No abstract

BACKGROUND The ability to control the spread of COVID-19 continues to be hampered by a lack of rapid, scalable, and easily deployable diagnostic solutions. METHODS : We developed a diagnostic method based on CRISPR that can deliver sensitive, specific, and high-throughput detection of Sudden Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2). The assay utilizes SHERLOCK (Specific High-sensitivity Enzymatic Reporter unLOCKing) for the qualitative detection of SARS-CoV-2 RNA and may be performed directly on a swab or saliva sample without nucleic acid extraction. The assay uses a 384-well format and provides results in less than one hour. RESULTS Assay performance was evaluated with 105 (55 negative, 50 positive) remnant SARS-CoV-2 specimens previously identified as positive using Food and Drug Administration emergency use authorized assays and re-tested with a modified version of the Centers for Disease Control and Prevention (CDC) RT-qPCR assay. When combined with magnetic bead-based extraction, the high throughput SHERLOCK SARS-CoV-2 assay was 100% concordant (n = 60) with the CDC RT-qPCR. When used with direct sample addition the high throughput assay was also 100% concordant with the CDC RT-qPCR direct method (n = 45). With direct saliva sample addition, the negative and positive percent agreements were 100% (15/15, 95% CI : 81.8-100%) and 88% (15/17, 95% CI : 63.6-98.5%), respectively, compared with results from a collaborating clinical laboratory. CONCLUSIONS This high throughput assay identifies SARS-CoV-2 from patient samples with or without nucleic acid extraction with high concordance to RT-qPCR methods. This test enables high complexity laboratories to rapidly increase their testing capacities with simple equipment.

No abstract

Background: Melanocytic neoplasms can be challenging to diagnose. One wellestablished diagnostic aid is the detection of copy number variation (CNV) in a few key genetic loci using conventional methods such as fluorescence in situ hybridization (FISH) and chromosomal microarray (CMA). Droplet digital polymerase chain reaction (ddPCR) is a novel, cost-effective, rapid, and automated method to detect CNV. Methods:We perform the first investigation of ddPCR to assay Ras-responsive element-binding protein-1 (RREB1), the most common CNV in melanoma using formalin-fixed, paraffin-embedded (FFPE) melanocytic lesion samples; CMA data are used as the gold standard. Archival samples from 2013 to 2021 were analyzed, including 153 data points from 39 FFPE samples representing 34 patients. Benign, borderline, malignant, and metastatic melanocytic neoplasms were examined.Results: ddPCR showed a sensitivity and specificity of 93.8% and 95.7% using one reference gene, and 87.5% and 100% using a different reference gene for RREB1 gain detection.Conclusions: Here we show that ddPCR can provide inexpensive, rapid, and robust data on the commonest copy number alteration in melanoma. Future development and validation could provide a useful ancillary tool in the diagnosis of challenging melanocytic lesions.

Despite vaccination efforts, the delta and omicron variants of SARS-CoV-2 have caused global surges of COVID-19. As the COVID-19 pandemic continues, it is important to find new ways of tracking early signs of SARS-CoV-2 outbreaks.

No abstract

Supplemental Digital Content is Available in the Text.

The characteristics of tumor-infiltrating lymphocytes (TIL) are essential in cancer prognostication and treatment through the ability to indicate the tumor's capacity to evade the immune system (e.g., as evidenced by nodal involvement). Machine learning technologies have demonstrated remarkable success for localizing TILs, though these methods require extensive curation of manual annotations or restaining procedures that can degrade tissue quality, resulting in imprecise annotation. In this study, we co-registered tissue slides stained for both hematoxylin and eosin (H\&E) and immunofluorescence (IF) as means to rapidly perform large-scale annotation of nuclei. We integrated the following approaches to improve the prediction of TILs: 1) minimized tissue degradation on same-section tissue restaining, 2) developed a scoring algorithm to improve the selection of patches for machine learning modeling and 3) utilized a graph neural network deep learning approach to identify relevant contextual features for lymphocyte prediction. Our graph neural network approach accounts for surrounding contextual micro/macro-architecture tissue features to facilitate interpretation of registered IF. The graph neural network compares favorably (F1-score=0.9235, AUROC=0.9462) to two alternative modeling approaches. This study brings insight to the importance of contextual information leveraged from within and around neighboring cells in a nuclei classification workflow, as well as elucidate approaches which enable the rapid generation of large-scale annotations of lymphocytes for machine learning approaches for immune phenotyping. Such approaches can help further interrogate the spatial biology of colorectal cancer tumors and tumor metastasis.