Biology subjects

Sedigh, A.

Publications and source records attributed to Sedigh, A..

6 recordsLinked to original sources

bioRxiv2025

The Effect of Celecoxib and muMab911 on Strain Adaptive Bone Remodeling and Fracture Repair in Female Mice: Implications for Rapidly Progressive Osteoarthritis

Debilitating pain is the primary clinical feature of osteoarthritis (OA) that drives the enormous healthcare costs. Osteoarthritis-related pain is often treated with non-steroidal anti-inflammatory drugs (NSAIDs), which effectively relieve pain and inflammation by inhibition of prostaglandin synthesis. Antibodies directed against nerve growth factor (NGF) were tested some time ago as an alternative potential analgesic for musculoskeletal pain, including osteoarthritis-related pain. Unfortunately, clinical development of these drugs was put on hold due to adverse outcomes - primarily rapidly progressive osteoarthritis. Both prostaglandin synthesis and NGF have been implicated as critical mediators of strain adaptive bone remodeling, which may play a role in rapid osteoarthritis progression. Therefore, this study was designed to investigate the effects of celecoxib, an NSAID, and muMab911, an anti-NGF antibody, as well as the combination therapy on strain adaptive bone remodeling, bone mass and geometry, and bone healing in a murine model. Adult female C57BL/6J mice received celecoxib through drinking water, up to 3 IP injections of muMab911, or both treatments over a period of two weeks. As expected, all treatments were effective for relieving injury-associated pain. Consistent with previous studies, we found that celecoxib alone and in combination with muMab911 impaired periosteal load-induced bone formation induced by axial forelimb compression. Furthermore, both treatments had minimal effects on osteoblast and osteocyte populations, bone structural and material properties, and cortical and trabecular bone mass. Mice subjected to damaging axial forelimb compression did not have impaired healing or callus morphology due to treatment. Nonetheless, both drugs decreased NGF expression during fracture healing. In total, our results indicate that these medications both suppress NGF expression and load-induced bone formation which may support rapid OA disease progression by diminishing strain adaptive bone remodeling in subchondral bone of OA patients.

bioengineering↗

bioRxiv2024

Design and Evaluation of Nanoscale Materials with Programmed Responsivity towards Epigenetic Enzymes

Self-assembled materials capable of modulating their assembly properties in response to specific enzymes play a pivotal role in advancing intelligent encapsulation platforms for biotechnological applications. Here, we introduce a previously unreported class of synthetic nanomaterials that programmatically interact with histone deacetylase (HDAC) as the triggering stimulus for disassembly. These nanomaterials consist of co-polypeptides comprising poly (acetyl L-lysine) and poly(ethylene glycol) blocks. Under neutral pH conditions, they self-assemble into particles. However, their stability is compromised upon exposure to HDACs, depending on enzyme concentration and exposure time. Our investigation, utilizing HDAC8 as the model enzyme, revealed that the primary mechanism behind disassembly involves a decrease in amphiphilicity within the block copolymer due to the deacetylation of lysine residues within the particles hydrophobic domains. To elucidate the response mechanism, we encapsulated a fluorescent dye within these nanoparticles. Upon incubation with HDAC, the nanoparticle structure collapsed, leading to controlled release of the dye over time. Notably, this release was not triggered by denatured HDAC8, other proteolytic enzymes like trypsin, or the co-presence of HDAC8 and its inhibitor. We further demonstrated the biocompatibility and cellular effects of these materials and conducted a comprehensive computational study to unveil the possible interaction mechanism between enzymes and particles. By drawing parallels to the mechanism of naturally occurring histone proteins, this research represents a pioneering step toward developing functional materials capable of harnessing the activity of epigenetic enzymes such as HDACs.

bioengineering↗

bioRxiv2022

Approximating Scaffold Printability Utilizing Computational Methods

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for various applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of materials (bioinks) that can be utilized, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that affect bioprinting utility, accuracy, and precision. Here, we have extended our previous work to achieve high precision (i.e., repeatability) across samples by optimizing bioink-specific printing parameters. Specifically, we hypothesized that an adaptive neuro-fuzzy inference system (ANFIS) could be used as a computational method to address the imprecision in 3D bioprinting test data and uncover the optimal printing parameters for a specific bioink that result in high accuracy and precision. To test this hypothesis, we have implemented an ANFIS model consisting of four inputs (bioink concentration, printing pressure, speed, and temperature) and a single output to quantify the precision (scaffold bioprinted linewidth range). We validate our use of the bioprinting precision index (BPI) with both standard and normalized printability factors. In total, our results indicate that computational methods are a cost-efficient measure to improve the precision and robustness of extrusion 3D bioprinting with gelatin-based bioinks.

