Case report and mini-review: Sarcina ventriculi in the stomach of an 80-year-old female.

Sarcina ventriculi, also known as Zymosarcina ventriculi and, incorrectly, as Clostridium ventriculi, is rarely encountered in clinical settings. A patient with a complicated gastrointestinal (GI) history, who was acutely presenting with small-bowel obstruction, was found to be colonized by S. ventriculi. The distinctive morphology of this species, with large Gram-variable cocci (up to 3 µm) arranged in two-by-two cuboid clusters reaching up to 20 µm, was key in identifying this bacterium in a stomach biopsy specimen. Sarcina ventriculi appears to be ubiquitously found in nature, and related bacterial species can cause GI-related disease in various animals. Clinical manifestations in humans are broad and often related to other underlying comorbidities. Isolation of S. ventriculi in the laboratory requires anaerobic culture on select media but its absence from standard MALDI-TOF databases complicates identification. Susceptibility data do not exist, so empiric treatment is the only option for this rare pathogen.

Universal PCR for bacteria, mycobacteria, and fungi: a 10-year retrospective review of clinical indications and patient outcomes.

IMPORTANCE: Our work provides a retrospective analysis of universal PCR orders for bacteria, mycobacteria, and fungi across our institution across a 10-year period. We assessed the positivity rates for this diagnostic tool by test type and specimen type and, critically, studied whether and how the results influenced the outcomes from treatment change, to readmission, to death.

ENRICHing medical imaging training sets enables more efficient machine learning.

OBJECTIVE: Deep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model's performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets. MATERIALS AND METHODS: To address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set. RESULTS: First, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets. CONCLUSIONS: ENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.

Sars-Cov-2 antigen tests predict infectivity based on viral culture: comparison of antigen, PCR viral load, and viral culture testing on a large sample cohort.

OBJECTIVE: To define the relationship of SARS-CoV-2 antigen, viral load determined by RT-qPCR, and viral culture detection. Presumptively, viral culture can provide a surrogate measure for infectivity of sampled individuals and thereby inform how and where to most appropriately deploy antigen and nucleic acid amplification-based diagnostic testing modalities. METHODS: We compared the antigen testing results from three lateral flow and one microfluidics assay to viral culture detection and viral load determination performed in parallel in up to 189 nasopharyngeal swab samples positive for SARS-CoV-2. Sample viral loads, determined by RT-qPCR, were distributed across the range of viral load values observed in our testing population. RESULTS: Antigen tests were predictive of viral culture positivity, with the LumiraDx microfluidics method showing enhanced sensitivity (90%; 95% CI 83-94%) compared with the BD Veritor (74%, 95% CI 65-81%), CareStart (74%, 95% CI 65-81%) and Oscar Corona (74%, 95% CI 65-82%) lateral flow antigen tests. Antigen and viral culture positivity were also highly correlated with sample viral load, with areas under the receiver operator characteristic curves of 0.94 to 0.97 and 0.92, respectively. A viral load threshold of 100 000 copies/mL was 95% sensitive (95% CI, 90-98%) and 72% specific (95% CI, 60-81%) for predicting viral culture positivity. Adjusting for sample dilution inherent in our study design, sensitivities of antigen tests were ≥95% for detection of viral culture positive samples with viral loads >106 genome copies/mL, although specificity of antigen testing was imperfect. DISCUSSION: Antigen testing results and viral culture were correlated. For culture positive samples, the sensitivity of antigen tests was high at high viral loads that are likely associated with significant infectivity. Therefore, our data provides support for use of antigen testing in ruling out infectivity at the time of sampling.

Scaling Monte-Carlo-Based Inference on Antibody and TCR Repertoires.

