Κατασκευή νανοσωματιδίων χιτοζάνης φορτωμένων με αντισώματα μέσω μικρορευστοποίησης
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) has become one of the most common chronic conditions which has affected millions of people around the world. Conventional therapies might ultimately result in chronic illness or surgery. Biological therapies with anti-tumor necrosis factor alpha (anti-TNF-α) Monoclonal Antibodies, such as Infliximab (INF), has been very effective in remission and tissue healing. Unfortunately, its systemic administrations result in severe side effects. In this study, Chitosan (CHI) Nanoparticles (NPs) and Carboxy Methyl Chitosan (CMC) NPs were produced by microfluidics-based on-a-chip systems to efficiently encapsulate INF for oral delivery to reduce side effects and increase patient compliances. This study also includes comparison between two well-known Microfluidics platforms i.e., the Automated Nanoparticle (ANP) System (Dolomite, Roystone, United Kingdom) and the NanoAssemblr® Benchtop (Precision NanoSystems, Vancouver, Canada). CHI NPs and CMC NPs were successfully prepared by Microfluidics technique when using both equipment, and INF was also encapsulated successfully in both types of NPs with encapsulation efficiency (EE%) ranging from 9.06 to 67.45 of for different NPs prepared with different ingredients ratios and at flow rates. Comparison between two types of equipment showed that the NPs preparation in terms of size and PDI are quite comparable, but EE% is quite different. Stability studies of CHI NPs and CMC NPs with and without antibody was performed for one week and NPs were found to be very stable. Results obtained in this study seems to be promising for the potential of CHI NPs and CMC NPs for oral delivery of INF. Further studies are required to evaluate the efficacy of INF through in-vitro and in-vivo studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.472 | 0.156 |
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; both teacher heads agree on what is shown here.
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".