourably to the treatment with the tumorous tissues showing a reduced growth rate and greater bioavailability of the drug. Conclusion: The liposomal drug delivery mechanism presents a novel method by which anti-tumoral drugs can both cross the blood-brain barrier and precisely target the tumorous mass, thereby reducing the risk of drugs getting lost within the vasculature and expanding the horizons for brain tumour prognoses.

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)0.018
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
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 score0.333
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.009
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.115
GPT teacher head0.422
Teacher spread0.307 · 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; both teacher heads agree on what is shown here.

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 routes1
Has abstractyes

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