(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.

This study shows the modification of the surface of polymer-layered glass substrates to form biofunctional micropatterns through femtosecond laser ablation in an aqueous solution. Domains of micrometer size on a substrate can be selectively converted from proteinphobic (resistant to protein adsorption) to proteinphilic, allowing patterning of protein features under physiological aqueous conditions. When femtosecond laser pulses (800 nm, 1 kHz, 200–500 nJ per pulse) were focused on and scanned on the substrate, which was glass covered with the proteinphobic polymer 2-methacryloyloxyethylphosphorylcholine (MPC), the surface became proteinphilic. Surface analysis by X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM) reveals that the laser ablates the MPC polymer. Extracellular matrix (ECM) proteins were bound to the laser-ablated surface by physisorption. Since femtosecond laser ablation is induced under physiological aqueous conditions, this approach can form micropatterns of functional ECM proteins with minimal damage. This method was applied to pattern collagen, laminin, and gelatin on the substrate. Removal of an ECM protein from the substrate followed by replacement with another ECM protein was achieved on demand at a specific location and time by the same laser ablation method. Living cells adhered to the fabricated domains where ECM proteins were arranged. The modification of patterning during cell culture was used to control cell migration and form arrays of different cells.

Multi-responsive and optically-active diarylethene (DAE)-based [2]pseudo-rotaxane polymers were prepared via host–guest interactions of Zn ²⁺ coordinated with two terpyridyl ligands bearing crown-ethers as an energy donor host and DAE containing two ammonium salts as an acceptor guest.