Pepsin is generally used in the preparation of F(ab) fragments from antibodies. The antibodies that are one of the largest and fastest growing categories of bio- pharmaceutical candidates. Differential scanning calorimetric is principally suitable method to follow the energetics of a multi-domain, fragment to perform a more exhaustive description of the thermodynamics in an associating system. The thermodynamical models of analysis include the construction of a simultaneous fitting of a theoretical expression. The expression depending on the equilibrium unfolding data from multimeric proteins that have a two-state monomer. The aim of the present study is considering the DSC data in connection with pepsin going through reversible thermal denaturation. Afterwards, we calculate the homology modeling identification of pepsin in complex multi-domain families with varied domain architectures. In order to analyze the DSC data, the thermal denaturation of multimer proteins were considered, the "two independent two-state sequential transitions with domains dissociation model" was introduced by using of the effective Δ concept. The reversible unfolding of the protein description was followed by the two-state transition quantities which is a slower irreversible process of aggregation. The protein unfolding is best described by two non-ideal transitions, suggesting the presence of unfolding intermediates. These evaluations are also applicable for high throughput investigation of protein stability.
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http://dx.doi.org/10.1016/j.bbrep.2017.01.005 | DOI Listing |
Med Biol Eng Comput
January 2025
Department of Computer Science and Engineering, Shri Shankaracharya Institute of Professional Management and Technology, Raipur, (C.G.), India.
This study presents an advanced methodology for 3D heart reconstruction using a combination of deep learning models and computational techniques, addressing critical challenges in cardiac modeling and segmentation. A multi-dataset approach was employed, including data from the UK Biobank, MICCAI Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, and clinical datasets of congenital heart disease. Preprocessing steps involved segmentation, intensity normalization, and mesh generation, while the reconstruction was performed using a blend of statistical shape modeling (SSM), graph convolutional networks (GCNs), and progressive GANs.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Department of Radiology, University of Chicago, Chicago, IL, USA.
Purpose: Thyroid nodules are common, and ultrasound-based risk stratification using ACR's TIRADS classification is a key step in predicting nodule pathology. Determining thyroid nodule contours is necessary for the calculation of TIRADS scores and can also be used in the development of machine learning nodule diagnosis systems. This paper presents the development, validation, and multi-institutional independent testing of a machine learning system for the automatic segmentation of thyroid nodules on ultrasound.
View Article and Find Full Text PDFMagn Reson Imaging
December 2024
Department of Radiology and Diagnostic Imaging, Nicolaus Copernicus University, Collegium Medicum, Bydgoszcz, Poland.
Background: Brain tumors exhibit diverse genetic landscapes and hemodynamic properties, influencing diagnosis and treatment outcomes.
Purpose: To explore the relationship between MRI perfusion metrics (rCBV, rCBF), genetic markers, and contrast enhancement patterns in gliomas, aiming to enhance diagnostic accuracy and inform personalized therapeutic strategies. Additionally, other radiological features, such as the T2/FLAIR mismatch sign, are evaluated for their predictive utility in IDH mutations.
PLoS One
December 2024
School of Aerospace Engineering, Xiamen University, Xiamen, China.
To address the problem of insufficient feature extraction abilities of traditional fault diagnosis methods under conditions of sample scarcity and strong noise interference, a rolling bearing fault diagnosis method based on the Gramian Angular Difference Field (GADF) and Dynamic Self-Calibrated Convolution (DSC) is proposed. First, the GADF method converts one-dimensional signals into GADF images to capture nonlinear relationships and periodic information in time-series data. Second, a dynamic self-calibrated convolution module is introduced to enhance the feature extraction ability of the model.
View Article and Find Full Text PDFEpilepsia
December 2024
Department of Neuroradiology, University Hospital Bonn, Bonn, Germany.
Objective: Focal cortical dysplasia (FCD) is a common cause of drug-resistant focal epilepsy but can be challenging to detect visually on magnetic resonance imaging. Three artificial intelligence models for automated FCD detection are publicly available (MAP18, deepFCD, MELD) but have only been compared on single-center data. Our first objective is to compare them on independent multicenter test data.
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