Richter Wichmann (stovesarah37)

We review classical analytical and data-driven approaches for outcomes prediction that serve as antecedents to both radiomic and radiogenomic strategies. Discussion then focuses on uses of conventional and deep machine learning in radiomics. We further consider promising strategies for the harmonization of high-dimensional, heterogeneous multiomics datasets (panomics) and techniques for nonparametric validation of best-fit models. Strategies to overcome common pitfalls that are unique to data-intensive radiomics are also discussed.Despite significant advances in cystic fibrosis (CF) treatments, a one-time treatment for this life-shortening disease remains elusive. Stable complementation of the disease-causing mutation with a normal copy of the CF transmembrane conductance regulator (CFTR) gene fulfills that goal. Integrating lentiviral vectors are well suited for this purpose, but widespread airway transduction in humans is limited by achievable titers and delivery barriers. Since airway epithelial cells are interconnected through gap junctions, small numbers of cells expressing supraphysiologic levels of CFTR could support sufficient channel function to rescue CF phenotypes. Here, we investigated promoter choice and CFTR codon optimization (coCFTR) as strategies to regulate CFTR expression. VX-661 modulator We evaluated two promoters-phosphoglycerate kinase (PGK) and elongation factor 1-α (EF1α)-that have been safely used in clinical trials. We also compared the wild-type human CFTR sequence to three alternative coCFTR sequences generated by different algorithms. With the use of the CFTR-mediated anion current in primary human CF airway epithelia to quantify channel expression and function, we determined that EF1α produced greater currents than PGK and identified a coCFTR sequence that conferred significantly increased functional CFTR expression. Optimized promoter and CFTR sequences advance lentiviral vectors toward CF gene therapy clinical trials.Gene therapeutic approaches to aortic diseases require efficient vectors and delivery systems for transduction of endothelial cells (ECs) and smooth muscle cells (SMCs). Here, we developed a novel strategy to efficiently deliver a previously described vascular-specific adeno-associated viral (AAV) vector to the abdominal aorta by application of alginate hydrogels. To efficiently transduce ECs and SMCs, we used AAV9 vectors with a modified capsid (AAV9SLR) encoding enhanced green fluorescent protein (EGFP), as wild-type AAV vectors do not transduce ECs and SMCs well. AAV9SLR vectors were embedded into a solution containing sodium alginate and polymerized into hydrogels. Gels were surgically implanted around the adventitia of the infrarenal abdominal aorta of adult mice. Three weeks after surgery, an almost complete transduction of both the endothelium and tunica media adjacent to the gel was demonstrated in tissue sections. Hydrogel-mediated delivery resulted in induction of neutralizing antibodies but did not cause inflammatory responses in serum or the aortic wall. To further determine the translational potential, aortic tissue from patients was embedded ex vivo into AAV9SLR-containing hydrogel, and efficient transduction could be confirmed. These findings demonstrate that alginate hydrogel harboring a vascular-targeting AAV9SLR vector allows efficient local transduction of the aortic wall.Spinal muscular atrophy is a progressive, recessively inherited monogenic neurologic disease, the genetic root cause of which is the absence of a functional survival motor neuron 1 gene. Onasemnogene abeparvovec (formerly AVXS-101) is an adeno-associated virus serotype 9 vector-based gene therapy that delivers a fully functional copy of the human survival motor neuron gene. We report anti-adeno-associated virus serotype 9 antibody titers for patients with spinal muscular atrophy when they were screened for eligibility in the onasemnogene abeparvovec clinical trials (intravenous and intrathec