Efficacy of In Vitro Lithium Chloride Treatments on .

(Fabr., 1794) (Acari: Ixodidae) is parasite that spreads many diseases which are dangerous to humans and animals. Microelement lithium was found to have promising potential against the detrimental bee pest Furthermore, its effectiveness was confirmed against a major parasite of poultry, in vitro.

Lithium Chloride Shows Effectiveness against the Poultry Red Mite ().

The poultry red mite () is the main pest of poultry, causing severe problems by being a vector of several animal and human pathogens. The number of miticides is few, and their efficacy in practice implies problems of residues and resistance; therefore, the demand for a new and safe agent is constant. The present publication investigated the effectiveness of lithium chloride under in vitro conditions on poultry red mites.

Essential and Detrimental - an Update on Intracellular Iron Trafficking and Homeostasis.

Chloroplasts, mitochondria and vacuoles represent characteristic organelles of the plant cell, with a predominant function in cellular metabolism. Chloroplasts are the site of photosynthesis and therefore basic and essential for photoautotrophic growth of plants. Mitochondria produce energy during respiration and vacuoles act as internal waste and storage compartments.

A Time-and-Motion Study of Clinical Trial Eligibility Screening in a Pediatric Emergency Department.

Objective: Challenges with efficient patient recruitment including sociotechnical barriers for clinical trials are major barriers to the timely and efficacious conduct of translational studies. We conducted a time-and-motion study to investigate the workflow of clinical trial enrollment in a pediatric emergency department.

Methods: We observed clinical research coordinators during 3 clinically staffed shifts.

Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.

The objective of this study was to determine whether the Food and Drug Administration's Adverse Event Reporting System (FAERS) data set could serve as the basis of automated electronic health record (EHR) monitoring for the adverse drug reaction (ADR) subset of adverse drug events. We retrospectively collected EHR entries for 71 909 pediatric inpatient visits at Cincinnati Children's Hospital Medical Center. Natural language processing (NLP) techniques were used to identify positive diseases/disorders and signs/symptoms (DDSSs) from the patients' clinical narratives.

Suboptimal Clinical Documentation in Young Children with Severe Obesity at Tertiary Care Centers.

The prevalence of severe obesity in children has doubled in the past decade. The objective of this study is to identify the clinical documentation of obesity in young children with a BMI ≥ 99th percentile at two large tertiary care pediatric hospitals. We used a standardized algorithm utilizing data from electronic health records to identify children with severe early onset obesity (BMI ≥ 99th percentile at age <6 years).

Electronic Health Record Based Algorithm to Identify Patients with Autism Spectrum Disorder.

Objective: Cohort selection is challenging for large-scale electronic health record (EHR) analyses, as International Classification of Diseases 9th edition (ICD-9) diagnostic codes are notoriously unreliable disease predictors. Our objective was to develop, evaluate, and validate an automated algorithm for determining an Autism Spectrum Disorder (ASD) patient cohort from EHR. We demonstrate its utility via the largest investigation to date of the co-occurrence patterns of medical comorbidities in ASD.

Developing an Algorithm to Detect Early Childhood Obesity in Two Tertiary Pediatric Medical Centers.

Objective: The objective of this study is to develop an algorithm to accurately identify children with severe early onset childhood obesity (ages 1-5.99 years) using structured and unstructured data from the electronic health record (EHR).

Introduction: Childhood obesity increases risk factors for cardiovascular morbidity and vascular disease.

Will they participate? Predicting patients' response to clinical trial invitations in a pediatric emergency department.

Objective: (1) To develop an automated algorithm to predict a patient's response (ie, if the patient agrees or declines) before he/she is approached for a clinical trial invitation; (2) to assess the algorithm performance and the predictors on real-world patient recruitment data for a diverse set of clinical trials in a pediatric emergency department; and (3) to identify directions for future studies in predicting patients' participation response.

Materials And Methods: We collected 3345 patients' response to trial invitations on 18 clinical trials at one center that were actively enrolling patients between January 1, 2010 and December 31, 2012. In parallel, we retrospectively extracted demographic, socioeconomic, and clinical predictors from multiple sources to represent the patients' profiles.

Automated detection of medication administration errors in neonatal intensive care.

