Genetic tracing of market wildlife and viruses at the epicenter of the COVID-19 pandemic.

Zoonotic spillovers of viruses have occurred through the animal trade worldwide. The start of the COVID-19 pandemic was traced epidemiologically to the Huanan Seafood Wholesale Market. Here, we analyze environmental qPCR and sequencing data collected in the Huanan market in early 2020. We demonstrate that market-linked severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic diversity is consistent with market emergence and find increased SARS-CoV-2 positivity near and within a wildlife stall. We identify wildlife DNA in all SARS-CoV-2-positive samples from this stall, including species such as civets, bamboo rats, and raccoon dogs, previously identified as possible intermediate hosts. We also detect animal viruses that infect raccoon dogs, civets, and bamboo rats. Combining metagenomic and phylogenetic approaches, we recover genotypes of market animals and compare them with those from farms and other markets. This analysis provides the genetic basis for a shortlist of potential intermediate hosts of SARS-CoV-2 to prioritize for serological and viral sampling.

Genomic surveillance reveals dynamic shifts in the connectivity of COVID-19 epidemics.

The maturation of genomic surveillance in the past decade has enabled tracking of the emergence and spread of epidemics at an unprecedented level. During the COVID-19 pandemic, for example, genomic data revealed that local epidemics varied considerably in the frequency of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage importation and persistence, likely due to a combination of COVID-19 restrictions and changing connectivity. Here, we show that local COVID-19 epidemics are driven by regional transmission, including across international boundaries, but can become increasingly connected to distant locations following the relaxation of public health interventions. By integrating genomic, mobility, and epidemiological data, we find abundant transmission occurring between both adjacent and distant locations, supported by dynamic mobility patterns. We find that changing connectivity significantly influences local COVID-19 incidence. Our findings demonstrate a complex meaning of "local" when investigating connected epidemics and emphasize the importance of collaborative interventions for pandemic prevention and mitigation.

Wastewater sequencing reveals early cryptic SARS-CoV-2 variant transmission.

As SARS-CoV-2 continues to spread and evolve, detecting emerging variants early is critical for public health interventions. Inferring lineage prevalence by clinical testing is infeasible at scale, especially in areas with limited resources, participation, or testing and/or sequencing capacity, which can also introduce biases1-3. SARS-CoV-2 RNA concentration in wastewater successfully tracks regional infection dynamics and provides less biased abundance estimates than clinical testing4,5. Tracking virus genomic sequences in wastewater would improve community prevalence estimates and detect emerging variants. However, two factors limit wastewater-based genomic surveillance: low-quality sequence data and inability to estimate relative lineage abundance in mixed samples. Here we resolve these critical issues to perform a high-resolution, 295-day wastewater and clinical sequencing effort, in the controlled environment of a large university campus and the broader context of the surrounding county. We developed and deployed improved virus concentration protocols and deconvolution software that fully resolve multiple virus strains from wastewater. We detected emerging variants of concern up to 14 days earlier in wastewater samples, and identified multiple instances of virus spread not captured by clinical genomic surveillance. Our study provides a scalable solution for wastewater genomic surveillance that allows early detection of SARS-CoV-2 variants and identification of cryptic transmission.

Outbreak.info genomic reports: scalable and dynamic surveillance of SARS-CoV-2 variants and mutations.

In response to the emergence of SARS-CoV-2 variants of concern, the global scientific community, through unprecedented effort, has sequenced and shared over 11 million genomes through GISAID, as of May 2022. This extraordinarily high sampling rate provides a unique opportunity to track the evolution of the virus in near real-time. Here, we present outbreak.info , a platform that currently tracks over 40 million combinations of Pango lineages and individual mutations, across over 7,000 locations, to provide insights for researchers, public health officials and the general public. We describe the interpretable visualizations available in our web application, the pipelines that enable the scalable ingestion of heterogeneous sources of SARS-CoV-2 variant data and the server infrastructure that enables widespread data dissemination via a high-performance API that can be accessed using an R package. We show how outbreak.info can be used for genomic surveillance and as a hypothesis-generation tool to understand the ongoing pandemic at varying geographic and temporal scales.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

Identification of Spatial Proteomic Signatures of Colon Tumor Metastasis: A Digital Spatial Profiling Approach.

