Circulating tumor DNA (ctDNA) trajectories predict survival in trifluridine/tipiracil-treated metastatic colorectal cancer patients.

Late-line treatment in metastatic colorectal cancer (mCRC) can improve prognosis. However, not every patient has a benefit and may experience severe side effects. Thus, predictive/prognostic biomarkers are urgently needed.

From MRI to FEM: an automated pipeline for biomechanical simulations of vertebrae and intervertebral discs.

Introduction: Biomechanical simulations can enhance our understanding of spinal disorders. Applied to large cohorts, they can reveal complex mechanisms beyond conventional imaging. Therefore, automating the patient-specific modeling process is essential.

Neural network surrogate and projected gradient descent for fast and reliable finite element model calibration: A case study on an intervertebral disc.

Accurate calibration of finite element (FE) models is essential across various biomechanical applications, including human intervertebral discs (IVDs), to ensure their reliability and use in diagnosing and planning treatments. However, traditional calibration methods are computationally intensive, requiring iterative, derivative-free optimization algorithms that often take days to converge. This study addresses these challenges by introducing a novel, efficient, and effective calibration method demonstrated on a human L4-L5 IVD FE model as a case study using a neural network (NN) surrogate.

Gene-editing in patient and humanized-mice primary muscle stem cells rescues dysferlin expression in dysferlin-deficient muscular dystrophy.

Dystrophy-associated fer-1-like protein (dysferlin) conducts plasma membrane repair. Mutations in the DYSF gene cause a panoply of genetic muscular dystrophies. We targeted a frequent loss-of-function, DYSF exon 44, founder frameshift mutation with mRNA-mediated delivery of SpCas9 in combination with a mutation-specific sgRNA to primary muscle stem cells from two homozygous patients.

SPINEPS-automatic whole spine segmentation of T2-weighted MR images using a two-phase approach to multi-class semantic and instance segmentation.

Objectives: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images.

Material And Methods: This local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones.