Biology subjects

Shamsi, M.

Publications and source records attributed to Shamsi, M..

4 recordsLinked to original sources

bioRxiv2026

Multi-material biomaterial model of scaffold-defect integration at the wound margins

Critical-sized craniomaxillofacial (CMF) defects affect the skull, face, and jaw, arising from conditions such as cleft palate, oncologic resections, and high energy impacts, and due to their large size and irregular geometry, cannot heal naturally by the body, thus requiring surgery. The field of biomedical research has long recognized the need to develop higher order biomaterial model systems for improved disease characterization and translational therapeutic/material progress. There is, however, difficulty in developing these workflows at the scale of conventional two-dimensional cell culture screening systems while simultaneously approaching a level of complexity necessary to consider translation to in vivo animal models. Here, we describe a three-dimensional (3D), in vitro model system to investigate the impact of stromal cell migration from one microenvironment to another at a medium-throughput scale. Importantly, we demonstrate the ability of this workflow to be utilized as a screening tool for collagen-based biomaterial motifs of interest in promoting craniomaxillofacial bone defect repair. Taken together we provide a strategy for interpreting cell-to-cell, cell-to-material, and material-to-material interactions across a multidimensional spatiotemporal scale.

bioengineering↗

bioRxiv2021

Depth Dataset Using Microsoft Kinect-v2

In biomedical imaging studies, numerous methods have been used to capture human data, mostly by using magnetic resonance imaging (MRI) and computed tomography (CT). However, due to being inexpensive and accessibility of Microsoft Kinect, its usage has been significantly increased in recent years. In this study, we aimed to represent the procedure of data acquisition, which includes a set of depth images from individuals back surfaces. The goal of image acquisition is to investigate spinal deformities and landmark detection of the back surface. Traditional imaging systems are challenging, most notably because of ionized beams in the data acquisition process, which has not been solved yet. Indeed, noninvasiveness is the most crucial advantage of our study. Our imaging system was set in a dim laboratory, and the University approved the ethical letter of Medical Sciences before data acquisition. After that, the subjects (total 105; 50 women and 55 men) were recruited, and data images were captured from the back surface. Then, we increased the imaging data size by using the augmentation method to use deep learning methods in future works. Finally, this Dataset leads us to the desired output in our study procedure.

bioengineering↗

bioRxiv2021

Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities that cause physical dysfunction and correspondingly reduce peoples quality of life. Radiography is the most common method for detecting these deformities and requires monitoring and follow-up until full treatment; however, it frequently exposes the patient to X-rays and ionization and as a result, cancer risk is increased in the patient and could be highly dangerous for children or pregnant women. To prevent this, several solutions have been proposed using topographic data analysis of the human back surface. The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and practical information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach - Deep convolution neural network-was used as a model to estimate the location of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed that 86.9% of PDJ and 80% of PCK. According to the results, this study was more effective than other methods with more than thousands of training data.

bioengineering↗

bioRxiv2017

BPG: Seamless, Automated and Interactive Visualization of Scientific Data

We introduce BPG, an easy-to-use framework for generating publication-quality, highly-customizable plots in the R statistical environment. This open-source package includes novel methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it ideal for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for seamless integration with computational pipelines. BPG is available at http://labs.oicr.on.ca/boutros-lab/software/bpg

bioinformatics↗