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Development and application of intelligent follow-up platform for colostomy patients after colorectal cancer surgery

ABSTRACT: Interventions: control group:Telephone follow-up;intervention group 1:Use the intelligent follow-up mobile phone APP platform;intervention group 2:Use the mobile APP and turn off the automatic measurement of stoma Primary outcome(s): Complications of enterostomy;self-efficacy;anxiety;depression;sleep;caregiver burden Study Design: Parallel

DISEASE(S): Colostomy Patients After Colorectal Cancer Surgery

PROVIDER: 2683793 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Project description:BACKGROUND AND AIMS: The increasing incidence of cardiovascular diseases has created an urgent need for safe and effective anti-thrombotic agents. Leech, as a traditional Chinese medicine, has the effect of promoting blood circulation and removing blood stasis, but its real material basis and mechanism of action for the treatment of diseases such as blood stasis and thrombosis have not been reported.METHODS: In this study, Whitmania Pigra Whitman (WPW), Hirudo nipponica Whitman (HNW) and Whitmania acranutata Whitman (WAW) were hydrolyzed by biomimetic enzymatic hydrolysis to obtain the active peptides of WPW (APP), the active peptides of HNW (APH) and the active peptides of WAW (APA), respectively. Then their structures were characterized by sykam amino acid analyzer, fourier transform infrared spectrometer (FT-IR), circular dichroism (CD) spectrometer and LC-MS. Next, the anti-thrombotic activities of APP, APH and APA were determined by carrageenan-induced tail vein thrombosis model in mice, and the anti-thrombotic mechanisms of high-dose APP group (HAPP), high-dose APH group (HAPH) and high-dose APA group (HAPA) were explored based on UHPLC-Q-Exactive Orbitrap mass spectrometry.RESULTS: The results showed that the amino acid composition of APP, APH and APA was consistent, and the proportion of each amino acid was few different. The results of FT-IR and CD showed that there were no significant differences in the proportion of secondary structures (such as β-sheet and random coil) and infrared absorption peaks between APP, APH and APA. Mass spectrometry data showed that there were 43 common peptides in APP, APH and APA, indicating that the three have common material basis. APP, APH and APA could significantly inhibit platelet aggregation, reduce black-tail length, whole blood viscosity (WBV), plasma viscosity (PV), and Fibrinogen (FIB), and prolong coagulation time, including activated partial thrombin time (APTT), prothrombin time (PT) and thrombin time (TT). In addition, 24 metabolites were identified as potential biomarkers associated with thrombosis development. Among these, 19, 23, and 20 metabolites were significantly normalized after administration of HAPP, HAPH, and HAPA in the mice, respectively. Furthermore, the intervention mechanism of HAPP, HAPH and HAPA on tail vein thrombosis mainly involved in linoleic acid metabolism, primary bile acid biosynthesis and ether lipid metabolism.CONCLUSION: Our findings suggest that APP, APH and APA can exert their anti-blood stasis and anti-thrombotic activities by interfering with disordered metabolic pathways in vivo, and there is no significant difference in their efficacies.

Project description:BackgroundAbout 30% of cell lines have been cellular cross-contaminated and misidentification, which can result in invalidated experimental results and unusable therapeutic products. Cell morphology under the microscope was observed routinely, and further DNA sequencing analysis was performed periodically to verify cell line identity, but the sequencing analysis was costly, time-consuming, and labor intensive. The purpose of this study was to construct a novel artificial intelligence (AI) technology for "cell face" recognition, in which can predict DNA-level identification labels only using cell images.MethodsSeven commonly used cell lines were cultured and co-cultured in pairs (totally 8 categories) to simulated the situation of pure and cross-contaminated cells. The microscopy images were obtained and labeled of cell types by the result of short tandem repeat profiling. About 2 million patch images were used for model training and testing. AlexNet was used to demonstrate the effectiveness of convolutional neural network (CNN) in cell classification. To further improve the feasibility of detecting cross-contamination, the bilinear network for fine-grained identification was constructed. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the cell semantic segmentation was conducted by DilatedNet.ResultsThe cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%.ConclusionsThe deep CNN model proposed in this study has the ability to recognize small differences in cell morphology, and achieved high classification accuracy.

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Development and application of intelligent follow-up platform for colostomy patients after colorectal cancer surgery

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