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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 < 0.001). For the detection of 13 diseases, the test group achieved significant higher sensitivities (72.2% ~ 100.0%) and comparable specificities (90.8% ~ 98.7%) comparing with the control group (sensitivities, 50% ~ 100%; specificities 96.7 ~ 99.8%). The DLS group presented similar performance to the test group in the detection of any fundus abnormality (sensitivity, 95.7%; specificity, 87.2%) and each of the 13 diseases (sensitivity, 83.3% ~ 100.0%; specificity, 89.0 ~ 98.0%). The proposed DLS provided a novel approach for the automatic detection of 13 major fundus diseases with high diagnostic consistency and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses. ClinicalTrials.gov NCT04723160., (© 2024. The Author(s).)

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 MFe hi 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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Li, Ren, Chang, Wu, Griffiths, Lu and Gao.)

As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods are promising when training and test data are well aligned, but perform poorly on independent tests. Moreover, due to the absence of authentic test images, their image-level detection specificity is in doubt. The key question is how to design and train a deep neural network capable of learning generalizable features sensitive to manipulations in novel data, whilst specific to prevent false alarms on the authentic. We propose multi-view feature learning to jointly exploit tampering boundary artifacts and the noise view of the input image. As both clues are meant to be semantic-agnostic, the learned features are thus generalizable. For effectively learning from authentic images, we train with multi-scale (pixel / edge / image) supervision. We term the new network MVSS-Net and its enhanced version MVSS-Net++. Experiments are conducted in both within-dataset and cross-dataset scenarios, showing that MVSS-Net++ performs the best, and exhibits better robustness against JPEG compression, Gaussian blur and screenshot based image re-capturing.

Aim: 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., Methods: 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., Results: 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., Conclusions: 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., (© 2022 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.)

Background: There are various differences in response to different antipsychotics and antioxidant defense systems (ADS) by sex. Previous studies have shown that several ADS enzymes are closely related to the treatment response of patients with antipsychotics-naïve first-episode (ANFE) schizophrenia., Objective: Therefore, the main goal of this study was to assess the sex difference in the relationship between changes in ADS enzyme activities and risperidone response., Methods: The plasma activities of glutathione peroxidase (GPx), catalase (CAT), superoxide dismutase (SOD), and total antioxidant status (TAS) were measured in 218 patients and 125 healthy controls. Patients were treated with risperidone for 3 months, and we measured PANSS for psychopathological symptoms and ADS biomarkers at baseline and at the end of 3 months of treatment. We compared sex-specific group differences between 50 non-responders and 168 responders at baseline and at the end of the three months of treatment., Results: We found that female patients responded better to risperidone treatment than male patients. At baseline and 3-month follow-up, there were no significant sex differences in TAS levels and three ADS enzyme activities. Interestingly, only in female patients, after 12 weeks of risperidone treatment, the GPx activity of responders was higher than that of non-responders., Conclusion: These results indicate that after treatment with risperidone, changes in GPx activity were associated with treatment response, suggesting that changes in GPx may be a predictor of response to risperidone treatment in female patients with ANFE schizophrenia., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

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., (© 2022. The Author(s).)

Background: Oxidative stress plays an important role in weight gain induced by antipsychotics in schizophrenia (SCZ). However, little is known about how antioxidant enzymes are involved in weight gain caused by risperidone monotherapy in antipsychotics-naïve first-episode (ANFE) patients with SCZ. Therefore, the main purpose of this study was to investigate the effects of risperidone on several antioxidant enzymes in patients with ANFE SCZ and the relationship between weight gain and changes in antioxidant enzyme activities., Objective: The activities of plasma superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx), as well as the levels of malondialdehyde (MDA) were measured in 225 ANFE patients and 125 healthy controls., Methods: Patients were treated with risperidone monotherapy for 12 weeks. Clinical symptoms, antioxidant enzyme activities, and MDA levels were measured at baseline and during follow-up., Results: Compared with healthy controls, the patients showed higher activities of SOD and CAT but lower MDA levels and GPx activity. At baseline, the CAT activity was associated with body weight or BMI. Further, based on a 7% weight increase from baseline to follow-up, we found 75 patients in the weight gain (WG) group and 150 patients in the non-WG group. Comparing SOD, CAT, GPx activities and MDA levels between the WG group and the non-WG group at baseline and during the 12-week follow-up, it was found that after treatment, the SOD activity in the WG group increased while the MDA level decreased in the non-WG group. Moreover, baseline SOD and GPx activities were predictors of weight gain at 12-week follow-up., Conclusion: These results suggest that the antioxidant defense system may have predictive value for the weight gain of ANFE SCZ patients after risperidone treatment., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

