Pub Date : 2024-10-01DOI: 10.1177/00236772241279473
Jordi L Tremoleda
{"title":"Our 61st Annual Meeting: An exciting programme is shaping up!","authors":"Jordi L Tremoleda","doi":"10.1177/00236772241279473","DOIUrl":"https://doi.org/10.1177/00236772241279473","url":null,"abstract":"","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142372231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-08-05DOI: 10.1177/00236772241244519
Natasha A Karp, Alan Sharpe, Benjamin Phillips
Pilots are small-scale initial experiments that are intended to guide the design of future, larger studies, with a view to increasing their effectiveness. In this statistical primer we highlight five common mistakes that limit the utility of pilot studies and provide practical guidance to avoid such errors and increase their effectiveness. The common thread connecting these mistakes is insufficient planning and over-interpretation of the results. This approach compromises the ultimate goals of the research programme and the future experimental cascade. In support of our view that over-interpretation is an error, we present a simple simulation to demonstrate that pilots will generally generate an inaccurate estimate of the variability of the biological endpoint under study and that frequent under-estimation will lead to inconclusive and unethical subsequent experiments. We argue that well planned pilots are an important part of the research cascade and still need to be implemented to a high standard.
{"title":"Preclinical pilot studies: Five common pitfalls and how to avoid them.","authors":"Natasha A Karp, Alan Sharpe, Benjamin Phillips","doi":"10.1177/00236772241244519","DOIUrl":"10.1177/00236772241244519","url":null,"abstract":"<p><p>Pilots are small-scale initial experiments that are intended to guide the design of future, larger studies, with a view to increasing their effectiveness. In this statistical primer we highlight five common mistakes that limit the utility of pilot studies and provide practical guidance to avoid such errors and increase their effectiveness. The common thread connecting these mistakes is insufficient planning and over-interpretation of the results. This approach compromises the ultimate goals of the research programme and the future experimental cascade. In support of our view that over-interpretation is an error, we present a simple simulation to demonstrate that pilots will generally generate an inaccurate estimate of the variability of the biological endpoint under study and that frequent under-estimation will lead to inconclusive and unethical subsequent experiments. We argue that well planned pilots are an important part of the research cascade and still need to be implemented to a high standard.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141893767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Random treatment assignment is essential in demonstrating a causal relationship between a treatment and the outcome of interest. Randomisation ensures that animals assigned to different treatment groups do not differ from each other systematically, except for the randomly assigned treatment. The randomisation pattern should also dictate the statistical analysis.
{"title":"Treatment randomisation at animal or pen level? : Statistical analysis should follow the randomisation pattern!","authors":"Luc Duchateau, Robrecht Dockx, Klara Goethals, Matthijs Vynck, Frédéric Vangroenweghe, Christian Burvenich","doi":"10.1177/00236772241247274","DOIUrl":"10.1177/00236772241247274","url":null,"abstract":"<p><p>Random treatment assignment is essential in demonstrating a causal relationship between a treatment and the outcome of interest. Randomisation ensures that animals assigned to different treatment groups do not differ from each other systematically, except for the randomly assigned treatment. The randomisation pattern should also dictate the statistical analysis.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142000315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-09-24DOI: 10.1177/00236772241260173
Bernhard Voelkl, Hanno Würbel
Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.
{"title":"Heterogeneity of animal experiments and how to deal with it.","authors":"Bernhard Voelkl, Hanno Würbel","doi":"10.1177/00236772241260173","DOIUrl":"10.1177/00236772241260173","url":null,"abstract":"<p><p>Heterogeneity of study samples is ubiquitous in animal experiments. Here, we discuss the different options of how to deal with heterogeneity in the statistical analysis of a single experiment. Specifically, data from different sub-groups (e.g. sex, strain, age cohorts) may be analysed separately, heterogenization factors may be ignored and data pooled for analysis, or heterogenization factors may be included as additional variables in the statistical model. The cost of ignoring a heterogenization factor is an inflated estimate of the variance and a consequent loss of statistical power. Therefore, it is usually preferable to include the heterogenization factor in the statistical model, especially if the heterogenization factor has been introduced intentionally (e.g. using both sexes). If heterogenization factors are included, they can be treated either as fixed factors in an analysis of variance design or sometimes as random effects in mixed effects regression models. Finally, for an appropriate sample size estimation, it is necessary to decide whether to treat heterogenization factors as nuisance variables, or whether the experiment should be powered to be able to detect not only the main effect of the treatment but also interactions between heterogenization factors and the treatment variable.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-09-20DOI: 10.1177/00236772241281044
{"title":"Vacancy for EDITOR position to join the EIC team.","authors":"","doi":"10.1177/00236772241281044","DOIUrl":"10.1177/00236772241281044","url":null,"abstract":"","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-08-11DOI: 10.1177/00236772231217777
Stanley E Lazic
The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.
