Hospital treatment, mortality and healthcare costs in relation to socioeconomic status among people with bipolar affective disorder

BACKGROUND: Evidence regarding the relationships between the socioeconomic status and long-term outcomes of individuals with bipolar affective disorder (BPD) is lacking. AIMS: We aimed to estimate the effects of baseline socioeconomic status on longitudinal outcomes. METHOD: A national cohort of adult participants with newly diagnosed BPD was identified in 2008. The effects of personal and household socioeconomic status were explored on outcomes of hospital treatment, mortality and healthcare costs, over a 3-year follow-up period (2008–2011). RESULTS: A total of 7987 participants were recruited. The relative risks of hospital treatment and mortality were found elevated for the ones from low-income households who also had higher healthcare costs. Low premium levels did not correlate with future healthcare costs. CONCLUSIONS: Socioeconomic deprivation is associated with poorer outcome and higher healthcare costs in BPD patients. Special care should be given to those with lower socioeconomic status to improve outcomes with potential benefits of cost savings in the following years. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © 2016 The Royal College of Psychiatrists. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence

Decoupled Contrastive Learning

Contrastive learning (CL) is one of the most successful paradigms for self-supervised learning (SSL). In a principled way, it considers two augmented "views" of the same image as positive to be pulled closer, and all other images as negative to be pushed further apart. However, behind the impressive success of CL-based techniques, their formulation often relies on heavy-computation settings, including large sample batches, extensive training epochs, etc. We are thus motivated to tackle these issues and establish a simple, efficient, yet competitive baseline of contrastive learning. Specifically, we identify, from theoretical and empirical studies, a noticeable negative-positive-coupling (NPC) effect in the widely used InfoNCE loss, leading to unsuitable learning efficiency concerning the batch size. By removing the NPC effect, we propose decoupled contrastive learning (DCL) loss, which removes the positive term from the denominator and significantly improves the learning efficiency. DCL achieves competitive performance with less sensitivity to sub-optimal hyperparameters, requiring neither large batches in SimCLR, momentum encoding in MoCo, or large epochs. We demonstrate with various benchmarks while manifesting robustness as much less sensitive to suboptimal hyperparameters. Notably, SimCLR with DCL achieves 68.2% ImageNet-1K top-1 accuracy using batch size 256 within 200 epochs pre-training, outperforming its SimCLR baseline by 6.4%. Further, DCL can be combined with the SOTA contrastive learning method, NNCLR, to achieve 72.3% ImageNet-1K top-1 accuracy with 512 batch size in 400 epochs, which represents a new SOTA in contrastive learning. We believe DCL provides a valuable baseline for future contrastive SSL studies.Comment: Accepted by ECCV202

Association of metabolic syndrome with erosive esophagitis and Barrett’s esophagus in a Chinese population

AbstractBackgroundMetabolic syndrome has been highlighted as a risk factor for several gastrointestinal diseases, including gastroesophageal reflux disease and Barrett’s esophagus (BE). The aim of this study was to investigate the association of metabolic syndrome with erosive esophagitis (EE) and BE.MethodsData were retrospectively collected from patients who visited the Medical Screening Center at Taichung Veterans General Hospital, Taichung, Taiwan from January 2006 to December 2009. All patients underwent an open-access transoral upper gastrointestinal endoscopy, and serum laboratory data were collected. The exclusion criteria included prior gastric surgery, or presence of esophageal varices or peptic ulcers. These patients were assigned to groups according to their endoscopic findings as follows: (1) normal group; (2) EE group; and (3) BE group. Metabolic syndrome was diagnosed based on the International Diabetes Federation criteria.ResultsThere were 560/6499 (8.6%) patients, 214/1118 (9.6%) patients, and 19/95 (20%) patients with metabolic syndrome in the normal, EE, and BE groups, respectively. There was a significantly higher percentage of cases with hypertriglyceridemia in the EE group (67%) compared with the other groups. The BE group had significantly higher rates of central obesity (33%) and hypertension (29.5%) compared with rates in the normal and EE groups. After adjusting for confounders, the positive association with metabolic syndrome still existed in both the EE group (adjusted odds ratio=2.43; 95% confidence interval=1.02–3.44) and the BE group (adjusted odds ratio=2.82; 95% confidence interval=2.05–3.88).ConclusionOur research indicated that in fact there is a greater risk of concurrent metabolic syndrome in patients with EE or BE

Image operator learning coupled with CNN classification and its application to staff line removal

Many image transformations can be modeled by image operators that are characterized by pixel-wise local functions defined on a finite support window. In image operator learning, these functions are estimated from training data using machine learning techniques. Input size is usually a critical issue when using learning algorithms, and it limits the size of practicable windows. We propose the use of convolutional neural networks (CNNs) to overcome this limitation. The problem of removing staff-lines in music score images is chosen to evaluate the effects of window and convolutional mask sizes on the learned image operator performance. Results show that the CNN based solution outperforms previous ones obtained using conventional learning algorithms or heuristic algorithms, indicating the potential of CNNs as base classifiers in image operator learning. The implementations will be made available on the TRIOSlib project site.Comment: To appear in ICDAR 201

