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.

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.

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.

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.

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.

The mitochondrial RNA polymerase POLRMT promotes skin squamous cell carcinoma cell growth.

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.

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.

An Artificial-Intelligence-Based Automated Grading and Lesions Segmentation System for Myopic Maculopathy Based on Color Fundus Photographs.

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.

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.

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.

Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning.

AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema (DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness (CRT) and apply it in follow-up of DME patients. METHODS: Optical coherence tomography (OCT) scans of 229 eyes from 160 patients were collected. We manually annotated cystoid macular edema (CME), subretinal fluid (SRF) and fovea as ground truths. Deep convolution neural networks (DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation (OCR) for fluid (CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study (ETDRS) grid. RESULTS: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients (DSC) of segmentation reached 0.78 (CME), 0.82 (SRF), and 0.95 (retina). In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 µm. The generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 µm. Thickness maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT B-scan. CONCLUSION: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.

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.

Development and evaluation of a deep learning model for the detection of multiple fundus diseases based on colour fundus photography.

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.

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.

Sex-Specific Association between Antioxidant Defense System and Therapeutic Response to Risperidone in Schizophrenia: A Prospective Longitudinal Study.

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.

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.

Inorganic Lead-Free B-γ-CsSnI3 Perovskite Solar Cells Using Diverse Electron-Transporting Materials: A Simulation Study.

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

Plasma Lysophosphatidylcholine and Lysophosphatidylethanolamine Levels Were Associated With the Therapeutic Response to Olanzapine in Female Antipsychotics-naïve First-episode Patients With Schizophrenia.

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.

A multicenter survey of perioperative anxiety in China: Pre- and postoperative associations.

OBJECTIVES: To describe patient characteristics associated with preoperative anxiety and subsequently assess the relationship between preoperative anxiety and postoperative anxiety, pain, sleep quality, nausea and vomiting. METHODS: The study collected data from patients undergoing elective operation from 12 hospitals in China. The State-Trait Anxiety Inventory (STAI) and the Athens Insomnia Scale (AIS) were used to assess anxiety and sleep quality before surgery. Evaluations of anxiety, pain, sleep quality, nausea and vomiting were quantified using the Visual Analogue Scale on postoperative days 1 and 2. RESULTS: Data from 997 patients were analyzed. Preoperatively, 258 (25.9%) patients had high anxiety (STAI-State>44). Multivariate analyses showed a significant relationship between high anxiety and female gender (OR: 1.66, 95% CI: 1.08-2.57, p = 0.02), highly invasive surgery (OR: 2.29, 95% CI: 1.29-4.06, p = 0.005), higher trait anxiety (OR: 1.24, 95% CI: 1.20-1.28, p < 0.001) and insomnia (AIS ≥ 6, OR: 1.79, 95% CI: 1.17-2.76, p = 0.008). Preoperative anxiety demonstrated a negative correlation with postoperative anxiety following highly invasive surgery; this became a positive relationship following less invasive surgery. Preoperative anxiety was also positively related to postoperative pain and poor sleep quality. The correlation between preoperative anxiety and postoperative nausea and vomiting was not statistically significant. CONCLUSION: Female gender, highly invasive surgery, higher trait anxiety and insomnia are independent risk factors for high preoperative anxiety. Surgical invasiveness influences association between pre- and postoperative anxiety. Higher preoperative anxiety is related to poorer sleep quality and more severe pain postoperatively.

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.