Precision Nanomedicine with Bio-Inspired Nanosystems: Recent Trends and Challenges in Mesenchymal Stem Cells Membrane-Coated Bioengineered Nanocarriers in Targeted Nanotherapeutics
Bibliographic record
Abstract
In the recent past, the formulation and development of nanocarriers has been elaborated into the broader fields and opened various avenues in their preclinical and clinical applications. In particular, the cellular membrane-based nanoformulations have been formulated to surpass and surmount the limitations and restrictions associated with naïve or free forms of therapeutic compounds and circumvent various physicochemical and immunological barriers including but not limited to systemic barriers, microenvironmental roadblocks, and other cellular or subcellular hinderances-which are quite heterogeneous throughout the diseases and patient cohorts. These limitations in drug delivery have been overcome through mesenchymal cells membrane-based precision therapeutics, where these interventions have led to the significant enhancements in therapeutic efficacies. However, the formulation and development of nanocarriers still focuses on optimization of drug delivery paradigms with a one-size-fits-all resolutions. As mesenchymal stem cell membrane-based nanocarriers have been engineered in highly diversified fashions, these are being optimized for delivering the drug payloads in more and better personalized modes, entering the arena of precision as well as personalized nanomedicine. In this Review, we have included some of the advanced nanocarriers which have been designed and been utilized in both the non-personalized as well as precision applicability which can be employed for the improvements in precision nanotherapeutics. In the present report, authors have focused on various other aspects of the advancements in stem cells membrane-based nanoparticle conceptions which can surmount several roadblocks and barriers in drug delivery and nanomedicine. It has been suggested that well-informed designing of these nanocarriers will lead to appreciable improvements in the therapeutic efficacy in therapeutic payload delivery applications. These approaches will also enable the tailored and customized designs of MSC-based nanocarriers for personalized therapeutic applications, and finally amending the patient outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".