A triple signal amplification method for chemiluminescent detection of the cancer marker microRNA-21.

Mesoporous silica nanospheres (MSNs) are used in a triple signal amplification chemiluminescent (CL) assay for microRNA-21. It is based on (a) the synergistic amplification via loading and controlled-release of signal reagents by MSNs, (b) target recycling amplification, and (c) the enhancement effect of graphene oxide quantum dots (GOQD). CL is generated by the bis(2,4,6-trichlorophenyl) oxalate (TCPO) and H2O2 reaction in the presence of the fluorophore rhodamine B (RB). RB is firstly loaded into the pores of MSNs modified with amino groupsand coupled with ssDNA. Then, the pores are capped by GOQD. Upon the addition of microRNA-21 into the system, the designed ssDNA assumes a double stranded structure. With the aid of duplex-specific nuclease, the double strand structure is cleaved and the free microRNA-21 enters into the next cycling process to combine with other ssDNA forming double strand structures. After several cycling process, amounts of GOQDs departing from the surface of MSNs cause the opening of the pores of MSNs and the release of RB causes the CL of TCPO-H2O2 reaction system. Free GOQDs can lead to a further CL enhancement. By this method, even a low amount of microRNA-21 leads to a large number of released RB molecules and triggers high-intensity CL. The method was applied in an assay where the CL signal increases linearly with the logarithm of the microRNA-21 concentration in the range of 0.005-50 pmol L-1 and the detection limit is 1.7 fmol L-1 (at 3σ). Graphical abstract Schematic presentation of a triple signal amplification chemiluminescence (CL) analysis platform based on rodamine B (RB) loading and controlled release, target recycling amplification and graphene oxide quantum dots (GOQD) as the enhancer for analysis of microRNA-21 in human serum.

Predictive Value of CT Perfusion Imaging on the Basis of Automatic Segmentation Algorithm to Evaluate the Collateral Blood Flow Status on the Outcome of Reperfusion Therapy for Ischemic Stroke.

Our objective was to study the predictive value of CT perfusion imaging based on automatic segmentation algorithm for evaluating collateral blood flow status in the outcome of reperfusion therapy for ischemic stroke. All data of 30 patients with ischemic stroke reperfusion in our hospital were collected and examined by CT perfusion imaging. Convolutional neural network (CNN) algorithm was used to segment perfusion imaging map and evaluate the results. The patients were grouped by regional leptomeningeal collateral score (rLMCs). Binary logistic regression was used to analyze the independent influencing factors of collateral blood flow on brain CT perfusion. The modified Scandinavian Stroke Scale was used to evaluate the prognosis of patients, and the effects of different collateral flow conditions on prognosis were obtained. The accuracy of CNN segmentation image is 62.61%, the sensitivity is 87.42%, the similarity coefficient is 93.76%, and the segmentation result quality is higher. Blood glucose (95% CI = 0.943, P=0.028) and ischemic stroke history (95% CI = 0.855, P=0.003) were independent factors affecting the collateral blood flow status of stroke patients. CBF (95% CI = 0.818, P=0.008) and CBV (95% CI = 0.796, P=0.016) were independent influencing factors of CT perfusion parameters. After 3 weeks of onset, the prognostic function defect score of the good collateral flow group (11.11%) was lower than that of the poor group (41.67%) (P < 0.05). The automatic segmentation algorithm has more accurate segmentation ability for stroke CT perfusion imaging and plays a good auxiliary role in the diagnosis of clinical stroke reperfusion therapy. The collateral blood flow state based on CT perfusion imaging is helpful to predict the treatment outcome of patients with ischemic stroke and further predict the prognosis of patients.