Protein profile pattern analysis: A multifarious, in vitro diagnosis technique for universal screening

Universal health care is attracting increased attention nowadays, because of the large increase in population all over the world, and a similar increase in life expectancy, leading to an increase in the incidence of non-communicable (various cancers, coronary diseases, neurological and old-age-related diseases) and communicable diseases/pandemics like SARS-COVID 19. This has led to an immediate need for a healthcare technology that should be cost-effective and accessible to all. A technology being considered as a possible one at present is liquid biopsy, which looks for markers in readily available samples like body fluids which can be accessed non- or minimally- invasive manner. Two approaches are being tried now towards this objective. The first involves the identification of suitable, specific markers for each condition, using established methods like various Mass Spectroscopy techniques (Surface-Enhanced Laser Desorption/Ionization Mass Spectroscopy (SELDI-MS), Matrix-Assisted Laser Desorption/Ionization (MALDI-MS), etc., immunoassays (Enzyme-Linked Immunoassay (ELISA), Proximity Extension Assays, etc.) and separation methods like 2-Dimensional Polyacrylamide Gel Electrophoresis (2-D PAGE), Sodium Dodecyl-Sulfate Polyacrylamide Gel Electrophoresis (SDS-PAGE), Capillary Electrophoresis (CE), etc. In the second approach, no attempt is made the identification of specific markers; rather an efficient separation method like High-Performance Liquid Chromatography/ Ultra-High-Performance Liquid Chromatography (HPLC/UPLC) is used to separate the protein markers, and a profile of the protein pattern is recorded, which is analysed by Artificial Intelligence (AI)/Machine Learning (MI) methods to derive characteristic patterns and use them for identifying the disease condition. The present report gives a summary of the current status of these two approaches and compares the two in the use of their suitability for universal healthcare.

Correlation studies of Hippocampal Morphometry and Plasma NFL Levels in Cognitively Unimpaired Subjects

Alzheimer's disease(AD) is being the burden of society and family. Applying computing-aided strategies to reveal its pathology is one of the research highlights. Plasma neurofilament light (NFL) is an emerging noninvasive and economic biomarker for AD molecular pathology. It is valuable to reveal the correlations between the plasma NFL levels and neurodegeneration, especially hippcampal deformations at the preclinical stage. The negative correlation between plasma NFL levels and hippocampal volumes has been documented. However, the relationship between the plasma NFL levels and the hippocampal morphometry details at the preclinical stage is still elusive. This study seeks to demonstrate the capacity of our proposed surface-based hippocampal morphometry system to discern the plasma NFL positive (NFL+>41.9 pg/L) level and plasma NFL negative (NFL-<41.9pg/L) level and illustrate its superiority to the hippocampal volume measurement by drawing the cohort of 154 CU middle aged and elderly adults. We also apply this morphometry measure and a proposed sparse coding based classification algorithm to classify CU individuals with NFL+ and NFL- levels. Experimental results show that the proposed hippocampal morphometry system offers stronger statistical power to discriminate CU subjects with NFL+ and NFL- levels, comparing with the hippocampal volume measure. Furthermore, this system can discriminate plasma NFL levels in CU individuals (Accuracy=0.86). Both the group level and individual level analysis results indicate that the association between plasma NFL levels and the hippocampal shapes can be mapped at the preclinical stage.

Using audit and feedback to guide tailored implementations of measurement-based care in community mental health: a multiple case study

BACKGROUND

Audit and feedback (A&F) is an implementation strategy that can facilitate implementation tailoring by identifying gaps between desired and actual clinical care. While there are several theory-based propositions on which A&F components lead to better implementation outcomes, many have not been empirically investigated, and there is limited guidance for stakeholders when applying A&F in practice. The current study aims to illustrate A&F procedures in six community mental health clinics, with an emphasis on reporting A&F components that are relevant to theories of how feedback elicits behavior change.

METHODS

Six clinics from a larger trial using a tailored approach to implement measurement-based care (MBC) were analyzed for feedback content, delivery mechanisms, barriers to feedback, and outcomes of feedback using archival data. Pattern analysis was conducted to examine relations between A&F components and changes in MBC use.

RESULTS

Several sites utilized both aggregate and individualized data summaries, and data accuracy concerns were common. Feedback cycles featuring individual-level clinician data, data relevant to MBC barriers, and information requested by data recipients were related to patterns of increased MBC use.

CONCLUSIONS

These findings support extant theory, such as Feedback Intervention Theory. Mental health professionals wishing to apply A&F should consider establishing reciprocal feedback mechanisms on the quality and amount of data being received and adopting specific roles communicating and addressing data quality concerns.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT02266134.

Profiling of multiple classes of flame retardants in house dust in China: Pattern analysis and human exposure assessment

Legacy [e.g., brominated- (BFRs)] and alternative [e.g., organophosphate- (OPFRs) and nitrogenous- (NFRs)] flame retardants have a propensity to migrate out of consumer products, and thus are dispersed in indoor microenvironments. In this study, simultaneous presence of 11 BFRs, 18 OPFRs and 11 NFRs were measured in house dust collected from Tianjin, China. OPFRs were found at the highest concentrations, with a median value of 3200 ng/g, followed by NFRs (2600) and BFRs (1600). Tris(2-butoxyethyl) phosphate (median: 1800 ng/g), melamine (1100), and BDE-209 (870) were the top three most abundant chemicals in the respective groups. Location-specific patterns of flame retardant concentrations were found with 30%, 20% and 10% of samples were predominated by OPFRs, NFRs and BFRs, respectively, and the remaining samples contained by two or more of the chemical groups occurring concurrently. Network and cluster analysis results indicated the existence of multiple sources of flame retardants in the indoor microenvironment. Estimated human daily intakes via indoor dust ingestion were approximately several tens of ng/kg bw/day and were below their respective reference dose values. Our results indicate widespread occurrence of multiple flame retardant families in indoor dust and suggest need for continued monitoring and efforts to reduce exposures through dust ingestion.

Analyzing SARS-CoV-2 Sequence Patterns by Semantic Trajectories

Since the beginning of the pandemic due to the SARS-CoV-2 emergence, several variants has been observed all over the world. One of the last known, Omicron, caused a large spread of the virus in few days, and several countries reached a record number of contaminations. Indeed, the mutation in the Spike region of the virus played an important role in altering its behavior. Therefore, it is important to understand the virus evolution by extracting and analyzing the virus structure of each variant. In this work we show how patterns sequence could be analyzed and extracted by means of semantic trajectories modeling. To do so, we designed a graph-based model in which the genome organization is handled using nodes and edges to represent respectively the nucleotides and sequence connection (point of interest and routes for trajectories). The modeling choices and pattern extraction from the graph allowed to retrieve a region where a mutation occurred in Omicron (NCBI version:OM011974.1).

Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol

Background

Given the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support.

Methods

The PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: “Pattern Analysis” to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: “Model Development” to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: “Translational Research” to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits.

Discussion

With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.

Strategies to gain novel Alzheimer's disease diagnostics and therapeutics using modulators of ABCA transporters

Adenosine-triphosphate-(ATP)-binding cassette (ABC) transport proteins are ubiquitously present membrane-bound efflux pumps that distribute endo- and xenobiotics across intra- and intercellular barriers. Discovered over 40 years ago, ABC transporters have been identified as key players in various human diseases, such as multidrug-resistant cancer and atherosclerosis, but also neurodegenerative diseases, such as Alzheimer’s disease (AD). Most prominent and well-studied are ABCB1, ABCC1, and ABCG2, not only due to their contribution to the multidrug resistance (MDR) phenotype in cancer, but also due to their contribution to AD. However, our understanding of other ABC transporters is limited, and most of the 49 human ABC transporters have been largely neglected as potential targets for novel small-molecule drugs. This is especially true for the ABCA subfamily, which contains several members known to play a role in AD initiation and progression. This review provides up-to-date information on the proposed functional background and pathological role of ABCA transporters in AD. We also provide an overview of small-molecules shown to interact with ABCA transporters as well as potential in silico, in vitro, and in vivo methodologies to gain novel templates for the development of innovative ABC transporter-targeting diagnostics and therapeutics.

Capturing and analyzing pattern diversity: an example using the melanistic spotted patterns of leopard geckos

Animal color patterns are widely studied in ecology, evolution, and through mathematical modeling. Patterns may vary among distinct body parts such as the head, trunk or tail. As large amounts of photographic data is becoming more easily available, there is a growing need for general quantitative methods for capturing and analyzing the full complexity and details of pattern variation. Detailed information on variation in color pattern elements is necessary to understand how patterns are produced and established during development, and which evolutionary forces may constrain such a variation. Here, we develop an approach to capture and analyze variation in melanistic color pattern elements in leopard geckos. We use this data to study the variation among different body parts of leopard geckos and to draw inferences about their development. We compare patterns using 14 different indices such as the ratio of melanistic versus total area, the ellipticity of spots, and the size of spots and use these to define a composite distance between two patterns. Pattern presence/absence among the different body parts indicates a clear pathway of pattern establishment from the head to the back legs. Together with weak within-individual correlation between leg patterns and main body patterns, this suggests that pattern establishment in the head and tail may be independent from the rest of the body. We found that patterns vary greatest in size and density of the spots among body parts and individuals, but little in their average shapes. We also found a correlation between the melanistic patterns of the two front legs, as well as the two back legs, and also between the head, tail and trunk, especially for the density and size of the spots, but not their shape or inter-spot distance. Our data collection and analysis approach can be applied to other organisms to study variation in color patterns between body parts and to address questions on pattern formation and establishment in animals.

Is there any incremental benefit to conducting neuroimaging and neurocognitive assessments in the diagnosis of ADHD in young children? A machine learning investigation

Given the negative trajectories of early behavior problems associated with ADHD, early diagnosis is considered critical to enable intervention and treatment. To this end, the current investigation employed machine learning to evaluate the relative predictive value of parent/teacher ratings, behavioral and neural measures of executive function (EF) in predicting ADHD in a sample consisting of 162 young children (ages 4–7, mean age 5.55, 82.6 % Hispanic/Latino). Among the target measures, teacher ratings of EF were the most predictive of ADHD. While a more extensive evaluation of neural measures, such as diffusion-weighted imaging, may provide more information as they relate to the underlying cognitive deficits associated with ADHD, the current study indicates that measures of cortical anatomy obtained in research studies, as well cognitive measures of EF often obtained in routine assessments, have little incremental value in differentiating typically developing children from those diagnosed with ADHD. It is important to note that the overlap between some of the EF questions in the BRIEF, and the ADHD symptoms could be enhancing this effect. Thus, future research evaluating the importance of such measures in predicting children’s functional impairment in academic and social areas would provide additional insight into their contributing role in ADHD.

NEURO-LEARN: a Solution for Collaborative Pattern Analysis of Neuroimaging Data

The development of neuroimaging instrumentation has boosted neuroscience researches. Consequently, both the fineness and the cost of data acquisition have profoundly increased, leading to the main bottleneck of this field: limited sample size and high dimensionality of neuroimaging data. Therefore, the emphasis of ideas of data pooling and research collaboration has increased over the past decade. Collaborative analysis techniques emerge as the idea developed. In this paper, we present NEURO-LEARN, a solution for collaborative pattern analysis of neuroimaging data. Its collaboration scheme consists of four parts: projects, data, analysis, and reports. While data preparation workflows defined in projects reduce the high dimensionality of neuroimaging data by collaborative computation, pooling of derived data and sharing of pattern analysis workflows along with generated reports on the Web enlarge the sample size and ensure the reliability and reproducibility of pattern analysis. Incorporating this scheme, NEURO-LEARN provides an easy-to-use Web application that allows users from different sites to share projects and processed data, perform pattern analysis, and obtain result reports. We anticipate that this solution will help neuroscientists to enlarge sample size, conquer the curse of dimensionality and conduct reproducible studies on neuroimaging data with efficiency and validity.

[Ichthyoses: a dermatopathological spectrum from heterogeneous cornification disorders to psoriasiform dermatitis]

Ichthyoses are hereditary cornification disorders that occur in isolation (nonsyndromic) or with associated internal diseases (syndromic) and can lead to life-threatening complications. The identification of the genetic causes has led to an understanding of the molecular mechanisms, but also to reclassification. The pathological changes in skin biopsies were also more precisely characterized. Certain histological patterns could be defined, which are based on the defects of epidermal differentiation but also on the inflammatory pattern. Complementary histo- and immunohistochemical methods sometimes allow a precise diagnosis, or at least a limitation of the differential diagnoses.

[Pattern analysis of inflammatory skin diseases according to A. B. Ackerman-always up to date]

The exact microscopic diagnosis of inflammatory skin diseases requires the linking of histopathological findings with clinical features. This is not easy when skin biopsies are rarely assessed and the terminology of dermatopathology and dermatology is itself unfamiliar.The infiltrates of almost all inflammatory skin diseases tend to show eight specific patterns in high magnification. By further classifying according to architectural and cytological features, a specific diagnosis can be made in most cases. At the same time, clinically suspected diagnoses are simply excluded or greatly reduced in number. This procedure, starting with the overview magnification and the recognition of clearly defined histomorphological features, corresponds to an algorithm.Another algorithmic approach uses histomorphological changes under high magnification. Here, "nonspecific" findings are added to the pattern analysis as a diagnostic vehicle.Occasionally, inflammatory skin diseases cannot be assessed conclusively with current modern methods. Such pathology reports should be written descriptively and possible differential diagnoses should be mentioned as notes. The report should be written in a language understandable to the clinician.Artificial intelligence, with its ability to transform and integrate extensive clinical as well as image data, will play an important role in the future of decision making, diagnosing, and personalizing medicine. In the field of pathology, it could be seen as a second opinion. It is important that physicians always contribute their opinion where important algorithmic decisions are made, such as in algorithm design, data quality, interpretation, action, and feedback.

Glucocorticoids and cortical decoding in the phobic brain

Glucocorticoids reduce phobic fear in anxiety disorders and enhance psychotherapy, possibly by reducing the retrieval of fear memories and enhancing the consolidation of new corrective memories. Glucocorticoid signaling in the basolateral amygdala can influence connected fear and memory-related cortical regions, but this is not fully understood. Previous studies investigated specific pathways moderated by glucocorticoids, for example, visual-temporal pathways; however, these analyses were limited to a-priori selected regions. Here, we performed whole-brain pattern analysis to localize phobic stimulus decoding related to the fear-reducing effect of glucocorticoids. We reanalyzed functional magnetic resonance imaging (fMRI) data from a previously published study with spider-phobic patients and healthy controls. The patients received glucocorticoids or a placebo before the exposure to spider images. There was moderate evidence that patients with phobia had higher decoding of phobic content in the anterior cingulate cortex (ACC) and the left and right anterior insula compared to controls. Decoding in the ACC and the right insula showed strong evidence for correlation with experienced fear. Patients with cortisol reported a reduction of fear by 10-13%; however, there was only weak evidence for changes in neural decoding compared to placebo which was found in the precuneus, the opercular cortex, and the left cerebellum.

Development of an Index Score for Intestinal Inflammation-Associated Dysbiosis Using Real-World Stool Test Results

BACKGROUND

Gut microbiota play an important role in human health. However, the application of gut microbiome in regular clinical practice is limited by interindividual variations and complexity of test results.

HYPOTHESIS

It is possible to address interindividual variation by using large data-based exploratory-pattern analysis.

METHODS

The current study was conducted using a large data set (n = 173,221) of nonselective incoming patients' test results from a stool test. The data set included assays for the detection of 24 selected commensal microorganisms and multiple biomarkers in feces. Patients were grouped based on their levels of inflammation biomarkers such as calprotectin, eosinophil protein X, and IgA. Group mean values of biomarkers and commensal microbes were used in an exploratory-pattern analysis for association from which an index score for intestinal inflammation-associated dysbiosis (IAD) was developed. The IAD score was evaluated in one questionnaire-based study (n = 7263) and one prospective case series study (n = 122) with patients of inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and celiac disease.

RESULTS

We identified a microbial profile strongly associated with fecal inflammation biomarkers. Developed on the pattern of the microbial profile, the IAD score demonstrated a strong association with fecal inflammation biomarkers and was significantly different between patients with IBD and those with IBS or celiac disease.

CONCLUSION

Using real-world data, we have developed a method to predict gut dysbiosis associated with different GI disease conditions. It may help clinicians simplify the process of interpreting gut microbial status and provide gut health assessment and treatment evaluation.

Autoregressive modeling to assess stride time pattern stability in individuals with Huntington's disease

Background

Huntington’s disease (HD) is a progressive, neurological disorder that results in both cognitive and physical impairments. These impairments affect an individual’s gait and, as the disease progresses, it significantly alters one’s stability. Previous research found that changes in stride time patterns can help delineate between healthy and pathological gait. Autoregressive (AR) modeling is a statistical technique that models the underlying temporal patterns in data. Here the AR models assessed differences in gait stride time pattern stability between the controls and individuals with HD. Differences in stride time pattern stability were determined based on the AR model coefficients and their placement on a stationarity triangle that provides a visual representation of how the patterns mean, variance and autocorrelation change with time. Thus, individuals who exhibit similar stride time pattern stability will reside in the same region of the stationarity triangle. It was hypothesized that individuals with HD would exhibit a more altered stride time pattern stability than the controls based on the AR model coefficients and their location in the stationarity triangle.

Methods

Sixteen control and twenty individuals with HD performed a five-minute walking protocol. Time series’ were constructed from consecutive stride times extracted during the protocol and a second order AR model was fit to the stride time series data. A two-sample t-test was performed on the stride time pattern data to identify differences between the control and HD groups.

Results

The individuals with HD exhibited significantly altered stride time pattern stability than the controls based on their AR model coefficients (AR1 p < 0.001; AR2 p < 0.001).

Conclusions

The AR coefficients successfully delineated between the controls and individuals with HD. Individuals with HD resided closer to and within the oscillatory region of the stationarity triangle, which could be reflective of the oscillatory neuronal activity commonly observed in this population. The ability to quantitatively and visually detect differences in stride time behavior highlights the potential of this approach for identifying gait impairment in individuals with HD.

Analysis of nonstandardized stress echocardiography sequences using multiview dimensionality reduction

Alternative stress echocardiography protocols such as handgrip exercise are potentially more favorable towards large-scale screening scenarios than those currently adopted in clinical practice. However, these are still underexplored because the maximal exercise levels are not easily quantified and regulated, requiring the analysis of the complete data sequences (thousands of images), which represents a challenging task for the clinician. We propose a framework for the analysis of these complex datasets, and illustrate it on a handgrip exercise dataset including complete acquisitions of 10 healthy controls and 5 ANT1 mutation patients (1377 cardiac cycles). The framework is based on an unsupervised formulation of multiple kernel learning, which is used to integrate information coming from myocardial velocity traces and heart rate to obtain a lower-dimensional representation of the data. Such simplified representation is then explored to discriminate groups of response and understand the underlying pathophysiological mechanisms. The analysis pipeline involves the reconstruction of population-specific signatures using multiscale kernel regression, and the clustering of subjects based on the trajectories defined by their projected sequences. The results confirm that the proposed framework is able to detect distinctive clusters of response and to provide insight regarding the underlying pathophysiology.

Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson's disease induced presynaptic alterations

Most neurodegenerative diseases are known to affect several aspects of brain function, including neurotransmitter systems, metabolic and functional connectivity. Diseases are generally characterized by common clinical characteristics across subjects, but there are also significant inter-subject variations. It is thus reasonable to expect that in terms of brain function, such clinical behaviors will be related to a general overall multi-system pattern of disease-induced alterations and additional brain system-specific abnormalities; these additional abnormalities would be indicative of a possible unique system response to disease or subject-specific propensity to a specific clinical progression. Based on the above considerations we introduce and validate the use of a joint pattern analysis approach, canonical correlation analysis and orthogonal signal correction, to analyze multi-tracer PET data to identify common (reflecting functional similarities) and unique (reflecting functional differences) information provided by each tracer/target. We apply the method to [11C]-DTBZ (VMAT2 marker) and [11C]-MP (DAT marker) data from 15 early Parkinson's disease (PD) subjects; the behavior of these two tracers/targets is well characterized providing robust reference information for the method's outcome. Highly significant common subject profiles were identified that decomposed the characteristic dopaminergic changes into three distinct orthogonal spatial patterns: 1) disease-induced asymmetry between the less and more affected dorsal striatum; 2) disease-induced gradient with caudate and ventral striatum being relatively spared compared to putamen; 3) progressive loss in the less affected striatum, which correlated significantly with disease duration (p < 0.01 for DTBZ, p < 0.05 for MP). These common spatial patterns reproduce all known aspects of these two targets/tracers. In addition, orthogonality of the patterns may indicate different mechanisms underlying disease initiation or progression. Information unique to each tracer revealed a residual striatal asymmetry when targeting VMAT2, consistent with the notion that VMAT2 density is highly related to terminal degeneration; and a residual DAT disease-induced gradient in the striatum with relative DAT preservation in the substantia nigra. This finding may be indicative either of a possible DAT specific early disease compensation and/or related to disease origin. These results demonstrate the applicability and relevance of the joint pattern analysis approach to datasets obtained with two PET tracers; this data driven method, while recapitulating known aspects of the PD-induced tracer/target behaviour, was found to be statistically more robust and provided additional information on (i) correlated behaviors of the two systems, identified as orthogonal patterns, possibly reflecting different disease-induced alterations and (ii) system specific effects of disease. It is thus expected that this approach will be very well suited to the analysis of multi-tracer and/or multi-modality data and to relating the outcomes to different aspects of disease.

Quantitative and Comparative Analysis of Global Patterns of (Microtubule) Cytoskeleton Organization with CytoskeletonAnalyzer2D

The microtubule cytoskeleton plays important roles in cell morphogenesis. To investigate the mechanisms of cytoskeletal organization, for example, during growth or development, in genetic studies, or in response to environmental stimuli, image analysis tools for quantitative assessment are needed. Here, we present a method for texture measure-based quantification and comparative analysis of global microtubule cytoskeleton patterns and subsequent visualization of output data. In contrast to other approaches that focus on the extraction of individual cytoskeletal fibers and analysis of their orientation relative to the growth axis, CytoskeletonAnalyzer2D quantifies cytoskeletal organization based on the analysis of local binary patterns. CytoskeletonAnalyzer2D thus is particularly well suited to study cytoskeletal organization in cells where individual fibers are difficult to extract or which lack a clearly defined growth axis, such as leaf epidermal pavement cells. The tool is available as ImageJ plugin and can be combined with publicly available software and tools, such as R and Cytoscape, to visualize similarity networks of cytoskeletal patterns.

Impact of Nodule Size on Malignancy Risk Differs according to the Ultrasonography Pattern of Thyroid Nodules

Objective

To test whether the impact of thyroid-nodule size on the malignancy risk differs according to the ultrasonography (US) patterns of nodules.

Materials and Methods

This study is a post hoc analysis using data from the Thyroid Imaging Reporting and Data System (TIRADS) multicenter retrospective study which included 2000 consecutive thyroid nodules (≥ 1 cm) with final diagnoses. A total of 2000 consecutive thyroid nodules from 1802 patients (1387 women and 613 men; mean age, 51.2 ± 12.2 years) were enrolled in this study. The malignancy risk of the nodules was assessed according to the nodule size and US patterns (Korean-TIRADS).

