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Steps in Outbreak Investigation

Steps in Outbreak Investigation

In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:

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Updated: May 17, 2025

Urinary Bladder Distention Evoked Visceromotor Responses as a Model for Bladder Pain in Mice

Urinary Bladder Distention Evoked Visceromotor Responses as a Model for Bladder Pain in Mice

Published on: April 27, 2014

从炎症标志物预测过度活跃的膀:使用NHANES 2005-2020的机器学习方法.

Haoxun Zhang¹, Guoling Zhang¹, Chunyang Wang¹

¹Department of Urology, The First Affiliated Hospital of Harbin Medical University, Harbin, China.

Biomolecules & biomedicine

|May 16, 2025

概括

系统性炎症,如SII和NLR等全血细胞计 (CBC) 生物标志物所表明,与过度活性膀 (OAB) 的更高风险有关. 这些CBC标记可能有助于识别患有OAB风险增加的个体.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
炎症研究炎症研究
生物统计学生物统计学

背景情况:

过度活跃的膀 (OAB) 显著影响生活质量和医疗费用.
系统性炎症越来越被认为是OAB发展的一个因素.
对OAB风险的全血细胞计 (CBC) 衍生的炎症生物标志物没有得到充分的研究.

研究的目的:

调查CBC衍生的炎症生物标志物与OAB之间的关联.
用统计和机器学习模型评估这些生物标志物的预测性能.

主要方法:

对35,394名NHANES参与者的横截面分析 (2005-2020年).
使用了后勤回归,值分析和随机森林/极端梯度提升模型.
根据社会人口统计学,生活方式和临床因素进行调整.

主要成果:

系统性免疫炎症指数 (SII),系统性炎症反应指数 (SIRI),中性细胞与淋巴细胞比率 (NLR),单细胞与淋巴细胞比率 (MLR) 和中性细胞与淋巴细胞比率 (NMLR) 的升高与OAB风险增加有关 (P <0.05).
观察到非线性关系,NLR和MLR的临界值.
机器学习模型将SII和SIRI确定为关键预测因子,表现良好 (测试时AUC为0.76).

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Evaluation of Biomaterials for Bladder Augmentation using Cystometric Analyses in Various Rodent Models

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结论:

由CBC衍生的炎症生物标志物与OAB风险有关.
这些生物标志物可能为OAB风险分层提供一种具有成本效益的方法.
需要进一步的纵向研究来确认因果关系和治疗影响.