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Choroidal neovascularization (CNV) generally occurs at the late stage of senile macular degeneration (AMD),and accurate segmentation of CNV in optical coherence tomography (SD-OCT) is of great significance for the diagnosis and treatment of AMD.This paper proposes a CNV multi-task segmentation network that combines time series model and attention mechanism.The continuous SD-OCT image is input into the segmentation network,and the multi-scale information of the picture is extracted in the encoder part.In order to better extract the local features of the picture,the attention gate is added in the skip connection part.In order to solve the problem of discontinuous scanning segmentation,after the segmentation network is pooled,the timing constraint network is passed to generate the continuity constraint of adjacent frames and gradient constraints are added to the loss function to better preserve the lesion boundary.The spatial pyramid is used to fuse the two parts of the network feature map to produce segmentation loss,which improves the final segmentation accuracy.Based on patient independence,effective cross-validation is performed on 200 eyes of 12 patients.The Dice coefficient reaches 76.3% and the overlap reaches 60.7%.CNV can be reliably segmented in SD-OCT images.

Single‐molecule white‐light and near‐infrared circularly polarized luminescent materials are effective complements to the demonstrated enhancements of visible emission in the high contrast 3D images or organic light‐emitting diodes. In this contribution, two binaphthalene‐containing luminogenic enantiomers (BIT or BITM) with aggregation‐induced emission are synthesized which exhibit circularly polarized luminescence in solution with dissymmetry factors of ±5.0 × 10−3 and ±4.8 × 10−3, respectively. Additionally, the dual‐fluorescence spectra of single‐molecule white emission (CIE: 0.33, 0.34) in toluene solution are observed with (R)/(S)‐BIT in low polarity solvent originating from locally excited and twisted intramolecular charge‐transfer emission. While (R)/(S)‐BITM only shows single NIR luminescence with peaks at 720 nm in powder state. The nondoped OLEDs exhibit high maximum external quantum efficiencies of up to 4.2% with small efficiency roll‐off and NIR electroluminescence peak at 709 nm. [ABSTRACT FROM AUTHOR]

At present, one of the major factors limiting the further improvement of inverted (p‐i‐n) perovskite solar cells (PSCs) is trap‐assisted non‐radiative recombination at the perovskite/electron transporting layer (ETL) interface. Surface passivation with organic ammonium salt is a powerful strategy to improve the performance of PSCs. Herein, an effective method by using propylamine hydroiodide (PAI) and 1,3‐diaminopropane dihydroiodide (PDADI) is reported to modify the perovskite/ETL interface. These two ammonium salts do not form new perovskite but directly passivate the defects on the perovskite surface after annealing. The results show that the PDADI‐modified perovskite films possess a lower surface defect density and less non‐radiative recombination as well as improved charge carrier transport. Based on this strategy, the PDADI‐modified p‐i‐n PSCs deliver an impressive efficiency of 25.09% (certified 24.58%) with an open‐circuit voltage of 1.184 V. Furthermore, the unencapsulated PDADI‐modified PSCs also exhibit good storage stability, retaining 91% of initial PCE at 65 °C in a N2 glove box for 1300 h. This strategy provides an efficient route to fabricate highly efficient and stable inverted p‐i‐n structured PSCs. [ABSTRACT FROM AUTHOR]

Singlet oxygen (1O2), representing an important reactive oxygen species, has promising applications in biomedical, material, and environmental sciences. Photosensitized production of 1O2 using organic dyes is highly desirable and the exploration of highly efficient photosensitizers has received considerable attention. Herein, two tridentate Pt(II) complexes, i.e., cationic 1(PF6) and neutral 2, modified with the ethynylnaphthalimide chromophore, were designed and prepared for the application in 1O2 generation. Spectroscopic studies and computational results suggest that 1(PF6) and 2 display the lowest-energy absorption bands centered at 435–465 nm with the molar extinction coefficients of 0.6–3.2 × 104 M−1 cm−1, originating from the singlet ligand-to-ligand charge transfer (1LLCT) and a mixture of 1LLCT and singlet ligand-centered (LC) transitions, respectively. Moreover, they show similar phosphorescence at 620–640 nm assigned to the Pt-perturbed triplet LC emission of the ethynylnaphthalimide moiety. Thanks to the relatively long phosphorescence lifetimes, these complexes exhibit O2-dependent phosphorescence intensities with good reversibility and stability. They are able to behave as efficient triplet photosensitizers to promote the 1O2 generation with high quantum yields (84–89%). This work indicates that the combination of an organic chromophore with Pt(II) complexes provides an effective method to obtain photosensitizers for 1O2 generation. [ABSTRACT FROM AUTHOR]

