Systematic enhancement of protein crystallization efficiency by bulk lysine-to-arginine (KR) substitution.

Structural genomics consortia established that protein crystallization is the primary obstacle to structure determination using x-ray crystallography. We previously demonstrated that crystallization propensity is systematically related to primary sequence, and we subsequently performed computational analyses showing that arginine is the most overrepresented amino acid in crystal-packing interfaces in the Protein Data Bank. Given the similar physicochemical characteristics of arginine and lysine, we hypothesized that multiple lysine-to-arginine (KR) substitutions should improve crystallization. To test this hypothesis, we developed software that ranks lysine sites in a target protein based on the redundancy-corrected KR substitution frequency in homologs. This software can be run interactively on the worldwide web at https://www.pxengineering.org/. We demonstrate that three unrelated single-domain proteins can tolerate 5-11 KR substitutions with at most minor destabilization, and, for two of these three proteins, the construct with the largest number of KR substitutions exhibits significantly enhanced crystallization propensity. This approach rapidly produced a 1.9 Å crystal structure of a human protein domain refractory to crystallization with its native sequence. Structures from Bulk KR-substituted domains show the engineered arginine residues frequently make hydrogen-bonds across crystal-packing interfaces. We thus demonstrate that Bulk KR substitution represents a rational and efficient method for probabilistic engineering of protein surface properties to improve crystallization.

A deep neural network for MRI spinal inflammation in axial spondyloarthritis.

OBJECTIVE: To develop a deep neural network for the detection of inflammatory spine in short tau inversion recovery (STIR) sequence of magnetic resonance imaging (MRI) on patients with axial spondyloarthritis (axSpA). METHODS: A total 330 patients with axSpA were recruited. STIR MRI of the whole spine and clinical data were obtained. Regions of interests (ROIs) were drawn outlining the active inflammatory lesion consisting of bone marrow edema (BME). Spinal inflammation was defined by the presence of an active inflammatory lesion on the STIR sequence. The 'fake-color' images were constructed. Images from 270 and 60 patients were randomly separated into the training/validation and testing sets, respectively. Deep neural network was developed using attention UNet. The neural network performance was compared to the image interpretation by a radiologist blinded to the ground truth. RESULTS: Active inflammatory lesions were identified in 2891 MR images and were absent in 14,590 MR images. The sensitivity and specificity of the derived deep neural network were 0.80 ± 0.03 and 0.88 ± 0.02, respectively. The Dice coefficient of the true positive lesions was 0.55 ± 0.02. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the deep neural network was 0.87 ± 0.02. The performance of the developed deep neural network was comparable to the interpretation of a radiologist with similar sensitivity and specificity. CONCLUSION: The developed deep neural network showed similar sensitivity and specificity to a radiologist with four years of experience. The results indicated that the network can provide a reliable and straightforward way of interpreting spinal MRI. The use of this deep neural network has the potential to expand the use of spinal MRI in managing axSpA.

Deep Learning Algorithm of the SPARCC Scoring System in SI Joint MRI.

