Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Background: Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis.

Methods: Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM).

A prognostic and predictive computational pathology immune signature for ductal carcinoma in situ: retrospective results from a cohort within the UK/ANZ DCIS trial.

Background: The density of tumour-infiltrating lymphocytes (TILs) could be prognostic in ductal carcinoma in situ (DCIS). However, manual TIL quantification is time-consuming and suffers from interobserver and intraobserver variability. In this study, we developed a TIL-based computational pathology biomarker and evaluated its association with the risk of recurrence and benefit of adjuvant treatment in a clinical trial cohort.

In vivo gene delivery to immune cells.

Immune cell therapies are an emerging class of living drugs that rely on the delivery of therapeutic transgenes to enhance, modulate, or restore cell function, such as those that encode for tumor-targeting receptors or replacement proteins. However, many cellular immunotherapies are autologous treatments that are limited by high manufacturing costs, typical vein-to-vein time of 3-4 weeks, and severe immune-related adverse effects. To address these issues, different classes of gene delivery vehicles are being developed to target specific immune cell subsets in vivo to address the limitations of ex vivo manufacturing, modulate therapeutic responses in situ, and reduce on- and off-target toxicity.

mRNA-encoded Cas13 treatment of Influenza via site-specific degradation of genomic RNA.

The CRISPR-Cas13 system has been proposed as an alternative treatment of viral infections. However, for this approach to be adopted as an antiviral, it must be optimized until levels of efficacy rival or exceed the performance of conventional approaches. To take steps toward this goal, we evaluated the influenza viral RNA degradation patterns resulting from the binding and enzymatic activity of mRNA-encoded LbuCas13a and two crRNAs from a prior study, targeting PB2 genomic and messenger RNA.

CohortFinder: an open-source tool for data-driven partitioning of digital pathology and imaging cohorts to yield robust machine-learning models.

Batch effects (BEs) refer to systematic technical differences in data collection unrelated to biological variations whose noise is shown to negatively impact machine learning (ML) model generalizability. Here we release CohortFinder (http://cohortfinder.com), an open-source tool aimed at mitigating BEs via data-driven cohort partitioning.

Protocol for the creation and characterization of SARS-CoV-2 variant testing panels using remnant clinical samples for diagnostic assay testing.

The emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Alpha variant in 2020 demonstrated the need for reanalysis of diagnostic tests to ensure detection of emerging variants. Here, we present a protocol for creating and characterizing SARS-CoV-2 variant testing panels using remnant clinical samples for diagnostic assay testing. We describe steps for characterizing SARS-CoV-2 remnant clinical samples and preparing them into pools and their use in preparing varying quantities of virus.

PatchSorter: a high throughput deep learning digital pathology tool for object labeling.

The discovery of patterns associated with diagnosis, prognosis, and therapy response in digital pathology images often requires intractable labeling of large quantities of histological objects. Here we release an open-source labeling tool, PatchSorter, which integrates deep learning with an intuitive web interface. Using >100,000 objects, we demonstrate a >7x improvement in labels per second over unaided labeling, with minimal impact on labeling accuracy, thus enabling high-throughput labeling of large datasets.

Towards equitable AI in oncology.

Artificial intelligence (AI) stands at the threshold of revolutionizing clinical oncology, with considerable potential to improve early cancer detection and risk assessment, and to enable more accurate personalized treatment recommendations. However, a notable imbalance exists in the distribution of the benefits of AI, which disproportionately favour those living in specific geographical locations and in specific populations. In this Perspective, we discuss the need to foster the development of equitable AI tools that are both accurate in and accessible to a diverse range of patient populations, including those in low-income to middle-income countries.

mRNA-encoded Cas13 can be used to treat dengue infections in mice.

Dengue is a major global health threat, and there are no approved antiviral agents. Prior research using Cas13 only demonstrated dengue mitigation in vitro. Here we demonstrate that systemic delivery of mRNA-encoded Cas13a and guide RNAs formulated in lipid nanoparticles can be used to treat dengue virus (DENV) 2 and 3 in mice.

self-organizes developmental rosettes.

