Bone tissue engineering via application of a PCL/Gelatin/Nanoclay/Hesperetin 3D nanocomposite scaffold

Ganji, Saeed Mahdipour; Tehranchi, Maryam; Ehterami, Arian; Semyari, H; Taleghani, Ferial; Habibzadeh, M A; Tayeed, Mohammad Hossein; Mehrnia, Nika; Karimi, Anita; Salehi, Majid

doi:10.1016/j.jddst.2022.103704

Cited by 7 publications

(5 citation statements)

References 60 publications

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“…Poly(ε-caprolactone) (PCL) is a useful biodegradable biomaterial that can slowly degrade (in few years) under physiological conditions and is therefore used for longterm implants (in the treatment of bone defects), drug delivery systems [254] and as delivery platforms for various extracellular matrix proteins [255] or 3D scaffolds [256]. Polyanhydrides are hydrophobic polymers whose degradation occurs more through surface erosion than volume.…”

Section: Biodegradable Polymersmentioning

confidence: 99%

Advanced Biomedical Applications of Multifunctional Natural and Synthetic Biomaterials

Chelu,

Musuc

2023

Processes

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Biomaterials are mostly any natural and synthetic materials which are compatible from a biological point of view with the human body. Biomaterials are widely used to sustain, increase, reestablish or substitute the biological function of any injured tissue and organ from the human body. Additionally, biomaterials are uninterruptedly in contact with the human body, i.e., tissue, blood and biological fluids. For this reason, an essential feature of biomaterials is their biocompatibility. Consequently, this review summarizes the classification of different types of biomaterials based on their origin, as natural and synthetic ones. Moreover, the advanced applications in pharmaceutical and medical domains are highlighted based on the specific mechanical and physical properties of biomaterials, concerning their use. The high-priority challenges in the field of biomaterials are also discussed, especially those regarding the transfer and implementation of valuable scientific results in medical practice.

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Section: Biodegradable Polymersmentioning

confidence: 99%

Advanced Biomedical Applications of Multifunctional Natural and Synthetic Biomaterials

Chelu,

Musuc

2023

Processes

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“…These techniques depend on image processing functions that maintain class details. Applying offline techniques to new projects may need domain knowledge [32], [33].…”

Section: A Online Data Augmentationmentioning

confidence: 99%

A Novel Method for Myocardial Image Classification using Data Augmentation

Zhu¹

2023

IJACSA

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Myocarditis is an important public health concern since it can cause heart failure and abrupt death. It can be diagnosed with magnetic resonance imaging (MRI) of the heart, a non-invasive imaging technology with the potential for operator bias. The study provides a deep learning-based model for myocarditis detection using CMR images to support medical professionals. The proposed architecture comprises a convolutional neural network (CNN), a fully-connected decision layer, a generative adversarial network (GAN)-based algorithm for data augmentation, an enhanced DE for pre-training weights, and a reinforcement learning-based method for training. We present a new method of employing produced images for data augmentation based on GAN to improve the classification performance of the provided CNN. Unbalanced data is one of the most significant classification issues, as negative samples are more than positive, decimating system performance. To solve this issue, we offer an RL-based training method that learns minority class examples with attention. In addition, we tackle the challenges associated with the training step, which typically relies on gradient-based techniques for the learning process; however, these methods often face issues like sensitivity to initialization. To start the BP process, we present an improved differential evolution (DE) technique that leverages a clustering-based mutation operator. It recognizes a successful cluster for DE and applies an original updating strategy to produce potential solutions. We assess our suggested model on the Z-Alizadeh Sani myocarditis dataset and show that it outperforms other methods.

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“…Their work employs a receptive regularization on the convolution and deconvolution layers of a decoder block in the V-Net. Huang et al [53] proposed a CAD-based method that utilizes a 3D CNN [54] to detect lung nodules in low-dose CTs. The proposed method merges a priori intensity and geometrical knowledge about nodules and complicates anatomical structures with features and classifiers.…”

Section: B Deep Learningmentioning

confidence: 99%

Recognition of Lung Nodules in Computerized Tomography Lung Images using a Hybrid Method with Class Imbalance Reduction

Wang¹,

Wang²,

Dong³

2023

IJACSA

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Lung cancer is among the deadly diseases affecting millions globally every year. Physicians' and radiologists' manual detection of lung nodules has low efficiency due to the variety of shapes and nodule locations. The paper aims to recognize the lung nodules in computerized tomography (CT) lung images utilizing a hybrid method to reduce the problem space at every step. First, the suggested method uses the fast and robust fuzzy cmeans clustering method (FRFCM) algorithm to segment CT images and extract two lungs, followed by a convolutional neural network (CNN) to identify the sick lung for use in the next step. Then, the adaptive thresholding method detects the suspected regions of interest (ROIs) among all available objects in the sick lung. Next, some statistical features are selected from every ROI, and then a restricted Boltzmann machine (RBM) is considered a feature extractor that extracts rich features among the selected features. After that, an artificial neural network (ANN) is employed to classify ROIs and determine whether the ROI includes nodules or non-nodules. Finally, cancerous ROIs are localized by the Otsu thresholding algorithm. Naturally, sick ROIs are more than healthy ones, leading to a class imbalance that substantially decreases ANN ability. To solve this problem, a reinforcement learning (RL)-based algorithm is used, in which the states are sampled. The agent receives a larger reward/penalty for correct/incorrect classification of the examples related to the minority class. The proposed model is compared with state-of-the-art methods on the lung image database consortium image collection (LIDC-IDRI) dataset and standard performance metrics. The results of the experiments demonstrate that the proposed model outperforms its rivals.

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Bone tissue engineering via application of a PCL/Gelatin/Nanoclay/Hesperetin 3D nanocomposite scaffold

Cited by 7 publications

References 60 publications

Advanced Biomedical Applications of Multifunctional Natural and Synthetic Biomaterials

Advanced Biomedical Applications of Multifunctional Natural and Synthetic Biomaterials

A Novel Method for Myocardial Image Classification using Data Augmentation

Recognition of Lung Nodules in Computerized Tomography Lung Images using a Hybrid Method with Class Imbalance Reduction

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