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.

Objective:To construct an intelligent endoscopic image analysis system that could monitor the blind spot of the upper gastrointestinal tract, and to test its performance.Methods:A total of 87 167 upper gastrointestinal endoscopy images (dataset 1) including 75 551 for training and 11 616 for testing, and a total of 2 414 pharyngeal images (dataset 2) including 2 233 for training and 181 for testing were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University between 2016 to 2020. A 27-category-classification model for blind spot monitoring in the upper gastrointestinal tract (model 1, which distinguished 27 anatomical sites such as the pharynx, esophagus, and stomach) and a 5-category-classification model for blind spot monitoring in the pharynx (model 2, which distinguished palate, posterior pharyngeal wall, larynx, left and right pyriform sinuses) were constructed. The above models were trained and tested based on dataset 1 and 2, respectively, and trained based on the EfficientNet-B4, ResNet50 and VGG16 models of the keras framework. Thirty complete upper gastrointestinal endoscopy videos were retrospectively collected from the Digestive Endoscopy Center of Renmin Hospital of Wuhan University in 2021 to test model 2 blind spot monitoring performance.Results:The cross-sectional comparison results of the accuracy of model 1 in identifying 27 anatomical sites of the upper gastrointestinal tract in images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 90.90%, 90.24%, and 89.22%, respectively, with the EfficientNet-B4 model performance the best, and the accuracy of EfficientNet-B4 model for each site ranged from 80.49% to 97.80%. The cross-sectional comparison results of the accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the images showed that the mean accuracy of EfficientNet-B4, ResNet50, and VGG16 were 99.40%, 98.56%, and 97.01%, respectively, in which the EfficientNet-B4 model had the best performance, and the accuracy of EfficientNet-B4 model for each site ranged from 96.15% to 100.00%. The overall accuracy of model 2 in identifying the 5 anatomical sites of the pharynx in the video was 97.33% (146/150).Conclusion:The intelligent endoscopic image analysis system based on deep learning can monitor blind spots in the upper gastrointestinal tract, coupled with pharyngeal blind spot monitoring and esophagogastroduodenal blind spot monitoring functions. The system shows high accuracy in both images and videos, which is expected to have a potential role in clinical practice and assisting endoscopists to achieve full observation of the upper gastrointestinal tract.

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 develop an artificial intelligence model based on multi-modal endoscopic images for identifying gastric neoplasms and to compare its diagnostic efficacy with traditional models and endoscopists.Methods:A total of 3 267 images of gastric neoplasms and non-neoplastic lesions under white light (WL) endoscopy and weak magnification (WM) endoscopy from 463 patients at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from March 2018 to December 2019 were utilized. Two single-modal models (WL model and WM model) were constructed based on WL and WM images separately. WL and WM images of corresponding lesions were combined into image pairs for creating a multi-modal (MM) characteristics integration model. A test set consisting of 696 images of 102 lesions from 97 patients from March 2020 to March 2021 was used to compare the diagnostic efficacy of the single-modal models and a multi-modal model for gastric neoplastic lesions at both the image and the lesion levels. Additionally, video clips of 80 lesions from 80 patients from January 2022 to June 2022 were employed to compare diagnostic efficacy of the WM model, the MM model and 7 endoscopists at the lesion level for gastric neoplasms.Results:In the image test set, the sensitivity and accuracy of MM model were 84.96% (576/678), and 86.89% (1 220/1 289), respectively, for diagnosing gastric neoplasms at the image level, which were superior to 63.13% (113/179) and 80.59% (353/438) of WM model ( χ2=42.81, P<0.001; χ2=10.33, P=0.001), and also better than those of WL model [70.47% (74/105), χ2=13.52, P<0.001; 67.82% (175/258), χ2=57.27, P<0.001]. The MM model showed a sensitivity of 87.50% (28/32), a specificity of 88.57% (62/70), and an accuracy of 88.24% (90/102) at the lesion level. The specificity ( χ2=22.99, P<0.001) and accuracy ( χ2=19.06, P<0.001) were significantly higher than those of WL model; however, there was no significant difference compared with those of the WM model ( P>0.05). In the video test, the sensitivity, specificity and accuracy of the MM model at the lesion level were 95.00% (19/20), 93.33% (56/60) and 93.75% (75/80). These results were significantly better than those of endoscopists, who had a sensitivity of 77.14% (108/140), a specificity of 79.29% (333/420), and an accuracy of 78.75% (441/560), with significant differences ( χ2=18.62, P<0.001; χ2=35.07, P<0.001; χ2=53.12, P<0.001), and was higher than the sensitivity of advanced endoscopists [83.33% (50/60)] with significant difference ( χ2=4.23, P=0.040). Conclusion:The artificial intelligence model based on multi-modal endoscopic images for the diagnosis of gastric neoplasms shows high efficacy in both image and video test sets, outperforming the average diagnostic performance of endoscopists in the video test.

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.