Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models.

Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

Human Activity Recognition Based on Deep Learning and Micro-Doppler Radar Data.

Activity recognition is one of the significant technologies accompanying the development of the Internet of Things (IoT). It can help in recording daily life activities or reporting emergencies, thus improving the user's quality of life and safety, and even easing the workload of caregivers. This study proposes a human activity recognition (HAR) system based on activity data obtained via the micro-Doppler effect, combining a two-stream one-dimensional convolutional neural network (1D-CNN) with a bidirectional gated recurrent unit (BiGRU). Initially, radar sensor data are used to generate information related to time and frequency responses using short-time Fourier transform (STFT). Subsequently, the magnitudes and phase values are calculated and fed into the 1D-CNN and Bi-GRU models to extract spatial and temporal features for subsequent model training and activity recognition. Additionally, we propose a simple cross-channel operation (CCO) to facilitate the exchange of magnitude and phase features between parallel convolutional layers. An open dataset collected through radar, named Rad-HAR, is employed for model training and performance evaluation. Experimental results demonstrate that the proposed 1D-CNN+CCO-BiGRU model demonstrated superior performance, achieving an impressive accuracy rate of 98.2%. This outperformance of existing systems with the radar sensor underscores the proposed model's potential applicability in real-world scenarios, marking a significant advancement in the field of HAR within the IoT framework.

Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

This research examines the application of non-invasive acoustic analysis for detecting obstructions in vascular access (fistulas) used by kidney dialysis patients. Obstructions in these fistulas can interrupt essential dialysis treatment. In this study, we utilized a condenser microphone to capture the blood flow sounds before and after angioplasty surgery, analyzing 3819 sound samples from 119 dialysis patients. These sound signals were transformed into spectrogram images to classify obstructed and unobstructed vascular accesses, that is fistula conditions before and after the angioplasty procedure. A novel lightweight two-dimension convolutional neural network (CNN) was developed and benchmarked against pretrained CNN models such as ResNet50 and VGG16. The proposed model achieved a prediction accuracy of 100%, surpassing the ResNet50 and VGG16 models, which recorded 99% and 95% accuracy, respectively. Additionally, the study highlighted the significantly smaller memory size of the proposed model (2.37 MB) compared to ResNet50 (91.3 MB) and VGG16 (57.9 MB), suggesting its suitability for edge computing environments. This study underscores the efficacy of diverse deep-learning approaches in the obstructed detection of dialysis fistulas, presenting a scalable solution that combines high accuracy with reduced computational demands.

The Role of Ultrasound in Cancer and Cancer-Related Pain-A Bibliometric Analysis and Future Perspectives.

Ultrasound has a deep penetrating ability with minimal or no tissue injury, while cancer-mediated complications during diagnosis, therapy, and surgery have become a serious challenge for clinicians and lead to the severity of the primary condition (cancer). The current study highlights the importance of ultrasound imaging and focused ultrasound therapy during cancer diagnosis, pain reduction, guidance for surgical resection of cancer, and the effectiveness of chemotherapy. We performed the bibliometric analysis on research domains involving ultrasound, cancer management, pain, and other challenges (chemotherapy, surgical guidance, and postoperative care), to observe the trend by which the research field has grown over the years and propose a possible future trend. The data was obtained from the Web of Science, processed, and exported as plain text files for analysis in the Bibliometrix R web interface using the Biblioshiny package. A total of 3248 documents were identified from 1100 journal sources. A total of 390 articles were published in 2022, with almost a 100% growth rate from previous years. Based on the various network analysis, we conclude that the outcome of the constant research in this domain will result in better patient care during the management of various diseases, including cancer and other co-morbidities.

Prioritizing cardiovascular surgical care in COVID-19 pandemic: Shall we operate or defer?

BACKGROUND: The coronavirus disease (COVID-19) has affected a large population across the world. Patients with cardiovascular disease have increased morbidity and mortality due to coronavirus disease. The burden over the health care system has to be reduced in this global pandemic to provide optimal care of patients with COVID-19, as well not compromising those who are in need of emergent cardiovascular care. METHODS: There is a very limited data published defining which cardiovascular procedures are to be performed or to be deferred in the COVID-19 pandemic. In this article, we have reviewed a few published guidelines regarding cardiovascular surgery in COVID-19 pandemics. CONCLUSION: After reviewing a few available guidelines regarding cardiovascular surgery in COVID-19, we conclude to perform only those surgeries which cannot be deferred to a certain period of time, to reduce the burden of the health care system of the country, provide optimal care to patients with COVID-19, and to protect health care workers and cardiovascular patients from COVID-19.

Retrograde radial arteriovenous fistula, post coronary artery intervention.

BACKGROUND: Radial arteriovenous fistula (AVF) post coronary artery intervention is rare. METHODS AND CONCLUSION: Here we describe an extremely rare case of radial AVF with retrograde filling through the deep palmar arch and presenting as early ischemic changes.

STB: synthetic minority oversampling technique for tree-boosting models for imbalanced datasets of intrusion detection systems.

