Construction of an Electron Capture and Transfer Center for Highly Efficient and Selective Solar-Light-Driven CO₂ Conversion

Exploring high-efficiency photocatalysts for selective CO2 reduction is still challenging because of the limited charge separation and surface reactions. In this study, a noble-metal-free metallic VSe2 nanosheet was incorporated on g-C3N4 to serve as an electron capture and transfer center, activating surface active sites for highly efficient and selective CO2 photoreduction. Quasi in situ X-ray photoelectron spectroscopy (XPS), soft X-ray absorption spectroscopy (sXAS), and femtosecond transient absorption spectroscopy (fs-TAS) unveiled that VSe2 could capture electrons, which are further transferred to the surface for activating active sites. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) and density functional theory (DFT) calculations revealed a kinetically feasible process for the formation of a key intermediate and confirmed the favorable production of CO on the VSe2/PCN (protonated C3N4) photocatalyst. As an outcome, the optimized VSe2/PCN composite achieved 97% selectivity for solar-light-driven CO2 conversion to CO with a high rate of 16.3 μmol·g–1·h–1, without any sacrificial reagent or photosensitizer. This work offers new insights into the photocatalyst design toward highly efficient and selective CO2 conversion

A machine learning model to predict the pyrolytic kinetics of different types of feedstocks

An in-depth knowledge of pyrolytic kinetics is vital for understanding the thermal decomposition process. Numerous experimental studies have investigated the kinetic performance of the pyrolysis of different raw materials. An accurate prediction of pyrolysis kinetics could substantially reduce the efforts of researchers and decrease the cost of experiments. In this work, a model to predict the mean values of model-free activation energies of pyrolysis for five types of feedstocks was successfully constructed using the random forest machine learning method. The coefficient of determination of the fitting result reached a value as high as 0.9964, which indicates significant potential for making a quick initial pyrolytic kinetic estimation using machine learning methods. Specifically, from the results of a partial dependence analysis of the lignocellulose-type feedstock, the atomic ratios of H/C and O/C were found to have negative correlations with the pyrolytic activation energies. However, the effect of the ash content on the activation energy strongly depended on the organic component species present in the lignocellulose feedstocks. This work confirms the possibility of predicting model-free pyrolytic activation energies by utilizing machine learning methods, which can improve the efficiency and understanding of the kinetic analysis of pyrolysis for biomass and fossil investigations

E-Cadherin–Mediated Cell Contact Controls the Epidermal Damage Response in Radiation Dermatitis

Radiotherapy is a primary oncological treatment modality that also damages normal tissue, including the skin, and causes radiation dermatitis (RD). Here, we explore the mechanism of acute epidermal damage in radiation dermatitis. Two distinctive phases in the damage response were identified: an early destructive phase, where a burst of reactive oxygen species induces loss of E-cadherin-mediated cell contact, followed by a regenerative phase, during which Wnt and Hippo signaling are activated. A blocking peptide, as well as a neutralizing antibody to E-cadherin, works synergistically with ionizing radiation to promote the epidermal damage. In addition, ROS disassembles adherens junctions in epithelial cells via posttranslational mechanisms, that is, activation of Src/Abl kinases and degradation of β-catenin/E-cadherin. The key role of tyrosine kinases in this process is further substantiated by the rescue effect of the tyrosine kinase inhibitor genistein, and the more specific Src/Abl kinase inhibitor dasatinib: both reduced ROS-induced degradation of β-catenin/E-cadherin in vitro and ameliorated skin damage in rodent models. Finally, we confirm that the same key molecular events are also seen in human radiation dermatitis. Therefore, we propose that loss of cell contact in epidermal keratinocytes through reactive oxygen species-mediated disassembly of adherens junctions is pivotal for the acute epidermal damage in radiation dermatitis

