Enabling next‐generation therapies: A foreword to a special issue on nanotechnology in medicine
Bibliographic record
Abstract
The Spring of 2022 coincided with a long-awaited return of the conference series on Nanotechnology in Medicine (Calabria, Italy), chaired on this occasion by Dr Milica Radisic (University of Toronto) and Dr Victor Shahin (University of Münster) under the auspices of Engineering Conference International (ECI). To celebrate the main highlights of such event, the special issue of Bioengineering & Translational Medicine (Volume X, Issue X) brings together a curated collection of stimulating contributions from plenary, keynote, and invited speakers of the conference under the unifying theme of “enabling next-generation therapies.” The third edition of this conference provided an intimate yet lively scientific forum whose purpose expanded upon the scope of the past two previous editions of the conference series (see, e.g., Bioengineering & Translational Medicine Vol. 4, Issues 2 & 3, 2019) in discussing recent research developments in the aforementioned field. Among the leading topics emphasized in this 2022 edition of the conference were (i) a deepening of the mechanistic understanding of biodistribution of systematically targeted nanoparticles (NPs), (ii) exploring the effects of mechanical environments of tissues and cells, (iii) the use of tissue and organ-on-chip (OoC) models in the studies of NP distribution and toxicity, (iv) generating an improved mechanistic understanding of the factors necessary to control in vivo NP targeting; and (v) exploiting such understanding to generate highly effective nanotechnologies for the early detection, imaging, and treatment of human diseases. In this short editorial, we briefly take the opportunity to highlight a few contributions of interest that mark the special issue. Resonating with the timeliness of the COVID-19 pandemic, Lu et al. (https://doi.org/10.1002/btm2.10581) discuss recent advances in heart-on-a-chip platforms for elucidating SARS-CoV-2 pathogenesis, including the potential mechanisms that drive heart failure whereby viral infection induces myocardial dysfunction, with an outlook toward more advanced models for disease modeling and pharmacological discovery. Continuing in the area of OoC, Spitz et al. (https://doi.org/10.1002/btm2.10604) provide an overview of recent OoC advances in the field of neurodegenerative diseases (NDDs) directed toward non-invasive sensing strategies encompassing electrical, electrochemical and optical sensors. Motivated by the lack of insufficient predictive validity of animal-based disease models for clinical trials, the authors discuss promising on- and integrable off-chip sensing OoC strategies applicable to NDD research to advance the translational value of microphysiological systems in preclinical settings. In parallel, Ramezani et al. (https://doi.org/10.1002/btm2.10652) discuss the potential of dye supramolecular assemblies for broad applications such as photoacoustic and fluorescence imaging, as well as photothermal and photodynamic therapies. There, the authors expand on emerging applications of dyes as drug-stabilizing agents used together with aggregator molecules to form stable NPs in view of further translational in vivo endpoints for clinical use. In the area of joint diseases (e.g., osteoarthritis), intra-articular delivery of drugs to cartilage remains an unresolved challenge due to their rapid clearance within joints. Here, Gonzales et al. (https://doi.org/10.1002/btm2.10612) discuss the development of new cationic nanocarriers with variable charge that form reversible electrostatic interactions with the anionic extra-cellular matrix of cartilage. The authors present results both in vitro and in mouse cartilage explants supporting a proof-of-concept study with the transport of cationic, branched poly-l-lysine nanocarriers through negatively charged cartilaginous tissues that can promote deeper penetration and prolongment of drug retention. As a final example, in the areas of systemic delivery in the cardiovascular system, the review of Asaad et al. (https://doi.org/10.1002/btm2.10669) discusses interactions of several key NP types (e.g., polymeric, ceramic, silica, dendrimers, and metallic) on circulating platelets in blood, with a focus on the physicochemical parameters that may modulate the therapeutic potential of such NPs when designing safe and effective therapies that can be translated into clinical practice. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1002/btm2.10678.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.025 | 0.016 |
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".