Rosettes are self-organizing, circular multicellular communities that initiate developmental processes, like organogenesis and embryogenesis, in complex organisms. Their formation results from the active repositioning of adhered sister cells and is thought to distinguish multicellular organisms from unicellular ones. Though common in eukaryotes, this multicellular behavior has not been reported in bacteria.

"An app is just available at all times"-the process and outcomes of converting the Georgia Tuberculosis Reference Guide into a mobile application.

Background: The physical, paper-based Georgia TB Reference Guide has served as the clinical reference handbook on tuberculosis (TB) diagnostic and treatment guidelines for the state of Georgia in the United States. Supported by the Georgia Department of Public Health, the production of the 112-page palm-sized booklet was previously led by a team of Georgia-based TB experts at Emory University and printed every three-five years with updates to clinical management guidelines and TB consult contact information. However, the costs associated with editorial printing combined with delays in updating a static printed booklet with revised guidance hampered the utility of the tool.

Gait signature changes with walking speed are similar among able-bodied young adults despite persistent individual-specific differences.

Understanding individuals' distinct movement patterns is crucial for health, rehabilitation, and sports. Recently, we developed a machine learning-based framework to show that "gait signatures" describing the neuromechanical dynamics governing able-bodied and post-stroke gait kinematics remain individual-specific across speeds. However, we only evaluated gait signatures within a limited speed range and number of participants, using only sagittal plane (i.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

Background: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.

Purpose: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.

Study Type: Retrospective.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings.

Objectives: To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods: Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians.

Disturbed flow regulates protein disulfide isomerase A1 expression via microRNA-204.

Redox processes can modulate vascular pathophysiology. The endoplasmic reticulum redox chaperone protein disulfide isomerase A1 (PDIA1) is overexpressed during vascular proliferative diseases, regulating thrombus formation, endoplasmic reticulum stress adaptation, and structural remodeling. However, both protective and deleterious vascular effects have been reported for PDIA1, depending on the cell type and underlying vascular condition.

BRAND: a platform for closed-loop experiments with deep network models.

Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g.

Identifying Distinct Neural Features between the Initial and Corrective Phases of Precise Reaching Using AutoLFADS.

Many initial movements require subsequent corrective movements, but how the motor cortex transitions to make corrections and how similar the encoding is to initial movements is unclear. In our study, we explored how the brain's motor cortex signals both initial and corrective movements during a precision reaching task. We recorded a large population of neurons from two male rhesus macaques across multiple sessions to examine the neural firing rates during not only initial movements but also subsequent corrective movements.

Comparison of RT-PCR and antigen test sensitivity across nasopharyngeal, nares, and oropharyngeal swab, and saliva sample types during the SARS-CoV-2 omicron variant.

Limited data highlight the need to understand differences in SARS-CoV-2 omicron (B.1.1.

Distinct Cortical Correlates of Perception and Motor Function in Balance Control.

Fluctuations in brain activity alter how we perceive our body and generate movements but have not been investigated in functional whole-body behaviors. During reactive balance, we recently showed that evoked brain activity is associated with the balance ability in young individuals. Furthermore, in PD, impaired whole-body motion perception in reactive balance is associated with impaired balance.

Low-force human-human hand interactions induce gait changes through sensorimotor engagement instead of direct mechanical effects.

Physical human-robot interactions (pHRI) often provide mechanical force and power to aid walking without requiring voluntary effort from the human. Alternatively, principles of physical human-human interactions (pHHI) can inspire pHRI that aids walking by engaging human sensorimotor processes. We hypothesize that low-force pHHI can intuitively induce a person to alter their walking through haptic communication.

Failing to Make the Grade: Conventional Cardiac Allograft Rejection Grading Criteria Are Inadequate for Predicting Rejection Severity.

Background: Cardiac allograft rejection is the leading cause of early graft failure and is a major focus of postheart transplant patient care. While histological grading of endomyocardial biopsy samples remains the diagnostic standard for acute rejection, this standard has limited diagnostic accuracy. Discordance between biopsy rejection grade and patient clinical trajectory frequently leads to both overtreatment of indolent processes and delayed treatment of aggressive ones, spurring the need to investigate the adequacy of the current histological criteria for assessing clinically important rejection outcomes.

MRI-based prostate cancer classification using 3D efficient capsule network.

Background: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information.