Objective To investigate and analyze the status quo of professional identity of nursing students with different educational backgrounds.Methods With stratified-cluster random sampling,635 students were surveyed using the nursing students' professional identity questionnaire.Results The total score of professional identity of junior college nursing students was significantly higher than that of undergraduate students.Junior college nursing students had a higher score in professional knowledge dimension than undergraduate and graduate students,while the score of nursing undergraduate students was higher than that of graduate students.Junior college nursing students had a higher score in professional will dimension than undergraduate and graduate students.Nursing graduate students had a higher score in professional expectation dimension than junior college and undergraduate students,while the score of undergraduate students was higher than that of junior college students.Whatever the educational background,professional skill and professional expectation were the top two dimensions,while professional knowledge and professional emotion were the last two dimensions.The difference was statistically significant between the answers of nursing students with different educational backgrounds to the following questions,such as which is the most probable factor affecting your learning enthusiasm,who has the greatest impact on your professional thought about nursing and which nursing field are you most willing to go in for.There was significant difference in professional identity score between different grades of junior college and undergraduate nursing students,while there is no significant grade difference in graduate students.Conclusions There were some differences of professional identity of nursing students with different educational backgrounds.Nursing educators should carry out the professional education according to the student's educational backgrounds.

OBJECTIVE Tostudytheeffectofmetforminonglucolipidmetabolicdisorderscaused byolanzapineorquetiapineinrats.METHODS Olanzapine1mg·kg-1·d-1wasgivenigorquetiapine 20 mg·kg -1·d -1 was given ig for 4 d,and the dose increased to 40 mg·kg -1·d -1 from the 5th day.Met-formin 100 mg·kg -1·d -1 was given ig from the 15th day.The treatment lasted 8 weeks.Body mass, fasting blood sugar (FBS)and postprandial 2 hours blood glucose (2hPBG)were measured at base-line,3 d,1 week,2 week,4 week,6 week and 8 week.At the end of the 8th week,serum cholesterol (TC),triglyceride (TG),low density lipoprotein (LDL-C),high density lipoprotein (HDL-C),fruc-tosamine(FA)andinsulin(IRS)weremeasured.RESULTS Therewasnosignificantstatisticaldiffer-ence between normal control group and metformin 1 00 mg·kg -1·d -1 group.At the end of the 6th week, compared with normal control group,the body mass and 2hPBG were significantly increased in olanzap-ine 1 mg·kg -1·d -1 group and quetiapine 40 mg·kg -1·d -1 group (P<0.05),respectively.At the end of the 8th week,body mass,2hPBG,INS,FA,TC,TG,LDL-C were significantly increased (P<0.05), and HDL-C decreased in olanzapine group and quetiapine group(P<0.05),respectively.FBS was increased only in olanzapine group(P<0.05).Compared with olanzapine group or quetiapine group, body mass,FBS,2hPBG,INS,FA,TC,TG,LDL-C were significantly decreased by metformin admin-istration(P<0.05).CONCLUSION Metformincaneffectivelypreventandtreatweightgainand glucolipid metabolic disorder caused by olanzapine or quetiapine.

Objective To develop and apply an ICU orientation program based on the Miller pyramid competency model. Methods 45 new ICU nurses in 2013 and 2014 were selected as the experimental group. The experimental group was oriented by the new program,including the orientation of preceptors, workshop-centered orientation courses, checklists-precepting and comprehensive competency evaluation methods. 39 new ICU nurses in 2011 and 2012 were selected as the control group. The control group was oriented by the conventional program,including focused theory classes and traditional precepting.New nurses′ satisfaction with the orientation and the competency were analyzed before and after intervention in two groups. Results Satisfaction with the orientation, self-evaluated competency and peer-evaluated competency in the experimental group were(16.71±1.19) scores, (126.52±3.31) scores and (90.00±2.68) scores respectively,which were much better than the control group and had statistically significant difference in two groups. Conclusions The ICU new nurse orientation program based on the Miller pyramid competency model can improve the new nurses′ satisfaction with the orientation and the competency after the orientation.