Attacks on the Intrusion Detection System (IDS) can result in an imbalanced dataset, making it difficult to predict what types of attacks will occur. A novel method called SMOTE Tree Boosting (STB) is proposed to generate synthetic tabular data from imbalanced datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. In this experiment, multiple datasets were used along with three boosting-based machine learning algorithms (LightGBM, XGBoost, and CatBoost). Our results show that using SMOTE improves the content accuracy of the LightGBM and XGBoost algorithms. Using SMOTE also helps to better predict computational processes. proven by its accuracy and F1 score, which average 99%, which is higher than several previous studies attempting to solve the same problem known as imbalanced IDS datasets. Based on an analysis of the three IDS datasets, the average computation time required for the LightGBM model is 2.29 seconds, 11.58 seconds for the XGBoost model, and 52.9 seconds for the CatBoost model. This shows that our proposed model is able to process data quickly.

Use of roller pump in venovenous extracorporeal membrane oxygenation as an emergency rescue procedure.

A 41 years old male patient, having acute respiratory distress syndrome (ARDS) due to viral pneumonia, was put on venovenous (VV) extracorporeal membrane oxygenation (ECMO). After 3 h of initiation, the ECMO pump malfunctioned. The patient was initially managed on a hand crank for 5 h. However, as another machine would be available after 12-24 h, we decided to use a roller pump with a conventional cardiopulmonary bypass (CPB) circuit. We successfully manage this crisis with this technique without any complications. This technique can be lifesaving in catastrophic situations such as ECMO console or pump failures when there are neither backup machines nor service engineers available.

Successful open surgical repair of large ilio-caval fistula presenting with congestive heart failure.

Ilio-caval arteriovenous fistula is a rare sequel of lumbar disc surgery or penetrating injury. We report a case of 40-year gentleman presenting with symptoms of congestive heart failure and continuous murmur heard over right iliac fossa. His past history was significant for lumbar disc surgery done twice 1 year back. Computed tomography angiogram confirmed a large fistulous communication between right common iliac artery and inferior vena cava. Open surgical repair was performed as the fistula was not well suited for endovascular repair. We successfully managed this case by open surgical ligation of right common iliac artery proximal and distal to fistula and continuity restored with interposition Dacron graft. He was relieved of symptoms and performing well on follow-up.

Extracranial carotid artery pseudoaneurysm due to Mycobacterium tuberculosis.

Mycobacterium tuberculosis is a very rare cause of extracranial carotid artery pseudoaneurysm. A 39-year-old man presented with a rapidly increasing neck mass and hoarseness of voice for 15 days. He was on antitubercular treatment for pulmonary tuberculosis. Computed tomography angiography showed a large pseudoaneurysm of the right common carotid artery. Emergency surgery was performed to excise the pseudoaneurysm sac and repair the artery with a Dacron graft. Postoperative angiography showed normal flow in the carotids and cerebral circulation. Histology of the excised tissue was consistent with a tubercular etiology of the pseudoaneurysm.

Solution structures of conformationally equilibrium forms of holo-acyl carrier protein (PfACP) from Plasmodium falciparum provides insight into the mechanism of activation of ACPs.

Acyl Carrier Protein (ACP) from the malaria parasite, Plasmodium falciparum (PfACP) in its holo form is found to exist in two conformational states in solution. Unique 3D solution structures of holo-PfACP have been determined for both equilibrium conformations, using high-resolution NMR methods. Twenty high-resolution solution structures for each of the two forms of holo-PfACP have been determined on the basis of 1226 and 1218 unambiguously assigned NOEs (including NOEs between 4'-phosphopantetheine prosthetic group (4'-PP) and protein), 55 backbone dihedral angles and 26 hydrogen bonds. The atomic rmsd values of the determined structures of two equilibrium forms, about the mean coordinates of the backbone and heavy atoms, are 0.48 +/- 0.09 and 0.92 +/- 0.10 and 0.49 +/- 0.08 and 0.97 +/- 0.11 A, respectively. The interaction of 4'-PP with the polypeptide backbone is reported here for the first time for any of the ACPs. The structures of holo-PfACP consist of three well-defined helices that are tightly packed. The structured regions of the molecule are stabilized by extensive hydrophobic interactions. The difference between the two forms arises from a reorientation of the 4'-PP group. The enthalpy difference between the two forms, although small, implies that a conformational switch is essential for the activation of holo-ACP. Sequence and structures of holo-PfACP have been compared with those of the ACPs from type I and type II fatty acid biosynthesis pathways (FAS), in particular with the ACP from rat and the butyryl-ACP from E. coli. The PfACP structure, thus determined has several novel features hitherto not seen in other ACPs.

A novel approach for over-expression, characterization, and isotopic enrichment of a homogeneous species of acyl carrier protein from Plasmodium falciparum.

Acyl carrier protein (ACP) plays a central role in fatty acid biosynthesis by transferring the acyl groups from one enzyme to another for the completion of the fatty acid synthesis cycle. Holo-ACP is the obligatory substrate for the synthesis of acyl-ACPs which act as the carrier and donor for various metabolic reactions. Despite its interactions with numerous proteins in the cell, its mode of interaction is poorly understood. Here, we report the over-expression of PfACP in minimal medium solely in its holo form and in high yield. Expression in minimal media provides a means to isotopically label PfACP for high resolution multi-nuclear and multi-dimensional NMR studies. Indeed, the proton-nitrogen correlated NMR spectrum exhibits very high chemical shift dispersion and resolution. We also show that holo-PfACP thus expressed is amenable to acylation reactions using Escherichia coli acyl-ACP synthetase as well as by standard chemical methods.