Alternative splicing modulates chromatin interactome and phase separation of the RIF1 C-terminal domain.

RIF1 (RAP1 interacting factor) fulfills diverse roles in DNA double-strand break repair, DNA replication, and nuclear organization. RIF1 is expressed as two splice variants, RIF1-Long (RIF1-L) and RIF1-Short (RIF1-S), from the alternative splicing (AS) of Exon 32 (Ex32) which encodes a 26 aa Ser/Lys-rich cassette peptide in the RIF1 C-terminal domain (CTD). Here we demonstrate that Ex32 inclusion was repressed by DNA damage and oncogenesis but peaked at G/M phase of the cell cycle.

Efficacy and Toxicity of [Lu]Lu-PSMA-617 for Metastatic Castration-Resistant Prostate Cancer: Results from the U.S. Expanded-Access Program and Comparisons with Phase 3 VISION Data.

The phase 3 VISION trial demonstrated that [Lu]Lu-PSMA-617 prolonged progression-free survival and overall survival (OS) in prostate-specific membrane antigen [PSMA]-positive metastatic castration-resistant prostate cancer (mCRPC) patients who progressed on taxane-based chemotherapy and androgen receptor-signaling inhibitors (ARSIs). The U.S.

Altering translation allows to overcome chemically stabilized G-quadruplexes.

G-quadruplex (G4) structures can form in guanine-rich DNA or RNA and have been found to modulate cellular processes including replication, transcription, and translation. Many studies on the cellular roles of G4s have focused on eukaryotic systems, with far fewer probing bacterial G4s. Using a chemical-genetic approach, we identified genes in that are important for growth in G4-stabilizing conditions.

Elevated Baseline Mean Corpuscular Volume Predicts the Development of Severe Hematologic Toxicity After Lu-DOTATATE Therapy.

Lu-DOTATATE is an effective second-line treatment for metastatic or nonresectable neuroendocrine tumors. This treatment can result in hematologic severe adverse reactions (SARs). Preemptive identification of patients at risk of SARs could mitigate this risk and improve treatment safety and outcomes.

An Automated Deep Learning-Based Framework for Uptake Segmentation and Classification on PSMA PET/CT Imaging of Patients with Prostate Cancer.

Uptake segmentation and classification on PSMA PET/CT are important for automating whole-body tumor burden determinations. We developed and evaluated an automated deep learning (DL)-based framework that segments and classifies uptake on PSMA PET/CT. We identified 193 [F] DCFPyL PET/CT scans of patients with biochemically recurrent prostate cancer from two institutions, including 137 [F] DCFPyL PET/CT scans for training and internally testing, and 56 scans from another institution for external testing.

Comparison of Multiple Segmentation Methods for Volumetric Delineation of Primary Prostate Cancer with Prostate-Specific Membrane Antigen-Targeted F-DCFPyL PET/CT.

This study aimed to assess the accuracy of intraprostatic tumor volume measurements on prostate-specific membrane antigen-targeted F-DCFPyL PET/CT made with various segmentation methods. An accurate understanding of tumor volumes versus segmentation techniques is critical for therapy planning, such as radiation dose volume determination and response assessment. Twenty-five men with clinically localized, high-risk prostate cancer were imaged with F-DCFPyL PET/CT before radical prostatectomy.

A Practical Guide to the Pearls and Pitfalls of PSMA PET Imaging.

Prostate-specific membrane antigen (PSMA)-targeted PET agents have revolutionized the care of patients with prostate cancer, supplanting traditional methods of imaging prostate cancer, and improving the selection and delivery of therapies. This has led to a rapid expansion in both the number of PSMA PET scans performed and the imaging specialists required to interpret those scans. To aid those imagers and clinicians who are new to the interpretation of PSMA PET, this review provides an overview of the interpretation of PSMA PET/CT imaging and pearls for overcoming commonly encountered pitfalls.

Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries.

Traditional small-molecule drug discovery is a time-consuming and costly endeavor. High-throughput chemical screening can only assess a tiny fraction of drug-like chemical space. The strong predictive power of modern machine-learning methods for virtual chemical screening enables training models on known active and inactive compounds and extrapolating to much larger chemical libraries.

Image acquisition and interpretation of 18F-DCFPyL (piflufolastat F 18) PET/CT: How we do it.

Prostate-specific membrane antigen (PSMA)-targeted positron emission tomography (PET) is rapidly becoming widely accepted as the standard-of-care for imaging of men with prostate cancer. Labeled indications for regulatoryapproved agents include primary staging and recurrent disease in men at risk of metastases. The first commercial PSMA PET agent to become available was 18F-DCFPyL (piflufolastat F 18), a radiofluorinated small molecule with high-affinity for PSMA.

Piflufolastat F-18 (F-DCFPyL) for PSMA PET imaging in prostate cancer .

Introduction: Accurate imaging is essential for staging prostate cancer and guiding management decisions. Conventional imaging modalities are hampered by a limited sensitivity for metastatic disease. Nearly all prostate cancers express prostate-specific membrane antigen (PSMA) and F-DCFPyL (piflufolastat F 18) is a new FDA-approved positron emission tomography (PET) agent that targets PSMA for improved staging of prostate cancer.

Machine learning-driven multiscale modeling reveals lipid-dependent dynamics of RAS signaling proteins.

RAS is a signaling protein associated with the cell membrane that is mutated in up to 30% of human cancers. RAS signaling has been proposed to be regulated by dynamic heterogeneity of the cell membrane. Investigating such a mechanism requires near-atomistic detail at macroscopic temporal and spatial scales, which is not possible with conventional computational or experimental techniques.

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Cervical Spine Fractures.

