Biophysical Reviews and Letters

The regeneration and repair of bone tissue is a multiphase process that requires a lot of attention, especially if stimulated through scaffold implantation. This review analyzes the process from both a biological and mechanical point of view through the analysis of the porosity and characteristics of the biomaterials that can provide optimal regeneration of bone tissue and functional vascularization that prevents implant failure. Particular attention is paid to the porosity of the new biomaterials and the related physiological effects and the angiogenesis process that the biomaterials themselves can stimulate, analyzing some of the works present in the literature.

When many histopathological breast images with different magnification levels need to be analyzed, diagnosing benign or malignant cancer from the images can be time-consuming. Automatic classification of histopathological images of breast cancer can support the diagnostic workflow in pathology, reducing analysis time. Recently, convolutional neural networks (CNNs) have been used for more accurate classification of breast cancer histopathological images. CNNs typically highlight semantic information to extract discriminative features. However, the traditional softmax loss used by CNNs usually lacks sufficient discriminative power. To address this problem, several angular margin-based softmax loss functions have been proposed, including Large Margin Softmax Loss (A-Softmax), Large Margin Cosine Loss (CosFace), Additive Angular Margin Loss (ArcFace), and Linear ArcFace (Li-ArcFace). All of these improved losses are based on the same concept: maximizing inter-class variance while minimizing intra-class distance. This paper focuses on these four losses and their effectiveness in extracting discriminative features and creating decision margins between classes. Extensive experimental evaluations were conducted on a public and well-known histopathological breast cancer image dataset (BreakHis). Further experiments with the BACH dataset for breast cancer classification and the SARS-CoV-2 CT-scan dataset for COVID-19 detection affirm the generalization capability of angular margin-based softmax losses in medical image classification.

Purpose. A virus, when it infects a host organism, multiplies its ribonucleic acid (RNA) to make it more harmful. We analyze if a view-based temporal template method, the motion history image (MHI) algorithm, may be of use to extract information on the progression of viral interactions over time. Methods. We employ an alternative online processing modality to comprehend and analyze the replication dynamics of severe acute respiratory syndrome coronavirus 2 (SARS CoV 2). The data are evaluated using the computation of a quantitative algorithm, the absolute value of differences (AVD), followed by the generation of temporal template map, the MHI of viral contact. Results. We show that the MHI identifies various stages to examine cultural characteristics and information on the progression of viral interactions over time. The results are more easily obtained compared to a visual inspection of the frames constituting the recordings. MHI also has the advantage of relying on a simple algorithm. The AVD index further provides deeper insights into the dynamics of the viral interaction over time, and its measurement suggests that SARS CoV-2 virus is becoming more widespread. This study shows that the replication process damages the host cell over time, in addition to causing SARS CoV-2 to proliferate. Conclusions. The MHI provides a framework for automated feature extraction from time series data. The combined approach results in decreased computational complexity and enables online assessment of the assay. Clinical and research studies can be conducted to evaluate MHI and AVD and their utility in aiding diagnostic analysis.

A substitution box is regarded as an eminent source of nonlinearity and perplexity in most of the block ciphers. In this work, we propose that the involvement of a local ring has a favorable influence on the highly desirable features of this nonlinear cryptographic system. We deploy the unit group of a local ring by considering its correspondence with the Galois field and design an algorithm that utilizes the algebraic properties of the local ring ${\mathbb{Z}}_{512}$, to synthesize a highly efficient S-box.

The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.

