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基于 CASA 技术的人类运动学和形态计量学精子亚群分析:一种新的精子分类方法。

Human kinematic and morphometric sperm subpopulation analysis using CASA technology: A new approach to spermatozoa classification.

机构信息

Proiser R+D, Scientific Park, Universitat de València, 46980 Paterna, Valencia, Spain.

Instituto Valenciano de Infertilidad, 46015 Valencia, Spain.

出版信息

Rev Int Androl. 2022 Oct-Dec;20(4):257-265. doi: 10.1016/j.androl.2021.05.003. Epub 2022 Jul 29.

Abstract

INTRODUCTION

Semen analysis is a clinical method aimed at determining the fertility of a male individual. The traditional subjective method lacks the reliability that can be achieved by computer-assisted sperm analysis (CASA) technology. Unfortunately, this technology has only been used when taking into consideration individually different sperm characteristics. The aim of this work is to present an integrative mathematical approach that considers different seminal variables to establish human sperm subpopulations.

METHODS

Samples were obtained from thirteen volunteers via masturbation and were analyzed by the routine subjective method and two objective systems, CASA Motility (CASA-Mot) and CASA Morphology (CASA-Morph).

RESULTS

Seminogram variables were reduced to three principal components (PC) showing two subpopulations. Kinematics and morphometric variables each rendered three PCs for four subpopulations.

CONCLUSIONS

These results lay the foundations for future studies including different geographical, social, ethnic and age range conditions with the aim of achieving a definitive view of the human semen picture.

摘要

简介

精液分析是一种旨在确定男性个体生育能力的临床方法。传统的主观方法缺乏计算机辅助精子分析(CASA)技术所能达到的可靠性。不幸的是,这项技术仅在考虑个体不同精子特征时才会使用。本研究的目的是提出一种综合的数学方法,考虑不同的精液变量来建立人类精子亚群。

方法

通过自慰从 13 名志愿者中获取样本,并通过常规主观方法和两个客观系统 CASA 活力(CASA-Mot)和 CASA 形态学(CASA-Morph)进行分析。

结果

精液参数可简化为三个主成分(PC),显示出两个亚群。运动学和形态计量学变量分别为四个亚群生成三个 PC。

结论

这些结果为未来的研究奠定了基础,包括不同的地理、社会、种族和年龄范围的条件,旨在全面了解人类精液的情况。

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