Hospital treatment, mortality and healthcare costs in relation to socioeconomic status among people with bipolar affective disorder

BACKGROUND: Evidence regarding the relationships between the socioeconomic status and long-term outcomes of individuals with bipolar affective disorder (BPD) is lacking. AIMS: We aimed to estimate the effects of baseline socioeconomic status on longitudinal outcomes. METHOD: A national cohort of adult participants with newly diagnosed BPD was identified in 2008. The effects of personal and household socioeconomic status were explored on outcomes of hospital treatment, mortality and healthcare costs, over a 3-year follow-up period (2008–2011). RESULTS: A total of 7987 participants were recruited. The relative risks of hospital treatment and mortality were found elevated for the ones from low-income households who also had higher healthcare costs. Low premium levels did not correlate with future healthcare costs. CONCLUSIONS: Socioeconomic deprivation is associated with poorer outcome and higher healthcare costs in BPD patients. Special care should be given to those with lower socioeconomic status to improve outcomes with potential benefits of cost savings in the following years. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © 2016 The Royal College of Psychiatrists. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) licence

Renal Protective Effect of Xiao-Chai-Hu-Tang on Diabetic Nephropathy of Type 1-Diabetic Mice

Xiao-Chai-Hu-Tang (XCHT), a traditional Chinese medicine formula consisting of seven medicinal plants, is used in the treatment of various diseases. We show here that XCHT could protect type-1 diabetic mice against diabetic nephropathy, using streptozotocin (STZ)-induced diabetic mice and high-glucose (HG)-exposed rat mesangial cell (RMC) as models. Following 4 weeks of oral administration with XCHT, renal functions and renal hypertrophy significantly improved in the STZ-diabetic mice, while serum glucose was only moderately reduced compared to vehicle treatment. Treatment with XCHT in the STZ-diabetic mice and HG-exposed RMC resulted in a decrease in expression levels of TGF-β1, fibronectin, and collagen IV, with concomitant increase in BMP-7 expression. Data from DPPH assay, DHE stain, and CM-H2DCFDA analysis indicated that XCHT could scavenge free radicals and inhibit high-glucose-induced ROS in RMCs. Taken together, these results suggest that treatment with XCHT can improve renal functions in STZ-diabetic mice, an effect that is potentially mediated through decreasing oxidative stress and production of TGF-β1, fibronectin, and collagen IV in the kidney during development of diabetic nephropathy. XCHT, therefore merits further investigation for application to improve renal functions in diabetic disorders

BIOSMILE: A semantic role labeling system for biomedical verbs using a maximum-entropy model with automatically generated template features

Background: Bioinformatics tools for automatic processing of biomedical literature are invaluable for both the design and interpretation of large-scale experiments. Many information extraction (IE) systems that incorporate natural language processing (NLP) techniques have thus been developed for use in the biomedical field. A key IE task in this field is the extraction of biomedical relations, such as protein-protein and gene-disease interactions. However, most biomedical relation extraction systems usually ignore adverbial and prepositional phrases and words identifying location, manner, timing, and condition, which are essential for describing biomedical relations. Semantic role labeling (SRL) is a natural language processing technique that identifies the semantic roles of these words or phrases in sentences and expresses them as predicate-argument structures. We construct a biomedical SRL system called BIOSMILE that uses a maximum entropy (ME) machine-learning model to extract biomedical relations. BIOSMILE is trained on BioProp, our semi-automatic, annotated biomedical proposition bank. Currently, we are focusing on 30 biomedical verbs that are frequently used or considered important for describing molecular events. Results: To evaluate the performance of BIOSMILE, we conducted two experiments to (1) compare the performance of SRL systems trained on newswire and biomedical corpora; and (2) examine the effects of using biomedical-specific features. The experimental results show that using BioProp improves the F-score of the SRL system by 21.45% over an SRL system that uses a newswire corpus. It is noteworthy that adding automatically generated template features improves the overall F-score by a further 0.52%. Specifically, ArgM-LOC, ArgM-MNR, and Arg2 achieve statistically significant performance improvements of 3.33%, 2.27%, and 1.44%, respectively. Conclusion: We demonstrate the necessity of using a biomedical proposition bank for training SRL systems in the biomedical domain. Besides the different characteristics of biomedical and newswire sentences, factors such as cross-domain framesets and verb usage variations also influence the performance of SRL systems. For argument classification, we find that NE (named entity) features indicating if the target node matches with NEs are not effective, since NEs may match with a node of the parsing tree that does not have semantic role labels in the training set. We therefore incorporate templates composed of specific words, NE types, and POS tags into the SRL system. As a result, the classification accuracy for adjunct arguments, which is especially important for biomedical SRL, is improved significantly