Detecting differential transcript usage in complex diseases with SPIT.

Differential transcript usage (DTU) plays a crucial role in determining how gene expression differs among cells, tissues, and different developmental stages, thereby contributing to the complexity and diversity of biological systems. In abnormal cells, it can also lead to deficiencies in protein function, potentially leading to pathogenesis of diseases. Detecting such events for single-gene genetic traits is relatively uncomplicated; however, the heterogeneity of populations with complex diseases presents an intricate challenge due to the presence of diverse causal events and undetermined subtypes.

Effect of pre-operative warm-up on trainee intraoperative performance during robot-assisted hysterectomy: a randomized controlled trial.

Introduction And Hypothesis: The objective was to study the effect of immediate pre-operative warm-up using virtual reality simulation on intraoperative robot-assisted laparoscopic hysterectomy (RALH) performance by gynecology trainees (residents and fellows).

Methods: We randomized the first, non-emergent RALH of the day that involved trainees warming up or not warming up. For cases assigned to warm-up, trainees performed a set of exercises on the da Vinci Skills Simulator immediately before the procedure.

Brain Tumor Segmentation for Multi-Modal MRI with Missing Information.

Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity in tumor shape and appearance. The fusion of multiple MRI sequences allows networks to explore complementary tumor information for segmentation. However, developing a network that maintains clinical relevance in situations where certain MRI sequence(s) might be unavailable or unusual poses a significant challenge.

CEST2022: Amide proton transfer-weighted MRI improves the diagnostic performance of multiparametric non-contrast-enhanced MRI techniques in patients with post-treatment high-grade gliomas.

New or enlarged lesions in malignant gliomas after surgery and chemoradiation can be associated with tumor recurrence or treatment effect. Due to similar radiographic characteristics, conventional-and even some advanced MRI techniques-are limited in distinguishing these two pathologies. Amide proton transfer-weighted (APTw) MRI, a protein-based molecular imaging technique that does not require the administration of any exogenous contrast agent, was recently introduced into the clinical setting.

Accuracy of Artificial Intelligence in Estimating Best-Corrected Visual Acuity From Fundus Photographs in Eyes With Diabetic Macular Edema.

Importance: Best-corrected visual acuity (BCVA) is a measure used to manage diabetic macular edema (DME), sometimes suggesting development of DME or consideration of initiating, repeating, withholding, or resuming treatment with anti-vascular endothelial growth factor. Using artificial intelligence (AI) to estimate BCVA from fundus images could help clinicians manage DME by reducing the personnel needed for refraction, the time presently required for assessing BCVA, or even the number of office visits if imaged remotely.

Objective: To evaluate the potential application of AI techniques for estimating BCVA from fundus photographs with and without ancillary information.

Opportunity Cost to Attending Surgeons of Intraoperative Training for Residents in Cataract Surgery.

Purpose: To estimate the opportunity cost to attending surgeons of teaching residents cataract surgery in the operating room.

Patients And Methods: Operating room records at an academic teaching hospital from July 2016 to July 2020 were analyzed in this retrospective review of cases. Cases were identified using Current Procedural Terminology (CPT) codes 66982 and 66984 for cataract surgery.

Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single cell RNA-sequencing datasets.

Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding the pathologies of diseases. However, several experimental and computational challenges remain in developing and implementing transcriptomics-based deconvolution approaches, especially those using a single cell/nuclei RNA-seq reference atlas, which are becoming rapidly available across many tissues. Notably, deconvolution algorithms are frequently developed using samples from tissues with similar cell sizes.

Performance and Usability of Code-Free Deep Learning for Chest Radiograph Classification, Object Detection, and Segmentation.

Purpose: To evaluate the performance and usability of code-free deep learning (CFDL) platforms in creating DL models for disease classification, object detection, and segmentation on chest radiographs.

Materials And Methods: Six CFDL platforms were evaluated in this retrospective study (September 2021). Single- and multilabel classifiers were trained for thoracic pathologic conditions using Guangzhou pediatric and NIH-CXR14 (ie, National Institutes of Health ChestX-ray14) datasets, and external testing was performed using subsets of NIH-CXR14 and Stanford CheXpert datasets, respectively.

escheR: Unified multi-dimensional visualizations with Gestalt principles.

The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables.

Interrater Reliability of Cervical Neural Foraminal Stenosis Using Traditional and Splayed Reconstructions: Analysis of One Hundred Scans.

