Objective To explore the correlation of pulmonary heart disease (PHD) with erythrocyte immunity function and serum erythropoietin(EPO)level. Methods Forty-eight patients with PHD were selected as PHD group, while forty people were chosen as control group. The erythrocyte C3b receptor (E-C3bRR), erythrocyte immunity complex (RBC-ICR) and serum EPO content were detected by yeast rosette method and enzyme-linked immunosorbent assay (ELISA),and blood gas indexes were examined by blood gas analyser.Results The E-C3bRR and serum EPO content were lower, while the RBC-ICR was higher in PHD group than in control group(both P< 0. 01). Compared with control group, PCOz and HCO3-levels were higher, but blood oxygen saturation(SaO2) level was lower in the PHD group than in control group(both P<0. 01). There were no differences in pH value and PO2between two groups(both P>0. 05). The E-C3bRR was positively related to serum EPO content (r=0. 623, P<0.01), and HCO3-was positively related to pH value and PCO2(r=0. 219 ,P<0. 05;r=0. 585,P<0. 01) ,whereas PCO2was negatively related to pH value(r=-0. 529,P<0.01),and PO2was positively related to SaO2(r=0. 682,P<0.01)in PHD group. Conclusions There is a correlation between E-C3bRR and serum EPO content in PHD patients.

Objective To investigate the expression and clinical significance of microRNA-224 and microRNA-378e in colorectal cancer tissues and normal mucosa adjacent to tumor lesions. Methods The gene chip technology was used to detect the different expression of miRNA in colorectal carcinoma tissues and adjacent normal tissues, which was then confirmed by real-time PCR. The relationship between the pathology and clinical data was analyzed. Results The expres-sion level of miR-224 was significantly up-regulated in tumor tissue, while miR-378e was down-regulated in tumor tissue, which was confirmed by real-time PCR. The expression of miR-224 was strongly associated with histological types, while miR-378e was strongly associated with the infiltration depth of colorectal cancer. Conclusion miR-224 is a potent tumor promoter, while miR-378e is a potent tumor suppressor. Both miR-224 and miR-378e can be used as potential colorectal cancer molecular markers.

Background and purpose: MicroRNAs (miRNAs) play an important role in tumor biological behavior. miRNAs are down-regulated or up-regulated in various cancer types, triggering abnormal cell differentiation, proliferation and apoptosis. This study was designed to investigate the expression and clinical signiifcance of miR-187*in colorectal cancer (CRC), and further to investigate its roles in promoting cell apoptosis. Methods:The expressions of miR-187* in 40 CRC cases were examined by real-time quantitative reverse transcription-PCR (qRT-PCR). The relationship between miR-187*expression and clinical features of CRC was analyzed. HCT116 cells were transfected with a miR-187*mimic and the apoptosis of the transfected cells were examined by lfow cytometry (FCM). Results:The expression of miR-187*was down-regulated in CRC tissues 0.165 (0.106, 0.428) compared with those in normal tissues 0.334 (0.211, 0.712) (P<0.05), especially in mucinous carcinoma and older age CRC (P<0.05). Transfection of HCT116 cells with a miR-187*mimic up-regulated the expression of miR-187*and increased cell early apoptosis (P<0.05). Conclusion: The expression level of miR-187* was lower in CRC. miR-187* expression correlates with histological type and age. Transfection of HCT116 cells with a miR-187*mimic accelerates apoptosis of tumor cells, suggesting that miR-187*is a potent tumor suppressor.

Brain-computerinterface(BCI) is a kind of direct channel for information communication and control established between the human brain and computer or other electronic equipment.BCI is a novel information communication system which does not depend on the conventional brain information pathways.The asynchronous brain-computer interface technology is based on alpha wave control,and can automatically switch system mode between working and idle and select the larger EEG signal associated with motion imagination.In this paper,the basic knowledge of BCI and alpha wave-based asynchronous BCI technology were introduced.The key technology and application prospect of the novel alpha wave-based asynchronous BCI technology were summarized,and the status and existing problems were analyzed.

