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Digitally Ventilated Cages Mice

DVC ANALYTICS

Tecniplast

Available
DK, SE, NO & FI

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The DVC® Analytics is the scientific cloud-based software developed by Tecniplast in order to access and analyze DVC raw data, transforming them into valuable animal activity information.

Take the utmost from any experiment where spontaneous locomotor activity is a scientifically valuable information!

DVC Analytics can be easily accessed from any web-connected device. Researchers and Lab Managers can now complement their undergoing experiments with animal locomotor activity.

  • Group cages together and make comparisons across cages
  • Monitor cage location thanks to the DVC Inventory module: if during the experiment a cage is moved from an IVC rack position to another, the corresponding DVC raw data are automatically re-aggregated in the DVC Analytics interface
  • 2 different available metrics (and more to come!), to interpret and analyze raw data: Raw data average and Activations
  • The DVC Analytics platform provides different chart visualizations and different types of data aggregation. Easily achieve cross-sectional or longitudinal study analysis and exploit several comparison opportunities. Immediately detect discrepancies among groups and complement the outcome of your experiments without any extra effort
  • Any DVC metric data can be easily downloaded in a .csv format for further statistical analysis with your own tools

The Digital Ventilated Cage (DVC®) documents animal activity at the home cage level throughout your study and supports surveillance of your animals. 

DVC monitors the animal activity in the home cage. By means of a sensor plate the system’s software captures data 24/7 and compares it to different days and similar cages. 

With DVC you can document the level of activity in any cage on the IVC rack throughout your study. Peaks of high or low activity are detected and indicate which cages you should pay special attention to during the daily inspection. This helps you detect animals that may need special care, ensures that humane endpoints are met early and that you do not loose valuable data.

“By using continuous home-cage recordings we observed that food and water restriction induced a reversible reduction of overall activity levels that went undetected using the instantaneous scoring method.” (Goltstein et al. 2018).

“DVC is effective in identifying mouse cages with patterns of high activity levels, signaling possible aggression incidences, thus potentially allowing for early intervention and consequently improving animal welfare.” (Giles et al. 2018).

“The system detected an increase in activity preceding and peaking around lights-on followed by a decrease to a rest pattern. At lights off, activity increased substantially displaying a distinct temporal variation across this period. We also documented impact on mouse activity that standard animal handling procedures have, e.g. cage-changes, and show that such procedures are stressors impacting in-cage activity. These key observations replicated across the three test-sites… These data demonstrate that home cage monitoring is scalable and run in real time, providing complementary information for animal welfare measures, experimental design and phenotype characterization. (Pernold et al. 2019).

“The results show that the proposed home-cage monitoring system can provide animal activity metrics that are comparable to the ones derived via a conventional video tracking system, with the advantage of system scalability, limited amount of both data generated, and computational capabilities required to derive metrics.” (Iannello F. 2018).

”In summary, our results indicate that, for the measures recorded, there was no significant impact on the behaviour and welfare of low frequency EMF exposure experienced continuously over a six-week period as an integrated part of this IVC housing system for BALB/cAnNCrl and C57BL/6NCrl mice.” (Burman et al. 2018).

References:

Goltstein, PM et al 2018: Food and water restriction lead to differential learning behaviors in a head-fixed two-choice visual discrimination task for mice. PLoS ONE 13 (9): 1-19

Giles, JM et al 2018: Effect of Environmental Enrichment on Aggression in BALB/cJ and BALB/cByJ Mice Monitored by Using an Automated System. JAALAS 57 (3): 236-243

Pernold, K et al 2019: Towards large scale automated cage monitoring – Diurnal rhythm and impact of interventions on in-cage activity of C57BL/6J mice recorded 24/7 with a non-disrupting capacitive-based technique. PLoS ONE 14 (2): 1-20

Iannello, F 2018: Non-intrusive high throughput automated data collection from the home cage. Heliyon 5: e01454

Burman, O et al 2018: The effect of exposure to low frequency electromagnetic fields (EMF) as an integral part of the housing system on anxiety-related behaviour, cognition and welfare in two strains of laboratory mouse. PLoS ONE 13 (5): e0197054

Additional reading:

Electromagnetic fields (EMF) – Clearing up a natural phenomena

Home-Cage Monitoring and Its Effects on Research Capability and Outcomes - An Expert Panel Discussion

Recordati, C et al 2019: Long-Term Study on the Effects of Housing C57BL/6NCRL Mice in Cages Equipped with Wireless Technology Generating Extremely Low-Intensity Electromagnetic Fields. Toxicologic Pathology: https://doi.org/10.1177/0192623319852353 

Technology that remotely tracks mouse cages can reduce animal stress and reveal environmental factors that undermine the reliability of experimental data. Experiences from leading universities