Singleton exome sequencing of 90 fetuses with ultrasound anomalies revealing novel disease-causing variants and genotype–phenotype correlations

Exome sequencing has been increasingly implemented in prenatal genetic testing for fetuses with morphological abnormalities but normal rapid aneuploidy detection and microarray analysis. We present a retrospective study of 90 fetuses with different abnormal ultrasound findings, in which we employed the singleton exome sequencing (sES; 75 fetuses) or to a lesser extent (15 fetuses) a multigene panel analysis of 6713 genes as a primary tool for the detection of monogenic diseases. The detection rate of pathogenic or likely pathogenic variants in this study was 34.4%. The highest diagnostic rate of 56% was in fetuses with multiple anomalies, followed by cases with skeletal or renal abnormalities (diagnostic rate of 50%, respectively). We report 20 novel disease-causing variants in different known disease-associated genes and new genotype–phenotype associations for the genes KMT2D, MN1, CDK10, and EXOC3L2. Based on our data, we postulate that sES of fetal index cases with a concurrent sampling of parental probes for targeted testing of the origin of detected fetal variants could be a suitable tool to obtain reliable and rapid prenatal results, particularly in situations where a trio analysis is not possible.

Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework

Global and national surveillance of SARS-CoV-2 epidemiology is mostly based on targeted schemes focused on testing individuals with symptoms. These tested groups are often unrepresentative of the wider population and exhibit test positivity rates that are biased upwards compared with the true population prevalence. Such data are routinely used to infer infection prevalence and the effective reproduction number, Rt, which affects public health policy. Here, we describe a causal framework that provides debiased fine-scale spatiotemporal estimates by combining targeted test counts with data from a randomized surveillance study in the United Kingdom called REACT. Our probabilistic model includes a bias parameter that captures the increased probability of an infected individual being tested, relative to a non-infected individual, and transforms observed test counts to debiased estimates of the true underlying local prevalence and Rt. We validated our approach on held-out REACT data over a 7-month period. Furthermore, our local estimates of Rt are indicative of 1-week- and 2-week-ahead changes in SARS-CoV-2-positive case numbers. We also observed increases in estimated local prevalence and Rt that reflect the spread of the Alpha and Delta variants. Our results illustrate how randomized surveys can augment targeted testing to improve statistical accuracy in monitoring the spread of emerging and ongoing infectious disease.

Modeling for COVID-19 college reopening decisions: Cornell, a case study

We consider epidemiological modeling for the design of COVID-19 interventions in university populations, which have seen significant outbreaks during the pandemic. A central challenge is sensitivity of predictions to input parameters coupled with uncertainty about these parameters. Nearly 2 y into the pandemic, parameter uncertainty remains because of changes in vaccination efficacy, viral variants, and mask mandates, and because universities’ unique characteristics hinder translation from the general population: a high fraction of young people, who have higher rates of asymptomatic infection and social contact, as well as an enhanced ability to implement behavioral and testing interventions. We describe an epidemiological model that formed the basis for Cornell University’s decision to reopen for in-person instruction in fall 2020 and supported the design of an asymptomatic screening program instituted concurrently to prevent viral spread. We demonstrate how the structure of these decisions allowed risk to be minimized despite parameter uncertainty leading to an inability to make accurate point estimates and how this generalizes to other university settings. We find that once-per-week asymptomatic screening of vaccinated undergraduate students provides substantial value against the Delta variant, even if all students are vaccinated, and that more targeted testing of the most social vaccinated students provides further value.

COVID-19 in Low- and Middle-Income Countries (LMICs): A Narrative Review from Prevention to Vaccination Strategy

The management of the COVID-19 pandemic represents a challenging process, especially for low- and middle-income countries (LMICs) due to the serious economic and health resource problems it generates. In this article, we assess COVID-19 situation in LMICs and outline emerging problems and possible solutions. The prevention and control of COVID-19 would be based on focused tests exploiting those systems (e.g., GeneXpert®) already used in other scenarios. This would be less stressful for the healthcare system in LMICs. Avoiding close contact with people suffering from acute respiratory infections, frequent handwashing, and avoiding unprotected contact with farm or wild animals are recommended infection control interventions. The appropriate use of personal protective equipment (PPE) is required, despite its procurement being especially difficult in LMICs. Patients’ triage should be based on a simple and rapid logarithm to decide who requires isolation and targeted testing for SARS-CoV-2. Being able to estimate which patients will develop severe disease would allow hospitals to better utilize the already limited resources more effectively. In LMICs, laboratories are often in the capital cities; therefore, early diagnosis and isolation become difficult. The number of ICU beds is often insufficient, and the equipment is often old and poorly serviced. LMICs will need access to COVID-19 treatments at minimal prices to ensure that all who need them can be treated. Year-to-date, different vaccines have been approved and are currently available. The main obstacle to accessing them is the limited ability of LMICs to purchase significant quantities of the vaccine.

