Objective:To investigate the clinical value of radiomics based on computed tomography (CT) examination in preoperative differential diagnosis of pancreatic serous cystadenoma (SCA) and mucinous cystadenoma (MCA).Methods:The retrospective case-control study was conducted. The clinicopathological and imaging data of 154 patients with pancreatic cystic neoplasms who were admitted to the First Affiliated Hospital, Zhejiang University School of Medicine from January 2012 to December 2019 were collected. There were 24 males and 130 females, aged (50±13)years. Of the 154 patients, 99 cases were diagnosed as SCA and 55 cases were diagnosed as MCA. All the 154 patients underwent plain and enhanced CT scan of pancreas before operation. The clinical characteristics, radiology features and radiomics features of all patients were collected to construct the clinical characteristics model, radiology model, radiomics model and fused model. The receiver operating characteristic (ROC) curve of each model was drawn, and those constructed models were evaluated by area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value and negative predictive value. Based on the optimal model, the nomogram was constructed. Observation indicators: (1) establishment and validation of clinical characteristics model; (2) establishment and validation of radiology model; (3) establishment and validation of radiomics model; (4) establishment and validation of fused model; (5) nomogram of fused model. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was analyzed using the chi-square test or Fisher exact probability. Results:(1) Establishment and validation of clinical characteristics model: 3 clinical characteristics, including age, symptoms and preoperative serum CA19-9, were selected using multinomial logistic linear regression analysis to construct the clinical characteristics model. Result of the multinomial logistic linear regression analysis was expressed by formula ①: clinical characteristics model score=0.635-0.007×age+0.054×clinical symptoms+0.108×preoperative serum CA19-9. The ROC curve for the test dataset of clinical characteristics model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of clinical characteristics model were 0.611(95% confidence interval as 0.488?0.734, P<0.05), 56.6%, 66.7%, 56.3%, 41.5%, 78.4% for the training dataset and 0.771(95% confidence interval as 0.624?0.919, P<0.05), 77.8%, 63.1%, 88.5%, 80.1%, 76.7% for the test dataset, respectively. (2) Establishment and validation of radiology model: 5 radiology characteristics, including tumor location, the number of tumors, tumor diameter of cross section, lobulated tumor and polycystic tumor (more than 6), were selected using multinomial logistic linear regression analysis to construct the radiology model. Result of the multinomial logistic linear regression analysis was expressed by formula ②: radiology model score=?0.034+0.300×tumor location+0.202×the number of tumors+0.014×tumor diameter of cross section?0.251×lobulated tumor?0.170×polycystic tumor (more than 6). The ROC curve for the test dataset of radiology model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of radiology model were 0.862(95% confidence interval as 0.791?0.932, P<0.05), 78.8%, 81.8%, 77.5%, 62.8%, 90.2% for the training dataset and 0.853(95% confidence interval as 0.713?0.994), P<0.05), 88.9%, 89.4%, 88.5%, 85.0%, 92.0% for the test dataset, respectively. (3) Establishment and validation of radiomics model: 4 categories of a total 1 067 radiomics features were extracted from 154 patients with pancreatic cystic neoplasms, including 7 first-order histogram features, 53 texture features, 848 wavelet features and 159 local binary pattern features. A total of 896 stable radiomics features were retained to construct the model, based on the condition of intraclass correlation coefficient >0.9. After selected by variance threshold and correlation coefficient threshold, 350 radiomics features were retained. Fifty synthetic radiomics features were constructed based on the original features in order to obtain potential radiomics features, and the total number of radiomics features was 400. After analyzed by the five-fold recursive feature elimination, 22 radiomics features were screened out, including 13 wavelet features, 7 synthetic radiomics features and 2 local binary pattern features. The support vector machine algorithm was used to construct the radiomics model. The penalty coefficient 'C' and parameter 'γ' of the radiomics model were 35.938 and 0.077, respectively. The kernel function of the radiomics model was 'radial basis function kernel'. The ROC curve of radiomics model using 5-fold cross validation was drawn. The average AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the radiomics model were 0.870 ( P<0.05), 83.1%, 81.8%, 83.8%, 73.8% and 89.2%, respectively. (4) Establishment and validation of fused model: the fused model was constructed after selecting the tumor location and lobulated tumor of radiology characteristics and radiomics score. Result of the multinomial logistic linear regression analysis was expressed by formula ③: fused model socre=?0.154+0.218×tumor location?0.223×lobulated tumor+0.621×radiomics score. The ROC curve for the test dataset of fused model was drawn. The AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of fused model were 0.893(95% confidence interval as 0.828?0.958, P<0.05), 83.7%, 81.8%, 84.5%, 71.1%, 90.9% for the training dataset and 0.966(95% confidence interval as 0.921?0.999, P<0.05), 91.1%, 84.2%, 96.2%, 94.1%, 89.3% for the test dataset, respectively. (5) Nomogram of fused model: the nomogram of fused model was illustrated with the Youden index of 0.416. Conclusion:The prediction model based on the radiomics signature and radiological features extracted from preoperative CT examination can make the differential diagnosis of pancreatic SCA from MCA.

