Pharmaceutical firms have begun offering online prescription management systems to facilitate prescription processing. This study evaluated the impact of the HUMIRA Complete Pro (HCPro) online prescription management system on the rate of abandonment and the time to first fill for patients prescribed adalimumab (ADA). A retrospective cohort analysis of patients initiating ADA treatment with or without use of the HCPro online prescription processing system was used to evaluate the impact of HCPro on treatment initiation outcomes.

Plants are important as foods, pharmaceuticals, biorenewable chemicals, fuel resources, bioremediation tools and general tools for recombinant technology. The study of plant biological pathways is advanced by easy access to integrated data sources. Today, various plant data sources are scattered throughout the web, making it increasingly complicated to build comprehensive datasets.

Pathway-oriented experimental and computational studies have led to a significant accumulation of biological knowledge concerning three major types of biological pathway events: molecular signaling events, gene regulation events, and metabolic reaction events. A pathway consists of a series of molecular pathway events that link molecular entities such as proteins, genes, and metabolites. There are approximately 300 biological pathway resources as of April 2009 according to the Pathguide database; however, these pathway databases generally have poor coverage or poor quality, and are difficult to integrate, due to syntactic-level and semantic-level data incompatibilities.

In the Internet era, many web-based appointment systems for hospitals have been established to replace traditional systems. Our study aimed to highlight the features of online appointment systems for hospitals in Taiwan, where patients can visit outpatient departments without a referral.

The integration of genomics, transcriptomics, proteomics and phenotypic traits across genetically diverse populations is a powerful approach to discover novel biological regulators. The increasing volume of complex data require new and easy-to-use tools accessible to a variety of scientists for the discovery and visualization of functionally relevant associations. To meet this requirement, we developed CoffeeProt, an open-source tool that analyses genetic variants associated to protein networks, other omics datatypes and phenotypic traits. CoffeeProt uses transcriptomics or proteomics data to perform correlation network analyses and annotates results with protein-protein interactions, subcellular localisations and drug associations. It then integrates genetic variants associated with gene expression (eQTLs) or protein abundance (pQTLs) and includes predictions of the potential consequences of variants on gene function. Finally, genetic variants are co-mapped to molecular or phenotypic traits either provided by the user or retrieved directly from publicly available GWAS results. We demonstrate its utility with the analysis of mouse and human population data enabling the rapid identification of genetic variants associated with druggable proteins and clinical traits. We expect that CoffeeProt will serve the systems genetics and basic science research communities, leading to the discovery of novel biologically relevant associations. CoffeeProt is available at www.coffeeprot.com.

Chronic pain conditions are the top reason patients seek care, the most common reason for disability and addiction, and the biggest driver of healthcare costs; their treatment costs more than cancer, heart disease, dementia, and diabetes care. The personal impact in terms of suffering, disability, depression, suicide, and other problems is incalculable. There has been much effort to prevent many medical and dental conditions, but little effort has been directed toward preventing chronic pain. To address this deficit, a massive open online course (MOOC) was developed for students and healthcare professionals. "Preventing Chronic Pain: A Human Systems Approach" was offered by the University of Minnesota through the online platform Coursera. The first offering of this free open course was in the spring of 2014 and had 23 650 participants; 53% were patients or consumers interested in pain. This article describes the course concepts in preventing chronic pain, the analytic data from course participants, and postcourse evaluation forms.

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response. Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization. The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

Electronic nicotine delivery systems (ENDS) were created to vape nicotine e-liquids; however, social media demonstrates increased ENDS modifications to vape cannabis. Analysis of social media content helps with understanding ENDS modifications for cannabis use, overlapping markets for ENDS and cannabis, and the need for additional regulation.

Sensor drift is a common issue in E-Nose systems and various drift compensation methods have received fruitful results in recent years. Although the accuracy for recognizing diverse gases under drift conditions has been largely enhanced, few of these methods considered online processing scenarios. In this paper, we focus on building online drift compensation model by transforming two domain adaptation based methods into their online learning versions, which allow the recognition models to adapt to the changes of sensor responses in a time-efficient manner without losing the high accuracy. Experimental results using three different settings confirm that the proposed methods save large processing time when compared with their offline versions, and outperform other drift compensation methods in recognition accuracy.