BACKGROUND: The Spondyloarthritis Research Consortium of Canada (SPARCC) scoring system is a sacroiliitis grading system. PURPOSE: To develop a deep learning-based pipeline for grading sacroiliitis using the SPARCC scoring system. STUDY TYPE: Prospective. POPULATION: The study included 389 participants (42.2-year-old, 44.6% female, 317/35/37 for training/validation/testing). A pretrained algorithm was used to differentiate image with/without sacroiliitis. FIELD STRENGTH/SEQUENCE: 3-T, short tau inversion recovery (STIR) sequence, fast spine echo. ASSESSMENT: The regions of interest as ground truth for models' training were identified by a rheumatologist (HYC, 10-year-experience) and a radiologist (KHL, 6-year-experience) using the Assessment of Spondyloarthritis International Society definition of MRI sacroiliitis independently. Another radiologist (YYL, 4.5-year-experience) solved the discrepancies. The bone marrow edema (BME) and sacroiliac region models were for segmentation. Frangi-filter detected vessels used as intense reference. Deep learning pipeline scored using SPARCC scoring system evaluating presence and features of BMEs. A rheumatologist (SCWC, 6-year-experience) and a radiologist (VWHL, 14-year-experience) scored using the SPARCC scoring system once. The radiologist (YYL) scored twice with 5-day interval. STATISTICAL TESTS: Independent samples t-tests and Chi-squared tests were used. Interobserver and intraobserver reliability by intraclass correlation coefficient (ICC) and Pearson coefficient evaluated consistency between readers and the deep learning pipeline. We evaluated the performance using sensitivity, accuracy, positive predictive value, and Dice coefficient. A P-value <0.05 was considered statistically significant. RESULTS: The ICC and the Pearson coefficient between the SPARCC scores from three readers and the deep learning pipeline were 0.83 and 0.86, respectively. The sensitivity in identifying BME and accuracy of identifying SI joints and blood vessels was 0.83, 0.90, and 0.88, respectively. The dice coefficients were 0.82 (sacrum) and 0.80 (ilium). DATA CONCLUSION: The high consistency with human readers indicated that deep learning pipeline may provide a SPARCC-informed deep learning approach for scoring of STIR images in spondyloarthritis. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.

Comparative genetic, biochemical, and biophysical analyses of the four E. coli ABCF paralogs support distinct functions related to mRNA translation.

Multiple paralogous ABCF ATPases are encoded in most genomes, but the physiological functions remain unknown for most of them. We herein compare the four Escherichia coli K12 ABCFs - EttA, Uup, YbiT, and YheS - using assays previously employed to demonstrate EttA gates the first step of polypeptide elongation on the ribosome dependent on ATP/ADP ratio. A Δ uup knockout, like Δ ettA , exhibits strongly reduced fitness when growth is restarted from long-term stationary phase, but neither Δ ybiT nor Δ yheS exhibits this phenotype. All four proteins nonetheless functionally interact with ribosomes based on in vitro translation and single-molecule fluorescence resonance energy transfer experiments employing variants harboring glutamate-to-glutamine active-site mutations (EQ 2 ) that trap them in the ATP-bound conformation. These variants all strongly stabilize the same global conformational state of a ribosomal elongation complex harboring deacylated tRNA Val in the P site. However, EQ 2 -Uup uniquely exchanges on/off the ribosome on a second timescale, while EQ 2 -YheS-bound ribosomes uniquely sample alternative global conformations. At sub-micromolar concentrations, EQ 2 -EttA and EQ 2 -YbiT fully inhibit in vitro translation of an mRNA encoding luciferase, while EQ 2 -Uup and EQ 2 -YheS only partially inhibit it at ~10-fold higher concentrations. Moreover, tripeptide synthesis reactions are not inhibited by EQ 2 -Uup or EQ 2 -YheS, while EQ 2 -YbiT inhibits synthesis of both peptide bonds and EQ 2 -EttA specifically traps ribosomes after synthesis of the first peptide bond. These results support the four E. coli ABCF paralogs all having different activities on translating ribosomes, and they suggest that there remains a substantial amount of functionally uncharacterized "dark matter" involved in mRNA translation.

Multiplatform analyses reveal distinct drivers of systemic pathogenesis in adult versus pediatric severe acute COVID-19.

The pathogenesis of multi-organ dysfunction associated with severe acute SARS-CoV-2 infection remains poorly understood. Endothelial damage and microvascular thrombosis have been identified as drivers of COVID-19 severity, yet the mechanisms underlying these processes remain elusive. Here we show alterations in fluid shear stress-responsive pathways in critically ill COVID-19 adults as compared to non-COVID critically ill adults using a multiomics approach. Mechanistic in-vitro studies, using microvasculature-on-chip devices, reveal that plasma from critically ill COVID-19 adults induces fibrinogen-dependent red blood cell aggregation that mechanically damages the microvascular glycocalyx. This mechanism appears unique to COVID-19, as plasma from non-COVID sepsis patients demonstrates greater red blood cell membrane stiffness but induces less significant alterations in overall blood rheology. Multiomics analyses in pediatric patients with acute COVID-19 or the post-infectious multi-inflammatory syndrome in children (MIS-C) demonstrate little overlap in plasma cytokine and metabolite changes compared to adult COVID-19 patients. Instead, pediatric acute COVID-19 and MIS-C patients show alterations strongly associated with cytokine upregulation. These findings link high fibrinogen and red blood cell aggregation with endotheliopathy in adult COVID-19 patients and highlight differences in the key mediators of pathogenesis between adult and pediatric populations.

