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Objective

The objective of this study was to determine and compare the influence of osteoporosis on biomechanics of the spine after lumbar interbody fusion (LIF) surgery or non-fusion dynamic stabilization (NFDS) surgery under whole body vibration (WBV) which is typically present in moving vehicles.

Methods

Based on a previously validated finite element (FE) model of normal human lumbosacral spine, four surgical models including LIF, LIF with osteoporosis (LIF-OST), NFDS, and NFDS with osteoporosis (NFDS-OST) were constructed. Biomechanical responses of the surgical models to an axial cyclic load were calculated using transient dynamic analysis. Response parameters include vibration amplitudes of the endplate stress and screw stress at surgical L4–L5 level, vibration amplitudes of the disc bulge and intradiscal pressure at adjacent L3–L4 level.

Results

Osteoporosis increased vibration amplitudes of all these investigated response parameters. Further, we found that vibration amplitudes of the endplate stress and screw stress for the LIF-OST model were significantly higher than those for the NFDS-OST model, but there was very small difference in vibration amplitudes of the disc bulge and intradiscal pressure between the LIF-OST and NFDS-OST models.

Conclusions

For both the LIF and NFDS surgeries, osteoporosis might increase the risk for implant failure and accelerate adjacent segment degeneration (ASD) under WBV. When osteoporosis occurs, LIF might be associated with a higher likelihood of implant failure at the surgical level compared with NFDS, and the surgical approach (LIF or NFDS) might have little influence on biomechanics of the adjacent level.

Localisation-capable technologies are becoming more readily available as off-the-shelf components. In this paper, we highlight the need for such a service in the field of health and autonomy, especially for disabled people. We then explore this idea through a study that leverages this localisation functionality.

We introduce a model for Semantic Position Description (SPD) (“The pill organiser in on the kitchen table”) as well as various algorithms that transform raw distance estimations to SPD related to proximity, alignment and room identification.

Two measurement campaigns have been conducted. The first one focused on algorithm performance and exploited the LocURa4IoT testbed. The second confronted the system's output (SPDs) to real user perception in a smart-home environment. This experiment involved ten human participants in the Maison Intelligente de Blagnac. The results indicate that both processes (human and machine perception) converge 90% of the time. This convergence confirms the relevance of our locaisation-based approach and encourages future explorations of its application to various domains.

Objectives

How changes in intracranial compliance (ICC), resistance to cerebrospinal fluid (CSF)-outflow (R_out), and pressure-volume index (PVI) can play a prominent role in clarifying the complexities in the biomechanism and treatment outcomes of hydrocephalus. This study aims to provide a comprehensive review to find the correlation between ICC, R_out, and PVI with intracranial pressure (ICP) characteristics, as well as morphometric parameters, in hydrocephalus patients.

Material and methods

Electronic searches were conducted using the PubMed/MEDLINE, Scopus, Google Scholar, Ovid, Cochrane, and EMBASE databases from their dates of inception to December 2022 using filters for English-language articles. The article selection and data collection were conducted in accordance with PRISMA guidelines and recommendations. We assigned a quality score for each study to evaluate the information and selection bias. The certainty of the pooled data for all articles was assessed based on GRADE criteria. The Funnel plot asymmetry study was used to evaluate publication bias.

Results

The overall pre-treatment ICC, R_out, and PVI were 0.45 ml/mmHg (95% CI, 0.33-0.57; I²=99.7%; P<0.001), 14.93 mmHg/(ml/min) (95% CI, 13.65-16.21; I²= 99.7%; P<0.001) and 19.26 ml (95% CI, 15.63-22.89; I²=98.7%; P<0.001), respectively. The pooled R_out was observed to be higher in adult hydrocephalus compared to pediatric hydrocephalus, whereas the opposite trend was found for PVI. Patients with normal pressure hydrocephalus (NPH) exhibited a higher pooled R_out compared to communicating hydrocephalus. Patients with unimproved outcomes demonstrated a decrease in the effect size of the pooled R_out. The results also showed significant correlations were observed between all ICP characteristics and both the ICC and R_out. The correlation between ICC and the ventricular score was significant. R_out had a significant correlation with the third ventricle index. PVI had significant correlations with Evan's ratio, inverse cella media, and ventricular score.

Conclusion

The results indicate the possible reason for publication bias in some subgroups may be related to methodological heterogeneity in the measurement of pressure-volume parameters. This finding motivates researchers to investigate the effects of different invasive and non-invasive methods on the measurement results of ICC, R_out, and PVI.

Objectives

The role of controllers is inevitable in the design of powered orthosis to achieve ideal gait characteristics. Despite the fact that electrical actuators are preferred for most of the orthoses, a pneumatic actuator proves to have low cost and less weight. In this study, suitable controllers are designed and implemented for the knee and hip joints of a pneumatically actuated orthosis for the afflicted people.

