The effects of different inner pressures of air insoles and walking durations on peak plantar pressure

Exercise plays a crucial role in mitigating chronic complications associated with diabetes and peripheral vascular diseases. One intervention, the use of air insoles, has been suggested to reduce peak plantar pressure (PPP) and subsequently lower the risk of diabetic foot ulcers (DFUs). Despite its potential benefits, there are no established guidelines for selecting the optimal air insole pressure to effectively reduce PPP.This study investigates the effects of various air insole pressures on PPP across different walking durations. We conducted a repeated measures study with 13 participants, evaluating three air insole pressures (80, 160, and 240 mmHg) and two walking durations (10 and 20 minutes) across six walking conditions. PPP values were measured at the first toe, first metatarsal head, and second metatarsal head.

Using cross-correlation analysis of multi-channel near infrared spectroscopy to assess the hemodynamic response to cupping therapy

Cupping therapy is a prevalent intervention for managing musculoskeletal impairments, and prior research has highlighted its benefits in improving muscle hemodynamic responses through single-channel near-infrared spectroscopy (NIRS). However, the spatial hemodynamic responses and the relationship between oxyhemoglobin and deoxyhemoglobin during cupping therapy have remained largely unexplored.Our study employs the cross-correlation function (CCF) algorithm to analyze time-series NIRS signals from both inside and outside the cup, as well as the correlation between oxyhemoglobin and deoxyhemoglobin under various cupping intensities. We investigated four cupping conditions, including −225 and −300 mmHg for durations of 5 and 10 minutes.

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

Detecting lumbar disc herniation (LDH) through magnetic resonance imaging (MRI) poses significant challenges due to the diverse shapes, sizes, angles, and regions associated with disc abnormalities. Deep learning methods, particularly the YOU ONLY LOOK ONCE (YOLO) model series, offer promising solutions for the automatic identification and initial interpretation of LDH in clinical settings.

This study aims to evaluate the performance of various YOLO models (YOLOv5, YOLOv6, and YOLOv7) in detecting LDH across different regions of the lumbar intervertebral disc. Our methodology involved converting DICOM images to JPEG, selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on a comprehensive dataset.

Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

Alzheimer’s disease (AD) remains a significant public health priority, with the hippocampus being one of the most affected brain regions. MRI imaging of the hippocampus is a valuable biomarker for diagnosing AD using machine learning techniques. Our research addresses the challenge of lower accuracy in AD classification when utilizing entire MRI image slices.

Effect of negative pressure therapy on the treatment response to scar thickness and viscoelasticity

Patients with scars often face significant challenges to their mental and physical well-being. Our recent research delves into the efficacy of negative pressure therapy in promoting scar healing by creating a microenvironment that stimulates cell regeneration. The study addresses a critical gap in understanding the optimal pressure magnitudes necessary to prevent the disruption of scar tissue regeneration.