Effects of HAT-CN Layer Thickness on Molecular Orientation and Energy-Level Alignment with ZnPc.

Efficient energy-level alignment is crucial for achieving high performance in organic electronic devices. Because the electronic structure of an organic semiconductor is significantly influenced by its molecular orientation, comprehensively understanding the molecular orientation and electronic structure of the organic layer is essential. In this study, we investigated the interface between a 1,4,5,8,9,11-hexaazatriphenylene hexacarbonitrile (HAT-CN) hole injection layer and a zinc-phthalocyanine (ZnPc) p-type organic semiconductor. To determine the energy-level alignment and molecular orientation, we conducted in situ ultraviolet and X-ray photoelectron spectroscopies, as well as angle-resolved X-ray absorption spectroscopy. We found that the HAT-CN molecules were oriented relatively face-on (40°) in the thin (5 nm) layer, whereas they were oriented relatively edge-on (62°) in the thick (100 nm) layer. By contrast, ZnPc orientation was not significantly altered by the underlying HAT-CN orientation. The highest occupied molecular orbital (HOMO) level of ZnPc was closer to the Fermi level on the 100 nm thick HAT-CN layer than on the 5 nm thick HAT-CN layer because of the higher work function. Consequently, a considerably low energy gap between the lowest unoccupied molecular orbital level of HAT-CN and the HOMO level of ZnPc was formed in the 100 nm thick HAT-CN case. This may improve the hole injection ability of the anode system, which can be utilized in various electronic devices.

Anti-inflammatory flavonoids from root bark of Broussonetia papyrifera in LPS-stimulated RAW264.7 cells.

Broussonetia papyrifera has been used as a diuretic, tonic and suppressor of edema. Bioactivity-guided fractionation and metabolite investigation of root bark extracts of this plant resulted in the isolation and identification of six 1,3-diphenylpropanes (1, 2, 8, 10, 17, 20), flavanone (3), two chalcones (4, 5), five flavans (6, 11, 14-16), dihydroflavonol (7) and five flavonols (9, 12, 13, 18, 19), including five new compounds (5, 7, 8, 19, 20) that inhibit NO production in LPS-induced RAW264.7 cells. The structures of compounds 1-20 were elucidated on the basis of spectroscopic data (1D and 2D NMR, MS, MS/MS, and HRMS). In particular, compounds 3, 5, 7, 12, and 20 exhibited significant inhibitory effects on the NO, iNOS, and pro-inflammatory cytokine (TNF-α and IL-6) production. Therefore, this study suggests that the flavonoid-rich products of B. papyrifera, including the new compounds, could be valuable candidates for the development of pharmaceuticals or functional foods in the prevention and treatment of anti-inflammatory disease.

Development of Deep Ensembles to Screen for Autism and Symptom Severity Using Retinal Photographs.

Importance: Screening for autism spectrum disorder (ASD) is constrained by limited resources, particularly trained professionals to conduct evaluations. Individuals with ASD have structural retinal changes that potentially reflect brain alterations, including visual pathway abnormalities through embryonic and anatomic connections. Whether deep learning algorithms can aid in objective screening for ASD and symptom severity using retinal photographs is unknown. Objective: To develop deep ensemble models to differentiate between retinal photographs of individuals with ASD vs typical development (TD) and between individuals with severe ASD vs mild to moderate ASD. Design, Setting, and Participants: This diagnostic study was conducted at a single tertiary-care hospital (Severance Hospital, Yonsei University College of Medicine) in Seoul, Republic of Korea. Retinal photographs of individuals with ASD were prospectively collected between April and October 2022, and those of age- and sex-matched individuals with TD were retrospectively collected between December 2007 and February 2023. Deep ensembles of 5 models were built with 10-fold cross-validation using the pretrained ResNeXt-50 (32×4d) network. Score-weighted visual explanations for convolutional neural networks, with a progressive erasing technique, were used for model visualization and quantitative validation. Data analysis was performed between December 2022 and October 2023. Exposures: Autism Diagnostic Observation Schedule-Second Edition calibrated severity scores (cutoff of 8) and Social Responsiveness Scale-Second Edition T scores (cutoff of 76) were used to assess symptom severity. Main Outcomes and Measures: The main outcomes were participant-level area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. The 95% CI was estimated through the bootstrapping method with 1000 resamples. Results: This study included 1890 eyes of 958 participants. The ASD and TD groups each included 479 participants (945 eyes), had a mean (SD) age of 7.8 (3.2) years, and comprised mostly boys (392 [81.8%]). For ASD screening, the models had a mean AUROC, sensitivity, and specificity of 1.00 (95% CI, 1.00-1.00) on the test set. These models retained a mean AUROC of 1.00 using only 10% of the image containing the optic disc. For symptom severity screening, the models had a mean AUROC of 0.74 (95% CI, 0.67-0.80), sensitivity of 0.58 (95% CI, 0.49-0.66), and specificity of 0.74 (95% CI, 0.67-0.82) on the test set. Conclusions and Relevance: These findings suggest that retinal photographs may be a viable objective screening tool for ASD and possibly for symptom severity. Retinal photograph use may speed the ASD screening process, which may help improve accessibility to specialized child psychiatry assessments currently strained by limited resources.