This paper attacks the challenging problem of video retrieval by text. In such a retrieval paradigm, an end user searches for unlabeled videos by ad-hoc queries described exclusively in the form of a natural-language sentence, with no visual example provided. Given videos as sequences of frames and queries as sequences of words, an effective sequence-to-sequence cross-modal matching is crucial. To that end, the two modalities need to be first encoded into real-valued vectors and then projected into a common space. In this paper we achieve this by proposing a dual deep encoding network that encodes videos and queries into powerful dense representations of their own. Our novelty is two-fold. First, different from prior art that resorts to a specific single-level encoder, the proposed network performs multi-level encoding that represents the rich content of both modalities in a coarse-to-fine fashion. Second, different from a conventional common space learning algorithm which is either concept based or latent space based, we introduce hybrid space learning which combines the high performance of the latent space and the good interpretability of the concept space. Dual encoding is conceptually simple, practically effective and end-to-end trained with hybrid space learning. Extensive experiments on four challenging video datasets show the viability of the new method. Code and data are available at https://github.com/danieljf24/hybrid&#95;space.

Aim: To explore and evaluate an appropriate deep learning system (DLS) for the detection of 12 major fundus diseases using colour fundus photography., Methods: Diagnostic performance of a DLS was tested on the detection of normal fundus and 12 major fundus diseases including referable diabetic retinopathy, pathologic myopic retinal degeneration, retinal vein occlusion, retinitis pigmentosa, retinal detachment, wet and dry age-related macular degeneration, epiretinal membrane, macula hole, possible glaucomatous optic neuropathy, papilledema and optic nerve atrophy. The DLS was developed with 56 738 images and tested with 8176 images from one internal test set and two external test sets. The comparison with human doctors was also conducted., Results: The area under the receiver operating characteristic curves of the DLS on the internal test set and the two external test sets were 0.950 (95% CI 0.942 to 0.957) to 0.996 (95% CI 0.994 to 0.998), 0.931 (95% CI 0.923 to 0.939) to 1.000 (95% CI 0.999 to 1.000) and 0.934 (95% CI 0.929 to 0.938) to 1.000 (95% CI 0.999 to 1.000), with sensitivities of 80.4% (95% CI 79.1% to 81.6%) to 97.3% (95% CI 96.7% to 97.8%), 64.6% (95% CI 63.0% to 66.1%) to 100% (95% CI 100% to 100%) and 68.0% (95% CI 67.1% to 68.9%) to 100% (95% CI 100% to 100%), respectively, and specificities of 89.7% (95% CI 88.8% to 90.7%) to 98.1% (95%CI 97.7% to 98.6%), 78.7% (95% CI 77.4% to 80.0%) to 99.6% (95% CI 99.4% to 99.8%) and 88.1% (95% CI 87.4% to 88.7%) to 98.7% (95% CI 98.5% to 99.0%), respectively. When compared with human doctors, the DLS obtained a higher diagnostic sensitivity but lower specificity., Conclusion: The proposed DLS is effective in diagnosing normal fundus and 12 major fundus diseases, and thus has much potential for fundus diseases screening in the real world., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. No commercial re-use. See rights and permissions. Published by BMJ.)