Objective: To investigate the hospital employee satisfaction and trustworthiness in the background of the trusteeship mode and to find out the problems after trusteeship.Methods: Minnesota satisfaction questionnaire (MSQ) and the self-made questionnaire were used to conduct the survey.Results: The overall employee satisfaction (3.80±0.86) and trustworthiness (3.95±0.77)were higher.The highest level of trustworthiness concerned the cultural connotation (84.8%) followed by the management concept (82.8%) for overall satisfaction.The lowest level of employee satisfaction concerned income and workload (53.7%), followed by the working conditions and environment (55.3%).The administrative staff satisfaction was higher compared to that of medical staff (p=0.001), which showed significant statistical differences.Conclusions: The hospital has made some achievements after the trusteeship system reform, however, it still needs improvement and further strengthening in many aspects.The hospital must always keep abreast of the demands of workforce and improve the staff satisfaction so as to promote its continuous and sustainable development.

Objective To establish the Nifedipine-induced liver cell damage model,and to investigate the cellular or molecular mechanism for children's liver cell damage.Methods The HepG2 cells were utilized to establish liver cell damage models.The optimal concentration and the optimal pretreatment time of Nifedipine-induced liver cell injury were confirmed.Western blot and real-time PCR(RT-PCR)were used to check the alteration in proteins and mRNAs level of the liver function-associated classic markers,which contained alkaline phosphatase(ALP),aspartate amino transferase(AST),glutamyl transpeptidase(γ-GT)and alanine aminotransferase(ALT).Additionally,flow cytometry(FCM),colony formation assay(CFA)and cell wound healing assay(CWHA)were utilized to check the effect of Nifedipine on the cell cycle progression and proliferation of HepG2.Results (1)The optimal concentration of Nifedipine was 20 mg/L and the optimal treatment period was 21 days for liver damage.(2)Western blot:intracellular ALT protein content after Nifedipine group was less than that of control group,while the protein levels of AST,γ-GT and ALP in the culture medium after Nifedipine addition(3.55 ± 0.05,4.91 ± 0.055,3.51 ± 0.05,3.08 ± 0.08) were higher than those of control group(0.96 ± 0.02,1.03 ± 0.02,1.00 ± 0.05,0.90 ± 0.13),and the differences were all significant(t = -85.695,-117.582,-47.371,-33.260,all P<0.05).(3)The findings of RT-PCR showed that the mRNA levels of intracellular ALT,γ-GT and ALP in Nifedipine group(0.26 ± 0.02,0.05 ± 0.04, 0.05 ± 0.02)were lower than those of control group(1.13 ± 0.21,0.94 ± 0.10,1.03 ± 0.06),and the differences were all significant(t=7.233,127.436,25.687,all P<0.05).However,the mRNAs levels in purified culture me-dium in Nifedipine group(5.95 ± 0.05,3.13 ± 0.10,3.32 ± 0.08)were higher than those in control group(1.01 ± 0.08,1.00 ± 0.05,1.00 ± 0.05),and the differences were all significant(t= -92.339,-31.250,-43.007,all P<0.05).(4)The portion of G0/G1 phase in Nifedipine group[(84.09 ± 0.43)%]was more than that of control group[(30.93 ± 0.32)%],which had statistical significance(t=173.084,P=0.000).(5)In contrast with control group,the colony formation of cells in Nifedipine group declined from(97.10 ± 1.17)% to(38.56 ± 1.51)%(t=92.088,P=0.000)and the migration rate of cells wound healing was(56.37 ± 2.06)%,(25.00 ± 1.71)% sepa-rately(t=20.285,P=0.000).Conclusion Nifedipine may promote children's liver injury through regulating cell cycle related proteins.

