Maternal obesity and childhood asthma risk: Exploring mediating pathways.

Background: Growing evidence for the effect of maternal obesity on childhood asthma motivates investigation of mediating pathways.

Objective: To investigate if childhood body mass index (BMI), gestational weight gain (GWG) and preterm birth mediate the association of maternal obesity on childhood asthma risk.

Methods: We used electronic medical records from mother-child pairs enrolled in Kaiser Permanente Northern California integrated healthcare system.

Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD.

MetaMSD is a proteomic software that integrates multiple quantitative mass spectrometry data analysis results using statistical summary combination approaches. By utilizing this software, scientists can combine results from their pilot and main studies to maximize their biomarker discovery while effectively controlling false discovery rates. It also works for combining proteomic datasets generated by different labeling techniques and/or different types of mass spectrometry instruments.

Gender differences in the risk and protective factors of marijuana use among U.S. College students.

Gender differences in the risk and protective factors of marijuana use among college students were explored by analyzing online survey responses from 464 undergraduates. Women perceived higher risk and used marijuana less than men, with no gender difference in peer disapproval. In addition, women had higher objective knowledge regarding the health effects of marijuana, although they exhibited lower confidence in their knowledge.

US direct-to-consumer medical service advertisements fail to provide adequate information on quality and cost of care.

Background: In the 1970s, the Federal Trade Commission declared that allowing medical providers to advertise directly to consumers would be "providing the public with truthful information about the price, quality or other aspects of their service." However, our understanding of the advertising content is highly limited.

Objective: To assess whether direct-to-consumer medical service advertisements provide relevant information on access, quality and cost of care, a content analysis was conducted.

MGMS2: Membrane glycolipid mass spectrum simulator for polymicrobial samples.

Rationale: Polymicrobial samples present unique challenges for mass spectrometric identification. A recently developed glycolipid technology has the potential to accurately identify individual bacterial species from polymicrobial samples. In order to develop and validate bacterial identification algorithms (e.

Marijuana knowledge, confidence in knowledge, and information efficacy as the protective and risk factors of marijuana use among college students.

This study explored the relationships between marijuana knowledge, confidence in knowledge, and information efficacy and marijuana use. Furthermore, the effects of the knowledge-related variables were examined on intention to use, resistance efficacy, and intention to vote for legalization. Undergraduate students ( = 215) were surveyed in Fall 2018.

Acute associations between PM and ozone concentrations and asthma exacerbations among patients with and without allergic comorbidities.

Acute effects of outdoor air pollution on asthma exacerbations may vary by asthma phenotype (allergic vs nonallergic). Associations of ambient PM and ozone concentrations with acute asthma visits (office, urgent, emergency, and hospitalization) were investigated using electronic medical records. International Classification of Disease codes were used to identify asthmatics, and classify them based on the presence or absence of an allergic comorbidity in their medical records.

Model-Based Spectral Library Approach for Bacterial Identification via Membrane Glycolipids.

By circumventing the need for a pure colony, MALDI-TOF mass spectrometry of bacterial membrane glycolipids (lipid A) has the potential to identify microbes more rapidly than protein-based methods. However, currently available bioinformatics algorithms (e.g.

MetaMSD: meta analysis for mass spectrometry data.

Mass spectrometry-based proteomics facilitate disease understanding by providing protein abundance information about disease progression. For the same type of disease studies, multiple mass spectrometry datasets may be generated. Integrating multiple mass spectrometry datasets can provide valuable information that a single dataset analysis cannot provide.

Detecting differential protein expression in large-scale population proteomics.

Motivation: Mass spectrometry (MS)-based high-throughput quantitative proteomics shows great potential in large-scale clinical biomarker studies, identifying and quantifying thousands of proteins in biological samples. However, there are unique challenges in analyzing the quantitative proteomics data. One issue is that the quantification of a given peptide is often missing in a subset of the experiments, especially for less abundant peptides.

Bioinformatics tools to identify and quantify proteins using mass spectrometry data.

Proteomics tries to understand biological function of an organism by studying its protein expressions. Mass spectrometry is used in the field of shotgun proteomics, and it generates mass spectra that are used to identify and quantify proteins in biological samples. In this chapter, we discuss the bioinformatics algorithms to analyze mass spectrometry data.

Beyond the proteome: Mass Spectrometry Special Interest Group (MS-SIG) at ISMB/ECCB 2013.

wenzhong.xiao@mgh.harvard.

Brief report: genome sequence and construction of an infectious cDNA clone of Ribgrass mosaic virus from Chinese cabbage in Korea.

Ribgrass mosaic virus (RMV) has severely decreased the production and lowered quality of Chinese cabbage co-infected with Turnip mosaic virus (63.4%) in Korea. The complete genome sequence of RMV isolated from Brassica rapa ssp.