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies

Abstract Background Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal malignancies upon initial attempt. Here, we aimed to develop an artificial intelligence tool to detect nasopharyngeal malignancies under endoscopic examination based on deep learning. Methods An endoscopic images-based nasopharyngeal malignancy detection model (eNPM-DM) consisting of a fully convolutional network based on the inception architecture was developed and fine-tuned using separate training and validation sets for both classification and segmentation. Briefly, a total of 28,966 qualified images were collected. Among these images, 27,536 biopsy-proven images from 7951 individuals obtained from January 1st, 2008, to December 31st, 2016, were split into the training, validation and test sets at a ratio of 7:1:2 using simple randomization. Additionally, 1430 images obtained from January 1st, 2017, to March 31st, 2017, were used as a prospective test set to compare the performance of the established model against oncologist evaluation. The dice similarity coefficient (DSC) was used to evaluate the efficiency of eNPM-DM in automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images, by comparing automatic segmentation with manual segmentation performed by the experts. Results All images were histopathologically confirmed, and included 5713 (19.7%) normal control, 19,107 (66.0%) nasopharyngeal carcinoma (NPC), 335 (1.2%) NPC and 3811 (13.2%) benign diseases. The eNPM-DM attained an overall accuracy of 88.7% (95% confidence interval (CI) 87.8%–89.5%) in detecting malignancies in the test set. In the prospective comparison phase, eNPM-DM outperformed the experts: the overall accuracy was 88.0% (95% CI 86.1%–89.6%) vs. 80.5% (95% CI 77.0%–84.0%). The eNPM-DM required less time (40 s vs. 110.0 ± 5.8 min) and exhibited encouraging performance in automatic segmentation of nasopharyngeal malignant area from the background, with an average DSC of 0.78 ± 0.24 and 0.75 ± 0.26 in the test and prospective test sets, respectively. Conclusions The eNPM-DM outperformed oncologist evaluation in diagnostic classification of nasopharyngeal mass into benign versus malignant, and realized automatic segmentation of malignant area from the background of nasopharyngeal endoscopic images

DataSheet_1_Prediction model for the pretreatment evaluation of mortality risk in anti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease.docx

BackgroundAnti-melanoma differentiation-associated gene 5 antibody-positive dermatomyositis with interstitial lung disease (anti-MDA5 DM-ILD) is a disease with high mortality. We sought to develop an effective and convenient prediction tool to estimate mortality risk in patients with anti-MDA5 DM-ILD and inform clinical decision-making early.MethodsThis prognostic study included Asian patients with anti-MDA5 DM-ILD hospitalized at the Nanjing Drum Hospital from December 2016 to December 2020. Candidate laboratory indicators were retrospectively collected. Patients hospitalized from 2016 to 2018 were used as the discovery cohort and applied to identify the optimal predictive features using a least absolute shrinkage and selection operator (LASSO) logistic regression model. A risk score was determined based on these features and used to construct the mortality risk prediction model in combination with clinical characteristics. Results were verified in a temporal validation comprising patients treated between 2019 and 2020. The primary outcome was mortality risk within one year. The secondary outcome was overall survival. The prediction model’s performance was assessed in terms of discrimination, calibration, and clinical usefulness.ResultsThis study included 127 patients, (72 men [56.7%]; median age, 54 years [interquartile range, 48-63 years], split into discovery (n = 87, 70%) and temporal validation (n=37, 30%) cohorts. Five optimal features were selected by LASSO logistic regression in the discovery cohort (n = 87) and used to construct a risk score, including lymphocyte counts, CD3+CD4+ T-cell counts, cytokeratin 19 fragment (CYFRA21-1), oxygenation index, and anti-Ro52 antibody. The retained predictive variables in the final prediction model were age, Heliotrope, fever, and risk score, and the most predictive factor was the risk score. The prediction model showed good discrimination (AUC: 0.915, 95% CI: 0.846–0.957), good calibration (Hosmer–Lemeshow test, P = 0.506; Brier score, 0.12), and fair clinical usefulness in the discovery cohort. The results were verified among patients in the temporal validation cohort (n = 38). We successfully divided patients into three risk groups with very different mortality rates according to the predictive score in both the discovery and validation cohorts (Cochran-Armitage test for trend, P ConclusionsWe developed and validated a mortality risk prediction tool with good discrimination and calibration for Asian patients with anti-MDA5 DM-ILD. This tool can offer individualized mortality risk estimation and inform clinical decision-making.</p