Novel fusion architecture of multi-location blood flow sounds for arteriovenous fistula stenosis diagnosis.

Background And Objective: Arteriovenous fistula (AVF) is a surgical intervention that establishes an artificial conduit between an artery and a vein, which is essential for hemodialysis treatment. The blood flow sounds has the potential to diagnose AVF stenosis. The research primarily addresses two critical questions: whether there is stenosis within the vascular pathway, and the precise location of the stenosis within the vascular pathway.

Asthma Detection Research Based on Voice Signal Processing and Machine Learning.

To analyze voice signals and identify asthma patients using voice signal analysis and machine learning techniques, we collected clear, low-noise fixed-pattern voice signals from 50 asthma patients and 50 healthy controls to build an analysis database. The research conducted multi-dimensional voice signal analysis based on MATLAB and selected voice feature indicators with significant differences between asthma patients and healthy controls. After dimensionality reduction analysis on differential phonetic features, the processed features were incorporated into subsequent SVM and RF modeling and classification research.

Application of Pulsed Laser Deposition (PLD) Technology in the Preparation of Two-Dimensional (2D) Film Materials.

Two-dimensional film materials with unique atomic structures and electronic operation modes have demonstrated amazing application potential and value in the field of high technology. Among the various methods for preparing 2D film materials, PLD technology has become the preferred technology for rapid and green preparation of high-quality, complex structured 2D film materials due to its features such as maintaining the excellent stoichiometric ratio of the target, strong process flexibility, and non-polluting environment. Therefore, this paper discusses the exciting topic of PLD technology in the preparation and application of 2D film materials.

Non-invasive Detection of Adenoid Hypertrophy Using Deep Learning Based on Heart-Lung Sounds.

Adenoid hypertrophy is one of the most common upper respiratory tract disorders during childhood, leading to a range of symptoms such as nasal congestion, mouth breathing and obstructive sleep apnea. Current diagnostic methods, including computerized tomography scans and nasal endoscopy, are invasive or involve ionizing radiation, rendering them unsuitable for long-term assessments. To address these clinical challenges, this paper proposes a novel deep learning approach for the non-invasive detection of adenoid hypertrophy using heartlung sounds.

A comprehensive validation study on the influencing factors of cough-based COVID-19 detection through multi-center data with abundant metadata.

Objective: In recent years, COVID-19 has placed enormous burdens on healthcare systems. Currently, hundreds of thousands of new cases are reported monthly. World Health Organization is managing COVID-19 as a long-term disease, indicating that an efficient and low-cost detection method remains necessary.

Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification.

Respiratory diseases are one of the major health problems worldwide. Early diagnosis of the disease types is of vital importance. As one of the main symptoms of many respiratory diseases, cough may contain information about different pathological changes in the respiratory system.

Automated valvular heart disease detection using heart sound with a deep learning algorithm.

Background: Insufficient clinicians' auscultation ability delays the diagnosis and treatment of valvular heart disease (VHD); artificial intelligence provides a solution to compensate for the insufficiency in auscultation ability by distinguishing between heart murmurs and normal heart sounds. However, whether artificial intelligence can automatically diagnose VHD remains unknown. Our objective was to use deep learning to process and compare raw heart sound data to identify patients with VHD requiring intervention.

Rethinking terrestrial dissolved organic matter in dam reservoirs before mixing: Linking photodegradation and biodegradation and the phenanthrene binding behavior.

With the increased construction of dam reservoirs and the demand for water security, terrestrial dissolved organic matter (DOM) has received attention because of its role in regulating water quality, ecological functions, and the fate and transport of pollutants in dam reservoirs. This study investigated the transformations of soil DOM and vegetation DOM of dam reservoirs following photodegradation and biodegradation before conservative mixing, as well as the resultant effects on phenanthrene binding. Based on the results, terrestrial DOM could undergo transformation via photodegradation and biodegradation before conservative mixing in dam reservoirs.