Objective:To explore the effects of the γ-aminobutyric acid(GABA) neurons and melanin-concentrating hormone (MCH) neurons of the nucleus accumbens (NAc)-lateral hypothalamic area (LHA) neural pathway on the rewarding feeding(palatable food sweat condensed milk) in the obesity rats.Methods:Total 142 male Wistar rats of SPF grade were divided into normal diet (ND) group ( n=68) and high-fat diet induced obesity (DIO) group ( n=74) according to the principle of body mass matching. The rats in the two groups were given normal diet and high-fat diet for 8 weeks. Eight weeks later, 6 DIO rats were randomly selected to observe the nerve projection from GABA neurons in NAc to MCH neurons in LHA by fluorogold retrograde tracing combined fluorescence immunohistochemistry. And the expressions of c-Fos and MCH in LHA after ingestion of sweet condensed milk(rewarding feeding) were observed by fluorescence immunohistochemistry (6 rats in each group). GABA receptor agonist Musimol or GABA receptor antagonist Bicuculine was microinjected into the nucleus of LHA to observe the effect of GABA on rewarding food intake in ND and DIO rats ( n=8 in each group), and the changes of rewarding food intake after blocking MCH signal ( n=8 in each group). SPSS 17.0 was used for statistical analysis, two-way ANOVA and post hoc Bonferroni test were used for comparison among multiple groups, and t-test was used for comparison between two groups. Results:After 8 weeks of high-fat diet modeling, the intake of delicious food in DIO rats was significantly higher than that in ND rats((12.52±2.29) mL, (7.45±1.23) mL, t=4.778, P<0.01) after satiety.The results of fluorogold retrograde tracing combined with fluorescence immunohistochemistry showed that GABA neurons in NAc projected nerve fibers to neurons in LHA, and GABA A receptors in some neurons in LHA coexisted with MCH.The results of NAc-LHA pathway on delicious food intake showed that the interaction between rat group and drug intervention was significant( F=9.869, P<0.01). Simple effect analysis showed that the intake of delicious food after microinjection of Musimol into LHA nucleus of ND rats was significantly lower than that of microinjection normal saline ((4.25±1.38) mL, (7.29±1.49) mL, P<0.01), while the intake of delicious food after injection of Bicuculine was significantly higher than that of microinjection normal saline((10.72±2.11) mL, (7.29±1.49) mL, P<0.05). The intake of delicious food after microinjection of Musimol into LHA nucleus in DIO group was significantly lower than that of microinjection normal saline((3.51±1.77)mL, (13.68±2.95) mL, P<0.01), but there was no significant difference between microinjection Bicuculine and microinjection normal saline ((14.83±3.44) mL, (13.68±2.95) mL, P>0.05). The results of blocking MCH signal on delicious food intake showed that the interaction effect between SNAP-94847 and Bicuculine intervention was not significant ( F=1.468, P>0.05). The main effect of SNAP-94847 intervention was significant ( F=15.880, P<0.01)and the main effect of Bicuculine intervention was significant ( F=6.930, P<0.05). After intracerebroventricular injection of MCH receptor blocker SNAP-94847, the delicious food intake of ND rats was significantly less than that of injection normal saline((4.78±1.72) mL, (7.63±2.77) mL, P<0.05), and it was not affected by pre injection of Bicuculine in LHA ((6.24±2.18) mL, (4.78±1.72) mL, P>0.05). In the DIO rats, the interaction effect between SNAP-94847 and Bicuculine intervention was not significant( F=0.006, P>0.05). The main effect of SNAP-94847 intervention was significant ( F=18.46, P<0.01) and the main effect of Bicuculine intervention was not significant ( F=2.059, P>0.05). After intracerebroventricular injection of MCH receptor blocker SNAP-94847, the delicious food intake of DIO rats was significantly lower than that of injection normal saline((6.89±2.11) mL, (12.19±4.36) mL, P<0.05), and it was not affected by pre injection of Bicuculine in LHA ((8.72±2.26) mL, (6.89±2.11) mL, P>0.05). Conclusion:GABAergic signal in NAc can regulate the expression of MCH in neurons of LHA. In the DIO rats, the sensitivity of MCH neurons in LHA to satiety signal decreases and the hedonic feeding increases.

