A hybrid Neural Network Model for Joint Prediction of Presence and Period Assertions of Medical Events in Clinical Notes

In this paper, we propose a novel neural network architecture for clinical text mining. We formulate this hybrid neural network model (HNN), composed of recurrent neural network and deep residual network, to jointly predict the presence and period assertion values associated with medical events in clinical texts. We evaluate the effectiveness of our model on a corpus of expert-annotated longitudinal Electronic Health Records (EHR) notes from Cancer patients. Our experiments show that HNN improves the joint assertion classification accuracy as compared to conventional baselines

MetaMT,a MetaLearning Method Leveraging Multiple Domain Data for Low Resource Machine Translation

Manipulating training data leads to robust neural models for MT

Detection of Bleeding Events in Electronic Health Record Notes Using Convolutional Neural Network Models Enhanced With Recurrent Neural Network Autoencoders: Deep Learning Approach

BACKGROUND: Bleeding events are common and critical and may cause significant morbidity and mortality. High incidences of bleeding events are associated with cardiovascular disease in patients on anticoagulant therapy. Prompt and accurate detection of bleeding events is essential to prevent serious consequences. As bleeding events are often described in clinical notes, automatic detection of bleeding events from electronic health record (EHR) notes may improve drug-safety surveillance and pharmacovigilance. OBJECTIVE: We aimed to develop a natural language processing (NLP) system to automatically classify whether an EHR note sentence contains a bleeding event. METHODS: We expert annotated 878 EHR notes (76,577 sentences and 562,630 word-tokens) to identify bleeding events at the sentence level. This annotated corpus was used to train and validate our NLP systems. We developed an innovative hybrid convolutional neural network (CNN) and long short-term memory (LSTM) autoencoder (HCLA) model that integrates a CNN architecture with a bidirectional LSTM (BiLSTM) autoencoder model to leverage large unlabeled EHR data. RESULTS: HCLA achieved the best area under the receiver operating characteristic curve (0.957) and F1 score (0.938) to identify whether a sentence contains a bleeding event, thereby surpassing the strong baseline support vector machines and other CNN and autoencoder models. CONCLUSIONS: By incorporating a supervised CNN model and a pretrained unsupervised BiLSTM autoencoder, the HCLA achieved high performance in detecting bleeding events

Carotenoids synthesis affects the salt tolerance mechanism of Rhodopseudomonas palustris

Rhodopseudomonas palustris CGA009 is a Gram-negative, purple non-sulfur, metabolically diverse bacterium with wide-ranging habitats. The extraordinary ability of R. palustris to decompose a variety of raw materials and convert them into high-value products makes it an attractive host for biotechnology and industrial applications. However, being a freshwater bacterium R. palustris has limited application in highly-saline environments. Therefore, it is of great significance to obtain the salt-tolerant strain of R. palustris and understand its tolerance mechanism. In this study, R. palustris CGA009 was successfully evolved into eight salt-tolerant strains using an adaptive laboratory evolution technique. RPAS-11 (R. palustris anti-salt strain 11) was selected as the best salt-tolerant strain and was used in further studies to explore the salt-tolerance mechanism. The expression of most genes associated with the carotenoid synthesis in RPAS-11 increased significantly under high concentration of salt stress, suggesting that carotenoid synthesis is one of the reasons for the salt tolerance of RPAS-11. Gene overexpression and knockout experiments were performed to get clear about the role of carotenoids in salt stress tolerance. RPAS-11-IDI, the mutant with overexpression of IDI (Isopentenyl diphosphate isomerase) exhibited enhanced salt tolerance, whereas the knockout mutant CGA009-∆crtI showed a decline in salt tolerance. In addition, the results indicated that rhodopin, a carotenoid compound, was the key pigment responsible for the salt tolerance in R. palustris. Furthermore, the production of lycopene, a widely-used carotenoid, was also increased. Taken together, our research helps to deepen the understanding of the salt tolerance mechanism of R. palustris and also widens the application of R. palustris in highly-saline environments

Early Prediction of Alzheimers Disease Leveraging Symptom Occurrences from Longitudinal Electronic Health Records of US Military Veterans

