Editorial: Insights in biomaterials 2022 / 2023—novel developments, current challenges and future perspectives
Bibliographic record
Abstract
Insights in biomaterials 2022 / 2023-novel developments, current challenges, and future perspectivesWe are now entering the third decade of the 21st century, and, especially in the last few years, the achievements made by scientists have been exceptional, leading to major advancements in the rapidly growing fields of bioengineering and biotechnology.New biomaterials designed with advanced features have been the driving force behind the applications in bioengineering and biotechnology fields.This annual Research Topic, which highlights article submissions typically from our Editorial Board members, looks to explore new insights, novel developments, current challenges, latest discoveries, recent advances, and future perspectives in the field of Biomaterials.This Research Topic solicited brief, forward-looking contributions that describe the state of the art, outlining recent developments and major accomplishments that have been achieved and that need to occur to move the field forward.We wanted to shed light on the progress made in the past decade in the biomaterials field, and on its future challenges, to provide a thorough overview of the field.This article Research Topic is intended to inspire, inform and provide direction and guidance to researchers in the field.Eleven articles have been finally accepted for this Research Topic which were a mixture of review papers and technical articles.The review paper by Tiemeijer and Tel summarized the current state of affairs in utilization of hydrogels for single cell microgel production.Hydrogels, with their high-water content and being readily tunable for physicochemical features, offer an opportunity to create suitable microenvironments for cell cultivation at microscale so as to foster selection of individual cells (or clones) for selection for a variety of reasons.While microdrop production has been used for cell encapsulation since 1980s, using microfluidics devices that emerged in the last decade provides exquisite control over the hydrogel features at the micro scale.Common materials used for microgel production was reviewed as well as a survey of applications including sequencing, biosensing using entrapped cells and enhanced cultivation for various purposes, including therapy.The technology is bound to yield
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.026 | 0.022 |
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".