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https://www.readbyqxmd.com/read/29149763/formulation-and-anti-neurotoxic-activity-of-baicalein-incorporating-neutral-nanoliposome
#1
Farhang Aliakbari, Ali Akbar Shabani, Hassan Bardania, Hossein Mohammad-Beigi, Amir Tayaranian Marvian, Faezeh Dehghani Esmatabad, Abbas Ali Vafaei, Seyed Abbas Shojaosadati, Ali Akbar Saboury, Gunna Christiansen, Daniel E Otzen, Dina Morshedi
Despite extensive studies of the effects of herbal-derived small molecules in the biopharmaceutical and biomedical sciences, their low solubility and stability remain a challenge. Here we focus on baicalein, a small molecule showing potential against neurodegenerative diseases such as Parkinson's and Alzheimer's. However, therapeutic usage in vivo is challenged by low solubility and stability. To address this we have applied neutrally-charged nanoliposome (NLP) as carrier for baicalein. Baicalein was incorporated into NLP to form NLP-Ba at molar baicalain:lipid ratios of up to 1:3, giving a drug entrapment efficiency of 96...
November 11, 2017: Colloids and Surfaces. B, Biointerfaces
https://www.readbyqxmd.com/read/29141128/immune-enhancing-effect-of-nanometric-lactobacillus-plantarum-nf1-nlp-nf1-in-a-mouse-model-of-cyclophosphamide-induced-immunosuppression
#2
Dae-Woon Choi, Sun Young Jung, Jisu Kang, Young-Do Nam, Seong-Il Lim, Ki Tae Kim, Hee Soon Shin
Nanometric Lactobacillus plantarum nF1 (nLp-nF1) is a biogenics consisting of dead L. plantarum pre-treated with heat and a nanodispersion process. In this study, we investigated the immune-enhancing effects of nLp-nF1 in vivo and in vitro. To evaluate the immunostimulatory effects of nLp-nF1, mice immunosuppressed by cyclophosphamide (CPP) treatment were administered with nLp-nF1. As expected, CPP restricted the immune response of mice, whereas oral administration of nLp-nF1 significantly increased total IgG in serum, and cytokine production [interleukin-12 (IL-12) and tumor necrosis factor alpha (TNF-α)] in bone marrow cells...
November 15, 2017: Journal of Microbiology and Biotechnology
https://www.readbyqxmd.com/read/29135365/deep-learning-to-classify-radiology-free-text-reports
#3
Matthew C Chen, Robyn L Ball, Lingyao Yang, Nathaniel Moradzadeh, Brian E Chapman, David B Larson, Curtis P Langlotz, Timothy J Amrhein, Matthew P Lungren
Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE...
November 13, 2017: Radiology
https://www.readbyqxmd.com/read/29123330/markov-logic-networks-for-adverse-drug-event-extraction-from-text
#4
Sriraam Natarajan, Vishal Bangera, Tushar Khot, Jose Picado, Anurag Wazalwar, Vitor Santos Costa, David Page, Michael Caldwell
Adverse drug events (ADEs) are a major concern and point of emphasis for the medical profession, government, and society. A diverse set of techniques from epidemiology, statistics, and computer science are being proposed and studied for ADE discovery from observational health data (e.g., EHR and claims data), social network data (e.g., Google and Twitter posts), and other information sources. Methodologies are needed for evaluating, quantitatively measuring, and comparing the ability of these various approaches to accurately discover ADEs...
May 2017: Knowledge and Information Systems
https://www.readbyqxmd.com/read/29121053/adept-a-semantically-enriched-pipeline-for-extracting-adverse-drug-events-from-free-text-electronic-health-records
#5
Ehtesham Iqbal, Robbie Mallah, Daniel Rhodes, Honghan Wu, Alvin Romero, Nynn Chang, Olubanke Dzahini, Chandra Pandey, Matthew Broadbent, Robert Stewart, Richard J B Dobson, Zina M Ibrahim
Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals...
2017: PloS One
https://www.readbyqxmd.com/read/29117551/distinct-roles-of-sensory-neurons-in-mediating-pathogen-avoidance-and-neuropeptide-dependent-immune-regulation
#6
Xiou Cao, Rie Kajino-Sakamoto, Argenia Doss, Alejandro Aballay
Increasing evidence implies an extensive and universal interaction between the immune system and the nervous system. Previous studies showed that OCTR-1, a neuronal G-protein-coupled receptor (GPCR) analogous to human norepinephrine receptors, functions in sensory neurons to control the gene expression of both microbial killing pathways and the unfolded protein response (UPR) in Caenorhabditis elegans. Here, we found that OCTR-1-expressing neurons, ASH, are involved in controlling innate immune pathways. In contrast, another group of OCTR-1-expressing neurons, ASI, was shown to promote pathogen avoidance behavior...
November 7, 2017: Cell Reports
https://www.readbyqxmd.com/read/29109070/artificial-intelligence-learning-semantics-via-external-resources-for-classifying-diagnosis-codes-in-discharge-notes
#7
Chin Lin, Chia-Jung Hsu, Yu-Sheng Lou, Shih-Jen Yeh, Chia-Cheng Lee, Sui-Lung Su, Hsiang-Cheng Chen
BACKGROUND: Automated disease code classification using free-text medical information is important for public health surveillance. However, traditional natural language processing (NLP) pipelines are limited, so we propose a method combining word embedding with a convolutional neural network (CNN). OBJECTIVE: Our objective was to compare the performance of traditional pipelines (NLP plus supervised machine learning models) with that of word embedding combined with a CNN in conducting a classification task identifying International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes in discharge notes...
November 6, 2017: Journal of Medical Internet Research
https://www.readbyqxmd.com/read/29104964/a-machine-learning-algorithm-for-identifying-atopic-dermatitis-in-adults-from-electronic-health-records
#8
Erin Gustafson, Jennifer Pacheco, Firas Wehbe, Jonathan Silverberg, William Thompson
The current work aims to identify patients with atopic dermatitis for inclusion in genome-wide association studies (GWAS). Here we describe a machine learning-based phenotype algorithm. Using the electronic health record (EHR), we combined coded information with information extracted from encounter notes as features in a lasso logistic regression. Our algorithm achieves high positive predictive value (PPV) and sensitivity, improving on previous algorithms with low sensitivity. These results demonstrate the utility of natural language processing (NLP) and machine learning for EHR-based phenotyping...
August 2017: IEEE International Conference on Healthcare Informatics IEEE International Conference on Healthcare Informatics
https://www.readbyqxmd.com/read/29094145/population-based-analysis-of-histologically-confirmed-melanocytic-proliferations-using-natural-language-processing
#9
Jason P Lott, Denise M Boudreau, Ray L Barnhill, Martin A Weinstock, Eleanor Knopp, Michael W Piepkorn, David E Elder, Steven R Knezevich, Andrew Baer, Anna N A Tosteson, Joann G Elmore
Importance: Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic understanding of skin biopsy outcomes, specifically those of melanocytic origin. Objective: To determine population-based frequencies and distribution of histologically confirmed melanocytic lesions. Design, Setting, and Participants: A natural language processing (NLP)-based analysis of EMR pathology reports of adult patients who underwent skin biopsies at a large integrated health care delivery system in the US Pacific Northwest from January 1, 2007, through December 31, 2012...
November 1, 2017: JAMA Dermatology
https://www.readbyqxmd.com/read/29093611/aggregating-and-predicting-sequence-labels-from-crowd-annotations
#10
An T Nguyen, Byron C Wallace, Junyi Jessy Li, Ani Nenkova, Matthew Lease
Despite sequences being core to NLP, scant work has considered how to handle noisy sequence labels from multiple annotators for the same text. Given such annotations, we consider two complementary tasks: (1) aggregating sequential crowd labels to infer a best single set of consensus annotations; and (2) using crowd annotations as training data for a model that can predict sequences in unannotated text. For aggregation, we propose a novel Hidden Markov Model variant. To predict sequences in unannotated text, we propose a neural approach using Long Short Term Memory...
2017: Proceedings of the Conference on Computational Linguistics
https://www.readbyqxmd.com/read/29093610/automating-biomedical-evidence-synthesis-robotreviewer
#11
Iain J Marshall, Joël Kuiper, Edward Banner, Byron C Wallace
We present RobotReviewer, an open-source web-based system that uses machine learning and NLP to semi-automate biomedical evidence synthesis, to aid the practice of Evidence-Based Medicine. RobotReviewer processes full-text journal articles (PDFs) describing randomized controlled trials (RCTs). It appraises the reliability of RCTs and extracts text describing key trial characteristics (e.g., descriptions of the population) using novel NLP methods. RobotReviewer then automatically generates a report synthesising this information...
July 2017: Proceedings of the Conference on Computational Linguistics
https://www.readbyqxmd.com/read/29089288/ranking-medical-terms-to-support-expansion-of-lay-language-resources-for-patient-comprehension-of-electronic-health-record-notes-adapted-distant-supervision-approach
#12
Jinying Chen, Abhyuday N Jagannatha, Samah J Fodeh, Hong Yu
BACKGROUND: Medical terms are a major obstacle for patients to comprehend their electronic health record (EHR) notes. Clinical natural language processing (NLP) systems that link EHR terms to lay terms or definitions allow patients to easily access helpful information when reading through their EHR notes, and have shown to improve patient EHR comprehension. However, high-quality lay language resources for EHR terms are very limited in the public domain. Because expanding and curating such a resource is a costly process, it is beneficial and even necessary to identify terms important for patient EHR comprehension first...
October 31, 2017: JMIR Medical Informatics
https://www.readbyqxmd.com/read/29084368/electronic-health-record-phenotypes-for-precision-medicine-perspectives-and-caveats-from-treatment-of-breast-cancer-at-a-single-institution
#13
Matthew K Breitenstein, Hongfang Liu, Kara N Maxwell, Jyotishman Pathak, Rui Zhang
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health record(EHR)s are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer(BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing(NLP)...
October 30, 2017: Clinical and Translational Science
https://www.readbyqxmd.com/read/29084046/using-clinical-notes-and-natural-language-processing-for-automated-hiv-risk-assessment
#14
Daniel J Feller, Jason Zucker, Michael T Yin, Peter Gordon, Noémie Elhadad
OBJECTIVE: Universal HIV screening programs are costly, labor-intensive, and often fail to identify high-risk individuals. Automated risk assessment methods that leverage longitudinal electronic health records (EHRs) could catalyze targeted screening programs. While social and behavioral determinants of health are typically captured in narrative documentation, previous analyses have considered only structured EHR fields. We examined whether natural language processing (NLP) would improve predictive models of HIV diagnosis...
October 27, 2017: Journal of Acquired Immune Deficiency Syndromes: JAIDS
https://www.readbyqxmd.com/read/29081577/neural-tree-indexers-for-text-understanding
#15
Tsendsuren Munkhdalai, Hong Yu
Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-independent tree structured model, Neural Tree Indexers (NTI) that provides a middle ground between the sequential RNNs and the syntactic tree-based recursive models...
April 2017: Proceedings of the Conference on Computational Linguistics
https://www.readbyqxmd.com/read/29079959/integrating-natural-language-processing-and-machine-learning-algorithms-to-categorize-oncologic-response-in-radiology-reports
#16
Po-Hao Chen, Hanna Zafar, Maya Galperin-Aizenberg, Tessa Cook
A significant volume of medical data remains unstructured. Natural language processing (NLP) and machine learning (ML) techniques have shown to successfully extract insights from radiology reports. However, the codependent effects of NLP and ML in this context have not been well-studied. Between April 1, 2015 and November 1, 2016, 9418 cross-sectional abdomen/pelvis CT and MR examinations containing our internal structured reporting element for cancer were separated into four categories: Progression, Stable Disease, Improvement, or No Cancer...
October 27, 2017: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
https://www.readbyqxmd.com/read/29070036/comparing-clinician-descriptions-of-frailty-and-geriatric-syndromes-using-electronic-health-records-a-retrospective-cohort-study
#17
Laura J Anzaldi, Ashwini Davison, Cynthia M Boyd, Bruce Leff, Hadi Kharrazi
BACKGROUND: Geriatric syndromes, including frailty, are common in older adults and associated with adverse outcomes. We compared patients described in clinical notes as "frail" to other older adults with respect to geriatric syndrome burden and healthcare utilization. METHODS: We conducted a retrospective cohort study on 18,341 Medicare Advantage enrollees aged 65+ (members of a large nonprofit medical group in Massachusetts), analyzing up to three years of administrative claims and structured and unstructured electronic health record (EHR) data...
October 25, 2017: BMC Geriatrics
https://www.readbyqxmd.com/read/29066897/an-annotated-corpus-with-nanomedicine-and-pharmacokinetic-parameters
#18
Nastassja A Lewinski, Ivan Jimenez, Bridget T McInnes
A vast amount of data on nanomedicines is being generated and published, and natural language processing (NLP) approaches can automate the extraction of unstructured text-based data. Annotated corpora are a key resource for NLP and information extraction methods which employ machine learning. Although corpora are available for pharmaceuticals, resources for nanomedicines and nanotechnology are still limited. To foster nanotechnology text mining (NanoNLP) efforts, we have constructed a corpus of annotated drug product inserts taken from the US Food and Drug Administration's Drugs@FDA online database...
2017: International Journal of Nanomedicine
https://www.readbyqxmd.com/read/29063569/making-sense-of-big-textual-data-for-health-care-findings-from-the-section-on-clinical-natural-language-processing
#19
A Névéol, P Zweigenbaum
Objectives: To summarize recent research and present a selection of the best papers published in 2016 in the field of clinical Natural Language Processing (NLP). Method: A survey of the literature was performed by the two section editors of the IMIA Yearbook NLP section. Bibliographic databases were searched for papers with a focus on NLP efforts applied to clinical texts or aimed at a clinical outcome. Papers were automatically ranked and then manually reviewed based on titles and abstracts. A shortlist of candidate best papers was first selected by the section editors before being peer-reviewed by independent external reviewers...
August 2017: Yearbook of Medical Informatics
https://www.readbyqxmd.com/read/29063568/capturing-the-patient-s-perspective-a-review-of-advances-in-natural-language-processing-of-health-related-text
#20
G Gonzalez-Hernandez, A Sarker, K O'Connor, G Savova
Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts...
August 2017: Yearbook of Medical Informatics
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