This paper tackles automated categorization of Age-related Macular Degeneration (AMD), a common macular disease among people over 50. Previous research efforts mainly focus on AMD categorization with a single-modal input, let it be a color fundus photograph (CFP) or an OCT B-scan image. By contrast, we consider AMD categorization given a multi-modal input, a direction that is clinically meaningful yet mostly unexplored. Contrary to the prior art that takes a traditional approach of feature extraction plus classifier training that cannot be jointly optimized, we opt for end-to-end multi-modal Convolutional Neural Networks (MM-CNN). Our MM-CNN is instantiated by a two-stream CNN, with spatially-invariant fusion to combine information from the CFP and OCT streams. In order to visually interpret the contribution of the individual modalities to the final prediction, we extend the class activation mapping (CAM) technique to the multi-modal scenario. For effective training of MM-CNN, we develop two data augmentation methods. One is GAN-based CFP/OCT image synthesis, with our novel use of CAMs as conditional input of a high-resolution image-to-image translation GAN. The other method is Loose Pairing, which pairs a CFP image and an OCT image on the basis of their classes instead of eye identities. Experiments on a clinical dataset consisting of 1,094 CFP images and 1,289 OCT images acquired from 1,093 distinct eyes show that the proposed solution obtains better F1 and Accuracy than multiple baselines for multi-modal AMD categorization. Code and data are available at https://github.com/li-xirong/mmc-amd.

Introduction: Olanzapine (OLA) is one of the most commonly used second-generation antipsychotics for the treatment of schizophrenia. However, the heterogeneity of therapeutic response to OLA among schizophrenia patients deserves further exploration. The role of carnitine in the clinical response to OLA monotherapy remains unclear., Objectives: The current study was designed to investigate whether carnitine and its derivatives are linked to the response to OLA treatment. Drug-naïve first-episode patients with schizophrenia were recruited and treated with OLA for 4 weeks. Psychiatric symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS) in pre and post treatment., Results: After treatment, we found a significant decrease in 2-Octenoylcarnitine levels and a significant increase in linoelaidyl carnitine, 11Z-Octadecenylcarnitine and 9-Decenoylcarnitine levels. Furthermore, baseline linoelaidyl carnitine levels were correlated with the reduction of PANSS positive symptom subscore. Linear regression and logistic regression analyses found that the baseline linoelaidyl carnitine level was a predictive marker for the therapeutic response to OLA monotherapy for 4 weeks., Conclusion: Our pilot study suggests that linoelaidyl carnitine levels at baseline may have a predictive role for the improvement of positive symptoms after OLA monotherapy in the patients with schizophrenia., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: To develop deep learning models based on color fundus photographs that can automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and segment myopia-related lesions., Methods: Photographs were graded and annotated by four ophthalmologists and were then divided into a high-consistency subgroup or a low-consistency subgroup according to the consistency between the results of the graders. ResNet-50 network was used to develop the classification model, and DeepLabv3+ network was used to develop the segmentation model for lesion identification. The two models were then combined to develop the classification-and-segmentation-based co-decision model., Results: This study included 1395 color fundus photographs from 895 patients. The grading accuracy of the co-decision model was 0.9370, and the quadratic-weighted κ coefficient was 0.9651; the co-decision model achieved an area under the receiver operating characteristic curve of 0.9980 in diagnosing pathologic myopia. The photograph-level F1 values of the segmentation model identifying optic disc, peripapillary atrophy, diffuse atrophy, patchy atrophy, and macular atrophy were all >0.95; the pixel-level F1 values for segmenting optic disc and peripapillary atrophy were both >0.9; the pixel-level F1 values for segmenting diffuse atrophy, patchy atrophy, and macular atrophy were all >0.8; and the photograph-level recall/sensitivity for detecting lacquer cracks was 0.9230., Conclusions: The models could accurately and automatically grade myopic maculopathy, diagnose pathologic myopia, and identify and monitor progression of the lesions., Translational Relevance: The models can potentially help with the diagnosis, screening, and follow-up for pathologic myopic in clinical practice.