Over 150,000 Americans are diagnosed with colorectal cancer (CRC) every year, and annually >50,000 individuals are estimated to die of CRC, necessitating improvements in screening, prognostication, disease management, and therapeutic options. CRC tumors are removed en bloc with surrounding vasculature and lymphatics. Examination of regional lymph nodes at the time of surgical resection is essential for prognostication. Developing alternative approaches to indirectly assess recurrence risk would have utility in cases where lymph node yield is incomplete or inadequate. Spatially dependent, immune cell-specific (eg, tumor-infiltrating lymphocytes), proteomic, and transcriptomic expression patterns inside and around the tumor-the tumor immune microenvironment-can predict nodal/distant metastasis and probe the coordinated immune response from the primary tumor site. The comprehensive characterization of tumor-infiltrating lymphocytes and other immune infiltrates is possible using highly multiplexed spatial omics technologies, such as the GeoMX Digital Spatial Profiler. In this study, machine learning and differential co-expression analyses helped identify biomarkers from Digital Spatial Profiler-assayed protein expression patterns inside, at the invasive margin, and away from the tumor, associated with extracellular matrix remodeling (eg, granzyme B and fibronectin), immune suppression (eg, forkhead box P3), exhaustion and cytotoxicity (eg, CD8), Programmed death ligand 1-expressing dendritic cells, and neutrophil proliferation, among other concomitant alterations. Further investigation of these biomarkers may reveal independent risk factors of CRC metastasis that can be formulated into low-cost, widely available assays.

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal using intraoperative margin assessment for basal cell carcinoma. However, the varied morphologies of cSCC present challenges for AI margin assessment. The aim of this study was to develop and evaluate the accuracy of an AI algorithm for real-time histologic margin analysis of cSCC. To do this, a retrospective cohort study was conducted using frozen cSCC section slides. These slides were scanned and annotated, delineating benign tissue structures, inflammation and tumour to develop an AI algorithm for real-time margin analysis. A convolutional neural network workflow was used to extract histomorphological features predictive of cSCC. This algorithm demonstrated proof of concept for identifying cSCC with high accuracy, highlighting the potential for integration of AI into the surgical workflow. Incorporation of AI algorithms may improve efficiency and completeness of real-time margin assessment for cSCC removal, particularly in cases of moderately and poorly differentiated tumours/neoplasms. Further algorithmic improvement incorporating surrounding tissue context is necessary to remain sensitive to the unique epidermal landscape of well-differentiated tumours, and to map tumours to their original anatomical position/orientation.

Characterizing Veteran suicide decedents that were not classified as high-suicide-risk.

BACKGROUND: Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide. METHODS: Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups. RESULTS: High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients. CONCLUSIONS: Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.

Investigating the role of imaging factors in the variability of CT-based texture analysis metrics.

