Objectives To explore the relationship between the imaging manifestations and clinical classification of COVID-19. Methods We conducted a retrospective single-center study on patients with COVID-19 from Jan. 18, 2020 to Feb. 7, 2020 in Zhuhai, China. Patients were divided into 3 types based on Chinese guideline: mild (patients with minimal symptoms and negative CT findings), common, and severe-critical (patients with positive CT findings and different extent of clinical manifestations). CT visual quantitative evaluation was based on summing up the acute lung inflammatory lesions involving each lobe, which was scored as 0 (0%), 1 (1-25%), 2 (26-50%), 3 (51-75%), or 4 (76-100%), respectively. The total severity score (TSS) was reached by summing the five lobe scores. The consistency of two observers was evaluated. The TSS was compared with the clinical classification. ROC was used to test the diagnosis ability of TSS for severe-critical type. Results This study included 78 patients, 38 males and 40 females. There were 24 mild (30.8%), 46 common (59.0%), and 8 severecritical (10.2%) cases, respectively. The median TSS of severe-critical-type group was significantly higher than common type (p < 0.001). The ICC value of the two observers was 0.976 (95% CI 0.962-0.985). ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918. The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. Conclusions The proportion of clinical mild-type patients with COVID-19 was relatively high; CT was not suitable for independent screening tool. The CT visual quantitative analysis has high consistency and can reflect the clinical classification of COVID-19. Key Points • CT visual quantitative evaluation has high consistency (ICC value of 0.976) among the observers. The median TSS of severecritical type group was significantly higher than common type (p < 0.001). • ROC analysis showed the area under the curve (AUC) of TSS for diagnosing severe-critical type was 0.918 (95% CI 0.843-0.994). The TSS cutoff of 7.5 had 82.6% sensitivity and 100% specificity. • The proportion of confirmed COVID-19 patients with normal chest CT was relatively high (30.8%); CT was not a suitable screening modality

A series of novel cationic gemini surfactants with diethylammonium headgroups and a diamido spacer were synthesized, and their surface and bulk properties were investigated by surface tension, electrical conductivity, fluorescence, viscosity, dynamic light scattering (DLS), and transmission electron microscopy (TEM) measurements. An interesting phenomenon, that is, the obvious decline in surface tension upon increasing concentration above the critical micelle concentration (cmc), was found in these gemini surfactant solutions, and two explanations were proposed. This surface tension behavior could be explained by the rapid increase in the counterion activity in the bulk phase or the continued filling of the interface with increasing surfactant concentration above the cmc. More interestingly, not only vesicles but also the surfactant-concentration-induced vesicle to larger aggregate (spongelike aggregate) transition and the salt-induced vesicle and spongelike aggregate to micelle transition were found in the aqueous solutions of these gemini surfactants. The spongelike aggregate that is first reported in the cationic gemini surfactant-water binary system is probably caused by the adhesion and fusion of vesicles at high surfactant concentration.

We explore the interactions between a fluorescein (FAM)-labeled single-stranded DNA (P), graphene oxide (GO), and a cationic conjugated polymer, poly [(9,9-bis(6'-N,N,N-trimethylammonium)hexyl)-fluorenylene phenylene dibromide] (PFP). It is found that the fluorescence change of P-GO-PFP system is dependent on the addition order of P and PFP. When adding PFP into P/GO complex, the fluorescence resonance energy transfer (FRET) from PFP to P is inefficient. If P is added to PFP/GO complex, efficient FRET is obtained. This may be attributed to the equal binding ability for P and PFP to GO. The results of time-resolved fluorescence and fluorescence anisotropy support the different fluorescent response under different addition order of P and PFP to GO. Based on the above phenomenon, we demonstrate a method to reduce the high background signal of a traditional PFP-based DNA sensor by introducing GO. In comparison to the use of single PFP, the combination of PFP with GO-based method shows enhanced sensitivity with a detection limit as low as 40 pM for target DNA detection.

Background: It is a permanent challenge to differentiate small solid lung nodules. Massive data, extracted from medical image through radiomics analysis, may help early diagnosis of lung cancer. The aim of this study was to assess the usefulness of a quantitative radiomic model developed from baseline low-dose computed tomography (LDCT) screening for the purpose of predicting malignancy in small solid pulmonary nodules (SSPNs). Methods: This retrospective study included malignant and benign SSPNs (6 to 15 mm) detected in baseline low-dose CT screening. The malignancy was confirmed pathologically, and benignity was confirmed by long term follow-up or pathological diagnosis. The non-contrast CT images were reconstructed with a lung kernel of a slice thickness of 1 mm and were processed to extract 385 quantitative radiomic features using Analysis-Kinetic software. A predictive model was established with the training set of 156 benign and 40 malignant nodules, and was tested with the validation set of 77 benign and 21 malignant nodules through the analysis of R square. The performance of the radiomic model in predicting malignancy was compared with that of the ACR Lung Imaging Reporting and Data System (ACR lung-RADS). Results: In 294 cases of SSPNs, 61 lung cancers and 24 benign nodules were confirmed pathologically and the remaining 209 nodules were stable with long-term follow-up (4.1±0.9 years). Eleven non-redundant features, including 8 high-order texture features, were extracted from the training data set. The sensitivity and specificity of the prediction model in malignancy differentiation were 81.0% and 92.2% respectively. The accuracy was superior to ACR-lung RADS (89.8% vs. 76.5%). Conclusions: A radiomic model based on baseline low-dose CT screening for lung cancer can improve the accuracy in predicting malignancy of SSPNs.