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Biology subjects

Wong, C. F.

Publications and source records attributed to Wong, C. F..

2 recordsLinked to original sources

A novel biopolymer synergizes type I IFN and IL-1β production through STING

N-dihydrogalactochitosan (GC) is developed for inducing immune responses. Synthesized from chitosan and galactose, GC is a new chemical entity that significantly enhances the immune-stimulating properties of its parental material, chitosan, making it a promising therapeutic agent. When used in combination with antigenic material, GC stimulates innate and adaptive antitumor and antiviral immunities. However, the mechanism of GC has not been fully investigated. Herein we demonstrate that GC drives type I IFN production and IFN responses in antigen presenting cells (APCs) and has superior potency compared to its corresponding chitosan. More importantly, GC drives alternative activation of STING leading to inflammatory cell death that enhances dendritic cell (DC) activation, which triggers a variety of nucleic acid sensing pattern recognition receptors (PRRs) and IL-1{beta} production. In vivo, GC induced a potent response of type I IFN and upregulated genes associated with STING signaling within the tumor microenvironment (TME). Moreover, intratumoral delivery of GC reduced the numbers of M2-like macrophages residing within the TME, while subsequently increasing the number of DCs. Our findings demonstrate GCs unique ability to activate STING and stimulate a broad type I IFN response which holds therapeutic promise in generating antitumor and antiviral immunities.

cancer biology↗

An AI-assisted Tool For Efficient Prostate Cancer Diagnosis

Pathologists diagnose prostate cancer by core needle biopsy. For low-grade and low-volume cases, the pathologists look for the few malignant glands out of hundreds within a core. They may miss the few malignant glands, resulting in repeat biopsies or missed therapeutic opportunities. This study developed a multi-resolution deep learning pipeline detecting malignant glands in core needle biopsies to help pathologists effectively and accurately diagnose prostate cancer in low-grade and low-volume cases. The pipeline consisted of two stages: the gland segmentation model detected the glands within the sections and the multi-resolution model classified each detected gland into benign vs. malignant. Analyzing a gland at multiple resolutions provided the classification model to exploit both morphology information (of nuclei and glands) and neighborhood information (for architectural patterns), important in prostate gland classification. We developed and tested our pipeline on the slides of a local cohort of 99 patients in Singapore. The images were made publicly available, becoming the first digital histopathology dataset of prostatic carcinoma patients of Asian ancestry. Our pipeline successfully classified the core needle biopsy parts (81 parts: 50 benign and 31 malignant) into benign vs. malignant. It achieved an AUROC value of 0.997 (95% CI: 0.987 - 1.000). Moreover, it produced heatmaps highlighting the malignancy of each gland in core needle biopsies. Hence, our pipeline can effectively assist pathologists in core needle biopsy analysis.

cancer biology↗