Enhancing Efficiency and Stability in Carbon-Based Perovskite Solar Cells by Double Passivation with Ultralow-Cost Coal-Derived Graphene and Its Derivatives.

Coal-derived carbon nanomaterials possess numerous superior features compared to other classic carbon, such as readily accessible surfaces, tunable pore structure, and facile and precise surface functionalization. Therefore, the controllable preparation of coal-derived carbon nanomaterials is anticipated to be of great significance for the performance improvement and commercialization process of carbon-based perovskite solar cells (C-PSCs). In this study, we successfully synthesized highly stable and commercially valuable graphene oxide (GO) and reduced graphene oxide (rGO) utilizing coal. Compared to traditional methods and commercial graphene, the chemical oxidation and pyrolysis process used in this study is mild and simple, offering the advantages of controlled composition and the absence of other impurities. GO or rGO was incorporated into the top of the SnO2 electron transport layer (ETL) of C-PSCs. Under optimized conditions and ultraviolet-ozone (UVO) irradiation, the ultimate power conversion efficiency (PCE) increased from the unmodified 12.4 to 14.04% (based on rGO) and 15.18% (based on GO), representing improvements of 22 and 31%, respectively. The improved photovoltaic performance is mainly owing to enhanced charge transport capabilities, denser interfacial contacts, improved carrier separation properties, increased conductivity, and abundance of hydrophilic functional groups in GO, which can form more stable hydrogen bonds with SnO2. After being stored at room temperature and ambient humidity for 30 days, the modified, unpacked devices retained 87% of the highest power conversion efficiency (PCE). This study introduces a practical and manageable method to enhance the performance of C-PSCs by using functional carbon nanomaterials derived from coal.

Assessment of a Radiomic Signature Developed in a General NSCLC Cohort for Predicting Overall Survival of ALK-Positive Patients With Different Treatment Types.

BACKGROUND: The purpose of the study was to investigate the potential of a radiomic signature developed in a general non-small-cell lung cancer (NSCLC) cohort for predicting the overall survival of anaplastic lymphoma kinase (ALK)-positive (ALK+) patients with different treatment types. MATERIALS AND METHODS: After test-retest in the Reference Image Database to Evaluate Therapy Response data set, 132 features (intraclass correlation coefficient > 0.9) were selected in the least absolute shrinkage and selection operator Cox regression model with a leave-one-out cross-validation. The NSCLC radiomics collection from The Cancer Imaging Archive was randomly divided into a training set (n = 254) and a validation set (n = 63) to develop a general radiomic signature for NSCLC. In our ALK+ set, 35 patients received targeted therapy and 19 patients received nontargeted therapy. The developed signature was tested later in this ALK+ set. Performance of the signature was evaluated with the concordance index (C-index) and stratification analysis. RESULTS: The general signature had good performance (C-index > 0.6; log rank P < .05) in the NSCLC radiomics collection. It includes 5 features: Geom_va_ratio, W_GLCM_Std, W_GLCM_DV, W_GLCM_IM2, and W_his_mean. Its accuracy of predicting overall survival in the ALK+ set achieved 0.649 (95% confidence interval [CI], 0.640-0.658). Nonetheless, impaired performance was observed in the targeted therapy group (C-index = 0.573; 95% CI, 0.556-0.589) whereas significantly improved performance was observed in the nontargeted therapy group (C-index = 0.832; 95% CI, 0.832-0.852). Stratification analysis also showed that the general signature could only identify high- and low-risk patients in the nontargeted therapy group (log rank P = .00028). CONCLUSION: This preliminary study suggests that the applicability of a general signature to ALK+ patients is limited. The general radiomic signature seems to be only applicable to ALK+ patients who had received nontargeted therapy, which indicates that developing special radiomics signatures for patients treated with tyrosine kinase inhibitors might be necessary.

Application of radiomics signature captured from pretreatment thoracic CT to predict brain metastases in stage III/IV ALK-positive non-small cell lung cancer patients.

