Objective: To gradually implement electronic signature in medical records included outpatient prescription, examination report, test report, hospitalization record and others so as to realize paperless archiving for electronic medical record(EMR) of hospital. Methods: The national law of electronic signature was used as the legal basis and the electronic signature technique was used as the technological foundation to establish electronic signature mechanism about signature, encryption and authentication. The dean who was responsible for informatization of hospital was appointed group leader, and section chief of each relevantly functional department, director of clinical department and head nurse were members of the group. Through perfect the leading group for informatization to ensure electronic signature was effectively implemented. Results: Through adhered to some principles included top-level design, gradual implementation and from easiness to difficulty, the electronic signatures about out-patient prescription, examination reports, test reports, documents of medical record and the paperless archiving for hospitalization record were realized after technique development of many years. Conclusion: The implementation of electronic signature can ensure the legality and authenticity of EMR, and the traceability of modified behavior. And it has realized paperless management for medical archives and has strengthened internal sharing of medical information in hospital.

BACKGROUNDChemokine-like factor 1 (CKLF1) was recently identified as a novel cytokine. The full-length CKLF1 cDNA contains 530 bp encoding 99 amino acid residues with a CC motif similar to that of other CC family chemokines. Recombinant CKLF1 exhibits chemotactic activity on leucocytes and stimulates proliferation of murine skeletal muscle cells. We questioned whether CKLF1 could be involved in the pathogenesis of inflammation and proliferation in the lung. Therefore we used efficient in vivo gene delivery method to investigate the biological effect of CKLF1 in the murine lung.

METHODSCKLF1-expressing plasmid, pCDI-CKLF1, was constructed and injected into the skeletal muscles followed by electroporation. Lung tissues were obtained at the end of week 1, 2, 3 and 4 respectively after injection. The pathological changes in the lungs were observed by light microscope.

RESULTSA single intramuscular injection of CKLF1 plasmid DNA into BALB/c mice caused dramatic pathological changes in the lungs of treated mice. These changes included peribronchial leukocyte infiltration, epithelial shedding, collagen deposition, proliferation of bronchial smooth muscle cells and fibrosis of the lung.

CONCLUSIONSThe sustained morphological abnormalities of the bronchial and bronchiolar wall, the acute pneumonitis and interstitial pulmonary fibrosis induced by CKLF1 were similar to phenomena observed in chronic persistent asthma, acute respiratory distress syndrome and severe acute respiratory syndrome. These data suggest that CKLF1 may play an important role in the pathogenesis of these important diseases and the study also implies that gene electro-transfer in vivo could serve as a valuable approach for evaluating the function of a novel gene in animals.

Animals ; Base Sequence ; Bronchoalveolar Lavage Fluid ; cytology ; Cell Movement ; Chemokines ; genetics ; physiology ; Electroporation ; Humans ; Lung ; pathology ; MARVEL Domain-Containing Proteins ; Mice ; Mice, Inbred BALB C ; Molecular Sequence Data ; Plasmids ; Pulmonary Fibrosis ; etiology

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.