OBJECTIVE: This study assesses the robustness of first-order radiomic texture features namely interquartile range (IQR), coefficient of variation (CV) and standard deviation (SD) derived from computed tomography (CT) images by varying dose, reconstruction algorithms and slice thickness using scans of a uniform water phantom, a commercial anthropomorphic liver phantom, and a human liver in-vivo. MATERIALS AND METHODS: Scans were acquired on a 16 cm detector GE Revolution Apex Edition CT scanner with variations across three different nominal slice thicknesses: 0.625, 1.25, and 2.5 mm, three different dose levels: CTDIvol of 13.86 mGy for the standard dose, 40% reduced dose and 60% reduced dose and two different reconstruction algorithms: a deep learning image reconstruction (DLIR-high) algorithm and a hybrid iterative reconstruction (IR) algorithm ASiR-V50% (AV50) were explored, varying one at a time. To assess the effect of non-linear modifications of images by AV50 and DLIR-high, images of the water phantom were also reconstructed using filtered back projection (FBP). Quantitative measures of IQR, CV and SD were extracted from twelve pre-selected, circular (1 cm diameter) regions of interest (ROIs) capturing different texture patterns across all scans. RESULTS: Across all scans, imaging, and reconstruction settings, CV, IQR and SD were observed to increase with reduction in dose and slice thickness. An exception to this observation was found when using FBP reconstruction. Lower values of CV, IQR and SD were observed in DLIR-high reconstructions compared to AV50 and FBP. The Poisson statistics were more stringently noted in FBP than DLIR-high and AV50, due to the non-linear nature of the latter two algorithms. CONCLUSION: Variation in image noise due to dose reduction algorithms, tube current, and slice thickness show a consistent trend across phantom and patient scans. Prospective evaluation across multiple centers, scanners and imaging protocols is needed for establishing quality assurance standards of radiomics.

Technical and clinical considerations of a physical liver phantom for CT radiomics analysis.

OBJECTIVE: This study identifies key characteristics to help build a physical liver computed tomography (CT) phantom for radiomics harmonization; particularly, the higher-order texture metrics. MATERIALS AND METHODS: CT scans of a radiomics phantom comprising of 18 novel 3D printed inserts with varying size, shape, and material combinations were acquired on a 64-slice CT scanner (Brilliance 64, Philips Healthcare). The images were acquired at 120 kV, 250 mAs, CTDIvol of 16.36 mGy, 2 mm slice thickness, and iterative noise-reduction reconstruction (iDose, Philips Healthcare, Andover, MA). Radiomics analysis was performed using the Cancer Imaging Phenomics Toolkit (CaPTk), following automated segmentation of 3D regions of interest (ROI) of the 18 inserts. The findings were compared to three additional ROI obtained of an anthropomorphic liver phantom, a patient liver CT scan, and a water phantom, at comparable imaging settings. Percentage difference in radiomic metrics values between phantom and tissue was used to assess the biological equivalency and <10% was used to claim equivalent. RESULTS: The HU for all 18 ROI from the phantom ranged from -30 to 120 which is within clinically observed HU range of the liver, showing that our phantom material (T3-6B) is representative of biological CT tissue densities (liver) with >50% radiomic features having <10% difference from liver tissue. Based on the assessment of the Neighborhood Gray Tone Difference Matrix (NGTDM) metrics it is evident that the water phantom ROI show extreme values compared to the ROIs from the phantom. This result may further reinforce the difference between a structureless quantity such as water HU values and tissue HU values found in liver. CONCLUSION: The 3-D printed patterns of the constructed radiomics phantom cover a wide span of liver tissue textures seen in CT images. Using our results, texture metrics can be selectively harmonized to establish clinically relevant and reliable radiomics panels.

An eosinophil peroxidase activity assay accurately predicts eosinophilic chronic rhinosinusitis.

BACKGROUND: A definitive diagnosis of eosinophilic chronic rhinosinusitis (eCRS) requires invasive surgical tissue sampling and histologic enumeration of intact eosinophils. Eosinophil peroxidase (EPX) is an accurate biomarker of sinonasal tissue eosinophilia in CRS regardless of polyp status. A less invasive and rapid method that accurately identifies tissue eosinophilia would be of great benefit to patients. OBJECTIVE: We sought to evaluate a new clinical tool that uses a nasal swab and colorimetric EPX activity assay to predict a diagnosis of eCRS. METHODS: A prospective, observational cohort study was conducted using nasal swabs and sinonasal tissue biopsies obtained from patients with CRS electing endoscopic sinus surgery. Patients were classified as non-eCRS (n = 19) and eCRS (n = 35) on the basis of pathologically determined eosinophil counts of less than 10 or greater than or equal to 10 eosinophils/HPF, respectively. Swab-deposited EPX activity was measured and compared with tissue eosinophil counts, EPX levels, and CRS-specific disease metrics. RESULTS: EPX activity was significantly increased in patients with eCRS than in patients without eCRS (P < .0001). With a relative absorbance unit cutoff value of greater than or equal to 0.80, the assay demonstrated high sensitivity (85.7%) and moderate specificity (79.0%) for confirming eCRS. Spearman correlations between EPX activity and tissue eosinophil counts (rs = 0.424), EPX levels (rs = 0.503), and Lund-Kennedy endoscopy scores (rs = 0.440) in eCRS were significant (P < .05). CONCLUSIONS: This investigation evaluates a nasal swab sampling method and EPX activity assay that accurately confirms eCRS. This method could potentially address the unmet need to identify sinonasal tissue eosinophilia at the point-of-care, as well as to longitudinally monitor eosinophil activity and treatment response.