Purpose: The purpose of this study is to develop and validate the intelligent diagnosis of severe DR with lesion recognition based on color fundus photography., Methods: 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., Results: 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., Conclusions: 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., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

B-γ-CsSnI 3 perovskite solar cells (PSCs) are simulated employing diverse electron-transporting layers (ETLs, including TiO 2 , ZnO, SnO 2 , GaN, C 60 , 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 ( J sc ) than the inverted structures, but the inverted structures have larger fill factor (FF). All of the simulated optimal PSCs have similar open-circuit voltages ( V oc ) of ∼0.96 V. The PSCs with TiO 2 ETLs have the best photovoltaic performance, and the optimum structure exhibits the highest efficiency of 20.2% with a V oc of 0.97 V, J sc of 29.67 mA/cm 2 , and FF of 0.70. The optimal PSCs with ZnO, GaN, C 60 , and PCBM ETLs exhibit efficiencies of 17.88, 18.09, 16.71, and 16.59%, respectively. The optimal PSC with SnO 2 ETL exhibits the lowest efficiency of 15.5% in all of the simulated PSCs due to its cliff-like band offset at the SnO 2 /CsSnI 3 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 CsSnI 3 PSCs., Competing Interests: The authors declare no competing financial interest., (© 2021 American Chemical Society.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Liu, Xiu, Liu, Wang and Li.)

Purpose: To investigate the detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus imaging system (Optos) with convolutional neural network technology., Methods: This study included 1500 Optos color images for tessellated fundus confirmation and peripheral retinal lesion (lattice degeneration, retinal breaks, and retinal detachment) assessment. Three retinal specialists evaluated all images and proposed the reference standard when an agreement was achieved. Then, 722 images were used to train and verify a combined deep-learning system of 3 optimal binary classification models trained using seResNext50 algorithm with 2 preprocessing methods (original resizing and cropping), and a test set of 189 images were applied to verify the performance compared to the reference standard., Results: With optimal preprocessing approach (original resizing method for lattice degeneration and retinal detachment, cropping method for retinal breaks), the combined deep-learning system exhibited an area under curve of 0.888, 0.953, and 1.000 for detection of lattice degeneration, retinal breaks, and retinal detachment respectively in tessellated eyes. The referral accuracy of this system was 79.8% compared to the reference standard., Conclusion: A deep-learning system is feasible to detect lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field images. And this system may be considered for screening and telemedicine., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature.)

Aims: To establish an automated method for identifying referable diabetic retinopathy (DR), defined as moderate nonproliferative DR and above, using deep learning-based lesion detection and stage grading., Materials and Methods: A set of 12,252 eligible fundus images of diabetic patients were manually annotated by 45 licenced ophthalmologists and were randomly split into training, validation, and internal test sets (ratio of 7:1:2). Another set of 565 eligible consecutive clinical fundus images was established as an external test set. For automated referable DR identification, four deep learning models were programmed based on whether two factors were included: DR-related lesions and DR stages. Sensitivity, specificity and the area under the receiver operating characteristic curve (AUC) were reported for referable DR identification, while precision and recall were reported for lesion detection., Results: Adding lesion information to the five-stage grading model improved the AUC (0.943 vs. 0.938), sensitivity (90.6% vs. 90.5%) and specificity (80.7% vs. 78.5%) of the model for identifying referable DR in the internal test set. Adding stage information to the lesion-based model increased the AUC (0.943 vs. 0.936) and sensitivity (90.6% vs. 76.7%) of the model for identifying referable DR in the internal test set. Similar trends were also seen in the external test set. DR lesion types with high precision results were preretinal haemorrhage, hard exudate, vitreous haemorrhage, neovascularisation, cotton wool spots and fibrous proliferation., Conclusions: The herein described automated model employed DR lesions and stage information to identify referable DR and displayed better diagnostic value than models built without this information., (© 2021 John Wiley & Sons Ltd.)