CD8+ lymphocytes do not impact SIV reservoir establishment under ART.

Persistence of the human immunodeficiency virus type-1 (HIV-1) latent reservoir in infected individuals remains a problem despite fully suppressive antiretroviral therapy (ART). While reservoir formation begins during acute infection, the mechanisms responsible for its establishment remain unclear. CD8+ T cells are important during the initial control of viral replication. Here we examined the effect of CD8+ T cells on formation of the latent reservoir in simian immunodeficiency virus (SIV)-infected macaques by performing experimental CD8+ depletion either before infection or before early (that is, day 14 post-infection) ART initiation. We found that CD8+ depletion resulted in slower decline of viremia, indicating that CD8+ lymphocytes reduce the average lifespan of productively infected cells during acute infection and early ART, presumably through SIV-specific cytotoxic T lymphocyte (CTL) activity. However, CD8+ depletion did not change the frequency of infected CD4+ T cells in the blood or lymph node as measured by the total cell-associated viral DNA or intact provirus DNA assay. In addition, the size of the persistent reservoir remained the same when measuring the kinetics of virus rebound after ART interruption. These data indicate that during early SIV infection, the viral reservoir that persists under ART is established largely independent of CTL control.

Sports Bra Pressure: Effect on Body Skin Temperature and Wear Comfort.

Sports bras are an essential apparel for active women, but may exert excessive pressure that negatively affects thermoregulation, thermal comfort and wear sensation. This study measures skin temperature changes during short durations of exercise on a treadmill with different bra pressures. The results based on 21 female subjects (age: 27.2 ± 4.5 years old) show that bras with more pressure at the underband or shoulder straps do not cause statistically significant skin temperature changes during exercise (p > 0.05). Nevertheless, compared to the optimal bra fit, significant differences in bra-breast skin temperature are found during running, cooling down and sitting when the bra pressure is increased (p < 0.05), particularly under bra cup (T1) in this study. The FLIR thermal images can visualize the skin temperature changes at abdomen throughout the four activity stages. Subjective sensations of bra thermal comfort, pressure and breast support are assessed. Despite the increased pressure on the shoulders and chest wall, perceptions towards thermal comfort remain unchanged. The perceived pressure comfort and support sensation amongst the 4 bra conditions are comparable. Interestingly, positive sensations of pressure comfort and breast support are perceived with a tight-fitting sports bra during treadmill exercise. High pressures induced by sports bras (>4 kPa) that habitually considered harmful to the human body may not lead to wear discomfort but enhance bra support sensation and a sense of security to the wearers.

MRI lesions in SpA: a comparison with noninflammatory back pain using propensity score adjustment method.