Objective:To investigate the effects of Bifidobacterium animalis subsp. lactis BB-12 on hippocampal neuroinflammation and cognitive function of mice after whole brain radiotherapy. Methods:A total of sixty male C57BL/6J mice aged 7-8 weeks were randomly divided into 5 groups with 12 mice in each group: control group (Con group), probiotic group (BB-12 group), irradiation group (IR group), irradiation and Memantine group (IR+ Memantine group), irradiation and probiotic group (IR+ BB-12 group). The model of radiation-induced brain injury of mice was established by 10 Gy whole brain radiotherapy with a medical linear accelerator. Y-maze test was used to evaluate the cognitive function. The activation of microglia and astrocytes was observed by immunofluorescence staining. The expressions of inflammatory cytokines interleukin-1β (IL-1β), IL-6 and tumor necrosis factor-α (TNF-α) were detected by quantitative real-time reverse transcription polymerase chain reaction (QRT-PCR) and Western blot.Results:Y-maze test showed that, compared with Con group, the percentage of the times of reaching the novel arm in the total times of the three arms decreased significantly in the IR group ( t=5.04, P<0.05). BB-12 mitigated radiation-induced cognitive dysfunction ( t=4.72, P<0.05). Compared with Con group, the number ( t=3.05, 7.18, P<0.05) and circularity index ( t=6.23, 2.52, P<0.05) of Iba1 and GFAP positive cells were increased, the microglia and astrocytes were activated in the hippocampus of IR group, but these alterations were eliminated by BB-12. After whole brain IR, the mRNA and protein expression levels of inflammatory cytokines IL-1β, IL-6 and TNF-α in the hippocampus of mice were significantly increased compared with Con group ( tmRNA =4.10, 3.04, 4.18, P<0.05; tprotein=11.49, 7.04, 8.42, P<0.05), which were also significantly reduced by BB-12 compared with IR group ( tmRNA=4.20, 3.40, 2.84, P<0.05; tprotein=6.36, 4.03, 3.75, P<0.05). Conclusions:Bifidobacterium animalis BB-12 can suppress neuroinflammation mediated by microglia and astrocytes in the hippocampus of mice after radiotherapy and alleviates IR-induced cognitive dysfunction. Therefore, BB-12 has potential application in alleviating radiation induced brain injury.

In order to study the functions of the HSPs (Heat shock proteins) of Eimeria tenella, we cloned a novel gene (which designated EtHSP) coding HSP of Eimeria tenella by RT-PCR and RACE (Rapid-amplification of cDNA ends). The full-length cDNA sequence of EtHSP was 1802 bp, containing a 1455 bp ORF (Open reading frame) (GenBank Accession No. FJ911605) encoding a deduced protein of 484 amino acids. Real-time PCR revealed that the mRNA level of EtHSP was much higher in sporozoites of E. tenella than other developmental stages (unsporulated oocysts, sporulated oocysts and merozoites). We constructed the recombinant plasmids pET28a(+)-EtHSP, then transformed it into E. coli BL21(DE3) for expression. SDS-PAGE indicated that the fusion protein was expressed in included bodies, with peak expression 6 h after induction by IPTG Western blotting revealed that the protein was specifically recognized by polyclonal antibodies against E. tenella, showing that the fusion protein was native antigen.
Animals ; Chickens ; Eimeria tenella ; genetics ; metabolism ; Escherichia coli ; genetics ; metabolism ; Heat-Shock Proteins ; genetics ; immunology ; metabolism ; Inclusion Bodies ; metabolism ; Male ; Molecular Sequence Data ; Open Reading Frames ; genetics ; Rabbits ; Recombinant Fusion Proteins ; genetics ; immunology ; metabolism ; Sequence Analysis, Protein

Objective:To explore the clinical application of a new type of medical drainage device designed to measure the intra-abdominal pressure of ICU patients.Methods:The 65 patients with severe acute pancreatitis treated in our hospital from April to September 2018 were selected as the experimental group; the 54 patients with severe acute pancreatitis treated from October 2017 to March 2018 were selected as the control group. Patients in the control group used traditional drainage bags to measure intra-abdominal pressure, while patients in the experimental group used a new-designed medical drainage device to measure intra-abdominal pressure. Compare the cost of consumables used for the first time in the experimental group with the control group, the incidence of acupuncture injuries, the incidence of urethral leakage, the incidence of rupture of the catheter balloon, and the satisfaction of the nurses. The 32 patients admitted from January to March 2018 in the control group were measured again using a new drainage device after the abdominal pressure measurement operation was stopped in order to compare the accuracy of the two methods.Results:The per capita consumable cost of the experimental group was 5.71 yuan, 0 cases of needle stick injury, 0 case of catheter leakage, which were lower than the control group (22.36 yuan, 1 case, 7 cases), the difference was significant ( P<0.05). The nurse operation satisfaction score was 13.85±0.93, which was higher than the control group (10.00±1.05). The difference was statistically significant ( t value was -20.323, P<0.05). Conclusion:In the operation of intra-abdominal pressure measurement, the use of a new type of medical drainage device can ensure the accuracy of the measurement, reduce the cost of consumables, needle stick injuries and the incidence of urinary catheter leakage, and improve nurse operation satisfaction.