Objective: To improve neonatal patient safety through automated detection of medication administration errors (MAEs) in high alert medications including narcotics, vasoactive medication, intravenous fluids, parenteral nutrition, and insulin using the electronic health record (EHR); to evaluate rates of MAEs in neonatal care; and to compare the performance of computerized algorithms to traditional incident reporting for error detection.

Methods: We developed novel computerized algorithms to identify MAEs within the EHR of all neonatal patients treated in a level four neonatal intensive care unit (NICU) in 2011 and 2012. We evaluated the rates and types of MAEs identified by the automated algorithms and compared their performance to incident reporting.

Thymic Atrophy and Apoptosis of CD4+CD8+ Thymocytes in the Cuprizone Model of Multiple Sclerosis.

Previous studies on the degenerative animal model of multiple sclerosis suggested that the copper-chelator cuprizone might directly suppress T-cell functions. Peripheral T-cell function in the cuprizone model has already been explored; therefore, in the present study, we investigated, for the first time, how cuprizone feeding affects the thymus, the organ of T-cell maturation and selection. We found that even one week of cuprizone treatment induced significant thymic atrophy, affecting the cortex over the medulla.

An end-to-end hybrid algorithm for automated medication discrepancy detection.

Background: In this study we implemented and developed state-of-the-art machine learning (ML) and natural language processing (NLP) technologies and built a computerized algorithm for medication reconciliation. Our specific aims are: (1) to develop a computerized algorithm for medication discrepancy detection between patients' discharge prescriptions (structured data) and medications documented in free-text clinical notes (unstructured data); and (2) to assess the performance of the algorithm on real-world medication reconciliation data.

Methods: We collected clinical notes and discharge prescription lists for all 271 patients enrolled in the Complex Care Medical Home Program at Cincinnati Children's Hospital Medical Center between 1/1/2010 and 12/31/2013.

Increasing the efficiency of trial-patient matching: automated clinical trial eligibility pre-screening for pediatric oncology patients.

Background: Manual eligibility screening (ES) for a clinical trial typically requires a labor-intensive review of patient records that utilizes many resources. Leveraging state-of-the-art natural language processing (NLP) and information extraction (IE) technologies, we sought to improve the efficiency of physician decision-making in clinical trial enrollment. In order to markedly reduce the pool of potential candidates for staff screening, we developed an automated ES algorithm to identify patients who meet core eligibility characteristics of an oncology clinical trial.

Automated clinical trial eligibility prescreening: increasing the efficiency of patient identification for clinical trials in the emergency department.

Objectives: (1) To develop an automated eligibility screening (ES) approach for clinical trials in an urban tertiary care pediatric emergency department (ED); (2) to assess the effectiveness of natural language processing (NLP), information extraction (IE), and machine learning (ML) techniques on real-world clinical data and trials.

Data And Methods: We collected eligibility criteria for 13 randomly selected, disease-specific clinical trials actively enrolling patients between January 1, 2010 and August 31, 2012. In parallel, we retrospectively selected data fields including demographics, laboratory data, and clinical notes from the electronic health record (EHR) to represent profiles of all 202795 patients visiting the ED during the same period.

Developing and evaluating a machine learning based algorithm to predict the need of pediatric intensive care unit transfer for newly hospitalized children.

Background: Early warning scores (EWS) are designed to identify early clinical deterioration by combining physiologic and/or laboratory measures to generate a quantified score. Current EWS leverage only a small fraction of Electronic Health Record (EHR) content. The planned widespread implementation of EHRs brings the promise of abundant data resources for prediction purposes.

Preparing an annotated gold standard corpus to share with extramural investigators for de-identification research.

Objective: The current study aims to fill the gap in available healthcare de-identification resources by creating a new sharable dataset with realistic Protected Health Information (PHI) without reducing the value of the data for de-identification research. By releasing the annotated gold standard corpus with Data Use Agreement we would like to encourage other Computational Linguists to experiment with our data and develop new machine learning models for de-identification. This paper describes: (1) the modifications required by the Institutional Review Board before sharing the de-identification gold standard corpus; (2) our efforts to keep the PHI as realistic as possible; (3) and the tests to show the effectiveness of these efforts in preserving the value of the modified data set for machine learning model development.

Phenotyping for patient safety: algorithm development for electronic health record based automated adverse event and medical error detection in neonatal intensive care.