Aims: To investigate the efficacy of a bi-modality deep convolutional neural network (DCNN) framework to categorise age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) from colour fundus images and optical coherence tomography (OCT) images., Methods: A retrospective cross-sectional study was proposed of patients with AMD or PCV who came to Peking Union Medical College Hospital. Diagnoses of all patients were confirmed by two retinal experts based on diagnostic gold standard for AMD and PCV. Patients with concurrent retinal vascular diseases were excluded. Colour fundus images and spectral domain OCT images were taken from dilated eyes of patients and healthy controls, and anonymised. All images were pre-labelled into normal, dry or wet AMD or PCV. ResNet-50 models were used as the backbone and alternate machine learning models including random forest classifiers were constructed for further comparison. For human-machine comparison, the same testing data set was diagnosed by three retinal experts independently. All images from the same participant were presented only within a single partition subset., Results: On a test set of 143 fundus and OCT image pairs from 80 eyes (20 eyes per-group), the bi-modal DCNN demonstrated the best performance, with accuracy 87.4%, sensitivity 88.8% and specificity 95.6%, and a perfect agreement with diagnostic gold standard (Cohen's κ 0.828), exceeds slightly over the best expert (Human1, Cohen's κ 0.810). For recognising PCV, the model outperformed the best expert as well., Conclusion: A bi-modal DCNN for automated classification of AMD and PCV is accurate and promising in the realm of public health., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background/aims: The aim of this study was to generate and evaluate individualised post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of antivascular endothelial growth factor therapy for typical neovascular age-related macular degeneration (nAMD) based on pretherapeutic images using generative adversarial network (GAN)., Methods: A total of 476 pairs of pretherapeutic and post-therapeutic OCT images of patients with nAMD were included in training set, while 50 pretherapeutic OCT images were included in the tests set retrospectively, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. The pix2pixHD method was adopted for image synthesis. Three experiments were performed to evaluate the quality, authenticity and predictive power of the synthetic images by retinal specialists., Results: We found that 92% of the synthetic OCT images had sufficient quality for further clinical interpretation. Only about 26%-30% synthetic post-therapeutic images could be accurately identified as synthetic images. The accuracy to predict macular status of wet or dry was 0.85 (95% CI 0.74 to 0.95)., Conclusion: Our results revealed a great potential of GAN to generate post-therapeutic OCT images with both good quality and high accuracy., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: To assess the association of single nucleotide polymorphisms (SNPs) CHRNA4 gene with response to selective serotonin re-uptake inhibitors (SSRIs) for the treatment of major depressive disorder (MDD)., Methods: For 304 patients receiving drug treatment for major depression, 2 SNPs, namely rs4522666 and rs4603829, of the CHRNA4 gene were determined by matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry. HAMD-17 was adopted as the primary rating tool to evaluate the severity of depression on the baseline and at the end of 1st, 2nd, 4th and 6th weeks treatment., Results: The frequency of GG genotype/G allele for rs4522666 differed significantly from that of TT and GT genotypes/T allele between responders and non-responders (P=0.015 and P=0.006, respectively). No significant difference was found in genotypic and allelic frequencies of rs4603829 between the two groups (P> 0.05)., Conclusion: SNPs of the CHRNA4 gene may play an important role in the response to antidepressant drugs among ethnic Han Chinese with MDD.