Results

Overall, the malignancy risk did not increase as nodules enlarged. In high-suspicion nodules, the malignancy rate had no association with nodule size (p = 0.467), whereas in intermediate- or low-suspicion nodules there was a trend toward an increasing malignancy risk as the nodule size increased (p = 0.004 and 0.002, respectively). The malignancy rate of large nodules (≥ 3 cm) was higher than that of small nodules (< 3 cm) in intermediate-suspicion nodules (40.3% vs. 22.6%, respectively; p = 0.001) and low-suspicion nodules (11.3% vs. 7.0%, respectively; p = 0.035). There was a trend toward a decreasing risk and proportion of papillary carcinoma and an increasing risk and proportion of follicular carcinoma or other malignant tumors as nodule size increased (p < 0.001, respectively).

Conclusion

The impact of nodule size on the malignancy risk differed according to the US pattern. A large nodule size (≥ 3 cm) showed a higher malignancy risk than smaller nodules in intermediate- and low-suspicion nodules.

Food sources of free sugars in children's diet and identification of lifestyle patterns associated with free sugars intake: the GRECO (Greek Childhood Obesity) study

OBJECTIVE

Excessive free sugars consumption has a possible role in health issues, diet quality and obesity development. The present cross-sectional study aimed to identify the major food sources of free sugars in Greek children's diet and investigate possible associations of dietary patterns with free sugars intake.

DESIGN

Anthropometric measurements and information on dietary and physical activity habits were obtained. Energy and free sugars intake coming from foods were estimated and principal components analysis was applied to identify dietary patterns.

SETTING

The GRECO (Greek Childhood Obesity) study.

SUBJECTS

Nationwide sample of 3089 children (aged 10-12 years).

RESULTS

Adopting WHO criteria, 44·2 % of participants were categorized as having free sugars intake above 10 % of total energy intake. Mean contribution of free sugars to energy intake was 11·2 %, and the major food sources of free sugars differed from those of other childhood populations. Free sugars intake was not associated with overweight/obesity. Multiple linear regression analysis revealed that two lifestyle and dietary patterns, characterized by higher consumption of sweets, fast foods, fries, sugared drinks, frequently ordering/eating outside home and having meals in front of a screen (pattern 1) and higher consumption of whole fruits, 100 % fruit juices, vegetables, legumes and honey/jam (pattern 2), were positively associated with free sugars intake.

CONCLUSIONS

A large proportion of children exceeded the recommended cut-off and free sugars intake was associated with lifestyle patterns rather than single foods. Public health programmes aiming to reduce free sugars consumption should be tailored on promoting the correct dietary habits of specific childhood populations.

Amplification and bioinformatics analysis of conserved FAD-binding region of L-amino acid oxidase (LAAO) genes in gastropods compared to other organisms

This study aimed to investigate the conserved FAD-binding region of the L-amino acid oxidase (LAAO) genes in twelve gastropod genera commonly found in Thailand compared to those in other organisms using molecular cloning, nucleotide sequencing and bioinformatics analysis. Genomic DNA of gastropods and other invertebrates was extracted and screened using primers specific to the conserved FAD-binding region of LAAO. The amplified 143-bp fragments were cloned and sequenced. The obtained nucleotide sequences of 21 samples were aligned and phylogenetically compared to the LAAO-conserved FAD-binding regions of 210 other organisms from the NCBI database. Translated amino acid sequences of these samples were used in phylogenetics and pattern analyses. The phylogenetic trees showed clear separation of the conserved regions in fungi, invertebrates, and vertebrates. Alignment of the conserved 47-amino-acid FAD-binding region of the LAAOs showed 150 unique sequences among the 231 samples and these patterns were different from those of other flavoproteins in the amine oxidase family. An amino acid pattern analysis of five sub-regions (bFAD, FAD, FAD-GG, GG, and aGG) within the FAD-binding sequence showed high variation at the FAD-GG sub-region. Pattern analysis of secondary structures indicated the aGG sub-region as having the highest structural variation. Cluster analysis of these patterns revealed two major clusters representing the mollusc clade and the vertebrate clade. Thus, molecular phylogenetics and pattern analyses of sequence and structural variations could reflect evolutionary relatedness and possible structural conservation to maintain specific function within the FAD-binding region of the LAAOs in gastropods compared to other organisms.

Evaluation of a New Digital Automated Glycemic Pattern Detection Tool

BACKGROUND

Blood glucose meters are reliable devices for data collection, providing electronic logs of historical data easier to interpret than handwritten logbooks. Automated tools to analyze these data are necessary to facilitate glucose pattern detection and support treatment adjustment. These tools emerge in a broad variety in a more or less nonevaluated manner. The aim of this study was to compare eDetecta, a new automated pattern detection tool, to nonautomated pattern analysis in terms of time investment, data interpretation, and clinical utility, with the overarching goal to identify early in development and implementation of tool areas of improvement and potential safety risks.

