New horizons: expert systems for flow cytometry

A global effort to dissect the human genetic basis of resistance to SARS-CoV-2 infection

SARS-CoV-2 infections display tremendous interindividual variability, ranging from asymptomatic infections to life-threatening disease. Inborn errors of, and autoantibodies directed against, type I interferons (IFNs) account for about 20% of critical COVID-19 cases among SARS-CoV-2-infected individuals. By contrast, the genetic and immunological determinants of resistance to infection per se remain unknown. Following the discovery that autosomal recessive deficiency in the DARC chemokine receptor confers resistance to Plasmodium vivax, autosomal recessive deficiencies of chemokine receptor 5 (CCR5) and the enzyme FUT2 were shown to underlie resistance to HIV-1 and noroviruses, respectively. Along the same lines, we propose a strategy for identifying, recruiting, and genetically analyzing individuals who are naturally resistant to SARS-CoV-2 infection.

A knowledge-based system for the interpretation of flow cytometry data in leukemias and lymphomas

Flow cytometry immunophenotyping and analysis of DNA ploidy and proliferative activity have become extremely helpful techniques for diagnosing and subclassifying hematopoietic cell populations in a modern, full-service hematopathology laboratory. The number of physicians with special training in the interpretation of these studies is limited. A knowledge-based computer system has been designed to aid in the interpretation of immunophenotyping and DNA flow cytometry results in hematopoietic disease. The system, known as "Professor Fidelio," is a heuristic classification system that reasons on the basis of defined diagnostic patterns. In this study, Fidelio was tested as a stand-alone system on 366 specimens from two large tertiary medical centers. Fidelio's interpretation was considered to be appropriate in all cases. In 300 of 366 (82%) specimens, the system's interpretation agreed with the diagnosis of record. Many of the disagreements could be traced to errors in the recording of the original diagnosis and minor differences in diagnostic criteria between Fidelio's knowledge base and the criteria in use at the medical centers. When used in a stand-alone mode, Fidelio's interpretation was less specific than the diagnosis of record in certain lymphoproliferative disorders that require morphologic information for subclassification. Professor Fidelio is one module in a workstation for the diagnostic hematology laboratory. This workstation is designed for interpretive reporting, education, and database functions for clinical research. Clinical and morphologic information are shared between Fidelio and the other modules for peripheral blood analysis, bone marrow morphology, and lymph node interpretation by means of a relational database. The system will be useful in hospitals that lack individuals specially trained in flow cytometry.(ABSTRACT TRUNCATED AT 250 WORDS)