Previously, it has been shown that maximum-entropy models of immune-repertoire sequence can be used to determine a person's vaccination status. However, this approach has the drawback of requiring a computationally intensive method to compute each model's partition function (Z), the normalization constant required for calculating the probability that the model will generate a given sequence. Specifically, the method required generating approximately 1010 sequences via Monte-Carlo simulations for each model. This is impractical for large numbers of models. Here we propose an alternative method that requires estimating Z this way for only a few models: it then uses these expensive estimates to estimate Z more efficiently for the remaining models. We demonstrate that this new method enables the generation of accurate estimates for 27 models using only three expensive estimates, thereby reducing the computational cost by an order of magnitude. Importantly, this gain in efficiency is achieved with only minimal impact on classification accuracy. Thus, this new method enables larger-scale investigations in computational immunology and represents a useful contribution to energy-based modeling more generally.

Repertoire-scale measures of antigen binding.

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

Urine Proteomics Reveals Sex-Specific Response to Total Pancreatectomy With Islet Autotransplantation.

OBJECTIVES: Total pancreatectomy with islet autotransplantation (TPIAT) is a surgical option for refractory chronic pancreatitis-related pain. Despite the known clinical implications of TPIAT, the molecular effects remain poorly investigated. We performed the first hypothesis-generating study of the urinary proteome before and after TPIAT. METHODS: Twenty-two patients eligible for TPIAT were prospectively enrolled. Urine samples were collected the week before and 12 to 18 months after TPIAT. The urine samples were prepared for bottom-up label-free quantitative proteomics using the "MStern" protocol. RESULTS: Using 17 paired samples, we identified 2477 urinary proteins, of which 301 were significantly changed post-TPIAT versus pre-TPIAT. Our quantitative analysis revealed that the molecular response to TPIAT was highly sex-specific, with pronounced sex differences pre-TPIAT but minimal differences afterward. Comparing post-TPIAT versus pre-TPIAT, we found changes in cell-cell adhesion, intracellular vacuoles, and immune response proteins. After surgery, immunoglobulins, complement proteins, and cathepsins were increased, findings that may reflect glomerular damage. Finally, we identified both known and novel markers for immunoglobulin A nephropathy after 1 patient developed the disease 2 years after TPIAT. CONCLUSIONS: We found distinct changes in the urinary proteomic profile after TPIAT and the response to TPIAT is highly sex-specific.

Visualizing omicron: COVID-19 deaths vs. cases over time.

For most of the COVID-19 pandemic, the daily focus has been on the number of cases, and secondarily, deaths. The most recent wave was caused by the omicron variant, first identified at the end of 2021 and the dominant variant through the first part of 2022. South Africa, one of the first countries to experience and report data regarding omicron (variant 21.K), reported far fewer deaths, even as the number of reported cases rapidly eclipsed previous peaks. However, as the omicron wave has progressed, time series show that it has been markedly different from prior waves. To more readily visualize the dynamics of cases and deaths, it is natural to plot deaths per million against cases per million. Unlike the time-series plots of cases or deaths that have become daily features of pandemic updates during the pandemic, which have time as the x-axis, in a plot of deaths vs. cases, time is implicit, and is indicated in relation to the starting point. Here we present and briefly examine such plots from a number of countries and from the world as a whole, illustrating how they summarize features of the pandemic in ways that illustrate how, in most places, the omicron wave is very different from those that came before. Code for generating these plots for any country is provided in an automatically updating GitHub repository.

Verification of the Abbott Alinity m Resp-4-Plex assay for detection of SARS-CoV-2, influenza A/B, and respiratory syncytial virus.

COVID-19 symptomology may overlap with other circulating respiratory viruses that may also cause severe disease and for which there are specific and potentially life-saving treatments. The Abbott Alinity m Resp-4-Plex assay is a multiplex PCR assay that simultaneously detects and differentiates infection with SARS-CoV-2, influenza A, influenza B, and respiratory syncytial virus (RSV). We characterized its accuracy, precision, and analytical sensitivity. All were found to be robust for measures examined. In the context of sample-to-answer, near random access automation on the Alinity m platform, we believe that the Resp-4-Plex assay offers significant utility in addressing the current needs of the SARS-CoV-2 pandemic and future needs during anticipated endemic circulation of SARS-CoV-2 with other respiratory viruses.

