Combining machine and deep transfer learning for mediastinal lymph node evaluation in patients with lung cancer

Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.

Switching CO_(2) Electroreduction Selectivity Between C_(1) and C_(2) Hydrocarbons on Cu Gas-Diffusion Electrodes

Regulating the selectivity toward a target hydrocarbon product is still the focus of CO_(2)electroreduction.Here,we discover that the original surface Cu species in Cu gas-diffusion electrodes plays a more important role than the surface roughness,local pH,and facet in governing the selectivity toward C_(1)or C_(2)hydrocarbons.The selectivity toward C_(2)H_(4) progressively increases,while CH_(4) decreases steadily upon lowering the Cu oxidation species fraction.At a relatively low electrodeposition voltage of 1.5 V,the Cu gas-diffusion electrode with the highest Cu^(δ+)/Cu^(0)ratio favors the pathways of∗CO hydrogenation to form CH_(4) with maximum Faradaic efficiency of 65.4%and partial current density of 228 mA cm^(−2)at−0.83 V vs RHE.At 2.0 V,the Cu gas-diffusion electrode with the lowest Cu^(δ+)/Cu^(0)ratio prefers C-C coupling to form C_(2)+products with Faradaic efficiency topping 80.1%at−0.75 V vs RHE,where the Faradaic efficiency of C_(2)H_(4) accounts for 46.4%and the partial current density of C_(2)H_(4) achieves 279 mA cm^(−2).This work demonstrates that the selectivity from CH_(4) to C_(2)H_(4) is switchable by tuning surface Cu species composition of Cu gas-diffusion electrodes.

Distribution of Asbestos Enterprises and Asbestosis Cases--China, 1997−2019

What is already known on this topic?Asbestos is classified as a Class I Carcinogen by the International Agency for Research on Cancer(IARC)because exposure causes mesothelioma and lung cancer in addition to asbestosis and plaques.So far,asbestos has been banned in 67 countries,but chrysotile,a commonly encountered form of asbestos,is still widely used in China and most developing countries.Most asbestos-caused cancers are not reported,recorded,and compensated in many countries.What is added by this report?Enterprises manufacturing asbestos products have been migrating from economically developed Eastern China to relatively underdeveloped central and western regions between 2010 and 2019.Asbestosis cases reported in Tianjin,Beijing,Shandong,Xinjiang,Gansu,Qinghai,and Sichuan accounted for a large proportion of the total cases in China,which was inconsistent with the distribution of asbestos-related enterprises(AREs).The reported asbestosis cases versus total pneumoconiosis cases declined from 2.81%to 0.39%from 2006–2017,and this proportion reached 0.69%in 2018.What are the implications for public health practice?Robust occupational and environmental health assessments and reporting are needed to define the epidemiology of asbestos-related lung diseases,and management of using asbestos and existing products containing asbestos need strengthening and follow-up.Enterprises should be encouraged to use safer substitutes and gradually ban asbestos materials in China.