The lack of a robust system to reproducibly propagate HRV-C, a family of viruses refractory to cultivation in standard cell lines, has substantially hindered our understanding of this common respiratory pathogen. We sought to develop an organoid-based system to reproducibly propagate HRV-C, and characterize virus-host interaction using respiratory organoids. We demonstrate that airway organoids sustain serial virus passage with the aid of CYT387-mediated immunosuppression, whereas nasal organoids that more closely simulate the upper airway achieve this without any intervention. Nasal organoids are more susceptible to HRV-C than airway organoids. Intriguingly, upon HRV-C infection, we observe an innate immune response that is stronger in airway organoids than in nasal organoids, which is reproduced in a Poly(I:C) stimulation assay. Treatment with α-CDHR3 and antivirals significantly reduces HRV-C viral growth in airway and nasal organoids. Additionally, an organoid-based immunofluorescence assay is established to titrate HRV-C infectious particles. Collectively, we develop an organoid-based system to reproducibly propagate the poorly cultivable HRV-C, followed by a comprehensive characterization of HRV-C infection and innate immunity in physiologically active respiratory organoids. The organoid-based HRV-C infection model can be extended for developing antiviral strategies. More importantly, our study has opened an avenue for propagating and studying other uncultivable human and animal viruses.

Short-term fasting (STF) is a technique to reduce nutrient intake for a specific period. Since metabolism plays a pivotal role in tumor progression, it can be hypothesized that STF can improve the efficacy of chemotherapy. Recent studies have demonstrated the efficacy of STF in cell and animal tumor models. However, large-scale clinical trials must be conducted to verify the safety and effectiveness of these diets. In this review, we re-examine the concept of how metabolism affects pathophysiological pathways. Next, we provided a comprehensive discussion of the specific mechanisms of STF on tumor progression, derived through studies carried out with tumor models. There are currently at least four active clinical trials on fasting and cancer treatment. Based on these studies, we highlight the potential caveats of fasting in clinical applications, including the onset of metabolic syndrome and other metabolic complications during chemotherapy, with a particular focus on the regulation of the epithelial to mesenchymal pathway and cancer heterogeneity. We further discuss the advantages and disadvantages of the current state-of-art tumor models for assessing the impact of STF on cancer treatment. Finally, we explored upcoming fasting strategies that could complement existing chemotherapy and immunotherapy strategies to enable personalized medicine. Overall, these studies have the potential for breakthroughs in cancer management.

Cancer cells undergo phenotypic changes or mutations during treatment, making detecting protein-based or gene-based biomarkers challenging. Here, we used algorithmic analysis combined with patient-derived tumor models to derive an early prediction tool using patient-derived cell clusters from liquid biopsy (LIQBP) for cancer prognosis in a label-free manner. The LIQBP platform incorporated a customized microfluidic biochip that mimicked the tumor microenvironment to establish patient clusters, and extracted physical parameters from images of each sample, including size, thickness, roughness, and thickness per area (n = 31). Samples from healthy volunteers (n = 5) and cancer patients (pretreatment; n = 4) could be easily distinguished with high sensitivity (91.16 ± 1.56%) and specificity (71.01 ± 9.95%). Furthermore, we demonstrated that the multiple unique quantitative parameters reflected patient responses. Among these, the ratio of normalized gray value to cluster size (RGVS) was the most significant parameter correlated with cancer stage and treatment duration. Overall, our work presented a novel and less invasive approach for the label-free prediction of disease prognosis to identify patients who require adjustments to their treatment regime. We envisioned that such efforts would promote the management of personalized patient care conveniently and cost effectively.

Cancer spatial and temporal heterogeneity fuels resistance to therapies. To realize the routine assessment of cancer prognosis and treatment, we demonstrate the development of an Intelligent Disease Detection Tool (IDDT), a microfluidic-based tumor model integrated with deep learning-assisted algorithmic analysis. IDDT was clinically validated with liquid blood biopsy samples (n = 71) from patients with various types of cancers (e.g., breast, gastric, and lung cancer) and healthy donors, requiring low sample volume (∼200 μl) and a high-throughput 3D tumor culturing system (∼300 tumor clusters). To support automated algorithmic analysis, intelligent decision-making, and precise segmentation, we designed and developed an integrative deep neural network, which includes Mask Region-Based Convolutional Neural Network (Mask R-CNN), vision transformer, and Segment Anything Model (SAM). Our approach significantly reduces the manual labeling time by up to 90% with a high mean Intersection Over Union (mIoU) of 0.902 and immediate results (<2 s per image) for clinical cohort classification. The IDDT can accurately stratify healthy donors (n = 12) and cancer patients (n = 55) within their respective treatment cycle and cancer stage, resulting in high precision (∼99.3%) and high sensitivity (∼98%). We envision that our patient-centric IDDT provides an intelligent, label-free, and cost-effective approach to help clinicians make precise medical decisions and tailor treatment strategies for each patient.