Association between the risk of malnutrition and functional capacity in patients with peripheral arterial disease: A cross-sectional study

Introduction The risk of malnutrition is an important predictor of functional capacity in the elderly population. However, whether malnutrition is associated with functional capacity in patients with peripheral artery disease (PAD) is poorly known. Purpose To analyse the association between the risk of malnutrition and functional capacity in patients with PAD. Methods This cross-sectional study included 135 patients with PAD of both genders, ≥50 years old, with symptomatic PAD (Rutherford stage I to III) in one or both limbs and with ankle-brachial index ≤0.90. The risk of malnutrition was assessed by the short form of the Mini Nutritional Assessment-Short Form and patients were classified as having normal nutritional status (n = 92) and at risk of malnutrition (n = 43). Functional capacity was objectively assessed using the six-minute walking test (6MWT, absolute maximal distance and relativized and expressed as a percentage of health subjects), short-physical performance battery (SPPB, balance, gait speed and the sit and stand test) and the handgrip test, and subjectively, using the Walking Impairment Questionnaire and Walking Estimated-Limitation Calculated by History. The association between the risk of malnutrition and functional capacity was analysed using bivariate and multivariate logistic regression adjustments for gender, age, ankle-brachial index, body mass index, use of statins, coronary arterial disease and stroke. For all statistical analyses, significance was accepted at p<0.05. Results Thirty-two per cent of our patients were classified with a risk of malnutrition. The risk of malnutrition was associated with the absolute 6MWT total distance (OR = 0.994, P = 0.031) relative 6MWT total distance (OR = 0.971, P = 0.038), lowest SPPB total score (OR = 0.682, P = 0.011), sit and stand (OR = 1.173, P = 0.003) and usual 4-meter walk test (OR = 1.757, P = 0.034). Conclusion In patients with PAD, the risk of malnutrition was associated with objective measurements of functional capacity

An approach to the diagnosis of lumbar disc herniation using deep learning models

Background: In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc.Materials and methods: The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist’s labeling and annotation, and then the deep learning models were trained using the training/validation dataset.Results: Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH.Conclusion: Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients

The effectiveness of modern cardiac rehabilitation : A systematic review of recent observational studies in non-attenders versus attenders

BACKGROUND: The beneficial effects of cardiac rehabilitation (CR) have been challenged in recent years and there is now a need to investigate whether current CR programmes, delivered in the context of modern cardiology, still benefit patients. METHODS: A systematic review of non-randomised controlled studies was conducted. Electronic searches of Medline, Embase, CINAHL, science citation index (web of science), CIRRIE and Open Grey were undertaken. Non-randomised studies investigating the effects of CR were included when recruitment occurred from the year 2000 onwards in accordance with significant CR guidance changes from the late 1990's. Adult patients diagnosed with acute myocardial infarction (AMI) were included. Non-English articles were considered. Two reviewers independently screened articles according to pre-defined selection criteria as reported in the PROSPERO database (CRD42015024021). RESULTS: Out of 2,656 articles, 8 studies involving 9,836 AMI patients were included. Studies were conducted in 6 countries. CR was found to reduce the risk of all-cause and cardiac-related mortality and improve Health-Related Quality of Life (HRQOL) significantly in at least one domain. The benefits of CR in terms of recurrent MI were inconsistent and no significant effects were found regarding re-vascularisation or re-hospitalisation following AMI. CONCLUSION: Recent observational evidence draws different conclusions to the most current reviews of trial data with respect to total mortality and re-hospitalisation, questioning the representativeness of historic data in the modern cardiological era. Future work should seek to clarify which patient and service level factors determine the likelihood of achieving improved all-cause and cardiac mortality and reduced hospital re-admissions

Stage Call: Cardiovascular Reactivity to Audition Stress in Musicians

Auditioning is at the very center of educational and professional life in music and is associated with significant psychophysical demands. Knowledge of how these demands affect cardiovascular responses to psychosocial pressure is essential for developing strategies to both manage stress and understand optimal performance states. To this end, we recorded the electrocardiograms (ECGs) of 16 musicians (11 violinists and 5 flutists) before and during performances in both low- and high-stress conditions: with no audience and in front of an audition panel, respectively. The analysis consisted of the detection of R-peaks in the ECGs to extract heart rate variability (HRV) from the notoriously noisy real-world ECGs. Our data analysis approach spanned both standard (temporal and spectral) and advanced (structural complexity) techniques. The complexity science approaches—namely, multiscale sample entropy and multiscale fuzzy entropy—indicated a statistically significant decrease in structural complexity in HRV from the low- to the high-stress condition and an increase in structural complexity from the pre-performance to performance period, thus confirming the complexity loss theory and a loss in degrees of freedom due to stress. Results from the spectral analyses also suggest that the stress responses in the female participants were more parasympathetically driven than those of the male participants. In conclusion, our findings suggest that interventions to manage stress are best targeted at the sensitive pre-performance period, before an audition begins

