Loss of ß-catenin reveals a role for glutathione in regulating oxidative stress during cholestatic liver disease.

BACKGROUND: Cholestasis is an intractable liver disorder that results from impaired bile flow. We have previously shown that the Wnt/ß-catenin signaling pathway regulates the progression of cholestatic liver disease through multiple mechanisms, including bile acid metabolism and hepatocyte proliferation. To further explore the impact of these functions during intrahepatic cholestasis, we exposed mice to a xenobiotic that causes selective biliary injury. METHODS: α-naphthylisothiocyanate (ANIT) was administered to liver-specific knockout (KO) of ß-catenin and wild-type mice in the diet. Mice were killed at 6 or 14 days to assess the severity of cholestatic liver disease, measure the expression of target genes, and perform biochemical analyses. RESULTS: We found that the presence of ß-catenin was protective against ANIT, as KO mice had a significantly lower survival rate than wild-type mice. Although serum markers of liver damage and total bile acid levels were similar between KO and wild-type mice, the KO had minor histological abnormalities, such as sinusoidal dilatation, concentric fibrosis around ducts, and decreased inflammation. Notably, both total glutathione levels and expression of glutathione-S-transferases, which catalyze the conjugation of ANIT to glutathione, were significantly decreased in KO after ANIT. Nuclear factor erythroid-derived 2-like 2, a master regulator of the antioxidant response, was activated in KO after ANIT as well as in a subset of patients with primary sclerosing cholangitis lacking activated ß-catenin. Despite the activation of nuclear factor erythroid-derived 2-like 2, KO livers had increased lipid peroxidation and cell death, which likely contributed to mortality. CONCLUSIONS: Loss of ß-catenin leads to increased cellular injury and cell death during cholestasis through failure to neutralize oxidative stress, which may contribute to the pathology of this disease.

Developing a synergistic rate-retarding polymeric implant for controlling monoclonal antibody delivery in minimally invasive glaucoma surgery.

Monoclonal antibodies (mAbs) have garnered substantial attention within the field of ophthalmology and can be used to suppress scar formation after minimally invasive glaucoma surgeries. Here, by controlling mAb passive diffusion, we developed a polymeric, rate-controlling membrane reservoir loaded with poly(lactic-co-glycolic acid) microspheres to deliver mAb for several weeks. Different parameters were tested to ensure that the microspheres achieved a good quality characteristic, and our results showed that 1 %W/V emulsifier with 5 %W/V NaCl achieved mAb-loaded microspheres with the highest stability, encapsulation efficiency and minimal burst release. Then, we fabricated and compared 10 types of microporous films based on polylactic acid (PLA), polycaprolactone (PCL), and polyethylene glycol (PEG). Our results revealed distinct pore characteristics and degradation patterns in different films due to varying polymer properties, and all the polymeric film formulations showed good biocompatibility in both human trabecular meshwork cells and human conjunctival fibroblasts. Finally, the optimized microspheres were loaded into the reservoir-type polymeric implant assembled by microporous membranes with different surface coating modifications. The implant formulation, which was fabricated by 60 PCL: 40 PEG (3 %W/V) polymer with 0.1 %W/V poly(lactic-co-glycolic acid) barrier, exerted the best drug release profile that can sustained release mAb (83.6 %) for 4 weeks.

Towards a general-purpose foundation model for computational pathology.

Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H&E-stained WSIs (>77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology.

A General-Purpose Self-Supervised Model for Computational Pathology.

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.

Artificial intelligence for multimodal data integration in oncology.

In oncology, the patient state is characterized by a whole spectrum of modalities, ranging from radiology, histology, and genomics to electronic health records. Current artificial intelligence (AI) models operate mainly in the realm of a single modality, neglecting the broader clinical context, which inevitably diminishes their potential. Integration of different data modalities provides opportunities to increase robustness and accuracy of diagnostic and prognostic models, bringing AI closer to clinical practice. AI models are also capable of discovering novel patterns within and across modalities suitable for explaining differences in patient outcomes or treatment resistance. The insights gleaned from such models can guide exploration studies and contribute to the discovery of novel biomarkers and therapeutic targets. To support these advances, here we present a synopsis of AI methods and strategies for multimodal data fusion and association discovery. We outline approaches for AI interpretability and directions for AI-driven exploration through multimodal data interconnections. We examine challenges in clinical adoption and discuss emerging solutions.