Biologic manufacturing processes typically employ clarification technologies like depth filtration to remove insoluble and soluble impurities. Conventional depth filtration media used in these processes contain naturally-derived components like diatomaceous earth and cellulose. These components may introduce performance variability and contribute extractable/leachable components like beta-glucans that could interfere with limulus amebocyte lysate endotoxin assays. Recently a novel, all-synthetic depth filtration media is developed (Millistak+ HC Pro X0SP) that may improve process consistency, efficiency, and drug substance product quality by reducing soluble process impurities. This new media is evaluated against commercially available benchmark filters containing naturally-derived components (Millistak+ HC X0HC and B1HC). Using model proteins, the synthetic media demonstrates increased binding capacity of positively charged proteins (72-126 mg g media) compared to conventional media (0.3-8.6 mg g media); and similar values for negatively charged species (1.3-5.6 mg g media). Several CHO-derived monoclonal antibodies (mAbs) or mAb-like molecules are also evaluated. The X0SP filtration performance behaves similarly to benchmarks, and exhibits improved HCP reduction (at least 50% in 55% of cases tested). X0SP filtrates contained increased silicon extractables relative to benchmarks, but these were readily removed downstream. Finally, the X0SP devices demonstrates suitable lot-to-lot robustness when specific media components are altered intentionally to manufacturing specification limits.
The first fully integrated 2D CMOS imaging sensor with on-chip signal processing for applications in laser Doppler blood flow (LDBF) imaging has been designed and tested. To obtain a space efficient design over 64 × 64 pixels means that standard processing electronics used off-chip cannot be implemented. Therefore the analog signal processing at each pixel is a tailored design for LDBF signals with balanced optimization for signal-to-noise ratio and silicon area. This custom made sensor offers key advantages over conventional sensors, viz. the analog signal processing at the pixel level carries out signal normalization; the AC amplification in combination with an anti-aliasing filter allows analog-to-digital conversion with a low number of bits; low resource implementation of the digital processor enables on-chip processing and the data bottleneck that exists between the detector and processing electronics has been overcome. The sensor demonstrates good agreement with simulation at each design stage. The measured optical performance of the sensor is demonstrated using modulated light signals and in vivo blood flow experiments. Images showing blood flow changes with arterial occlusion and an inflammatory response to a histamine skin-prick demonstrate that the sensor array is capable of detecting blood flow signals from tissue.
What is believed to be the first fully integrated two-dimensional complementary metal oxide semiconductor (CMOS) imaging array for laser Doppler blood flow imaging is demonstrated. The sensor has 64×64 pixels and includes both analog and digital on-chip processing electronics. This offers several potential advantages over commercial sensors as the processing is tailored to the signals of interest and the data bottleneck that exists between the sensor and processing electronics is overcome. To obtain a space efficient design over 64×64 pixels means that standard processing electronics used off-chip cannot be implemented. Images of both simulated blood flow responses and a blood flow occlusion test demonstrate the capability.
We introduce a new benchmark dataset, namely VinDr-RibCXR, for automatic segmentation and labeling of individual ribs from chest X-ray (CXR) scans. The VinDr-RibCXR contains 245 CXRs with corresponding ground truth annotations provided by human experts. A set of state-of-the-art segmentation models are trained on 196 images from the VinDr-RibCXR to segment and label 20 individual ribs. Our best performing model obtains a Dice score of 0.834 (95% CI, 0.810-0.853) on an independent test set of 49 images. Our study, therefore, serves as a proof of concept and baseline performance for future research.
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