Objective: The oblique sagittal orientation of the cervical neural foramina hinders the evaluation of cervical neural foraminal stenosis (CNFS) on traditional axial and sagittal slices. Traditional image reconstruction techniques to generate oblique slices provide only a view of the foramina unilaterally. We present a simple technique for generating splayed slices that show the bilateral neuroforamina simultaneously and assess its reliability compared with traditional axial windows.

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, OCT, and Clinical Data.

Purpose: To assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data.

Design: A retrospective cohort study.

Subjects: In total, 4536 eyes from 2962 patients.

Integrated single cell and unsupervised spatial transcriptomic analysis defines molecular anatomy of the human dorsolateral prefrontal cortex.

Generation of a molecular neuroanatomical map of the human prefrontal cortex reveals novel spatial domains and cell-cell interactions relevant for psychiatric disease. The molecular organization of the human neocortex has been historically studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally-defined spatial domains that move beyond classic cytoarchitecture.

Identification of the Language Network from Resting-State fMRI in Patients with Brain Tumors: How Accurate Are Experts?

Background And Purpose: Resting-state fMRI helps identify neural networks in presurgical patients who may be limited in their ability to undergo task-fMRI. The purpose of this study was to determine the accuracy of identifying the language network from resting-state-fMRI independent component analysis (ICA) maps.

Materials And Methods: Through retrospective analysis, patients who underwent both resting-state-fMRI and task-fMRI were compared by identifying the language network from the resting-state-fMRI data by 3 reviewers.

Effectiveness of Netarsudil versus Brimonidine in Eyes already Being Treated with Glaucoma Medications at a Single Academic Tertiary Care Practice: A Comparative Study.

Background: Rho kinase inhibitors, such as netarsudil, are a relatively new class of medications recently introduced into the market for the treatment of glaucoma, the leading cause of irreversible blindness in the world. Previous clinical trials have studied netarsudil's efficacy when used as a first- or second-line agent but limited studies have investigated its effectiveness in the real world where it is more commonly used as a third, fourth, or fifth agent in combination with other topical medications. Equally important, prior studies have not compared its effectiveness to its peer medications in these settings.

Direct Gene Expression Profile Prediction for Uveal Melanoma from Digital Cytopathology Images via Deep Learning and Salient Image Region Identification.

Objective: To demonstrate that deep learning (DL) methods can produce robust prediction of gene expression profile (GEP) in uveal melanoma (UM) based on digital cytopathology images.

Design: Evaluation of a diagnostic test or technology.

Subjects Participants And Controls: Deidentified smeared cytology slides stained with hematoxylin and eosin obtained from a fine needle aspirated from UM.

Monocyte distribution width as a pragmatic screen for SARS-CoV-2 or influenza infection.

Monocyte distribution width (MDW) is a novel marker of monocyte activation, which is known to occur in the immune response to viral pathogens. Our objective was to determine the performance of MDW and other leukocyte parameters as screening tests for SARS-CoV-2 and influenza infection. This was a prospective cohort analysis of adult patients who underwent complete blood count (CBC) and SARS-CoV-2 or influenza testing in an Emergency Department (ED) between January 2020 and July 2021.

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

Introduction: Trauma patients have diverse resource needs due to variable mechanisms and injury patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS).

Methods: Data was collected from the trauma registry at an urban level one adult trauma center and included patients from 1/1/2014 to 3/31/2019.

Active shape model registration of ocular structures in computed tomography images.

. The goal of this work is to create an active shape model segmentation method based on the statistical shape model of five regions of the globe on computed tomography (CT) scans and to use the method to categorize normal globe from globe injury..

Causal determinants of postoperative length of stay in cardiac surgery using causal graphical learning.

Objective: We aimed to learn the causal determinants of postoperative length of stay in cardiac surgery patients undergoing isolated coronary artery bypass grafting or aortic valve replacement surgery.

Methods: For patients undergoing isolated coronary artery bypass grafting or isolated aortic valve replacement surgeries between 2011 and 2016, we used causal graphical modeling on electronic health record data. The Fast Causal Inference (FCI) algorithm from the Tetrad software was used on data to estimate a Partial Ancestral Graph (PAG) depicting direct and indirect causes of postoperative length of stay, given background clinical knowledge.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability.