简介：TanshinoneIIAisapharmacologicallyactivecompoundisolatedfromDanshen(Salviamiltiorrhiza),atraditionalChineseherbalmedicineforthemanagementofcardiacdiseasesandotherdisorders.Butitsunderlyingmolecularmechanismsofactionarestillunclear.ThepresentinvestigationutilizedadataminingapproachbasedonnetworkpharmacologytouncoverthepotentialproteintargetsofTanshinoneIIA.Networkpharmacology,anintegratedmultidisciplinarystudy,incorporatessystemsbiology,networkanalysis,connectivity,redundancy,andpleiotropy,providingpowerfulnewtoolsandinsightsintoelucidatingthefinedetailsofdrug-targetinteractions.Inthepresentstudy,twoseparatedrug-targetnetworksforTanshinoneIIAwereconstructedusingtheAgilentLiteratureSearch(ALS)andSTITCH(searchtoolforinteractionsofchemicals)methods.AnalysisoftheALS-constructednetworkrevealedatargetnetworkwithascale-freetopologyandfivetopnodes(proteintargets)correspondingtoFos,Jun,Src,phosphatidylinositol-4,5-bisphosphate3-kinase,catalyticsubunitalpha(PIK3CA),andmitogen-activatedproteinkinasekinase1(MAP2K1),whereasanalysisoftheSTITCH-constructednetworkrevealedthreetopnodescorrespondingtocytochromeP4503A4(CYP3A4),cytochromeP450A1(CYP1A1),andnuclearfactorkappaB1(NFκB1).Thediscrepancieswereprobablyduetothedifferencesinthedivergentcomputerminingtoolsanddatabasesemployedbythetwomethods.However,itisconceivablethatalleightproteinsmediateimportantbiologicalfunctionsofTanshinoneIIA,contributingtoitsoveralldrug-targetnetwork.Inconclusion,thecurrentresultsmayassistindevelopingacomprehensiveunderstandingofthemolecularmechanismsandsignalingpathwaysofinasimple,compact,andvisualmanner.

简介：AbstractObjectives:Polycystic ovary syndrome (PCOS) is a common endocrine disease in women of childbearing age. Although it is a leading cause of menstrual disorders, infertility, obesity, and other diseases, its molecular mechanism remains unclear. This study aimed to analyze the target genes, pathways, and potential drugs for PCOS through text mining.Methods:First, three different keywords ( "polycystic ovary syndrome", "obesity/adiposis", and "anovulation" ) were uploaded to GenCLiP3 to obtain three different gene sets. We then chose the common genes among these gene sets. Second, we performed gene ontology and signal pathway enrichment analyses of these common genes, followed by protein-protein interaction (PPI) network analysis. Third, the most significant gene module clustered in the protein-protein network was selected to identify potential drugs for PCOS via gene-drug analysis.Results:A total of 4291 genes related to three different keywords were obtained through text mining, 72 common genes were filtered among the three gene sets, and 69 genes participated in PPI network construction, of which 23 genes were clustered in the gene modules. Finally, six of the 23 genes were targeted by 30 existing drugs.Conclusions:The discovery of the six genes (CYP19A1, ESR1, IGF1R, PGR, PTGS2, and VEGFA) and 30 targeted drugs, which are associated with ovarian steroidogenesis (P <0.001), may be used in potential therapeutic strategies for PCOS.

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简介：AbstractPurpose:The triage and initial care of injured patients and a subsequent right level of care is paramount for an overall outcome after traumatic injury. Early recognition of patients is an important case of such decision-making with risk of worse prognosis. This article is to answer if clinical and paraclinical signs can predict the critical conditions of injured patients after traumatic injury resuscitation.Methods:The study included 1107 trauma patients, 16 years and older. The patients were trauma victims of Levels I and II triage and admitted to the Rajaee (Emtiaz) Trauma Hospital, Shiraz, in 2014-2015. The cross-industry process for data mining methodology and modeling was used for assessing the best early clinical and paraclinical variables to predict the patients' prognosis. Five modeling methods including the support vector machine, K-nearest neighbor algorithms, Bagging and Adaboost, and the neural network were compared by some evaluation criteria.Results:Learning algorithms can predict the deterioration of injured patients by monitoring the Bagging and SVM models with 99% accuracy. The most-fitted variables were Glasgow Coma Scale score, base deficit, and diastolic blood pressure especially after initial resuscitation in the algorithms for overall outcome predictions.Conclusion:Data mining could help in triage, initial treatment, and further decision-making for outcome measures in trauma patients. Clinical and paraclinical variables after resuscitation could predict short-term outcomes much better than variables on arrival. With artificial intelligence modeling system, diastolic blood pressure after resuscitation has a greater association with predicting early mortality rather than systolic blood pressure after resuscitation. Artificial intelligence monitoring may have a role in trauma care and should be further investigated.