PubMed Health⌕ Search

PubMed · 3207202

Comparative study of two computerized semen motility analyzers.

Abstract

Semen analysis is one of the primary tests carried out to investigate the infertile male. Subjective evaluation of semen is often prone to observer bias and error. To eliminate this, a number of computerized semen analyzers have recently been introduced into the market and we have evaluated two of the more popular models, the Cell Soft Semen Analyzer and the Hamilton Thorn Motility Analyzer (HTM 2000). The Cell Soft identifies sperm on the basis of user defined values for cell size and luminosity whereas the Hamilton Thorn identifies sperm by motility, and then applies the computer-calculated average size and luminosity of all moving objects to non moving sperm cells. Semen samples from 25 normal donors and 25 subfertile patients were analyzed using these two models of computerized semen analyzers, and also by an experienced technician using both the Makler chamber and the hemocytometer. The results obtained from the two automated analyzers were compared with those obtained by subjective evaluation. Variation in sperm count and motility were analyzed according to the sperm density. Four groups, less than 30 million/ml with debris, less than 30 million/ml, 30-50 million/ml, and greater than 50 million/ml were studied. The majority of patients fit into the first two groups. We observed that the HTM 2000 is superior to the Cell Soft in evaluating sperm count within the patient population group. For our donor population with an average sperm count of greater than 85 million/ml both systems provide extremely accurate counts.(ABSTRACT TRUNCATED AT 250 WORDS)

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

H S Gill, K Van Arsdalen, J Hypolite, R M Levin, J V Ruzich. Comparative study of two computerized semen motility analyzers.. https://pubmed.ncbi.nlm.nih.gov/3207202/

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↗