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.

Background: Metastasis is a complex process that affects patient treatment and survival. To routinely monitor cancer plasticity and guide treatment strategies, it is highly desired to provide information about metastatic status in real-time. Here, we proposed a worm-based (WB) microfluidic biosensor to rapidly monitor biochemical cues related to metastasis in a well-defined environment. Compared to conventional biomarker-based methods, the WB biosensor allowed high throughput screening under low cost, requiring only visual quantification of outputs; Methods: Caenorhabditis elegans were placed in the WB biosensor and exposed to samples conditioned with cancer cell clusters. The chemotactic preference of these worms was observed under discontinuous imaging to minimize the impact on physiological activity; Results: A chemotaxis index (CI) was defined to standardize the quantitative assessment from the WB biosensor, where moderate (3.24–6.5) and high (>6.5) CI levels reflected increased metastasis risk and presence of metastasis, respectively. We demonstrated that the secreted metabolite glutamate was a chemorepellent, and larger clusters associated with increased metastatic potential also enhanced CI levels; Conclusions: Overall, this study provided a proof of concept for the WB biosensors in assessing metastasis status, with the potential to evaluate patient-derived cancer clusters for routine management.

Bacterial detection is pivotal for the timely diagnosis and effective treatment of infectious diseases. Microfluidic platforms offer advantages over traditional methods, including heightened sensitivity, rapid analysis, and minimal sample volume requirements. Traditional clinical methods for bacterial identification often involve extended processing times and necessitate high pathogen concentrations, resulting in delayed diagnoses and missed treatment opportunities. Microfluidic technology overcomes these limitations by facilitating rapid bacterial identification at lower biomass levels, thus ensuring prompt and precise treatment interventions. Additionally, bacteria-driven inflammation has been associated with the development and progression of various diseases, including cancer. Elucidating the complex interplay between bacteria, inflammation, and disease is essential for devising effective disease models and therapeutic strategies. Microfluidic platforms have been used to construct in vitro disease models that accurately replicate the intricate microenvironment that bacteria-driven inflammation affects. These models offer valuable insights into bacteria-driven inflammation and its impact on disease progression, such as cancer metastasis and therapeutic responses. This review examines recent advancements in bacterial detection using microfluidics and assesses the potential of this technology as a robust tool for exploring bacteria-driven inflammation in the context of cancer.

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.

Microfluidic technology has gained widespread application in the field of biomedical research due to its exceptional sensitivity and high specificity. Particularly when combined with nanomaterials, the synergy between the two has significantly advanced fields such as precision medicine, drug delivery, disease detection, and treatment. This article aims to provide an overview of the latest research achievements of microfluidic nanomaterials in disease detection and treatment. It delves into the applications of microfluidic nanomaterials in detecting blood parameters, cardiovascular disease markers, neurological disease markers, and tumor markers. Special emphasis is placed on their roles in disease treatment, including models such as blood vessels, the blood–brain barrier, lung chips, and tumors. The development of microfluidic nanomaterials in emerging medical technologies, particularly in skin interactive devices and medical imaging, is also introduced. Additionally, the challenges and future prospects of microfluidic nanomaterials in current clinical applications are discussed. In summary, microfluidic nanomaterials play an indispensable role in disease detection and treatment. With the continuous advancement of technology, their applications in the medical field will become even more profound and extensive.