Generating universal anti-CD19 CAR T cells with a defined memory phenotype by CRISPR/Cas9 editing and safety evaluation of the transcriptome.

Introduction: Chimeric antigen receptor-expressing T cells (CAR T cells) have revolutionized cancer treatment, particularly in B cell malignancies. However, the use of autologous T cells for CAR T therapy presents several limitations, including high costs, variable efficacy, and adverse effects linked to cell phenotype.

Methods: To overcome these challenges, we developed a strategy to generate universal and safe anti-CD19 CAR T cells with a defined memory phenotype.

Lentiviral vectors for inducible, transactivator-free advanced therapy medicinal products: Application to CAR-T cells.

Controlling transgene expression through an externally administered inductor is envisioned as a potent strategy to improve safety and efficacy of gene therapy approaches. Generally, inducible ON systems require a chimeric transcription factor (transactivator) that becomes activated by an inductor, which is not optimal for clinical translation due to their toxicity. We generated previously the first all-in-one, transactivator-free, doxycycline (Dox)-responsive (Lent-On-Plus or LOP) lentiviral vectors (LVs) able to control transgene expression in human stem cells.

Efficacy and safety of universal (TCRKO) ARI-0001 CAR-T cells for the treatment of B-cell lymphoma.

Autologous T cells expressing the Chimeric Antigen Receptor (CAR) have been approved as advanced therapy medicinal products (ATMPs) against several hematological malignancies. However, the generation of patient-specific CAR-T products delays treatment and precludes standardization. Allogeneic CAR-T cells are an alternative to simplify this complex and time-consuming process.

Erratum: Physiological lentiviral vectors for the generation of improved CAR-T cells.

[This corrects the article DOI: 10.1016/j.omto.

Physiological lentiviral vectors for the generation of improved CAR-T cells.

Anti-CD19 chimeric antigen receptor (CAR)-T cells have achieved impressive outcomes for the treatment of relapsed and refractory B-lineage neoplasms. However, important limitations still remain due to severe adverse events (i.e.

Predicting Treatment Outcomes Using Explainable Machine Learning in Children with Asthma.

Asthma in children is a heterogeneous disease manifested by various phenotypes and endotypes. The level of disease control, as well as the effectiveness of anti-inflammatory treatment, is variable and inadequate in a significant portion of patients. By applying machine learning algorithms, we aimed to predict the treatment success in a pediatric asthma cohort and to identify the key variables for understanding the underlying mechanisms.

Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning.

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria.

Using Gene Editing Approaches to Fine-Tune the Immune System.

Genome editing technologies not only provide unprecedented opportunities to study basic cellular system functionality but also improve the outcomes of several clinical applications. In this review, we analyze various gene editing techniques used to fine-tune immune systems from a basic research and clinical perspective. We discuss recent advances in the development of programmable nucleases, such as zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeat (CRISPR)-Cas-associated nucleases.

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.

Motivation: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible in some contexts and leave a portion of the genome unqueried. Hence, we devised a novel, supervised, integrative learning framework to perform whole-genome methylation and hydroxymethylation predictions in CpG dinucleotides.

Stents for malignant ureteral obstruction.

Malignant ureteral obstruction can result in renal dysfunction or urosepsis and can limit the physician's ability to treat the underlying cancer. There are multiple methods to deal with ureteral obstruction including regular polymeric double J stents (DJS), tandem DJS, nephrostomy tubes, and then more specialized products such as solid metal stents (e.g.