Cooperation under Pressure: Lessons from the COVID-19 Swab Crisis.

The early months of the COVID-19 pandemic were marked by a desperate need for nasopharyngeal swabs to test for SARS-CoV-2, with demand far outstripping supply. April marked the anniversary of an unprecedented nationwide multibusiness/multihospital partnership that successfully met this need, a fitting occasion to review lessons learned. Here, I briefly recount the key events, constraints, and thought processes surrounding the effort in order to better inform responses to future crises. Overall, the experience was a strong validation of Joy's Law and illustrated the utility of recognizing temptations to avoid, in order to reap the rewards of cooperation. I conclude by summarizing lessons learned.

Saliva is Comparable to Nasopharyngeal Swabs for Molecular Detection of SARS-CoV-2.

The continued need for molecular testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the potential for self-collected saliva as an alternative to nasopharyngeal (NP) swabs for sample acquisition led us to compare saliva to NP swabs in an outpatient setting without restrictions to avoid food, drink, smoking, or tooth-brushing. A total of 385 pairs of NP and saliva specimens were obtained, the majority from individuals presenting for initial evaluation, and were tested on two high-sensitivity reverse transcriptase PCR (RT-PCR) platforms, the Abbott m2000 and Abbott Alinity m (both with limits of detection [LoD] of 100 copies of viral RNA/ml). Concordance between saliva and NP swabs was excellent overall (Cohen's κ = 0.93) for both initial and follow-up testing, for both platforms, and for specimens treated with guanidinium transport medium as preservative as well as for untreated saliva (κ = 0.88 to 0.95). Viral loads were on average 16× higher in NP specimens than saliva specimens, suggesting that only the relatively small fraction of outpatients (∼8% in this study) who present with very low viral loads (<1,600 copies/ml from NP swabs) would be missed by testing saliva instead of NP swabs when using sensitive testing platforms. Special attention was necessary to ensure leak-resistant specimen collection and transport. The advantages of self-collection of saliva, without behavioral restrictions, will likely outweigh a minor potential decrease in clinical sensitivity in individuals less likely to pose an infectious risk to others for many real-world scenarios, especially for initial testing. IMPORTANCE In general, the most accurate COVID-19 testing is hands-on and uncomfortable, requiring trained staff and a "brain-tickling" nasopharyngeal swab. Saliva would be much easier on both fronts, since patients could collect it themselves, and it is after all just spit. However, despite much interest, it remains unclear how well saliva performs in real-world settings when just using it in place of an NP swab without elaborate or cumbersome restrictions about not eating/drinking before testing, etc. Also, almost all studies of COVID-19 testing, whether of NP swabs, saliva, or otherwise, have been restricted to reporting results in the abstruse units of "CT values," which only mean something in the context of a specific assay and testing platform. Here, we compared saliva versus NP swabs in a real-world setting without restriction and report all results in natural units-the amount of virus being shed-showing that saliva is essentially just as good as NP swabs.

Nasal Swab Performance by Collection Timing, Procedure, and Method of Transport for Patients with SARS-CoV-2.

