Objective:To explore potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods:The prospective multicenter study was conducted in Zhejiang, China from May 1 st, 2019 to January 31 st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results:A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95% CI 0.593-0.771, P<0.01). Conclusion:In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.

Purpose/Significance To explore the research status,research hotspots and frontiers in the field of clinical decision sup-port based on electronic medical records(EMR).Method/Process The bibliometric method and CiteSpace 6.2.R2 software are used to draw scientific knowledge graph of country/region distribution,author cooperation,institutional cooperation,keyword co-occurrence and clustering for visualized comparative analysis.Python is used for clustering hotspot mining and analysis.Result/Conclusion The field of clinical decision support based on EMR data shows a rapid development trend,with the United States and China as the main research countries and strong cooperation between domestic and foreign institutions.The keywords mainly involve EMR,artificial intelligence(AI),etc.

The article analyzes chief physician SUN Wuquan's empirical characteristics in treating neck-type cervical spondylosis:disease differentiation combined with pattern differentiation,emphasizing the assessment of tendons and bones,with DING's Tuina(Chinese therapeutic massage)manipulations and static Gongfa(Qigong exercise)as the predominant treatment,inherits the academic features of DING's Tuina school,"paying equal attention to tendons and bones,putting function first";thus provides a reference for treating neck-type cervical spondylosis with Tuina therapy.

Objective:To evaluate the performance of an artificial intelligent (AI)-based automated digital cell morphology analyzer (hereinafter referred as AI morphology analyzer) in detecting peripheral white blood cells (WBCs).Methods:A multi-center study. 1. A total of 3010 venous blood samples were collected from 11 tertiary hospitals nationwide, and 14 types of WBCs were analyzed with the AI morphology analyzers. The pre-classification results were compared with the post-classification results reviewed by senior morphological experts in evaluate the accuracy, sensitivity, specificity, and agreement of the AI morphology analyzers on the WBC pre-classification. 2. 400 blood samples (no less than 50% of the samples with abnormal WBCs after pre-classification and manual review) were selected from 3 010 samples, and the morphologists conducted manual microscopic examinations to differentiate different types of WBCs. The correlation between the post-classification and the manual microscopic examination results was analyzed. 3. Blood samples of patients diagnosed with lymphoma, acute lymphoblastic leukemia, acute myeloid leukemia, myelodysplastic syndrome, or myeloproliferative neoplasms were selected from the 3 010 blood samples. The performance of the AI morphology analyzers in these five hematological malignancies was evaluated by comparing the pre-classification and post-classification results. Cohen′s kappa test was used to analyze the consistency of WBC pre-classification and expert audit results, and Passing-Bablock regression analysis was used for comparison test, and accuracy, sensitivity, specificity, and agreement were calculated according to the formula.Results:1. AI morphology analyzers can pre-classify 14 types of WBCs and nucleated red blood cells. Compared with the post-classification results reviewed by senior morphological experts, the pre-classification accuracy of total WBCs reached 97.97%, of which the pre-classification accuracies of normal WBCs and abnormal WBCs were more than 96% and 87%, respectively. 2. The post-classification results reviewed by senior morphological experts correlated well with the manual differential results for all types of WBCs and nucleated red blood cells (neutrophils, lymphocytes, monocytes, eosinophils, basophils, immature granulocytes, blast cells, nucleated erythrocytes and malignant cells r>0.90 respectively, reactive lymphocytes r=0.85). With reference, the positive smear of abnormal cell types defined by The International Consensus Group for Hematology, the AI morphology analyzer has the similar screening ability for abnormal WBC samples as the manual microscopic examination. 3. For the blood samples with malignant hematologic diseases, the AI morphology analyzers showed accuracies higher than 84% on blast cells pre-classification, and the sensitivities were higher than 94%. In acute myeloid leukemia, the sensitivity of abnormal promyelocytes pre-classification exceeded 95%. Conclusion:The AI morphology analyzer showed high pre-classification accuracies and sensitivities on all types of leukocytes in peripheral blood when comparing with the post-classification results reviewed by experts. The post-classification results also showed a good correlation with the manual differential results. The AI morphology analyzer provides an efficient adjunctive white blood cell detection method for screening malignant hematological diseases.

