Objective:To explore the application effect of the modified sandwich teaching method based on constructivism theory in clinical teaching of neurology nursing interns.Methods:A total of 29 nursing interns who practiced in the Department of Neurology of the First Affiliated Hospital of University of Science and Technology of China (Anhui Provincial Hospital) from June 2020 to May 2021 were selected as the control group, using the conventional clinical nursing teaching method, and 28 nursing interns who practiced from June 2021 to May 2022 were selected as the observation group, using the modified sandwich teaching method based on constructivism theory. Before graduating from the Department of Neurology, nursing interns in the two groups were compared in terms of the teaching effects, such as the score of learning initiative, theoretical examination, operation examination, and nursing round report. SPSS 25.0 was used for t test and chi-square test. Results:The score for each dimension (learning driving force, learning objectives, in-depth learning, controlled learning, and solid learning) and total score in the observation group were significantly higher than those in the control group ( P<0.05). There was no significant difference between the two groups in the scores of theoretical examination and operation examination (89.11±3.58 vs. 88.97±2.74, 93.79±2.48 vs. 93.86±2.20; P>0.05); the scores of nursing rounds in the observation group were significantly higher than those in the control group (88.61±2.60 vs. 83.38±3.97, P<0.05). Conclusions:The modified sandwich teaching method based on constructivism theory can enhance the learning initiative and comprehensive analysis ability of nursing interns in the Department of Neurology. It is suitable for clinical nursing teaching in the Department of Neurology and is worth popularizing.

Objective:To aid in the detection of thyroid cancer by using deep learning to differentiate the unique bioimpedance parameter patterns of different thyroid tissues.Methods:An electrical impedance system was designed to measure 331 ex-vivo thyroid specimens from 321 patients during surgery. The impedance data was then analyzed with one dimensional convolution neural (1D-CNN) combining with long short-term memory (LSTM) network models of deep learning. In the process of analysis, we assigned 80% of the data to training set (1072/1340) and the remaining 20% data to the test set (268/1340). The performance of final model was assessed using receiver operating characteristic (ROC) curves. In addition, sensitivity, specificity, positive predictive value, negative predictive value, Youden index were applied to compare impedance model with ultrasound results.Results:The ROC curve of the two-classification (malignant /non-malignant tissue) model showed a good performance (area-under-the-curve AUC=0.94), with an overall accuracy of 91.4%. To better fit clinical practice, we further performed a three-classification (malignant/ benign/ normal tissue) model, of which the areas under ROC curve were 0.91, 0.85, 0.92 for normal, benign, and malignant group, respectively. The results indicated that the area under micro-average ROC curve and the macro-average ROC curve were 0.91 and 0.90, respectively. Moreover, compared with ultrasound, the impedance model exhibited higher specificity.Conclusions:A deep learning model (CNN-LSTM) trained by thyroid electrical impedance spectroscopy (EIS) parameters shows an excellent performance in distinguishing among different in-vitro thyroid tissues, which is promising for applications. In future clinical utility, our study does not replace existing tests, but rather complements others, thus contributing to therapeutic decision-making and management of thyroid disease.

Objective:To develop a Self-rating Scale for Scientific Research Ability of Nursing Staff and conduct reliability and validity test.Methods:Based on literature review, qualitative interview and expert letter consultation, the first draft of the Self-rating Scale for Scientific Research Ability of Nursing Staff was formed. By the convenient sampling method, a total of 924 nursing staff from Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology were selected as the research objects from February to March 2021 for questionnaire survey to test the reliability and validity of the scale.Results:Two common factors were extracted from exploratory factor analysis, including 13 items, and the cumulative variance contribution rate was 82.088%. Confirmatory factor analysis suggested that the model fit well. Content validity index at item level was 0.833~1.000, and content validity index at scale level was 0.865. The Cronbach's α coefficient of the total scale was 0.971, and the Cronbach's α coefficient of each dimension was 0.913 and 0.974. The total split-half reliability coefficient of the scale was 0.941, and split-half reliability coefficient of each dimension was 0.932 and 0.964.Conclusions:The self-rating scale for scientific research ability of nursing staff has good reliability and validity, which can be used as an evaluation tool of research ability of nursing staff.

