Biomedical Engineering: Applications, Basis and Communications

Mesenchymal stem cells (MSCs) differentiate to cells that are part of tissues, such as bone and cartilage, both during ontogeny and for the repair/regeneration and renewal of tissues during postnatal life. Accordingly, MSCs can be a powerful tool for regenerative medicine and tissue engineering interventions in musculoskeletal deficiencies. MSCs found in the bone marrow and connective tissues are exposed to biochemical and biophysical factors that modulate their biological behavior. Given that biochemical factors have been widely studied, research on the effect of biophysical stimuli on the differentiation of MSCs is scarce. An overview of the impact of biophysical factors on MSCs osteogenic differentiation could shed light on some of the signals to consider when developing new cell therapies or tissue engineering products for the treatment of bone traumatic defects and diseases. The aim of this review is to provide an overview of the effect of biophysical stimuli on osteogenic differentiation. It focuses on reviewing what has been published on the effect of compression, tension, hydrostatic pressure, shear stress, electromagnetic fields, low-intensity ultrasound, and niche signaling – including topography and substrate stiffness, paracrine and juxtracrine regulation – on MSCs osteogenic differentiation. The knowledge gained from studying how MSCs respond to mechanical forces has the potential to drive the creation of more advanced treatments for conditions where bones are injured. These therapies could rely on either physical methods or a combination of physical and chemical approaches, offering new possibilities for improving bone health.

The majority of patients today have vascular illness that could cause pulmonary hypertension and pulmonary emboli which can lead to serious conditions. To diagnose changes in veins and arteries, manual, semi-automatic and automatic examination of a individuals chest computed tomography image or CT Pulmonary Angiogram (CTPA) is performed. Manual CT scan analysis is more tedious, takes more time and is not standardized. Thus the separation of vascular trees in CTPA scans is now semi-automatic and automatic which enables doctors to precisely identify aberrant conditions. Recent research has focused on applying machine learning along with deep learning approaches to find and categorize pulmonary vascular disorders like pulmonary embolism and pulmonary hypertension. Here, a brand-new technique is put out for the automatically classifying pulmonary veins in order to more accurately diagnose lung illnesses related to Pulmonary Embolism (PE). The proposed method uses Scaled Siamese-based Convoutional Neural Network (CNN) for classifying Pulmonary Embolism images with an accuracy more than 90 while using two publicly available datasets FUMPE and RSNA. Finally, comparison of the proposed method with other pretrained CNN-based algorithms for detecting PE is done in terms of Area Under Curve (AUC), recall, precision, accuracy, $F$1 score, etc. and values are tabulated and our proposed method shows good results. Comparison was also done using deep learning models like PENET, Inception $V$3 and $K$ fold cross validation and our proposed method values are outstanding compared to other methods.

The most serious nervous system ailment, a brain tumor impairs one’s health seriously and ultimately results in death. MRI, one of the most frequently used medical imaging modalities for brain tumors, has emerged as the main diagnostic system for the treatment and study of brain tumors. It was challenging to segment and classify the many kinds of brain tumors. The swarm intelligence approach has the potential to more efficiently and effectively tackle a number of issues. Therefore, this work develops a novel model for the classification of brain tumors that includes various phases. Primarily, the input image is preprocessed via the proposed median filtering that aids in removing noises. Subsequently, segmentation is done via optimal U-Net. For precise segmentation, the weights are tuned optimally by battle royale optimization with the Bernoulli randomization (BROBR) algorithm. Then, features like the proposed local Gabor XOR pattern (PLGXP), texton features, gray level co-occurrence matrix (GLCM), and correlation features are extracted. Finally, BTC is done using a deep maxout network (DMO) that provides the final output on the absence or presence of a brain tumor.

