Objective To observe the histopathological changes of tumor tissue after intraoperative regional chemotherapy of rectal cancer. Methods After the treatment of intraoperative regional chemotherapy with 5-FU (15 mg/kg)， the histopathological changes of tumor tissue were detected. Results Slight changes with cancer cells in all the cases accepted intraoperative regional chemotherapy were found under light microscope such as karyopyknosis, nuclear swelling, coagulation and necrosis of cytoplasm, hydropsia of intercellular substance, invasion of inflammatory cells, and 9/15 cases with slight inflammation of vessels were observed; While those changes were found in individual cells of the cases without regional chemotherapy. The more enlarged intercellular space of cancer cell was observed under electron microscope in the case with regional chemotherapy. Conclusion The intraoperative regional chemotherapy of rectal tumor can change the histopathological appearance of tumor tissue, that is significant in preventing cancer cells diffusing during operation and relapsing after operation.
Objective To observe the drug distributional characteristics after regional arterial perfusion chemotherapy (RAC) during gastric cancer radical resection, postoperative histopathological change and clinical toxic and adverse reactions. Methods According to the indications of RAC, 60 patients admitted in this department from September 2007 to November 2008 were included and divided into treated group and control group randomly. Treated group underwent the treatment of RAC with the 100 ml perfusion fluid including 5-FU (1 000 mg/m2), MMC (10 mg/m2) and 2 ml methylene blue injection by which the control group were not treated. Then the methylene blue distributional characteristics during operation, postoperative histopathological changes of tumors and clinical toxic and adverse reactions were observed. Results In the treated group, after RAC with injection contained methylene blue by primary supply arterial, the tumor region colored immediately and then dropped slowly, but it presented blue during whole operation. After operation, light microscope examination revealed a mild change of karyopyknosis, nuclear swelling, coagulation of cytoplasm in cancer cells, mild hydropsia of intercellular substance, invasion of inflammatory cells and mild vasculitis in some cases. Transmission electron microscope showed that nuclear swelling or coagulation, nuclear heterochromatin agglutination, nuclear-week gap expansion, mitochondrial swelling, endoplasmic reticulum expansion, and Golgi complex expansion. AST of treated group increased apparently on the first day (Plt;0.01), and recovered normal on the third day (Pgt;0.05). There was no significant difference between the two groups in renal function, ALT, ALP, GGT, LDH of liver function, medullary restraining, ECG by bed or reaction of gastrointestinal tract (Pgt;0.05). And stomal leak was not found in two groups. Conclusions The RAC during radical resection of gastric cancer enables gastric tumor to expose to therapeutics during whole operation and depresses the activity of cancer cells. Its clinical toxicity is little, so it can be used as an important supplementary means to prevent intraoperational extension and postoperative recurrence.
Meditation aims to guide individuals into a state of deep calm and focused attention, and in recent years, it has shown promising potential in the field of medical treatment. Numerous studies have demonstrated that electroencephalogram (EEG) patterns change during meditation, suggesting the feasibility of using deep learning techniques to monitor meditation states. However, significant inter-subject differences in EEG signals poses challenges to the performance of such monitoring systems. To address this issue, this study proposed a novel model—calibrated multi-source adversarial adaptation network (CMAAN). The model first trained multiple domain-adversarial neural networks in a pairwise manner between various source-domain individuals and the target-domain individual. These networks were then integrated through a calibration process using a small amount of labeled data from the target domain to enhance performance. We evaluated the proposed model on an EEG dataset collected from 18 subjects undergoing methamphetamine rehabilitation. The model achieved a classification accuracy of 73.09%. Additionally, based on the learned model, we analyzed the key EEG frequency bands and brain regions involved in the meditation process. The proposed multi-source domain adaptation framework improves both the performance and robustness of EEG-based meditation monitoring and holds great promise for applications in biomedical informatics and clinical practice.