Objective To investigate natural killer(NK) and NKT cells in acute pancreatitis(AP).Methods Changes of NK and NKT cells in peripheral blood of 86 AP cases were detected using muhiparameter flow cytometry.Results Compared with control group,the NKT cells decreased in AP patients (t =5.23,P =0.00),but NK cells didn't (t =-1.15,P =0.25).NKT cells in severe SAP and mnoderate MAP were lower than that in the control group (t =-3.92,P =0.00;t =4.84,P =0.00).There was no statistically significant difference of NK cells between MAP and the controls (t =-0.54,P =0.59),but NK cells in SAP group was obviously higher than that in control group (t =3.12,P =0.00).After one week treatment,NK cells significantly decreased (t =8.43,P =0.00).NKT cells were higher than control group (t =-4.44,P =0.00).Dynamic monitoring in AP patients found continuous declination in NK cells,and NKT cells experienced an increase before a falling.Conclusion Monitoring of NK and NKT cells can be used as an important index for the severity and response to treatment in acute pancreatitis.

Data quality management system is essential to ensure accurate, complete, consistent, and reliable data collection in clinical research. This paper is devoted to various choices of data quality metrics. They are categorized by study status, e.g. study start up, conduct, and close-out. In each category, metrics for different purposes are listed according to ALCOA+ principles such us completeness, accuracy, timeliness, traceability, etc. Some general quality metrics frequently used are also introduced. This paper contains detail information as much as possible to each metric by providing definition, purpose, evaluation, referenced benchmark, and recommended targets in favor of real practice. It is important that sponsors and data management service providers establish a robust integrated clinical trial data quality management system to ensure sustainable high quality of clinical trial deliverables. It will also support enterprise level of data evaluation and bench marking the quality of data across projects, sponsors, data management service providers by using objective metrics from the real clinical trials. We hope this will be a significant input to accelerate the improvement of clinical trial data quality in the industry.

Objective:To develop and validate a nomogram model for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on preoperative enhanced computed tomography imaging features and clinical data.Methods:The clinical data of 210 patients with HCC undergoing surgery in the Second Affiliated Hospital of Anhui Medical University from May 2018 to May 2022 were retrospectively analyzed, including 172 males and 38 females, aged (59±10) years old. Patients were randomly divided into the training group ( n=147) and validation group ( n=63) by systematic sampling at a ratio of 7∶3. Preoperative enhanced computed tomography imaging features and clinical data of the patients were collected. Logistic regression was conducted to analyze the risk factors for HCC with MVI, and a nomogram model containing the risk factors was established and validated. The diagnostic efficacy of predicting MVI status in patients with HCC was assessed by receiver operating characteristic (ROC) curve, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) of the subjects in the training and validation groups. Results:The results of multifactorial analysis showed that alpha fetoprotein ≥400 μg/ml, intra-tumor necrosis, tumor length diameter ≥3 cm, unclear tumor border, and subfoci around the tumor were independent risk factors predicting MVI in HCC. A nomogram model was established based on the above factors, in which the area under the curve (AUC) of ROC were 0.866 (95% CI: 0.807-0.924) and 0.834 (95% CI: 0.729-0.939) in the training and validation groups, respectively. The DCA results showed that the predictive model thresholds when the net return is >0 ranging from 7% to 93% and 12% to 87% in the training and validation groups, respectively. The CIC results showed that the group of patients with predictive MVI by the nomogram model are highly matched with the group of patients with confirmed MVI. Conclusion:The nomogram model based on the imaging features and clinical data could predict the MVI in HCC patients prior to surgery.

Objective : To study the relationship between the expression of eIF4F complex and human colorectal cancer (CRC), and the possible mechanism of sulforaphane on the proliferation of CRC cells. Methods : Immunohistochemical staining was used to detect the expression of eIF4F complex related proteins in tumor and adjacent tissues of 12 colorectal cancer patients. MTT colorimetry was used to detect the cell activity of colorectal cancer cells treated with sulforaphane. Western blot was used to detect the expression of eIF4F complex related proteins in colorectal cancer cells treated with sulforaphane. In the animal experiment ( tumor formation in nude mice), the growth of subcutaneous tumors in nude mice after intraperitoneal injection of sulforaphane was compared, and the expressions of PI3K/AKT/mTOR/4EBP1 signal pathway related proteins and eIF4F complex related proteins were detected by immunohistochemical staining. Results : In the specimens of CRC patients, the expression of eIF4F complex related protein in tumor tissues was significantly higher than that in paracancerous tissues. SFN could inhibit the proliferation of CRC cells, and the higher the concentration, the stronger the inhibitory ability. SFN could inhibit the growth and development of CRC cells and down⁃regulate the expression of eIF4F complex in CRC cells. Conclusion The expression of eIF4F complex is closely related to the development of colorectal cancer. Sulforaphane may affect the up⁃regulation of eIF4F complex through PI3K/AKT/mTOR/4EBP1 signal pathway, thus affecting the development of colorectal cancer cells.