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Herein, α-cyanostilbene-based luminogen with an electron donor-π-electron acceptor (D-π-A) architecture was formylated into the salicylaldehyde-analogue luminogen, followed by the Schiff base reaction with phenylamine, a red-emitting luminogen was elaborately designed and successfully synthesized in a high yield of 89%. Its well-defined structure was confirmed by FT-IR, MALDI-TOF-MS, HR-MS and 1H/13C NMR technologies. Based on the synergistic mechanisms of aggregation-induced emission (AIE), excited-state intramolecular proton transfer (ESIPT) and intramolecular charge transfer (ICT), it enjoyed a red-fluorescence emission at 627 nm in THF/water mixtures (fw = 95%) and was used as a probe. Moreover, the TLC-based test strips loaded with the probe not only exhibited the reversible fluorescence response to amine/acid vapor but also showed sensitive and selective fluorescence response towards Cu2+. Furthermore, the fluorescence titration experiment between the probe and Cu2+ in THF/water mixtures (fw = 95%, pH = 7.4) revealed that the detection limit was 1.18 × 10-7 M and the binding constant was 1.59 × 105. Job's plot experiment and HR-MS analysis revealed the 2:1 binding stoichiometry of the probe with Cu2+. The method enabled real-time assessment for Cu2+ in real water samples. This study could offer insightful opinions on the development of long-wavelength emissive luminogens based on α-cyanostilbene. [ABSTRACT FROM AUTHOR]

Background: Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique. Methods: We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model. Results: Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it. Conclusion: Our model can be used to predict the rupture risk of MCA aneurysm. [ABSTRACT FROM AUTHOR]

Objectives: Anterior communicating artery (ACOM) aneurysms are the most common intracranial aneurysms, and predicting their rupture risk is challenging. We aimed to predict this risk using a two-layer feed-forward artificial neural network (ANN).Materials and Method: 594 ACOM aneurysms, 54 unruptured and 540 ruptured, were reviewed. A two-layer feed-forward ANN was designed for ACOM aneurysm rupture-risk analysis. To improve ANN efficiency, an adaptive synthetic (ADASYN) sampling approach was applied to generate more synthetic data for unruptured aneurysms. Seventeen parameters (13 morphological parameters of ACOM aneurysm measured from these patients' CT angiography (CTA) images, two demographic factors, and hypertension and smoking histories) were adopted as ANN input.Results: Age, vessel size, aneurysm height, perpendicular height, aneurysm neck size, aspect ratio, size ratio, aneurysm angle, vessel angle, aneurysm projection, A1 segment configuration, aneurysm lobulations and hypertension were significantly different between the ruptured and unruptured groups. Areas under the ROC curve for training, validating, testing and overall data sets were 0.953, 0.937, 0.928 and 0.950, respectively. Overall prediction accuracy for raw 594 samples was 94.8 %.Conclusion: This ANN presents good performance and offers a valuable tool for prediction of rupture risk in ACOM aneurysms, which may facilitate management of unruptured ACOM aneurysms.Key Points: • A feed-forward ANN was designed for the prediction of rupture risk in ACOM aneurysms. • Two demographic parameters, 13 morphological aneurysm parameters, and hypertension/smoking history were acquired. • An ADASYN sampling approach was used to improve ANN quality. • Overall prediction accuracy of 94.8 % for the raw samples was achieved. [ABSTRACT FROM AUTHOR]