METHODS

Multicenter web-based evaluation in which 37 endocrinologists were asked to assess glycemic patterns of 4 real reports (2 continuous subcutaneous insulin infusion [CSII] and 2 multiple daily injection [MDI]). Endocrinologist and eDetecta analyses were compared on time spent to analyze each report and agreement on the presence or absence of defined patterns.

RESULTS

eDetecta module markedly reduced the time taken to analyze each case on the basis of the emminens eConecta reports (CSII: 18 min; MDI: 12.5), compared to the automatic eDetecta analysis. Agreement between endocrinologists and eDetecta varied depending on the patterns, with high level of agreement in patterns of glycemic variability. Further analysis of low level of agreement led to identifying areas where algorithms used could be improved to optimize trend pattern identification.

CONCLUSION

eDetecta was a useful tool for glycemic pattern detection, helping clinicians to reduce time required to review emminens eConecta glycemic reports. No safety risks were identified during the study.

A look inside the nerve - Morphology of nerve fascicles in healthy controls and patients with polyneuropathy

OBJECTIVE

Polyneuropathies are increasingly analyzed by ultrasound. Summarizing, diffuse enlargement is typical in Charcot-Marie Tooth type 1 (CMT1a), regional enlargement occurs in inflammatory neuropathies. However, a distinction of subtypes is still challenging. Therefore, this study focused on fascicle size and pattern in controls and distinct neuropathies.

METHODS

Cross-sectional area (CSA) of the median, ulnar and peroneal nerve (MN, UN, PN) was measured at predefined landmarks in 50 healthy controls, 15 CMT1a and 13 MMN patients. Additionally, largest fascicle size and number of visible fascicles was obtained at the mid-upper arm cross-section of the MN and UN and in the popliteal fossa cross-section of the PN.

RESULTS

Cut-off normal values for fascicle size in the MN, UN and PN were defined (<4.8mm², <2.8mm² and <3.5mm²). In CMT1a CSA and fascicle values are significantly enlarged in all nerves, while in MMN CSA and fascicles are regionally enlarged with predominance in the upper arm nerves. The ratio of enlarged fascicles and all fascicles was significantly increased in CMT1a (>50%) in all nerves (p<0.0001), representing diffuse fascicle enlargement, and moderately increased in MMN (>20%), representing differential fascicle enlargement (enlarged and normal fascicles at the same location) sparing the peroneal nerve (regional fascicle enlargement). Based on these findings distinct fascicle patterns were defined.

CONCLUSION

Normal values for fascicle size could be evaluated; while CMT1a features diffuse fascicle enlargement, MMN shows regional and differential predominance with enlarged fascicles as single pathology.

SIGNIFICANCE

Pattern analysis of fascicles might facilitate distinction of several otherwise similar neuropathies.

Modeling Temporal Interaction Dynamics in Organizational Settings

Most workplace phenomena take place in dynamic social settings and emerge over time, and scholars have repeatedly called for more research into the temporal dynamics of organizational behavior. One reason for this persistent research gap could be that organizational scholars are not aware of the methodological advances that are available today for modeling temporal interactions and detecting behavioral patterns that emerge over time. To facilitate such awareness, this Methods Corner contribution provides a hands-on tutorial for capturing and quantifying temporal behavioral patterns and for leveraging rich interaction data in organizational settings. We provide an overview of different approaches and methodologies for examining temporal interaction patterns, along with detailed information about the type of data that needs to be gathered in order to apply each method as well as the analytical steps (and available software options) involved in each method. Specifically, we discuss and illustrate lag sequential analysis, pattern analysis, statistical discourse analysis, and visualization methods for identifying temporal patterns in interaction data. We also provide key takeaways for integrating these methods more firmly in the field of organizational research and for moving interaction analytical research forward.

Concurrent validation of an index to estimate fall risk in community dwelling seniors through a wireless sensor insole system: A pilot study

Falls are a major health problem for older adults with immediate effects, such as fractures and head injuries, and longer term effects including fear of falling, loss of independence, and disability. The goals of the WIISEL project were to develop an unobtrusive, self-learning and wearable system aimed at assessing gait impairments and fall risk of older adults in the home setting; assessing activity and mobility in daily living conditions; identifying decline in mobility performance and detecting falls in the home setting. The WIISEL system was based on a pair of electronic insoles, able to transfer data to a commercially available smartphone, which was used to wirelessly collect data in real time from the insoles and transfer it to a backend computer server via mobile internet connection and then onwards to a gait analysis tool. Risk of falls was calculated by the system using a novel Fall Risk Index (FRI) based on multiple gait parameters and gait pattern recognition. The system was tested by twenty-nine older users and data collected by the insoles were compared with standardized functional tests with a concurrent validity approach. The results showed that the FRI captures the risk of falls with accuracy that is similar to that of conventional performance-based tests of fall risk. These preliminary findings support the idea that theWIISEL system can be a useful research tool and may have clinical utility for long-term monitoring of fall risk at home and in the community setting.

