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Abstract Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P

Catastrophic optical mirror damage (COMD) and catastrophic optical bulk damage (COBD) are the main factors that affect the reliability of semiconductor lasers. In this paper, we characterize the COMD and COBD failure modes by examining the voltage changes at the current point where failure occurs, as well as by using the electroluminescent technique. Our study reveals that the voltage has an increase at the failure current point for COMD samples, in which failure occurs due to the damage to the facet mirrors; for COBD samples, in which failure occurs inside the laser cavity, the voltage exhibits a decrease and the amount of voltage decrease is roughly proportional to the size of the damaged area.

Abstract RNA polymerase mitochondrial (POLRMT) expression and the potential biological functions in skin squamous cell carcinoma (SCC) were explored. We showed that POLRMT is significantly elevated in skin SCC. Genetic depletion of POLRMT, using shRNA-induced knockdown or CRISPR/Cas9-mediated knockout (KO), resulted in profound anti-skin SCC cell activity. In patient-derived primary skin SCC cells or immortalized lines (A431 and SCC-9), POLRMT shRNA or KO potently suppressed mitochondrial DNA (mtDNA) transcription and suppressed cell viability, proliferation and migration. POLRMT shRNA or KO impaired mitochondrial functions in different skin SCC cells, leading to production of ROS (reactive oxygen species), depolarization of mitochondria and depletion of ATP. Moreover, mitochondrial apoptosis cascade was induced in POLRMT-depleted skin SCC cells. IMT1, a POLRMT inhibitor, largely inhibited proliferation and migration, while inducing depolarization of mitochondria and apoptosis in primary skin SCC cells. Contrarily, ectopic overexpression of POLRMT increased mtDNA transcription and augmented skin SCC cell growth. Importantly, POLRMT shRNA adeno-associated virus injection robustly hindered growth of the subcutaneous A431 xenografts in mice. In the POLRMT shRNA virus-treated A431 xenograft tissues, POLRMT depletion, mtDNA transcription inhibition, cell apoptosis, lipid peroxidation and ATP depletion were detected. Together, overexpressed POLRMT increases mtDNA transcription and promotes skin SCC growth.

Macrophage infiltration into adipose tissue is a key pathological factor inducing adipose tissue dysfunction and contributing to obesity-induced inflammation and metabolic disorders. In this review, we aim to present the most recent research on macrophage heterogeneity in adipose tissue, with a focus on the molecular targets applied to macrophages as potential therapeutics for metabolic diseases. We begin by discussing the recruitment of macrophages and their roles in adipose tissue. While resident adipose tissue macrophages display an anti-inflammatory phenotype and promote the development of metabolically favorable beige adipose tissue, an increase in pro-inflammatory macrophages in adipose tissue has negative effects on adipose tissue function, including inhibition of adipogenesis, promotion of inflammation, insulin resistance, and fibrosis. Then, we presented the identities of the newly discovered adipose tissue macrophage subtypes (e.g. metabolically activated macrophages, CD9+ macrophages, lipid-associated macrophages, DARC+ macrophages, and MFehi macrophages), the majority of which are located in crown-like structures within adipose tissue during obesity. Finally, we discussed macrophage-targeting strategies to ameliorate obesity-related inflammation and metabolic abnormalities, with a focus on transcriptional factors such as PPARγ, KLF4, NFATc3, and HoxA5, which promote macrophage anti-inflammatory M2 polarization, as well as TLR4/NF-κB-mediated inflammatory pathways that activate pro-inflammatory M1 macrophages. In addition, a number of intracellular metabolic pathways closely associated with glucose metabolism, oxidative stress, nutrient sensing, and circadian clock regulation were examined. Understanding the complexities of macrophage plasticity and functionality may open up new avenues for the development of macrophage-based treatments for obesity and other metabolic diseases.

Background: Accumulating studies have shown that the pathophysiology of schizophrenia may be associated with aberrant lysophospolipid metabolism in the early stage of brain development. Recent evidence demonstrates that antipsychotic medication can regulate the phospholipase activity. However, it remains unclear whether lysophospolipid is associated with the therapeutic response to antipsychotic medication in schizophrenia. This study aimed to investigate the influence of olanzapine monotherapy on lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) and the association between symptom improvement and changes of LPC and LPE levels during treatment in antipsychotic-naïve first-episode (ANFE) patients.Materials and Methods: The psychotic symptoms were evaluated by the Positive and Negative Syndrome Scale (PANSS). 25 ANFE patients were treated with olanzapine for 1 mo. The levels of LPC and LPE were determined and psychotic symptoms were assessed at baseline and at 1-mo follow-up.Results: Relative to baseline, the psychotic symptoms were significantly reduced after olanzapine treatment, except for negative symptoms. Moreover, the levels of most LPC and LPE increased after treatment. Interestingly, increased LPC(18:3) and LPC(20:2) levels were positively associated with the reduction rates of PANSS positive subscore. In addition, baseline levels of LPE(20:5), LPE(18:3) and LPE(22:5) were predictors for the reduction of positive symptoms.Conclusion: Our study reveals that the levels of lysophospolipid are associated with the improvement of positive symptoms, indicating that LPC may be a potential therapeutic target for olanzapine in schizophrenia. Moreover, baseline LPE levels were predictive biomarkers for the therapeutic response to olanzapine in the early stage of treatment in ANFE patients.

