Automated Cytometric Gating with Human-Level Performance Using Bivariate Segmentation.

Recent advances in cytometry technology have enabled high-throughput data collection with multiple single-cell protein expression measurements. The significant biological and technical variance between samples in cytometry has long posed a formidable challenge during the gating process, especially for the initial gates which deal with unpredictable events, such as debris and technical artifacts. Even with the same experimental machine and protocol, the target population, as well as the cell population that needs to be excluded, may vary across different measurements. To address this challenge and mitigate the labor-intensive manual gating process, we propose a deep learning framework UNITO to rigorously identify the hierarchical cytometric subpopulations. The UNITO framework transformed a cell-level classification task into an image-based semantic segmentation problem. For reproducibility purposes, the framework was applied to three independent cohorts and successfully detected initial gates that were required to identify single cellular events as well as subsequent cell gates. We validated the UNITO framework by comparing its results with previous automated methods and the consensus of at least four experienced immunologists. UNITO outperformed existing automated methods and differed from human consensus by no more than each individual human. Most critically, UNITO framework functions as a fully automated pipeline after training and does not require human hints or prior knowledge. Unlike existing multi-channel classification or clustering pipelines, UNITO can reproduce a similar contour compared to manual gating for each intermediate gating to achieve better interpretability and provide post hoc visual inspection. Beyond acting as a pioneering framework that uses image segmentation to do auto-gating, UNITO gives a fast and interpretable way to assign the cell subtype membership, and the speed of UNITO will not be impacted by the number of cells from each sample. The pre-gating and gating inference takes approximately 2 minutes for each sample using our pre-defined 9 gates system, and it can also adapt to any sequential prediction with different configurations.

[The effects of hinge structure on the biological activity of antimicrobial peptides and its application in molecular design: a review].

The hinge structure, also known as hinge region or bend, is a special structure found in some antimicrobial peptides. Most studies on antimicrobial peptides focused on the standard secondary structure of α-helix and ß-sheet, while the hinge structure and its functions were rarely studied. The hinge structure confers the antimicrobial peptides an improved structural flexibility, which may promote their disruptive effect on bacterial membrane or their binding efficiency to the intracellular targets, thus resulting in a higher antibacterial activity. Meanwhile, the hinge structure may reduce the structural rigidity, which may eliminate the cytotoxicity of antimicrobial peptides to eukaryotic cells. This article reviews the structural characteristics of the hinge structure, its effects on the biological activity of antimicrobial peptides and application in the molecular design, with the aim to provide a reference for the design and development of new antimicrobial peptides.

Palladium/Brønsted-Acid-Catalyzed Diastereoselective Cyclization with Chiral Sulfinamides as Nucleophiles.

This article reports diastereoselective cyclization with chiral sulfinamides as nucleophiles in two reaction pathways: (1) intramolecular allylic substitution and (2) sequential aerobic oxidation with aza-Michael addition. These reactions were enabled by synergistic palladium and Brønsted acid catalysis and produced chiral isoindolines with good yields of 55-92% and high diastereoselectivities of 10:1 to >20:1 dr.

A clinical study on the treatment of severe hepatitis by a combined artificial liver.

BACKGROUND/AIMS: To focus on determining efficacy and safety of the combined artificial liver in treating severe hepatitis. METHODOLOGY: Ten patients with acute and chronic severe hepatitis were chosen for this study. A total of 19 cases were treated with a combination of selective plasma absorption and selective plasma exchange and 1 was treated with plasma perfusion absorption. Clinical symptoms and physical signs were observed. In addition, the changes in biochemical markers, coagulation function, and aminogram before and after selective plasma perfusion absorption treatment were compared. RESULTS: Ten patients were able to tolerate the treatment; 8 patients were cured or improved whereas 2 worsened. Statistically significant differences (p<0.05) were observed in the serum levels of total bilirubin, direct bilirubin, indirect bilirubin, alanine transaminase, aspartate transaminase, alkaline phosphatase, total bile acids, albumin and globulin before and after the selective perfusion absorption treatment. In contrast, no statistically significant differences (p>0.05) were observed in the serum concentrations of potassium, sodium, creatinine and urea nitrogen, as well as in prothrombin time, partial thromboplastin time and aminogram changes. CONCLUSIONS: The new perfusion absorber can markedly improve hepatic function without influencing the metabolism of micro-molecules and coagulation factors.