Digital pathology coupled with advanced machine learning (e.g., deep learning) has been changing the paradigm of whole-slide histopathological images (WSIs) analysis. Major applications in digital pathology using machine learning include automatic cancer classification, survival analysis, and subtyping from pathological images. While most pathological image analyses are based on patch-wise processing due to the extremely large size of histopathology images, there are several applications that predict a single clinical outcome or perform pathological diagnosis per slide (e.g., cancer classification, survival analysis). However, current slide-based analyses are task-dependent, and a general framework of slide-based analysis in WSI has been seldom investigated. We propose a novel slide-based histopathology analysis framework that creates a WSI representation map, called HipoMap, that can be applied to any slide-based problems, coupled with convolutional neural networks. HipoMap converts a WSI of various shapes and sizes to structured image-type representation. Our proposed HipoMap outperformed existing methods in intensive experiments with various settings and datasets. HipoMap showed the Area Under the Curve (AUC) of 0.96±0.026 (5% improved) in the experiments for lung cancer classification, and c-index of 0.787±0.013 (3.5% improved) and coefficient of determination ( $$R^2$$R 2 ) of 0.978±0.032 (24% improved) in survival analysis and survival prediction with TCGA lung cancer data respectively, as a general framework of slide-based analysis with a flexible capability. The results showed significant improvement comparing to the current state-of-the-art methods on each task. We further discussed experimental results of HipoMap as pathological viewpoints and verified the performance using publicly available TCGA datasets. A Python package is available at https://pypi.org/project/hipomap, and the package can be easily installed using Python PIP. The open-source codes in Python are available at: https://github.com/datax-lab/HipoMap.

In recent years, pure iron (Fe) has attracted significant attention as a promising biodegradable orthopedic implant material due to its excellent mechanical and biological properties. However, in physiological conditions, Fe has an extremely slow degradation rate with localized and irregular degradation, which is problematic for practical applications. In this study, we developed a novel combination of a nanostructured surface topography and galvanic reaction to achieve uniform and accelerated degradation of an Fe implant. The target-ion induced plasma sputtering (TIPS) technique was applied on the Fe implant to introduce biologically compatible and electrochemically noble tantalum (Ta) onto its surface and develop surface nano-galvanic couples. Electrochemical tests revealed that the uniformly distributed nano-galvanic corrosion cells of the TIPS-treated sample (nano Ta–Fe) led to relatively uniform and accelerated surface degradation compared to that of bare Fe. Furthermore, the mechanical properties of nano Ta–Fe remained almost constant during a long-term in vitro immersion test (~40 weeks). Biocompatibility was also assessed on surfaces of bare Fe and nano Ta–Fe using in vitro osteoblast responses through direct and indirect contact assays and an in vivo rabbit femur medullary cavity implantation model. The results revealed that nano Ta–Fe not only enhanced cell adhesion and spreading on its surface, but also exhibited no signs of cellular or tissue toxicity. These results demonstrate the immense potential of Ta-implanted surface nanostructures as an effective solution for the practical application of Fe-based orthopedic implants, ensuring long-term biosafety and clinical efficacy.

Biliary strictures are characterized by the narrowing of the bile duct lumen, usually caused by surgical biliary injury, cancer, inflammation, and scarring from gallstones. Endoscopic stent placement is a well-established method for the management of biliary strictures. However, maintaining optimal mechanical properties of stents and designing surfaces that can prevent stent-induced tissue hyperplasia and biofilm formation are challenges in the fabrication of biodegradable biliary stents (BBSs) for customized treatment. This study proposes a novel approach to fabricating functionalized polymer BBSs with nanoengineered surfaces using 3D printing. The 3D printed stents, fabricated from bioactive silica poly(ε-carprolactone) (PCL) via a sol–gel method, exhibited tunable mechanical properties suitable for supporting the bile duct while ensuring biocompatibility. Furthermore, a nanoengineered surface layer was successfully created on a sirolimus (SRL)-coated functionalized PCL (fPCL) stent using Zn ion sputtering-based plasma immersion ion implantation (S-PIII) treatment to enhance the performance of the stent. The nanoengineered surface of the SRL-coated fPCL stent effectively reduced bacterial responses and remarkably inhibited fibroblast proliferation and initial burst release of SRL in vitro systems. The physicochemical properties and biological behaviors, including in vitro biocompatibility and in vivo therapeutic efficacy in the rabbit bile duct, of the Zn-SRL@fPCL stent demonstrated its potential as a versatile platform for clinical applications in bile duct tissue engineering.

The rapid growth of uncharacterized enzymes and their functional diversity urge accurate and trustworthy computational functional annotation tools. However, current state-of-the-art models lack trustworthiness on the prediction of the multilabel classification problem with thousands of classes. Here, we demonstrate that a novel evidential deep learning model (named ECPICK) makes trustworthy predictions of enzyme commission (EC) numbers with data-driven domain-relevant evidence, which results in significantly enhanced predictive power and the capability to discover potential new motif sites. ECPICK learns complex sequential patterns of amino acids and their hierarchical structures from 20 million enzyme data. ECPICK identifies significant amino acids that contribute to the prediction without multiple sequence alignment. Our intensive assessment showed not only outstanding enhancement of predictive performance on the largest databases of Uniprot, Protein Data Bank (PDB) and Kyoto Encyclopedia of Genes and Genomes (KEGG), but also a capability to discover new motif sites in microorganisms. ECPICK is a reliable EC number prediction tool to identify protein functions of an increasing number of uncharacterized enzymes.