PubMed Health⌕ Search

PubMed · 8176848

[Lung sound analysis and pulmonary function studies].

Abstract

Because of technical difficulties, the study of lung sounds has been neglected for the last 200 years since the age of Laennec. However, recent advances in signal processing and in technology have made it possible to record lung sounds routinely at the bedside or in clinical laboratories. Lung sound analysis has many advantages because it is safe, non-invasive, low-cost, and repeatable. Normal lung sounds can be classified into tracheal, bronchial, and alveolar (vesicular) sounds. They are all common in that the origin of the sound is the turbulence in the airways. The most important and convenient way of distinguishing them is the place where they are heard and the difference in intensity in relation to breathing cycle. Alveolar sounds are heard at the bases and are definitely larger during inspiration, while bronchial sounds are heard at the apex, over the sternum and inter-scapular area, and are equal or louder during expiration. The abnormal, or adventitious lung sounds are classified into continuous and discontinuous. Wheezes are most common continuous sounds. However, in patients with stenosis of trachea or major bronchus large continuous sounds often referred to as rhonchi are heard. They are easily heard over the neck and are important clinical signs which nurses and laboratory technicians must be aware of. By the use of adaptive digital filters we can reduce contaminating noises without the use of sound proof rooms. Because of many advantages, the lung sound analysis has a promising future as a method to supplement other pulmonary function studies.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

M Mori. 1994. [Lung sound analysis and pulmonary function studies].. https://pubmed.ncbi.nlm.nih.gov/8176848/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