Hydrogel-Based Bioinks for Three-Dimensional Bioprinting: Patent Analysis

2021 ◽  
Vol 7 (1) ◽  
pp. 3
Author(s):  
Ahmed Fatimi
Keyword(s):  
Tissue Engineering ◽  
Detailed Analysis ◽  
State Of The Art ◽  
Three Dimensional ◽  
Patent Analysis ◽  
The State ◽  
Driving Factors ◽  
3D Bioprinting ◽  
Preparation Methods

There are a variety of hydrogel-based bioinks commonly used in three-dimensional bioprinting. In this study, in the form of patent analysis, the state of the art has been reviewed by introducing what has been patented in relation to hydrogel-based bioinks. Furthermore, a detailed analysis of the patentability of the used hydrogels, their preparation methods and their formulations, as well as the 3D bioprinting process using hydrogels, have been provided by determining publication years, jurisdictions, inventors, applicants, owners, and classifications. The classification of patents reveals that most inventions intended for hydrogels used as materials for prostheses or for coating prostheses are characterized by their function or properties Knowledge clusters and expert driving factors show that biomaterials, tissue engineering, and biofabrication research is concentrated in the most patents.

scholarly journals State-of-the-art on monolingual lexicography for Croatia (Croatian)

2019 ◽  
Vol 7 (1) ◽  
pp. 65-77
Author(s):  
Kristina Štrkalj Despot ◽  
Lana Hudeček ◽  
Tomislav Stojanov ◽  
Nikola Ljubešić
Keyword(s):  
State Of The Art ◽  
The State ◽  
Writing Systems ◽  
Insight Into

In this minireview, the state of the art of the Croatian monolingual lexicography is presented. A brief overview and classification of all existing lexicographic resources is provided in the firts part of the minireview, followed by somewhat more detailed insight into the existing Croatian monolingual dictionaries and monolingual lexicographic projects, orthography dictionaries, and dictionary writing systems used.

scholarly journals Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1459 ◽  
Author(s):  
Tamás Czimmermann ◽  
Gastone Ciuti ◽  
Mario Milazzo ◽  
Marcello Chiurazzi ◽  
Stefano Roccella ◽  
...  
Keyword(s):  
Deep Learning ◽  
Defect Detection ◽  
Visual Processing ◽  
State Of The Art ◽  
Industrial Applications ◽  
The State ◽  
Shape Error ◽  
Supervised Classifiers ◽  
Processing Techniques

This paper reviews automated visual-based defect detection approaches applicable to various materials, such as metals, ceramics and textiles. In the first part of the paper, we present a general taxonomy of the different defects that fall in two classes: visible (e.g., scratches, shape error, etc.) and palpable (e.g., crack, bump, etc.) defects. Then, we describe artificial visual processing techniques that are aimed at understanding of the captured scenery in a mathematical/logical way. We continue with a survey of textural defect detection based on statistical, structural and other approaches. Finally, we report the state of the art for approaching the detection and classification of defects through supervised and non-supervised classifiers and deep learning.

scholarly journals Fabrication of 3D Printing Scaffold with Porcine Skin Decellularized Bio-Ink for Soft Tissue Engineering

Materials ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 3522
Author(s):  
Su Jeong Lee ◽  
Jun Hee Lee ◽  
Jisun Park ◽  
Wan Doo Kim ◽  
Su A Park
Keyword(s):  
Tissue Engineering ◽  
Extracellular Matrix ◽  
3D Printing ◽  
Three Dimensional ◽  
3D Bioprinting ◽  
Research Groups ◽  
Porcine Skin ◽  
Alginate Hydrogel ◽  
Chemical Treatments ◽  
Soft Tissue Engineering

Recently, many research groups have investigated three-dimensional (3D) bioprinting techniques for tissue engineering and regenerative medicine. The bio-ink used in 3D bioprinting is typically a combination of synthetic and natural materials. In this study, we prepared bio-ink containing porcine skin powder (PSP) to determine rheological properties, biocompatibility, and extracellular matrix (ECM) formation in cells in PSP-ink after 3D printing. PSP was extracted without cells by mechanical, enzymatic, and chemical treatments of porcine dermis tissue. Our developed PSP-containing bio-ink showed enhanced printability and biocompatibility. To identify whether the bio-ink was printable, the viscosity of bio-ink and alginate hydrogel was analyzed with different concentration of PSP. As the PSP concentration increased, viscosity also increased. To assess the biocompatibility of the PSP-containing bio-ink, cells mixed with bio-ink printed structures were measured using a live/dead assay and WST-1 assay. Nearly no dead cells were observed in the structure containing 10 mg/mL PSP-ink, indicating that the amounts of PSP-ink used were nontoxic. In conclusion, the proposed skin dermis decellularized bio-ink is a candidate for 3D bioprinting.

scholarly journals 3D Printing in Heterogeneous Catalysis—The State of the Art

Materials ◽  
2020 ◽  
Vol 13 (20) ◽  
pp. 4534 ◽  
Author(s):  
Elżbieta Bogdan ◽  
Piotr Michorczyk
Keyword(s):  
Additive Manufacturing ◽  
Heterogeneous Catalysis ◽  
3D Printing ◽  
Chemical Synthesis ◽  
State Of The Art ◽  
Three Dimensional ◽  
Production Method ◽  
The State ◽  
Monolithic Catalysts ◽  
Selection Of

This paper describes the process of additive manufacturing and a selection of three-dimensional (3D) printing methods which have applications in chemical synthesis, specifically for the production of monolithic catalysts. A review was conducted on reference literature for 3D printing applications in the field of catalysis. It was proven that 3D printing is a promising production method for catalysts.