For the chromatographic analysis of biological samples, sample preparation requires efficient matrix removal and retention of the analytes. The development of online pretreatment technologies provides a fully automated solution for biological sample analysis. Online pretreatment solutions improve both throughput and precision. In this study, we compared two online extraction systems, an online solid-phase extraction (SPE) system and an online turbulent flow chromatography (TFC) extraction system, which are being applied by more analysts at present. The comparison showed that the TFC extraction produced better matrix removal effects, while, peak analysis showed that the online SPE system had obvious advantages in peak shape and efficiency. Thus, we developed an automated online SPE-high performance liquid chromatography (HPLC)-diode array detector (DAD) method for the simultaneous determination of ten β-amino alcohols. The results provide a reference for analysts to choose an appropriate online pretreatment method and provide a solution for biological sample analysis of β-amino alcohol drugs.

Currently, not all children that need speech therapy have access to a therapist. With the current international shortage of speech-language pathologists (SLPs), there is a demand for online tools to support SLPs with their daily tasks. Several online speech therapy (OST) systems have been designed and proposed in the literature; however, the implementation of these systems is lacking. The technical knowledge that is needed to use these programs is a challenge for SLPs. There has been limited effort to systematically identify, analyze and report the findings of prior studies. We provide the results of an extensive literature review of OST systems for childhood speech communication disorders. We systematically review OST systems that can be used in clinical settings or from home as part of a treatment program for children with speech communication disorders. Our search strategy found 4481 papers, of which 35 were identified as focusing on speech therapy programs for speech communication disorders. The features of these programs were examined, and the main findings are extracted and presented. Our analysis indicates that most systems which are designed mainly to support the SLPs adopt and use supervised machine learning approaches that are either desktop-based or mobile-phone-based applications. Our findings reveal that speech therapy systems can provide important benefits for childhood speech. A collaboration between computer programmers and SLPs can contribute to implementing useful automated programs, leading to more children having access to good speech therapy.

Social media platforms are increasingly used by the general public as a source of information on health-related and legal concerns, among other topics. Reddit.com, one of the top 10 most visited websites in the United States, is a popular social media platform that allows users to anonymously discuss various topics, including workers' compensation (WC). Understanding the candid concerns of workers who are navigating WC systems will allow for the development of more effective educational resources that are tailored to the needs of this population.  Methods: In January-March 2023, a cross-sectional review of anonymous public posts submitted to the r/WorkersComp section of the Reddit social media website between December 2021 and December 2022 was performed. Post content was extracted from a systematic random sample and coded into themes/sub-themes and emotional tones by two independent reviewers. A third reviewer resolved any discrepancies in coding in order to reach consensus prior to data analysis. The data were analyzed using Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA).

Understanding metabolic function requires knowledge of the dynamics, interdependence, and regulation of metabolic networks. However, multiple professional societies have recognized that most undergraduate biochemistry students acquire only a surface-level understanding of metabolism. We hypothesized that guiding students through interactive computer simulations of metabolic systems would increase their ability to recognize how individual interactions between components affect the behavior of a system under different conditions. The computer simulations were designed with an interactive activity (i.e., module) that used the predict-observe-explain model of instruction to guide students through a process in which they iteratively predict outcomes, test their predictions, modify the interactions of the system, and then retest the outcomes. We found that biochemistry students using modules performed better on metabolism questions compared with students who did not use the modules. The average learning gain was 8% with modules and 0% without modules, a small to medium effect size. We also confirmed that the modules did not create or reinforce a gender bias. Our modules provide instructors with a dynamic, systems-driven approach to help students learn about metabolic regulation and equip students with important cognitive skills, such as interpreting and analyzing simulation results, and technical skills, such as building and simulating computer-based models.

Free and open-source software projects have become essential digital infrastructure over the past decade. These projects are largely created and maintained by unpaid volunteers, presenting a potential vulnerability if the projects cannot recruit and retain new volunteers. At the same time, their development on open collaborative development platforms provides a nearly complete record of the community's interactions; this affords the opportunity to study naturally occurring language dynamics at scale and in a context with massive real-world impact. The present work takes a dynamical systems view of language to understand the ways in which communicative context and community membership shape the emergence and impact of language use-specifically, sentiment and expressions of gratitude. We then present evidence that these language dynamics shape newcomers' likelihood of returning, although the specific impacts of different community responses are crucially modulated by the context of the newcomer's first contact with the community.