Isolated pontine infarction (IPI) is the main type of acute brainstem infarction. Due to the application of high-resolution MRI, the research on the etiology of IPI has gradually increased in recent years. This article reviews the latest research progress on the characteristics of basilar artery plaques and disease progression mechanisms of IPI, aiming to provide reference for the etiology research of IPI.

Purpose To explore the artificial intelligence(AI)-assisted diagnosis system of thyroid cancer based on deep transfer learning and evaluate its clinical application value.Methods The HE sections of 682 cases thyroid disease patients(including benign lesions,papillary carcinoma,follicular carci-noma,medullary carcinoma and undifferentiated carcinoma)in the Pathology Department of the Renmin Hospital of Wuhan Uni-versity were collected,scanned into digital sections,divided into training sets and internal test sets according to the ratio of 8 ∶ 2,and the training sets were labeled at the pixel level by patholo-gists.The thyroid cancer classification model was established u-sing VGG image classification algorithm model.In the process of model training,the parameters of the breast cancer region recog-nition model were taken as the initial values,and the parameters of the thyroid cancer region recognition model were optimized through the transfer learning strategy.Then the test set and 633 intraoperative frozen HE section images of thyroid disease in Jianli County People's Hospital,Jingzhou City,Hubei Province wereused as the external test set to evaluate the performance of the established AI-assisted diagnostic model.Results In the internal test set,without the use of the breast cancer region rec-ognition model transfer learning,the accuracy of the AI-assisted diagnosis model was 0.882,and the area under the Receiver op-erating characteristic(AUC)valuewas0.938;However,inthe use of the Transfer learning model,the accuracy of the AI-assis-ted diagnosis model for was 0.926,and the AUC value was 0.956.In the external test set,the accuracy of the zero learning model was 0.872,the AUC value was 0.915,and the accuracy of the Transfer learning model was 0.905,the AUC value was 0.930.Conclusion The AI-assisted diagnosis method for thy-roid cancer established in this study has good accuracy and gen-eralization.With the continuous development of pathological AI research,transfer learning can help improve the performance and generalization ability of the model,and improve the accura-cy of the diagnostic model.

Klebsiella pneumoniae(K.pneumoniae)is an important pathogen that can cause severe hospital-and community-acquired infections.To systematically investigate its methylation features,we determined the whole-genome sequences of 14 K.pneumoniae strains covering varying serotypes,multilocus sequence types,clonal groups,viscosity/virulence,and drug resistance.Their methy-lomes were further characterized using Pacific Biosciences single-molecule real-time and bisulfite technologies.We identified 15 methylation motifs[13 N6-methyladenine(6mA)and two 5-methylcytosine(5mC)motifs],among which eight were novel.Their corresponding DNA methyl-transferases were also validated.Additionally,we analyzed the genomic distribution of GATC and CCWGG methylation motifs shared by all strains,and identified differential distribution pat-terns of some hemi-/un-methylated GATC motifs,which tend to be located within intergenic regions(IGRs).Specifically,we characterized the in vivo methylation kinetics at single-base resolu-tion on a genome-wide scale by simulating the dynamic processes of replication-mediated passive demethylation and MTase-catalyzed re-methylation.The slow methylation of the GATC motifs in the replication origin(oriC)regions and IGRs implicates the epigenetic regulation of replication initiation and transcription.Our findings illustrate the first comprehensive dynamic methylome map of K.pneumoniae at single-base resolution,and provide a useful reference to better understand epigenetic regulation in this and other bacterial species.

Dental resin materials have been widely used in the treatment of dental defects. However, the polymerization shrinkage of the resin materials tends to cause microleakage and accumulation of bacterial plaque, which leads to secondary dental caries. Endowing dental resin with antibacterial properties is an important way to solve this problem. Adding antibacterial agents to dental resin is the main method to give it antibacterial properties. Antimicrobial agents are mainly divided into three types: release type, non-release type and mixed type. In terms of antibacterial effects, the selection and addition of antibacterial agents will affect the antibacterial and mechanical properties of dental resin materials; and the long-term antibacterial effect of antimicrobial agents in the oral cavity remains to be verified; as antimicrobial agents or other environmental factors can lead to drug resistance and even dormant persistent bacteria. In recent years, researchers have been committed to improving the antibacterial effect by modifying antibacterial agents. The sustained release of antimicrobial agents via carriers is also the main research direction. This paper reviews the research progress on the antibacterial properties of dental resin materials.

