Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by synovial tissue inflammation and joint destruction associated with the activation of angiogenesis. Exosomes, which play a role in cell-to-cell communication as carriers of genetic information, transfer microRNAs (miRNAs or miRs) between cells and have been studied as delivery vehicles for therapeutic molecules. The aim of the current study was to investigate the therapeutic effect of mesenchymal stem cell (MSC)–derived miR-150-5p exosomes on joint destruction in RA. The expression and secretion of miR-150-5p, matrix metalloproteinase (MMP) 14, and vascular endothelial growth factor (VEGF) in RA patients and fibroblast-like synoviocytes (FLS) were examined by quantitative RT-PCR, ELISA, and Western blotting. Immunohistochemistry was used to assess angiogenesis. MSCs were transfected with an miR-150-5p expression plasmid, and MSC-derived exosomes were harvested. The effect of MSC-derived miR-150-5p exosomes (Exo-150) on MMP14 and VEGF expression was examined. The effects of Exo-150 on cell migration and invasion in cytokine-stimulated FLS from RA patients were examined by HUVEC tube formation and transwell assays. The effect of Exo-150 in vivo was examined in a collagen-induced arthritis mouse model. Exo-150 decreased migration and invasion in RA FLS and downregulated tube formation in HUVECs by targeting MMP14 and VEGF. Injection of Exo-150 reduced hind paw thickness and the clinical arthritic scores in collagen-induced arthritis mice. Exo-150 reduced joint destruction by inhibiting synoviocyte hyperplasia and angiogenesis. Exosomes facilitate the direct intracellular transfer of miRNAs between cells and represent a potential therapeutic strategy for RA.

Objective: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using low-dose chest computed tomography (LDCT) scans obtained for lung cancer screening. Methods: First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis. Results: Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964–0.968 and mean errors of 2.2–4.0 mg/cm³. The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD. Conclusion: The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening. Key Points: • Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fracture. • A deep learning-based system was developed to fully automate bone mineral density measurement in low-dose chest computed tomography scans. • The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

To automatically measure the Cobb angle and diagnose scoliosis on chest X-rays, a computer-aided method was proposed and the reliability and accuracy were evaluated.

Coronavirus Disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronaviruses 2 (SARS-CoV-2) has become a serious global pandemic in the past few months and caused huge loss to human society worldwide. For such a large-scale pandemic, early detection and isolation of potential virus carriers is essential to curb the spread of the pandemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the pandemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health conditions of people wearing masks through analysis of the respiratory characteristics from RGB-infrared sensors. We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with an attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status of respiratory with 83.69% accuracy, 90.23% sensitivity and 76.31% specificity on the real-world dataset. This work demonstrates that the proposed RGB-infrared sensors on portable device can be used as a pre-scan method for respiratory infections, which provides a theoretical basis to encourage controlled clinical trials and thus helps fight the current COVID-19 pandemic. The demo videos of the proposed system are available at: https://doi.org/10.6084/m9.figshare.12028032.

Local minimally invasive injection of anticancer therapies is a compelling approach to maximize the utilization of drugs and reduce the systemic adverse drug effects. However, the clinical translation is still hampered by many challenges such as short residence time of therapeutic agents and the difficulty in achieving multi‐modulation combination therapy. Herein, mesoporous silica‐coated gold nanorods (AuNR@SiO₂) core‐shell nanoparticles are fabricated to facilitate drug loading while rendering them photothermally responsive. Subsequently, AuNR@SiO₂ is anchored into a monodisperse photocrosslinkable gelatin (GelMA) microgel through one‐step microfluidic technology. Chemotherapeutic drug doxorubicin (DOX) is loaded into AuNR@SiO₂ and 5,6‐dimethylxanthenone‐4‐acetic acid (DMXAA) is loaded in the microgel layer. The osteosarcoma targeting ligand alendronate is conjugated to AuNR@SiO₂ to improve the tumor targeting. The microgel greatly improves the injectability since they can be dispersed in buffer and the injectability and degradability are adjustable by microfluidics during the fabrication. The drug release can, in turn, be modulated by multi‐round light‐trigger. Importantly, a single super low drug dose (1 mg kg⁻¹ DOX with 5 mg kg⁻¹ DMXAA) with peritumoral injection generates long‐term therapeutic effect and significantly inhibited tumor growth in osteosarcoma bearing mice. Therefore, this nanocomposite@microgel system can act as a peritumoral reservoir for long‐term effective osteosarcoma treatment.

Objective: Cell death plays an important role in the pathology associated with inflammatory diseases such as osteoarthritis. It has been reported that autophagy can protect cells against tumour necrosis factor-α (TNF-α)-induced apoptosis. This study aimed to determine the potential role of microRNA-30b (miR-30b) in TNF-α-induced apoptosis, autophagy and differentiation in the chondrogenic ADTC5 cell line. Methods: To analyse the effect of TNF-α on the viability of ADTC5 cells, cell counting kit-8 and Hoechst 33342 staining were employed and the expression levels of caspase-3 and -9 were assessed. Autophagy was examined by analysing the levels of LC3B-II and p62 and quantitating GFP-LC3B by fluorescence microscopy. A luciferase reporter assay investigated the putative binding sites of miR-30b. The effects of miR-30b and antimiR-30b on autophagy, apoptosis and osteogenic differentiation of TNF-α-treated cells were determined by autophagosome, apoptosis and alkaline phosphatase assays, respectively. Results: TNF-α exposure decreased cell viability, increased apoptosis and positively regulated autophagy in ADTC5 cells. A direct interaction was detected between miR-30b and the mRNA 3ʹ-UTRs of autophagy genes BECN1 and ATG5. Overexpression of miR-30b downregulated autophagy genes and upregulated pro-apoptotic gene expression in TNF-α-treated cells, while treatment with antimiR-30b had the inverse effect. Overexpression of miR-30b also downregulated ECM degradation and anti-miR-30b reverse TNF-α-induced ECM degradation. Conclusions: Anti-miR-30b enhanced autophagy and attenuated cartilage degradation and played a protective role in TNF-α-induced apoptosis of ATDC5 cells. Anti-miR-30b may therefore elevate cellular survival during inflammation and has therapeutic potential for inflammatory diseases such as osteoarthritis.