The urgent need for large-scale diagnostic testing for SARS-CoV-2 has prompted interest in sample collection methods of sufficient sensitivity to replace nasopharynx (NP) sampling. Nasal swab samples are an attractive alternative; however, previous studies have disagreed over how nasal sampling performs relative to NP sampling. Here, we compared nasal versus NP specimens collected by health care workers in a cohort of individuals clinically suspected of COVID-19 as well as SARS-CoV-2 reverse transcription (RT)-PCR-positive outpatients undergoing follow-up. We compared subjects being seen for initial evaluation versus follow-up, two different nasal swab collection protocols, and three different transport conditions, including traditional viral transport media (VTM) and dry swabs, on 307 total study participants. We compared categorical results and viral loads to those from standard NP swabs collected at the same time from the same patients. All testing was performed by RT-PCR on the Abbott SARS-CoV-2 RealTime emergency use authorization (EUA) (limit of detection [LoD], 100 copies viral genomic RNA/ml transport medium). We found low concordance overall, with Cohen's kappa (κ) of 0.49, with high concordance only for subjects with very high viral loads. We found medium concordance for testing at initial presentation (κ = 0.68) and very low concordance for follow-up testing (κ = 0.27). Finally, we show that previous reports of high concordance may have resulted from measurement using assays with sensitivity of ≥1,000 copies/ml. These findings suggest nasal-swab testing be used for situations in which viral load is expected to be high, as we demonstrate that nasal swab testing is likely to miss patients with low viral loads.

The Future of Blood Testing Is the Immunome.

It is increasingly clear that an extraordinarily diverse range of clinically important conditions-including infections, vaccinations, autoimmune diseases, transplants, transfusion reactions, aging, and cancers-leave telltale signatures in the millions of V(D)J-rearranged antibody and T cell receptor [TR per the Human Genome Organization (HUGO) nomenclature but more commonly known as TCR] genes collectively expressed by a person's B cells (antibodies) and T cells. We refer to these as the immunome. Because of its diversity and complexity, the immunome provides singular opportunities for advancing personalized medicine by serving as the substrate for a highly multiplexed, near-universal blood test. Here we discuss some of these opportunities, the current state of immunome-based diagnostics, and highlight some of the challenges involved. We conclude with a call to clinicians, researchers, and others to join efforts with the Adaptive Immune Receptor Repertoire Community (AIRR-C) to realize the diagnostic potential of the immunome.

The Limit of Detection Matters: The Case for Benchmarking Severe Acute Respiratory Syndrome Coronavirus 2 Testing.

BACKGROUND: Resolving the coronavirus disease 2019 (COVID-19) pandemic requires diagnostic testing to determine which individuals are infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current gold standard is to perform reverse-transcription polymerase chain reaction (PCR) on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of approximately 100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss infected patients. However, the relative clinical sensitivity of these assays remains unknown. METHODS: Here we model the clinical sensitivities of assays based on their LoD. Cycle threshold (Ct) values were obtained from 4700 first-time positive patients using the Abbott RealTime SARS-CoV-2 Emergency Use Authorization test. We derived viral loads from Ct based on PCR principles and empiric analysis. A sliding scale relationship for predicting clinical sensitivity was developed from analysis of viral load distribution relative to assay LoD. RESULTS: Ct values were reliably repeatable over short time testing windows, providing support for use as a tool to estimate viral load. Viral load was found to be relatively evenly distributed across log10 bins of incremental viral load. Based on these data, each 10-fold increase in LoD is expected to lower assay sensitivity by approximately 13%. CONCLUSIONS: The assay LoD meaningfully impacts clinical performance of SARS-CoV-2 tests. The highest LoDs on the market will miss a majority of infected patients. Assays should therefore be benchmarked against a universal standard to allow cross-comparison of SARS-CoV-2 detection methods.

Machine Learning and the Future of Cardiovascular Care: JACC State-of-the-Art Review.

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clinical experts in some of these tasks. Machine learning has the potential to benefit patients and cardiologists, but only if clinicians take an active role in bringing these new algorithms into practice. The aim of this review is to introduce clinicians who are not data science experts to key concepts in machine learning that will allow them to better understand the field and evaluate new literature and developments. The current published data in machine learning for cardiovascular disease is then summarized, using both a bibliometric survey, with code publicly available to enable similar analysis for any research topic of interest, and select case studies. Finally, several ways that clinicians can and must be involved in this emerging field are presented.

Open Development and Clinical Validation Of Multiple 3D-Printed Sample-Collection Swabs: Rapid Resolution of a Critical COVID-19 Testing Bottleneck.

