Osteolysis: A Literature Review of Basic Science and Potential Computer-Based Image Processing Detection Methods

Britanin - a beacon of hope against gastrointestinal tumors?

Britanin is a bioactive sesquiterpene lactone known for its potent anti-inflammatory and anti-oxidant properties. It also exhibits significant anti-tumor activity, suppressing tumor growth in vitro and in vivo. The current body of research on Britanin includes thirty papers predominantly related to neoplasms, the majority of which are gastrointestinal tumors that have not been summarized before. To drive academic debate, the present paper reviews the available research on Britanin in gastrointestinal tumors. It also outlines novel research directions using data not directly concerned with the digestive system, but which could be adopted in future gastrointestinal research. Britanin was found to counteract liver, colorectal, pancreatic, and gastric tumors, by regulating proliferation, apoptosis, autophagy, immune response, migration, and angiogenesis. As confirmed in pancreatic, gastric, and liver cancer, its most commonly noted molecular effects include nuclear factor kappa B and B-cell lymphoma 2 downregulation, as well as Bcl-2-associated X protein upregulation. Moreover, it has been found to induce the Akt kinase and Forkhead box O1 axis, activate the AMP-activated protein kinase pathway, elevate interleukin-2 and peroxisome proliferator-activated receptor-γ levels, reduce interleukin-10, as well as downregulate matrix metalloproteinase-9, Twist family bHLH transcription factor 1, and cyclooxygenase-2. It also inhibits Myc-HIF1α interaction and programmed death ligand 1 transcription by interrupting the Ras/ RAF/MEK/ERK pathway and mTOR/P70S6K/4EBP1 signaling. Future research should aim to unravel the link between Britanin and acetylcholinesterase, mast cells, osteolysis, and ischemia, as compelling data have been provided by studies outside the gastrointestinal context. Since the cytotoxicity of Britanin on noncancerous cells is significantly lower than that on tumor cells, while still being effective against the latter, further in-depth studies with the use of animal models are merited. The compound exhibits pleiotropic biological activity and offers considerable promise as an anti-cancer agent, which may address the current paucity of treatment options and high mortality rate among patients with gastrointestinal tumors.

Complications after total wrist arthroplasty

We reviewed the incidence and management of complications after total wrist arthroplasty, as reported in the literature, with so-called fourth-generation implants and other recent designs. While early intraoperative and postoperative complications, including fractures, tendon lacerations, infection, nerve compression, tendonitis, stiffness and chronic regional pain syndrome, had an acceptable incidence, late complications, such as periprosthetic osteolysis and implant loosening, occurred more frequently. Implant survival at 10 years was in the range of 70%-80% in most publications. Several of the implants have been modified or withdrawn. Instability and dislocation were frequent after a pyrocarbon spacer. Failed arthroplasties can be salvaged by revision arthroplasty or total wrist arthrodesis. Revision arthroplasty has a lower survival rate than primary arthroplasty and does not clearly offer important significant advantages over total wrist arthrodesis in terms of patient-reported outcome measures. Further development of prosthetic design, new materials and more knowledge on patient-related risk factors are needed.

Brain tumor segmentation based on optimized convolutional neural network and improved chimp optimization algorithm

Reliable and accurate brain tumor segmentation is a challenging task even with the appropriate acquisition of brain images. Tumor grading and segmentation utilizing Magnetic Resonance Imaging (MRI) are necessary steps for correct diagnosis and treatment planning. There are different MRI sequence images (T1, Flair, T1ce, T2, etc.) for identifying different parts of the tumor. Due to the diversity in the illumination of each brain imaging modality, different information and details can be obtained from each input modality. Therefore, by using various MRI modalities, the diagnosis system is capable of finding more unique details that lead to a better segmentation result, especially in fuzzy borders. In this study, to achieve an automatic and robust brain tumor segmentation framework using four MRI sequence images, an optimized Convolutional Neural Network (CNN) is proposed. All weight and bias values of the CNN model are adjusted using an Improved Chimp Optimization Algorithm (IChOA). In the first step, all four input images are normalized to find some potential areas of the existing tumor. Next, by employing the IChOA, the best features are selected using a Support Vector Machine (SVM) classifier. Finally, the best-extracted features are fed to the optimized CNN model to classify each object for brain tumor segmentation. Accordingly, the proposed IChOA is utilized for feature selection and optimizing Hyperparameters in the CNN model. The experimental outcomes conducted on the BRATS 2018 dataset demonstrate superior performance (Precision of 97.41 %, Recall of 95.78 %, and Dice Score of 97.04 %) compared to the existing frameworks.

