Citations

Abstract Objectives Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. Methods An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti–infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. Results Performance of the ML models was verified against the XN-31 software using P falciparum–infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). Conclusions This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.

Objective To evaluate maternal risk factors associated with chronic villitis of unknown etiology (VUE) and to describe cooccurring placental pathologies. Study Design A retrospective case–control study was conducted using placental pathology records from deliveries ≥ 20 weeks between 2010 and 2018. Cases were placentas with documented chronic villitis without infectious cause, hereafter called VUE. Controls were placentas without this diagnosis, matched to the cases 2:1. Maternal and neonatal demographic and clinical data were collected. Descriptive statistics are reported with Fisher's exact test or a chi-squared test, as appropriate, and multivariable conditional logistic regression was conducted. Results Our study included 352 cases with VUE and 657 controls. A diagnosis of gestational diabetes (p = 0.03) and gestational hypertension (p = 0.06) was 1.5 times more likely to occur in those with a VUE diagnosis. A trend was also seen for chronic hypertension (odds ratio [OR] = 1.7, p = 0.07) and preeclampsia (OR = 1.5, p = 0.09) compared with controls. Placentas with VUE, specifically high-grade VUE, were more likely to be small for gestational age (p = 0.01), and to be diagnosed with other placental findings including lymphoplasmacytic or chronic deciduitis (p < 0.01), maternal (p < 0.01) and fetal vascular malperfusion (p = 0.02), and chorionitis (acute or chronic; p < 0.01). Conclusion Gestational diabetes and hypertension were associated with a diagnosis of VUE, and overall, VUE placentas have more abnormal placental findings compared with control. Understanding VUE risk factors may facilitate prenatal care strategies and counseling to achieve the best outcomes for pregnant patients and their neonates. Key Points

Abstract Background The worldwide prevalence of cytomegalovirus (CMV) infection is estimated to be 83-100%.1,2 In pregnancy, transmission to the fetus can occur through the placenta with 0.4-2.3% of all newborns affected.3 The phenotype of congenital CMV (cCMV) has a spectrum of severity; 10-20% of exposed neonates show severe clinical manifestations.4 CMV has a wide range of tropism and can infect epithelial, myeloid, and endothelial cells. However, the cellular mechanisms of cCMV infection, and how they influence disease severity in exposed fetuses during gestation are not fully outlined,5 Here, we used the NanoString™ Digital Spatial Profiler (DSP) to characterize the cell-specific proteomic profile in placental tissue from infants with cCMV. Methods Placental tissue from 5 CMV-affected and 4 CMV-unaffected pregnancies were retrieved from the Mayo Clinic tissue repository. Immune cells were identified by CD45 positivity and cytotrophoblasts (CTBs) identified by CK7. The cell-specific proteome of these tissues was identified using the NanoString™ immune cell and cell death panels. Data was analyzed by NanoString™ GeoMx technology. Results Unsupervised clustering illustrated a distinct proteome in the immune cell compartment of CMV-positive compared to CMV-negative placental tissue (Figure 1). Linear Mixed Model (LMM) analysis revealed that immune cell related proteins were significantly overexpressed in CMV+ tissue as were four proteins associated with cell death: BIM, BCLXL, PARP, neurofibromin (Figure 2). Clustering did not demonstrate a distinct proteome in the CTB compartment (Figure 3), but LMM showed that two immune markers (CD44 and HLA-DR) and a cell death protein (BCL6) were overexpressed by CTBs in CMV+ tissue (Figure 4). The proteome of immune cells, and to a lesser extent in CTBs in CMV-infected placentae differs from CMV-uninfected placentae with overexpression of proteins associated with immune activation and cell death. With new screening initiatives set to improve identification of cCMV, our results provide a starting point to identify specific biomarkers and immune pathways associated with disease severity. These biomarkers and pathways may serve as novel antiviral targets in the treatment and management of cCMV. The proteome of immune cells, and to a lesser extent in CTBs in CMV-infected placentae differs from CMV-uninfected placentae with overexpression of proteins associated with immune activation and cell death. With new screening initiatives set to improve identification of cCMV, our results provide a starting point to identify specific biomarkers and immune pathways associated with disease severity. These biomarkers and pathways may serve as novel antiviral targets in the treatment and management of cCMV. Fig 3 - The proteome of immune cells, and to a lesser extent in CTBs in CMV-infected placentae differs from CMV-uninfected placentae with overexpression of proteins associated with immune activation and cell death. With new screening initiatives set to improve identification of cCMV, our results provide a starting point to identify specific biomarkers and immune pathways associated with disease severity. These biomarkers and pathways may serve as novel antiviral targets in the treatment and management of cCMV. Fig 4 - Volcano plot illustrating the differential proteomic expression in CK7+ tissue compartment of CMV-positive placental tissue compared to CMV-negative placental tissue. Conclusion The proteome of immune cells, and to a lesser extent in CTBs in CMV-infected placentae differs from CMV-uninfected placentae with overexpression of proteins associated with immune activation and cell death. With new screening initiatives set to improve identification of cCMV, our results provide a starting point to identify specific biomarkers and immune pathways associated with disease severity. These biomarkers and pathways may serve as novel antiviral targets in the treatment and management of cCMV. Disclosures Mark R. Schleiss, MD, Moderna Vaccines: Grant Support to my Division but no personal honoraria