Early prediction of Alzheimer's disease (AD) is crucial for timely intervention and treatment. This study aims to use machine learning approaches to analyze longitudinal electronic health records (EHRs) of patients with AD and identify signs and symptoms that can predict AD onset earlier. We used a case-control design with longitudinal EHRs from the U.S. Department of Veterans Affairs Veterans Health Administration (VHA) from 2004 to 2021. Cases were VHA patients with AD diagnosed after 1/1/2016 based on ICD-10-CM codes, matched 1:9 with controls by age, sex and clinical utilization with replacement. We used a panel of AD-related keywords and their occurrences over time in a patient's longitudinal EHRs as predictors for AD prediction with four machine learning models. We performed subgroup analyses by age, sex, and race/ethnicity, and validated the model in a hold-out and "unseen" VHA stations group. Model discrimination, calibration, and other relevant metrics were reported for predictions up to ten years before ICD-based diagnosis. The study population included 16,701 cases and 39,097 matched controls. The average number of AD-related keywords (e.g., "concentration", "speaking") per year increased rapidly for cases as diagnosis approached, from around 10 to over 40, while remaining flat at 10 for controls. The best model achieved high discriminative accuracy (ROCAUC 0.997) for predictions using data from at least ten years before ICD-based diagnoses. The model was well-calibrated (Hosmer-Lemeshow goodness-of-fit p-value = 0.99) and consistent across subgroups of age, sex and race/ethnicity, except for patients younger than 65 (ROCAUC 0.746). Machine learning models using AD-related keywords identified from EHR notes can predict future AD diagnoses, suggesting its potential use for identifying AD risk using EHR notes, offering an affordable way for early screening on large population.Comment: 24 page

Dissecting causal associations of type 2 diabetes with 111 types of ocular conditions: a Mendelian randomization study

BackgroundDespite the well-established findings of a higher incidence of retina-related eye diseases in patients with diabetes, there is less investigation into the causal relationship between diabetes and non-retinal eye conditions, such as age-related cataracts and glaucoma.MethodsWe performed Mendelian randomization (MR) analysis to examine the causal relationship between type 2 diabetes mellitus (T2DM) and 111 ocular diseases. We employed a set of 184 single nucleotide polymorphisms (SNPs) that reached genome-wide significance as instrumental variables (IVs). The primary analysis utilized the inverse variance-weighted (IVW) method, with MR-Egger and weighted median (WM) methods serving as supplementary analyses.ResultsThe results revealed suggestive positive causal relationships between T2DM and various ocular conditions, including “Senile cataract” (OR= 1.07; 95% CI: 1.03, 1.11; P=7.77×10-4), “Glaucoma” (OR= 1.08; 95% CI: 1.02, 1.13; P=4.81×10-3), and “Disorders of optic nerve and visual pathways” (OR= 1.10; 95% CI: 0.99, 1.23; P=7.01×10-2).ConclusionOur evidence supports a causal relationship between T2DM and specific ocular disorders. This provides a basis for further research on the importance of T2DM management and prevention strategies in maintaining ocular health

RNA-Seq Analyses of Midgut and Fat Body Tissues Reveal the Molecular Mechanism Underlying Spodoptera litura Resistance to Tomatine

Plants produce secondary metabolites to provide chemical defense against herbivorous insects, whereas insects can induce the expression of detoxification metabolism-related unigenes in counter defense to plant xenobiotics. Tomatine is an important secondary metabolite in tomato (Lycopersicon esculentum L.) that can protect the plant from bacteria and insects. However, the mechanism underlying the adaptation of Spodoptera litura, a major tomato pest, to tomatine in tomato is largely unclear. In this study, we first found that the levels of tomatine in tomatoes subjected to S. litura treatment were significantly increased. Second, we confirmed the inhibitory effect of tomatine on S. litura by adding moderate amounts of commercial tomatine to an artificial diet. Then, we utilized RNA-Seq to compare the differentially expressed genes (DEGs) in the midgut and fat body tissues of S. litura exposed to an artificial diet supplemented with tomatine. In total, upon exposure to tomatine, 134 and 666 genes were upregulated in the S. litura midgut and fat body, respectively. These DEGs comprise a significant number of detoxification-related genes, including 7 P450 family genes, 8 glutathione S-transferases (GSTs) genes, 6 ABC transport enzyme genes, 9 UDP-glucosyltransferases genes and 3 carboxylesterases genes. Moreover, KEGG analysis demonstrated that the upregulated genes were enriched in xenobiotic metabolism by cytochrome P450s, ABC transporters and drug metabolism by other enzymes. Furthermore, as numerous GSTs were induced by tomatine in S. litura, we chose one gene, namely GSTS1, to confirm the detoxification function on tomatine. Expression profiling revealed that GSTS1 transcripts were mainly expressed in larvae, and the levels were the highest in the midgut. Finally, when larvae were injected with double-stranded RNA specific to GSTS1, the transcript levels in the midgut and fat body decreased, and the negative effect of the plant xenobiotic tomatine on larval growth was magnified. These results preliminarily clarified the molecular mechanism underlying the resistance of S. litura to tomatine, establishing a foundation for subsequent pest control