Background: Although electronic health records (EHRs) have the potential to provide a foundation for quality and safety algorithms, few studies have measured their impact on automated adverse event (AE) and medical error (ME) detection within the neonatal intensive care unit (NICU) environment.

Objective: This paper presents two phenotyping AE and ME detection algorithms (ie, IV infiltrations, narcotic medication oversedation and dosing errors) and describes manual annotation of airway management and medication/fluid AEs from NICU EHRs.

Methods: From 753 NICU patient EHRs from 2011, we developed two automatic AE/ME detection algorithms, and manually annotated 11 classes of AEs in 3263 clinical notes.

EMR-linked GWAS study: investigation of variation landscape of loci for body mass index in children.

Unlabelled: Common variations at the loci harboring the fat mass and obesity gene (FTO), MC4R, and TMEM18 are consistently reported as being associated with obesity and body mass index (BMI) especially in adult population. In order to confirm this effect in pediatric population five European ancestry cohorts from pediatric eMERGE-II network (CCHMC-BCH) were evaluated.

Method: Data on 5049 samples of European ancestry were obtained from the Electronic Medical Records (EMRs) of two large academic centers in five different genotyped cohorts.

Developing and evaluating an automated appendicitis risk stratification algorithm for pediatric patients in the emergency department.

Objective: To evaluate a proposed natural language processing (NLP) and machine-learning based automated method to risk stratify abdominal pain patients by analyzing the content of the electronic health record (EHR).

Methods: We analyzed the EHRs of a random sample of 2100 pediatric emergency department (ED) patients with abdominal pain, including all with a final diagnosis of appendicitis. We developed an automated system to extract relevant elements from ED physician notes and lab values and to automatically assign a risk category for acute appendicitis (high, equivocal, or low), based on the Pediatric Appendicitis Score.

Evaluating the impact of pre-annotation on annotation speed and potential bias: natural language processing gold standard development for clinical named entity recognition in clinical trial announcements.

Objective: To present a series of experiments: (1) to evaluate the impact of pre-annotation on the speed of manual annotation of clinical trial announcements; and (2) to test for potential bias, if pre-annotation is utilized.

Methods: To build the gold standard, 1400 clinical trial announcements from the clinicaltrials.gov website were randomly selected and double annotated for diagnoses, signs, symptoms, Unified Medical Language System (UMLS) Concept Unique Identifiers, and SNOMED CT codes.

Mining FDA drug labels for medical conditions.

Background: Cincinnati Children's Hospital Medical Center (CCHMC) has built the initial Natural Language Processing (NLP) component to extract medications with their corresponding medical conditions (Indications, Contraindications, Overdosage, and Adverse Reactions) as triples of medication-related information ([(1) drug name]-[(2) medical condition]-[(3) LOINC section header]) for an intelligent database system, in order to improve patient safety and the quality of health care. The Food and Drug Administration's (FDA) drug labels are used to demonstrate the feasibility of building the triples as an intelligent database system task.

Methods: This paper discusses a hybrid NLP system, called AutoMCExtractor, to collect medical conditions (including disease/disorder and sign/symptom) from drug labels published by the FDA.

Web 2.0-based crowdsourcing for high-quality gold standard development in clinical natural language processing.

Background: A high-quality gold standard is vital for supervised, machine learning-based, clinical natural language processing (NLP) systems. In clinical NLP projects, expert annotators traditionally create the gold standard. However, traditional annotation is expensive and time-consuming.

Building gold standard corpora for medical natural language processing tasks.

We present the construction of three annotated corpora to serve as gold standards for medical natural language processing (NLP) tasks. Clinical notes from the medical record, clinical trial announcements, and FDA drug labels are annotated. We report high inter-annotator agreements (overall F-measures between 0.

A sequence labeling approach to link medications and their attributes in clinical notes and clinical trial announcements for information extraction.

Objective: The goal of this work was to evaluate machine learning methods, binary classification and sequence labeling, for medication-attribute linkage detection in two clinical corpora.

Data And Methods: We double annotated 3000 clinical trial announcements (CTA) and 1655 clinical notes (CN) for medication named entities and their attributes. A binary support vector machine (SVM) classification method with parsimonious feature sets, and a conditional random fields (CRF)-based multi-layered sequence labeling (MLSL) model were proposed to identify the linkages between the entities and their corresponding attributes.