COVID-19 Vaccine: A Common Suspect but Rare Culprit in Drug Rash With Eosinophilia and Systemic Symptoms (DRESS) Syndrome.

Drug rash with eosinophilia and systemic symptoms (DRESS) syndrome is a rare drug reaction that commonly presents with rash, fever, lymphadenopathy, eosinophilia, and multiorgan involvement. We present a case of this syndrome in a 31-year-old male who presented with a diffuse erythematous morbilliform rash with high fever and elevated liver enzymes. Upon history taking, the patient reported acute onset of multiple seizures that required intubation and ICU admission six weeks prior, which started 24 hours after receiving the Johnson and Johnson Janssen coronavirus disease 2019 (COVID-19) vaccine. During that hospitalization, he was given antiseizure medications Keppra (levetiracetam) and Dilantin (phenytoin), which he was eventually discharged home with. During our encounter with the patient, Dermatology was consulted and recommended punch skin biopsy, which revealed spongiotic dermatitis with subcorneal pustules along with superficial perivascular and mixed lymphocytic and neutrophilic infiltrate with dermal edema and rare eosinophils. Given these findings in conjunction with the patient's fever, elevated liver function, and cervical lymphadenopathy, the rash was consistent with DRESS syndrome or a pustular drug eruption likely secondary to phenytoin or levetiracetam. This case was eventually resolved with treatment with oral and topical corticosteroids and close outpatient follow-up with Dermatology. Prompt diagnosis and treatment of DRESS syndrome are therefore critical as the mortality rate can be as high as 10% in the setting of liver failure.

Biochemical, biophysical, and immunological characterization of respiratory secretions in severe SARS-CoV-2 infections.

Thick, viscous respiratory secretions are a major pathogenic feature of COVID-19, but the composition and physical properties of these secretions are poorly understood. We characterized the composition and rheological properties (i.e., resistance to flow) of respiratory secretions collected from intubated COVID-19 patients. We found the percentages of solids and protein content were greatly elevated in COVID-19 compared with heathy control samples and closely resembled levels seen in cystic fibrosis, a genetic disease known for thick, tenacious respiratory secretions. DNA and hyaluronan (HA) were major components of respiratory secretions in COVID-19 and were likewise abundant in cadaveric lung tissues from these patients. COVID-19 secretions exhibited heterogeneous rheological behaviors, with thicker samples showing increased sensitivity to DNase and hyaluronidase treatment. In histologic sections from these same patients, we observed increased accumulation of HA and the hyaladherin versican but reduced tumor necrosis factor-stimulated gene-6 staining, consistent with the inflammatory nature of these secretions. Finally, we observed diminished type I interferon and enhanced inflammatory cytokines in these secretions. Overall, our studies indicated that increases in HA and DNA in COVID-19 respiratory secretion samples correlated with enhanced inflammatory burden and suggested that DNA and HA may be viable therapeutic targets in COVID-19 infection.

Biochemical, Biophysical, and Immunological Characterization of Respiratory Secretions in Severe SARS-CoV-2 (COVID-19) Infections.

Thick, viscous respiratory secretions are a major pathogenic feature of COVID-19 disease, but the composition and physical properties of these secretions are poorly understood. We characterized the composition and rheological properties (i.e. resistance to flow) of respiratory secretions collected from intubated COVID-19 patients. We find the percent solids and protein content are greatly elevated in COVID-19 compared to heathy control samples and closely resemble levels seen in cystic fibrosis, a genetic disease known for thick, tenacious respiratory secretions. DNA and hyaluronan (HA) are major components of respiratory secretions in COVID-19 and are likewise abundant in cadaveric lung tissues from these patients. COVID-19 secretions exhibit heterogeneous rheological behaviors with thicker samples showing increased sensitivity to DNase and hyaluronidase treatment. In histologic sections from these same patients, we observe increased accumulation of HA and the hyaladherin versican but reduced tumor necrosis factorâ€"stimulated gene-6 (TSG6) staining, consistent with the inflammatory nature of these secretions. Finally, we observed diminished type I interferon and enhanced inflammatory cytokines in these secretions. Overall, our studies indicate that increases in HA and DNA in COVID-19 respiratory secretion samples correlate with enhanced inflammatory burden and suggest that DNA and HA may be viable therapeutic targets in COVID-19 infection.

