Different DLCO Parameters as Predictors of Postoperative Pulmonary Complications in Mild Chronic Obstructive Pulmonary Disease Patients with Lung Cancer.

Background: Numerous studies have investigated methods of predicting postoperative pulmonary complications (PPCs) in lung cancer surgery, with chronic obstructive pulmonary disease (COPD) and low forced expiratory volume in 1 second (FEV1) being recognized as risk factors. However, predicting complications in COPD patients with preserved FEV1 poses challenges. This study considered various diffusing capacity of the lung for carbon monoxide (DLCO) parameters as predictors of pulmonary complication risks in mild COPD patients undergoing lung resection. Methods: From January 2011 to December 2019, 2,798 patients undergoing segmentectomy or lobectomy for non-small cell lung cancer (NSCLC) were evaluated. Focusing on 709 mild COPD patients, excluding no COPD and moderate/severe cases, 3 models incorporating DLCO, predicted postoperative DLCO (ppoDLCO), and DLCO divided by the alveolar volume (DLCO/VA) were created for logistic regression. The Akaike information criterion and Bayes information criterion were analyzed to assess model fit, with lower values considered more consistent with actual data. Results: Significantly higher proportions of men, current smokers, and patients who underwent an open approach were observed in the PPC group. In multivariable regression, male sex, an open approach, DLCO <80%, ppoDLCO <60%, and DLCO/VA <80% significantly influenced PPC occurrence. The model using DLCO/VA had the best fit. Conclusion: Different DLCO parameters can predict PPCs in mild COPD patients after lung resection for NSCLC. The assessment of these factors using a multivariable logistic regression model suggested DLCO/VA as the most valuable predictor.

Self-Emitting Artificial Cilia Produced by Field Effect Spinning.

In nature, many cells possess cilia that provide them with motor or sensory functions, allowing organisms to adapt to their environment. The development of artificial cilia with identical or similar sensory functions will enable high-performance and flexible sensing. Here, we investigate a method of producing artificial cilia composed of various polymer materials, such as polyethylene terephthalate, polyurethane, poly(methyl methacrylate), polyvinylpyrrolidone, polystyrene, polyvinyl chloride, and poly (allylamine hydrochloride), using a field effect spinning (FES) method. Unlike wet- or electro-spinning, in which single or multiple strands of fibers are pulled without direction, the FES method can grow fiber arrays vertically and uniformly on a substrate in cilia-like patterns. The lengths and diameters of the vertically grown artificial cilia can be controlled by the precursor polymer concentration in the solution, applied electric current and voltage, and shape and size of the needle tip used for FES. The red, green, and blue emission characteristics of the polymer-quantum dot-based self-emitting artificial cilia prepared in polymer-inorganic nanoparticle hybrid form were determined. In addition, an artificial cilia-based humidity sensor made of the polymer-polymer composite was fabricated.