Autophagy induction and PDGFR-β knockdown by siRNA-encapsulated nanoparticles reduce chlamydia trachomatis infection
Bibliographic record
Abstract
C. trachomatis is the most common sexually transmitted bacterial infection in the world. Although the infection can be easily controlled by the use of antibiotics, several reports of clinical isolates that are resistant to antibiotics have prompted us to search for alternative strategies to manage this disease. In this paper, we developed a nanoparticle formulation (PDGFR-β siRNA-PEI-PLGA-PEG NP) that can simultaneously induce autophagy in human cells and knock down PDGFR-β gene expression, an important surface binding protein for C. trachomatis, as a strategy to reduce vaginal infection of C. trachomatis. PDGFR-β siRNA-PEI-PLGA-PEG NP significantly induced autophagy in human vaginal epithelial cells (VK2/E6E7) 48 hr post treatment by improving autophagic degradation activity without causing inflammation, apoptosis or any decrease in cell viability. Beclin-1, VPS34 (markers for initiation stage of autophagy), UVRAG, TECPR-1 (markers for degradation stage of autophagy) were found to be significantly upregulated after treatment with PDGFR-β siRNA-PEI-PLGA-PEG NP. Furthermore, PDGFR-β siRNA-PEI-PLGA-PEG NP decreased PDGFR-β mRNA expression by 50% and protein expression by 43% in VK2/E6E7 cells 48 hr post treatment. Treatment of cells with PDGFR-β siRNA-PEI-PLGA-PEG NP significantly decreased the intracellular C. trachomatis and extracellular release of C. trachomatis by approximately 65% and 67%, respectively, in vitro through augmenting autophagic degradation pathways and reducing bacterial binding simultaneously.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".