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BackgroundDecellularized allograft tendons are highly regarded for their accessibility and the reduced risk of immune rejection, making them a promising choice for grafting due to their favorable characteristics. However, effectively integrating reconstructed tendons with host bone remains a significant clinical challenge.PurposeThis study aims to investigate the relationship between the duration of tendon exposure to trypsin and its impact on tendon biomechanical properties and healing capacity.MethodsMorphological assessments and biochemical quantifications were conducted. Allograft tendons underwent heterotopic transplantation into the anterior cruciate ligament (ACL) in a rabbit model, with specimens harvested 6 weeks post-surgery for a comparative analysis of cell adhesion strength and mechanical performance. Duration-response curves were constructed using maximum stress and cell adhesion quantity as primary indicators.ResultsThe trypsin treatment enhanced cell adhesion on the tendon surface. Adhesion rates in the control group vs. the experimental groups were as follows: 3.10 ± 0.56% vs. 4.59 ± 1.51%, 5.36 ± 1.24%, 6.12 ± 1.98%, and 8.27 ± 2.34% (F = 6.755, p = 0.001). However, increasing treatment duration led to a decline in mechanical properties, with the ultimate load (N) in the control vs. experimental groups reported as 103.30 ± 10.51 vs. 99.59 ± 4.37, 93.15 ± 12.38, 90.42 ± 7.87, and 82.68 ± 6.89, F = 4.125 (p = 0.013).ConclusionThe findings reveal an increasing trend in adhesion effectiveness with prolonged exposure duration, while mechanical strength declines. The selection of the optimal processing duration should involve careful consideration of the benefits derived from both outcomes.

Background: Breast cancer (BC), the most common cancer among women globally, has been shown by numerous studies to significantly involve non-apoptotic regulatory cell death (RCD) in its pathogenesis and progression. Methods: We obtained the RNA sequences and clinical data of BC patients from The Cancer Genome Atlas (TCGA) database for the training set, while datasets GSE96058, GSE86166, and GSE20685 from The Gene Expression Omnibus (GEO) database were utilized as validation cohorts. Initially, we performed non-negative matrix factorization (NMF) clustering analysis on the BC samples from the TCGA database to discern non-apoptotic RCD-related molecular subtypes. To identify prognostically-relevant non-apoptotic RCD genes (NRGs) and construct a prognostic model, we implemented three machine learning algorithms: lasso regression, random forest, and XGBoost analysis. The expression of selected genes was verified using real-time quantitative polymerase chain reaction (RT-qPCR), single-cell RNA-sequencing (scRNA-seq) analysis, and The Human Protein Atlas (HPA) database. The risk signature was evaluated concerning clinical characteristics and drug sensitivity. Furthermore, we developed a nomogram to predict BC patient survival. Results: The NMF method successfully compartmentalized patients from the TCGA database into three distinct non-apoptotic RCD-related subtypes, with significant variations observed in immune characteristics and prognostic stratification across these subtypes. We identified 5 differentially expressed NRGs used in establishing the risk signature. Patients with different risk groups exhibited distinct clinicopathological features, drug sensitivity, and prognostic outcomes. A nomogram was subsequently developed, incorporating the NRGs-related risk signature, age, T stage, and N stage, to aid clinical decision-making. Conclusion: We identified a novel NRGs-related risk signature, which was expected to become a potential prognostic marker in BC.