Chronic alcohol use primes bronchial cells for altered inflammatory response and barrier dysfunction during SARS-CoV-2 infection.

Alcohol use disorder (AUD) is a significant public health concern and people with AUD are more likely to develop severe acute respiratory distress syndrome (ARDS) in response to respiratory infections. To examine whether AUD was a risk factor for more severe outcome in response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we examined early responses to infection using cultured differentiated bronchial epithelial cells derived from brushings obtained from people with AUD or without AUD. RNA-seq analysis of uninfected cells determined that AUD cells were enriched for expression of epidermal genes as compared with non-AUD cells. Bronchial epithelial cells from patients with AUD showed a significant decrease in barrier function 72 h postinfection, as determined by transepithelial electrical resistance. In contrast, barrier function of non-AUD cells was enhanced 72 h after SARS-CoV-2 infection. AUD cells showed claudin-7 that did not colocalize with zonula occludens-1 (ZO-1), indicative of disorganized tight junctions. However, both AUD and non-AUD cells showed decreased ß-catenin expression following SARS-CoV-2 infection. To determine the impact of AUD on the inflammatory response to SARS-CoV-2 infection, cytokine secretion was measured by multiplex analysis. SARS-CoV-2-infected AUD bronchial cells had enhanced secretion of multiple proinflammatory cytokines including TNFα, IL-1ß, and IFNγ as opposed to non-AUD cells. In contrast, secretion of the barrier-protective cytokines epidermal growth factor (EGF) and granulocyte macrophage-colony stimulating factor (GM-CSF) was enhanced for non-AUD bronchial cells. Taken together, these data support the hypothesis that AUD is a risk factor for COVID-19, where alcohol primes airway epithelial cells for increased inflammation and increased barrier dysfunction and increased inflammation in response to infection by SARS-CoV-2.NEW & NOTEWORTHY Alcohol use disorder (AUD) is a significant risk factor for severe acute respiratory distress syndrome. We found that AUD causes a phenotypic shift in gene expression in human bronchial epithelial cells, enhancing expression of epidermal genes. AUD cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had higher levels of proinflammatory cytokine secretion and barrier dysfunction not present in infected non-AUD cells, consistent with increased early COVID-19 severity due to AUD.

Prevalence of SARS-CoV-2 in hemoglobinopathies is modified by age and race.

Prior literature has established a positive association between sickle cell disease and risk of contracting SARS-CoV-2. Data from a cross-sectional study evaluating COVID-19 testing devices (n = 10,567) was used to examine the association between underlying health conditions and SARS-CoV-2 infection in an urban metropolis in the southern United States. Firth's logistic regression was used to fit the model predicting SARS-CoV-2 positivity using vaccine status and different medical conditions commonly associated with COVID-19. Another model using the same method was built using SARS-CoV-2 positivity as the outcome and hemoglobinopathy presence, age (<16 Years vs. ≥16 Years), race/ethnicity and comorbidities, including hemoglobinopathy, as the factors. Our first model showed a significant association between hemoglobinopathy and SARS-CoV-2 infection (OR: 2.28, 95 % CI: (1.17,4.35), P = 0.016). However, in the second model, this association was not maintained (OR: 1.35, 95 % CI: (0.72,2.50), P = 0.344). We conclude that the association between SARS-CoV-2 positivity and presence of hemoglobinopathies like sickle cell disease is confounded by race, age, and comorbidity status. Our results illuminate previous findings by identifying underlying clinical/demographic factors that confound the reported association between hemoglobinopathies and SARS-CoV-2. These findings demonstrate how social determinants of health may influence disease manifestations more than genetics alone.

