Objective To investigate the status quo of application of fall risk factors assessment scale in nurses of different position in geriatric ward . Method Eighty-six nurses in different position were investigated by fall risk factors assessment scale . Results About 89 . 5%of the nurses could assess the fall risk factors on time and 80 . 2%could do it accurately , and only 62 . 8%of them worked out their nursing orders based on the possible falls. In terms of the accuracy in using fall risk factors assessment, the primary nurses was poorer than the senior nurses, with statistically significant difference between them (P<0.05). Yet there were no significant differences between them in timeliness and pertinence at working out nursing orders (P>0.05). Conclusion We should strengthen the training to the clinical nurses in correctly using the fall assessment scale , in order to exert the diagnostic value of the fall assessment scale, reduce the incidence of falls and ensure the safety of the patients.

Objective:To develop an artificial intelligence (AI) quality control model of fetal heart in the first trimester and verify its effectiveness.Methods:A total of 18 694 images of the four-chamber view(4CV) and three-vessel and tracheal view(3VT) of fetal heart in the first trimester were selected from Shenzhen Maternal and Child Health Hospital Affiliated to Southern Medical University since January 2022 to December 2022. A total of 14 432 images were manually annotated. The one-stage target detection algorithm YOLO V5 was used to train the AI quality control model in the first trimester of fetal heart, and 4 262 images (golden standard set by expert group) were used to evaluate the application effectiveness of AI quality control model. Kappa consistency test was used to compare the results of section classification and standard degree judgment from AI quality control model, Doctor 1(D1) and Doctor 2(D2).Results:①Precision of the AI quality control model was 0.895, recall was 0.852, mean average precision (mAP 50) was 0.873.The average precision(AP) of the AI quality control model for section classification was 0.907 (4CV) and 0.989 (3VT), respectively. ②Compared with the gold standard, the overall coincidence rate and consistency of section classification of AI quality control model, D1 and D2 were 99.91% (Kappa=0.998), 100% (Kappa=1.000), 100% (Kappa=1.000), respectively. The coincidence rate and consistency of the plane standard degree evaluation from the AI quality control model, D1 and D2 were 97.46% (Weighted Kappa=0.932), 93.73% (Weighted Kappa=0.847), and 93.12% (Weighted Kappa=0.832), respectively. Strong consistency was displayed. Moreover, AI quality control model showed the highest coincidence rate and the strongest consistency in judging section standard degree, which was superior to manual quality control. The time-consuming of AI quality control (0.012 s/sheet) was significantly less than the way of manual quality control (4.76-6.11 s/sheet)( Z=-8.079, P<0.001). Conclusions:The use of artificial intelligent fetal heart quality control model in the first trimester can effectively and accurately control the image quality.

OBJECTIVE: To explore the exact mechanisms of programmed cell death (PCD) induced by Type II anti-CD20 mAb in CD20+ non-Hodgkin lymphoma (NHL) cells, and to provide theoretical basis for anti-tumor ability of new CD20 mAb.
 METHODS: After incubation with Rituximab (a Type I anti-CD20 mAb) and Tositumomab (a Type II anti-CD20 mAb), Raji cells were stained by annexin V & propidium iodide (PI). The ratio of programmed death cells were measured by two channel flow cytometry (FCM). Before the treatment of anti-CD20 mAbs, Raji cells was incubated with a caspase inhibitor carbobenzoxy-valyl-alanyl-aspartyl-[O-methyl]- fluoromethylketone (Z-VAD-FMK) and a dihydroceramide synthase inhibitor fumonisin B1 (FB1) for 30 minutes to assess their inhibitory effect on PCD. High performance liquid chromatography (HPLC) was utilized to compare the ratio of programmed death cells between the pretreatment group (treated by Rituximab and Tositumomab) and the non-pretreatment group. The anti-CD20 mAbs-treated Raji cells were collected, and the ceramide levels in the Raji cells in the different pretreatment groups were also examined by HPLC, and the inhibitory effect of FB1 on the changes of ceramide levels in the Raji cells was measured. The Raji cells were incubated with different concentration C2-ceramide, C2-Ceramide-induced PCD was also evaluated by annexin V & PI staining after 16 hours. 
 RESULTS: Tositumomab (10 µg/mL) but not Rituximab (10 µg/mL) can induce significant PCD (28.6±4.2)% in Raji cells, with significant difference (t=26.48, P<0.01), which cannot be blocked by Z-VAD-FMK with a concentration range from 10 to 30 µmol/L (F=3.01, P>0.05). The cellular ceramide levels in Raji cells were significantly elevated after the treatment of Tositumomab (t=28.48, P<0.01). C2-ceramide can significantly induce PCD in Raji cells in a dose-dependent manner with a concentration range from 5 to 40 µmol/L (F=2.71, P>0.05). The dihydroceramide synthase inhibitor FB1 can significantly inhibit the elevated cellular ceramide levels (F=20.18, P<0.01) and cell programmed death induced by Tositumomab (F=17.02, P<0.01).
 CONCLUSION Type II but not Type I anti-CD20 mAbs can induce caspase independent PCD in CD20+ NHL cells through the elevation of cellular ceramide levels. The PCD is not associated with classic caspase pathway.
Amino Acid Chloromethyl Ketones ; Apoptosis ; drug effects ; Cell Line, Tumor ; drug effects ; Humans ; Lymphoma, Non-Hodgkin ; Rituximab ; pharmacology ; Sphingosine ; analogs & derivatives ; pharmacology