The SARS-CoV-2 pandemic has caused a severe international shortage of the nasopharyngeal swabs that are required for collection of optimal specimens, creating a critical bottleneck in the way of high-sensitivity virological testing for COVID-19. To address this crisis, we designed and executed an innovative, radically cooperative, rapid-response translational-research program that brought together healthcare workers, manufacturers, and scientists to emergently develop and clinically validate new swabs for immediate mass production by 3D printing. We performed a rigorous multi-step preclinical evaluation on 160 swab designs and 48 materials from 24 companies, laboratories, and individuals, and shared results and other feedback via a public data repository (http://github.com/rarnaout/Covidswab/). We validated four prototypes through an institutional review board (IRB)-approved clinical trial that involved 276 outpatient volunteers who presented to our hospital's drive-through testing center with symptoms suspicious for COVID-19. Each participant was swabbed with a reference swab (the control) and a prototype, and SARS-CoV-2 reverse-transcriptase polymerase chain reaction (RT-PCR) results were compared. All prototypes displayed excellent concordance with the control (κ=0.85-0.89). Cycle-threshold (Ct) values were not significantly different between each prototype and the control, supporting the new swabs' non-inferiority (Mann-Whitney U [MWU] p>0.05). Study staff preferred one of the prototypes over the others and the control swab overall. The total time elapsed between identification of the problem and validation of the first prototype was 22 days. Contact information for ordering can be found at http://printedswabs.org. Our experience holds lessons for the rapid development, validation, and deployment of new technology for this pandemic and beyond.

Nasal-Swab Testing Misses Patients with Low SARS-CoV-2 Viral Loads.

The urgent need for large-scale diagnostic testing for SARS-CoV-2 has prompted pursuit of sample-collection methods of sufficient sensitivity to replace sampling of the nasopharynx (NP). Among these alternatives is collection of nasal-swab samples, which can be performed by the patient, avoiding the need for healthcare personnel and personal protective equipment. Previous studies have reached opposing conclusions regarding whether nasal sampling is concordant or discordant with NP. To resolve this disagreement, we compared nasal and NP specimens collected by healthcare workers in a cohort consisting of individuals clinically suspected of COVID-19 and outpatients known to be SARS-CoV-2 RT-PCR positive undergoing follow-up. We investigated three different transport conditions, including traditional viral transport media (VTM) and dry swabs, for each of two different nasal-swab collection protocols on a total of 308 study participants, and compared categorical results and Ct values to those from standard NP swabs collected at the same time from the same patients. All testing was performed by RT-PCR on the Abbott SARS-CoV-2 RealTime EUA (limit of detection [LoD], 100 copies viral genomic RNA/mL transport medium). We found high concordance (Cohen's kappa >0.8) only for patients with viral loads above 1,000 copies/mL. Those with viral loads below 1,000 copies/mL, the majority in our cohort, exhibited low concordance (Cohen's kappa = 0.49); most of these would have been missed by nasal testing alone. Previous reports of high concordance may have resulted from use of assays with higher LoD (≥1,000 copies/mL). These findings counsel caution in use of nasal testing in healthcare settings and contact-tracing efforts, as opposed to screening of asymptomatic, low-prevalence, low-risk populations. Nasal testing is an adjunct, not a replacement, for NP.

SARS-CoV2 Testing: The Limit of Detection Matters.

Resolving the COVID-19 pandemic requires diagnostic testing to determine which individuals are infected and which are not. The current gold standard is to perform RT-PCR on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of ~100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, resulting in more false negatives. However, the false-negative rate for a given LoD remains unknown. Here we address this question using over 27,500 test results for patients from across our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 13%, missing an additional one in eight infected patients. The highest LoDs on the market will miss a majority of infected patients, with false negative rates as high as 70%. These results suggest that choice of assay has meaningful clinical and epidemiological consequences. The limit of detection matters.