Background: Suicidal ideation (SI) is a common and serious clinical concern in people with major depressive disorder (MDD). Inconsistent evidence suggests that individuals with SI may have a lower measured intelligence quotient (IQ) than those without SI. The aims of this study were to examine SI prevalence and its associations with demographic, clinical variables and IQ in Chinese drug-naïve MDD patients., Methods: 488 drug-naïve Chinese Han patients (male: 203, 41.6%) meeting a DSM-IV diagnosis of MDD were enrolled in a cross-sectional study involving seven hospitals. All participants were asked to complete a series of questionnaires, which include information on socio-demographic and clinical variables. The Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA) and Brief Psychiatric Rating Scale (BPRS) were also administered. Verbal IQ (VIQ), performance IQ (PIQ) and full-scale IQ (FIQ) scores were measured using the Chinese version of the Wechsler Adult Intelligence Scale-Ⅲ (WAIS-Ⅲ). We categorized patients with SI based on a cut-off score of ≥3 on HAMD item 3., Results: An estimated prevalence 32.8% (160/488) of drug-naïve MDD patients experienced SI during the current episode of illness. Patients with SI were more likely to be divorced (x 2  = 6.93, p = 0.031), male (x 2  = 6.04, p = 0.014), had higher severity of depression and anxiety symptoms (t = -8.14, p < 0.001, t = -3.28, p < 0.001, respectively), comorbid psychotic features (t = -5.71, p < 0.001), and lower FIQ levels (t = -4.21, p < 0.001), when compared to patients without SI. Using logistic regression analysis and adjusting for confounding variables, the following variables were independently associated with SI: divorced marital status compared to married (OR=4.674, 95% CI: 1.676~13.036), severity of depression symptoms (OR=1.312, 95% CI: 1.196~1.440), psychotic features (OR=1.044; 1.012~1.077), and FIQ /PIQ levels which expressed in OR per SD increase in IQ score (OR=0.740, 95% CI: 0.561~0.977; OR=0.744, 95% CI: 0.557~0.994, respectively)., Limitations: A cross-sectional study which did not assess the influence of severity of SI., Conclusions: The prevalence of SI in drug-naive Chinese patients with MDD is high, and associated with marital status, severity of depression, psychotic features and measured IQ. Further research is needed to further explore these and other potentially relevant risk factors which might affect clinical outcomes., (Copyright © 2018. Published by Elsevier B.V.)

Objective: To assess the association of TPH2 gene polymorphisms with the response or remission to selective serotonin reuptake inhibitors (SSRIs) drugs during treatment of major depressive disorder., Methods: For 304 patients receiving SSRIs treatment for major depression disorder, 3 single nucleotide polymorphisms (SNPs) (rs1007023, rs1023990 and rs4570625) in the TPH2 gene were genotyped by MALDI-TOF mass spectrometry using a MassArray Analyzer 4 system. HAMD-17 was adopted as the primary rating tool to evaluate the severity of depression on the baseline and at the end of 1st, 2nd, 4th and 6th week of treatment., Results: The frequency of GG genotype/G allele for rs4570625 differed significantly with the frequency of TT and GT genotypes/T allele between responders and non-responders (P=0.013 and 0.007, respectively). Genotypic and allelic frequencies of the other polymorphisms did not differ significantly between the two groups (P>0.05). No association of TPH2 gene polymorphism with remission was found with the 3 SNPs., Conclusion: The polymorphisms of TPH2 gene may play an important role in response to antidepressant drug therapy.

BACKGROUND This study aimed to study the brain structural and functional changes after 8 courses of electroconvulsive therapy (ECT) on patients with major depressive disorder (MDD). MATERIAL AND METHODS MRI scans were performed on 12 depressive patients before and after 8 courses of ECT and compared with those of 15 normal controls. Data were analyzed by voxel-based morphometry (VBM) using SPM8 software. Functional MRI (fMRI) and regional homogeneity (ReHo) analyses were used to assess the functional changes after ECT. RESULTS Grey matter volumes were smaller in the right cingulate gyrus of depressive patients before ECT compared with normal controls. After false discovery rate (FDR) correction, post-ECT grey matter volumes were increased in bilateral amygdala and hippocampus compared with pre-ECT. Resting-state ReHo maps showed significant differences in brain activity pre- and post-ECT. Compared with healthy controls, MDD patients treated with 8 courses of ECT showed higher ReHo values in the bilateral frontal lobe, bilateral parietal lobe, and right caudate nucleus. Decreased ReHo values were observed in the right medial temporal gyrus, right superior temporal gyrus, right cingulate gyrus, and left anterior cerebellar lobe. CONCLUSIONS Results suggested that there were both structural and functional differences between the brains of MDD patients and healthy controls. After ECT, both structural and functional changes occurred, but without complete recovery to normal. ECT may display effects through regulating other brain regions to compensate for the original defects., Competing Interests: The authors declare that they have no conflict of interest.