Objective:To survey the training requirements for doctoral candidates in general practice.Methods:A questionnaire survey was conducted to investigate the requirements for general practice doctorate training; the items included the training target, training objectives and training contents. A total of 130 stakeholders of general practice participated in the survey, including administrators in health committees, general practice educators of medical schools, general practitioners in hospital and community health service centers and students in general medicine. The participants were divided into training unit group ( n=42), employer group ( n=42) and doctor and student group ( n=46). Results:Among all training objectives, the training "general medicine scientific research ability" ranked the first (121, 93.1%); while 114 (87.7%) respondents believed that the training target should be the leading talents in general medicine. Regarding the content of clinical competence training, 86 (66.1%) respondents believed that there was a lack of medical care training for whole life-cycle and whole population currently. The training requirement for acute disease management and referral competence in employer group was significantly higher than the training unit group (χ2=25.73, P<0.01) and the doctor and student group (χ2=24.37, P<0.01). Most survey respondents believed that the doctorate candidates in general medicine should focus on scientific research training in community clinical medicine ( n=110, 84.6%), community preventive medicine and epidemiology ( n=114, 87.7%); 117 (90.0%) respondents believed that clinical research design and methodology training should be strengthened, and 125 (96.2%) respondents believed that clinical teaching thinking training should be strengthened; 108 (83.1%) survey respondents believed that it is necessary for their units to recruit personnel with doctoral degree in general medicine. Conclusion:The training of doctorate candidates in general medicine should aim at cultivating leading talents in general medicine, strengthen the training of clinical scientific research capabilities, and focus on cultivating their comprehensive abilities of "being able to research, be good at teaching, and understand management", so that they can truly become the leaders in this discipline.

Objective:To assess the effect of serum heat inactivation on the detection of 2019 novel coronavirus (2019-nCoV) specific IgM and IgG antibodies by colloidal gold method.Methods:The serum specimens were collected from a total of 106 Coronavirus Disease 2019 (COVID-19) patients and 52 control subjects. Both the fresh serum and the heat inactivated serum samples from the same patient were detected simultaneously with the 2019-nCoV IgM and IgG antibodies detection kit (colloidal gold method). According to the patient′s onset time, the positive rates of antibodies production profile were calculated. The influence of heat inactivation on the detection rates of antibodies at different stages of disease after onset was analyzed.Results:The test results of the specimens of the healthy control group before and after inactivation were all negative. For the 106 specimens of COVID-19 patients, the detection rates of 2019-nCoV specific IgM and IgG antibodies were reduced after heating at 56 ℃ for 30 min. The positive rates of IgM antibodies significantly decreased from 66.04% (70/106) to 43.40% (46/106) ( χ2=22.042, P=0.000), while the positive rates of IgG antibodies slightly decreased from 81.13% (86/106) to 76.42% (81/106) ( χ2=0.800, P=0.063). Further analysis revealed that there was a significant difference in the positive rates of IgM antibodies before and after heat inactivation in the 3rd, 5th and 6th week after onset. However, there was no statistically significant difference in the detection rates of IgG antibodies before and after serum heat inactivation in different periods of onset. Conclusions:Heat inactivation significantly decreased the detection rates of 2019-nCoV specific IgM antibodies, which may lead to serological false negative results.

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

Objective:To investigate the etiology of necrotizing pneumonia (NP) in children and the clinical characteristics of NP caused by different pathogens in China.Methods:A retrospective, case-control study was performed in children with NP who were admitted to 13 hospitals in China from January 2008 to December 2019. The demographic and clinical information, laboratory data, etiological and radiological findings were analyzed. The data were divided into three groups based on the following years: 2008-2011, 2012-2015 and 2016-2019, and the distribution characteristics of the pathogens in different period were compared. Meanwhile, the pathogens of pediatric NP in the southern and northern China were compared. And the clinical characteristics of the Mycoplasma pneumoniae (MP) NP and the bacterial NP were also compared. T-test or Mann-Whitney nonparametric test was used for comparison of numerical variables, and χ 2 test was used for categorical variables. Results:A total of 494 children with NP were enrolled, the median ages were 4.7 (0.1-15.3) years, including 272 boys and 222 girls. Among these patients, pathogens were identified in 347 cases and the pathogen was unclear in the remaining 147 cases. The main pathogens were MP (238 cases), Streptococcus pneumoniae (SP) (61 cases), Staphylococcus aureus (SA) (51 cases), Pseudomonas aeruginosa (13 cases), Haemophilus influenzae (10 cases), adenovirus (10 cases), and influenza virus A (7 cases), respectively. MP was the most common pathogen in all three periods and the proportion increased yearly. The proportion of MP in 2016-2019 was significantly higher than that in 2012-2015 (52.1% (197/378) vs. 36.8% (32/87), χ 2= 6.654, P=0.010), while there was no significant difference in the proportion of MP in 2012-2015 and that in 2008-2011 (36.8% (32/87) vs. 31.0% (9/29), χ2=0.314, P=0.575).Regarding the regional distribution, 342 cases were in the southern China and 152 in the northern China. Also, MP was the most common pathogen in both regions, but the proportion of MP was higher and the proportion of SP was lower in the north than those in the south (60.5% (92/152) vs. 42.7% (146/342), χ 2=13.409, P<0.010; 7.9% (12/152) vs. 14.3% (49/342), χ 2= 4.023, P=0.045). Comparing the clinical characteristics of different pathogens, we found that fever and cough were the common symptoms in both single MP and single bacterial groups, but chest pain was more common (17.0% (34/200) vs. 6.1% (6/98), χ 2=6.697, P=0.010) while shortness of breath and wheezing were less common in MP group (16.0% (32/200) vs. 60.2% (59/98), χ 2=60.688, P<0.01; 4.5% (9/200) vs. 21.4% (21/98), χ 2=20.819, P<0.01, respectively). The white blood cell count, C-reactive protein and procalcitonin in the bacterial group were significantly higher than those in the MP group (14.7 (1.0-67.1)×10 9/L vs. 10.5 (2.5-32.2)×10 9/L, 122.5 (0.5-277.3) mg/L vs. 51.4 (0.5-200.0) g/L, 2.13 (0.05-100.00) μg/L vs. 0.24 (0.01-18.85) μg/L, Z=-3.719, -5.901 and -7.765, all P<0.01). Conclusions:The prevalence of pediatric NP in China shows an increasing trend during the past years. MP, SP and SA are the main pathogens of NP, and the most common clinical symptoms are fever and cough. The WBC count, C-reactive protein and procalcitonin in bacterial NP are significantly higher than those caused by MP.

Background: Endoscopic screening is an effective approach for detecting early gastric cancer. The interval of endoscopic surveillance should be defined based on the risk stratification of gastric cancer, so as to improve the screening efficiency. Aims: To investigate whether gastric cancer risk could be stratified according to endoscopic atrophic gastritis grading. Methods: Subjects who underwent gastroscopy at least two times between Jan. 2015 and Dec. 2019 at the 900th Hospital of Joint Logistics Support Force, PLA were enrolled in a retrospective study. The demographic data and information on endoscopy were recorded. The extent and degree of gastric atrophy under endoscopy was graded using Kimura-Takemoto classification system. Then the subjects were allocated into mild group (none atrophy and C-1), moderate group (C-2 and C-3) and severe group (O-1, O-2 and O-3) based on the grading. The correlation of endoscopic grading of atrophy with the risk of gastric cancer was analyzed. Results: A total of 8 736 subjects were enrolled, 4 154 were in mild group (47.6%), 2 409 in moderate group (27.6%), and 2 173 in severe group (24.9%). The mean endoscopic follow-up time was (1 052±643) d, and the mean endoscopic surveillance interval was (518±271) d. The overall coincidence rate of endoscopic diagnosis and pathological diagnosis for atrophy was 88.9%. During the follow-up period, gastric cancer was detected in 41 cases; the detection rates of mild group, moderate group and severe group were 0.07%, 0.54% and 1.15%, respectively (P<0.001). Conclusions: The risk of gastric cancer can be stratified according to the endoscopic atrophic gastritis grading, which is helpful for the decision of individualized endoscopic surveillance interval.

Objective To evaluate the influence of hematocrit(HCT),vitamin C(Vc)and galactose on three portable blood glucose meters to provide some reference for clinical selection of appropriate blood glucose me-ter.Methods 20 heparin anticoagulant venous blood samples in the clinical laboratory department of Nanfang Hospital were selected for verifying the accuracy of blood glucose meter.2 healthy volunteers were selected for collecting 5 w hole blood samples in interferent test.Referring to the detection results of Roche automatic bio-chemical analyzer,the Nova StatStrip Xpress glucometer,Bayer Contour Plus glucometer and Roche Accu-chek Performa blood glucose meter were performed the accuracy verification.The influence of HCT,Vc and galac-tose on the detection results of above three portable blood glucose meters were evaluated.Results The detec-tion accuracy of Nova StatStrip Xpress and the Roche Accu-Chek glucometers all conformed to the require-ments of ISO15197:2013 standards.The detection accuracy of Bayer Contour Plus glucometer only conformed to the requirements of ISO15197:2003 standards.Under the interference of different levels of HCT,Vc and galactose,the detection results of Nova StatStrip Xpress glucometer conformed to the requirements of ISO15197:2013 standards;w hen detecting low concentration of blood glucose,the Bayer Contour Plus glucom-eter was interfered by 10 mg/dL Vc,and other detection results conformed to the requirements of ISO15197:2003 standards;the anti-interference performance of Roche Accu-chek Performa glucometer conformed to the