BACKGROUND: The purpose of this study is to develop a radiomics approach to predict brain metastasis (BM) for stage III/IV ALK-positive non-small cell lung cancer (NSCLC) patients. METHODS: Patients with ALK-positive III/IV NSCLC from 2014 to 2017 were enrolled retrospectively. Their pretreatment thoracic CT images were collected, and the gross tumor volume (GTV) was defined by two experienced radiation oncologists. An in-house feature extraction code-set was performed based on MATLAB 2015b (Mathworks, Natick, MA, USA) in patients' CT images to extract features. Patients were randomly divided into training set and test set (4:1) by using createDataPartition function in caret package. A test-retest in RIDER NSCLC dataset was performed to identify stable radiomics features. LASSO Cox regression and a leave-one-out cross-validation were conducted to identify optimal features for the logistic regression model to evaluate the predictive value of radiomics feature(s) for BM. Furthermore, extended validation for the radiomics feature(s) and Cox regression analyses which combined radiomics feature(s) and treatment elements were implemented to predict the risk of BM during follow-up. RESULTS: In total, 132 patients were included, among which 27 patients had pretreatment BM. The median follow-up time was 11.8 (range, 0.1-65.2) months. In the training set, one radiomics feature (W_GLCM_LH_Correlation) showed discrimination ability of BM (P value =0.014, AUC =0.687, 95% CI: 0.551-0.824, specificity =83.5%, sensitivity =57.1%). It also exhibited reposeful performance in the test set (AUC =0.642, 95% CI: 0.501-0.783, specificity =60.0%, sensitivity =83.3%). Those 105 patients without pretreatment BM were divided into stage III (n=57) and stage IV (n=48) groups. The radiomics feature (W_GLCM_LH_Correlation) had moderate performance to predict BM during/after treatment in separate groups (stage III: AUC =0.682, 95% CI: 0.537-0.826, specificity =64.4%, sensitivity =75.0%; stage IV: AUC =0.653, 95% CI: 0.503-0.804, specificity =70.4%, sensitivity =75.0%). Meanwhile, stage III patients could be divided into low risk and high risk groups for BM during surveillance according to Cox regression analysis (log-rank P value =0.021). CONCLUSIONS: We identified one wavelet texture feature derived from pretreatment thoracic CT that presented potential in predicting BM in stage III/IV ALK-positive NSCLC patients. This could be beneficial to risk stratification for such patients. Further investigation is necessary to include expanded sample size investigation and external multicenter validation.

Evaluation of the prognostic value of surgery and postoperative radiotherapy for patients with thymic neuroendocrine tumors: A propensity-matched study based on the SEER database.

BACKGROUND: The prognostic value of surgery and postoperative radiotherapy (PORT) for primary thymic neuroendocrine tumors (TNETs) was estimated using the SEER database. METHODS: This retrospective study used SEER data of TNET patients between 1998 and 2015. Propensity score matching (PSM) was performed according to whether surgery was performed. The prognostic effects on overall survival (OS) and cancer-specific survival (CSS) were evaluated using multivariate Cox regression. RESULTS: A total of 3947 patients were included: 293 (7.4%) TNET, 2788 (70.6%) thymoma, and 866 (21.9%) thymic carcinoma. Compared to other subtypes, TNET patients were younger, included a larger proportion of men, had a well or moderately differentiated histological grade, higher disease stage at diagnosis, and were more likely to have regional lymph node metastasis. The median OS and CSS for TNET were 82.9 (95% confidence interval 74.3-91.4) and 101.9 (95% confidence interval 91.9-111.8) months, respectively, significantly shorter than for thymomas. In the matched cohort of TNET patients, multivariate analysis of OS and CSS revealed a significantly poorer prognosis in the non-surgery group (P < 0.001). Compared to total/radical resection, TNET patients who underwent debulking resection had significantly inferior outcomes (P < 0.05). Postoperative radiotherapy favorably impacted OS and CSS in Masaoka-Koga stage III-IV TNET patients; this OS impact was also observed in stage IIB patients. CONCLUSION: TNETs are extremely rare with relatively dismal outcomes. This analysis revealed the role of complete surgical resection and the favorable effect of postoperative radiotherapy in specific TNET subgroups.