Objective:To investigate the serotype distribution and drug resistance of Streptococcus pneumoniae ( S. pneumoniae) isolated in Urumqi Children′s Hospital and to evaluate the significances of 13-valent pneumococcal conjugate vaccine (PCV13) in preventing infection and controlling drug resistance. Methods:The S. pneumoniae isolates stored in clinical laboratory of Urumqi Children′s Hospital from January to December in 2018 were re-cultured.The serotypes were detected by capsule swelling experiment to assess the coverage rate of PCV13.The minimum inhibitory concentration (MIC) of Penicillin, Amoxicillin, Cefotaxime and Ceftriaxone were detected by E-test method, and the susceptibility of the isolates to Meropenem and other 9 antibiotics was detected by VITEC 2 Compact system. Results:A total of 225 S. pneumoniae strains were identified.The common serotypes were 19F (32.9%), 23F (12.0%), 19A (10.7%), 6B (10.2%) and 6A (8.0%). PCV13 coverage rate was 80.4%.There was no significant difference in serotype distribution and PCV13 coverage between children < 2 years old and ≥ 2 years old, as well as between Han and minority people.The 57.8% and 31.7% strains showed intermediate susceptibility and resistance against oral Penicillin, respectively.Based on the breakpoints for meningitis, 89.4% strains were resistant against pare-nteral Penicillin, and 47.5% and 64.6% strains were non-susceptible (mainly intermediately susceptible) to Ceftria-xone and Cefotaxime, respectively.The resistance rates of strains against Erythromycin, Sulfamethoxazole-trimethoprim and Tetracycline were as high as 98.1%, 67.6% and 89.6%, respectively.More than 90% tested isolates were susceptible to Amoxicillin, Meropenem, Levofloxacin or Moxifloxacin.PCV13 strains were more resistant to Penicillin than non-PCV13 strains. Conclusions:The serotypes 19F, 23F, 19A, 6B and 6A are common among the S. pneumoniae isolated in Urumqi.The coverage rate of PCV13 is about 80%.There was no significant difference in serotype distribution between Han and minority nationality children. S. pneumoniae were frequently resistant against Erythromycin.The high resistance to Penicillin and other beta-lactams should be taken into account when treatment is decided for suspected pneumococcal meningitis.Universal administration of PCV13 would be effective strategy to prevent pneumococcal infection in children and to control the drug resistance of S. pneumoniae.

Objective:To determine whether these Streptococcus pneumoniae isolates identified by routine cli-nical methods that cannot be serotyped by the quellung reaction contain other species of viridans group streptococci and to determine the antibiotic susceptibility to provide reference for clinical medicine. Methods:A total of 105 isolates identified as Streptococcus pneumoniae by routine methods with negative quellung reaction results were enrolled in this study.Multilocus sequence analysis (MLSA) and matrix-assisted laser desorption/ionization time of flight mass spectrometry(MALDI-TOF-MS) were used to identify species of these isolates.Broth microdilution method was used to detect susceptibilities of 14 antibiotics. Results:Twenty-four of the 105 isolates were identified as Streptococcus pseudopneumoniae by MLSA, and the remaining 81 were Streptococcus pneumoniae.Six isolates of Streptococcus pseudopneumoniae were misidentified as Streptococcus pneumoniae, and 3 isolates as Streptococcus mitis/ oralis by MALDI-TOF-MS; and 6 isolates of Streptococcus pneumoniae were misidentified as Streptococcus pseudopneumoniae.All isolates were susceptible to Vancomycin, Levofloxacin and Moxifloxacin.The non-susceptibility rates between Streptococcus Pneumoniae and Streptococcus pseudopneumoniae against Ceftriaxone(28.4% vs.58.4%), Chloramphenicol(39.5% vs.4.2%), Erythromycin(77.8% vs.95.8%) and Azithromycin(75.3% vs.95.8%) were obviously different. Conclusions:Routine clinical methods may misidentify some Streptococcus pseudopneumoniae as Streptococcus pneumoniae, and so does the MALDI-TOF-MS.In addition, Streptococcus pneumoniae isolates with negative results of the quellung reaction showed differences in antimicrobial resistance.And misidentification may affect the evaluation of pathogenic bacteria and antibiotic resistance.