Material and Methods

Different controllers (P, PI and PID) were tested for the position control of the orthosis by proper tuning of gain constants ($K_P$, $K_I$, $K_D$). By using Lagrange Euler Method, the optimal trajectory for the knee and hip joints were determined for the pneumatic system. Particle swarm optimization (PSO) based PID controller was further employed for optimizing the gain constants.

Results

With the healthy gait as reference, the knee and hip reference angles were manually set in the PID controller. The subject was made to walk five times at a distance of 5 m and the average knee and hip angles were calculated based on the gait trials. Knee and hip angles varied 0 to 45° and 0 to 35° for healthy subjects while they varied 0 to 41° and 0 to 45° for the implemented pneumatic leg. The values of gain constants obtained in manual tuning matched with the PSO based controller at 25^th iteration and the best fitness function was chosen with least error (0.7011).

Conclusion

The prototype of the orthosis is fabricated and the response of PID controller was found to be acceptable for a desired pressure (5 bar) with an angular velocity of 3 deg/s. Using a PID controlled pneumatic orthosis, exhibited less oscillation and showed an improved steady-state error when compared to the other controllers, thereby replicating healthy gait. A global best position with minimum error was obtained using PSO to find optimal controller gain constants.

INTRODUCTION: Heart disease (HD) has been identified as one of the deadly diseases, which affects the human beings of all ages worldwide. In such a scenario, Data Mining (DM) techniques have been found to be efficient in the analysis and the prediction of the phases of HD complications while handling larger patient datasets'. This dataset would consist of irrelevant and redundant features as well. These features would further impact the accuracy and the speed of data processing during the classification process.

OBJECTIVES: Hence, the feature selection techniques are required for removing the redundant features from the dataset. Therefore, in this study, feature selection techniques like genetic algorithm, particle swarm optimization and African buffalo algorithm have been implemented.

METHODS: To further enhance this process, a newly developed GSA (Genetic Sine Algorithm) is proposed as it is capable of selecting optimal features and avoid getting trapped in local optima. The selected features are subjected to the classification technique by RNN (Recurrent Neural Network) integrated with LSTM (Long Short Term Memory) algorithm. To filter out all the invalid informations and emphasize only on critical information, DPA-RNN+LSTM (Deep Progressive Attention-RNN+LSTM) has been developed so as to improve the classification rate.

RESULTS: The proposed results have been supported by the performance and comparative analysis performed on two benchmark datasets namely heart disease diagnosis UCI dataset and heart failure clinical dataset. Further, statistical analysis in terms of Mann-Whitney U-test, Pearson Correlation co-efficient, Friedman rank and Iman-Davenport significant values has been evaluated.

CONCLUSION: The obtained results show that the proposed system is comparatively more efficient for heart disease diagnosis than other conventional techniques.

Introduction

Frailty would affect 4 out of 5 people aged of 85 and over in France.

Methods

As part of a global project aimed to develop a multimodal system for the early detection of frailty among older adults living at home or in independent senior living apartments, we reported prospective quantitative study assessing a priori acceptability of this system based on Bel's integrative model devoted to the behavioral prediction use of an unknown technology. The platform is composed of 5 devices: a weight scale, a tensiometer, a wrist-worn step counter, an activity tracker and a tablet to exchange data with the aforementioned sensors over the internet. The inclusion criteria are: age 2: 80 years old, living at home or in independent senior living apartments, assessed as ‘robust’ or ‘pre-frail’. The a priori acceptability is assessed through self-evaluation questionnaires, mainly using a continuous scale (min-max score 1-7).

Results

We included 34 volunteers (24 women and 10 men), mean age 85.6 years (± 4.1), 24 of whom were classified as ‘robust’ and 10 as ‘pre-frail’. A priori utility of the multimodal system (mean score = 6.0 (± 0.9)), a priori intention of use (mean score = 5.9 (± 0.8)) and the rate of recommendation of the device before its first use (82.4% (± 17.4)) were clearly in favor of the device.

Conclusion

This study is in favor of an a priori acceptability and an a priori intention to use rather favorable to the developing system. Those results point a need for significant ease of use and almost-perfect functioning of this connected system for good acceptability.

Background

Vacuum-assisted biopsy is a minimally invasive sampling technique that relies on rotational cutting as a major tissue retrieving method. A precise diagnosis of the disease requires large, uncrushed samples, which are impacted by the cutting force of the biopsy needle.

Objectives

This study proposes a novel needle design with double-concave-curved cutting edges, which is more suited for rotational needle cutting. We aimed to optimize the design so that large, undamaged samples could be extracted with minimal cutting force.

Materials and Methods

Five-factor experiments were designed using the Taguchi method. Experiments involving rotational needle insertion and tissue sampling were conducted to examine the effects of these variables on the cutting force and sampling quality, respectively. The relationship between the cutting force and sampling quality was analyzed.

