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Innovative Solutions for the Control of Leishmaniases: Nanoscale Drug Delivery Systems

2019· review· en· W2951537607 on OpenAlexafffund
Victoria Wagner, Aida Minguez-Menendez, Joan Domingo Peña, Christopher Fernandez‐Prada

Bibliographic record

VenueCurrent Pharmaceutical Design · 2019
Typereview
Languageen
FieldMedicine
TopicResearch on Leishmaniasis Studies
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMiltefosineNanocarriersParomomycinLeishmaniasisDrug deliveryDrugSandflyLeishmaniaSolid lipid nanoparticleMedicinePharmacologyVisceral leishmaniasisNanotechnologyBiologyImmunologyAntibioticsMicrobiologyMaterials science

Abstract

fetched live from OpenAlex

BACKGROUND: Leishmania are sandfly-transmitted protozoan parasites that harbour within the macrophages of a mammalian host and cause leishmaniasis, a serious zoonotic disease that threatens the lives of millions worldwide. Its numerous forms (cutaneous, mucocutaneous, and visceral) are currently treated with a sparse arsenal of drugs, specifically antimonials, amphotericin B, miltefosine, and paromomycin, for which drug resistance and clinical failure are rampant. Medicine is presently trending towards nanotechnology to aid in the successful delivery of drugs. Vehicles such as lipid-based nanocarriers, polymer-based nanoparticles, and metal ions and oxides have been previously demonstrated to improve bioavailability of drugs and decrease toxicity for the patient. These cutting-edge solutions can be combined with existing active molecules, as well as novel drugs or plant extracts with promising antileishmanial activity. CONCLUSION: This review explores the current evidence for the treatment of leishmaniases using nanoscale drug delivery systems (specifically lipid-, polymer- and metal-based systems) and encourages further development of the aforementioned nanotechnologies for treatment of Leishmania.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.352
GPT teacher head0.463
Teacher spread0.110 · 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 designNot applicable
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

Citations13
Published2019
Admission routes2
Has abstractyes

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