Objective:To construct an artificial intelligence-assisted diagnosis system to recognize the characteristics of Helicobacter pylori ( HP) infection under endoscopy, and evaluate its performance in real clinical cases. Methods:A total of 1 033 cases who underwent 13C-urea breath test and gastroscopy in the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from January 2020 to March 2021 were collected retrospectively. Patients with positive results of 13C-urea breath test (which were defined as HP infertion) were assigned to the case group ( n=485), and those with negative results to the control group ( n=548). Gastroscopic images of various mucosal features indicating HP positive and negative, as well as the gastroscopic images of HP positive and negative cases were randomly assigned to the training set, validation set and test set with at 8∶1∶1. An artificial intelligence-assisted diagnosis system for identifying HP infection was developed based on convolutional neural network (CNN) and long short-term memory network (LSTM). In the system, CNN can identify and extract mucosal features of endoscopic images of each patient, generate feature vectors, and then LSTM receives feature vectors to comprehensively judge HP infection status. The diagnostic performance of the system was evaluated by sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC). Results:The diagnostic accuracy of this system for nodularity, atrophy, intestinal metaplasia, xanthoma, diffuse redness + spotty redness, mucosal swelling + enlarged fold + sticky mucus and HP negative features was 87.5% (14/16), 74.1% (83/112), 90.0% (45/50), 88.0% (22/25), 63.3% (38/60), 80.1% (238/297) and 85.7% (36 /42), respectively. The sensitivity, specificity, accuracy and AUC of the system for predicting HP infection was 89.6% (43/48), 61.8% (34/55), 74.8% (77/103), and 0.757, respectively. The diagnostic accuracy of the system was equivalent to that of endoscopist in diagnosing HP infection under white light (74.8% VS 72.1%, χ2=0.246, P=0.620). Conclusion:The system developed in this study shows noteworthy ability in evaluating HP status, and can be used to assist endoscopists to diagnose HP infection.

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.

Objective:To assess the diagnostic efficacy of upper gastrointestinal endoscopic image assisted diagnosis system (ENDOANGEL-LD) based on artificial intelligence (AI) for detecting gastric lesions and neoplastic lesions under white light endoscopy.Methods:The diagnostic efficacy of ENDOANGEL-LD was tested using image testing dataset and video testing dataset, respectively. The image testing dataset included 300 images of gastric neoplastic lesions, 505 images of non-neoplastic lesions and 990 images of normal stomach of 191 patients in Renmin Hospital of Wuhan University from June 2019 to September 2019. Video testing dataset was from 83 videos (38 gastric neoplastic lesions and 45 non-neoplastic lesions) of 78 patients in Renmin Hospital of Wuhan University from November 2020 to April 2021. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD for image testing dataset were calculated. The accuracy, the sensitivity and the specificity of ENDOANGEL-LD in video testing dataset for gastric neoplastic lesions were compared with those of four senior endoscopists.Results:In the image testing dataset, the accuracy, the sensitivity, the specificity of ENDOANGEL-LD for gastric lesions were 93.9% (1 685/1 795), 98.0% (789/805) and 90.5% (896/990) respectively; while the accuracy, the sensitivity and the specificity of ENDOANGEL-LD for gastric neoplastic lesions were 88.7% (714/805), 91.0% (273/300) and 87.3% (441/505) respectively. In the video testing dataset, the sensitivity [100.0% (38/38) VS 85.5% (130/152), χ2=6.220, P=0.013] of ENDOANGEL-LD was higher than that of four senior endoscopists. The accuracy [81.9% (68/83) VS 72.0% (239/332), χ2=3.408, P=0.065] and the specificity [ 66.7% (30/45) VS 60.6% (109/180), χ2=0.569, P=0.451] of ENDOANGEL-LD were comparable with those of four senior endoscopists. Conclusion:The ENDOANGEL-LD can accurately detect gastric lesions and further diagnose neoplastic lesions to help endoscopists in clinical work.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.