Detecting genes associated with antimicrobial resistance and pathogen virulence in three New Zealand rivers

The emergence of clinically significant antimicrobial resistance (AMR) in bacteria is frequently attributed to the use of antimicrobials in humans and livestock and is often found concurrently with human and animal pathogens. However, the incidence and natural drivers of antimicrobial resistance and pathogenic virulence in the environment, including waterways and ground water, are poorly understood. Freshwater monitoring for microbial pollution relies on culturing bacterial species indicative of faecal pollution, but detection of genes linked to antimicrobial resistance and/or those linked to virulence is a potentially superior alternative. We collected water and sediment samples in the autumn and spring from three rivers in Canterbury, New Zealand; sites were above and below reaches draining intensive dairy farming. Samples were tested for loci associated with the AMR-related group 1 CTX-M enzyme production (blaCTX-M) and Shiga toxin producing Escherichia coli (STEC). The blaCTX-M locus was only detected during spring and was more prevalent downstream of intensive dairy farms. Loci associated with STEC were detected in both the autumn and spring, again predominantly downstream of intensive dairying. This cross-sectional study suggests that targeted testing of environmental DNA is a useful tool for monitoring waterways. Further studies are now needed to extend our observations across seasons and to examine the relationship between the presence of these genetic elements and the incidence of disease in humans.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.

1386. Seroprevalence of Strongyloidiasis in Liver Transplant Candidates at a Tertiary-Level Hospital in Newark, NJ

Background The liver transplant center at University Hospital (Newark, NJ) is one of the busiest in northern NJ. Current guidelines for Strongyloides stercoralis (Ss) screening in solid transplant recipients recommend targeted testing. We propose a high seroprevalence of this infection in our facility given its significant percentage of foreign-born patients from Ss endemic areas such as Latin America, the Caribbean, and Africa. Methods Descriptive study from secondary data. We obtained the total number of Strongyloides antibody tests performed at University Hospital in the last two years (08/2018-10/2020). Subsequently, medical charts were reviewed to obtain epidemiological and clinical data. Results A total of 388 patients underwent screening for Strongyloides antibody, of whom 71 (18%) were positive. The test was mainly performed in male (58%) and foreign-born (55%) patients. More than half (55%) of the US-born individuals had history of travel overseas. The main reasons for testing were transplant evaluation (65%), immunosuppression (14%) and eosinophilia (9%). There was no association between transplant evaluation and seropositivity (81% vs 81%, p = 0.994). Being foreign-born was not associated with a positive test (19% vs 20%, p = 0.834), but for US-born patients, having a history of travel was associated with a positive test (33% vs 14%, p = 0.039). For the Ss positive patients, 34% had a HTLV-I/II test, 48% had at least one stool test, and 76% were given treatment. Conclusion There is a significant seroprevalence of Ss in our transplant candidate population, both non-foreign and foreign-born, prompting the indication for universal screening at our facility. Disclosures All Authors: No reported disclosures

Small Fiber Neuropathy Incidence, Prevalence, Longitudinal Impairments, and Disability

Background and Objectives:There is limited population-based data on small fiber neuropathy (SFN). We wished to determine SFN incidence, prevalence, comorbidities, longitudinal impairments, and disabilities.Methods:Test-confirmed patients with SFN in Olmsted, Minnesota and adjacent counties were compared 3:1 to matched controls (January 1st, 1998-December 31st, 2017).Results:Ninety-four patients with SFN were identified, incidence 1.3/100,000/year increasing over the study period, prevalence 13.3/100,000. Average follow-up was 6.1 years (0.7-43 years), mean onset age 54 years (range 14–83). Female (67%), obesity (BMI mean 30.4 versus 28.5), insomnia (86% versus 54%), analgesic-opioid prescriptions (72% versus 46%), hypertriglyceridemia (180 mg/dl mean versus 147 mg/dl) and diabetes mellitus (DM) (51% versus 22%, p<0.001) were more common (OR 3.8-9.0, all p<0.03). Patients with SFN did not self-identify as disabled with median mRS of 1.0 (range 0-6) versus controls 0.0 (0-6), p=0.04. Higher Charlson comorbidities (median 6, range 3-9) occurred versus controls (median 3, range 1-9) p<0.001. Myocardial infarctions occurred in 46% versus 27% of controls (p<0.0001). Classifications included: idiopathic (70%); DM (15%); Sjögrens (2%); AL-amyloid (1%); transthyretin-amyloid (1%); Fabry (1%); lupus (1%); post viral (1%); Lewy body (1%) and multifactorial (5%). Foot ulcers occurred in 17, with 71% having DM. Large fiber neuropathy developed in 36%, on average 5.3 years (range 0.2-14.3 years) from SFN onset. Median onset Composite Autonomic Severity Score (CASS) was 3, change/year 0.08 (range 0-2.0). Median Neuropathy Impairment Score (NIS) was 2 at onset (range 0 to 8), change/year +1.0 (range -7.9 to +23.3). NIS and CASS change >+1 point/year occurred in only AL-amyloid, hereditary transthyretin-amyloid, Fabry, uncontrolled DM, and Lewy body. Death from symptom onset was higher in patients with SFN 19% versus controls 12%, p<0.001, 50% secondary to DM complications.Discussion:Isolated SFN is uncommon but increasing in incidence. Most patients do not develop major neurological impairments and disability but have multiple comorbidities, including cardiovascular ischemic events, and increased mortality from SFN onsets. Development of large fiber involvements and DM are common over time. Targeted testing facilitates interventional therapies for DM but also rheumatologic and rare genetic forms.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CXoV-2 infections. In this study, we describe and compare two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+ Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt , county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. We also find that both methods perform adequately in both rural and non-rural predictions. Finally, we provide a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt, and the potential for further development of machine learning methods that are enhanced by Rt.