Objective To evaluate the value of risk score model based on clinical and multimodal ultrasound features for differentiating benign and malignant breast imaging reporting and data system(BI-RADS)4A lesions in breast.Methods Totally 177 patients with BI-RADS 4A lesions detected by multimodal breast ultrasound were prospectively enrolled,and the patients were divided into training set(n=123)and test set(n=54)at the ratio of 7∶3.Univariate and multivariate logistic regression analyses were used to analyze patients'clinical,gray-scale ultrasound,CDFI and elastography ultrasound parameters of lesions,and the independent risk factors for differentiating benign and malignant BI-RADS 4A lesions in breast were screened,so as a risk score model was constructed.Based on the results of sungical pathology or follow-up,the cut-off value of the model for differentiating benign and malignant BI-RADS 4A lesions in breast was obtained by receiver operating characteristic(ROC)curve,and the patients were divided into high-risk and low-risk subgroups according to the cut-off value,and the efficacy of the model was evaluated.Results Among 177 cases,39 were with benign lesions and 138 were with malignant lesions.Patients'age＞58 years,diameter of lesion＞15.1 mm,lesions with irregular shape,lesions with grade 1 or 2 of blood flow and standard deviation of sound touch elastography of a 2 mm circular area around the lesion(shell)(shell-STESD)＞16.33 kPa were all independent risk factors of malignant BI-RADS 4A lesions in breast.The cut-off value of risk score model for differential diagnosis was 6.5 points,and its sensitivity was 84.69%(83/98)and specificity was 88.00%(22/25)in distinguishing high-risk and low-risk subgroups in training set,while its sensitivity was 77.50%(31/40)and specificity was 71.43%(10/14)in test set.Conclusion Risk score model based on clinical and multimodal ultrasound features could effectively differentiate benign and malignant BI-RADS 4A lesions in breast.

Objective To evaluate the value of risk score model based on clinical and multimodal ultrasound features for differentiating benign and malignant breast imaging reporting and data system(BI-RADS)4A lesions in breast.Methods Totally 177 patients with BI-RADS 4A lesions detected by multimodal breast ultrasound were prospectively enrolled,and the patients were divided into training set(n=123)and test set(n=54)at the ratio of 7∶3.Univariate and multivariate logistic regression analyses were used to analyze patients'clinical,gray-scale ultrasound,CDFI and elastography ultrasound parameters of lesions,and the independent risk factors for differentiating benign and malignant BI-RADS 4A lesions in breast were screened,so as a risk score model was constructed.Based on the results of sungical pathology or follow-up,the cut-off value of the model for differentiating benign and malignant BI-RADS 4A lesions in breast was obtained by receiver operating characteristic(ROC)curve,and the patients were divided into high-risk and low-risk subgroups according to the cut-off value,and the efficacy of the model was evaluated.Results Among 177 cases,39 were with benign lesions and 138 were with malignant lesions.Patients'age＞58 years,diameter of lesion＞15.1 mm,lesions with irregular shape,lesions with grade 1 or 2 of blood flow and standard deviation of sound touch elastography of a 2 mm circular area around the lesion(shell)(shell-STESD)＞16.33 kPa were all independent risk factors of malignant BI-RADS 4A lesions in breast.The cut-off value of risk score model for differential diagnosis was 6.5 points,and its sensitivity was 84.69%(83/98)and specificity was 88.00%(22/25)in distinguishing high-risk and low-risk subgroups in training set,while its sensitivity was 77.50%(31/40)and specificity was 71.43%(10/14)in test set.Conclusion Risk score model based on clinical and multimodal ultrasound features could effectively differentiate benign and malignant BI-RADS 4A lesions in breast.

ObjectiveTo effectively organize the interdisciplinary knowledge of traditional Chinese medicine （TCM） and evidence-based medicine contained in the clinical trial literature of TCM and facilitate the processing and mapping of multi-source data， this paper organized the knowledge of clinical trial literature of TCM by ontology modeling. MethodThe seven-step method and skeleton method were used to develop the ontology. After the structure and language characteristics of TCM clinical trial literature were analyzed， the ontological and non-ontological resources such as top-level framework of Scientific Evidence and Provenance Information Ontology （SEPIO） and TCM language system （TCMLS） were reused to determine the domain concepts and attribute relationship. Finally， the core concepts and attribute relationship such as disease， syndrome， symptom， grouping， intervention measures， outcome indicators and literature quality information were determined. ResultThe information contained in the clinical trial literature of TCM was divided into five categories. According to the Cochrane risk of bias assessment tool and CONSORT 2010 statement， the literature quality evaluation information was mapped to the ontology， and a total of 68 categories， 8 object attributes， and 38 data attributes were established， which basically realized the structured expression of clinical trial literature. ConclusionThe ontology of TCM clinical trial literature constructed in this study can well organize， utilize， and present the construction and association of internal knowledge system in TCM clinical trial literature， underpinning the reasoning of strength of evidence and information of diagnosis and treatment in the future.