Objective:To develop early gastric cancer (EGC) detection system of magnifying blue laser imaging (ME-BLI) model and magnifying narrow-band imaging (ME-NBI) model based on deep convolutional neural network, to compare the performance differences of the two models and to explore the effects of training methods on the accuracy.Methods:The images of benign gastric lesions and EGC under ME-BLI and ME-NBI were respectively collected. A total of five data sets and three test sets were collected. Data set 1 included 2 024 noncancerous lesions and 452 EGC images under ME-BLI. Data set 2 included 2 024 noncancerous lesions and 452 EGC images under ME-NBI. Data set 3 was the combination of data set 1 and 2 (a total of 4 048 noncancerous lesions and 904 EGC images under ME-BLI and ME-NBI). Data set 4: on the basis of data set 2, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added (2 086 noncancerous lesions and 2 757 EGC images under ME-NBI). Data set 5: on the basis of data set 3, another 62 noncancerous lesions and 2 305 EGC images under ME-NBI were added(4 110 noncancerous lesions and 3 209 EGC images under ME-NBI and ME-BLI). Test set A included 422 noncancerous lesions and 197 EGC images under ME-BLI. Test set B included 422 noncancerous lesions and 197 EGC images under ME-NBI. Test set C was the combination of test set A and B (844 noncancerous and 394 EGC images under ME-BLI and ME-NBI). Five models were constructed according to these five data sets respectively and their performance was evaluated in the three test sets. Per-lesion video was collected and used to compare the performance of deep convolutional neural network models under ME-BLI and ME-NBI for the detection of EGC in clinical environment, and compared with four senior endoscopy doctors. The primary endpoint was the diagnostic accuracy of EGG, sensitivity and specificity. Chi-square test was used for statistical analysis.Results:The performance of model 1 was the best in test set A with the accuracy, sensitivity and specificity of 76.90% (476/619), 63.96% (126/197) and 82.94% (350/422), respectively. The performance of model 2 was the best in test set B with the accuracy, sensitivity and specificity of 86.75% (537/619), 92.89% (183/197) and 83.89% (354/422), respectively. The performance of model 3 was the best in test set B with the accuracy, sensitivity and specificity of 86.91% (538/619), 84.26% (166/197) and 88.15% (372/422), respectively. The performance of model 4 was the best in test set B with the accuracy, sensitivity and specificity of 85.46% (529/619), 95.43% (188/197) and 80.81% (341/422), respectively. The performance of model 5 was the best in test set B, with the accuracy, sensitivity and specificity of 83.52% (517/619), 96.95% (191/197) and 77.25% (326/422), respectively. In terms of image recognition of EGC, the accuracy of models 2 to 5 was higher than that of model 1, and the differences were statistically significant ( χ2=147.90, 149.67, 134.20 and 115.30, all P<0.01). The sensitivity and specificity of models 2 and 3 were higher than those of model 1, the specificity of model 2 was lower than that of model 3, and the differences were statistically significant ( χ2=131.65, 64.15, 207.60, 262.03 and 96.73, all P < 0.01). The sensitivity of models 4 and 5 was higher than those of models 1 to 3, and the specificity of models 4 and 5 was lower than those of models 1 to 3, and the differences were statistically significant ( χ2=151.16, 165.49, 71.35, 112.47, 132.62, 153.14, 176.93, 74.62, 14.09, 15.47, 6.02 and 5.80, all P<0.05). The results of video test based on lesion showed that the average accuracy of doctors 1 to 4 was 68.16%. And the accuracy of models 1 to 5 was 69.47% (66/95), 69.47% (66/95), 70.53% (67/95), 76.84% (73/95) and 80.00% (76/95), respectively. There were no significant differences in the accuracy among models 1 to 5 and between models 1 to 5 and doctors 1 to 4 (all P>0.05). Conclusions:ME-BLI EGC recognition model based on deep learning has good accuracy, but the diagnostic effecacy is sligntly worse than that of ME-NBI model. The effects of EGC recognition model of ME-NBI combined with ME-BLI is better than that of a single model. A more sensitive ME-NBI model can be obtained by increasing the number of ME-NBI images, especially the images of EGG, but the specificity is worse.

