Health Literacy, Medication Adherence, and Patient Satisfaction in Community Pharmacy

Background: Health literacy is defined as the ability to read, understand, and act on health information; almost half of adults have inadequate health literacy. Since inadequate health literacy is often followed by many negative outcomes, such as poor medication adherence, it is important to address health literacy, in order to rectify the low levels and improve outcomes. Patients see pharmacists regularly, and therefore, the pharmacist is crucial In improving medication adherence. If health literacy is a barrier to medication adherence, the community pharmacist should be able to provide health literacy-appropriate counseling to improve medication adherence. Objectives: To determine if pharmacists\u27 perception of patient health literacy and actual patient health literacy align, to assess the relationship between health literacy and medication adherence In patients who visit independent community pharmacies, to evaluate patients\u27 satisfaction with their pharmacists\u27 patient counseling, and to determine the pharmacists\u27 willingness to improve their communication technique with their patients according to their health literacy. Methods: Independent pharmacies were selected from the Cedarvlle network. Patients will complete Instruments to assess health literacy (Newest Vital Signs®), medication adherence (8-item Morisky Medication Adherence Scale©), and patient satisfaction with counseling (Likert-type questions derived from the literature). The patient satisfaction items will be peer-reviewed before finalizing the version given to the patients. Patients will consist of those at least 18 years of age, who speak English, and are obtaining a refill for a chronic condition. Results In Progress: Thus far, the selection process of pharmacies is being conducted, as well as securing the health literacy test, the medication adherence test, and formulating questions for patient satisfaction. Completion is anticipated by the end of spring 2014

Genomic Regions Associated with Root Traits under Drought Stress in Tropical Maize (Zea mays L.)

An association mapping panel, named as CIMMYT Asia association mapping (CAAM) panel, involving 396 diverse tropical maize lines were phenotyped for various structural and functional traits of roots under drought and well-watered conditions. The experiment was conducted during Kharif (summer-rainy) season of 2012 and 2013 in root phenotyping facility at CIMMYT-Hyderabad, India. The CAAM panel was genotyped to generate 955, 690 SNPs through GBS v2.7 using Illumina Hi-seq 2000/2500 at Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA. GWAS analysis was carried out using 331,390 SNPs filtered from the entire set of SNPs revealed a total of 50 and 67 SNPs significantly associated for root functional (transpiration efficiency, flowering period water use) and structural traits (rooting depth, root dry weight, root length, root volume, root surface area and root length density), respectively. In addition to this, 37 SNPs were identified for grain yield and shoot biomass under well-watered and drought stress. Though many SNPs were found to have significant association with the traits under study, SNPs that were common for more than one trait were discussed in detail. A total 18 SNPs were found to have common association with more than one trait, out of which 12 SNPs were found within or near the various gene functional regions. In this study we attempted to identify the trait specific maize lines based on the presence of favorable alleles for the SNPs associated with multiple traits. Two SNPs S3_128533512 and S7_151238865 were associated with transpiration efficiency, shoot biomass and grain yield under well-watered condition. Based on favorable allele for these SNPs seven inbred lines were identified. Similarly, four lines were identified for transpiration efficiency and shoot biomass under drought stress based on the presence of favorable allele for the common SNPs S1_211520521, S2_20017716, S3_57210184 and S7_130878458 and three lines were identified for flowering period water-use, transpiration efficiency, root dry weight and root volume based on the presence of favorable allele for the common SNPs S3_162065732 and S3_225760139

Trace-element variations and melt percolation within mantle xenoliths from Algeria

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Privacy preserving distributed learning classifiers-Sequential learning with small sets of data

Background: Artificial intelligence (AI) typically requires a significant amount of high-quality data to build reliable models, where gathering enough data within a single institution can be particularly challenging. In this study we investigated the impact of using sequential learning to exploit very small, siloed sets of clinical and imaging data to train AI models. Furthermore, we evaluated the capacity of such models to achieve equivalent performance when compared to models trained with the same data over a single centralized database. Methods: We propose a privacy preserving distributed learning framework, learning sequentially from each dataset. The framework is applied to three machine learning algorithms: Logistic Regression, Support Vector Machines (SVM), and Perceptron. The models were evaluated using four open-source datasets (Breast cancer, Indian liver, NSCLC-Radiomics dataset, and Stage III NSCLC). Findings: The proposed framework ensured a comparable predictive performance against a centralized learning approach. Pairwise DeLong tests showed no significant difference between the compared pairs for each dataset. Interpretation: Distributed learning contributes to preserve medical data privacy. We foresee this technology will increase the number of collaborative opportunities to develop robust AI, becoming the default solution in scenarios where collecting enough data from a single reliable source is logistically impossible. Distributed sequential learning provides privacy persevering means for institutions with small but clinically valuable datasets to collaboratively train predictive AI while preserving the privacy of their patients. Such models perform similarly to models that are built on a larger central dataset

Genome wide association scans for markers- S3_162065732 associated with root dry weight (RDW) and flowering period water use (WU) and S3_225760139 associated with root volume (RV) and Shoot biomass (SB) under drought stress condition.

<p>Genome wide association scans for markers- S3_162065732 associated with root dry weight (RDW) and flowering period water use (WU) and S3_225760139 associated with root volume (RV) and Shoot biomass (SB) under drought stress condition.</p

Linkage disequilibrium (LD) decay in the CAAM panel based on the SNPs

<p>Linkage disequilibrium (LD) decay in the CAAM panel based on the SNPs</p