Radiomics Biomarkers Correlate with CD8 Expression and Predict Immune Signatures in Melanoma Patients.

Treatment for metastatic melanoma includes targeted and/or immunotherapy. Although many patients respond, only a subset has complete response. As late-stage patients often have multiple tumors in difficult access sites, non-invasive techniques are necessary for the development of predictive/prognostic biomarkers. PET/CT scans from 52 patients with stage III/IV melanoma were assessed and CT image parameters were evaluated as prognostic biomarkers. Analysis indicated patients with high standard deviation or high mean of positive pixels (MPP) had worse progression-free survival (P = 0.00047 and P = 0.0014, respectively) and worse overall survival (P = 0.0223 and P = 0.0465, respectively). Whole-exome sequencing showed high MPP was associated with BRAF mutation status (P = 0.0389). RNA-sequencing indicated patients with immune "cold" signatures had worse survival, which was associated with CT biomarker, MPP4 (P = 0.0284). Multiplex immunofluorescence confirmed a correlation between CD8 expression and image biomarkers (P = 0.0028). IMPLICATIONS: CT parameters have the potential to be cost-effective biomarkers of survival in melanoma, and reflect the tumor immune-microenvironment. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/19/6/950/F1.large.jpg.

Biochemical and Biophysical Characterization of Respiratory Secretions in Severe SARS-CoV-2 (COVID-19) Infections.

Thick, viscous respiratory secretions are a major pathogenic feature of COVID-19 disease, but the composition and physical properties of these secretions are poorly understood. We characterized the composition and rheological properties (i.e. resistance to flow) of respiratory secretions collected from intubated COVID-19 patients. We found the percent solids and protein content are all greatly elevated in COVID-19 compared to heathy control samples and closely resemble levels seen in cystic fibrosis (CF), a genetic disease known for thick, tenacious respiratory secretions. DNA and hyaluronan are major components of respiratory secretions in COVID-19 and are likewise abundant in cadaveric lung tissues from these patients. COVID-19 secretions exhibited heterogeneous rheological behaviors with thicker samples showing increased sensitivity to DNase and hyaluronidase treatment. These results highlight the dramatic biophysical properties of COVID-19 respiratory secretions and suggest that DNA and hyaluronan may be viable therapeutic targets in COVID-19 infection.

Congenital myasthenic syndrome caused by a frameshift insertion mutation in GFPT1.

OBJECTIVE: Description of a new variant of the glutamine-fructose-6-phosphate transaminase 1 (GFPT1) gene causing congenital myasthenic syndrome (CMS) in 3 children from 2 unrelated families. METHODS: Muscle biopsies, EMG, and whole-exome sequencing were performed. RESULTS: All 3 patients presented with congenital hypotonia, muscle weakness, respiratory insufficiency, head lag, areflexia, and gastrointestinal dysfunction. Genetic analysis identified a homozygous frameshift insertion in the GFPT1 gene (NM_001244710.1: c.686dupC; p.Arg230Ter) that was shared by all 3 patients. In one of the patients, inheritance of the variant was through uniparental disomy (UPD) with maternal origin. Repetitive nerve stimulation and single-fiber EMG was consistent with the clinical diagnosis of CMS with a postjunctional defect. Ultrastructural evaluation of the muscle biopsy from one of the patients showed extremely attenuated postsynaptic folds at neuromuscular junctions and extensive autophagic vacuolar pathology. CONCLUSIONS: These results expand on the spectrum of known loss-of-function GFPT1 mutations in CMS12 and in one family demonstrate a novel mode of inheritance due to UPD.

Modelagem de plumas de efluentes entre Praia Grande e Peruíbe, litoral do estado de São Paulo, Brasil / Modeling effluent plumes between Praia Grande and Peruíbe, São Paulo State's shore, Brazil

RESUMO O presente trabalho teve como objetivo monitorar a dispersão de plumas de efluentes provenientes dos emissários submarinos de Praia Grande e do Rio Itanhaém, por meio do processamento do modelo Delft3D (módulos hidrodinâmico e de qualidade da água) e do modelo Visual Plumes (módulo UM3). A modelagem de campo próximo indicou que as correntes geradas pelo vento foram mais eficientes do que as correntes de maré na diluição inicial das plumas dos emissários; e a modelagem de campo distante, validada qualitativamente, demonstrou que as concentrações dos potenciais poluentes de todas as plumas simuladas não ultrapassam os limites máximos estabelecidos pelo Conselho Nacional do Meio Ambiente quando essas plumas atingem as praias da região de estudo.