Objective:To compare the ability of deep convolutional neural network-crop (DCNN-C) and deep convolutional neural network-whole (DCNN-W), 2 artificial intelligence systems based on different training methods to dignose early gastric cancer (EGC) diagnosis under magnifying image-enhanced endoscopy (M-IEE).Methods:The images and video clips of EGC and non-cancerous lesions under M-IEE under narrow band imaging or blue laser imaging mode were retrospectively collected in the Endoscopy Center of Renmin Hospital of Wuhan University, for the training set and test set for DCNN-C and DCNN-W. The ability of DCNN-C and DCNN-W in EGC identity in image test set were compared. The ability of DCNN-C, DCNN-W and 3 senior endoscopists (average performance) in EGC identity in video test set were also compared. Paired Chi-squared test and Chi-squared test were used for statistical analysis. Inter-observer agreement was expressed as Cohen′s Kappa statistical coefficient (Kappa value).Results:In the image test set, the accuracy, sensitivity, specificity and positive predictive value of DCNN-C in EGC diagnosis were 94.97%(1 133/1 193), 97.12% (202/208), 94.52% (931/985), and 78.91%(202/256), respectively, which were higher than those of DCNN-W(86.84%, 1 036/1 193; 92.79%, 193/208; 85.58%, 843/985 and 57.61%, 193/335), and the differences were statistically significant ( χ2=4.82, 4.63, 61.04 and 29.69, P=0.028, =0.035, <0.001 and <0.001). In the video test set, the accuracy, specificity and positive predictive value of senior endoscopists in EGC diagnosis were 67.67%, 60.42%, and 53.37%, respectively, which were lower than those of DCNN-C (93.00%, 92.19% and 87.18%), and the differences were statistically significant ( χ2=20.83, 16.41 and 11.61, P<0.001, <0.001 and =0.001). The accuracy, specificity and positive predictive value of DCNN-C in EGC diagnosis were higher than those of DCNN-W (79.00%, 70.31% and 64.15%, respectively), and the differences were statistically significant ( χ2=7.04, 8.45 and 6.18, P=0.007, 0.003 and 0.013). There were no significant differences in accuracy, specificity and positive predictive value between senior endoscopists and DCNN-W in EGC diagnosis (all P>0.05). The sensitivity of senior endoscopists, DCNN-W and DCNN-C in EGC diagnosis were 80.56%, 94.44%, and 94.44%, respectively, and the differences were not statistically significant (all P>0.05). The results of the agreement analysis showed that the agreement between senior endoscopists and the gold standard was fair to moderate (Kappa=0.259, 0.532, 0.329), the agreement between DCNN-W and the gold standard was moderate (Kappa=0.587), and the agreement between DCNN-C and the gold standard was very high (Kappa=0.851). Conclusion:When the training set is the same, the ability of DCNN-C in EGC diagnosis is better than that of DCNN-W and senior endoscopists, and the diagnostic level of DCNN-W is equivalent to that of senior endoscopists.

Objective:To assess the influence of an artificial intelligence (AI) -assisted diagnosis system on the performance of endoscopists in diagnosing gastric cancer by magnifying narrow banding imaging (M-NBI).Methods:M-NBI images of early gastric cancer (EGC) and non-gastric cancer from Renmin Hospital of Wuhan University from March 2017 to January 2020 and public datasets were collected, among which 4 667 images (1 950 images of EGC and 2 717 of non-gastric cancer)were included in the training set and 1 539 images (483 images of EGC and 1 056 of non-gastric cancer) composed a test set. The model was trained using deep learning technique. One hundred M-NBI videos from Beijing Cancer Hospital and Renmin Hospital of Wuhan University between 9 June 2020 and 17 November 2020 were prospectively collected as a video test set, 38 of gastric cancer and 62 of non-gastric cancer. Four endoscopists from four other hospitals participated in the study, diagnosing the video test twice, with and without AI. The influence of the system on endoscopists′ performance was assessed.Results:Without AI assistance, accuracy, sensitivity, and specificity of endoscopists′ diagnosis of gastric cancer were 81.00%±4.30%, 71.05%±9.67%, and 87.10%±10.88%, respectively. With AI assistance, accuracy, sensitivity and specificity of diagnosis were 86.50%±2.06%, 84.87%±11.07%, and 87.50%±4.47%, respectively. Diagnostic accuracy ( P=0.302) and sensitivity ( P=0.180) of endoscopists with AI assistance were improved compared with those without. Accuracy, sensitivity and specificity of AI in identifying gastric cancer in the video test set were 88.00% (88/100), 97.37% (37/38), and 82.26% (51/62), respectively. Sensitivity of AI was higher than that of the average of endoscopists ( P=0.002). Conclusion:AI-assisted diagnosis system is an effective tool to assist diagnosis of gastric cancer in M-NBI, which can improve the diagnostic ability of endoscopists. It can also remind endoscopists of high-risk areas in real time to reduce the probability of missed diagnosis.