An integrative pharmacological approach to radio telemetry and blood sampling in pharmaceutical drug discovery and safety assessment

<p>Abstract</p> <p>Background</p> <p>A successful integration of the automated blood sampling (ABS) and telemetry (ABST) system is described. The new ABST system facilitates concomitant collection of physiological variables with blood and urine samples for determination of drug concentrations and other biochemical measures in the same rat without handling artifact.</p> <p>Method</p> <p>Integration was achieved by designing a 13 inch circular receiving antenna that operates as a plug-in replacement for the existing pair of DSI's orthogonal antennas which is compatible with the rotating cage and open floor design of the BASi Culex^®ABS system. The circular receiving antenna's electrical configuration consists of a pair of electrically orthogonal half-toroids that reinforce reception of a dipole transmitter operating within the coil's interior while reducing both external noise pickup and interference from other adjacent dipole transmitters.</p> <p>Results</p> <p>For validation, measured baclofen concentration (ABST vs. satellite (μM): 69.6 ± 23.8 vs. 76.6 ± 19.5, p = NS) and mean arterial pressure (ABST vs. traditional DSI telemetry (mm Hg): 150 ± 5 vs.147 ± 4, p = NS) variables were quantitatively and qualitatively similar between rats housed in the ABST system and traditional home cage approaches.</p> <p>Conclusion</p> <p>The ABST system offers unique advantages over traditional between-group study paradigms that include improved data quality and significantly reduced animal use. The superior within-group model facilitates assessment of multiple physiological and biochemical responses to test compounds in the same animal. The ABST also provides opportunities to evaluate temporal relations between parameters and to investigate anomalous outlier events because drug concentrations, physiological and biochemical measures for each animal are available for comparisons.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing