In order to explore the colorectal cancer during perioperative period based on the structure model of artificial neural network, and to provide reference for the selection of perioperative nursing measures for patients with colorectal cancer, the artificial neural network models of rectal colon cancer patients with different nutritional interventions based on biomedicine were established. 264 patients with rectal colon cancer and accelerated rehabilitation nutritional nursing group. There were 132 patients in each group who were given different nutritional intervention measures to compare the quality of life after nursing. The results suggested that artificial neural network had a certain value in predicting the impact of different nutritional interventions on patients’ quality of life. In addition, the initial anal ventilation time, initial anal defecation time, infusion time and hospitalization time of patients in accelerated rehabilitation nursing group were shorter than those in routine nursing group, and the incidence of abdominal distension after operation was significantly lower than that of routine nursing group (P < 0.05). The cancer-related fatigue (P < 0.05). To sum up, artificial neural network can adapt to the non-linear effect of the model and has a strong advantage in survival analysis. Accelerated rehabilitation nursing intervention measures can promote the rehabilitation of patients with rectal colon cancer during perioperative period and improve the overall quality of life, which is worthy of clinical reference.

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Result & discussionFluorescence spectroscopy indicates GQDs have size dependent fluorescence emission. TEM and AFM conformed the size of GQDs around 20nm. NMR, Raman Spectroscopy, Gel electrophoresis and TLC data revealed the stable conjugation of GQDs-Protein/Antibody. Cell viability of SK-N-SH and N2a cell lines proved the cellular uptake, lower and targeting affinity of the GQD-conjugates to brain tumor. Future directionIn found that successive conjugation of GQDs-Protein. Which supports the hypothesis of targeted future cancer therapeutics of Glioblastoma.