Objective:To analyze the medication rules in the ancient book Pu Ji Fang for the external treatment of acne based on data mining method. Methods:By screening out the methods of treating acne externally in Pu Ji Fang and establishing a standardized medical record database, this paper adopted the web version of Ancient and Modern Medical Record Cloud Platform to calculate the frequency, properties, flavors, and meridians of those medicines, and conduct cluster analysis by using IBM SPSS Modeler 18.0 software to analyze the association rules. Results:A total of 87 prescriptions were selected, including 164 kinds of Chinese materia medica, among which. Radix Angelicae, Ligusticum Wallichii, Rhizoma Typhoni and lead powder are frequently appeared. The properties of those medicines are mainly warm, cold and mild; the flavors of those medicines are mainly spicy, acrid, sweet and bitter, and the meridians mainly belongs to lung, spleen, stomach and liver meridians. The medical pair and group with the strongest associationion are Ligusticum Wallichii- Radix Angelicae and Rhizoma Typhonii- Radix Angelicae- Ligusticum Wallichii. Those freuently appeared medicines could be grouped into three categories. The paste dosage that was frequently appeared has strong correlation with tallow, mercury and lead powder, while the powder dosage that was frequenctly appeared has strong correlation with Angelica Dahurica, Radix Saponicae, Gleditsia sinensis, Radices Ligustici Sinensis and Ligusticum Wallichii. Conclusions:The application of data mining method could preliminarily reveal the medication rules of Pu Ji Fang for the external treatment of acne. The main treatment method is XinSanFaYue. The three categories of Chinese materia medica are used to treat the syndrome of asthenic habitus attacked by exogenous pathogenic factors, exterior attacked by wind heat and hot blood stasis respectively, showing the rules of treating acne externally before Ming Dynasty and providing references for the clinical treatment of acne.

Objective:To collect and analyze the evaluation index of infertility treated by Traditional Chinese Medicine (TCM), which will lay a foundation for the establishment of the core index and evaluation index system of TCM treating infertility.Methods:By retriving the published literatures of randomized controlled trials of infertility treated by TCM in the database of CNKI, VIP, Wanfang, SinoMed, PubMed and Cochrane Library from January 1, 2011 to December 31, 2020, and after the screening according to the creteria of inclusion and exclusion, this paper analyzed the normal indexes of infertility and the pathogenic indexes of TCM.Results:A total of 95 RCTs were included, including 9 069 patients aged between 20-39 years old. The average sample size of each RCT study was 95, involving 78 evaluation criteria. The highest frequency of use was the total effective rate, etiological-index analysis found that the general indicators of infertility such as the total effective rate, pregnancy rate, TCM syndrome scores,and common etiological factors such as LH, FSH are the characteristic indicators.Conclusion:There are many problems in the evaluation indexes of clinical trials of treating infertility by TCM, such as great differences in number and not standardized usage. It is necessary to carry out research on the construction of core indicator and evaluation indicator systems of infertility according to different etiologies.

Objective:To anylyze the combination rule of prescriptions containing Cmnamomi Mmulus in the book of Treatise on Typhoid and Miscellaneous Diseases based on tree analysis algorith method. Methods:By collecting prescriptions contain Cmnamomi Mmulus in the book of Treatise on Typhoid and Miscellaneous Diseases, and applying the tree analysis algorithm method on the Ancient and Modern Medical Case Cloud Platform to co-occurrence calculate each layer of the prescriptions, we got the hierarchical tree structure diagram of Cmnamomi Mmulus prescriptions. Results:79 prescriptions containing 96 medicines were included, which appeared 529 times, with 7 different functions. The medicines that are frequently appeared include Cmnamomi Mmulus, Glycyrrhizae Radix Et Rhizoma Praeparata Cum Melle, Zingiberis Rhizoma Recens, Jujubae Fructus, etc. The main effects include relieving the exterior, warming the meridians, warming yang and promoting diuresis. The tree structure diagram of the prescription is divided into seven layers, including the largest items of Cmnamomi Mmulus, Glycyrrhizae Radix Et Rhizoma Praeparata Cum Melle,Zingiberis Rhizoma Recens, Jujubae Fructus, Paeoniae Radix Alba, Ephedrae Herba, Puerariae Lobatae Radix, and the collateral drugs of Poria, Atractylodis macrocephalae rhizoma, Alismatis Rhizoma, Zingiberis Rhizoma, etc. Conclusion:The formula tree analysis algorithm can connect the correlation between drugs in series, and show the relationship between a series of high-frequency co-occurrence drugs in the formula, which can be used for the learning of classics.