On the transmodality of signs and their interpretants: Evidence from Peirce’s MS 599, Reason’s Rules

Semiotica ◽  
2019 ◽  
Vol 2019 (228) ◽  
pp. 223-235
Author(s):  
Winfried Nöth
Keyword(s):  
Media Studies ◽  
State Of The Art ◽  
The State ◽  
Close Reading ◽  
Communicative Process ◽  
External Sign ◽  
The One ◽  
The Way

AbstractThe paper begins with a survey of the state of the art in multimodal research, an international trend in applied semiotics, linguistics, and media studies, and goes on to compare its approach to verbal and nonverbal signs to Charles S. Peirce’s approach to signs and their classification. The author introduces the concept of transmodality to characterize the way in which Peirce’s classification of signs reflects the modes of multimodality research and argues that Peirce’s classification of the signs takes modes and modalities in two different respects into consideration, (1) from the perspective of the sign and (2) from the one of its interpretant. While current research in multimodality has its focus on the (external) sign in a communicative process, Peirce considers additionally the multimodality of the interpretants, i.e., the mental icons and indexical scenarios evoked in the interpreters’ minds. The paper illustrates and comments on the Peircean method of studying the multi and transmodality of signs in an analysis of Peirce’s close reading of Luke 19:30 in MS 599, Reason’s Rules, of c. 1902. As a sign, this text is “monomodal” insofar as it consists of printed words only. The study shows in which respects the interpretants of this text evince trans and multimodality.

scholarly journals A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

2020 ◽  
Vol 10 (2) ◽  
pp. 84 ◽  
Author(s):  
Atif Mehmood ◽  
Muazzam Maqsood ◽  
Muzaffar Bashir ◽  
Yang Shuyuan
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Early Stage ◽  
The State ◽  
Test Accuracy ◽  
Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

scholarly journals Automatic Segmentation and Classification of Heart Sounds Using Modified Empirical Wavelet Transform and Power Features

2020 ◽  
Vol 10 (14) ◽  
pp. 4791 ◽  
Author(s):  
Pedro Narváez ◽  
Steven Gutierrez ◽  
Winston S. Percybrooks
Keyword(s):  
Wavelet Transform ◽  
Heart Sound ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Automatic Classification ◽  
The State ◽  
Heart Sounds ◽  
Empirical Wavelet Transform ◽  
Art Methods

A system for the automatic classification of cardiac sounds can be of great help for doctors in the diagnosis of cardiac diseases. Generally speaking, the main stages of such systems are (i) the pre-processing of the heart sound signal, (ii) the segmentation of the cardiac cycles, (iii) feature extraction and (iv) classification. In this paper, we propose methods for each of these stages. The modified empirical wavelet transform (EWT) and the normalized Shannon average energy are used in pre-processing and automatic segmentation to identify the systolic and diastolic intervals in a heart sound recording; then, six power characteristics are extracted (three for the systole and three for the diastole)—the motivation behind using power features is to achieve a low computational cost to facilitate eventual real-time implementations. Finally, different models of machine learning (support vector machine (SVM), k-nearest neighbor (KNN), random forest and multilayer perceptron) are used to determine the classifier with the best performance. The automatic segmentation method was tested with the heart sounds from the Pascal Challenge database. The results indicated an error (computed as the sum of the differences between manual segmentation labels from the database and the segmentation labels obtained by the proposed algorithm) of 843,440.8 for dataset A and 17,074.1 for dataset B, which are better values than those reported with the state-of-the-art methods. For automatic classification, 805 sample recordings from different databases were used. The best accuracy result was 99.26% using the KNN classifier, with a specificity of 100% and a sensitivity of 98.57%. These results compare favorably with similar works using the state-of-the-art methods.

The Fabrication of Tissue Engineering Scaffolds by Inkjet Printing Technology

2018 ◽  
Vol 934 ◽  
pp. 129-133 ◽  
Author(s):  
Chao Fan Lv ◽  
Li Ya Zhu ◽  
Jian Ping Shi ◽  
Zong An Li ◽  
Wen Lai Tang ◽  
...  
Keyword(s):  
Tissue Engineering ◽  
Sodium Alginate ◽  
Inkjet Printing ◽  
Three Dimensional ◽  
3D Bioprinting ◽  
Tissue Engineering Scaffolds ◽  
Printing Technology ◽  
3D Printed ◽  
Inkjet Printing Technology ◽  
Alginate Scaffold

Three-dimensional (3D) printing has been playing an important role in diverse areas in medicine. In order to promote the development of tissue engineering, this study attempts to fabricate tissue engineering scaffolds using the inkjet printing technology. Sodium alginate, exhibiting similar properties to the native human extracellular matrix (ECM), was used as bioink. The jetted fluid of sodium alginate would be gelatinized when printed into the calcium chloride solution. The characteristics of the 3D-printed sodium alginate scaffold were systematically measured and analyzed. The results show that, the pore size, porosity and degradation property of these scaffolds could be well controlled. This study indicates the capability of 3D bioprinting technology for preparing tissue engineering scaffolds.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

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