Using the daily temperature data from meteorological stations in Yunnan province and Guizhou province and Guizhou province from 2000 to 2019, NCEP/NCAR re-analyzed the data on a daily basis, and analyzed the temporal and spatial characteristics of extreme temperature events in the study area. Research shows that the extreme temperature in Yunnan province and Guizhou province region is warming; the extreme temperature index is different in spatial tendency, and all have abrupt changes; the maximum monthly maximum temperature and the heat persistence index will continue to increase in the future, the number of cold night days and the cold persistence index are on a strong and continuous decreasing trend.

In recent years, deep neural network (DNN) based scene classification methods have achieved promising performance. However, the data-driven training strategy requires a large number of labeled samples, making the DNN-based methods unable to solve the scene classification problem in the case of a small number of labeled images. As the number and variety of scene images continue to grow, the cost and difficulty of manual annotation also increase. Therefore, it is significant to deal with the scene classification problem with only a few labeled samples. In this paper, we propose an attention metric network (AMN) in the framework of the few-shot learning (FSL) to improve the performance of one-shot scene classification. AMN is composed of a self-attention embedding network (SAEN) and a cross-attention metric network (CAMN). In SAEN, we adopt the spatial attention and the channel attention of feature maps to obtain abundant features of scene images. In CAMN, we propose a novel cross-attention mechanism which can highlight the features that are more concerned about different categories, and improve the similarity measurement performance. A loss function combining mean square error (MSE) loss with multi-class N-pair loss is developed, which helps to promote the intra-class similarity and inter-class variance of embedding features, and also improve the similarity measurement results. Experiments on the NWPU-RESISC45 dataset and the RSD-WHU46 dataset demonstrate that our method achieves the state-of-the-art results on one-shot remote sensing image scene classification tasks.

AimThe aim of this study is to assess the depression of pregnant women in the aftermath of an earthquake, and to identify the social support that they obtained, their coping styles and socio-demographic factors associated with depression.MethodsA total of 128 pregnant women from three hospitals in the epicenter area were recruited immediately after the Ya'an earthquake. Their depression was investigated using the Edinburgh Postnatal Depression Scale (EPDS) with a cutoff score of 14; the social support that they obtained was measured using the Social Support Questionnaire; and their coping styles were assessed using the Coping Styles Questionnaire.ResultsImmediately after the earthquake, the incidence rate of depression in pregnant women was 35.2%, higher than that of the general pregnant population (7%-14%). The EPDS scores were significantly correlated with gestation age at the time of the earthquake, objective support, subjective support, use of support, negative coping style, and positive coping style. The regression analysis indicated that risk factors of prenatal depression include the number of children, relatives wounded, subjective support, and coping styles. A further analysis of the interaction between social support and two types of coping styles with depression showed that there was interaction effect between subjective social support and positive coping styles in relation to EPDS scores. There was an inverse relationship between low EPDS scores and positive coping styles and high social support, and vice versa.ConclusionThe timing of the occurrence of the earthquake may not necessarily affect the progress of the illness and recovery from depression, and psychological intervention could be conducted in the immediate aftermath after the earthquake. The impact of coping styles on prenatal depression appeared to be linked with social support. Helping pregnant women to adopt positive coping styles with good social support after a recent major earthquake, which is a stressor, may reduce their chances of developing prenatal depression.

Objective. Electroconvulsive therapy (ECT) is considered one of the most effective and fast-acting treatment options for depressive episodes. Little is known, however, about ECT’s enabling brain (neuro)plasticity effects, particular for plasticity of white matter pathway. Materials and Methods. We collected longitudinal diffusion tensor imaging in the first-episode, drug-naïve major depressive disorder (MDD) patients n=24 before and after a predefined time window ECT treatment. We constructed large-scale anatomical networks derived from white matter fiber tractography and evaluated the topological reorganization using graph theoretical analysis. We also assessed the relationship between topological reorganization with improvements in depressive symptoms. Results. Our investigation revealed three main findings: (1) the small-worldness was persistent after ECT series; (2) anatomical connections changes were found in limbic structure, temporal and frontal lobes, in which the connection changes between amygdala and parahippocampus correlate with depressive symptom reduction; (3) significant nodal strength changes were found in right paralimbic network. Conclusions. ECT elicits neuroplastic processes associated with improvements in depressive symptoms that act to specific local ventral frontolimbic circuits, but not small-world property. Overall, ECT induced topological reorganization in large-scale brain structural network, opening up new avenues to better understand the mode of ECT action in MDD.