ABSTRACT
Background and objective: PolyWare™ software (PW) has been exclusively used in the majority of polyethylene wear studies of total hip arthroplasty (THA). PW measurements can be significantly inaccurate and unrepeatable, depending on imaging conditions or subjective manipulation choices. In this regard, this study aims to shed light on the conditions needed to achieve the best accuracy and reliability of PW measurements. Methods: The experiment looked at how PW fluctuated based on several measurement conditions. x-ray images of in-vitro THA prostheses were acquired under a clinical x-ray scanning condition. A linear wear rate of 6.67â mm was simulated in combination with an acetabular lateral inclination of 36.6° and anteversion of 9.0°. Results: Among all the imported x-ray images, those with a resolution of 1,076 × 1,076 exhibited the best standard deviation in wear measurements as small as 0.01â mm and the lowest frequencies of blurriness. The edge detection area specified as non-square and off the femoral head center exhibited the most blurriness. The x-ray image that scans a femoral head eccentrically placed by 15â cm superior to the x-ray beam center led to a maximum acetabular anteversion measurement error of 5.3°. Conclusion: Because PW has been the only polyethylene wear measurement tool used, identifying its sources of error and devising a countermeasure are of the utmost importance. The results call for PW users to observe the following measurement protocols: (1) the original x-ray image must be a 1,076 × 1,076 square; (2) the edge detection area must be specified as a square with edge lengths of 5 times the diameter of the femoral head, centered at the femoral head center; and (3) the femoral head center or acetabular center must be positioned as close to the center line of the x-ray beam as possible when scanning.
ABSTRACT
Extracellular cues affect signaling, metabolic, and regulatory processes to elicit cellular responses. Although intracellular signaling, metabolic, and regulatory networks are highly integrated, previous analyses have largely focused on independent processes (e.g., metabolism) without considering the interplay that exists among them. However, there is evidence that many diseases arise from multifunctional components with roles throughout signaling, metabolic, and regulatory networks. Therefore, in this study, we propose a flux balance analysis (FBA)-based strategy, referred to as integrated dynamic FBA (idFBA), that dynamically simulates cellular phenotypes arising from integrated networks. The idFBA framework requires an integrated stoichiometric reconstruction of signaling, metabolic, and regulatory processes. It assumes quasi-steady-state conditions for "fast" reactions and incorporates "slow" reactions into the stoichiometric formalism in a time-delayed manner. To assess the efficacy of idFBA, we developed a prototypic integrated system comprising signaling, metabolic, and regulatory processes with network features characteristic of actual systems and incorporated kinetic parameters based on typical time scales observed in literature. idFBA was applied to the prototypic system, which was evaluated for different environments and gene regulatory rules. In addition, we applied the idFBA framework in a similar manner to a representative module of the single-cell eukaryotic organism Saccharomyces cerevisiae. Ultimately, idFBA facilitated quantitative, dynamic analysis of systemic effects of extracellular cues on cellular phenotypes and generated comparable time-course predictions when contrasted with an equivalent kinetic model. Since idFBA solves a linear programming problem and does not require an exhaustive list of detailed kinetic parameters, it may be efficiently scaled to integrated intracellular systems that incorporate signaling, metabolic, and regulatory processes at the genome scale, such as the S. cerevisiae system presented here.
