Allele-specific RNA imaging shows that allelic imbalances can arise in tissues through transcriptional bursting.

Extensive cell-to-cell variation exists even among putatively identical cells, and there is great interest in understanding how the properties of transcription relate to this heterogeneity. Differential expression from the two gene copies in diploid cells could potentially contribute, yet our ability to measure from which gene copy individual RNAs originated remains limited, particularly in the context of tissues. Here, we demonstrate quantitative, single molecule allele-specific RNA FISH adapted for use on tissue sections, allowing us to determine the chromosome of origin of individual RNA molecules in formaldehyde-fixed tissues. We used this method to visualize the allele-specific expression of Xist and multiple autosomal genes in mouse kidney. By combining these data with mathematical modeling, we evaluated models for allele-specific heterogeneity, in particular demonstrating that apparent expression from only one of the alleles in single cells can arise as a consequence of low-level mRNA abundance and transcriptional bursting.

Deep Learning Based on MRI for Differentiation of Low- and High-Grade in Low-Stage Renal Cell Carcinoma.

Pretreatment determination of renal cell carcinoma aggressiveness may help to guide clinical decision-making. PURPOSE: To evaluate the efficacy of residual convolutional neural network using routine MRI in differentiating low-grade (grade I-II) from high-grade (grade III-IV) in stage I and II renal cell carcinoma. STUDY TYPE: Retrospective. POPULATION: In all, 376 patients with 430 renal cell carcinoma lesions from 2008-2019 in a multicenter cohort were acquired. The 353 Fuhrman-graded renal cell carcinomas were divided into a training, validation, and test set with a 7:2:1 split. The 77 WHO/ISUP graded renal cell carcinomas were used as a separate WHO/ISUP test set. FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T/T2 -weighted and T1 contrast-enhanced sequences. ASSESSMENT: The accuracy, sensitivity, and specificity of the final model were assessed. The receiver operating characteristic (ROC) curve and precision-recall curve were plotted to measure the performance of the binary classifier. A confusion matrix was drawn to show the true positive, true negative, false positive, and false negative of the model. STATISTICAL TESTS: Mann-Whitney U-test for continuous data and the chi-square test or Fisher's exact test for categorical data were used to compare the difference of clinicopathologic characteristics between the low- and high-grade groups. The adjusted Wald method was used to calculate the 95% confidence interval (CI) of accuracy, sensitivity, and specificity. RESULTS: The final deep-learning model achieved a test accuracy of 0.88 (95% CI: 0.73-0.96), sensitivity of 0.89 (95% CI: 0.74-0.96), and specificity of 0.88 (95% CI: 0.73-0.96) in the Fuhrman test set and a test accuracy of 0.83 (95% CI: 0.73-0.90), sensitivity of 0.92 (95% CI: 0.84-0.97), and specificity of 0.78 (95% CI: 0.68-0.86) in the WHO/ISUP test set. DATA CONCLUSION: Deep learning can noninvasively predict the histological grade of stage I and II renal cell carcinoma using conventional MRI in a multiinstitutional dataset with high accuracy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Canonical Wnt signaling in the oligodendroglial lineage--puzzles remain.

The straightforward concept that accentuated Wnt signaling via the Wnt-receptor-ß-catenin-TCF/LEF cascade (also termed canonical Wnt signaling or Wnt/ß-catenin signaling) delays or blocks oligodendrocyte differentiation is very appealing. According to this concept, canonical Wnt signaling is responsible for remyelination failure in multiple sclerosis and for persistent hypomyelination in periventricular leukomalacia. This has given rise to the hope that pharmacologically inhibiting this signaling will be of therapeutic potential in these disabling neurological disorders. But current studies suggest that Wnt/ß-catenin signaling plays distinct roles in oligodendrogenesis, oligodendrocyte differentiation, and myelination in a context-dependent manner (central nervous system regions, developmental stages), and that Wnt/ß-catenin signaling interplays with, and is subjected to regulation by, other central nervous system factors and signaling pathways. On this basis, we propose the more nuanced concept that endogenous Wnt/ß-catenin activity is delicately and temporally regulated to ensure the seamless development of oligodendroglial lineage cells in different contexts. In this review, we discuss the role Wnt/ß-catenin signaling in oligodendrocyte development, focusing on the interpretation of disparate results, and highlighting areas where important questions remain to be answered about oligodendroglial lineage Wnt/ß-catenin signaling.

Analysis of Validation Performance of a Machine Learning Classifier in Interstitial Lung Disease Cases Without Definite or Probable Usual Interstitial Pneumonia Pattern on CT Using Clinical and Pathology-Supported Diagnostic Labels.

