Health and longevity in all organisms are strongly influenced by the environment. To fully understand how environmental factors interact with genetic and stochastic factors to modulate the aging process, it is crucial to precisely control environmental conditions for long-term studies. In the commonly used model organism Caenorhabditis elegans, existing assays for healthspan and lifespan have inherent limitations, making it difficult to perform large-scale longitudinal aging studies under precise environmental control. To address these constraints, we developed the Health and Lifespan Testing Hub (HeALTH), an automated, microfluidic-based system for robust longitudinal behavioral monitoring. Our system provides long-term (i.e. entire lifespan) spatiotemporal environmental control. We demonstrate healthspan and lifespan studies under a variety of genetic and environmental perturbations while observing how individuality plays a role in the aging process. This system is generalizable beyond aging research, particularly for short- or long-term behavioral assays, and could be adapted for other model systems.
BACKGROUND: Rituximab is a top-selling biologic that was first approved by the FDA in 1997 for a non-Hodgkin lymphoma orphan indication. It has since been approved for additional orphan indications, with rheumatoid arthritis as the only FDA-approved, nonorphan indication. Evidence suggests that rituximab is frequently used off-label, but information on its use over time and indications for use in the United States is limited. OBJECTIVE: To assess incident rituximab use over time in an integrated health care delivery system. METHODS: This was a cross-sectional, retrospective study. Data were collected from administrative databases and manual chart reviews. Patients who received their first rituximab infusion between October 1, 2009, and December 31, 2017, and who were not a part of a clinical trial were included. Indication for use (FDA-approved orphan/nonorphan, off-label) was determined. Proportions of use were assessed over time. Multivariable logistic regression modeling was performed to assess factors associated with receiving rituximab for an FDA-approved indication.RESULTS: A total of 1,674 patients were included. The majority (66.4%) of patients had an FDA-approved indication, with lymphoma being the most common approved indication (66.4%). The most common indication for off-label use was neurologic conditions (72.7%), predominantly demyelinating diseases. Off-label indication use increased from 1.2% in 2009 to 55.6% in 2017. Factors associated with rituximab use for an FDA-approved indication included increased age (adjusted odds ratio [AOR] = 1.05, 95% CI = 1.04-1.07) and increased burden of chronic disease (chronic disease score: AOR = 1.07, 95% CI = 1.02-1.12; Charlson Comorbidity Index score: AOR = 3.52, 95% CI = 3.03-4.10).CONCLUSIONS: Off-label use of rituximab grew dramatically over the course of the study. With the recent FDA approval of the rituximab biosimilar and its expected lower price, off-label use will likely continue to rise. Opportunities for cost savings and to ensure appropriate use of these medications should be evaluated.
Robust and accurate behavioral tracking is essential for ethological studies. Common methods for tracking and extracting behavior rely on user adjusted heuristics that can significantly vary across different individuals, environments, and experimental conditions. As a result, they are difficult to implement in large-scale behavioral studies with complex, heterogenous environmental conditions. Recently developed deep-learning methods for object recognition such as Faster R-CNN have advantages in their speed, accuracy, and robustness. Here, we show that Faster R-CNN can be employed for identification and detection of Caenorhabditis elegans in a variety of life stages in complex environments. We applied the algorithm to track animal speeds during development, fecundity rates and spatial distribution in reproductive adults, and behavioral decline in aging populations. By doing so, we demonstrate the flexibility, speed, and scalability of Faster R-CNN across a variety of experimental conditions, illustrating its generalized use for future large-scale behavioral studies.
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