Results

For needle insertion, the optimal design within the design space demonstrated a marked improvement in cutting force from 1.2107 to 0.1888 N. Furthermore, the optimized double concave-curved needle outperformed the blunt needle under the same needle speeds, showing a 24.4% reduction in cutting force (0.1888 vs. 0.2496 N). Increasing rotation-translation ratio or insertion speed would allow for extracting a larger sample (increased up to 21.95% in weight and 17.21% in total length) but may also increase the rotation speed, resulting in sample fragmentation. To simultaneously improve sampling quality and cutting force, a higher K value, larger rotation-translation ratio, and slower insertion speed are suggested. Based on the conditions examined in this study, the optimal needle configuration should include a sharpened cutting edge with a K value of 0.2, a rotation-translation ratio of 8, and an insertion speed of 1 mm/s.

Purpose

As a main etiology of myocardial ischemia, coronary microvascular dysfunction (CMD) can occur in patients with or without obstructive coronary artery disease. Currently, there is a lack of a non-invasive approach for early detection of CMD.

Aim

We aim to develop a multilayer perceptron (MLP) algorithm to achieve non-invasive early detection of CMD based on vectorcardiography (VCG) and cardiodynamicsgram (CDG) features.

Methods

Electrocardiograms of 82 CMD patients and 107 healthy controls were collected and synthesized into VCGs. The VCGs' ST-T segments were extracted and fed into a deterministic learning algorithm to develop CDGs. Temporal heterogeneity index, spatial heterogeneity index, sample entropy, approximate entropy, and complexity index were extracted from VCGs' ST-T segments and CDGs, entitled as STT- and CDG-based features, respectively. The most effective feature subsets were determined from CDG-based, STT-based, and the combined features (i.e., all features) via the sequential backward selection algorithm as inputs for CDG-, STT-, and CDG-STT-based MLP models optimized with an improved sparrow search algorithm, respectively. Finally, the classification capacity of the corresponding models was evaluated via five-fold cross-validations and tested on a testing dataset to verify the optimal one.

Results

The CDG-STT-based MLP model had significantly higher evaluated metrics than CDG- and STT-based ones on the validation dataset, with the accuracy, sensitivity, specificity, F1 score, and AUC of 0.904, 0.925, 0.870, 0.870, and 0.897 on the testing dataset respectively.

Conclusions

The MLP model based on VCG and CDG features showed high efficiency in identifying CMD. The CDG-STT-based MLP model may afford a potential computer-aided tool for non-invasive detection of CMD.

Purpose

To experimentally measure selected passive properties of skeletal muscle at three different scales (macroscopic scale: whole muscle, microscopic scale: single skinned fiber, and submicron scale: single myofibril) within the same animal model (mice), and to compare a primarily slow-twitch fiber muscle (soleus) and a primarily fast-twitch fiber muscle (extensor digitorum longus, EDL) for each scale.

Methods

Healthy 3 months old wild-type C57BL6 mice were used. To characterize each scale, soleus (N = 11), EDL (N = 9), slow fibers (N = 17), fast fibers (N = 16), and myofibrils from soleus (N = 11) and EDL (N = 11) were harvested. Passive mechanical (ramp, relaxation) tests were applied at each scale to compare the passive properties (Young's modulus, static and dynamic stresses) within a given scale, across scales and between muscle types.

Results

The soleus and EDL showed significant passive mechanical differences at the macroscopic scale while no variation was observed between both tissues at the microscopic and submicron scales. The results highlight the importance of the scale that is used to mechanically characterize a multiscale tissue.

Conclusion

The present work will allow for a better understanding of the multiscale passive mechanical properties for two muscles with vastly differing physiological and metabolic properties. This study provides referent data to the body of literature that can be built upon in future work.

Objectives

Laryngoscopy is a medical procedure for obtaining a view of the human larynx. It is challenging for clinicians to distinguish laryngeal neoplasms by human visual observation. Recent deep learning methods can assist clinicians in improving the accuracy of distinguishing. However, existed methods are often trained on large-scale private datasets, while other researchers and hospitals can neither access these private datasets nor afford to build such large-scale datasets. In this paper, we focus on identifying laryngeal neoplasms under the “small data” regime, which is more important for many small hospitals to investigate deep learning models for diagnosis.

Material and methods

We build an extremely small dataset consisting of 279 laryngoscopic images of different categories. We found that traditional deep learning models for image classification cannot achieve satisfactory performance for small data, due to the great variability of recording laryngoscopic images and the small area of the neoplasms. To address these difficulties, we propose to employ object detection methods for this small data problem. Concretely, a Faster R-CNN is implemented here, which combines the DropBlock regularization technique to alleviate overfitting additionally.

Results

Compared to previous methods, our model is more robust to overfitting and can predict the location and category of detected neoplasms simultaneously. Our method achieves 73.00% overall accuracy, which is higher than the average of clinicians (65.05%) and the recent state-of-the-art classification method (65.00%).

Conclusion

The proposed method shows great ability to detect both the category and location of neoplasms and can be served as a screening tool to help the final decisions of clinicians.