We previously validated Fibresolve, a machine learning classifier system that non-invasively predicts idiopathic pulmonary fibrosis (IPF) diagnosis. The system incorporates an automated deep learning algorithm that analyzes chest computed tomography (CT) imaging to assess for features associated with idiopathic pulmonary fibrosis. Here, we assess performance in assessment of patterns beyond those that are characteristic features of usual interstitial pneumonia (UIP) pattern. The machine learning classifier was previously developed and validated using standard training, validation, and test sets, with clinical plus pathologically determined ground truth. The multi-site 295-patient validation dataset was used for focused subgroup analysis in this investigation to evaluate the classifier's performance range in cases with and without radiologic UIP and probable UIP designations. Radiologic assessment of specific features for UIP including the presence and distribution of reticulation, ground glass, bronchiectasis, and honeycombing was used for assignment of radiologic pattern. Output from the classifier was assessed within various UIP subgroups. The machine learning classifier was able to classify cases not meeting the criteria for UIP or probable UIP as IPF with estimated sensitivity of 56-65% and estimated specificity of 92-94%. Example cases demonstrated non-basilar-predominant as well as ground glass patterns that were indeterminate for UIP by subjective imaging criteria but for which the classifier system was able to correctly identify the case as IPF as confirmed by multidisciplinary discussion generally inclusive of histopathology. The machine learning classifier Fibresolve may be helpful in the diagnosis of IPF in cases without radiological UIP and probable UIP patterns.

Virtual Resection Specimen Interaction Using Augmented Reality Holograms to Guide Margin Communication and Flap Sizing.

Head and neck surgeons often have difficulty in relocating sites of positive margins due to the complex 3-dimensional (3D) anatomy of the head and neck. We introduce a new technique where resection specimens are 3D scanned with a smartphone, annotated in computer-assisted design software, and immediately visualized on augmented reality (AR) glasses. The 3D virtual specimen can be accurately superimposed onto surgical sites for orientation and sizing applications. During an operative workshop, a surgeon using AR glasses projected virtual, annotated specimen models back into the resection bed onto a cadaver within approximately 10 minutes. Colored annotations can correspond with pathologic annotations and guide the orientation of the virtual 3D specimen. The model was also overlayed onto a flap harvest site to aid in reconstructive planning. We present a new technique allowing interactive, sterile inspection of tissue specimens in AR that could facilitate communication among surgeons and pathologists and assist with reconstructive surgery.

Pediatric Provider Utilization of a Clinical Decision Support Alert and Association with HIV Pre-exposure Prophylaxis Prescription Rates.

OBJECTIVES: An electronic clinical decision support (CDS) alert can provide real-time provider support to offer pre-exposure prophylaxis (PrEP) to youth at risk for human immunodeficiency virus (HIV). The purpose of this study was to evaluate provider utilization of a PrEP CDS alert in a large academic-community pediatric network and assess the association of the alert with PrEP prescribing rates. METHODS: HIV test orders were altered for patients 13 years and older to include a hard-stop prompt asking if the patient would benefit from PrEP. If providers answered "Yes" or "Not Sure," the CDS alert launched with options to open a standardized order set, refer to an internal PrEP specialist, and/or receive an education module. We analyzed provider utilization using a frequency analysis. The rate of new PrEP prescriptions for 1 year after CDS alert implementation was compared with the year prior using Fisher's exact test. RESULTS: Of the 56 providers exposed to the CDS alert, 70% (n = 39) responded "Not sure" to the alert prompt asking if their patient would benefit from PrEP, and 54% (n = 30) chose at least one clinical support tool. The PrEP prescribing rate increased from 2.3 prescriptions per 10,000 patients to 6.6 prescriptions per 10,000 patients in the year post-intervention (p = 0.02). CONCLUSION: Our findings suggest a knowledge gap among pediatric providers in identifying patients who would benefit from PrEP. A hard-stop prompt within an HIV test order that offers CDS and provider education might be an effective tool to increase PrEP prescribing among pediatric providers.

Systematically quantifying morphological features reveals constraints on organoid phenotypes.

Organoids recapitulate complex 3D organ structures and represent a unique opportunity to probe the principles of self-organization. While we can alter an organoid's morphology by manipulating the culture conditions, the morphology of an organoid often resembles that of its original organ, suggesting that organoid morphologies are governed by a set of tissue-specific constraints. Here, we establish a framework to identify constraints on an organoid's morphological features by quantifying them from microscopy images of organoids exposed to a range of perturbations. We apply this framework to Madin-Darby canine kidney cysts and show that they obey a number of constraints taking the form of scaling relationships or caps on certain parameters. For example, we found that the number, but not size, of cells increases with increasing cyst size. We also find that these constraints vary with cyst age and can be altered by varying the culture conditions. We observed similar sets of constraints in intestinal organoids. This quantitative framework for identifying constraints on organoid morphologies may inform future efforts to engineer organoids.

