Quantitative PCR (qPCR) is one of the most common techniques for quantification of nucleic acid molecules in biological and environmental samples. Although the methodology is perceived to be ...relatively simple, there are a number of steps and reagents that require optimization and validation to ensure reproducible data that accurately reflect the biological question(s) being posed. This review article describes and illustrates the critical pitfalls and sources of error in qPCR experiments, along with a rigorous, stepwise process to minimize variability, time, and cost in generating reproducible, publication quality data every time. Finally, an approach to make an informed choice between qPCR and digital PCR technologies is described.
qPCR is more complex than perceived by many scientists.
The production of an amplification curve and an associated quantitative cycle value does not necessarily mean interpretable data.
The MIQE guidelines and associated methodology articles published thereafter, underline the ongoing drive to help scientists produce reproducible data from qPCR, culminating in a simple, stepwise methodology to ensure high-quality, reproducible data from qPCR experiments.
The concept of data normalization has led to the ongoing publication of articles solely focused on this subject for various sample types and experimental parameters.
The analysis of qPCR data can be challenging, especially as experiments grow in sample number and complexity of biological groups. A defined approach to qPCR data analysis is necessary to clarify gene expression analysis.
Fruit and vegetables (F&V) have been a cornerstone of healthy dietary recommendations; the 2015-2020 U.S. Dietary Guidelines for Americans recommend that F&V constitute one-half of the plate at each ...meal. F&V include a diverse collection of plant foods that vary in their energy, nutrient, and dietary bioactive contents. F&V have potential health-promoting effects beyond providing basic nutrition needs in humans, including their role in reducing inflammation and their potential preventive effects on various chronic disease states leading to decreases in years lost due to premature mortality and years lived with disability/morbidity. Current global intakes of F&V are well below recommendations. Given the importance of F&V for health, public policies that promote dietary interventions to help increase F&V intake are warranted. This externally commissioned expert comprehensive narrative, umbrella review summarizes up-to-date clinical and observational evidence on current intakes of F&V, discusses the available evidence on the potential health benefits of F&V, and offers implementation strategies to help ensure that public health messaging is reflective of current science. This review demonstrates that F&V provide benefits beyond helping to achieve basic nutrient requirements in humans. The scientific evidence for providing public health recommendations to increase F&V consumption for prevention of disease is strong. Current evidence suggests that F&V have the strongest effects in relation to prevention of CVDs, noting a nonlinear threshold effect of 800 g per day (i.e., about 5 servings a day). A growing body of clinical evidence (mostly small RCTs) demonstrates effects of specific F&V on certain chronic disease states; however, more research on the role of individual F&V for specific disease prevention strategies is still needed in many areas. Data from the systematic reviews and mostly observational studies cited in this report also support intake of certain types of F&V, particularly cruciferous vegetables, dark-green leafy vegetables, citrus fruits, and dark-colored berries, which have superior effects on biomarkers, surrogate endpoints, and outcomes of chronic disease.
Cerebral atrophy in response to traumatic brain injury is a well-documented phenomenon in both primary investigations and review articles. Recent atrophy studies focus on exploring the ...region-specific patterns of cerebral atrophy; yet, there is no study that analyzes and synthesizes the emerging atrophy patterns in a single comprehensive review. Here we attempt to fill this gap in our current knowledge by integrating the current literature into a cohesive theory of preferential brain tissue loss and by identifying common risk factors for accelerated atrophy progression. Our review reveals that observations for mild traumatic brain injury remain inconclusive, whereas observations for moderate-to-severe traumatic brain injury converge towards robust patterns: brain tissue loss is on the order of 5% per year, and occurs in the form of generalized atrophy, across the entire brain, or focal atrophy, in specific brain regions. The most common regions of focal atrophy are the thalamus, hippocampus, and cerebellum in gray matter and the corpus callosum, corona radiata, and brainstem in white matter. We illustrate the differences of generalized and focal gray and white matter atrophy on emerging deformation and stress profiles across the whole brain using computational simulation. The characteristic features of our atrophy simulations—a widening of the cortical sulci, a gradual enlargement of the ventricles, and a pronounced cortical thinning—agree well with clinical observations. Understanding region-specific atrophy patterns in response to traumatic brain injury has significant implications in modeling, simulating, and predicting injury outcomes. Computational modeling of brain atrophy could open new strategies for physicians to make informed decisions for whom, how, and when to administer pharmaceutical treatment to manage the chronic loss of brain structure and function.
