Objective: To investigate the value of initial CT quantitative analysis of ground-glass opacity (GGO), consolidation, and total lesion volume and its relationship with clinical features for assessing the severity of coronavirus disease 2019 (COVID-19). Materials and Methods: A total of 84 patients with COVID-19 were retrospectively reviewed from January 23, 2020 to February 19, 2020. Patients were divided into two groups: severe group (n = 23) and non-severe group (n = 61). Clinical symptoms, laboratory data, and CT findings on admission were analyzed. CT quantitative parameters, including GGO, consolidation, total lesion score, percentage GGO, and percentage consolidation (both relative to total lesion volume) were calculated. Relationships between the CT findings and laboratory data were estimated. Finally, a discrimination model was established to assess the severity of COVID-19. Results: Patients in the severe group had higher baseline neutrophil percentage, increased high-sensitivity C-reactive protein (hs-CRP) and procalcitonin levels, and lower baseline lymphocyte count and lymphocyte percentage (p < 0.001). The severe group also had higher GGO score (p < 0.001), consolidation score (p < 0.001), total lesion score (p < 0.001), and percentage consolidation (p = 0.002), but had a lower percentage GGO (p = 0.008). These CT quantitative parameters were significantly correlated with laboratory inflammatory marker levels, including neutrophil percentage, lymphocyte count, lymphocyte percentage, hs-CRP level, and procalcitonin level (p < 0.05). The total lesion score demonstrated the best performance when the data cutoff was 8.2%. Furthermore, the area under the curve, sensitivity, and specificity were 93.8% (confidence interval [CI]: 86.8-100%), 91.3% (CI: 69.6-100%), and 91.8% (CI: 23.0-98.4%), respectively. Conclusion: CT quantitative parameters showed strong correlations with laboratory inflammatory markers, suggesting that CT quantitative analysis might be an effective and important method for assessing the severity of COVID-19, and may provide additional guidance for planning clinical treatment strategies.

Purpose: The EphA2 receptor tyrosine kinase is believed to play a role in tumor growth and metastasis.The clinical significance of the expression of EphA2 was observed in breast, prostate, colon, skin, cervical, ovarian, and lung cancers. The purpose of this work was to determine the expression of EphA2 and its ligand, Ephrin A-1, and E-cadherin in carcinoma of the urinary bladder, and determine EphA2 as a new target for therapy in bladder cancer. Experimental Design: EphA2 mRNA and protein expression was investigated by reverse transcription-PCR and Western blot, respectively, in bladder cancer cell lines. In addition, the expression of EphA2, Ephrin A-1, and E-cadherin in tissues from patients with different stages of urinary bladder cancer was determined by immunohistochemistry. Furthermore, the ability of Ephrin A-1 to inhibit growth of bladder cancer cells was also investigated using an adenoviral delivery system.

Autonomic nervous system plays an important role in the development of multiple cancers via regulating cancer cell proliferation, differentiation, apoptosis, migration and invasion. However, no detailed studies have been performed to study the role of autonomic nerve fibers in hepatocellular carcinoma (HCC) as well as its correlation with the progression of HCC. Here, we examined the distribution of the autonomic nerve fibers and analyzed the correlation between autonomic nerve fibers and the pathological characteristics of HCC patients. The transcriptional expression of adrenergic and cholinergic receptors was evaluated in both hepatoma cell lines and primary hepatoma cells. In addition, we summarized the function of receptors for neurotransmitters in different cancers recently reported. Our findings indicate that tissue of liver cancer is innervated by both sympathetic and parasympathetic nerves and the density of the nerve fibers is associated with patients' poor prognosis. Additionally, we report that adrenergic receptors β2 and cholinergic receptors α7, M1 and M3 are high expressed in both hepatoma cell lines and primary hepatoma cells, indicating these receptors may play essential roles in the regulation of autonomic nervous system triggered HCC.

BackgroundHigh-intensity focused ultrasound (HIFU) is a widely applied to treatment for unresectable hepatocellular carcinoma. However, insufficient HIFU can result in rapid progression of the residual tumor. The mechanism of such rapid growth of the residual tumor after HIFU ablation is poorly understood.ObjectiveThe aim of this study was to investigate the dynamic angiogenesis of residual tumor, and the temporal effect and mechanism of the HIF-1, 2α in the residual tumor angiogenesis.MethodsXenograft tumors of HepG2 cells were created by subcutaneously inoculating nude mice (athymic BALB/c nu/nu mice) with hepatoma cells. About thirty days after inoculation, all mice (except control group) were treated by HIFU and assigned randomly to 7 groups according to various time intervals (1st, 3rd, 5th day (d) and 1st, 2nd, 3rd, 4th week (w)). The residual tumor tissues were obtained from the experimental groups at various time points. Protein levels of HIF-1α, HIF-2α, VEGF-A, and EphA2 were quantified by immunohistochemistry analysis and Western Blot assays, and mRNA levels measured by Q-PCR. Microvascular density was calculated with counting of CD31 positive vascular endothelial cells by immunohistochemical staining.ResultsCompared with the control group, protein and mRNA levels of HIF-1α reached their highest levels on the 3rd day (P<0.01), then decreased (P<0.05). HIF-2α expression reached its highest level on the 2nd week compared with control group (P<0.01), then decreased (2w–4w) (P<0.05). The protein and mRNA levels of VEGF-A and EphA2 in the residual tumor tissues group that received HIFU were significantly decreased until 1 week compared with the control group (P<0.01). However, the levels increased compared to controls in 2–4 weeks (P<0.05). Similar results were obtained for MVD expression (P<0.05).ConclusionInsufficient HIFU ablation promotes the angiogenesis in residual carcinoma tissue over time. The data indicate that the HIF-1, 2α/VEGFA/EphA2 pathway is involved.

Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values < 0.01 for both cohorts). Multivariable logistic regression model showed the best performance (AUC = 0.92 ± 0.04 and 0.87 ± 0.02, accuracy = 0.87 and 0.86, and F-1score = 0.93 and 0.86 in training and validation cohorts, respectively) in comparison with any other model (P-value < 0.001). Good calibration was observed for multivariable logistic regression model. Conclusion CT radiomic models could help predict resectability of oesophageal SCC, and multivariable logistic regression model is the most predictive model.