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Record W2945631840 · doi:10.1080/17425247.2019.1621287

Novel therapeutic approaches for targeting TB and HIV reservoirs prevailing in lungs

2019· review· en· W2945631840 on OpenAlexaff
Mrunal Jadhav, Tabassum Khan, Chintan Bhavsar, Munira Momin, Abdelwahab Omri

Bibliographic record

VenueExpert Opinion on Drug Delivery · 2019
Typereview
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsLaurentian University
Fundersnot available
KeywordsCoinfectionMedicineTuberculosisIntensive care medicineHuman immunodeficiency virus (HIV)Mycobacterium tuberculosisRegimenDrug deliveryImmunologyPathologyNanotechnology

Abstract

fetched live from OpenAlex

INTRODUCTION: is the leading cause of death in HIV positive patients. In 2017, about 0.3 million HIV positive people died of tuberculosis. There is high load of mycobacteria and HIV in the lungs and eradication of the same is vital for patient survival. AREAS COVERED: This review focuses on the pathogenesis of HIV-TB coinfection and the current management approaches of this coinfection. It presents a detailed discussion of current investigations in novel drug delivery systems for effective targeting of HIV-TB lung reservoirs, especially via pulmonary drug delivery. Additionally, emphasis is given to the need of HIV-TB cotargeting, an unmet need in management of HIV-TB coinfection. EXPERT OPINION: To achieve the goal of complete eradication of HIV-TB reservoirs in lungs requires focused research strategies to be undertaken in the area of pulmonary delivery systems. These endeavors could eventually lead to better patient compliance and improved treatment outcomes. The treatment regimen of HIV-TB coinfection is associated with a major drawback of low therapeutic concentration of drugs in lungs. Nanotechnology provides an excellent platform for delivery of anti-TB and anti-HIV drugs via the pulmonary route thereby serving as a viable and effective means of managing the mycobacterial and HIV reservoirs in the lungs.

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 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 score0.964
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.159
GPT teacher head0.364
Teacher spread0.204 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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