ABSTRACT This study's goal was to monitor the dispersion of effluent plumes from the Praia Grande's submarine outfalls and from the Itanhaém River by processing the Delft3D model (hydrodynamic and water quality modules) and the Visual Plumes model (UM3 module). The near field modeling indicated that wind-driven currents were more efficient than tidal currents in the initial dilution of the outfalls' plumes; and the far field modeling, qualitatively validated, demonstrated that the potential pollutants' concentrations in all the simulated plumes don't exceed the maximum limits established by the National Environmental Council when these plumes reach the study region's beaches.

Peripheral TREM1 responses to brain and intestinal immunogens amplify stroke severity.

Stroke is a multiphasic process in which initial cerebral ischemia is followed by secondary injury from immune responses to ischemic brain components. Here we demonstrate that peripheral CD11b+CD45+ myeloid cells magnify stroke injury via activation of triggering receptor expressed on myeloid cells 1 (TREM1), an amplifier of proinflammatory innate immune responses. TREM1 was induced within hours after stroke peripherally in CD11b+CD45+ cells trafficking to ischemic brain. TREM1 inhibition genetically or pharmacologically improved outcome via protective antioxidant and anti-inflammatory mechanisms. Positron electron tomography imaging using radiolabeled antibody recognizing TREM1 revealed elevated TREM1 expression in spleen and, unexpectedly, in intestine. In the lamina propria, noradrenergic-dependent increases in gut permeability induced TREM1 on inflammatory Ly6C+MHCII+ macrophages, further increasing epithelial permeability and facilitating bacterial translocation across the gut barrier. Thus, following stroke, peripheral TREM1 induction amplifies proinflammatory responses to both brain-derived and intestinal-derived immunogenic components. Critically, targeting this specific innate immune pathway reduces cerebral injury.

Transverse relaxation-based assessment of mammographic density and breast tissue composition by single-sided portable NMR.

PURPOSE: Elevated mammographic density (MD) is an independent risk factor for breast cancer (BC) as well as a source of masking in X-ray mammography. High-frequency longitudinal monitoring of MD could also be beneficial in hormonal BC prevention, where early MD changes herald the treatment's success. We present a novel approach to quantification of MD in breast tissue using single-sided portable NMR. Its development was motivated by the low cost of portable-NMR instrumentation, the suitability for measurements in vivo, and the absence of ionizing radiation. METHODS: Five breast slices were obtained from three patients undergoing prophylactic mastectomy or breast reduction surgery. Carr-Purcell-Meiboom-Gill (CPMG) relaxation curves were measured from (1) regions of high and low MD (HMD and LMD, respectively) in the full breast slices; (2) the same regions excised from the full slices; and (3) excised samples after H2 O-D2 O replacement. T2 distributions were reconstructed from the CPMG decays using inverse Laplace transform. RESULTS: Two major peaks, identified as fat and water, were consistently observed in the T2 distributions of HMD regions. The LMD T2 distributions were dominated by the fat peak. The relative areas of the two peaks exhibited statistically significant (P < .005) differences between HMD and LMD regions, enabling their classification as HMD or LMD. The relative-area distributions exhibited no statistically significant differences between full slices and excised samples. CONCLUSION: T2 -based portable-NMR analysis is a novel approach to MD quantification. The ability to quantify tissue composition, combined with the low cost of instrumentation, make this approach promising for clinical applications.

In Silico Labeling: Predicting Fluorescent Labels in Unlabeled Images.

Microscopy is a central method in life sciences. Many popular methods, such as antibody labeling, are used to add physical fluorescent labels to specific cellular constituents. However, these approaches have significant drawbacks, including inconsistency; limitations in the number of simultaneous labels because of spectral overlap; and necessary perturbations of the experiment, such as fixing the cells, to generate the measurement. Here, we show that a computational machine-learning approach, which we call "in silico labeling" (ISL), reliably predicts some fluorescent labels from transmitted-light images of unlabeled fixed or live biological samples. ISL predicts a range of labels, such as those for nuclei, cell type (e.g., neural), and cell state (e.g., cell death). Because prediction happens in silico, the method is consistent, is not limited by spectral overlap, and does not disturb the experiment. ISL generates biological measurements that would otherwise be problematic or impossible to acquire.