Background: Magnetic resonance imaging (MRI) is important in the management of axial spondyloarthritis (SpA). However, many MRI lesions are not exclusive to axial SpA. Further characterization of these lesions may lead to better clinical decisions. Objective: The objective of this study was to compare the frequency of individual spinal MRI lesions between axial SpA and noninflammatory back pain. The factors associated with individual lesions in participants with axial SpA were also determined. Design: This was a cross-sectional observational study. Methods: MRI lesions in 447 participants with axial SpA and 122 participants with noninflammatory back pain were compared using the propensity score adjustment method. Individual lesions included discovertebral lesions (DVL), Modic type 1 lesions, DVL without Modic type 1 lesions, facet joint lesions, costovertebral joint lesions, corner inflammatory lesions (CIL), and fatty corner lesions (FCL). The factors associated with the lesions were determined using regression analyses. Results: Among participants with axial SpA, 81.9% were HLA-B27-positive, 55.0% had radiographic axial SpA, and 60.5% had radiographic features of spinal damage (mSASSS >2). Almost half (48.6% in axial SpA versus 31.1% in noninflammatory back pain) had inflammatory lesions on spinal MRI. In propensity score matching with noninflammatory back pain, axial SpA had an increased occurrence of DVL without Modic type 1 lesion (OR = 3.43, p = 0.01), costovertebral lesion (OR = 11.89, p = 0.02), number of CIL (B = 1.19, p < 0.001), and number of FCL (B = 3.33, p < 0.001). Similar associations were found in the regression models in the radiographic axial SpA subgroup: DVL without Modic type 1 lesion (OR = 2.46, p = 0.001), costovertebral lesion (OR = 3.86, p < 0.001), number of CIL (B = 1.13, p < 0.001), and FCL (B = 2.29, p < 0.01). Conclusion: MRI lesions including DVL without Modic type 1, costovertebral joint lesions, CIL, and FCL were more specific in axial SpA.

Clinical, radiological, and magnetic resonance imaging characteristics of axial spondyloarthritis with late onset.

We aimed to investigate the clinical, diagnostic, and imaging features of patients with late onset axial spondyloarthritis (SpA) with initial symptom manifestation aged over 45 years. Participants with axial SpA were consecutively recruited. Clinical, demographic, blood, and imaging parameters were compared between the groups with early (≤45 years) and late onset (>45 years) at a cross-sectional level. Logistic regressions were used to determine the independent associations with axial SpA with late onset. A total of 455 participants were recruited. Among them, 70 (15.4%) had late onset disease. Multivariate analyses showed that axial SpA with late onset was associated with higher C-reactive protein based ankylosing spondylitis disease activity index (ASDAS-CRP) (B = 0.10; P = .04), higher intensity of spinal inflammation as measured by maximum apparent diffusion coefficient (spinal ADC max) (B = 0.27; P = .03) and mean ADC (spinal ADC mean) (B = 0.30; P = .004), lower modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) (B = -0.12; P = .02), more tender joint count (B = 0.12; P = .02), and fewer inflammatory back pain (IBP) (OR = 0.26; P < .001). Axial SpA with late onset had higher clinical disease activity, higher intensity of spinal MRI inflammation, less radiographic damage, and more tender joint count. There was also less inflammatory back pain, which could make the diagnosis more difficult.

Deep learning algorithms for magnetic resonance imaging of inflammatory sacroiliitis in axial spondyloarthritis.

OBJECTIVE: The aim of this study was to develop a deep learning algorithm for detection of active inflammatory sacroiliitis in short tau inversion recovery (STIR) sequence MRI. METHODS: A total of 326 participants with axial SpA, and 63 participants with non-specific back pain (NSBP) were recruited. STIR MRI of the SI joints was performed and clinical data were collected. Region of interests (ROIs) were drawn outlining bone marrow oedema, a reliable marker of active inflammation, which formed the ground truth masks from which 'fake-colour' images were derived. Both the original and fake-colour images were randomly allocated into either the training and validation dataset or the testing dataset. Attention U-net was used for the development of deep learning algorithms. As a comparison, an independent radiologist and rheumatologist, blinded to the ground truth masks, were tasked with identifying bone marrow oedema in the MRI scans. RESULTS: Inflammatory sacroiliitis was identified in 1398 MR images from 228 participants. No inflammation was found in 3944 MRI scans from 161 participants. The mean sensitivity of the algorithms derived from the original dataset and fake-colour image dataset were 0.86 (0.02) and 0.90 (0.01), respectively. The mean specificity of the algorithms derived from the original and the fake-colour image datasets were 0.92 (0.02) and 0.93 (0.01), respectively. The mean testing dice coefficients were 0.48 (0.27) for the original dataset and 0.51 (0.25) for the fake-colour image dataset. The area under the curve of the receiver operating characteristic (AUC-ROC) curve of the algorithms using the original dataset and the fake-colour image dataset were 0.92 and 0.96, respectively. The sensitivity and specificity of the algorithms were comparable with the interpretation by a radiologist, but outperformed that of the rheumatologist. CONCLUSION: An MRI deep learning algorithm was developed for detection of inflammatory sacroiliitis in axial SpA.

