de Oliveira, A. K. | bioRxiv Science

bioRxiv2025

RAWtunda: a Tool to Convert a Multi-Channel Raw Image into TIFF and OME-TIFF Formats

MotivationHigh-resolution biological imaging in spatial biology produces data in many proprietary formats. The lack of compatibility between these formats restricts reproducibility and analysis, creates access issues, and makes early data analysis a challenge. ResultsHere, we introduce a graphical user interface (GUI) application designed to convert proprietary image formats into standardized formats like OME-TIFF and TIFF which we have termed Rawtunda. This tool addresses the need for easy and efficient handling of large, complex imaging data generated in spatial biology and microscopy, facilitating data sharing, analysis, and long-term storage. Featuring an intuitive interface the application supports users in converting.mcd and.ndpi formats, generated from Image Mass Cytometry (IMC) and Digital Pathology scanners, respectively. This resource aims to improve interoperability of spatial biology datasets, streamline data management workflows, and promote reproducibility in imaging research and analysis, preserving crucial image metadata. The app ensures compatibility with downstream tools and is designed for both bioinformaticians and bench biologists without experience in coding. Availability and implementationThe software, the documentation, and examples are available as open-source a https://med.virginia.edu/spatial-biology-core/rawtunda/ under the Copywrite of University of Virginia.

bioinformatics↗

bioRxiv2017

Prognostic biomarkers in oral squamous cell carcinoma: a systematic review

Over the years, several tumor biomarkers have been suggested to foresee the prognosis of oral squamous cell carcinoma (OSCC) patients. Here, we present a systematic review to identify, evaluate and summarize the evidence for OSCC reported markers. Eligible studies were identified through a literature search of MEDLINE/PubMed until January 2016. We included primary articles reporting overall survival, disease-free survival and cause-specific survival as outcomes. Our findings were analysed using REporting recommendations for tumor MARKer prognostic studies (REMARK), QuickGo tool and SciCurve trends. We found 41 biomarkers, mostly proteins evaluated by immunohistochemistry. The selected studies are of good quality, although, any study referred to a sample size determination. Considering the lack of follow-up studies, the molecules are still potential biomarkers. Further research is required to validate these biomarkers in well-designed clinical cohort-based studies.

pathology↗