Biomechanical analysis based on a full-body musculoskeletal model for evaluating the effect of a passive lower-limb exoskeleton on lumbar load

The present study investigated the effect of a passive lower-limb exoskeleton on lumbar load and verified the effectiveness of biomechanical analysis for evaluating the physical burden while wearing the exoskeleton. Twelve healthy male participants performed an assembly task under three conditions: standing and high and low sitting while wearing the exoskeleton. We mainly analyzed the joint compression force computed using the musculoskeletal model based on measurements obtained from a motion capture system and force platform. While wearing the exoskeleton, the lumbar joint compression force increased. Analysis of the joint compression force can determine the increase in the lumbar load when wearing an exoskeleton, which is not possible using the traditional analysis of posture and muscular activity. Therefore, biomechanical analysis based on a full-body musculoskeletal model provides valuable information in evaluating the effect of the exoskeleton that attempts to prevent musculoskeletal disorders.Comment: 46 pages, 10 figure

Swarm Intelligence Algorithm Based on Plant Root System in 1D Biomedical Signal Feature Engineering to Improve Classification Accuracy

The classification accuracy of one-dimensional (1D) biomedical signals is limited due to the lack of independence of the extracted features. To address this shortcoming, the study applies a swarm intelligence algorithm based on plant root systems (PRSs) to feature engineering. Some basic features of 1D biomedical signals are integrated into a digitized soil, and a root matrix is generated from this digitized soil and the PRS algorithm. The PRS features are extracted from the root matrix and used to classify the basic features. Following classification with the same biomedical signals and classifier, the accuracy of the added PRS set is generally higher than that of the base set. The result shows that the proposed algorithm can expand the application of 1D biomedical signals to include more biomedical signals in classification tasks for clinical diagnosis

A Plant Root System Algorithm Based on Swarm Intelligence for One-dimensional Biomedical Signal Feature Engineering

To date, very few biomedical signals have transitioned from research applications to clinical applications. This is largely due to the lack of trust in the diagnostic ability of non-stationary signals. To reach the level of clinical diagnostic application, classification using high-quality signal features is necessary. While there has been considerable progress in machine learning in recent years, especially deep learning, progress has been quite limited in the field of feature engineering. This study proposes a feature extraction algorithm based on group intelligence which we call a Plant Root System (PRS) algorithm. Importantly, the correlation between features produced by this PRS algorithm and traditional features is low, and the accuracy of several widely-used classifiers was found to be substantially improved with the addition of PRS features. It is expected that more biomedical signals can be applied to clinical diagnosis using the proposed algorithm

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters

This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals

Development of a Lower Limb Finite Element Musculoskeletal Gait Simulation Framework Driven Solely by Inertial Measurement Unit Sensors

Finite element musculoskeletal (FEMS) approaches using concurrent musculoskeletal and finite element models driven by motion data such as marker-based motion trajectory can provide insight into the interactions between the knee joint secondary kinematics, contact mechanics, and muscle forces in subject-specific biomechanical investigations. However, these data-driven FEMS systems have a major disadvantage that makes them challenging to apply in clinical environments, i.e., they require expensive and inconvenient equipment for data acquisition. In this study, we developed an FEMS model of the lower limb driven solely by inertial measurement unit sensors that include the tissue geometries of the entire knee joint, and that combine modeling of 16 muscles into a single framework. The model requires only the angular velocities and accelerations measured by the sensors as input. The target outputs (knee contact mechanics, secondary kinematics, and muscle forces) are predicted from the convergence results of iterative calculations of muscle force optimization and knee contact mechanics. To evaluate its accuracy, the model was compared with in vivo experimental data during gait. The maximum contact pressure (11.3 MPa) occurred on the medial side of the cartilage at the maximum loading response. The developed framework combines measurement convenience and accurate modeling, and shows promise for clinical applications aimed at understanding subject-specific biomechanics