Background: Modified electroconvulsive therapy (MECT) has been regarded as the most effective antidepressant therapy, despite its cognitive side effects. However, how MECT influences problem-solving capacity in major depressive disorder (MDD), as well as its underlying neurobiological mechanisms, remains unclear. The present study aimed to assess alterations in problem-solving capacity after MECT and to explore spontaneous brain activity using amplitudes of low-frequency fluctuations (ALFF)/fractional ALFF., Methods: Thirteen first-episode, treatment-naive MDD patients treated by MECT were recruited. We collected resting-state functional magnetic resonance imaging, and we evaluated their Modified Card Sorting Test performance before and after single-session MECT. Another 11 MDD patients without MECT were also recruited and interviewed with Modified Card Sorting Test twice as a control group., Results: After a single MECT, MDD patients showed significantly decreased ALFF in the right cerebellar posterior lobe. Compared to the control group, perseverative errors significantly decreased after MECT, controlling for practice effects. Some cognitive functional changes significantly correlated to changed ALFF in several brain regions, including Brodmann areas BA9, BA19, BA 21, and BA48, right thalamus, left cerebellum, and right postcentral gyrus., Conclusions: The MECT could improve problem-solving capacity, even after controlling for practice effects, and it could induce changes in spontaneous brain activity. These changes in cognitive functioning might result from changes in the cerebral functions of some regions, including frontal cortex, a key region for problem-solving capacity.

The mechanisms underlying the effects of electroconvulsive therapy (ECT) in major depressive disorder (MDD) are not fully understood. Resting-state functional magnetic resonance imaging (rs-fMRI) is a new tool to study the effects of brain stimulation interventions, particularly ECT. The authors aim to investigate the mechanisms of ECT in MDD by rs-fMRI.They used rs-fMRI to measure functional changes in the brain of first-episode, treatment-naive MDD patients (n = 23) immediately before and then following 8 ECT sessions (brief-pulse square-wave apparatus, bitemporal). They also computed voxel-wise amplitude of low-frequency fluctuation (ALFF) as a measure of regional brain activity and selected the left subgenual anterior cingulate cortex (sgACC) to evaluate functional connectivity between the sgACC and other brain regions.Increased regional brain activity measured by ALFF mainly in the left sgACC following ECT. Functional connectivity of the left sgACC increased in the ipsilateral parahippocampal gyrus, pregenual ACC, contralateral middle temporal pole, and orbitofrontal cortex. Importantly, reduction in depressive symptoms were negatively correlated with increased ALFF in the left sgACC and left hippocampus, and with distant functional connectivity between the left sgACC and contralateral middle temporal pole. That is, across subjects, as depression improved, regional brain activity in sgACC and its functional connectivity increased in the brain.Eight ECT sessions in MDD patients modulated activity in the sgACC and its networks. The antidepressant effects of ECT were negatively correlated with sgACC brain activity and connectivity. These findings suggest that sgACC-associated prefrontal-limbic structures are associated with the therapeutic effects of ECT in MDD.

Aim: The 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., Methods: A 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., Results: Immediately 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., Conclusion: The 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.

Selective serotonin reuptake inhibitors (SSRIs) are widely used drugs for major depressive disorder (MDD), although the treatment outcomes vary in different people. The vesicular glutamate transporter 1 coded by SLC17A7 gene has been reported associated with MDD. According to its role in glutamate transmission, it is reasonable to consider it as a potential pharmacogenetic candidate in SSRI treatment. A total of 290 MDD patients who had been taking SSRIs for 6 weeks were recruited. Their genotypes were assessed for the presence of 4 single-nucleotide polymorphisms, which were selected from either the HapMap Chinese ethnic group or the literature report. Treatment effects were evaluated by the change rate of Hamilton Rating Scale for Depression. After the adjustment for the false discovery rate, 1 single-nucleotide polymorphism (rs74174284, false discovery rate; P = 0.032) demonstrated significant association with SSRI treatment response at week 6. Our results suggest that genetic variants in the SLC17A7 gene may be indicators of treatment response in MDD patients treated by SSRIs.