Persistent detection of SARS-CoV-2 RNA in patients and healthcare workers with COVID-19.

BACKGROUND: Current guidelines for returning health care workers (HCW) to service after a positive SARS-CoV-2 RT-PCR test and ceasing of transmission precautions for patients is based on two general strategies. A test-based strategy that requires negative respiratory RT-PCR tests obtained after the resolution of symptoms. Alternatively, due to the limited availability of testing, many sites employ a symptom-based strategy that recommends excluding HCW from the workforce and keeping patients on contact precautions until a fixed period of time has elapsed from symptom recovery. The underlying assumption of the symptom-based strategy is that waiting for a fixed period of time is a surrogate for negative RT-PCR testing, which itself is a surrogate for the absence of shedding infectious virus. OBJECTIVES: To better understand the appropriate length of symptom based return to work and contact precaution strategies. STUDY DESIGN: We performed an observational analysis of 150 patients and HCW that transitioned from RT-PCR SARS-CoV-2 positive to negative over the course of 2 months at a US academic medical center. RESULTS: We found that the average time to transition from RT-PCR positive to negative was 24 days after symptom onset and 10 % remained positive even 33 days after symptom onset. No difference was seen in HCW and patients. CONCLUSIONS: These findings suggest until definitive evidence of the length of infective viral shedding is obtained that the fixed length of time before returning to work or ceasing contract precautions be revised to over one-month.

Deep learning-based classification of primary bone tumors on radiographs: A preliminary study.

BACKGROUND: To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists. METHODS: A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and pre-operative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varying level of experience using the Permutations tests. FINDINGS: For benign vs. not benign, model achieved area under curve (AUC) of 0•894 and 0•877 on cross-validation and external testing, respectively. For malignant vs. not malignant, model achieved AUC of 0•907 and 0•916 on cross-validation and external testing, respectively. For three-way classification, model achieved 72•1% accuracy vs. 74•6% and 72•1% for the two subspecialists on cross-validation (p = 0•03 and p = 0•52, respectively). On external testing, model achieved 73•4% accuracy vs. 69•3%, 73•4%, 73•1%, 67•9%, and 63•4% for the two subspecialists and three junior radiologists (p = 0•14, p = 0•89, p = 0•93, p = 0•02, p < 0•01 for radiologists 1-5, respectively). INTERPRETATION: Deep learning can classify primary bone tumors using conventional radiographs in a multi-institutional dataset with similar accuracy compared to subspecialists, and better performance than junior radiologists. FUNDING: The project described was supported by RSNA Research & Education Foundation, through grant number RSCH2004 to Harrison X. Bai.

Deep Learning to Distinguish Benign from Malignant Renal Lesions Based on Routine MR Imaging.

PURPOSE: With increasing incidence of renal mass, it is important to make a pretreatment differentiation between benign renal mass and malignant tumor. We aimed to develop a deep learning model that distinguishes benign renal tumors from renal cell carcinoma (RCC) by applying a residual convolutional neural network (ResNet) on routine MR imaging. EXPERIMENTAL DESIGN: Preoperative MR images (T2-weighted and T1-postcontrast sequences) of 1,162 renal lesions definitely diagnosed on pathology or imaging in a multicenter cohort were divided into training, validation, and test sets (70:20:10 split). An ensemble model based on ResNet was built combining clinical variables and T1C and T2WI MR images using a bagging classifier to predict renal tumor pathology. Final model performance was compared with expert interpretation and the most optimized radiomics model. RESULTS: Among the 1,162 renal lesions, 655 were malignant and 507 were benign. Compared with a baseline zero rule algorithm, the ensemble deep learning model had a statistically significant higher test accuracy (0.70 vs. 0.56, P = 0.004). Compared with all experts averaged, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.60, P = 0.053), sensitivity (0.92 vs. 0.80, P = 0.017), and specificity (0.41 vs. 0.35, P = 0.450). Compared with the radiomics model, the ensemble deep learning model had higher test accuracy (0.70 vs. 0.62, P = 0.081), sensitivity (0.92 vs. 0.79, P = 0.012), and specificity (0.41 vs. 0.39, P = 0.770). CONCLUSIONS: Deep learning can noninvasively distinguish benign renal tumors from RCC using conventional MR imaging in a multi-institutional dataset with good accuracy, sensitivity, and specificity comparable with experts and radiomics.