Tailored bioengineering and nanomedicine strategies for sex-specific healing of chronic wounds
Bibliographic record
Abstract
Chronic wounds, defined by their prolonged healing process, significantly impair patients' quality of life and impose a hefty financial burden on healthcare systems worldwide. Sex- and gender-specific mechanisms regulate inflammation and infection, angiogenesis, matrix synthesis and cell recruitment. All of these processes contribute to cutaneous wound healing but remain largely understudied. This review aims to spotlight the innovative realm of bioengineering and nanomedicine, which is at the helm of revolutionizing complex chronic wound care. It underscores the significance of integrating patient sex into the development and (pre)clinical testing of these avant-garde treatment modalities, in order to enhance healing prospects for all patients regardless of sex. Moreover, we explore the representation of male and female patients in clinical trials of bioengineered and nanomedicine products. Finally, we examine the primary reasons for the historical neglect in translating sex-specific wound healing research into clinical practice and propose strategic solutions. By tackling these issues, the article advocates advanced treatment frameworks that could significantly improve healing outcomes for individuals of all sexes, thereby optimizing both efficacy and inclusivity in chronic wound management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".