John M. Elizarraras

Enrichment analysis, crucial for interpreting genomic, transcriptomic, and proteomic data, is expanding into metabolomics. Furthermore, there is a rising demand for integrated enrichment analysis that combines data from different studies and omics platforms, as seen in meta-analysis and multi-omics research. To address these growing needs, we have updated WebGestalt to include enrichment analysis capabilities for both metabolites and multiple input lists of analytes. We have also significantly increased analysis speed, revamped the user interface, and introduced new pathway visualizations to accommodate these updates. Notably, the adoption of a Rust backend reduced gene set enrichment analysis time by 95% from 270.64 to 12.41 s and network topology-based analysis by 89% from 159.59 to 17.31 s in our evaluation. This performance improvement is also accessible in both the R package and a newly introduced Python package. Additionally, we have updated the data in the WebGestalt database to reflect the current status of each source and have expanded our collection of pathways, networks, and gene signatures. The 2024 WebGestalt update represents a significant leap forward, offering new support for metabolomics, streamlined multi-omics analysis capabilities, and remarkable performance enhancements. Discover these updates and more at https://www.webgestalt.org.

Cybersecurity has become one of the largest growing fields in computer science and the technology industry. Faulty security has cost the global economy immense losses. Oftentimes, the pitfall in such financial loss is due to the security of passwords. Companies and regular people alike do not do enough to enforce strict password guidelines like the NIST (National Institute of Standard Technology) recommends. When big security breaches happen, thousands to millions of passwords can be exposed and stored into files, meaning people are susceptible to dictionary and rainbow table attacks. Those are only two examples of attacks that are used to crack passwords. In this paper, we will be going over three open-source password managers, each chosen for their own uniqueness. Our results will conclude on the overall security of each password manager using a list of established attacks and development of new potential attacks on such software. Additionally, we will compare our research with the limited research already conducted on password managers. Finally, we will provide some general guidelines of how to develop a better and more secure password manager.

Neuroblastoma (NB) is a pediatric tumor of the peripheral nervous system. Approximately 80% of relapsed NB show RAS-MAPK pathway mutations that activate ERK, resulting in the promotion of cell proliferation and drug resistance. Ulixertinib, a first-in-class ERK-specific inhibitor, has shown promising antitumor activity in phase 1 clinical trials for advanced solid tumors. Here, we show that ulixertinib significantly and dose-dependently inhibits cell proliferation and colony formation in different NB cell lines, including PDX cells. Transcriptomic analysis revealed that ulixertinib extensively inhibits different oncogenic and neuronal developmental pathways, including EGFR, VEGF, WNT, MAPK, NGF, and NTRK1. The proteomic analysis further revealed that ulixertinib inhibits the cell cycle and promotes apoptosis in NB cells. Additionally, ulixertinib treatment significantly sensitized NB cells to the conventional chemotherapeutic agent doxorubicin. Furthermore, ulixertinib potently inhibited NB tumor growth and prolonged the overall survival of the treated mice in two different NB mice models. Our preclinical study demonstrates that ulixertinib, either as a single agent or in combination with current therapies, is a novel and practical therapeutic approach for NB.

Matching patients to optimal treatment is challenging, in part due to the limited availability of real-world clinical datasets for predictive biomarker identification. The growing integration of omics profiling into clinical trials presents a new opportunity to tackle this challenge. Here, we introduce ClinicalOmicsDB, a web application for exploring molecular associations of oncology drug responses in clinical trials. This database includes transcriptomic data from 40 clinical trial studies, with 5913 patients spanning 11 cancer types. These studies include 67 treatment arms with a variety of chemotherapy, targeted therapy and immunotherapy drugs, and their combinations, which we organize based on an established ontology for easier navigation. The web application provides users with three options to explore molecular associations of oncology drug responses, focusing on studies, treatments or genes, respectively. Gene set analysis further connects treatment response to pathway activity and tumor microenvironment attributes. The user-friendly web interface of ClinicalOmicsDB streamlines interactive analysis. A Rust-based backend speeds up response time, and application programming interfaces and an R package enable programmatic access. We use three case studies to demonstrate the utility of this resource in human cancer studies. ClinicalOmicsDB is freely available at http://trials.linkedomics.org/.