Objective: To investigate the clinical significance of a quick screening model for symptomatic bacterascites for guided antibiotic therapy. Methods: Data were collected prospectively from 24 cases of cirrhotic ascites who were newly admitted to Nanchang Ninth Hospital between September 2016 and February 2017. No clear indication for antibiotic treatment was used when the number of polymorphonuclear cells in ascites was <250 cells/mm³. A quick screening model for symptomatic bacterascites was determined by positivity and was randomly divided into the experimental (12 cases) and the control group (12 cases). The experimental group was given antibiotic treatment during the whole process, while the control group did not receive antibiotic treatment. The 10^th day of treatment was the end point of the study. The treatment responses of the two groups were compared. The treatment response results were divided into three categories: complete response, partial response, and no response. The sum of complete and partial response rates was used to determine the response rate. The Mann-Whitney U test and Fisher's exact test were used to compare the treatment responses between groups. Results: The baseline conditions of gender, age, screening score, and disease severity were consistent between the two groups (P>0.05). On the 10^th day of treatment, the number of complete responses between the experimental group and the control group was 1 to 0, the number of partial responses was 7 to 2, and the number of non-responses was 4 to 10, Z=-2.467, P=0.014. The response rate was significantly better in the experimental group than in the control group [66.7% (8/12) vs. 16.7% (2/12), P=0.036]. Conclusion: Guided antibiotic therapy is somehow important for the quick screening model for symptomatic bacterascites, and patients with cirrhotic ascites who test positive in this model can benefit from antibiotic therapy.
Humans ; Anti-Bacterial Agents/therapeutic use* ; Bacterial Infections/drug therapy* ; Male ; Female ; Mass Screening/methods*

Neurofibromatosis Type 1 (NF1) is an autosomal dominant genetic disorder caused by NF1 gene mutations. Café au lait spots, neurofibromatosis, Lisch nodules, axillary freckling, dermal neurofibromas and skeletal dysplasia are the most common manifestations for this disease. A 11-year-old boy visited Third Xiangya Hospital, Central South University due to growth-retardation. He was eventually diagnosed as NF1 with growth hormone deficiency. A novel heterozygous splicing mutation c.6579+2 T>C (IVS 34+2 T>C) of NF1 gene was identified in the patient and his mother. Considering NF1 may present with short stature due to growth hormone deficiency, all children with short stature combined with café au lait spots should be screened for NF1, which may assist the clinical diagnosis and the genetic counseling.
Cafe-au-Lait Spots ; diagnosis ; genetics ; Child ; Genes, Neurofibromatosis 1 ; Growth Hormone ; deficiency ; Humans ; Male ; Mutation ; Neurofibromatosis 1 ; blood ; diagnosis

In the outbreak of COVID-19,triage procedures based on epidemiology were implemented in a local hospital in Changsha to control the transmission of SARS-CoV-2 and avoid healthcare-associated infection.This re-trospective study analyzed the data collected during the triage period and found that COVID-19 patients were en-riched 7 folds into the Section A designated for patients with obvious epidemiological history.On the other side,nearly triple amounts of visits were received at the Section B for patients without obvious epidemiological history.8 COVID-19 cases were spotted out of 247 suspected patients.More than 50％of the suspected patients were submi-tted to multiple rounds of nucleic acid analysis for SARS-CoV-2 infection.Of the 239 patients who were diagnosed as negative of the virus infection,188 were successfully revisited and none was reported as COVID-19 case.Of the 8 COVID-19 patients,3 were confirmed only after multiple rounds of nucleic acid analysis.Besides comorbidities,delayed sharing of epidemiological history added complexity to the diagnosis in practice.The triaging experience and strategy will be helpful for the control of infectious diseases in the future.

Famous traditional formula Sanpian Decoction(SPD)comes from Dialectical Records of Chen Shiduo of the Qing Dynasty,and ranks among 100 classic prescriptions of Classic Famous Traditional Formula catalogue(the First Batch). SPD was prepared according to Management Standards for Traditional Chinese Medicine Decoction Room in Medical Institutions. According to the polarity of different components in SPD,two HPLC fingerprints were established, in which six herbs, namely Chuanxiong Rhizoma, Paeoniae Randix Alba, Sinapis Semen, Glycyrrhizae Radix et Rhizoma, Pruni Semen, Angelicae Dahuricae Radix,are all reflected in the fingerprints; The dry extract rate, transfer rate and similarities of fingerprints were used as indicators to study the relationship between the quality value transmitting of medicinal herbs-decoction pieces-whole decoction of Chuanxiong Rhizoma. Experiment result shows that,the transfer rate of ferulic acid from medicinal herbs to decoction pieces is between 72.00% and 108.36%; the transfer rate of ferulic acid from decoction pieces to SPD is between 31.76% and 64.09%; the dry extract rate of the whole decoction is between 14.69% and 20.16%;The similarity range of fingerprint 1 of 15 batches of SPD is between 0.971 and 0.998, and the similarity range of fingerprint 2 is between 0.980 and 0.996. The established fingerprint has rich information,and the established quality evaluation method is suitable for the quality control of medicinal herbs-decoction pieces-whole decoction of Chuanxiong Rhizoma, which can provide a certain reference for developing the quality control evaluation method for formulated granules, famous formulae and other terminal products derived from traditional Chinese medicine decoction.
Chromatography, High Pressure Liquid ; Drugs, Chinese Herbal/chemistry* ; Medicine, Chinese Traditional ; Quality Control ; Rhizome

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.