Rapid and, in many cases, unprecedented Arctic climate changes are having far-reaching impacts on natural and human systems. Despite state-of-the-art climate models capturing the rapid nature of ...Arctic climate change, termed Arctic amplification, they significantly disagree on its magnitude. Using a regional, process-oriented surface energy budget analysis, we argue that differences in seasonal energy exchanges in sea ice retreat regions via increased absorption and storage of sunlight in summer and increased upward surface turbulent fluxes in fall/winter contribute to the inter-model spread. Models able to more widely disperse energy drawn from the surface in sea ice retreat regions warm more, suggesting that differences in the local Arctic atmospheric circulation response contribute to the inter-model spread. We find that the principle mechanisms driving the inter-model spread in Arctic amplification operate locally on regional scales, requiring an improved understanding of atmosphere-ocean-sea ice interactions in sea ice retreat regions to reduce the spread.
Coronary flow is different from the flow in other parts of the arterial system because it is influenced by the contraction and relaxation of the heart. To model coronary flow realistically, the ...compressive force of the heart acting on the coronary vessels needs to be included. In this study, we developed a method that predicts coronary flow and pressure of three-dimensional epicardial coronary arteries by considering models of the heart and arterial system and the interactions between the two models. For each coronary outlet, a lumped parameter coronary vascular bed model was assigned to represent the impedance of the downstream coronary vascular networks absent in the computational domain. The intramyocardial pressure was represented with either the left or right ventricular pressure depending on the location of the coronary arteries. The left and right ventricular pressure were solved from the lumped parameter heart models coupled to a closed loop system comprising a three-dimensional model of the aorta, three-element Windkessel models of the rest of the systemic circulation and the pulmonary circulation, and lumped parameter models for the left and right sides of the heart. The computed coronary flow and pressure and the aortic flow and pressure waveforms were realistic as compared to literature data.
Immunotherapy has become a key therapeutic strategy in the treatment of many cancers. As a result, research efforts have been aimed at understanding mechanisms of resistance to immunotherapy and how ...anti-tumor immune response can be therapeutically enhanced. It has been shown that tumor cell recognition by the immune system plays a key role in effective response to T cell targeting therapies in patients. One mechanism by which tumor cells can avoid immunosurveillance is through the downregulation of Major Histocompatibility Complex I (MHC-I). Downregulation of MHC-I has been described as a mechanism of intrinsic and acquired resistance to immunotherapy in patients with cancer. Depending on the mechanism, the downregulation of MHC-I can sometimes be therapeutically restored to aid in anti-tumor immunity. In this article, we will review current research in MHC-I downregulation and its impact on immunotherapy response in patients, as well as possible strategies for therapeutic upregulation of MHC-I.
Archaea represent a significant fraction of Earth's biodiversity, yet they remain much less well understood than Bacteria. Gene surveys, a few metagenomic studies, and some single-cell sequencing ...projects have revealed numerous little-studied archaeal phyla. Certain lineages appear to branch deeply and may be part of a major phylum radiation. The structure of this radiation and the physiology of the organisms remain almost unknown.
We used genome-resolved metagenomic analyses to investigate the diversity, genomes sizes, metabolic capacities, and potential roles of Archaea in terrestrial subsurface biogeochemical cycles. We sequenced DNA from complex sediment and planktonic consortia from an aquifer adjacent to the Colorado River (USA) and reconstructed the first complete genomes for Archaea using cultivation-independent methods. To provide taxonomic context, we analyzed an additional 151 newly sampled archaeal sequences. We resolved two new phyla within a major, apparently deep-branching group of phyla (a superphylum). The organisms have small genomes, and metabolic predictions indicate that their primary contributions to Earth's biogeochemical cycles involve carbon and hydrogen metabolism, probably associated with symbiotic and/or fermentation-based lifestyles.
The results dramatically expand genomic sampling of the domain Archaea and clarify taxonomic designations within a major superphylum. This study, in combination with recently published work on bacterial phyla lacking cultivated representatives, reveals a fascinating phenomenon of major radiations of organisms with small genomes, novel proteome composition, and strong interdependence in both domains.
