Plasma linoelaidyl carnitine levels positively correlated with symptom improvement in olanzapine-treated first-episode drug-naïve schizophrenia.

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.

Automated detection of severe diabetic retinopathy using deep learning method.

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.

Deep learning-based detection and stage grading for optimising diagnosis of diabetic retinopathy.

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.

Development of a deep-learning system for detection of lattice degeneration, retinal breaks, and retinal detachment in tessellated eyes using ultra-wide-field fundus images: a pilot study.

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.

[Association of CHRNA4 gene polymorphisms with response to antidepressant among ethnic Han Chinese].

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.

[Association of TPH2 gene polymorphism with response to antidepressant treatment among ethnic Han Chinese population].

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.

Electroconvulsive Therapy-Induced Brain Structural and Functional Changes in Major Depressive Disorders: A Longitudinal Study.

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.

Changes in Problem-Solving Capacity and Association With Spontaneous Brain Activity After a Single Electroconvulsive Treatment in Major Depressive Disorder.

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.

Reorganization of Anatomical Connectome following Electroconvulsive Therapy in Major Depressive Disorder.

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.

SLC17A7 gene may be the indicator of selective serotonin reuptake inhibitor treatment response in the Chinese Han population.

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.

Sudden ventricular fibrillation due to absence of pericardium in left upper lobectomy -a case report.

BACKGROUND: Congenital absence of the pericardium (CAP) is a rare cardiac abnormality. As pericardial defects are usually asymptomatic, most cases are diagnosed during surgery or on autopsy. The patient in this case was found to have CAP during thoracoscope. CASE: We present the unusual case of a 69-year-old patient with CAP who experienced sudden ventricular arrhythmia and developed ventricular fibrillation during left upper lobectomy. Surgical operations, the lateral decubitus position, and other external stimuli may be important risk factors for ventricular fibrillation. The patient regained sinus rhythm soon after intrathoracic cardiac compression and pharmacological treatment, including lidocaine spray (2%, 10 ml) administered to the heart surface. The surgery was then completed without any additional instances of ventricular arrhythmia. CONCLUSIONS: Patients with CAP are more susceptible to cardiac-related adverse events during thoracotomy or thoracoscopy. Treatment of ventricular arrhythmias that occur during lung resection in patients with CAP should be emphasized.

Ozonated autohemotherapy combined with pulsed radiofrequency in the treatment of thoracic postherpetic neuralgia in older adults: a retrospective study.

Postherpetic neuralgia (PHN) seriously affects the quality of life of the elderly population. This study aimed to evaluate the efficacy of ozonated autohemotherapy (O3-AHT) combined with pulsed radiofrequency (PRF) in the treatment of thoracic PHN in older adults. The medical records of patients with thoracic PHN aged 65 years and older from June 2018 until March 2021 in Shengli Oilfield Central Hospital were reviewed. They were assigned into two groups: PRF alone (PRF group, n = 107) and PRF combined with O3-AHT (PRF + O3-AHT group, n = 109). Visual Analogue Scale for pain was evaluated at pre-treatment, 1 day, 1, 3 and 6 months after treatment. Quality of life and sleep quality were assessed using Short-Form 36 Health Survey and Athens Insomnia Scale at pre-treatment and 6 months post-treatment, respectively. The median age of patients in the PRF and PRF + O3-AHT groups were 69 (67-73) years and 68 (67-72) years, respectively. The former included 62 females and the latter included 51 females. Compared with pre-treatment, the Visual Analogue Scale scores of two groups declined at post-treatment. Patients in the PRF + O3-AHT group showed obviously lower Visual Analogue Scale scores compared with those in the PRF group at 1, 3, and 6 months after treatment and they had earlier withdrawal time for drugs. However, dizziness, tachycardia, sleepiness, and nausea were presented after combination therapy. These symptoms resolved spontaneously after a period of rest. Additionally, O3-AHT combined with PRF was associated with a significant decrease in the Athens Insomnia Scale score and with a significant improvement in every dimension of the Short-Form 36 Health Survey. To conclude, O3-AHT combined with PRF is an effective way to relieve thoracic PHN in older patients.

The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial.

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.

[Field evaluation of SD(BIOLINE) malaria antigen Plasmodium falciparum/Plasmodium vivax rapid test kit].

Four hundred and seventy-five patients with fever within 48 h were detected for Plasmodium using double blind field trials in China-Myanmar border from June to December 2011. The result showed that 202 of 475 were positive by SD(BIOLINE) kits, with 98 positive of Plasmodium falciparum and 104 positive of Plasmodium vivax. By microscope examination, 206 were positive. Taking the result of microscope examination as the reference standard, the general sensitivity and specificity were 98.1% (202/206) and 97.8% (263/269) respectively, and the general coincidence rate of SD(BIOLINE) kits with microscopy was 97.9% (465/475). The sensitivity and specificity of P. falciparum were 99.0% (98/99) and 97.8% (263/269) respectively, and the coincidence rate of SD(BIOLINE) with microscopy was 98.1% (361/368). The sensitivity and specificity of P. vivax were 97.2% (104/107) and 100% (269/269), and the coincidence rate of SD(BIOLINE) with microscopy was 99.2% (373/376). Therefore, the test results of SD(BIOLINE) are stable with a high specificity and sensitivity.

MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection.

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.

Adipose tissue macrophages as potential targets for obesity and metabolic diseases.

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.

Learning Two-Stream CNN for Multi-Modal Age-Related Macular Degeneration Categorization.

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.

Kynurenine pathway metabolites and therapeutic response to olanzapine in female patients with schizophrenia: A longitudinal study.

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.

Dual Encoding for Video Retrieval by Text.

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_space.

Antioxidant Enzymes and Weight Gain in Drug-naive First-episode Schizophrenia Patients Treated with Risperidone for 12 Weeks: A Prospective Longitudinal Study.

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.