{"title":"Causal mediation analysis: How to avoid fooling yourself that <i>X</i> causes <i>Y</i>.","authors":"Stanley E Lazic","doi":"10.1177/00236772231217777","DOIUrl":"10.1177/00236772231217777","url":null,"abstract":"<p><p>The purpose of many preclinical studies is to determine whether an experimental intervention affects an outcome through a particular mechanism, but the analytical methods and inferential logic typically used cannot answer this question, leading to erroneous conclusions about causal relationships, which can be highly reproducible. A causal mediation analysis can directly test whether a hypothesised mechanism is partly or completely responsible for a treatment's effect on an outcome. Such an analysis can be easily implemented with modern statistical software. We show how a mediation analysis can distinguish between three different causal relationships that are indistinguishable when using a standard analysis.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141917072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-09-20DOI: 10.1177/00236772241259518
Limeng Liu, Ashley Petersen
Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample t-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and p-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.
动物研究通常涉及在同一动物身上多次测量感兴趣的结果,无论是随时间推移还是针对不同的暴露。由于动物的特异性,在同一动物身上重复测量的结果具有相关性。虽然与单一结果数据相比,重复测量数据可以解决更复杂的研究问题,但统计分析必须考虑到研究设计导致的相关结果,这违反了标准统计方法(如双样本 t 检验、线性回归)的独立性假设。如果不正确地使用标准统计方法来分析相关结果数据,统计推断(即置信区间和 p 值)将是不正确的,有些设置往往会导致无效结果,而有些设置则会产生具有统计意义的结果,尽管数据中并不支持这种结果。相反,研究人员可以利用专为相关结果设计的方法。在本文中,我们将讨论导致相关结果数据的常见研究设计,激发对使用独立数据方法不当分析相关结果的影响的直觉,并介绍正确利用相关结果数据的方法。
{"title":"Incorporating sources of correlation between outcomes: An introduction to mixed models.","authors":"Limeng Liu, Ashley Petersen","doi":"10.1177/00236772241259518","DOIUrl":"10.1177/00236772241259518","url":null,"abstract":"<p><p>Animal research often involves measuring the outcomes of interest multiple times on the same animal, whether over time or for different exposures. These repeated outcomes measured on the same animal are correlated due to animal-specific characteristics. While this repeated measures data can address more complex research questions than single-outcome data, the statistical analysis must take into account the study design resulting in correlated outcomes, which violate the independence assumption of standard statistical methods (e.g. a two-sample <i>t</i>-test, linear regression). When standard statistical methods are incorrectly used to analyze correlated outcome data, the statistical inference (i.e. confidence intervals and <i>p</i>-values) will be incorrect, with some settings leading to null findings too often and others producing statistically significant findings despite no support for this in the data. Instead, researchers can leverage approaches designed specifically for correlated outcomes. In this article, we discuss common study designs that lead to correlated outcome data, motivate the intuition about the impact of improperly analyzing correlated outcomes using methods for independent data, and introduce approaches that properly leverage correlated outcome data.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142290374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-01Epub Date: 2024-09-24DOI: 10.1177/00236772241273002
Angela Jeffers, Kathryn Konrad, Gary Larson, Katherine Allen-Moyer, Helen Cunny, Keith Shockley
Null hypothesis significance testing is a statistical tool commonly employed throughout laboratory animal research. When experimental results are reported, the reproducibility of the results is of utmost importance. Establishing standard, robust, and adequately powered statistical methodology in the analysis of laboratory animal data is critical to ensure reproducible and valid results. Simulation studies are a reliable method for assessing the power of statistical tests, however, biologists may not be familiar with simulation studies for power despite their efficacy and accessibility. Through an example of simulated Harlan Sprague-Dawley (HSD) rat organ weight data, we highlight the importance of conducting power analyses in laboratory animal research. Using simulations to determine statistical power prior to an experiment is a financially and ethically sound way to validate statistical tests and to help ensure reproducibility of findings in line with the 4R principles of animal welfare.
