Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Identification of polcalcin as a novel allergen of Amaranthus retroflexus pollen

Introduction: Amaranthus retroflexus (Redroot Pigweed) is one of the main sources of allergenic pollens in temperate areas. Polcalcin is a well-known panallergen involved in cross-reactivity between different plants. The aim of this study was the molecular cloning and expression of polcalcin, as well as evaluating its IgE-reactivity with A. retroflexus sensitive patients’ sera. Methods: Allergenic extract was prepared from A. retroflexus pollen and the IgE-reactivity profile was determined by ELISA and immunoblotting using sera from twenty A. retroflexus sensitive patients. Polcalcin-coding sequence was amplified by conventional PCR method and the product was inserted into pET-21b(+) vector. The recombinant protein was expressed in E. coli BL21 and purified by metal affinity chromatography. The IgE-binding capability of the recombinant protein was analyzed by ELISA and immunoblotting assays, and compared with crude extract. Results: Of 20 skin prick test positive patients, 17 patients were positive in IgE-specific ELISA. Western blotting confirmed that approximately 53% of ELISA positive patients reacted with 10 kDa protein in crude extract. The A. retroflexus polcalcin gene, encoding to 80 amino acid residues was cloned and expressed as a soluble protein and designated as Ama r 3. The recombinant polcalcin showed rather identical IgE-reactivity in ELISA and western blotting with 10 kDa protein in crude extract. These results were confirmed by inhibition methods, too. Conclusion: The recombinant form of A. retroflexus polcalcin (Ama r 3) could be easily produced in E. coli in a soluble form and shows rather similar IgE-reactivity with its natural counterpart

CCDC 1958147: Experimental Crystal Structure Determination

Related Article: Timothy R. Lex, Maria I. Swasy, Soham Panda, Beau R. Brummel, Lauren N. Giambalvo, Kristopher G. Gross, Colin D. McMillen, Khadijatul Kobra, William T. Pennington, Daniel C. Whitehead|2020|Tetrahedron Lett.|61|151723|doi:10.1016/j.tetlet.2020.15172

Tryptase β regulation of joint lubrication and inflammation via proteoglycan-4 in osteoarthritis

Altered expression and function of the extracellular matrix protein PRG4 have been associated with osteoarthritis. Here, the authors show that mast cell tryptase β cleaves PRG4, resulting in a reduction of lubrication and activation of inflammation in this context