The primary diagnosis of non-Hodgkin lymphoma/leukemia by fine-needle aspiration (FNA) is still controversial and relatively underused. We evaluated our FNA experience with lymphomas using the revised European-American classification of lymphoid neoplasms to determine the reliability of FNA when combined with flow cytometry in the diagnosis of lymphoma, the types of diagnoses made, and the limitations of this technique. Slides and reports from all lymph node and extranodal FNAs performed during the period January 1, 1993, to December 31, 1998, with a diagnosis of lymphoma or benign lymphoid process were reviewed. There were 290 aspirates from 275 patients. These included 158 cases of lymphoma, of which 86 (54.4%) were primary and 72 (45.6%) were recurrent. There were 44 aspirates suggestive of lymphoma and 81 benign/reactive diagnoses. With diagnoses suggestive of lymphoma considered as positive for lymphoma, levels of diagnostic sensitivity and specificity were 95% and 85%, respectively. Specificity was 100% when only definitive diagnoses of lymphoma were considered. Clearly, FNA and immunophenotyping by flow cytometry are complementary and obviate a more invasive open biopsy for many patients with lymphadenopathy.

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

The COVID-19 pandemic has been threatening the healthcare and socioeconomic systems of entire nations. While population-based surveys to assess the distribution of SARS-CoV-2 infection have become a priority, pre-existing longitudinal studies are ideally suited to assess the determinants of COVID-19 onset and severity.The Cooperative Health Research In South Tyrol (CHRIS) study completed the baseline recruitment of 13,393 adults from the Venosta/Vinschgau rural district in 2018, collecting extensive phenotypic and biomarker data, metabolomic data, densely imputed genotype and whole-exome sequencing data.Based on CHRIS, we designed a prospective study, called CHRIS COVID-19, aimed at: 1) estimating the incidence of SARS-CoV-2 infections; 2) screening for and investigating the determinants of incident infection among CHRIS participants and their household members; 3) monitoring the immune response of infected participants prospectively.An online screening questionnaire was sent to all CHRIS participants and their household members. A random sample of 1450 participants representative of the district population was invited to assess active (nasopharyngeal swab) or past (serum antibody test) infections. We prospectively invited for complete SARS-CoV-2 testing all questionnaire completers gauged as possible cases of past infection and their household members. In positive tested individuals, antibody response is monitored quarterly for one year. Untested and negative participants receive the screening questionnaire every four weeks until gauged as possible incident cases or till the study end.Originated from a collaboration between researchers and community stakeholders, the CHRIS COVID-19 study aims at generating knowledge about the epidemiological, molecular, and genetic characterization of COVID-19 and its long-term sequelae.

In this study we assess a flow cytometric gating method and its correlation with a concurrent manual bone marrow differential in abnormal marrows. Like normal bone marrow cells, leukemic blasts fall into discrete areas when a cytogram of CD 45 expression and Right Angle Light Scatter is plotted in Log scale. We studied 50 specimens with a suspected diagnosis of leukemia. Gates were set on the eight discrete clusters typically found in normal bone marrow. We employed these gates to determine the differential of the abnormal bone marrows. Our results show a high correlation between the flow differential and the manual differential with the following r values: blasts 0.875; promyelocytes 0.914; myeloid precursors 0.879; neutrophils 0.776; lymphocytes 0.707; monocytes 0.913; and erythroid precursors 0.873. In addition some leukemic infiltrates appear to produce a characteristic pattern with the CD 45 vs. RALS cytogram. In this study, there is a total of 6 cases (3 false positives and 3 false negatives) where the flow differential does not render the same diagnosis as the manual differential, and 4 cases in which there is evidence of marked peripheral contamination leading to disagreement between the two methods. This method provides a relatively easy and powerful tool which can be applied to all bone marrow specimens that undergo flow cytometric analysis. It can greatly enhance the identification and lineage assessment of leukemic blasts in the bone marrow. The correlation with a manual differential is high, and this gating method may provide an inexpensive and easy means of obtaining an automated bone marrow differential.