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The present study was performed to establish whether intact human tendons exhibit time-dependent tensile properties, as they do in the in vitro state. Measurements were taken in seven men and involved ultrasound-based recording of the gastrocnemius tendon elongation during three sets of five repeated isometric plantarflexion contractions elicited by tetanic electrical stimulation. The plantarflexion moment corresponding to the tendon elongation in the fifth contraction presented a pattern dependent on the voltage applied: it was approximately constant when applying 50% of maximal voltage, but it decreased curvilinearly as a function of contraction number when applying 70 and 100% of maximal voltage, reaching in the fifth contraction 84% of the plantarflexion moment corresponding to the elongation examined in the first contraction. These results suggest that, once a threshold tendon elongation is undergone, in vivo tendons may exhibit substantial viscoelasticity. The present findings have implications for muscle and joint function and need to be accounted for by musculoskeletal models.

There have been no previous reports of tendon tissue engineering using mesenchymal stem cells (MSCs) with regard to quantitative evaluation of protein expression levels and observation of derived extracellular matrix (ECM) state. Therefore, we approached tendon tissue engineering from both perspectives. Human bone marrow MSCs (hBMSCs) were subjected to 8% or 10% cyclic stretching at 1 Hz to promote differentiation into tenocytes and ECM production. The type I collagen (Col I) and Tenascin-C (Tnc) protein expression levels were evaluated quantitatively by enzyme-linked immunosorbent assay (ELISA). Confocal fluorescence microscopy was employed to observe the derived ECM state. Col I state derived from 10%-stretched cells as ECM was elongated like actual tendon ECM, although the quantitative protein expression levels were slightly higher in 8%-stretched cells. The results suggested that the optimal uniaxial stretching ratio was different between protein expression levels and derived ECM state. Therefore, it is important to pay attention to both protein expression levels and ECM state in tendon tissue engineering.

Bone is a multiscale combination of collagen molecules merged with mineral crystals. Its high rigidity and stability stem amply from its polymeric organic matrix and secondly from the connections established between interdifferent and intradifferent scale components through cross-links. Several studies have shown that the cross-links inhibition results in a reduction in strength of bone but they do not quantify the degree to which these connections contribute to the bone rigidity and toughness. This report is classified among the few works that measure the cross-links multiscale impact on the ultrastructure bone mechanical behavior.

This work aims firstly to study the effect of cross-links at the molecule scale and secondly to gather from literature studies results handling with cross-links effects on the other bone ultrastructure scales in order to reveal the multiscale effect of cross-links. This study proves that cross-links increasing number improves the mechanical performance of each scale of bone ultrastructure. On the other hand, cross-links have a multiscale contribution that depends on its rank related to existing cross-links connecting the same geometries and it depends on mechanical characteristics of geometries connected.

Pathological analysis as well as biomechanical methods are powerful approaches for collagen assessment, which plays an important role in understanding the wound healing process and choosing a treatment method in clinical situations. Due to the limitations of preparing and evaluating pathological images, this study was designed to establish a machine learning technique to predict the wound collagen content through its biomechanical parameters. For this purpose, the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were compared. The wound was created with an incision on the back of 30 male BALB/c mice. On the 7th and 14th days, animals were sacrificed and 60 wound tissue samples were evaluated using histopathological and biomechanical methods to quantify the amount of collagen and wound tensile strength to feed the ANN and ANFIS developed models. Based on the results, both models have appropriate performance to predict the wound collagen content. However, the comparison of coefficient of determination ($R^2$) and root mean square error (RMSE) for testing dataset revealed that ANN ($R^2=0.95$, $\mathrm{\text{RMSE}}=0.29$) had more prediction capability than ANFIS ($R^2=0.84$, $\mathrm{\text{RMSE}}=0.87$). As a decision support system, ANN model could assist in the evaluation of wound healing process with collagen values prediction.