Raman microspectroscopy identifies fibrotic tissues in collagen-related disorders via deconvoluted collagen type I spectra

The importance of 3D fibre architecture in cancer and implications for biomaterial model design

The need for improved prediction of clinical response is driving the development of cancer models with enhanced physiological relevance. A new concept of 'precision biomaterials' is emerging, encompassing patient-mimetic biomaterial models that seek to accurately detect, treat and model cancer by faithfully recapitulating key microenvironmental characteristics. Despite recent advances allowing tissue-mimetic stiffness and molecular composition to be replicated in vitro, approaches for reproducing the 3D fibre architectures found in tumour extracellular matrix (ECM) remain relatively unexplored. Although the precise influences of patient-specific fibre architecture are unclear, we summarize the known roles of tumour fibre architecture, underlining their implications in cell-matrix interactions and ultimately clinical outcome. We then explore the challenges in reproducing tissue-specific 3D fibre architecture(s) in vitro, highlighting relevant biomaterial fabrication techniques and their benefits and limitations. Finally, we discuss imaging and image analysis techniques (focussing on collagen I-optimized approaches) that could hold the key to mapping tumour-specific ECM into high-fidelity biomaterial models. We anticipate that an interdisciplinary approach, combining materials science, cancer research and image analysis, will elucidate the role of 3D fibre architecture in tumour development, leading to the next generation of patient-mimetic models for mechanistic studies and drug discovery.

Gelatin/heparin coated bio-inspired polyurethane composite fibers to construct small-caliber artificial blood vessel grafts

Long-term patency and ability for revascularization remain challenges for small-caliber blood vessel grafts to treat cardiovascular diseases clinically. Here, a gelatin/heparin coated bio-inspired polyurethane composite fibers-based artificial blood vessel with continuous release of NO and biopeptides to regulate vascular tissue repair and maintain long-term patency is fabricated. A biodegradable polyurethane elastomer that can catalyze S-nitrosothiols in the blood to release NO is synthesized (NPU). Then, the NPU core-shell structured nanofiber grafts with requisite mechanical properties and biopeptide release for inflammation manipulation are fabricated by electrospinning and lyophilization. Finally, the surface of tubular NPU nanofiber grafts is coated with heparin/gelatin and crosslinked with glutaraldehyde to obtain small-caliber artificial blood vessels (ABVs) with the ability of vascular revascularization. We demonstrate that artificial blood vessel grafts promote the growth of endothelial cells but inhibit the growth of smooth muscle cells by the continuous release of NO; vascular grafts can regulate inflammatory balance for vascular tissue remodel without excessive collagen deposition through the release of biological peptides. Vascular grafts prevent thrombus and vascular stenosis to obtain long-term patency. Hence, our work paves a new way to develop small-caliber artificial blood vessel grafts that can maintain long-term patency in vivo and remodel vascular tissue successfully.

The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review

Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.