Both ASDAS and ADC are associated with spinal mobility in active axial spondyloarthritis: A comparison between early and later disease.

OBJECTIVE: Using diffusion-weighted imaging (DWI)-derived apparent diffusion coefficient (ADC), we aimed to determine the relationship between intensity of spinal inflammation and mobility in patients with axial spondyloarthritis (SpA) in early and later stages of active disease. The Ankylosing Spondylitis Disease Activity Score (ASDAS) was also used for a more comprehensive evaluation. METHODS: Participants with axial SpA and back pain were recruited from 10 rheumatology centers. Clinical, biochemical and radiological parameters were collected. Short tau inversion recovery (STIR) sequence magnetic resonance imaging (MRI) and DWI of the spine and sacroiliac (SI) joints were performed. ADC maps were generated. Participants were examined for Bath Ankylosing Spondylitis Metrology Index (BASMI). Linear regression models were used to determine associations between BASMI and various clinical, radiological, and MRI parameters in participants with active inflammation on spinal ADC maps. RESULTS: One-hundred and twenty-seven participants were included in the analyses. Multivariate linear regression showed that mean ADC spine (ß = .16; P = .03), ASDAS-C-reactive protein (CRP) (ß = .29, P < .001), and ASDAS-erythrocyte sedimentation rate (ESR) (ß = .25, P < .01) were associated with BASMI. In participants with duration of back pain ≤3 years, mean spine ADC (ß = .37; P = .03), ASDAS-CRP (ß = .44; P = .01), and ASDAS-ESR (ß = .42; P = .01) were associated with BASMI after adjustment for confounding factors. In participants with duration of back pain >3 years, only ASDAS-CRP (ß = .25; P < .01) and ASDAS-ESR (ß = .20; P = .20) were associated with BASMI. CONCLUSION: Intensity of inflammation and clinical disease activity were independently associated with impairment of spinal mobility. The associations were stronger in early (≤3 years) than later disease.

Length of Cervical Stenosis, Admission ASIA Motor Scores, and BASIC Scores Are Predictors of Recovery Rate Following Central Cord Syndrome.

STUDY DESIGN: A retrospective cohort study. OBJECTIVE: This study aims to determine whether quantitative magnetic resonance imaging (MRI) parameters and radiological scoring systems could be used as a reliable assessment tool for predicting neurological recovery trajectory following acute traumatic central cord injury syndrome (CCS). SUMMARY OF BACKGROUND DATA: Controversy remains in whether CCS should be managed conservatively or by early surgical decompression. It is essential to understand how clinical and radiological parameters correlate with neurological deficits and how they predict recovery trajectories. METHODS: We identified patients with CCS admitted between 2011 and 2018 with a minimum of 1-year follow-up. Cervical MRIs were analyzed for cord/canal dimensions, Brain and Spinal Injury Center (BASIC) scores and sagittal grading as ordinal scales of intraparenchymal cord injury. Japanese Orthopaedic Association (JOA) recovery rates (≥50% as good, < 50% as poor) were analyzed against these variables by logistic regression and receiver operator characteristic (ROC) curves. Additionally, we evaluated American Spinal Injury Association motor scale (AMS) scores/recovery rates. RESULTS: Sixty patients were included, of which 30 were managed conservatively and 30 via surgical decompression. The average follow-up duration for the entire cohort was (51.1 ±â€Š25.7) months. Upon admission, sagittal grading correlated with AMS and JOA scores (P < 0.01, ß = 0.48). Volume of the C2 to C7 canal and axial cord area over the site of maximal compression correlated with AMS and JOA scores respectively (P = 0.04, ß = 0.26; P = 0.01, ß = 0.28). We determined admission AMS more than 61 to be a clinical cutoff for good recovery (area under the receiver operating curve [AUC] = 0.74, 95% confidence interval [CI]: 0.61-0.85, sensitivity 80.9%, specificity 69.2%, P < 0.01). Radiological cutoffs to identify patients with poor recovery rates were length of cervical spinal stenosis more than 3.9 cm (AUC = 0.76, 95% CI: 0.63-0.87, specificity 91.7%, sensitivity 52.2%, P < 0.01), BASIC score of more than 1 (AUC = 0.69, 95% CI: 0.56-0.81, specificity 80.5%, sensitivity 51.1%, P = 0.02). Surgical decompression performed as a salvage procedure upon plateau of recovery did not improve neurological outcomes. CONCLUSION: Clinical and radiological parameters upon presentation were prognosticative of neurological recovery rates in CCS. Surgery performed beyond the acute post-injury period failed to improve outcomes.Level of Evidence: 3.