Whether the provision of online health care referral systems by the Indonesia National Health Insurance Agency has ensured healthcare referral compliance raises much concern due to the continuing deficit. This study examines the pattern of healthcare referral process, regional and referral compliance from 2015 to 2016. To provide comprehensive analysis on how people seek treatment, this study also aims to understand health-seeking behavior in Indonesia, the utilization of alternative treatment, and health information-seeking behavior on social media.

Research studies involving health-related online communities have focused on examining network structure to understand mechanisms underlying behavior change. Content analysis of the messages exchanged in these communities has been limited to the "social support" perspective. However, existing behavior change theories suggest that message content plays a prominent role reflecting several sociocognitive factors that affect an individual's efforts to make a lifestyle change. An understanding of these factors is imperative to identify and harness the mechanisms of behavior change in the Health 2.0 era.

Massive Open Online Courses (MOOCs) have gained in popularity over the last few years. The space of online learning resources has been increasing exponentially and has created a problem of information overload. To overcome this problem, recommender systems that can recommend learning resources to users according to their interests have been proposed. MOOCs contain a huge amount of data with the quantity of data increasing as new learners register. Traditional recommendation techniques suffer from scalability, sparsity and cold start problems resulting in poor quality recommendations. Furthermore, they cannot accommodate the incremental update of the model with the arrival of new data making them unsuitable for MOOCs dynamic environment. From this line of research, we propose a novel online recommender system, namely NoR-MOOCs, that is accurate, scales well with the data and moreover overcomes previously recorded problems with recommender systems. Through extensive experiments conducted over the COCO data-set, we have shown empirically that NoR-MOOCs significantly outperforms traditional KMeans and Collaborative Filtering algorithms in terms of predictive and classification accuracy metrics.

Online learning initiatives over the past decade have become increasingly comprehensive in their selection of courses and sophisticated in their presentation, culminating in the recent announcement of a number of consortium and startup activities that promise to make a university education on the internet, free of charge, a real possibility. At this pivotal moment it is appropriate to explore the potential for obtaining comprehensive bioinformatics training with currently existing free video resources. This article presents such a bioinformatics curriculum in the form of a virtual course catalog, together with editorial commentary, and an assessment of strengths, weaknesses, and likely future directions for open online learning in this field.

Golgi (http://www.usegolgi.com) is a prototype interactive brain map of the rat brain that helps researchers intuitively interact with neuroanatomy, connectomics, and cellular and chemical architecture. The flood of "-omic" data urges new ways to help researchers connect discrete findings to the larger context of the nervous system. Here we explore Golgi's underlying reasoning and techniques and how our design decisions balance the constraints of building both a scientifically useful and usable tool. We demonstrate how Golgi can enhance connectomic literature searches with a case study investigating a thalamocortical circuit involving the Nucleus Accumbens and we explore Golgi's potential and future directions for growth in systems neuroscience and connectomics.

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) plans to allow participants to redeem their food package benefits online, i.e., online ordering. As grocery shopping online has become more common, companies have developed strategies to market food products to customers using online (or mobile) grocery shopping platforms. There is a significant knowledge gap in how these strategies may influence WIC participants who choose to shop for WIC foods online. This review examines the relevant literature to (1) identify food marketing strategies used in online grocery shopping platforms, (2) understand how these strategies influence consumer behavior and consumer diet, and (3) consider the implications for WIC participants. A total of 1862 references were identified from a systematic database search, of which 83 were included for full-text screening and 18 were included for data extraction and evidence synthesis. The included studies provide policymakers and other stakeholders involved in developing WIC online order processes with valuable information about the factors that shape healthy food choices in the online food retail environment. Findings indicate that some marketing interventions, such as nutrition labeling and food swaps, may encourage healthier food choices in the online environment and could potentially be tailored to reinforce WIC messaging about a healthy diet.