Video-Based Deep Learning to Detect Dyssynergic Defecation with 3D High-Definition Anorectal Manometry.

BACKGROUND: We developed a deep learning algorithm to evaluate defecatory patterns to identify dyssynergic defecation using 3-dimensional high definition anal manometry (3D-HDAM). AIMS: We developed a 3D-HDAM deep learning algorithm to evaluate for dyssynergia. METHODS: Spatial-temporal data were extracted from consecutive 3D-HDAM studies performed between 2018 and 2020 at Dartmouth-Hitchcock Health. The technical procedure and gold standard definition of dyssynergia were based on the London consensus, adapted to the needs of 3D-HDAM technology. Three machine learning models were generated: (1) traditional machine learning informed by conventional anorectal function metrics, (2) deep learning, and (3) a hybrid approach. Diagnostic accuracy was evaluated using bootstrap sampling to calculate area-under-the-curve (AUC). To evaluate overfitting, models were validated by adding 502 simulated defecation maneuvers with diagnostic ambiguity. RESULTS: 302 3D-HDAM studies representing 1208 simulated defecation maneuvers were included (average age 55.2 years; 80.5% women). The deep learning model had comparable diagnostic accuracy [AUC 0.91 (95% confidence interval 0.89-0.93)] to traditional [AUC 0.93(0.92-0.95)] and hybrid [AUC 0.96(0.94-0.97)] predictive models in training cohorts. However, the deep learning model handled ambiguous tests more cautiously than other models; the deep learning model was more likely to designate an ambiguous test as inconclusive [odds ratio 4.21(2.78-6.38)] versus traditional/hybrid approaches. CONCLUSIONS: Deep learning is capable of considering complex spatial-temporal information on 3D-HDAM technology. Future studies are needed to evaluate the clinical context of these preliminary findings.

Dynamic suicide topic modelling: Deriving population-specific, psychosocial and time-sensitive suicide risk variables from Electronic Health Record psychotherapy notes.

In the machine learning subfield of natural language processing, a topic model is a type of unsupervised method that is used to uncover abstract topics within a corpus of text. Dynamic topic modelling (DTM) is used for capturing change in these topics over time. The study deploys DTM on corpus of electronic health record psychotherapy notes. This retrospective study examines whether DTM helps distinguish closely matched patients that did and did not die by suicide. Cohort consists of United States Department of Veterans Affairs (VA) patients diagnosed with Posttraumatic Stress Disorder (PTSD) between 2004 and 2013. Each case (those who died by suicide during the year following diagnosis) was matched with five controls (those who remained alive) that shared psychotherapists and had similar suicide risk based on VA's suicide prediction algorithm. Cohort was restricted to patients who received psychotherapy for 9+ months after initial PTSD diagnoses (cases = 77; controls = 362). For cases, psychotherapy notes from diagnosis until death were examined. For controls, psychotherapy notes from diagnosis until matched case's death date were examined. A Python-based DTM algorithm was utilized. Derived topics identified population-specific themes, including PTSD, psychotherapy, medication, communication and relationships. Control topics changed significantly more over time than case topics. Topic differences highlighted engagement, expressivity and therapeutic alliance. This study strengthens groundwork for deriving population-specific, psychosocial and time-sensitive suicide risk variables.

Predictive Value of Isolated Symptoms for Diagnosis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection in Children Tested During Peak Circulation of the Delta Variant.