Musculoskeletal Biomaterials: Stimulated and Synergized with Low Intensity Pulsed Ultrasound

Clinical biophysical stimulating strategies, which have significant effects on improving the function of organs or treating diseases by causing the salutary response of body, have shown many advantages, such as non-invasiveness, few side effects, and controllable treatment process. As a critical technique for stimulation, the low intensity pulsed ultrasound (LIPUS) has been explored in regulating osteogenesis, which has presented great promise in bone repair by delivering a combined effect with biomaterials. This review summarizes the musculoskeletal biomaterials that can be synergized with LIPUS for enhanced biomedical application, including bone regeneration, spinal fusion, osteonecrosis/osteolysis, cartilage repair, and nerve regeneration. Different types of biomaterials are categorized for summary and evaluation. In each subtype, the verified biological mechanisms are listed in a table or graphs to prove how LIPUS was effective in improving musculoskeletal tissue regeneration. Meanwhile, the acoustic excitation parameters of LIPUS that were promising to be effective for further musculoskeletal tissue engineering are discussed, as well as their limitations and some perspectives for future research. Overall, coupled with biomimetic scaffolds and platforms, LIPUS may be a powerful therapeutic approach to accelerate musculoskeletal tissue repair and even in other regenerative medicine applications.

Machine Learning and Explainable Artificial Intelligence Using Counterfactual Explanations for Evaluating Posture Parameters

Postural deficits such as hyperlordosis (hollow back) or hyperkyphosis (hunchback) are relevant health issues. Diagnoses depend on the experience of the examiner and are, therefore, often subjective and prone to errors. Machine learning (ML) methods in combination with explainable artificial intelligence (XAI) tools have proven useful for providing an objective, data-based orientation. However, only a few works have considered posture parameters, leaving the potential for more human-friendly XAI interpretations still untouched. Therefore, the present work proposes an objective, data-driven ML system for medical decision support that enables especially human-friendly interpretations using counterfactual explanations (CFs). The posture data for 1151 subjects were recorded by means of stereophotogrammetry. An expert-based classification of the subjects regarding the presence of hyperlordosis or hyperkyphosis was initially performed. Using a Gaussian progress classifier, the models were trained and interpreted using CFs. The label errors were flagged and re-evaluated using confident learning. Very good classification performances for both hyperlordosis and hyperkyphosis were found, whereby the re-evaluation and correction of the test labels led to a significant improvement (M_PRAUC = 0.97). A statistical evaluation showed that the CFs seemed to be plausible, in general. In the context of personalized medicine, the present study's approach could be of importance for reducing diagnostic errors and thereby improving the individual adaptation of therapeutic measures. Likewise, it could be a basis for the development of apps for preventive posture assessment.

Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network

In recent years, there has been a growing interest in developing next point-of-interest (POI) recommendation systems in both industry and academia. However, current POI recommendation strategies suffer from the lack of sufficient mixing of details of the features related to individual users and their corresponding contexts. To overcome this issue, we propose a deep learning model based on an attention mechanism in this study. The suggested technique employs an attention mechanism that focuses on the pattern's friendship, which is responsible for concentrating on the relevant features related to individual users. To compute context-aware similarities among diverse users, our model employs six features of each user as inputs, including user ID, hour, month, day, minute, and second of visiting time, which explore the influences of both spatial and temporal features for the users. In addition, we incorporate geographical information into our attention mechanism by creating an eccentricity score. Specifically, we map the trajectory of each user to a shape, such as a circle, triangle, or rectangle, each of which has a different eccentricity value. This attention-based mechanism is evaluated on two widely used datasets, and experimental outcomes prove a noteworthy improvement of our model over the state-of-the-art strategies for POI recommendation.