A flow-cytometry-based protocol using diverse cell types for detecting autoantibodies from human plasma and serum samples.

Here, we describe a protocol for cell-based detection of autoantibodies from human plasma and serum samples using a standard flow cytometer. The protocol allows detection of autoantibodies against a wide array of extracellular antigens. Antigen coverage is limited to the cell types tested, and researchers will need to further determine if autoantibody-positive samples correlate with cytotoxic or clinical outcomes. This protocol is less expensive and faster to perform when compared to protein microarrays and requires no prior knowledge of potential targets. For complete details on the use and execution of this protocol, please refer to Wong et al. (2021).

Statin-associated anti-3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) myopathy: Imaging findings on thigh-muscle magnetic resonance imaging (MRI) in six patients.

INTRODUCTION/AIMS: Anti-3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR) myopathy is a rare but serious complication of statin use. The aim of this study was to describe the imaging features of statin-associated anti-HMGCR myopathy on thigh muscle magnetic resonance imaging (MRI). METHODS: Thigh muscle MRI images of six patients with statin-associated anti-HMGCR myopathy were reviewed for muscle edema, fatty replacement, and fascial edema in four compartments of each thigh: gluteal, anterior, medial, and posterior. Signal intensity ratio (SIR) was calculated in 17 muscles in both thighs on T2-weighted fat-suppressed images. Intracompartmental comparison of T2 SIRs of different muscles were performed. RESULTS: All patients demonstrated bilateral symmetrical pan-compartmental muscle edema. Three patients had fatty infiltration, involving gluteal and/or posterior compartments. In the anterior compartment, rectus femoris and vastus lateralis most frequently demonstrated the highest T2 SIR. In the posterior compartment, semimembranosus most frequently demonstrated the highest T2 SIR. The long head of the biceps femoris always had a higher T2 SIR than the short head. DISCUSSION: Statin-associated anti-HMGCR myopathy commonly demonstrates bilateral symmetrical pan-compartmental edema on thigh muscle MRI, with anterolateral predilection in the anterior compartment, and greater involvement of the semimembranosus with relative sparing of the short head of the biceps femoris in the posterior compartment. These observations can contribute to the diagnosis of statin-associated anti-HMGCR myopathy.

Modular Lentiviral Vectors for Highly Efficient Transgene Expression in Resting Immune Cells.

Gene/cell therapies are promising strategies for the many presently incurable diseases. A key step in this process is the efficient delivery of genes and gene-editing enzymes to many cell types that may be resistant to lentiviral vector transduction. Herein we describe tuning of a lentiviral gene therapy platform to focus on genetic modifications of resting CD4+ T cells. The motivation for this was to find solutions for HIV gene therapy efforts. Through selection of the optimal viral envelope and further modification to its expression, lentiviral fusogenic delivery into resting CD4+ T cells exceeded 80%, yet Sterile Alpha Motif and HD domain 1 (SAMHD1) dependent and independent intracellular restriction factors within resting T cells then dominate delivery and integration of lentiviral cargo. Overcoming SAMHD1-imposed restrictions, only observed up to 6-fold increase in transduction, with maximal gene delivery and expression of 35%. To test if the biologically limiting steps of lentiviral delivery are reverse transcription and integration, we re-engineered lentiviral vectors to simply express biologically active mRNA to direct transgene expression in the cytoplasm. In this setting, we observed gene expression in up to 65% of resting CD4+ T cells using unconcentrated MS2 lentivirus-like particles (MS2-LVLPs). Taken together, our findings support a gene therapy platform that could be readily used in resting T cell gene editing.

