Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".