{"title":"Simulation methodologies to determine statistical power in laboratory animal research studies.","authors":"Angela Jeffers, Kathryn Konrad, Gary Larson, Katherine Allen-Moyer, Helen Cunny, Keith Shockley","doi":"10.1177/00236772241273002","DOIUrl":"10.1177/00236772241273002","url":null,"abstract":"<p><p>Null hypothesis significance testing is a statistical tool commonly employed throughout laboratory animal research. When experimental results are reported, the reproducibility of the results is of utmost importance. Establishing standard, robust, and adequately powered statistical methodology in the analysis of laboratory animal data is critical to ensure reproducible and valid results. Simulation studies are a reliable method for assessing the power of statistical tests, however, biologists may not be familiar with simulation studies for power despite their efficacy and accessibility. Through an example of simulated Harlan Sprague-Dawley (HSD) rat organ weight data, we highlight the importance of conducting power analyses in laboratory animal research. Using simulations to determine statistical power prior to an experiment is a financially and ethically sound way to validate statistical tests and to help ensure reproducibility of findings in line with the 4R principles of animal welfare.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142308038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Outbred stocks of mice are widely used in pre-clinical research as these animals possess a diversified genetic background when compared with inbred strains of mice. It is crucial to assess particular alterations in the physiological and functional profiles of laboratory animals using haematological and biochemical indicators. These values can also differ between laboratories because they are influenced by many different factors. We aimed to provide normal values and reference intervals for selected haematology and biochemistry analytes of 570 ICR mice at three different ages: 6-8 weeks, 10-14 weeks and 6-9 months. Reference values were calculated by non-parametric methods. For comparisons between sexes, the independent-sample t-test and Mann-Whitney test were employed, and analysis of variance was used for age differences. The findings of the study revealed age-related declines in haemoglobin concentration, haematocrit, mean corpuscular volume and mean corpuscular haemoglobin concentrations. Mice aged 6-9 months had statistically higher platelet counts in their blood than mice of other ages. The white blood cell count had a significant age effect and progressively decreased with age. As mice get older, the percentage of neutrophils, monocytes and basophils increases, but the percentage of lymphocytes decreases. For the biochemical values, age-related significant differences in glucose, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and albumin concentrations were found. It was also found that creatinine concentrations were comparable across all age ranges. The values presented in the present work can be used as a reference to interpret clinical pathology data for other studies and to evaluate health status.
与近交系小鼠相比,近交系小鼠具有多样化的遗传背景,因此被广泛用于临床前研究。使用血液学和生化指标评估实验动物生理和功能特征的特定变化至关重要。由于受到许多不同因素的影响,这些数值在不同实验室之间也可能存在差异。我们的目标是为 570 只 ICR 小鼠在三个不同年龄段的选定血液学和生物化学分析指标提供正常值和参考区间:6-8周、10-14周和6-9个月。参考值采用非参数方法计算。性别间的比较采用独立样本 t 检验和曼-惠特尼检验,年龄差异采用方差分析。研究结果显示,血红蛋白浓度、血细胞比容、平均血球容积和平均血红蛋白浓度的下降与年龄有关。据统计,6-9 个月大的小鼠血液中血小板计数高于其他年龄段的小鼠。白细胞计数有明显的年龄效应,并随着年龄的增长而逐渐减少。随着小鼠年龄的增长,中性粒细胞、单核细胞和嗜碱性粒细胞的比例会增加,但淋巴细胞的比例会下降。在生化值方面,发现葡萄糖、天门冬氨酸氨基转移酶、丙氨酸氨基转移酶、碱性磷酸酶和白蛋白的浓度存在与年龄相关的显著差异。研究还发现,各年龄段的肌酐浓度相当。本研究提供的数值可作为其他研究解释临床病理学数据和评估健康状况的参考。
{"title":"Establishment of reference intervals of haematology and biochemistry analytes in ICR mice of different ages.","authors":"Suresh Patel, Satish Patel, Ashvin Kotadiya, Samir Patel, Bhavesh Shrimali, Tushar Patel, Harshida Trivedi, Vishal Patel, Jogeswar Mahapatra, Mukul Jain","doi":"10.1177/00236772241260909","DOIUrl":"https://doi.org/10.1177/00236772241260909","url":null,"abstract":"<p><p>Outbred stocks of mice are widely used in pre-clinical research as these animals possess a diversified genetic background when compared with inbred strains of mice. It is crucial to assess particular alterations in the physiological and functional profiles of laboratory animals using haematological and biochemical indicators. These values can also differ between laboratories because they are influenced by many different factors. We aimed to provide normal values and reference intervals for selected haematology and biochemistry analytes of 570 ICR mice at three different ages: 6-8 weeks, 10-14 weeks and 6-9 months. Reference values were calculated by non-parametric methods. For comparisons between sexes, the independent-sample <i>t</i>-test and Mann-Whitney test were employed, and analysis of variance was used for age differences. The findings of the study revealed age-related declines in haemoglobin concentration, haematocrit, mean corpuscular volume and mean corpuscular haemoglobin concentrations. Mice aged 6-9 months had statistically higher platelet counts in their blood than mice of other ages. The white blood cell count had a significant age effect and progressively decreased with age. As mice get older, the percentage of neutrophils, monocytes and basophils increases, but the percentage of lymphocytes decreases. For the biochemical values, age-related significant differences in glucose, aspartate aminotransferase, alanine aminotransferase, alkaline phosphatase and albumin concentrations were found. It was also found that creatinine concentrations were comparable across all age ranges. The values presented in the present work can be used as a reference to interpret clinical pathology data for other studies and to evaluate health status.