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\"This book aims to highlight the gaps and the transparency issues in the clinical research and trials processes and how there is a lack of information flowing back to researchers and patients involved in those trials. Lack of data transparency is an underlying theme within the clinical research world and causes issues of corruption, fraud, errors and a problem of reproducibility. Blockchain can prove to be a method to ensure a much more joined up and integrated approach to data sharing and improving patient outcomes. Surveys undertaken by creditable organisations in the healthcare industry are analysed in this book that show strong support for using blockchain technology regarding strengthening data security, interoperability and a range of beneficial use cases where mostly all respondents of the surveys believe blockchain will be important for the future of the healthcare industry. Another aspect considered in the book is the coming surge of healthcare wearables using Internet of Things (IoT) and the prediction that the current capacity of centralised networks will not cope with the demands of data storage. The benefits are great for clinical research, but will add more pressure to the transparency of clinical trials and how this is managed unless a secure mechanism like, blockchain is used\"--Publisher's description.

Engagement with scientific manuscripts is frequently facilitated by Twitter and other social media platforms. As such, the demographics of a paper's social media audience provide a wealth of information about how scholarly research is transmitted, consumed, and interpreted by online communities. By paying attention to public perceptions of their publications, scientists can learn whether their research is stimulating positive scholarly and public thought. They can also become aware of potentially negative patterns of interest from groups that misinterpret their work in harmful ways, either willfully or unintentionally, and devise strategies for altering their messaging to mitigate these impacts. In this study, we collected 331,696 Twitter posts referencing 1,800 highly tweeted bioRxiv preprints and leveraged topic modeling to infer the characteristics of various communities engaging with each preprint on Twitter. We agnostically learned the characteristics of these audience sectors from keywords each user's followers provide in their Twitter biographies. We estimate that 96% of the preprints analyzed are dominated by academic audiences on Twitter, suggesting that social media attention does not always correspond to greater public exposure. We further demonstrate how our audience segmentation method can quantify the level of interest from nonspecialist audience sectors such as mental health advocates, dog lovers, video game developers, vegans, bitcoin investors, conspiracy theorists, journalists, religious groups, and political constituencies. Surprisingly, we also found that 10% of the preprints analyzed have sizable (>5%) audience sectors that are associated with right-wing white nationalist communities. Although none of these preprints appear to intentionally espouse any right-wing extremist messages, cases exist in which extremist appropriation comprises more than 50% of the tweets referencing a given preprint. These results present unique opportunities for improving and contextualizing the public discourse surrounding scientific research.

As of September 13, 2005, the International Committee of Medical Journal Editors has required that all trials submitted for publication be registered in a public database. This study examines the records registered in ClinicalTrials.gov from May 11 to October 11, 2005. The number of trials in the database approximately doubled, and the information about the trials became more specific. As of September 13, 2005, the International Committee of Medical Journal Editors required that all trials submitted for publication be registered in a public database. This study examines the records registered in ClinicalTrials.gov from May to October 2005. Concern about previously undisclosed safety problems with drugs such as paroxetine (Paxil, GlaxoSmithKline) and rofecoxib (Vioxx, Merck) has increased the public's desire for more complete information about clinical research studies. 1 , 2 The provision of basic information about clinical trial protocols in a publicly accessible registry and the public identification of all trials, whether or not their results are subsequently published, have been advocated as ways to address this issue. 3 – 6 Numerous groups have called for comprehensive registration by issuing statements or convening meetings to discuss policy and implementation details. 7 – 15 In the United States, the Food and Drug Administration (FDA) . . .

Examination of approximately 10,000 specimens obtained during surgery for intractable seizures in children and adults resulted in 36 distinct diagnoses in seven categories; the most common diagnoses were hippocampal sclerosis, ganglioglioma, and focal cortical dysplasia.

Abstract Objective: To compare the use and effect of a computer based information system for cancer patients that is personalised using each patient's medical record with a system providing only general information and with information provided in booklets. Design: Randomised trial with three groups. Data collected at start of radiotherapy, one week later (when information provided), three weeks later, and three months later. Participants: 525 patients started radical radiotherapy; 438 completed follow up. Interventions: Two groups were offered information via computer (personalised or general information, or both) with open access to computer thereafter; the third group was offered a selection of information booklets. Outcomes: Patients' views and preferences, use of computer and information, and psychological status; doctors' perceptions; cost of interventions. Results: More patients offered the personalised information said that they had learnt something new, thought the information was relevant, used the computer again, and showed their computer printouts to others. There were no major differences in doctors' perceptions of patients. More of the general computer group were anxious at three months. With an electronic patient record system, in the long run the personalised information system would cost no more than the general system. Full access to booklets cost twice as much as the general system. Conclusions: Patients preferred computer systems that provided information from their medical records to systems that just provided general information. This has implications for the design and implementation of electronic patient record systems and reliance on general sources of patient information.