Sanguinarine is the main active component of the Papaver plants, and protopine-6-hydroxylase(P6 H), involved in the sanguinarine biosynthetic pathway, can oxidize protopine to 6-hydroxyprotopine. The investigation on the diversity of P6 H genes in the medicinal Papaver plants contributes to the acquirement of P6 H with high activity to increase the biosynthesis of sanguinarine. Five P6 H genes in P. somniferum, P. orientale, and P. rhoeas were discovered based on the re-sequencing data of the Papaver species, followed by bioinformatics analysis. With the elongation factor 1α(EF-1α), which exhibits stable expression in the root and stem, as the internal reference gene, the transcription levels of P6H genes in roots and stems of the Papaver plants were detected by real-time fluorescent quantitative PCR. As indicated by the re-sequencing results, there were two genotypes of P6H in P. somniferum and P. orientale, respectively, and only one in P. rhoeas. The bioinformatics analysis showed that the P6 H proteins of the three Papaver plants contained the conserved domain cl12078, which is the characteristic of p450 supergene family, and transmembrane regions. The existence of signal peptide remained verification. Real-time fluorescent quantitative PCR results revealed that the transcription level of P6 H in roots of P. somniferum was about 1.44 times of that in stems(α=0.05). The present study confirmed genetic diversity of P6 H in the three medicinal Papaver plants, which lays a basis for the research on the biosynthesis pathway and mechanism of sanguinarine in Papaver species.
Benzophenanthridines ; Berberine Alkaloids ; Cytochrome P-450 Enzyme System/genetics* ; Genetic Variation ; Papaver/genetics*

OBJECTIVETo assess the relationship of high altitude de-adaptation response (HADAR) with acute high altitude response (AHAR) and cardiac function.

METHODSNinety-six military personnel of rapid entering into high altitude (3 700 to 4 800 m) with strong physical work were analyzed, all subjects were male, aged 18 - 35 years. According to the symptomatic scores of AHAR were divided into 3 groups: sever AHAR (group A, 24), mild to moderate AHAR (group B, 47) and non-AHAR (group C, 25) at high altitude. According to the symptomatic scores of HADAR were divided into 3 groups: severe HADAR (group E, 19), mild to moderate HADAR (group F, 40) and non-HADAR (group G, 37) after return to lower altitude (1 500 m). Mean pulmonary arterial pressure (mPAP), right ventricular internal dimension (RVID), outflow tract of right ventricle (RVOT), left ventricular internal dimension (LVID), left ventricular ejection fraction (LVEF), cardiac muscle work index (Tei index), creatine kinase isoenzymes-MB (CK-MB), lactic dehydrogenase isoenzyme-1 (LDH-1) were measured at high altitude stayed 50 days and after return to lower altitude 12 h, 15 d, and 30 d. Fifty healthy volunteers (group D) at 1 500 m altitude served as control.

RESULTSLevel of mPAP, RVID, RVOT, RVID/LVID ratio, Tei index, CK-MB,and LDH-1 were higher, and LVEF was lower in group A than those in group B, C and D, there were significant differences between group B and C, C and D (all P < 0.01). AHAR scores were positively correlated with HADAR scores (r = 0.863, P < 0.01). Twelve hours after return to lower altitude, level of mPAP, RVID, RVOT, RVI/LVID ratio, Tei index, CK-MB, and LDH-1 were higher, and LVEF was lower in group E than those in group F, G and D, there were significant differences between group F and G, G and D (all P < 0.01). Fifteen days after return to lower altitude, level of mPAP, RVID, RVOT, RVID/LVID ratio were higher in group E than those in group F, G, and D, there were significant differences between group F and G, and D (P < 0.01 or P < 0.05), there were no significant differences between group G and D (all P > 0.05), LVEF, Tei index, CK-MB, LDH-1 showed no significant differences among groups (all P > 0.05). Thirty days after return to lower altitude, these parameters in group E, F, and G showed no significantly differences compared with those of group D (all P > 0.05).

CONCLUSIONThe severity of HADAR is associated with severity of AHAR and cardiac injury, the more serious of AHAR and cardiac injury at high altitude, the more serious of HADAR and cardiac injury after return to lower altitude, the more long of restore of right cardiac morphologic injury.

Adaptation, Physiological ; Adolescent ; Adult ; Altitude ; Altitude Sickness ; metabolism ; physiopathology ; Case-Control Studies ; Heart ; physiopathology ; Heart Function Tests ; Humans ; Male ; Myocardium ; enzymology ; Young Adult

"The Expert Group on Tumor Ablation Therapy of Chinese Medical Doctor Association, The Tumor Ablation Committee of Chinese College of Interventionalists, The Society of Tumor Ablation Therapy of Chinese Anti-Cancer Association and The Ablation Expert Committee of the Chinese Society of Clinical Oncology" have organized multidisciplinary experts to formulate the consensus for thermal ablation of pulmonary subsolid nodules or ground-glass nodule (GGN). The expert consensus reviews current literatures and provides clinical practices for thermal ablation of GGN. The main contents include: (1) clinical evaluation of GGN, (2) procedures, indications, contraindications, outcomes evaluation and related complications of thermal ablation for GGN and (3) future development directions.
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