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New AI model sets benchmark in digital pathology with superior cancer diagnostics

Researchers developed Prov-GigaPath, a whole-slide pathology foundation model using a novel vision transformer architecture. The model demonstrates superior performance in mutation prediction, cancer subtyping, and vision-language tasks. It leverages large-scale real-world data from over 30,000 patients to enhance clinical diagnostics.

Convolutional neural networkLong sequence network longnetProvidence gigapixel pathology modelVision transformerLong sequence networkHierarchical image pyramid transformerContrastive learning based pathology modelData efficient generalizationSelf supervised machine learningDiagnostic imagingCancer genome atlasSimple frameworkContrastive learningVisual representationsAttention based multiple instance learningProvidence pathology

XRCC1 shows potential as a prognostic and immunological pan-cancer biomarker

A new research paper was published in Aging (listed by MEDLINE/PubMed as "Aging (Albany NY)" and "Aging-US" by Web of Science) Volume 16, Issue 1, entitled, "XRCC1: a potential prognostic and immunological biomarker in LGG based on systematic pan-cancer analysis."

Luling huangRui panGuobing wangGang tianYunyue liCongchao jiaGuanhu yangHao chiOhio universityHospital of southwest medical universityFourth military medical universitySchool of nanchang universityXisheng yinYuchen sheSouthwest medical universityAffiliated hospital

XRCC1: Prognostic and Immunological Tool in Low-Grade Gliomas

XRCC1: Prognostic and Immunological Tool in Low-Grade Gliomas
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Guobing wangYunyue liLuling huangGang tianCongchao jiaGuanhu yangRui panHao chiHospital of southwest medical universityOhio universityFourth military medical universitySchool of nanchang universityXisheng yinYuchen sheSouthwest medical universityAffiliated hospital

XRCC1: A potential prognostic and immunological biomarker in low-grade gliomas

XRCC1: A potential prognostic and immunological biomarker in low-grade gliomas
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Guobing wangTumor mutation burdenMicrosatellite instabilityHuman protein atlas

Comparing Genomic Landscapes of Oral and Cutaneous Squamous Cell Carci by Ruta Gupta, Dario Strbenac et al

Squamous cell carcinoma is the most common head and neck malignancy arising from the oral mucosa and the skin. The histologic and immunohistochemical features of oral squamous cell carcinoma (OSCC) and head and neck cutaneous squamous cell carcinoma (HNcSCC) are similar, making it difficult to identify the primary site in cases of metastases. With the advent of immunotherapy, reliable distinction of OSCC and HNcSCC at metastatic sites has important treatment and prognostic implications. Here, we investigate and compare the genomic landscape of OSCC and HNcSCC to identify diagnostically useful biomarkers. Whole-genome sequencing data from 57 OSCC and 41 HNcSCC patients were obtained for tumor and matched normal samples. Tumor mutation burden (TMB), Catalogue of Somatic Mutations in Cancer (COSMIC) mutational signatures, frequent chromosomal alterations, somatic single nucleotide, and copy number variations were analyzed. The median TMB of 3.75 in primary OSCC was significantly lower (P

Somatic mutationsHead and neckEratinizing squamous cell carcinomaUtation signatureTumor mutation burdenWhole genome sequencing