Essential hand tremor (EHT) is a prevalent neurological condition affecting geriatric populations, yet its underlying mechanisms remain poorly understood. This study aims to investigate the neural substrates associated with motor tasks in EHT patients, illuminating the complex neural activity characterizing this condition. Twenty participants underwent a thorough evaluation to ensure eligibility, excluding factors such as mental illness, drug dependency, or Parkinson’s disease (PD). Functional magnetic resonance imaging (fMRI) was utilized to examine brain activation patterns during non-handwriting tasks (NHWT) and handwriting tasks (HWT). Participants received training to standardize hand and forearm movements for effective fMRI assessment. fMRI data preprocessing included motion correction, filtering, removal of linear trends, normalization to Montreal Neurological Institute (MNI) space, and spatial smoothing. Distinctive patterns of brain activation were observed during motor tasks in individuals with EHT compared to controls. During NHWT, the EHT group showed significantly increased activation in the precentral gyrus, supplementary motor area, thalamus, and posterior cerebellar lobe, highlighting their role in mediating motor challenges in EHT. Similarly, during HWT, the EHT group exhibited heightened activation in the precentral gyrus and supplementary motor areas. In contrast, reduced activation was noted in the caudate nucleus, inferior temporal gyrus, and precuneus during HWT in the EHT group compared to controls. These findings align with previous research on involuntary movement disorders, such as early-stage PD, emphasizing the importance of the caudate nucleus and related regions in EHT. In conclusion, this study sheds light on the intricate neural activity underlying motor tasks in EHT patients. The identified neural regions and their functions offer insights into the neurophysiological basis of EHT-related motor impairments. These findings have the potential to enhance the understanding of EHT beyond its surface-level effects. This study has identified the brain regions involved in motor tasks affected by EHT. This sets a foundation for future research to better understand the complexities of this neurological condition. These discoveries may lead to novel therapeutic interventions tailored to address the unique challenges faced by individuals with EHT, representing a significant milestone in understanding and managing EHT.

The coronavirus or COVID-19 infectious virus is the deadliest and potentially dangerous disease for humans. Radiologists frequently employ medical imaging tools to visualize complex internal structures as well as the functioning of the body. With precise diagnosis, it is possible to identify the infectious COVID-19 virus earlier, especially in an individual having no visible symptoms. For the diagnosis and early detection of the infectious COVID-19 virus, chest X-rays (CXRs) have been utilized which are available at https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database. Applying the gray-level co-occurrence matrix (GLCM) and gray-level run length matrix (GLRLM) feature extraction techniques, the features of the four classes (normal, lung opacity, viral pneumonia, and COVID-19) have been extracted and then classified by utilizing a machine learning (ML) classifier. Six distinct ML classifiers SMO (Sequential Minimal Optimization), Random Tree, MLP (Multi-Layer Perceptron), Linear SVM, Ensemble Classifier (Boosted Tree), and Bayes Net (Bayesian Network) with respective accuracy of 98.85%, 93.19%, 93.35%, 91.5%, 96.4%, and 96.454% are utilized to classify. The classifiers successfully distinguish between normal individuals, viral pneumonia-affected persons, lung opacity individuals, and COVID-19 virus-infected individuals who were considered for the study. These advanced technologies for coronavirus identification may be helpful in areas where access to skilled medical professionals and modern facilities is limited. Hence, as per the analysis, the study may be helpful in disease detection and classification. To classify the virus, radiologists’ second opinion can be quick and accurate in this urgent scenario.

Adolescent idiopathic scoliosis (AIS) can impair postural stability and balance functions. Boston brace systems with varying external corrective force strategies are used as one of the common conservative treatments in AIS. Although multiple studies have found that various orthotic therapies enhance curve advancement, the effects of spinal bracing on postural equilibrium and stability remain unknown. The primary objective of this study was to examine the effect of the immediate use of custom-made Boston-type thoraco-lumbo-sacral orthosis (TLSO) on balance and postural stability in AIS. Postural balance using the center of pressure (CoP) measures was measured by the HUMAC^Ⓡ Balance & Tilt System (CSMi, Stoughton, MA). The line laser tool (Bosch, GLL 3 X P, Stuttgart, Germany) was used for measuring the decompensation distance for postural evaluation. The balance parameters such as stability score (%), path length (cm), average velocity (cm/s), and compensation–decompensation for posture were evaluated and compared between brace-on and brace-off conditions. Improvement of standing posture indicates spinal symmetry and cosmesis whereas enhancement of static balance was statistically nonsignificant ($p$ > 0.05). Although the mean stability score, path length and average velocity of CoP in braced condition (91.4 ± 4.57, 115.62 ± 22.34 cm and 1.05 ± 0.12 cm/s) was found to be improved than unbraced condition (90.5 ± 5.09, 121.46 ± 35.52 cm and 1.11 ± 0.24 cm/s), the results were nonsignificant ($p$ = 0.078, 0.425 and 0.263). The scoliotic compensation was improved in the braced condition (0.64 ± 0.60 cm) compared to brace-off condition (1.74 ± 0.61 cm) showing improved posture ($p$ = 0.00013). Wearing the custom-made Boston-type TLSO showed positive effects on static posture and mean balance parameters. In future studies, the long-term effectiveness of this brace should be examined in patients with different spinal curve patterns and degrees of scoliosis.