Background: Early-stage diagnosis and treatment can improve survival rates of liver cancer patients. Dynamic contrast-enhanced MRI provides the most comprehensive information for differential diagnosis of liver tumors. However, MRI diagnosis is affected by subjective experience, so deep learning may supply a new diagnostic strategy. We used convolutional neural networks (CNNs) to develop a deep learning system (DLS) to classify liver tumors based on enhanced MR images, unenhanced MR images, and clinical data including text and laboratory test results.Methods: Using data from 1,210 patients with liver tumors (N = 31,608 images), we trained CNNs to get seven-way classifiers, binary classifiers, and three-way malignancy-classifiers (Model A-Model G). Models were validated in an external independent extended cohort of 201 patients (N = 6,816 images). The area under receiver operating characteristic (ROC) curve (AUC) were compared across different models. We also compared the sensitivity and specificity of models with the performance of three experienced radiologists.Results: Deep learning achieves a performance on par with three experienced radiologists on classifying liver tumors in seven categories. Using only unenhanced images, CNN performs well in distinguishing malignant from benign liver tumors (AUC, 0.946; 95% CI 0.914–0.979 vs. 0.951; 0.919–0.982, P = 0.664). New CNN combining unenhanced images with clinical data greatly improved the performance of classifying malignancies as hepatocellular carcinoma (AUC, 0.985; 95% CI 0.960–1.000), metastatic tumors (0.998; 0.989–1.000), and other primary malignancies (0.963; 0.896–1.000), and the agreement with pathology was 91.9%.These models mined diagnostic information in unenhanced images and clinical data by deep-neural-network, which were different to previous methods that utilized enhanced images. The sensitivity and specificity of almost every category in these models reached the same high level compared to three experienced radiologists.Conclusion: Trained with data in various acquisition conditions, DLS that integrated these models could be used as an accurate and time-saving assisted-diagnostic strategy for liver tumors in clinical settings, even in the absence of contrast agents. DLS therefore has the potential to avoid contrast-related side effects and reduce economic costs associated with current standard MRI inspection practices for liver tumor patients.

Three monoruthenium complexes 1(PF6)2–3(PF6)2 bearing an N(CH3)-bridged ligand have been synthesized and characterized. These complexes have a general formula of [Ru(bpy)2(L)](PF6)2, where L is a 2,5-di(N-methyl-N'-(pyrid-2-yl)amino)pyrazine (dapz) derivative with various substituents, and bpy is 2,2′-bipyridine. The photophysical and electrochemical properties of these compounds have been examined. The solid-state structure of complex 3(PF6)2 is studied by single-crystal X-ray analysis. These complexes show two well-separated emission bands centered at 451 and 646 nm (Δλmax = 195 nm) for 1(PF6)2, 465 and 627 nm (Δλmax = 162 nm) for 2(PF6)2, and 455 and 608 nm (Δλmax = 153 nm) for 3(PF6)2 in dilute acetonitrile solution, respectively. The emission maxima of the higher-energy emission bands of these complexes are similar, while the lower-energy emission bands are dependent on the electronic nature of substituents. These complexes display two consecutive redox couples owing to the stepwise oxidation of the N(CH3)-bridged ligand and ruthenium component. Moreover, these experimental observations are analyzed by computational investigation. [ABSTRACT FROM AUTHOR]

Keywords: dibenzo[ f, h ]quinolones; polycyclic aromatic hydrocarbons; C-H arylation; reductive cyclodehydrogenation; ruthenium EN dibenzo[ f, h ]quinolones polycyclic aromatic hydrocarbons C-H arylation reductive cyclodehydrogenation ruthenium 2779 2785 7 08/17/23 20230901 NES 230901 Graph Polycyclic aromatic hydrocarbons (PAHs) typically contain two or more fused benzene rings and are structurally characterized by a high degree of rigidity and -conjugation. We present in this work a new approach to synthesize dibenzo[ I f i , I h i ]quinolines via a two-step procedure involving the ruthenium-catalyzed C-H arylation followed by the potassium-mediated reductive cyclodehydrogenation (Figure 1e). For the substituents, only the relatively electron-rich groups such as OMe, Me, and Ph were used, because electron-deficient substituents, e.g., Cl and CO SB 2 sb Et, are not tolerated in the subsequent reductive cyclodehydrogenation reaction with potassium. Zhang and co-workers developed a palladium-catalyzed double cross-coupling reaction between cyclic diaryliodonium salts and 2-chloropyridinyl acids to access dibenzo-[ I f i , I h i ]quinolines and their derivatives (Figure 1a). [Extracted from the article]