Broad auto-reactive IgM responses are common in critically ill patients, including those with COVID-19.

The pathogenesis of severe coronavirus disease 2019 (COVID-19) remains poorly understood. While several studies suggest that immune dysregulation plays a central role, the key mediators of this process are yet to be defined. Here, we demonstrate that plasma from a high proportion (93%) of critically ill COVID-19 patients, but not healthy controls, contains broadly auto-reactive immunoglobulin M (IgM) and less frequently auto-reactive IgG or IgA. Importantly, these auto-IgMs preferentially recognize primary human lung cells in vitro, including pulmonary endothelial and epithelial cells. By using a combination of flow cytometry, analytical proteome microarray technology, and lactose dehydrogenase (LDH)-release cytotoxicity assays, we identify high-affinity, complement-fixing, auto-reactive IgM directed against 260 candidate autoantigens, including numerous molecules preferentially expressed on the cellular membranes of pulmonary, vascular, gastrointestinal, and renal tissues. These findings suggest that broad IgM-mediated autoimmune reactivity may be involved in the pathogenesis of severe COVID-19, thereby identifying a potential target for therapeutic interventions.

A Novel Evidence-Based Predictor Tool for Hospitalization and Length of Stay: Insights from COVID19 Patients in New York City

BackgroundCoronavirus disease 2019 (COVID-19) has evolved into a true global pandemic infecting more than 30 million people worldwide. Predictive models for key outcomes have the potential to optimize resource utilization and patient outcome as outbreaks continue to occur worldwide. We aimed to design and internally validate a web-based calculator predictive of hospitalization and length of stay (LOS) in a large cohort of COVID-19 positive patients presenting to the Emergency Department (ED) in a New York City health system. MethodsThe study cohort consisted of consecutive adult (>18 years) patients presenting to the ED of one of the Mount Sinai Health System hospitals between March, 2020 and April, 2020 who were diagnosed with COVID-19. Logistic regression was utilized to construct predictive models for hospitalization and prolonged (>3 days) LOS. Discrimination was evaluated using area under the receiver operating curve (AUC). Internal validation with bootstrapping was performed, and a web-based calculator was implemented. ResultsThe cohort consisted of 5859 patients with a hospitalization rate of 65% and a prolonged LOS rate of 75% among hospitalized patients. Independent predictors of hospitalization included older age (OR=6.29; 95% CI [1.83-2.63], >65 vs. 18-44), male sex (OR=1.35 [1.17-1.55]), chronic obstructive pulmonary disease (OR=1.74; [1.00-3.03]), hypertension (OR=1.39; [1.13-1.70]), diabetes (OR=1.45; [1.16-1.81]), chronic kidney disease (OR=1.69; [1.23-2.32]), elevated maximum temperature (OR=4.98; [4.28-5.79]), and low minimum oxygen saturation (OR=13.40; [10.59-16.96]). Predictors of extended LOS included older age (OR=1.03 [1.02-1.04], per year), chronic kidney disease (OR=1.91 [1.35-2.71]), elevated maximum temperature (OR=2.91 [2.40- 3.53]), and low minimum percent oxygen saturation (OR=3.89 [3.16-4.79]). AUCs of 0.881 and 0.770 were achieved for hospitalization and LOS, respectively. A calculator was made available under the following URL: https://covid19-outcome-prediction.shinyapps.io/COVID19_Hospitalization_Calculator/ ConclusionThe prediction tool derived from this study can be used to optimize resource allocation, guide quality of care, and assist in designing future studies on the triage and management of patients with COVID-19.