Increase in Liposome Production: From Microfluidics to Milli-fluidics
Bibliographic record
Abstract
Liposomes are tiny vesicles of lipid layers enclosing medication for drug delivery, mostly used in cancer treatment, gene therapy and mRNA vaccines. One of the production technologies implies the use of microfluidic mixers, which produce liposomes at a very low yield. Previous research proved the viability of liposome production but at a very low yield. Based on the successful results of the liposome production at micro-scale, the assumption that scaling up the channel size may lead to an increased production of similar-sized liposomes. To begin evaluating its feasibility, simulations of the mixing of two fluids within scaled up channels were carried out. The objective of the simulations are to evaluate the mixing potential prior to experimental trials. Same linear velocity values and mixing ratio were considered in simulations. However, from the mathematical model, the resulting size of the liposomes cannot be predicted. It may be possible that along with larger channels, larger liposomes might be produced. The same fluid properties will be used during the mixing of a solution containing lipids and alcohol with water, which will result in liposomes formation in both micro and milli channels. The hypothesis behind this experiment states that the size of the liposome depends on the speed of mixing, which is bounded by fluid flow properties, such as velocity, pressure and concentration, that will need to remain similar in values in the enlarged microfluidic device. During simulation, similar mixing results were obtained as the base research, which indicate good mixing efficiency when scaling up the cross-section area by 10 and 25 times.. It seems that it may be possible to increase production of liposomes through larger devices if the pressure inside the channels is increased due to higher flow rate, which is also scaled by a factor corresponding to the dimension increase. A larger production rate could be a game changer in the pharma industry. Preliminary experiments yield liposomes of increased size - by 20 to 50% in diameter at a significant increase in productivity.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".