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Record W4399729733 · doi:10.31274/td-20240617-262

Pathogenesis of intracellular organisms

2023· dissertation· en· W4399729733 on OpenAlexfundno aff
K Phadke

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicBurkholderia infections and melioidosis
Canadian institutionsnot available
FundersMcGill University
KeywordsBurkholderia pseudomalleiIntracellular parasiteBiologyMelioidosisMicrobiologyPathogenesisPathogenVirologyImmune systemObligateIntracellularInfectious disease (medical specialty)Human pathogenDiseaseAntibioticsImmunologyBacteriaGeneticsMedicine

Abstract

fetched live from OpenAlex

Intracellular pathogens are responsible for most of the infectious diseases that pose a threat to public health. This dissertation highlights two intracellular pathogens, Burkholderia pseudomallei and SARS-CoV-2, and discusses methods to study and treat them. Burkholderia pseudomallei causes the disease melioidosis which is predominantly a respiratory disease with a mortality rate as high as 40% if left untreated. A peculiar characteristic of the pathogen is its ability to invade and survive inside host cells protecting it from external antibiotics and the host immune system. This dissertation introduces a polyanhydride nanoparticle-based drug delivery system to increase the efficacy of known drugs against B. pseudomallei. We highlight meropenem and ceftazidime as our lead formulations that when encapsulated in nanoparticles can kill the bacteria more efficiently in vitro and in vivo. Viral pathogens, such as SARS-CoV-2 are obligate intracellular pathogens that use the host mechanisms to survive and replicate. SARS-CoV-2 first emerged in 2019 and has since evolved and adapted into multiple different variants. This dissertation focuses on 3 variants, namely, Wild type, Omicron and Delta. Studying pathogenesis of these variants helps us understand mechanisms important for viral infection that can be used to develop vaccines and antivirals. Here, we compared pathogenesis of the three variants in 5 cells lines to emphasize the importance of 2 host proteins, ACE2 and TMPRSS2. This study introduces a uniform comparison technique that can be altered as new variants emerge to further understand the evolution of the virus. COVID-19, the disease caused by SARS-CoV-2, is primarily a respiratory disease, but can also affect other parts of the body such as the brain. The neurological propensity of the virus has been observed in my clinical studies where viral titers and neurological symptoms were associated with COVID-19. In this dissertation we aimed to further investigate this phenomenon by setting up an in vitro model to study viral pathogenesis in the brain. We used human microglial cells (HMC3) that are the first responder to any infection or inflammation in the brain. We compared pathogenesis of 3 variants, Wild type, Omicron and Delta in HMC3 cells which highlighted an evolutionary change that was observed in Omicron and Delta but not in Wild type. These unique characteristic pavs the way for further investigation of SARS-CoV-2 brain infections and the mechanisms behind it. The two pathogens discussed here, although different, have a lot of commonalities, intracellular pathogenesis being the main one. This dissertation highlights the importance of studying emerging intracellular pathogens in multiple cell culture models to understand the host-pathogen interactions clearly. Additionally, it introduces a polyanhydride nanoparticle-based drug delivery system as an effective strategy against a B. pseudomallei, which can also be used against other intracellular pathogens like SARS-CoV-2.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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