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A metabolomics approach has recently been used to identify metabolites associated with response to antipsychotic treatment. This study was designed to identify the predictive biomarkers of response to olanzapine monotherapy using a metabolomics-based strategy.Twenty-five first-episode and drug-naïve female patients with schizophrenia were recruited and treated with olanzapine for 4 weeks. Psychiatric symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) at baseline and 4-week follow-up.Positive subscore, general psychopathology subscore, and PANSS total score were significantly decreased after treatment. An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics approach identified 72 differential metabolites after treatment. In addition, the baseline levels of methyl n-formylanthranilate (MNFT) were correlated with the rate of reduction in the positive subscore or PANSS total score. However, increase in MNFT after treatment was not associated with the rate of reduction in the PANSS total score or its subscores. Subsequent regression analysis revealed that the baseline MNFT levels predicted the treatment outcomes after olanzapine monotherapy for 4 weeks in patients with schizophrenia.Our study results suggest that the baseline MNFT levels in the kynurenine pathway of tryptophan metabolism may be predictive of the treatment response to olanzapine in schizophrenia.

The purpose of this study is to develop and validate the intelligent diagnosis of severe DR with lesion recognition based on color fundus photography. The Kaggle public dataset for DR grading is used in the project, including 53,576 fundus photos in the test set, 28,101 in the training set, and 7,025 in the validation set. We randomly select 4,192 images for lesion annotation. Inception V3 structure is adopted as the classification algorithm. Both 299 × 299 pixel images and 896 × 896 pixel images are used as the input size. ROC curve, AUC, sensitivity, specificity, and their harmonic mean are used to evaluate the performance of the models. The harmonic mean and AUC of the model of 896 × 896 input are higher than those of the 299 × 299 input model. The sensitivity, specificity, harmonic mean, and AUC of the method with 896 × 896 resolution images as input for severe DR are 0.925, 0.907, 0.916, and 0.968, respectively. The prediction error mainly occurs in moderate NPDR, and cases with more hard exudates and cotton wool spots are easily predicted as severe cases. Cases with preretinal hemorrhage and vitreous hemorrhage are easily identified as severe cases, and IRMA is the most difficult lesion to recognize. We have studied the intelligent diagnosis of severe DR based on color fundus photography. This artificial intelligence–based technology offers a possibility to increase the accessibility and efficiency of severe DR screening.

B-γ-CsSnI3 perovskite solar cells (PSCs) are simulated employing diverse electron-transporting layers (ETLs, including TiO2, ZnO, SnO2, GaN, C60, and PCBM), and a comparative study has been made. Both regular and inverted planar structures are simulated. Effects of the thickness of absorbers and ETLs, doping of ETLs, and interface trap states on the photovoltaic performance are studied to optimize the device structures. The regular structures have larger short-circuit current density (Jsc) than the inverted structures, but the inverted structures have larger fill factor (FF). All of the simulated optimal PSCs have similar open-circuit voltages (Voc) of ∼0.96 V. The PSCs with TiO2 ETLs have the best photovoltaic performance, and the optimum structure exhibits the highest efficiency of 20.2% with a Voc of 0.97 V, Jsc of 29.67 mA/cm2, and FF of 0.70. The optimal PSCs with ZnO, GaN, C60, and PCBM ETLs exhibit efficiencies of 17.88, 18.09, 16.71, and 16.59%, respectively. The optimal PSC with SnO2 ETL exhibits the lowest efficiency of 15.5% in all of the simulated PSCs due to its cliff-like band offset at the SnO2/CsSnI3 interface. Furthermore, the increase of interface trap density and capture cross section is found to reduce the photovoltaic performance of PSCs. This work contributes to designing and fabricating CsSnI3 PSCs.