Fabrication of Self-Wrinkling Polymer Films with Tunable Patterns through an Interfacial-Fuming-Induced Surface Instability Process.

Inspired by the superglue fuming method for fingerprint collection, this study developed a novel interfacial-fuming-induced surface instability process to generate wrinkled patterns on polymeric substrates. High-electronegativity groups are introduced on the substrate surface to initiate the polymerization of monomer vapors, such as ethyl cyanoacrylate, which results in the formation of a stiff poly(ethyl cyanoacrylate) capping layer. Moreover, interfacial polymerization resulted in the covalent bonding of the substrate, which led to the volumetric shrinkage of the composite and the accumulation of compressive strain. This process ultimately resulted in the development and stabilization of wrinkled surface morphologies. The authors systematically examined parameters such as the modulus of the epoxy substrate, prestrain, the flow rate of fuming, and operating temperature. The aforementioned technique can be easily applied to architectures with complex outer morphologies and inner surfaces, thereby enabling the construction of surface patterns under ambient conditions without vacuum limitations or precise process control. This study is the first to combine fuming-induced interfacial polymerization with surface instability to create robust wrinkles. The proposed method enables the fabrication of intricate microwrinkled patterns and has considerable potential for use in various practical applications, including microfluidics, optical components, bioinspired adhesive devices, and interfacial engineering.

Preoperative CT Radiomics Nomogram for Predicting Microvascular Invasion in Stage I Non-Small Cell Lung Cancer.

RATIONALE AND OBJECTIVES: This study aims to develop and validate a nomogram integrating clinical-CT and radiomic features for preoperative prediction of microvascular invasion (MVI) in patients with stage I non­small cell lung cancer (NSCLC). MATERIALS AND METHODS: This retrospective study analyzed 188 cases of stage I NSCLC (63 MVI positives and 125 negatives), which were randomly assigned to training (n = 133) and validation cohorts (n = 55) at a ratio of 7:3. Preoperative non-contrast and contrast-enhanced CT (CECT) images were used to analyze computed tomography (CT) features and extract radiomics features. The student's t-test, the Mann-Whitney-U test, the Pearson correlation, the least absolute shrinkage and selection operator, and multivariable logistic analysis were used to select the significant CT and radiomics features. Multivariable logistic regression analysis was performed to build the clinical-CT, radiomics, and integrated models. The predictive performances were evaluated through the receiver operating characteristic curve and compared with the DeLong test. The integrated nomogram was analyzed regarding discrimination, calibration, and clinical significance. RESULTS: The rad-score was developed with one shape and four textural features. The integrated nomogram incorporating radiomics score, spiculation, and the number of tumor-related vessels (TVN) demonstrated better predictive efficacy than the radiomics and clinical-CT models in the training cohort (area under the curve [AUC], 0.893 vs 0.853 and 0.828, and p = 0.043 and 0.027, respectively) and validation cohort (AUC, 0.887 vs 0.878 and 0.786, and p = 0.761 and 0.043, respectively). The nomogram also demonstrated good calibration and clinical usefulness. CONCLUSION: The radiomics nomogram integrating the radiomics with clinical-CT features demonstrated good performance in predicting MVI status in stage I NSCLC. The nomogram may be a useful tool for physicians in improving personalized management of stage I NSCLC.

Does dual-layer spectral detector CT provide added value in predicting spread through air spaces in lung adenocarcinoma? A preliminary study.

OBJECTIVES: To examine the predictive value of dual-layer spectral detector CT (DLCT) for spread through air spaces (STAS) in clinical lung adenocarcinoma. METHODS: A total of 225 lung adenocarcinoma cases were retrospectively reviewed for demographic, clinical, pathological, traditional CT, and spectral parameters. Multivariable logistic regression analysis was carried out based on three logistic models, including a model using traditional CT features (traditional model), a model using spectral parameters (spectral model), and an integrated model combining traditional CT and spectral parameters (integrated model). Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were performed to assess these models. RESULTS: Univariable analysis showed significant differences between the STAS and non-STAS groups in traditional CT features, including nodule density (p < 0.001), pleural indentation types (p = 0.006), air-bronchogram sign (p = 0.031), the presence of spiculation (p < 0.001), long-axis diameter of the entire nodule (LD) (p < 0.001), and consolidation/tumor ratio (CTR) (p < 0.001). Multivariable analysis revealed that LD > 20 mm (odds ratio [OR] = 2.271, p = 0.025) and CTR (OR = 24.208, p < 0.001) were independent predictors in the traditional model, while electronic density (ED) in the venous phase was an independent predictor in the spectral (OR = 1.062, p < 0.001) and integrated (OR = 1.055, p < 0.001) models. The area under the curve (AUC) for the integrated model (0.84) was the highest (spectral model, 0.83; traditional model, 0.80), and the difference between the integrated and traditional models was statistically significant (p = 0.015). DCA showed that the integrated model had superior clinical value versus the traditional model. CONCLUSIONS: DLCT has added value for STAS prediction in lung adenocarcinoma. CLINICAL RELEVANCE STATEMENT: Spectral CT has added value for spread through air spaces prediction in lung adenocarcinoma so may impact treatment planning in the future. KEY POINTS: • Electronic density may be a potential spectral index for predicting spread through air spaces in lung adenocarcinoma. • A combination of spectral and traditional CT features enhances the performance of traditional CT for predicting spread through air spaces.