Abstract Background Deep convolutional neural networks (DNN) have been successfully applied to the detection and segmentation of objects in gigapixel digital slides. In this study, trained laboratory technologists utilized a DNN-assisted digital workflow (Techcyte, Inc.; Orem, UT) to evaluate clinical stool specimens for the presence and identification of protozoan parasites. Methods Slides were prepared from 142 stool specimens by spreading a thin layer of concentrated stool onto a slide, staining with modified-trichrome, and coverslipping with permanent mounting medium. Slides were then scanned using a NanoZoomer 360 digital slide scanner (Hamamatsu Photonics K.K., Japan) at 40x apparent magnification and prospectively analyzed by the Techcyte DNN. Objects of interest were identified and classified by the DNN (protozoa, leukocytes, erythrocytes) and presented to a laboratory technologist for interpretation. (See example image below.) DNN-assisted results were compared to those from conventional microscopic examination. To determine analytical sensitivity, slides were prepared from serial, 2-fold dilutions of 3 unique stool specimens (6 slides at each dilution/specimen) and analyzed by the DNN-assisted method and conventional microscopy. Example of Techcyte DNN analysis review screen Objects of interest most likely to be significant are presented to the technologist for review in an intuitive web-based format. Parasites are confirmed as present or absent by a trained technologist after reviewing these images. Results Digital analysis was highly reproducible, showing 100% slide level agreement (positive/negative) within and between runs using positive and negative slides. There was 98.7% and 98.3% positive and negative agreement, respectively, between conventional and DNN-assisted results. Of the 142 slides conventional microscopy detected parasites in 82, while the DNN-assisted method detected 83. The DNN-assisted method also detected more slides with leukocytes (29 vs 24) and erythrocytes (12 vs 8). All discrepant results were confirmed by repeat conventional microscopy. Analytical sensitivity of the DNN-assisted method was equivalent or superior to conventional microscopy review, detecting down to 1:64 dilutions in 6/6 slides per specimen (conventional = 1:32 to 1:64), extending for some slides to detection at dilutions of 1:2048. Conclusion The DNN-assisted method facilitates rapid screening of negative specimens, while providing sensitive and accurate detection and classification of protozoa. Disclosures All Authors: No reported disclosures

ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has had an unprecedented impact on human health and highlights the need for genomic epidemiology studies to increase our understanding of virus evolution and spread and to inform policy decisions. We sequenced viral genomes from over 22,000 patient samples tested at Mayo Clinic Laboratories between 2020 and 2022 and used Bayesian phylodynamics to describe county and regional spread in Minnesota. The earliest calculated introduction into Minnesota was to Hennepin County from a domestic source around 22 January 2020; 6 weeks before the first confirmed case in the state. This led to the virus spreading to Northern Minnesota and, eventually, the rest of the state. International introductions were most abundant in Hennepin (home to the Minneapolis/St. Paul International Airport) totaling 45 (out of 107) over the 2-year period. Southern Minnesota counties were most common for domestic introductions, with 19 (out of 64), potentially driven by bordering states such as Iowa and Wisconsin as well as Illinois, which is nearby. Hennepin also was, by far, the most dominant source of in-state transmissions to other Minnesota locations ( n = 772) over the 2-year period. We also analyzed the diversity of the location source of SARS-CoV-2 viruses in each county and noted the timing of state-wide policies as well as trends in clinical cases. Neither the number of clinical cases nor the major policy decisions, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, appeared to have an impact on virus diversity across each individual county. IMPORTANCE We analyzed over 22,000 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes of patient samples tested at Mayo Clinic Laboratories during a 2-year period in the COVID-19 pandemic, which included Alpha, Delta, and Omicron variants of concern to examine the roles and relationships of Minnesota virus transmission. We found that Hennepin County, the most populous county, drove the transmission of SARS-CoV-2 viruses in the state after including the formation of earlier clades including 20A, 20C, and 20G, as well as variants of concern Alpha and Delta. We also found that Hennepin County was the source for most of the county-to-county introductions after an initial predicted introduction with the virus in early 2020 from an international source, while other counties acted as transmission “sinks.” In addition, major policies, such as the end of the lockdown period in 2020 or the end of all restrictions in 2021, did not appear to have an impact on virus diversity across individual counties.

Abstract Antibiotics are critical complements to the immune response in combatting infection. Anaerobic bacteria are increasingly common causes of infection, and antibiotic resistance of anaerobes has been rising. Most laboratories do not perform antimicrobial susceptibility testing of anaerobes, and even fewer have compiled antibiogram data. Here we investigated antimicrobial susceptibility results of anaerobes from January 2012 to December 2021 obtained from inpatients at Mayo Clinic using reference agar dilution testing. Actinomyces (60–70 isolates annually), Bacteroides fragilis (30–80), Finegoldia magna (40–60), and Cutibacterium acnes (70–110) were included. Penicillin, clindamycin, metronidazole, ertapenem, piperacillin-tazobactam, and moxifloxacin were tested. Clinical and Laboratory Standards (CLSI) breakpoints were followed. B. fragilis showed ≥90% and Actinomyces 100% susceptibility to piperacillin-tazobactam and ertapenem. All F. magna and &gt;95% B. fragilis were susceptible to metronidazole. The most striking fluctuations in percent susceptibility from year to year were observed for clindamycin, with Actinomyces ranging from 66–79%, B. fragilis 34–67%, and F. magna 47–85%. In the absence of local anaerobe antibiograms, providers may refer to such antibiogram data to guide empiric anti-anaerobic therapy at their institutions. In conclusion, anaerobe antibiotic susceptibility patterns must be monitored to ensure appropriate antibiotic empiric therapy and stewardship.

Sequencing is increasingly used for infective endocarditis (IE) diagnosis. Here, the performance of 16S rRNA gene PCR/sequencing of heart valves utilized in routine clinical practice was compared with conventional IE diagnostics.