MyoFInDer: An AI-Based Tool for Myotube Fusion Index Determination.

The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error prone and subjective. Several software tools have been proposed for addressing these limitations but suffer from various drawbacks, including unintuitive interfaces and limited performance. In this study, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared with other tools, MyoFInDer stands out as it minimizes the interoperator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.

On the uncertainty quantification of the active uterine contraction during the second stage of labor simulation.

Uterine contractions in the myometrium occur at multiple scales, spanning both organ and cellular levels. This complex biological process plays an essential role in the fetus delivery during the second stage of labor. Several finite element models of active uterine contractions have already been developed to simulate the descent of the fetus through the birth canal. However, the developed models suffer severe reliability issues due to the uncertain parameters. In this context, the present study aimed to perform the uncertainty quantification (UQ) of the active uterine contraction simulation to advance our understanding of pregnancy mechanisms with more reliable indicators. A uterus model with and without fetus was developed integrating a transversely isotropic Mooney-Rivlin material with two distinct fiber orientation architectures. Different contraction patterns with complex boundary conditions were designed and applied. A global sensitivity study was performed to select the most valuable parameters for the uncertainty quantification (UQ) process using a copula-based Monte Carlo method. As results, four critical material parameters ( C 1 , C 2 , K , Ca 0 ) of the active uterine contraction model were identified and used for the UQ process. The stress distribution on the uterus during the fetus descent, considering first and second fiber orientation families, ranged from 0.144 to 1.234 MPa and 0.044 to 1.619 MPa, respectively. The simulation outcomes revealed also the segment-specific contraction pattern of the uterus tissue. The present study quantified, for the first time, the effect of uncertain parameters of the complex constitutive model of the active uterine contraction on the fetus descent process. As perspectives, a full maternal pelvis model will be coupled with reinforcement learning to automatically identify the delivery mechanism behind the cardinal movements of the fetus during the active expulsion process.

Decellularized tissue exhibits large differences of extracellular matrix properties dependent on decellularization method: novel insights from a standardized characterization on skeletal muscle.

Decellularized matrices are an attractive choice of scaffold in regenerative medicine as they can provide the necessary extracellular matrix (ECM) components, signals and mechanical properties. Various detergent-based protocols have already been proposed for decellularization of skeletal muscle tissue. However, a proper comparison is difficult due to differences in species, muscle origin and sample sizes. Moreover, a thorough evaluation of the remaining acellular matrix is often lacking. We compared an in-house developed decellularization protocol to four previously published methods in a standardized manner. Porcine skeletal muscle samples with uniform thickness were subjected to in-depth histological, ultrastructural, biochemical and biomechanical analysis. In addition, 2D and three-dimensional cytocompatibility experiments were performed. We found that the decellularization methods had a differential effect on the properties of the resulting acellular matrices. Sodium deoxycholate combined with deoxyribonuclease I was not an effective method for decellularizing thick skeletal muscle tissue. Triton X-100 in combination with trypsin, on the other hand, removed nuclear material but not cytoplasmic proteins at low concentrations. Moreover, it led to significant alterations in the biomechanical properties. Finally, sodium dodecyl sulphate (SDS) seemed most promising, resulting in a drastic decrease in DNA content without major effects on the ECM composition and biomechanical properties. Moreover, cell attachment and metabolic activity were also found to be the highest on samples decellularized with SDS. Through a newly proposed standardized analysis, we provide a comprehensive understanding of the impact of different decellularizing agents on the structure and composition of skeletal muscle. Evaluation of nuclear content as well as ECM composition, biomechanical properties and cell growth are important parameters to assess. SDS comes forward as a detergent with the best balance between all measured parameters and holds the most promise for decellularization of skeletal muscle tissue.