More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Japan has been facing challenges relating to specifically defined rare diseases, called Nan-Byo in Japanese (literally 'difficult'+'illness'), and has already taken measures for them since 1972. This governmental support has surely benefited Nan-Byo patients; however, those suffering from medically unidentified conditions do not fall into this scheme and thus still confront difficulty in obtaining an examination, a diagnosis, and a treatment. To identify such rare and often undiagnosed diseases, we must integrate systematic diagnosis by medical experts with phenotypic and genetic data matching. Thus, in collaboration with Nan-Byo researchers and the Japanese universal healthcare system, the Japan Agency for Medical Research and Development launched the Initiative on Rare and Undiagnosed Diseases (IRUD) in 2015. IRUD is an ambitious challenge to construct a comprehensive medical network and an internationally compatible data-sharing framework. Synergizing with existing next-generation sequencing capabilities and other infrastructure, the nationwide medical research consortium has successfully grown to accept more than 2000 undiagnosed registrants by December 2016. We also aim at expanding the concept of microattribution throughout the initiative; that is, proper credit as collaborators shall be given to local primary care physicians, nurses and paramedics, patients, their family members, and those supporting the affected individuals whenever appropriate. As it shares many challenges among similar global efforts, IRUD's future successes and lessons learned will significantly contribute to ongoing international endeavors, involving players in basic research, applied research, and societal implementation.

Background To compare the efficacy and tolerability of paliperidone extended-release (ER) with risperidone immediate-release using propensity score methodology. Methods Six double-blind, randomized, placebo-controlled, short-term clinical trials for acute schizophrenia with availability of individual patient-level data were identified (3 per compound). Propensity score pairwise matching was used to balance observed covariates between the paliperidone ER and risperidone patient populations. Scores were generated using logistic regression models, with age, body mass index, race, sex, baseline Positive and Negative Syndrome Scale (PANSS) total score and baseline Clinical Global Impressions–Severity (CGI-S) score as factors. The dosage range of paliperidone ER (6-12 mg/day) was compared with 2 risperidone dosage ranges: 2-4 and 4-6 mg/day. The primary efficacy measure was change in PANSS total score at week 6 end point. Tolerability end points included adverse event (AE) reports and weight. AEs with rates ≥5% and with a ≥2% difference between paliperidone ER and risperidone were identified. Results Completion rates for placebo-treated subjects in paliperidone ER trials (n = 95) and risperidone trials (n = 122) groups were 36.8% and 51.6%, respectively; end point changes on PANSS total scores were similar (p = 0.768). Completion rates for subjects receiving paliperidone ER 6-12 mg/day (n = 179), risperidone 2-4 mg/day (n = 113) or risperidone 4-6 mg/day (n = 129) were 64.8%, 54.0% and 66.7%, respectively (placebo-adjusted rates: paliperidone ER vs risperidone 2-4 mg/day, p = 0.005; paliperidone ER vs risperidone 4-6 mg/day, p = 0.159). PANSS total score improvement with paliperidone ER was greater than with risperidone 2-4 mg/day (difference in mean change score, -6.7; p < 0.05) and similar to risperidone 4-6 mg/day (0.2; p = 0.927). Placebo-adjusted AEs more common with paliperidone ER were insomnia, sinus tachycardia and tachycardia; more common with risperidone were somnolence, restlessness, nausea, anxiety, salivary hypersecretion, akathisia, dizziness and nasal congestion. Weight changes with paliperidone ER and risperidone were similar (paliperidone ER vs risperidone 2-4 mg/day, p = 0.489; paliperidone ER vs risperidone 4-6 mg/day, p = 0.236). Conclusions This indirect database analysis suggested that paliperidone ER 6-12 mg/day may be more efficacious than risperidone 2-4 mg/day and as efficacious as risperidone 4-6 mg/day. The AE-adjusted incidence rates suggest differences between treatments that may be relevant for individual patients. Additional randomized, direct, head-to-head clinical trials are needed to confirm these findings.

A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.

Comparison and comparability lie at the heart of any comparative social science. Still, precise comparison is virtually impossible without using similar methods and similar data. In recent decades, social demographers, historians, and economic historians have compiled and made available a large number of micro-level data sets of historical populations for North America and Europe. Studies using these data have already made important contributions to many academic disciplines. In a similar spirit, we introduce five new microlevel historical panel data sets from East Asia, including the China Multi-Generational Panel Dataset-Liaoning (CMGPD-LN) 1749-1909, the China Multi-Generational Panel Dataset-Shuangcheng (CMGPD-SC) 1866-1913, the Japanese Ninbetsu-Aratame-Cho Population Register Database-Shimomoriya and Niita (NAC-SN) 1716-1870, the Korea Multi-Generational Panel Dataset-Tansung (KMGPD-TS) 1678-1888, and the Colonial Taiwan Household Registration Database (CTHRD) 1906-1945. These data sets in total contain more than 3.7 million linked observations of 610,000 individuals and are the first such Asian data to be made available online or by application. We discuss the key features and historical institutions that originally collected these data; the subsequent processes by which the data were reconstructed into individual-level panels; their particular data limitations and strengths; and their potential for comparative social scientific research.