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches the facial data with existing databases to analyze the individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, the occlusions, different head positions, deformed faces, motion blur under unrestricted settings, and complicated backgrounds make it complex to analyze the facial images. In this paper, the formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model is utilized for facial emotion detection for which JAFFE and the RAVDESS facial expression datasets are used. DCNNs are proficient with built-in feature extraction strategies from images to map the various facial expressions to the corresponding emotional states adopted for effective FER. In addition, the intensity, directional, and edge patterns as well as the correlation features extracted utilizing the hybrid textual pattern, RESNET 101 and VGG 16-based correlation modules assist the DCNN to attain the informative feature from the high-resolution images. Further, the formicary swarm optimization (FSO) is incorporated that effectively tunes the DCNN to capture the complex relationships between the learned features that excel the FER capability. Evaluating the metrics, the face recognition model using the RAVDESS dataset achieves notable efficiencies during a training percentage (TP) of 90%, with values of 97.51%, 95.48%, 99.55%, 95.48%, 97.48%, 96.47%, and a minimum loss of 2.49%. Simultaneously, the emotion detection model demonstrates robust efficiencies with metric values of 96.75%, 98.49%, 95.01%, 98.49%, 96.72%, 97.59%, and a minimum loss of 3.25%. Finally, the obtained results reveal the efficacy of the FSO-opt DCNN, particularly in face recognition tasks, as it outperforms the existing models across various datasets, showcasing its versatility and potential in facial analysis applications.

The permanent damage occurring to the optical nerve, which transmits the visualized images to the brain leading to permanent vision loss, is called glaucoma. The detection and diagnosis of glaucoma at the beginning stage are essential to prevent permanent damage caused to vision as there are no initial symptoms to detect the onset of the damage, and there is no cure while the damage progresses to its extreme stage. Many models utilizing deep learning technology have been executed to identify the presence of glaucoma using digital Fundus Images (FI). However, these methods need specialized hardware and are computationally complicated as there is not much-labeled data available, leading to generalization issues in these models. Therefore, the development of a deep learning-oriented model for Glaucoma Detection (GD) using the dual feature pooling of cup and disc segmentation features with the aid of the FI is the main motive behind this work. At first, the digital FI is congregated from standardized online data sources. Then the gathered images are considered for the image segmentation procedure. The disc is segmented from the input image with the aid of the Transformer based Adaptive ResUNet++ (TA-ResUNet++) framework. The segmented disc image is now provided to hybrid convolution networks (HCN) for extracting the disc-oriented features. At the same time, the cup is segmented from the gathered input image using the executed TA-ResUnet++. Following this, the cup features are extracted using the same HCN model. The obtained features regarding the disc and the cup are concatenated in order to generate a single pool of glaucoma features. Then these features are inputted into the implemented bidirectional long short-term memory with multi-head attention (Bi-LSTM-MHA) mechanism to detect the presence of glaucoma in people. The segmentation and detection outcomes are made better by optimizing the parameters in the TA-ResUNet++ and Bi-LSTM-MHA models with the aid of hybrid sewing training with the Chameleon swarm optimization algorithm (HST-CSA). The experimental analysis is done to analyze the effective performance of the developed GD model. Jaccard analysis performed for the developed HST-CSOA-TA-ResUNet++-based disc segmentation gained better performance as 18.29% than MobileNet, 14.11% efficient than ResNet, 12.79% superior to Bi-LSTM and 5.43% higher than Bi-LSTM-MHA while considering best as the statistical measure.