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Omar, O.

Publications and source records attributed to Omar, O..

2 recordsLinked to original sources

Pyrophosphate-containing Calcium Phosphates Negatively Impact Heterotopic Bone Quality

In bone, critical size defects pose substantial challenge in maxillofacial and orthopaedic reconstructions as they are incapable of spontaneous regeneration. Autologous bone grafts remain the "gold standard" in bone repair but their limitations and associated morbidities drive the development of bone graft substitute materials. Assessing the osteoinductive properties of these substitutes can help predict their success in bone regeneration applications. Calcium phosphates (CaP) are widely used bone graft substitutes due to their biocompatibility, osteoconductive properties, and potential for osteoinductivity. CaP materials containing monetite, beta-tricalcium phosphate ({beta}-TCP), and a small amount of calcium pyrophosphate (Ca-PP) possess both osteoconductive and osteoinductive properties. However, the role of Ca-PP in osteoinduction and material degradation remains unexplored. This study investigates heterotopic bone formation in response to five CaP material compositions, maintaining a constant monetite and {beta}-TCP ratio, with varying amounts of Ca-PP (0 - 12.5%). Twelve adult female sheep (Ovis aries) were subcutaneously implanted with constructs made of six CaP tiles interconnected by a Ti6Al4V frame and a control implant. Histological analysis, backscattered electron scanning electron microscopy, and Raman spectroscopy of samples retrieved at 12- and 52 weeks reveal that Ca-PP does not hinder heterotopic bone formation and minimally impacts CaP degradation. While monetite and {beta}-TCP transform into apatite, the Ca-PP phase remains unchanged. The addition of Ca-PP to the CaP material influences heterotopic bone quality and inflammatory response during tissue regeneration.

bioengineering↗

Reducing structural non-identifiabilities in upstream bioprocess models using profile-likelihood

Process models are increasingly used to support upstream process development in the biopharmaceutical industry for process optimization, scale-up and to reduce experimental effort. Parametric unstructured models based biological mechanisms are highly promising, since they do not require large amounts of data. The critical part in the application is the certainty of the parameter estimates, since uncertainty of the parameter estimates propagates to model predictions and can increase the risk associated with those predictions. Currently Fisher-Information-Matrix based approximations or Monte-Carlo approaches are used to estimate parameter confidence intervals and regularization approaches to decrease parameter uncertainty. Here we apply profile likelihood to determine parameter identifiability of a recent upstream process model. We have investigated the effect of data amount on identifiability and found out that addition of data reduces non-identifiability. The likelihood profiles of non-identifiable parameters were then used to uncover structural model changes. These changes effectively alleviate the remaining non-identifiabilities except for a single parameter out of 21 total parameters. We present the first application of profile likelihood to a complete upstream process model. Profile likelihood is a highly suitable method to determine parameter confidence intervals in upstream process models and provides reliable estimates even with non-linear models and limited data.

bioengineering↗