One highly multi-drug-resistant, mucus-producing and foul-smelling strain of Klebsiella ozaenae was isolated from a patient in the ICU of a Chinese tertiary hospital. MICs of several clinical antimicrobials against the strain were obtained using the Vitek-2 Compact System with AST-GN13 cards and resistance genes were evaluated by PCR and gene sequencing. The strain was resistant to most of the β-lactams and quinolones tested and carried several antibiotic resistance genes, including bla(KPC-2), bla(TEM-98), bla(CTX-M-3), bla(SHV-26) and qnrS. To our knowledge, this is the first report of β-lactam and quinolone resistance genes co-existing in a K. ozaenae strain in China.

Dermatopontin (DPT) was recently found as a downstream target of vitamin D receptor, which is a key molecule in the 1,25-dihydroxy-vitamin D(3) anti-hepatoma proliferation pathway. MCTx-1 from Millepora, a homolog of DPT, is identified as a cytotoxin towards leukemia cells. The aim of this study was to analyze DPT expression in hepatocellular carcinoma (HCC) based on the analysis for DPT gene in normal tissues in order to estimate its function in the progression of HCC. DPT mRNA expression was analyzed in normal tissues and HCC cell lines by RT-PCR, and in HCC tissue by RT-PCR and real-time PCR. Its protein was examined in HCC tissues by Western blot and immunohistochemistry assays. Meanwhile, transforming growth factor-beta1 (TGF-beta1) that is closely associated with HCC and DPT was observed by immunohistochemistry in HCC tissues. The results showed that DPT mRNA was strongly expressed in human fetal and adult liver, kidney, and spleen, weakly in ovary and heart, and absent in other tissues and HCC cell lines examined. Its mRNA was significantly downregulated in HCC tissues, while its protein was weakly expressed in tumor compared with non-tumor. DPT is located mainly in the cytoplasm of several cell types in the liver; it has been identified also in the extracellular matrix of the skin. TGF-beta1 was observed in extensive tumor tissue of HCC. This fact suggests that DPT can play various roles in different tissues and might be a molecule related to carcinogenesis and the progression of HCC via possible interaction with TGF-beta1 and other potential mechanisms.

Although it has been studied for years by the computer vision and machine learning communities, image annotation is still far from practical. In this paper, we propose a novel attempt at model-free image annotation, which is a data-driven approach that annotates images by mining their search results. Some 2.4 million images with their surrounding text are collected from a few photo forums to support this approach. The entire process is formulated in a divide-and-conquer framework where a query keyword is provided along with the uncaptioned image to improve both the effectiveness and efficiency. This is helpful when the collected data set is not dense everywhere. In this sense, our approach contains three steps: 1) the search process to discover visually and semantically similar search results, 2) the mining process to identify salient terms from textual descriptions of the search results, and 3) the annotation rejection process to filter out noisy terms yielded by Step 2. To ensure real-time annotation, two key techniques are leveraged-one is to map the high-dimensional image visual features into hash codes, the other is to implement it as a distributed system, of which the search and mining processes are provided as Web services. As a typical result, the entire process finishes in less than 1 second. Since no training data set is required, our approach enables annotating with unlimited vocabulary and is highly scalable and robust to outliers. Experimental results on both real Web images and a benchmark image data set show the effectiveness and efficiency of the proposed algorithm. It is also worth noting that, although the entire approach is illustrated within the divide-and conquer framework, a query keyword is not crucial to our current implementation. We provide experimental results to prove this.