Machine learning systems are part of the field of artificial intelligence that automatically learn models from data to make better decisions. Natural language processing (NLP), by using corpora and ...learning approaches, provides good performance in statistical tasks, such as text classification or sentiment mining.
The primary aim of this systematic review was to summarize and characterize, in methodological and technical terms, studies that used machine learning and NLP techniques for mental health. The secondary aim was to consider the potential use of these methods in mental health clinical practice.
This systematic review follows the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines and is registered with PROSPERO (Prospective Register of Systematic Reviews; number CRD42019107376). The search was conducted using 4 medical databases (PubMed, Scopus, ScienceDirect, and PsycINFO) with the following keywords: machine learning, data mining, psychiatry, mental health, and mental disorder. The exclusion criteria were as follows: languages other than English, anonymization process, case studies, conference papers, and reviews. No limitations on publication dates were imposed.
A total of 327 articles were identified, of which 269 (82.3%) were excluded and 58 (17.7%) were included in the review. The results were organized through a qualitative perspective. Although studies had heterogeneous topics and methods, some themes emerged. Population studies could be grouped into 3 categories: patients included in medical databases, patients who came to the emergency room, and social media users. The main objectives were to extract symptoms, classify severity of illness, compare therapy effectiveness, provide psychopathological clues, and challenge the current nosography. Medical records and social media were the 2 major data sources. With regard to the methods used, preprocessing used the standard methods of NLP and unique identifier extraction dedicated to medical texts. Efficient classifiers were preferred rather than transparent functioning classifiers. Python was the most frequently used platform.
Machine learning and NLP models have been highly topical issues in medicine in recent years and may be considered a new paradigm in medical research. However, these processes tend to confirm clinical hypotheses rather than developing entirely new information, and only one major category of the population (ie, social media users) is an imprecise cohort. Moreover, some language-specific features can improve the performance of NLP methods, and their extension to other languages should be more closely investigated. However, machine learning and NLP techniques provide useful information from unexplored data (ie, patients' daily habits that are usually inaccessible to care providers). Before considering It as an additional tool of mental health care, ethical issues remain and should be discussed in a timely manner. Machine learning and NLP methods may offer multiple perspectives in mental health research but should also be considered as tools to support clinical practice.
Abstract
Background
ICare represents a consortium of European Investigators examining the effects of online mental health care for a variety of common mental health disorders provided in a variety of ...settings. This article provides an overview of the evidence of effectiveness for Internet-based treatment for four common mental health disorders that are the focus of much of this work: depression, anxiety, substance abuse and eating disorders.
Methods
The overview focused primarily on systematic reviews and meta-analyses identified through PubMed (Ovid) and other databases and published in English. Given the large number of reviews specific to depression, anxiety, substance abuse and/or eating disorders, we did not focus on reviews that examined the effects of Internet-based interventions on mental health disorders in general. Each article was reviewed and summarized by one of the senior authors, and this review was then reviewed by the other senior authors. We did not address issues of prevention, cost-effectiveness, implementation or dissemination, as these are addressed in other reviews in this supplement.
Results
Across Internet-based intervention studies addressing depression, anxiety, substance abuse and eating disorders primarily among adults, almost all reviews and meta-analyses found that these interventions successfully reduce symptoms and are efficacious treatments. Generally, effect sizes for Internet-based interventions treating eating disorders and substance abuse are lower compared with interventions for depression and anxiety.
Conclusions
Given the effectiveness of Internet-based interventions to reduce symptoms of these common mental health disorders, efforts are needed to examine issues of how they can be best disseminated and implemented in a variety of health care and other settings.
Occupant behavior is one of the major factors influencing building energy consumption and contributing to uncertainty in building energy use prediction and simulation. Currently the understanding of ...occupant behavior is insufficient both in building design, operation and retrofit, leading to incorrect simplifications in modeling and analysis. This paper introduced the most recent advances and current obstacles in modeling occupant behavior and quantifying its impact on building energy use. The major themes include advancements in data collection techniques, analytical and modeling methods, and simulation applications which provide insights into behavior energy savings potential and impact. There has been growing research and applications in this field, but significant challenges and opportunities still lie ahead.
PDF
