Editorial: Novel biomaterial strategies for osteogenic treatments
Bibliographic record
Abstract
Editorial on the Research Topic Novel biomaterial strategies for osteogenic treatments Our musculoskeletal system enables our body's movement and function.Accordingly, diseases and disorders that inhibit these functions can have an enormous impact on the quality of life for patients suffering from them.For example, osteoarthritis results from the body's inability to regenerate its joints after injury.Approximately 528 million people world-wide suffers from this disease, with a high prevalence (10%-14%) reported in adult populations across North America, North Africa, Middle East and Australasia by public health agencies.This disabling joint disease generates a significant socioeconomic burden on the healthcare system, with direct costs ranging between 1% and 2.5% of the gross national product, generating $65.5 billion annually in direct medical costs in the US alone (2008)(2009)(2010)(2011)(2012)(2013)(2014).Likewise, the global market for bone implants is estimated to range from $40 to $70 billion, depending on the source of reporting.These implants are used to treat diseased and damaged bones and examples include metal implants as replacements for missing bone tissue or damaged hip and knee joints.Current metallic implants serve as an end-stage solution to treat predominant pain symptoms as it is costly, highly invasive and the available implants does not restore full function of the bones or joint, nor do they last a lifetime.New implant technologies, which can combine a wide range of surface modifications, signaling molecules and cells with biomaterials to instead drive the regeneration of healthy joint tissues and modulate local immune cell activation are thus highly attractive alternatives.These advanced treatment paradigms ultimately seek to fully repair and maintain the native tissue function and longevity after injection or transplantations.This Research Topic contains four research studies and one review article that have examined unique strategies for promoting osteogenesis-the generation of bone tissues.For example, the surface properties of bone implants play a large role in their in vivo performance as it dictates cells' ability to adhere, infiltrate and secrete healthy bone tissue at the implant-host tissue interface (osseointegration).Accordingly, a study from Sun et al. examined how different methods of applying hydroxyapatite coatings on 3D-printed titanium scaffolds affected their performance.They compared using plasma spray and electrochemically deposition to coat these scaffolds with hydroxyapatite and then characterized their
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".