After the New Coronary Pneumonia pandemic, people^'s social behavior has changed, and the current health management and development approaches should also change accordingly. This article analyzes the changes of social behavior during different anti-epidemic periods, demonstrates the necessity and possibility to change the current health management formats and development paths, and proposes a new concept of family-centered health management. The main target of the current urgent need for family health management is to expand the household entry units, build learning and sports units, etc. This article provides ideas for health management in the post-pandemic era.

Objective:To analyze the inflammatory mechanism and potential intervention drugs related to angiotensin converting enzyme 2 (ACE2) inhibitory mutations in order to provide reference for the treatment of corona virus disease 2019 (COVID-19).Methods:The data of lung adenocarcinoma with ACE2 mutations were screened from the cancer genome atlas (TCGA) database. The data were analyzed by R program language edgeR package and cluster Profiler package, gene ontology (GO)functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Using String online analysis website for protein-protein interaction (PPI) network analysis, screening out the core genes, and finally using the Epigenomic Precision Medicine Prediction Platform (EpiMed) for multi-group association analysis of key genes, and drug candidates prediction.Results:A total of 1 005 differential genes were obtained, of which 91 were up-regulated and 914 down-regulated. A total of 71 GO were enriched, including 45 items related to biological processes, 16 items related to cell components, and 10 items related to molecular function. A total of 13 KEGG pathways were enriched, mainly in inflammatory pathways, various viral infectious diseases, transcriptional regulation, drug metabolism and protein digestion and absorption pathways. The differentially expressed genes were introduced into String online analysis website for PPI network analysis, a total of 252 proteins were obtained, and 10 core genes were H2A clustered histone 16(HIST1H2AL), H3 clustered histone 2 (HIST1H3B), H3 clustered histone 7 (HIST1H3F), H3 clustered histone 11 (HIST1H3I), H3 clustered histone 3 (HIST1H3C), H2B clustered histone 3 (HIST1H2BB), H2B clustered histone 6 (HIST1H2BI), H4 clustered histone 2 (HIST1H4B), H1-4 linker histone (HIST1H1E), H2A clustered histone 4 (HIST1H2AB). Interferon-α, resveratrol, celecoxib, heartleaf houttuynia herb, weeping forsythia capsule, dexamethasone, Chinese pulsatilla root, tumor necrosis factor-α inhibitors, liquorice root and famciclovir might be drugs for the treatment of ACE2 mutation-related inflammation.Conclusions:Inflammation associated with ACE2 inhibitory mutations is similar to the pathogenesis of COVID-19, which could lead to disease by promoting the activation of inflammatory pathways such as mitogen-activated protein kinase (MAPK), the Janus kinase signal transducer and activator of transcription (JAK/STAT), mammalian target of rapamycin (mTOR). Celecoxib, interferon and resveratrol may have the potential therapeutic effects on COVID-19.

Objective:To investigate the omics mechanism of SARS-related immune injury and predict targeted therapeutic drugs through clinical bioinformatics analysis of the transcriptome data of SARS virus in order to provide reference for clinical treatment of COVID-19.Methods:The transcriptome data of SARA virus were collected from the Gene Expression Oibus (GEO) and used to screen differential genes. Enrichment analysis and protein interaction analysis were performed to investigate the mechanism of immune damage associated with SARS. A platform of epigenetics in precision medicine (EpiMed) was established to predict potential therapeutic drugs.Results:The mechanism of SARS-related immune injury was complex, involving affecting the function of immune cells through signaling pathways such as Toll-like receptors, increasing cytokines in plasma through Th17 signaling pathway and inducing autoimmune responses after autoantibodies were generated by molecules such as IL-6, NF-κB, and TNF. Drugs such as Chuanqiong and Etanercept might have therapeutic effects on SARS-related immune damage.Conclusions:SARS virus could cause abnormal expression of many immune-related molecules and signaling pathways. Drugs such as Chuanqiong and Etanercept might have therapeutic effects on SARS-related immune damage. This study might provide reference for clinical treatment of COVID-19.