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma.

OBJECTIVES: To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. METHODS: A total of 542 patients with clinical stage 0-I peripheral LUAD and with preoperative CT data of 1-mm slice thickness were included. Maximal solid size on axial image (MSSA) was evaluated by two chest radiologists. MSSA, volume of solid component (SV), and mass of solid component (SM) were evaluated by DL. Consolidation-to-tumor ratios (CTRs) were calculated. For ground glass nodules (GGNs), solid parts were extracted with different density level thresholds. The prognosis prediction efficacy of DL was compared with that of manual measurements. Multivariate Cox proportional hazards model was used to find independent risk factors. RESULTS: The prognosis prediction efficacy of T-staging (TS) measured by radiologists was inferior to that of DL. For GGNs, MSSA-based CTR measured by radiologists (RMSSA%) could not stratify RFS and OS risk, whereas measured by DL using 0HU (2D-AIMSSA0HU%) could by using different cutoffs. SM and SV measured by DL using 0 HU (AISM0HU% and AISV0HU%) could effectively stratify the survival risk regardless of different cutoffs and were superior to 2D-AIMSSA0HU%. AISM0HU% and AISV0HU% were independent risk factors. CONCLUSION: DL algorithm can replace human for more accurate T-staging of LUAD. For GGNs, 2D-AIMSSA0HU% could predict prognosis rather than RMSSA%. The prediction efficacy of AISM0HU% and AISV0HU% was more accurate than of 2D-AIMSSA0HU% and both were independent risk factors. CLINICAL RELEVANCE STATEMENT: Deep learning algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma. KEY POINTS: • Deep learning (DL) algorithm could replace human for size measurements and could better stratify prognosis than manual measurements in patients with lung adenocarcinoma (LUAD). • For GGNs, maximal solid size on axial image (MSSA)-based consolidation-to-tumor ratio (CTR) measured by DL using 0 HU could stratify survival risk than that measured by radiologists. • The prediction efficacy of mass- and volume-based CTRs measured by DL using 0 HU was more accurate than of MSSA-based CTR and both were independent risk factors.

Recombinant ING4 suppresses the migration of SW579 thyroid cancer cells via epithelial to mesenchymal transition.

Thyroid cancer is a common endocrine malignancy that has rapidly increased in global incidence. Inhibitor of growth 4 (ING4) has been identified in various types of carcinoma; however, to the best of our knowledge, no previous studies have investigated the effects of ING4 on thyroid cancer. In the present study, SW579 thyroid cancer cells were treated with recombinant ING4 protein, and the results confirmed that recombinant ING4 protein was able to reduce the rate of proliferation, increase the rate of apoptosis and inhibit the mobility of SW579 cells. These results were obtained using a colony formation, fluoroscein isothiocyanate/propidium iodide double staining and Transwell assays, respectively. Furthermore, in the western blot analysis assays, ING4 was demonstrated to inhibit the Wnt/ß catenin signaling pathway and epithelial to mesenchymal transition (EMT). Therefore, the present study demonstrated the antitumor activities of recombinant ING4 and identified ING4 could inhibit EMT in thyroid cancer cell. However, additional studies are required to confirm these results in other cell types.

RhoBTB2 (DBC2) functions as a multifunctional tumor suppressor in thyroid cancer cells via mitochondrial apoptotic pathway.

Thyroid cancer is the most common endocrine malignancy worldwide. Tumor suppressor gene RhoBTB2 (also known as Deleted in Breast Cancer 2, DBC2) was observed in various carcinomas, however, no reports showed the effects of RhoBTB2 on thyroid cancer. In our study, we found that RhoBTB2 decreases proliferation, increases apoptosis, inhibits mobility, and induces mitochondria damage in SW579 cells through increased Bax and decreased Bcl-2 and Bcl-xL protein expression. The effects of RhoBTB2 on SW579 cells were inversed by using butin (an inhibitor of the mitochondrial apoptosis pathway). Our results suggest that RhoBTB2 suppresses the growth of SW579 cells through a mitochondrial apoptosis pathway.