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142349385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-30DOI: 10.1177/00236772241259857
Stéphane Tanguy, Agathe Cambier, Leandro Fontana-Pires, Timothé Flenet, Charles Eynard, Julie Fontecave-Jalon, Pierre-Yves Gumery, François Boucher
The development of alternative methods for monitoring cardiorespiratory function without restraint or surgical implantation is attracting growing interest for both ethical and scientific reasons. For this purpose, a new non-invasive jacketed telemetry tool consisting in a radio device maintained in a jacket worn by the animal was previously developed to improve cardiorespiratory monitoring. It allows simultaneous monitoring of cardiac activity by surface electrocardiagram, respiratory function by respiratory inductive plethysmography, and locomotor activity by accelerometry. However, this tool has only been validated under conditions of low/intermediate activity levels or in anesthetized animals. This study aimed to evaluate the feasibility of using this system in the challenging conditions of an exertion protocol. Male Wistar rats (n = 10, 8-9 weeks old) were subjected to an incremental treadmill exercise protocol including speed levels from 5 to 40 cm s-1 separated by 30-s breaks. Heart rate (HR) and minute ventilation (assessed by minute volume; MV) were continuously monitored. At the end of each running level and during the 30-s breaks, HR and MV showed a significant increase compared to resting values. They returned to the baseline within 60 min of post-exercise recovery. Overall, our results demonstrated (i) the ability of the animal to run while wearing the device and (ii) the ability of the device to reliably monitor cardiorespiratory adaptation to treadmill exercise despite significant mechanical disturbances. In conclusion, this study highlights the possibility of non-invasively monitoring cardiorespiratory functional variables that were previously unattainable under conditions of high activity in freely moving animals.
{"title":"Jacketed telemetry in rats: a novel non-invasive method for cardiorespiratory phenotyping during treadmill exercise.","authors":"Stéphane Tanguy, Agathe Cambier, Leandro Fontana-Pires, Timothé Flenet, Charles Eynard, Julie Fontecave-Jalon, Pierre-Yves Gumery, François Boucher","doi":"10.1177/00236772241259857","DOIUrl":"https://doi.org/10.1177/00236772241259857","url":null,"abstract":"<p><p>The development of alternative methods for monitoring cardiorespiratory function without restraint or surgical implantation is attracting growing interest for both ethical and scientific reasons. For this purpose, a new non-invasive jacketed telemetry tool consisting in a radio device maintained in a jacket worn by the animal was previously developed to improve cardiorespiratory monitoring. It allows simultaneous monitoring of cardiac activity by surface electrocardiagram, respiratory function by respiratory inductive plethysmography, and locomotor activity by accelerometry. However, this tool has only been validated under conditions of low/intermediate activity levels or in anesthetized animals. This study aimed to evaluate the feasibility of using this system in the challenging conditions of an exertion protocol. Male Wistar rats (<i>n</i> = 10, 8-9 weeks old) were subjected to an incremental treadmill exercise protocol including speed levels from 5 to 40 cm s<sup>-1</sup> separated by 30-s breaks. Heart rate (HR) and minute ventilation (assessed by minute volume; MV) were continuously monitored. At the end of each running level and during the 30-s breaks, HR and MV showed a significant increase compared to resting values. They returned to the baseline within 60 min of post-exercise recovery. Overall, our results demonstrated (i) the ability of the animal to run while wearing the device and (ii) the ability of the device to reliably monitor cardiorespiratory adaptation to treadmill exercise despite significant mechanical disturbances. In conclusion, this study highlights the possibility of non-invasively monitoring cardiorespiratory functional variables that were previously unattainable under conditions of high activity in freely moving animals.</p>","PeriodicalId":18013,"journal":{"name":"Laboratory Animals","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142349386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}