Background And Purpose: Artificial intelligence decision support systems are a rapidly growing class of tools to help manage ever-increasing imaging volumes. The aim of this study was to evaluate the performance of an artificial intelligence decision support system, Aidoc, for the detection of cervical spinal fractures on noncontrast cervical spine CT scans and to conduct a failure mode analysis to identify areas of poor performance.

Materials And Methods: This retrospective study included 1904 emergent noncontrast cervical spine CT scans of adult patients (60 [SD, 22] years, 50.

High-Throughput Screening to Identify Inhibitors of SSB-Protein Interactions.

The bacterial single-stranded DNA-binding protein (SSB) uses an acidic C-terminal tail to interact with over a dozen proteins, acting as a genome maintenance hub. These SSB-protein interactions are essential, as mutations to the C-terminal tail that disrupt these interactions are lethal in Escherichia coli. While the roles of individual SSB-protein interactions have been dissected with mutational studies, small-molecule inhibitors of these interactions could serve as valuable research tools and have potential as novel antimicrobial agents.

Diagnostic Accuracy and Failure Mode Analysis of a Deep Learning Algorithm for the Detection of Intracranial Hemorrhage.

Objective: To determine the institutional diagnostic accuracy of an artificial intelligence (AI) decision support systems (DSS), Aidoc, in diagnosing intracranial hemorrhage (ICH) on noncontrast head CTs and to assess the potential generalizability of an AI DSS.

Methods: This retrospective study included 3,605 consecutive, emergent, adult noncontrast head CT scans performed between July 1, 2019, and December 30, 2019, at our institution (51% female subjects, mean age of 61 ± 21 years). Each scan was evaluated for ICH by both a certificate of added qualification certified neuroradiologist and Aidoc.

Incidental discovery of a rare right ventricular aneurysm.

Three types of cardiac outpouchings are encountered on cardiovascular imaging: diverticula, aneurysms and pseudoaneurysms. The underlying physiology, imaging findings, risk of rupture, and optimal treatment varies for each and a correct diagnosis is critical. We report a case of a rare, incidentally discovered right ventricular aneurysm that was characterized by transthoracic echocardiogram, computed tomography, and cardiac MRI.

Parallel algorithms for hyperdynamics and local hyperdynamics.

Hyperdynamics (HD) is a method for accelerating the timescale of standard molecular dynamics (MD). It can be used for simulations of systems with an energy potential landscape that is a collection of basins, separated by barriers, where transitions between basins are infrequent. HD enables the system to escape from a basin more quickly while enabling a statistically accurate renormalization of the simulation time, thus effectively boosting the timescale of the simulation.

E. coli Rep helicase and RecA recombinase unwind G4 DNA and are important for resistance to G4-stabilizing ligands.

G-quadruplex (G4) DNA structures can form physical barriers within the genome that must be unwound to ensure cellular genomic integrity. Here, we report unanticipated roles for the Escherichia coli Rep helicase and RecA recombinase in tolerating toxicity induced by G4-stabilizing ligands in vivo. We demonstrate that Rep and Rep-X (an enhanced version of Rep) display G4 unwinding activities in vitro that are significantly higher than the closely related UvrD helicase.

Exascale applications: skin in the game.

As noted in Wikipedia, refers to having 'incurred risk by being involved in achieving a goal', where ' is a synecdoche for the person involved, and is the metaphor for actions on the field of play under discussion'. For exascale applications under development in the US Department of Energy Exascale Computing Project, nothing could be more apt, with the being exascale applications and the being delivering comprehensive science-based computational applications that effectively exploit exascale high-performance computing technologies to provide breakthrough modelling and simulation and data science solutions. These solutions will yield high-confidence insights and answers to the most critical problems and challenges for the USA in scientific discovery, national security, energy assurance, economic competitiveness and advanced healthcare.

Antigenic Variation in Occurs Independently of RecQ-Mediated Unwinding of the G Quadruplex.

The obligate human pathogen alters its cell surface antigens to evade the immune system in a process known as antigenic variation (AV). During pilin AV, portions of the expressed pilin gene () are replaced with segments of silent pilin genes () through homologous recombination. The exchange is initiated by formation of a parallel guanine quadruplex (G4) structure near the gene, which recruits the homologous recombination machinery.

Computing long time scale biomolecular dynamics using quasi-stationary distribution kinetic Monte Carlo (QSD-KMC).

It is a challenge to obtain an accurate model of the state-to-state dynamics of a complex biological system from molecular dynamics (MD) simulations. In recent years, Markov state models have gained immense popularity for computing state-to-state dynamics from a pool of short MD simulations. However, the assumption that the underlying dynamics on the reduced space is Markovian induces a systematic bias in the model, especially in biomolecular systems with complicated energy landscapes.

An Overview of Recent Standard and Accelerated Molecular Dynamics Simulations of Helium Behavior in Tungsten.

One of the most critical challenges for the successful adoption of nuclear fusion power corresponds to plasma-facing materials. Due to its favorable properties in this context (low sputtering yield, high thermal conductivity, high melting point, among others), tungsten is a leading candidate material. Nevertheless, tungsten is affected by the plasma and fusion byproducts.

Practical Model Selection for Prospective Virtual Screening.

Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the data set and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models.

A guanine-flipping and sequestration mechanism for G-quadruplex unwinding by RecQ helicases.

Homeostatic regulation of G-quadruplexes (G4s), four-stranded structures that can form in guanine-rich nucleic acids, requires G4 unwinding helicases. The mechanisms that mediate G4 unwinding remain unknown. We report the structure of a bacterial RecQ DNA helicase bound to resolved G4 DNA.