Assessing microscope image focus quality with deep learning.

BACKGROUND: Large image datasets acquired on automated microscopes typically have some fraction of low quality, out-of-focus images, despite the use of hardware autofocus systems. Identification of these images using automated image analysis with high accuracy is important for obtaining a clean, unbiased image dataset. Complicating this task is the fact that image focus quality is only well-defined in foreground regions of images, and as a result, most previous approaches only enable a computation of the relative difference in quality between two or more images, rather than an absolute measure of quality. RESULTS: We present a deep neural network model capable of predicting an absolute measure of image focus on a single image in isolation, without any user-specified parameters. The model operates at the image-patch level, and also outputs a measure of prediction certainty, enabling interpretable predictions. The model was trained on only 384 in-focus Hoechst (nuclei) stain images of U2OS cells, which were synthetically defocused to one of 11 absolute defocus levels during training. The trained model can generalize on previously unseen real Hoechst stain images, identifying the absolute image focus to within one defocus level (approximately 3 pixel blur diameter difference) with 95% accuracy. On a simpler binary in/out-of-focus classification task, the trained model outperforms previous approaches on both Hoechst and Phalloidin (actin) stain images (F-scores of 0.89 and 0.86, respectively over 0.84 and 0.83), despite only having been presented Hoechst stain images during training. Lastly, we observe qualitatively that the model generalizes to two additional stains, Hoechst and Tubulin, of an unseen cell type (Human MCF-7) acquired on a different instrument. CONCLUSIONS: Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler.

Lethal neonatal case and review of primary short-chain enoyl-CoA hydratase (SCEH) deficiency associated with secondary lymphocyte pyruvate dehydrogenase complex (PDC) deficiency.

Mutations in ECHS1 result in short-chain enoyl-CoA hydratase (SCEH) deficiency which mainly affects the catabolism of various amino acids, particularly valine. We describe a case compound heterozygous for ECHS1 mutations c.836T>C (novel) and c.8C>A identified by whole exome sequencing of proband and parents. SCEH deficiency was confirmed with very low SCEH activity in fibroblasts and nearly absent immunoreactivity of SCEH. The patient had a severe neonatal course with elevated blood and cerebrospinal fluid lactate and pyruvate concentrations, high plasma alanine and slightly low plasma cystine. 2-Methyl-2,3-dihydroxybutyric acid was markedly elevated as were metabolites of the three branched-chain α-ketoacids on urine organic acids analysis. These urine metabolites notably decreased when lactic acidosis decreased in blood. Lymphocyte pyruvate dehydrogenase complex (PDC) activity was deficient, but PDC and α-ketoglutarate dehydrogenase complex activities in cultured fibroblasts were normal. Oxidative phosphorylation analysis on intact digitonin-permeabilized fibroblasts was suggestive of slightly reduced PDC activity relative to control range in mitochondria. We reviewed 16 other cases with mutations in ECHS1 where PDC activity was also assayed in order to determine how common and generalized secondary PDC deficiency is associated with primary SCEH deficiency. For reasons that remain unexplained, we find that about half of cases with primary SCEH deficiency also exhibit secondary PDC deficiency. The patient died on day-of-life 39, prior to establishing his diagnosis, highlighting the importance of early and rapid neonatal diagnosis because of possible adverse effects of certain therapeutic interventions, such as administration of ketogenic diet, in this disorder. There is a need for better understanding of the pathogenic mechanisms and phenotypic variability in this relatively recently discovered disorder.

Trainable high resolution melt curve machine learning classifier for large-scale reliable genotyping of sequence variants.

High resolution melt (HRM) is gaining considerable popularity as a simple and robust method for genotyping sequence variants. However, accurate genotyping of an unknown sample for which a large number of possible variants may exist will require an automated HRM curve identification method capable of comparing unknowns against a large cohort of known sequence variants. Herein, we describe a new method for automated HRM curve classification based on machine learning methods and learned tolerance for reaction condition deviations. We tested this method in silico through multiple cross-validations using curves generated from 9 different simulated experimental conditions to classify 92 known serotypes of Streptococcus pneumoniae and demonstrated over 99% accuracy with 8 training curves per serotype. In vitro verification of the algorithm was tested using sequence variants of a cancer-related gene and demonstrated 100% accuracy with 3 training curves per sequence variant. The machine learning algorithm enabled reliable, scalable, and automated HRM genotyping analysis with broad potential clinical and epidemiological applications.