Brain tumor segmentation of MRI images: A comprehensive review on the application of artificial intelligence tools

BACKGROUND

Brain cancer is a destructive and life-threatening disease that imposes immense negative effects on patients' lives. Therefore, the detection of brain tumors at an early stage improves the impact of treatments and increases the patients survival rates. However, detecting brain tumors in their initial stages is a demanding task and an unmet need.

METHODS

The present study presents a comprehensive review of the recent Artificial Intelligence (AI) methods of diagnosing brain tumors using MRI images. These AI techniques can be divided into Supervised, Unsupervised, and Deep Learning (DL) methods.

RESULTS

Diagnosing and segmenting brain tumors usually begin with Magnetic Resonance Imaging (MRI) on the brain since MRI is a noninvasive imaging technique. Another existing challenge is that the growth of technology is faster than the rate of increase in the number of medical staff who can employ these technologies. It has resulted in an increased risk of diagnostic misinterpretation. Therefore, developing robust automated brain tumor detection techniques has been studied widely over the past years.

CONCLUSION

The current review provides an analysis of the performance of modern methods in this area. Moreover, various image segmentation methods in addition to the recent efforts of researchers are summarized. Finally, the paper discusses open questions and suggests directions for future research.

Breast tumor localization and segmentation using machine learning techniques: Overview of datasets, findings, and methods

The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case.

Object tracking in infrared images using a deep learning model and a target-attention mechanism

Small object tracking in infrared images is widely utilized in various fields, such as video surveillance, infrared guidance, and unmanned aerial vehicle monitoring. The existing small target detection strategies in infrared images suffer from submerging the target in heavy cluttered infrared (IR) maritime images. To overcome this issue, we use the original image and the corresponding encoded image to apply our model. We use the local directional number patterns algorithm to encode the original image to represent more unique details. Our model is able to learn more informative and unique features from the original and encoded image for visual tracking. In this study, we explore the best convolutional filters to obtain the best possible visual tracking results by finding those inactive to the backgrounds while active in the target region. To this end, the attention mechanism for the feature extracting framework is investigated comprising a scale-sensitive feature generation component and a discriminative feature generation module based on the gradients of regression and scoring losses. Comprehensive experiments have demonstrated that our pipeline obtains competitive results compared to recently published papers.

Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network

One of the leading algorithms and architectures in deep learning is Convolution Neural Network (CNN). It represents a unique method for image processing, object detection, and classification. CNN has shown to be an efficient approach in the machine learning and computer vision fields. CNN is composed of several filters accompanied by nonlinear functions and pooling layers. It enforces limitations on the weights and interconnections of the neural network to create a good structure for processing spatial and temporal distributed data. A CNN can restrain the numbering of free parameters of the network through its weight-sharing property. However, the training of CNNs is a challenging approach. Some optimization techniques have been recently employed to optimize CNN's weight and biases such as Ant Colony Optimization, Genetic, Harmony Search, and Simulated Annealing. This paper employs the well-known nature-inspired algorithm called Shuffled Frog-Leaping Algorithm (SFLA) for training a classical CNN structure (LeNet-5), which has not been experienced before. The training method is investigated by employing four different datasets. To verify the study, the results are compared with some of the most famous evolutionary trainers: Whale Optimization Algorithm (WO), Bacteria Swarm Foraging Optimization (BFSO), and Ant Colony Optimization (ACO). The outcomes demonstrate that the SFL technique considerably improves the performance of the original LeNet-5 although using this algorithm slightly increases the training computation time. The results also demonstrate that the suggested algorithm presents high accuracy in classification and approximation in its mechanism.