BackgroundFibroblast growth factor receptor 2 (FGFR2) play a vital role in skeletogenesis. However, the molecular mechanisms triggered by FGFR2 in osteoblasts are still not fully understood. In this study, proteomics and bioinformatics analysis were performed to investigate changes in the protein profiles regulated by FGFR2, with the goal of characterizing the molecular mechanisms of FGFR2 function in osteoblasts.MethodsIn this study, FGFR2-overexpression cell line was established using the lentivirus-packaging vector in human osteoblasts (hFOB1.19). Next, the isobaric tags for relative and absolute quantitation (iTRAQ) in combination with the liquid chromatography-tandem mass spectrometry (LC-MS/MS) method was used to compare the proteomic changes between control and FGFR2-overexpression cells. Thresholds (fold-change of ≥ 1.5 and a P-value of < 0.05) were selected to determine differentially expressed proteins (DEPs). The bioinformatics analysis including GO and pathway analysis were done to identify the key pathways underlying the molecular mechanism.ResultsA Total of 149 DEPs was identified. The DEPs mainly located within organelles and involved in protein binding and extracellular regulation of signal transduction. ColI, TNC, FN1 and CDKN1A were strikingly downregulated while UBE2E3, ADNP2 and HSP70 were significantly upregulated in FGFR2-overexpression cells. KEEG analysis suggested the key pathways included cell death, PI3K-Akt signaling, focal adhesion and cell cycle.ConclusionsTo our knowledge, this is the first protomic research to investigate alterations in protein levels and affected pathways in FGFR2-overexpression osteoblasts. Thus, this study not only provides a comprehensive dataset on overall protein changes regulated by FGFR2, but also shed light on its potential molecular mechanism in human osteoblasts.Electronic supplementary materialThe online version of this article (10.1186/s12953-018-0140-x) contains supplementary material, which is available to authorized users.

Under the new trend of industry 4.0 software-defined network, the value of meta heuristic algorithm was explored in the recognition of depression in patients with androgenic alopecia (AGA), and there was an analysis on the effect of comprehensive psychological interventions in the rehabilitation of AGA patients. Based on the meta heuristic algorithm, the Filter and Wrapper algorithms were combined in this study to form a new feature selection algorithm FAW-FS. Then, the classification accuracy of FAW-FS and the ability to identify depression disorders were verified under different open data sets. 54 patients with AGA who went to the Medical Cosmetic Center of Tongji Hospital were selected as the research objects and rolled into a control group (routine psychological intervention) and an intervention group (routine + comprehensive psychological interventions) according to different psychological intervention methods, with 27 cases in each group. The differences of the self-rating anxiety scale (SAS), self-rating depression scale (SDS), Hamilton depression scale (HAMD), and physical, psychological, social, and substance function scores before and after intervention were compared between the two groups of AGA patients, and the depression efficacy and compliance of the two groups were analyzed after intervention. The results showed that the classification accuracy of FAW-FS algorithm was the highest in logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN) algorithm, support vector machine (SVM) algorithm, and random forest (RF) algorithm, which was 80.87, 79.24, 80.42, 83.07, and 81.45%, respectively. The LR algorithm had the highest feature selection accuracy of 82.94%, and the classification accuracy of depression disorder in RF algorithm was up to 73.01%. Besides, the SDS, SAS, and HAMD scores of the intervention group were lower sharply than the scores of the control group (p &lt; 0.05). The physical function, psychological function, social function, and substance function scores of the intervention group were higher markedly than those of the control group (p &lt; 0.05). In addition, the proportions of cured, markedly effective, total effective, full compliance, and total compliance patients in the intervention group increased obviously in contrast to the proportions of the control group (p &lt; 0.05). Therefore, it indicated that the FAW-FS algorithm established in this study had significant advantages in the recognition of depression in AGA patients, and comprehensive psychological intervention had a positive effect in the rehabilitation of depression in AGA patients.

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