BackgroundWe attempted to train and validate a model of deep learning for the preoperative prediction of the response of patients with intermediate-stage hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE).MethodAll computed tomography (CT) images were acquired for 562 patients from the Nan Fang Hospital (NFH), 89 patients from Zhu Hai Hospital Affiliated with Jinan University (ZHHAJU), and 138 patients from the Sun Yat-sen University Cancer Center (SYUCC). We built a predictive model from the outputs using the transfer learning techniques of a residual convolutional neural network (ResNet50). The prediction accuracy for each patch was revaluated in two independent validation cohorts.ResultsIn the training set (NFH), the deep learning model had an accuracy of 84.3% and areas under curves (AUCs) of 0.97, 0.96, 0.95, and 0.96 for complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD), respectively. In the other two validation sets (ZHHAJU and SYUCC), the deep learning model had accuracies of 85.1% and 82.8% for CR, PR, SD, and PD. The ResNet50 model also had high AUCs for predicting the objective response of TACE therapy in patches and patients of three cohorts. Decision curve analysis (DCA) showed that the ResNet50 model had a high net benefit in the two validation cohorts.ConclusionThe deep learning model presented a good performance for predicting the response of TACE therapy and could help clinicians in better screening patients with HCC who can benefit from the interventional treatment.Key Points• Therapy response of TACE can be predicted by a deep learning model based on CT images. • The probability value from a trained or validation deep learning model showed significant correlation with different therapy responses. • Further improvement is necessary before clinical utilization. Electronic supplementary materialThe online version of this article (10.1007/s00330-019-06318-1) contains supplementary material, which is available to authorized users.

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Although there have been a number of publications focused on heterogeneous of NO2 on mineral particles, most of these studies were focused on β-Al2O3 and performed in the dark. Less was known about the reaction process of NO2 on α-Al2O3, especially the effect of sunlight factor. The heterogeneous reaction between NO2 and α-Al2O3 was investigated by using diffuse reflectance infrared Fourier transform spectrometry. The effects of NO2 and O2 concentrations as well as simulated sunlight were examined, and the reaction mechanism including the consumption of surface OH groups, oxidation process of nitrites, and the formation of water was also discussed in detail. It was observed that the formation rates of nitrates and nitrites were sensitive to NO2 concentrations and O2 concentrations. Nitrite was identified to be an intermediate production and disappeared very soon as [NO2] was up to 4.035 × 10(15) molecules/cm(3). Light played an important role in the changes of the electronic configuration of mineral dust, such as electronic donating ability, surface OH groups orientation, as well as the conversion efficiency between proton acid and nonproton acid, all of which could significantly enhance the heterogeneous reaction process. The reaction order for NO2 and O2 was determined to be 0.960 ± 0.111 and 0.620 ± 0.028, respectively. The uptake coefficient of NO2, which dominated the first step of the heterogeneous reaction, was calculated by the infrared absorbance with the use of ion chromatography and determined to be 9.9 × 10(-10) in the dark and varied from 2.54 to 3.33 × 10(-9) under simulated sunlight from 0.45 to 1.35 mW/cm(2). It was also found that γNO2 was independent of [NO2] and sunlight increased the uptake coefficient by three times, indicating that the heterogeneous reaction between NO2 and α-Al2O3 was enhanced under sunlight.

Chiral hybrid perovskites (CHPs), aggregating chirality and favorable semiconducting properties in one, have taken a prominent position in direct circularly polarized light detection (CPL). However, passive high circular polarization sensitivity (g res) photodetection in CHPs is still elusive and challenging. Benefitting from efficient control and turning of carrier transport of CHPs by dimensional engineering, here, we unprecedentedly proposed a chain-to-layer dimensionality engineering to realize high-g res passive photodetection. Two novel 2D layered CHPs (R/S-PPA)­EAPbBr4 (2R/2S) (PPA = 1-phenylpropylamine, EA = ethylammonium) are successfully synthesized by alloying an EA cation with small steric hindrance into the chained CHPs (R/S-PPA)­PbBr3 (1R/1S). Particularly, compared with the neglectable photoresponse in 1R, the obtained 2R by chain-to-layer dimensionality engineering gives rise to an excellent photoconductivity and robust polar photovoltage effect (PPE) with a giant open-circuit voltage of 2.5 V. Furthermore, such PPE promotes realizing an impressive g res in 2R up to 0.42 at zero bias because of the independent separation of photoexcited carriers, which is the highest value among the reported layered chiral perovskites. This work paves the way for the vigorous development of higher dimensional CHPs and will reveal their applications in the field of passive high-g res CPL detection.

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This study was conducted to evaluate the effects of clove extract (CE) (0.25%, 0.5%, 1%, and 2%) on the oxidative stability and quality deterioration of Chinese-style sausage stored for 21 d at 4°C. The addition of clove extract to sausages significantly retarded increases in Thiobarbituric Reactive Substances (TBARS) values (p<0.05), while also controlling the production of protein carbonyls (p<0.05). However, the addition of clove extract promoted reduced thiol group content in sausages (p<0.05). Sausages amended with clove extract also had decreased L* values (p<0.05) and increased a* values (p<0.05) when compared with the control. Similarly, texture deterioration was retarded in sausage containing added clove extract when compared with the control during refrigerated storage. Moreover, the addition of clove extract had no negative effects on the sensory properties of sausages. These results suggested that clove extract was effective at protecting sausages from oxidation and quality deterioration during refrigerated storage for 21 d.

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