(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants’ exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Peripheral blood lymphocytes (PBLs) are mature lymphocytes that circulate in the blood rather than being localized to organs. A reliable label-free collection approach that can viably and appropriately isolate PBLs to establish in vitro culture systems is crucial for basic research and clinical requirements. However, isolation of PBLs from whole blood is difficult, and so the development of a rapid and safe method to perform this task is required. Microfluidic technology offers opportunities that challenge the performance of macroscale methods. In this study, we proposed a simple spiral microfluidic chip for efficient and high-throughput isolation of lymphocytes from a sample with prelysed RBCs. This spiral microfluidic platform does not rely on antibodies or biological markers for labeling cells of interest while isolating lymphocytes but rather enriches B and T lymphocytes through the different physical properties that are intrinsic to lymphocytes and other blood cells. The device was used to achieve high-throughput (~1.3 × 10⁵ cells/min) separation of lymphocytes with high viability (>95%). Compared with previous approaches, our device provided rapid, label-free, high-throughput, and safe lymphocyte separation.

Lung cancer is one of the leading causes of death worldwide.

Blood tests provide crucial diagnostic information regarding several diseases. A key factor that affects the precision and accuracy of blood tests is the interference of red blood cells; however, the conventional methods of blood separation are often complicated and time consuming. In this study, we devised a simple but high-efficiency blood separation system on a self-strained microfluidic device that separates 99.7 ± 0.3% of the plasma in only 6 min. Parameters, such as flow rate, design of the filter trench, and the relative positions of the filter trench and channel, were optimized through microscopic monitoring. Moreover, this air-difference-driven device uses a cost-effective and easy-to-use heater device that creates a low-pressure environment in the microchannel within minutes. With the aforementioned advantages, this blood separation device could be another platform choice for point-of-care testing.