Raman-Enhanced Spectroscopy Distinguishes Anal Squamous Intraepithelial Lesions in Human Immunodeficiency Virus-Serodiscordant Couples

Based on biomedical index data: Risk prediction model for prostate cancer

ABSTRACT

To explore the influencing factors of prostate cancer occurrence, set up risk prediction model, require reference for the preliminary diagnosis of clinical doctors, this model searched database through the data of prostate cancer patients and prostate hyperplasia patients National Clinical Medical Science Data Center.With the help of Stata SE 12.0 and SPSS 25.0 software, the biases between groups were balanced by propensity score matching. Based on the matched data, the relevant factors were further screened by stepwise logistic regression analysis, the key variable and artificial neural network model are established. The prediction accuracy of the model is evaluated by combining the probability of test set with the area under receiver operating characteristic curve (ROC).After 1:2 PSM, 339 pairs were matched successfully. There are 159 cases in testing groups and 407 cases in training groups. And the regression model was P = 1 / (1 + e (0.122 ∗ age + 0.083 ∗ Apo lipoprotein C3 + 0.371 ∗ total prostate specific antigen (tPSA) -0.227 ∗ Apo lipoprotein C2-6.093 ∗ free calcium (iCa) + 0.428 ∗ Apo lipoprotein E-1.246 ∗ triglyceride-1.919 ∗ HDL cholesterol + 0.083 ∗ creatine kinase isoenzyme [CKMB])). The logistic regression model performed very well (ROC, 0.963; 95% confidence interval, 0.951 to 0.978) and artificial neural network model (ROC, 0.983; 95% confidence interval, 0.964 to 0.997). High degree of Apo lipoprotein E (Apo E) (Odds Ratio, [OR], 1.535) in blood test is a risk factor and high triglyceride (TG) (OR, 0.288) is a protective factor.It takes the biochemical examination of the case as variables to establish a risk prediction model, which can initially reflect the risk of prostate cancer and bring some references for diagnosis and treatment.

TriBAFF-CAR-T cells eliminate B-cell malignancies with BAFFR-expression and CD19 antigen loss

Background

To investigate the effect of TriBAFF-CAR-T cells on hematological tumor cells.

Methods

TriBAFF-CAR-T and CD19-CAR-T cells were co-cultured with BAFFR-bearing B-cell malignancies at different effector/target ratios to evaluate the anti-tumor effects. In vivo, TriBAFF-CAR-T and CD19-CAR-T cells were intravenously injected into Raji-luciferase xenograft mice. CD19 antigens losing lymphoblasts was simulated by Raji knocking out CD19 (CD19^KO) to investigate the effect of TriBAFF-CAR-T cells on CD19^KO Raji.

Results

Both TriBAFF-CAR-T and CD19-CAR-T cells significantly induced the lysis of Raji, BALL-1, and Jeko-1. Moreover, when CD19-CAR-T cells specifically caused the lysis of K562 with overexpressed CD19, the lethal effect of TriBAFF-CAR-T cells was also specific for BAFFR-bearing K562 with increasing levels of interleukin-2 and INF-γ. The TriBAFF-CAR-T have the same effect with CD19-CAR-T cells in treating Raji xenofraft mice. TriBAFF-CAR-T cells also have great effect in CD19^KO Raji cells.

Conclusions

In this study, we successfully constructed novel TriBAFF-CAR-T cells to eliminate BAFFR-bearing and CD19 antigen loss in hematological tumor cells.