BACKGROUND: Coronavirus disease 2019 (COVID-19) testing policies for symptomatic children attending US schools or daycare vary, and whether isolated symptoms should prompt testing is unclear. We evaluated children presenting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing to determine if the likelihood of having a positive SARS-CoV-2 test differed between participants with 1 symptom vs ≥2 symptoms, and to examine the predictive capability of isolated symptoms. METHODS: Participants aged <18 years presenting for clinical SARS-CoV-2 molecular testing in 6 sites in urban/suburban/rural Georgia (July-October, 2021; Delta variant predominant) were queried about individual symptoms. Participants were classified into 3 groups: asymptomatic, 1 symptom only, or ≥2 symptoms. SARS-CoV-2 test results and clinical characteristics of the 3 groups were compared. Sensitivity, specificity, positive predictive values (PPVs), and negative predictive values (NPVs) for isolated symptoms were calculated by fitting a saturated Poisson model. RESULTS: Of 602 participants, 21.8% tested positive and 48.7% had a known or suspected close contact. Children reporting 1 symptom (n = 82; odds ratio [OR], 6.00 [95% confidence interval {CI}, 2.70-13.33]) and children reporting ≥2 symptoms (n = 365; OR, 5.25 [95% CI, 2.66-10.38]) were significantly more likely to have a positive COVID-19 test than asymptomatic children (n = 155), but they were not significantly different from each other (OR, 0.88 [95% CI, .52-1.49]). Sensitivity and PPV were highest for isolated fever (33% and 57%, respectively), cough (25% and 32%), and sore throat (21% and 45%); headache had low sensitivity (8%) but higher PPV (33%). Sensitivity and PPV of isolated congestion/rhinorrhea were 8% and 9%, respectively. CONCLUSIONS: With high Delta variant prevalence, children with isolated symptoms were as likely as those with multiple symptoms to test positive for COVID-19. Isolated fever, cough, sore throat, or headache, but not congestion/rhinorrhea, offered the highest predictive value.

Passive order auditing associated with reductions in red blood cell utilization: National blood shortage experience.

BACKGROUND: Decreased blood collection during the Coronavirus Disease 2019 (COVID-19) pandemic resulted in long-term red blood cell (RBC) shortages in the United States. In an effort to conserve RBCs, the existing passive alert system for auditing inpatient transfusions was modified to activate at a lower hemoglobin threshold (6.5 g/dL instead of 7.0 g/dL for stable, nonbleeding inpatients) during a 9-month shortage at an academic medical center. Hemoglobin levels prior to RBC transfusions were compared for inpatients receiving RBC transfusions to determine whether RBC utilization changed during the intervention. STUDY DESIGN AND METHODS: This retrospective study compared the number of single-unit RBC transfusions and hemoglobin levels prior to RBC transfusion among inpatients during the 9 months of the intervention (Period 2, 06/01/2021-2/28/2022) to the same period of the previous year (Period 1, 06/01/2020-2/28/2021). RESULTS: Overall full unit RBC transfusions to inpatients decreased by 15% from 5182 to 4421. Of all transfusions, 50.3% and 49.8% were single-unit RBC transfusions in Period 1 and Period 2, respectively. The incidence rate difference and incidence rate ratio of single RBC units transfused per 1000 patient days were significantly decreased (p = 0.0007). The average pre-transfusion hemoglobin level significantly decreased from 7.18 g/dL to 7.05 g/dL (p = 0.0002), largely due to significant decreases in hemoglobin transfusion triggers for adult inpatient ward transfusions. DISCUSSION: Modification of the passive alert system was associated with significantly decreased RBC utilization during a long-term RBC shortage. Modification of transfusion criteria recommended by passive alerts may be a feasible option to decrease RBC utilization at centers during long-term RBC shortages.

Treatment strategies for postviral olfactory dysfunction: A systematic review.