Research motivation: Staging esophageal cancer is of paramount importance for treatment. With conventional methods, accuracy of staging is low, we aimed to improve the accuracy of the “T” stage of esophageal cancer by using deep learning techniques. Method/Material: Clinically diagnosed esophageal cancer patients were prospectively observed and their data were collected. jpeg images were collected from the Computed Tomography of patients. 80% of the data were used for training and 20% for tests. Pathology results were used as the gold standard in the training of deep learning algorithms. EfficientNetB7 and ResNet152V2 models were used in the study. Both architectures with convolutional neural networks have Convolutional layers, pool layers, and fully connected layers. Results: A total of 477 images of 50 patients were analyzed. EfficientNetB7 makes predictions with a total of 64,107,931 parameters, and ResNet152V2 58,339,844 parameters within seconds (2s) at rates close to the accuracy offered by humans. With the EfficientNetB7 architecture, one of the Convolutional Neural Networks used in this study, 90% accuracy was achieved in the “T” staging of esophageal cancer. Conclusion: Despite the very limited dataset, deep learning algorithms can perform effective and reliable staging under the supervision of an experienced radiologist. With more datasets, the precision of the estimation can increase.

Stock marketing prediction is really important in this digital world and for this reason, its forecasting tools are highly desired. This research examines the influence of Long Short-Term Memory (LSTM) in stock market prediction. The basic genetic algorithm behind the time series analysis tool is described with the help of a structure diagram, and its mathematical structure is explained. We also described the application of LSTM in the field of finance. LSTM is playing a vital role in prediction in several applications. Experiments on the proposed method have been conducted on stock market data. The results show that LSTM predicts the price quite fairly accurately. It outperformed other prediction models due to long-term dependency and due to its accuracy. The research demonstrates the LSTM tool’s impact on finance, particularly stock market prediction.

The double-pendulum overhead crane system belongs to a class of underactuated mechanical systems, which is challenging to control due to its strong nonlinearities. This study deals with the nonlinear controller design for a double-pendulum overhead crane based on hierarchical sliding mode control with state-dependent switching gain. The equivalent and switching controls are obtained with the aid of first-level and second-level sliding surfaces. The asymptotic stability of sliding surfaces is proved theoretically and also shown graphically. Numerical simulations show that the proposed controller performs better than conventional hierarchical sliding mode control. The proposed controller effectively suppresses the load and hook swing and precisely controls the trolley’s displacement. It has strong robustness to the payload displacement and the change of the crane parameters.

Bone is termed the smart material, and its modeling is of great interest in biomechanics, biochemistry, endocrinology, and oncology. The bone health of cancer patients is directly affected by hormonal imbalances, receptor-mediated tumor-targeted processes, and disturbed bone mineral density. Researchers have used different therapeutic approaches to monitor bone health during and after cancer treatment. This paper describes a reverse process of bone rebuilding after the resorption of bone via cancer treatment. A detailed model is used for hormonal therapy, which leads to the physical changes in trabecular structure. These changes are demonstrated with the aid of artificial neural networks and Petri nets. This paper connects the structural modeling of the bone trabecula with chemical kinetics. The main goal of this study is to provide a PN model of bone metastasis and an analysis of its structural properties. These properties are very helpful in demonstrating the complex dynamics of bone metastasis. Although both ANNs and PNs are well organized in the areas of machine learning and network modeling, neither technique is without limitations. ANNs, for example, are very efficient machine-learning applications, but their utter lack of explanation capabilities classifies them as a “black-box” technique. On the other hand, PNs are an effective modeling technique, but their theory does not include machine learning. This paper provides a hybrid approach to address the two approaches in a novel manner.

The conventional method for determining whether a drug is carcinogenic involves subjecting rodents to a 2-year bioassay, but this approach is both time-consuming and expensive, not to mention unethical. Consequently, machine learning techniques have emerged as a popular alternative. One such technique is ensemble learning, which aims to create more accurate and robust models. In this particular study, the LightGBM model was utilized to predict the carcinogenicity of chemicals using its fingerprints. Molecular fingerprints were generated from the Simplified Molecular-Input Line-Entry System (SMILES) of 1003 chemicals from the Carcinogenic Potency Database (CPDB) dataset. The performance of the LightGBM model was found to be superior to other machine learning models reported in previous research. To further validate the model, it was tested on a database related to humans from the International Agency for Research on Cancer (IARC), as well as on chemicals that were withdrawn from the market between 1950 and 2014. The results showed that the LightGBM model was effective in identifying carcinogenic chemicals, suggesting that this approach could potentially replace traditional methods of carcinogenicity testing in the future.