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Background: Preparation of del-Nido cardioplegia and its delivery technique can cause significant hemodilution. The resultant effects from hemodilution are largely proportionate to the use of a dual circuit. We opted for a custom-disposable single cardioplegia circuit instead of a dual circuit., Methods: We describe an alternative technique of del-Nido cardioplegia delivery and initial clinical experience with it at National University Hospital, Singapore. This is a retrospective analysis of data from January 2017 to April 2019, comprising of 177 patients of heart surgery and reflecting a single center database survey under the National Health Care Group., Results: Of the 177 patients who underwent surgery with del-Nido cardioplegia, 76 (42.9%) were valve-only procedures and 5 (2.8%) were coronary artery bypass graft-only procedures. Ultrafiltration was utilized in 132 (62.6%) patients, whereas filtrate volume was 2200 [150-9500] mL. The alternative technique of del-Nido cardioplegia delivery adopted by National University Hospital advocates for a single pump, single circuit system. The retrospective institutional data highlighted safe delivery of del-Nido cardioplegia using this technique in a range of procedures., Conclusion: Besides the safe delivery of del-Nido cardioplegia, the National University Hospital Technique reduces hemodilution and provides other technical benefits including a steeper temperature gradient, modification of circuit configuration to deliver another cardioplegia while on bypass, as well as re-configuration of clamps to spike the base solution.

Automated image processing is a critical and often rate-limiting step in high-content screening (HCS) workflows. The authors describe an open-source imaging-statistical framework with emphasis on segmentation to identify novel selective pharmacological inducers of autophagy. They screened a human alveolar cancer cell line and evaluated images by both local adaptive and global segmentation. At an individual cell level, region-growing segmentation was compared with histogram-derived segmentation. The histogram approach allowed segmentation of a sporadic-pattern foreground and hence the attainment of pixel-level precision. Single-cell phenotypic features were measured and reduced after assessing assay quality control. Hit compounds selected by machine learning corresponded well to the subjective threshold-based hits determined by expert analysis. Histogram-derived segmentation displayed robustness against image noise, a factor adversely affecting region growing segmentation.