Biology subjects

Kashani, E.

Publications and source records attributed to Kashani, E..

2 recordsLinked to original sources

bioRxiv2023

Systematic microRNA screening identifies miR-19b as regulator of temozolomide response through targeting PP2A serine-threonine phosphatase in glioblastoma

Despite the standard of care, glioblastoma IDH wildtype (GBM) inevitably recurs, underscoring the need to develop new treatment strategies. To address the role of microRNAs in temozolomide (TMZ) response, we performed functional microRNA screens and consistently identified miR-19b. Our study reveals a novel axis between miR-19b and PPP2R5E subunit of serine/threonine protein phosphatase PP2A and establishes a so far unappreciated contribution of miR-19b in TMZ resistance of GBM. Specifically, our results demonstrate that attenuation of miR-19b in GBM cell lines and glioblastoma stem cells (GSCs) induces DNA damage, which further enhances the cytotoxic effects of TMZ treatment. We confirmed TMZ resistance induced by knocking down PPP2R5E in orthotopic mouse xenografts of GSCs. Furthermore, our results indicate that treating cells with the PP2A-activating drug FTY720 or knocking down endogenous PP2A-inhibiting proteins potentiates the cytotoxic effects of TMZ. MiR-19b attenuation or PPP2R5E activation could potentially be exploited in adjuvant therapy of GBM patients.

cancer biology↗

bioRxiv2021

Information retrieval in an infodemic: the case of COVID-19 publications

The COVID-19 pandemic has led to an exponential surge and an enormous amount of published literature, both accurate and inaccurate, a term usually coined as an infodemic. In the context of searching for COVID-19 related scientific literature, we present an information retrieval methodology for effectively finding relevant publications for different information needs. Our multi-stage information retrieval architecture combines probabilistic weighting models and re-ranking algorithms based on neural masked language models. The methodology was evaluated in the context of the TREC-COVID challenge, achieving competitive results with the top ranking teams participating in the competition. Particularly, the ranking combination of bag-of-words and language models significantly outperformed a BM25-based baseline model (16 percentage points for the NDCG@20 metric), correctly retrieving more than 16 out of the top 20 documents retrieved. The proposed pipeline could thus support the effective search and discovery of relevant information in the case of an infodemic.

bioinformatics↗