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Sebastian, E.

Publications and source records attributed to Sebastian, E..

2 recordsLinked to original sources

CRISPR/Cas9 technology abolishes the BCR/ABL1 oncogene effect in chronic myeloid leukemia and restores normal hematopoiesis

Chronic myeloid leukemia (CML) is a hematopoietic stem cell disease produced by a unique oncogenic event involving the constitutively active tyrosine kinase (TK) BCR/ABL1. TK activity explains most features of CML, such as tumor development and maintenance. TK-inhibitory (TKI) drugs have changed its prognosis and natural history. Unfortunately, the ABL1 gene persists unaffected by TKIs, leukemic stem cells (LSCs) remains, resistant mutations arise and adverse effects may occur during treatment. To address this problem, we have designed a potential therapeutic alternative with CRISPR/Cas9 genome editing nucleases that target LSCs. The strategy was successfully developed in murine and human cell lines and finally was evaluated in primary LSCs isolated from CML transgenic mice and from CML patients. Mouse CML-LSCs edited were orthotopic transplanted in immunodeficient NSG niches where restored the normal hematopoiesis. Importantly, patient-derived xenografts with CD34+-LSCs edited, repopulated and restored the normal hematopoiesis in immunodeficient NSG niches. We show, for the first time, how CRISPR technology efficiently interrupts the BCR/ABL1 oncogene in murine and human LSCs to provide a significant therapeutic benefit. We propose human CML as a potential candidate for CRISPR therapy, providing proof-of-principle for genome editing in CML patients, and open new avenues for the application of this technique in other fusion genes. Key pointsCRISPR system destroys BCR/ABL oncogene and induces a therapeutic benefit in a CML mouse model and CML patient derived xenografts.

cancer biology

The first AI-based mobile application for antibiotic resistance testing

Antimicrobial resistance is a major global health threat and its development is promoted by antibiotic misuse. While disk diffusion antibiotic susceptibility testing (AST, also called antibiogram) is broadly used to test for antibiotic resistance in bacterial infections, it faces strong criticism because of inter-operator variability and the complexity of interpretative reading. Automatic reading systems address these issues, but are not always adapted or available to resource-limited settings. We present the first artificial intelligence (AI)-based, offline smartphone application for antibiogram analysis. The application captures images with the phones camera, and the user is guided throughout the analysis on the same device by a user-friendly graphical interface. An embedded expert system validates the coherence of the antibiogram data and provides interpreted results. The fully automatic measurement procedure of our applications reading system achieves an overall agreement of 90% on susceptibility categorization against a hospital-standard automatic system and 98% against manual measurement (gold standard), with reduced inter-operator variability. The applications performance showed that the automatic reading of antibiotic resistance testing is entirely feasible on a smartphone. Moreover our application is suited for resource-limited settings, and therefore has the potential to significantly increase patients access to AST worldwide.

microbiology