Affinity mesh screen materials for selective extraction and analysis of antibiotics using transmission mode desorption electrospray ionization mass spectrometry.

The extraction of active compounds from natural sources has shown to be an effective approach to drug discovery. However, the isolation and identification of natural products from complex extracts can be an arduous task. A novel approach to drug discovery is presented through the use of polymer screens functionalized with an l-lysine-d-alanine-d-alanine (Kaa) peptide to create new affinity capture mesh screen materials. The Kaa sequence is a well-characterized specific binding site for antibiotics that inhibit cell wall synthesis in Gram-positive bacteria. The detailed synthesis and characterization of these novel screen materials are presented in this work. Polypropylene mesh screens were first coated with a poly(acrylic acid) film by pulsed plasma polymerization. The synthesized Kaa peptide was then covalently attached to carboxylic acid groups through a condensation reaction. An analysis of captured compounds was performed in a rapid fashion with transmission-mode desorption electrospray ionization (TM-DESI) mass spectrometry. A proof of principle was demonstrated to show the ability of the novel affinity capture materials to select for a macrocyclic antibiotic, vancomycin, over a negative control compound, spectinomycin. With further development, this method may provide a rapid screening technique for new antibacterial compounds, for example, those extracted from natural product sources having a limited supply. Here, we show that the screen can capture vancomycin preferentially over spectinomycin in a spiked extract of tea leaves.

Continuous flow-extractive desorption electrospray ionization: analysis from "non-electrospray ionization-friendly" solvents and related mechanism.

Due to their low polarities and dielectric constants, analytes in solvents such as hexane, chloroform, and ethyl acetate exhibit poor electrospray ionization (ESI) efficiency. These are deemed to be "non-ESI-friendly" solvents. Continuous flow extractive desorption electrospray ionization (CF-EDESI) is a novel ambient ionization technique that was recently developed in our group to manipulate protein charge distributions. Here we demonstrate its potential for ionizing analytes from non-ESI-friendly solvents. This feature makes CF-EDESI attractive to the general analytical community due to its apparent potential in lipidomics, normal phase separations, and hyphenation of mass spectrometry with HPLC-NMR systems. In this context, interest was subsequently initiated to discern mechanistic aspects of CF-EDESI. To achieve this, mechanistic experiments associated with a seemingly similar ambient ionization technique, extractive electrospray ionization (EESI), were emulated to compare CF-EDESI and EESI. Analysis of a series of fatty acids in multiple solvents in the negative ionization mode revealed differences between the two techniques. Whereas EESI has been previously shown to operate via extraction of analytes into the spray solvent, data presented here for CF-EDESI point toward a liquid-liquid mixing process to facilitate ionization. Further, a partial factorial design experiment was performed to evaluate the effects of different experimental variables on signal intensity. Sample flow rate was confirmed to be among the most significant factors to affect sensitivity. As a whole, the work presented provides greater insight into a new ambient ionization process, which exhibits expanded capabilities over conventional ESI; in this case, for direct analysis from non-ESI-friendly solvents.

A surface topography assisted droplet manipulation platform for biomarker detection and pathogen identification.

This paper reports a droplet microfluidic, sample-to-answer platform for the detection of disease biomarkers and infectious pathogens using crude biosamples. The platform exploited the dual functionality of silica superparamagnetic particles (SSP) for solid phase extraction of DNA and magnetic actuation. This enabled the integration of sample preparation and genetic analysis within discrete droplets, including the steps of cell lysis, DNA binding, washing, elution, amplification and detection. The microfluidic device was self contained, with all reagents stored in droplets, thereby eliminating the need for fluidic coupling to external reagent reservoirs. The device incorporated unique surface topographic features to assist droplet manipulation. Pairs of micro-elevations were created to form slits that facilitated efficient splitting of SSP from droplets. In addition, a compact sample handling stage, which integrated the magnet manipulator, the droplet microfluidic device and a Peltier thermal cycler, was built for convenient droplet manipulation and real-time detection. The feasibility of the platform was demonstrated by analysing ovarian cancer biomarker Rsf-1 and detecting Escherichia coli with real time polymerase chain reaction and real time helicase dependent amplification.