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Record W4401174260 · doi:10.1142/s1088424624300040

Drug delivery strategies for porphyrin-based photosensitizers in photodynamic antimicrobial chemotherapy

2024· article· en· W4401174260 on OpenAlexfundno aff
Margaret W. Murage, Edith Amuhaya, Betty N. Mbatia, Edward Muge, Solomon Derese

Bibliographic record

VenueJournal of Porphyrins and Phthalocyanines · 2024
Typearticle
Languageen
FieldMedicine
TopicPhotodynamic Therapy Research Studies
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsPorphyrinChemistryPhotodynamic therapyAntimicrobialPhotosensitizerDrugAntimicrobial chemotherapyCombinatorial chemistryChemotherapyDrug deliveryAntimicrobial drugPharmacologyPhotochemistryOrganic chemistrySurgeryMedicine

Abstract

fetched live from OpenAlex

In the face of increasing antimicrobial resistance (AMR) and the looming threat of a return to a pre-antibiotics era, the exploration of alternative antimicrobial strategies becomes imperative. Among these, Photodynamic Antimicrobial Chemotherapy (PACT) stands out as a promising solution due to its multi-target principle, diverging from the key-lock mechanism inherent in conventional antibiotics. However, the hydrophobic nature, non-targeted distribution, stability issues, and limited tissue penetration of photosensitizers (PSs), particularly the extensively studied porphyrin-based compounds, pose structural limitations impacting their bioavailability and effective delivery to target sites. Therefore, various delivery strategies are adopted to improve their effectiveness in PACT. This includes attaching the PS to a variety of delivery vehicles such as nanoparticles, and liposomes, or encapsulating them in nanostructures, such as micelles or dendrimers. This helps protect the PS from degradation, improve their solubility and bioavailability, and enhance their photophysical and photochemical properties, ultimately improving their effectiveness in PACT. In this paper, recent studies focusing on strategies used to improve the delivery of porphyrin-based PS in PACT are reviewed.

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 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 score0.369
Threshold uncertainty score0.840

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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