The Virtual-Spine Platform—Acquiring, visualizing, and analyzing individual sitting behavior

Back pain is a serious medical problem especially for those people sitting over long periods during their daily work. Here we present a system to help users monitoring and examining their sitting behavior. The Virtual-Spine Platform (VSP) is an integrated system consisting of a real-time body position monitoring module and a data visualization module to provide individualized, immediate, and accurate sitting behavior support. It provides a comprehensive spine movement analysis as well as accumulated data visualization to demonstrate behavior patterns within a certain period. The two modules are discussed in detail focusing on the design of the VSP system with adequate capacity for continuous monitoring and a web-based interactive data analysis method to visualize and compare the sitting behavior of different persons. The data was collected in an experiment with a small group of subjects. Using this method, the behavior of five subjects was evaluated over a working day, enabling inferences and suggestions for sitting improvements. The results from the accumulated data module were used to elucidate the basic function of body position recognition of the VSP. Finally, an expert user study was conducted to evaluate VSP and support future developments

Advances in Diagnosis and Pathophysiology of Microvascular Dysfunction

Microcirculation is the system that brings oxygen and nutrients to local cells and removes metabolic wastes [...

Effect of viscoelastic properties of plantar soft tissues on plantar pressures at the first metatarsal head in diabetics with peripheral neuropathy

[[abstract]]Diabetic foot ulcers are one of the most serious complications associated with diabetes mellitus. Current research studies have demonstrated that biomechanical alterations of the diabetic foot contribute to the development of foot ulcers. However, the changes of soft tissue biomechanical properties associated with diabetes and its influences on the development of diabetic foot ulcers have not been investigated. The purpose of this study was to investigate the effect of diabetes on the biomechanical properties of plantar soft tissues and the relationship between biomechanical properties and plantar pressure distributions. We used the ultrasound indentation tests to measure force-deformation relationships of plantar soft tissues and calculate the effective Young's modulus and quasi-linear viscoelastic parameters to quantify biomechanical properties of plantar soft tissues. We also measured plantar pressures to calculate peak plantar pressure and plantar pressure gradient. Our results showed that diabetics had a significantly greater effective Young's modulus and initial modulus of quasi-linear viscoelasticity compared to non-diabetics. The plantar pressure gradient and biomechanical properties were significantly correlated. Our findings indicate that diabetes is linked to an increase in viscoelasticity of plantar soft tissues that may contribute to a higher peak plantar pressure and plantar pressure gradient in the diabetic foot

Using Artificial Neural Network To Determine Favorable Wheelchair Tilt and Recline Usage In People With Spinal Cord Injury Training ANN with Genetic Algorithm to Improve Generalization

Abstract-People with spinal cord injury (SCI) are at risk for pressure ulcers because of their poor motor function and consequent prolonged sitting in wheelchairs. The current clinical practice typically uses the wheelchair tilt and recline to attain specific seating angles (sitting postures) to reduce seating pressure in order to prevent pressure ulcers. The rationale is to allow the development of reactive hyperemia to re-perfuse the ischemic tissues. However, our study reveals that a particular tilt and recline setting may result in a significant increase of skin perfusion for one person with SCI, but may cause neutral or even negative effect on another person. Therefore, an individualized guidance on wheelchair tilt and recline usage is desirable in people with various levels of SCI. In this study, we intend to demonstrate the feasibility of using machine-learning techniques to classify and predict favorable wheelchair tilt and recline settings for individual wheelchair users with SCI. Specifically, we use artificial neural networks (ANNs) to classify whether a given tilt and recline setting would cause a positive, neutral, or negative skin perfusion response. The challenge, however, is that ANN is prone to overfitting, a situation in which ANN can perfectly classify the existing data while cannot correctly classify new (unseen) data. We investigate using the genetic algorithm (GA) to train ANN to reduce the chance of converging on local optima and improve the generalization capability of classifying unseen data. Our experimental results indicate that the GA-based ANN significantly improves the generalization ability and outperforms the traditional statistical approach and other commonly used classification techniques, such as BP-based ANN and support vector machine (SVM). To the best of our knowledge, there are no such intelligent systems available now. Our research fills in the gap in existing evidence