Background: The coronavirus disease 2019 (COVID-19) pandemic has been associated with a dramatic increase in postviral olfactory dysfunction (PVOD) among patients who are infected. A contemporary evidence-based review of current treatment options for PVOD is both timely and relevant to improve patient care. Objective: This review seeks to impact patient care by qualitatively reviewing available evidence in support of medical and procedural treatment options for PVOD. Systematic evaluation of data quality and of the level of evidence was completed to generate current treatment recommendations. Methods: A systematic review was conducted to identify primary studies that evaluated treatment outcomes for PVOD. A number of medical literature data bases were queried from January 1998 to May 2020, with completion of subsequent reference searches of retrieved articles to identify all relevant studies. Validated tools for the assessment of bias among both interventional and observational studies were used to complete quality assessment. The summary level of evidence and associated outcomes were used to generate treatment recommendations. Results: Twenty-two publications were identified for qualitative review. Outcomes of alpha-lipoic acid, intranasal and systemic corticosteroids, minocycline, zinc sulfate, vitamin A, sodium citrate, caroverine, intranasal insulin, theophylline, and Gingko biloba are reported. In addition, outcomes of traditional Chinese acupuncture and olfactory training are reviewed. Conclusion: Several medical and procedural treatments may expedite the return of olfactory function after PVOD. Current evidence supports olfactory training as a first-line intervention. Additional study is required to define specific treatment recommendations and expected outcomes for PVOD in the setting of COVID-19.

College competitiveness and medical school exam performance.

BACKGROUND: In medical school, students are tested through periodic USMLE Step 1 and 2 examinations before obtaining a medical license. Traditional predictors of medical school performance include MCAT scores, undergraduate grades, and undergraduate institutional selectivity. Prior studies indicate that admissions committees might unfairly discriminate against applicants who graduated from less competitive universities. However, there is limited literature to determine whether those who attended competitive colleges perform better on USMLE Step 1 and 2 examinations. OBJECTIVE: The purpose of our study is to determine if students who attended competitive undergraduate colleges outperform those who did not on medical school benchmarks. METHODS: We defined a Competitive College as having greater than 10% of its student body scoring 1400 or higher (on a 1600 scale) on the SAT. If this criteria was not met, colleges would be categorized as Non-Competitive. Descriptive statistics and unpaired t-tests were calculated to analyze average test scores on the MCAT, Phase 1 NBME, USMLE Step 1, Phase 2 NBME, and USMLE Step 2. RESULTS: Our findings suggest there are no statistically significant differences between students who do or do not attend competitive undergraduate colleges on these medical school benchmark examinations following the MCAT. CONCLUSION: Admissions committees should use this data to aid in their student selection as our research indicates that institutional selectivity accurately predicts MCAT scores, but not performance on standardized medical school examinations once admitted.

The role of aspirin desensitization followed by oral aspirin therapy in managing patients with aspirin-exacerbated respiratory disease: A Work Group Report from the Rhinitis, Rhinosinusitis and Ocular Allergy Committee of the American Academy of Allergy, Asthma & Immunology.

Aspirin-exacerbated respiratory disease (AERD) is characterized by the clinical triad of chronic rhinosinusitis with nasal polyps, asthma, and an intolerance to medications that inhibit the cycloxgenase-1 enzyme. Patients with AERD on average have more severe respiratory disease compared with patients with chronic rhinosinusitis with nasal polyps and/or asthma alone. Although patients with AERD traditionally develop significant upper and lower respiratory tract symptoms on ingestion of cycloxgenase-1 inhibitors, most of these same patients report clinical benefit when desensitized to aspirin and maintained on daily aspirin therapy. This Work Group Report provides a comprehensive review of aspirin challenges, aspirin desensitizations, and maintenance aspirin therapy in patients with AERD. Identification of appropriate candidates, indications and contraindications, medical and surgical optimization strategies, protocols, medical management during the desensitization, and recommendations for maintenance aspirin therapy following desensitization are reviewed. Also included is a summary of studies evaluating the clinical efficacy of aspirin therapy after desensitization as well as a discussion on the possible cellular and molecular mechanisms explaining how this therapy provides unique benefit to patients with AERD.