bioengineering↗

bioRxiv2021

Enhancing Precision in Bioprinting Utilizing Fuzzy Systems

Bioprinting facilitates the generation of complex, three-dimensional (3D), cell-based constructs for a variety of applications. Although multiple bioprinting technologies have been developed, extrusion-based systems have become the dominant technology due to the diversity of substrate materials (bioinks) that can be accommodated, either individually or in combination. However, each bioink has unique material properties and extrusion characteristics that limit bioprinting precision, particularly when generating constructs from different bioinks. Here, we aimed to achieve high precision (i.e. repeatability) across samples by generating bioink-specific printing parameters using a systematic approach. We hypothesized that a fuzzy system could be used as a "black box" method to tackle the inherent vagueness and imprecision in 3D bioprinting data and uncover the optimal printing parameters for a specific bioink that would result in high accuracy and precision. Our fuzzy model was used to approximate and quantify the precision and ease of printability for two common bioinks - type I collagen and Pluronic F127, with or without dilution in MEM culture media. The model consisted of three inputs (pressure, speed, and dilution percent of bioink) and a single output (layer width). Using this system, we introduce the Bioink Precision Index (BPI), a metric that can be used to quantify and compare the precision of any bioink. Here, we show that printing with parameters optimized using BPI increases the precision for collagen (+15%) and Pluronic F127 (+29%) as compared to the manufacturers recommended printing parameters.

bioinformatics↗

bioRxiv2021

Comparison of Type-1 and Type-2 Fuzzy Systems forMineralization of Bioprinted Bone

Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinting parameters, such as pressure, nozzle size, and speed, have a large influence on the quality of the bioprinted construct. Moreover, cell suspension density, cell culture period, and other critical biological parameters directly impact the biological function of the final product. Therefore, an approximation model that can be used to find the values of bioprinting parameters that will result in optimal bioprinted constructs is highly desired. Here, we propose type-1 and type-2 fuzzy systems to handle the uncertainty and imprecision in optimizing the input values. Specifically, we focus on the biological parameters, such as culture period, that can be used to maximize the output value (mineralization volume). To achieve a more accurate approximation, we have compared a type-2 fuzzy system with a type-1 fuzzy system using two levels of uncertainty. We hypothesized that type-2 fuzzy systems may be preferred in biological systems, due to the inherent vagueness and imprecision of the input data. Here, our results demonstrate that the type-2 fuzzy system with a high uncertainty boundary (30%) is superior to type-1 and type-2 with low uncertainty boundary fuzzy systems in the overall output approximation error for bone bioprinting inputs.

bioinformatics↗

bioRxiv2020

Utilizing Q-Learning to Generate 3D Vascular Networks for Bioprinting Bone

Bioprinting is an emerging tissue engineering method used to generate cell-laden scaffolds with high spatial resolution. Bioprinted vascularized bone grafts are a potential application of this technology that would meet a critical clinical need, since current approaches to volumetric bone repair have significant limitations. However, generation of vascular networks suitable for bioprinting is challenging. Here, we propose a novel Q-learning approach to quickly generate 3D vascular networks within patient-specific bone geometry that are optimized for bioprinting. First, the inlet and outlet locations are specified and the scenario is modeled using a grid world for initial agent training. Next, the path planned in the grid world environment is converted to a Bezier curve, which is then used to generate the final 3D vascularized bone model. The vessels generated using this procedure have minimal tortuosity, which increases the likelihood of successful bioprinting. Furthermore, the ability to specify inlet and outlet position is necessary for both surgical feasibility as well as generation of more complex vascular networks. In total, this study demonstrates the reliability of our reinforcement learning method for automated generation of 3D vascular networks within patient-specific geometry that can be used for bioprinting vascularized bone grafts.

bioengineering↗