Taking inspiration from diverse interlocking and adhesion structures found in nature, a biaxially interlocking interface is developed in this work. This interface is formed by interconnecting two electrostatically flocked substrates and its mechanical strength is enhanced through the incorporation of enoki mushroom-shaped microfibers and deposited extracellular matrix (ECM). Tips of flocked straight fibers can be transformed into mushroom shapes through thermal treatment. The tensile strength of interlocked scaffolds with mushroom-shaped tips drastically increased when compared to scaffolds made of straight fibers, which was not reported previously. More cells proliferate within interlocked scaffolds with mushroom-shaped tips than scaffolds with straight fibers. Additionally, the mechanical strength (e.g., compressive, tensile, and shear) of cell-seeded interlocked scaffolds increased proportionally to the amount of ECM deposited by dermal fibroblasts. The biaxially interlocking interface developed in this study holds promise for applications in engineering interfacial tissues, modelling tissue interfaces, investigating tissue-tissue interactions, and facilitating tissue bridging or binding.

Quantitative and volumetric assessment of filamentous actin fibers (F-actin) remains challenging due to their interconnected nature, leading researchers to utilize threshold based or qualitative measurement methods with poor reproducibility. Here we introduce a novel machine learning based methodology for accurate quantification and reconstruction of nuclei-associated F-actin. Utilizing a Convolutional Neural Network (CNN), we segment actin filaments and nuclei from 3D confocal microscopy images and then reconstruct each fiber by connecting intersecting contours on cross-sectional slices. This allowed measurement of the total number of actin filaments and individual actin filament length and volume in a reproducible fashion. Focusing on the role of F-actin in supporting nucleocytoskeletal connectivity, we quantified apical F-actin, basal F-actin, and nuclear architecture in mesenchymal stem cells (MSCs) following the disruption of the Linker of Nucleoskeleton and Cytoskeleton (LINC) Complexes. Disabling LINC in mesenchymal stem cells (MSCs) generated F-actin disorganization at the nuclear envelope characterized by shorter length and volume of actin fibers contributing a less elongated nuclear shape. Our findings not only present a new tool for mechanobiology but introduce a novel pipeline for developing realistic computational models based on quantitative measures of F-actin.

Understanding the effects of inflammation and cirrhosis on the regulation of drug metabolism during the progression of hepatocellular carcinoma (HCC) is critical for developing patient-specific treatment strategies. In this work, we created novel three-dimensional vascularized HCC-on-a-chips (HCCoC), composed of HCC, endothelial, stellate, and Kupffer cells tuned to mimic normal or cirrhotic liver stiffness. HCC inflammation was controlled by tuning Kupffer macrophage numbers, and the impact of cytochrome P450-3A4 (CYP3A4) was investigated by culturing HepG2 HCC cells transfected with CYP3A4 to upregulate expression from baseline. This model allowed for the simulation of chemotherapeutic delivery methods such as intravenous injection and transcatheter arterial chemoembolization (TACE). We showed that upregulation of metabolic activity, incorporation of cirrhosis and inflammation, increase vascular permeability due to upregulated inflammatory cytokines leading to significant variability in chemotherapeutic treatment efficacy. Specifically, we show that further modulation of CYP3A4 activity of HCC cells by TACE delivery of doxorubicin provides an additional improvement to treatment response and reduces chemotherapy-associated endothelial porosity increase. The HCCoCs were shown to have utility in uncovering the impact of the tumor microenvironment (TME) during cancer progression on vascular properties, tumor response to therapeutics, and drug delivery strategies.

Patients infected with SARS-CoV-2 and influenza display similar symptoms, but treatment requirements are different. Clinicians need to accurately distinguish SARS-CoV-2 from influenza to provide appropriate treatment. Here, the authors develope a color-based technique to differentiate between patients infected with SARS-CoV-2 and influenza A using a nucleic acid enzyme-gold nanoparticle (GNP) molecular test requiring minimal equipment. The MNAzyme and GNP probes are designed to be robust to viral mutations. Conserved regions of the viral genomes are targeted, and two MNAzymes are created for each virus. The ability of the system to distinguish between SARS-CoV-2 and influenza A using 79 patient samples is tested. When detecting SARS-CoV-2 positive patients, the clinical sensitivity is 90%, and the specificity is 100%. When detecting influenza A, the clinical sensitivity and specificity are 93% and 100%, respectively. The high clinical performance of the MNAzyme-GNP assay shows that it can be used to help clinicians choose effective treatments.

Platelets possess distinct surface moieties responsible for modulating their adhesion to various disease-relevant substrates involving vascular damage, immune evasion, and pathogen interactions. Such broad biointerfacing capabilities of platelets have inspired the development of platelet-mimicking drug carriers that preferentially target drug payloads to disease sites for enhanced therapeutic efficacy. Among these carriers, platelet membrane-coated nanoparticles (denoted 'PNPs') made by cloaking synthetic substrates with the plasma membrane of platelets have emerged recently. Their 'top-down' design combines the functionalities of natural platelet membrane and the engineering flexibility of synthetic nanomaterials, which together create synergy for effective drug delivery and novel therapeutics. Herein, we review the recent progress of engineering PNPs with different structures for targeted drug delivery, focusing on three areas, including targeting injured blood vessels to treat vascular diseases, targeting cancer cells for cancer treatment and detection, and targeting drug-resistant bacteria to treat infectious diseases. Overall, current studies have established PNPs as versatile nanotherapeutics for drug targeting with strong potentials to improve the treatment of various diseases.