Quantifying camouflage: how to predict detectability from appearance

BACKGROUND

Quantifying the conspicuousness of objects against particular backgrounds is key to understanding the evolution and adaptive value of animal coloration, and in designing effective camouflage. Quantifying detectability can reveal how colour patterns affect survival, how animals' appearances influence habitat preferences, and how receiver visual systems work. Advances in calibrated digital imaging are enabling the capture of objective visual information, but it remains unclear which methods are best for measuring detectability. Numerous descriptions and models of appearance have been used to infer the detectability of animals, but these models are rarely empirically validated or directly compared to one another. We compared the performance of human 'predators' to a bank of contemporary methods for quantifying the appearance of camouflaged prey. Background matching was assessed using several established methods, including sophisticated feature-based pattern analysis, granularity approaches and a range of luminance and contrast difference measures. Disruptive coloration is a further camouflage strategy where high contrast patterns disrupt they prey's tell-tale outline, making it more difficult to detect. Disruptive camouflage has been studied intensely over the past decade, yet defining and measuring it have proven far more problematic. We assessed how well existing disruptive coloration measures predicted capture times. Additionally, we developed a new method for measuring edge disruption based on an understanding of sensory processing and the way in which false edges are thought to interfere with animal outlines.

RESULTS

Our novel measure of disruptive coloration was the best predictor of capture times overall, highlighting the importance of false edges in concealment over and above pattern or luminance matching.

CONCLUSIONS

The efficacy of our new method for measuring disruptive camouflage together with its biological plausibility and computational efficiency represents a substantial advance in our understanding of the measurement, mechanism and definition of disruptive camouflage. Our study also provides the first test of the efficacy of many established methods for quantifying how conspicuous animals are against particular backgrounds. The validation of these methods opens up new lines of investigation surrounding the form and function of different types of camouflage, and may apply more broadly to the evolution of any visual signal.

Peripheral nerve ultrasound scoring systems: benchmarking and comparative analysis

Ultrasound of the nerves is an additive diagnostic tool to evaluate polyneuropathy. Recently, the need for standardized scoring systems has widely been discussed; different scores are described so far. Therefore, 327 patients with polyneuropathy were analyzed by ultrasound in our laboratory. Consequently, several ultrasound scoring tools were applied, i.e., the nerve pattern classification according to Padua et al. in all patients with CIDP and variants, the Bochum ultrasound score (BUS) and the neuritis ultrasound protocol in immune-mediated neuritis, the ultrasound pattern sum score, the homogeneity score, and the nerve enlargement distribution score in all neuropathies if possible. For all scores good accuracy was found. Most patients with CIDP revealed hypoechoic enlarged nerves (Class 1), the BUS/NUP was useful to identify GBS (sensitivity >85%), MMN (100%) and CIDP (>70%), while the UPSS showed high sensitivity and positive/negative predictive values (N/PPV) in the diagnosis of GBS (>70%), CIDP (>85%) and axonal non-inflammatory neuropathies (>90%). Homogeneous nerves were found in most CMT1 patients (66.7%), while immune-mediated neuropathies mostly show regional nerve enlargement. The HS was suitable to identify CMT patients with an HS ≥5 points. All scores were easily applicable with high accuracy. The former-reported results could be similarly confirmed. However, all sores have some incompleteness concerning unselected polyneuropathy population, particularly rare and focal types. Scoring systems are useful and easily applicable. They show high accuracy in certain neuropathies, but also offer some gaps and can, therefore, only be used in addition to standard diagnostic routines such as electrophysiology.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges.

Plasma fatty acid patterns reflect dietary habits and metabolic health: A cross-sectional study

SCOPE

Using pattern analysis, we investigated the relationship between plasma fatty acid patterns, dietary intake, and biomarkers of metabolic health using data from the Irish National Adult Nutrition Survey.

METHODS AND RESULTS

Plasma fatty acid patterns were derived from 26 plasma fatty acids using k-means cluster analysis. Four clusters were identified, each with a distinct fatty acid profile. Cluster 1 included high proportions of linoleic acid (LA) and low proportions of stearic acid (SA); cluster 2 was higher in n-3 polyunsaturated fatty acids and SA; the profile of cluster 3 was higher in very-long-chain saturated fatty acid (VLCSFA) and lower in α-linolenic acid (ALA) (cluster 3); while cluster 4 was higher in fatty acids related to de novo lipogenesis and 20:3n-6 and lower in LA (cluster 4). In general, cluster 4 was associated with adverse metabolic profile and higher metabolic risk (p < 0.033). Clusters 2 and 3 were associated with healthier and protective phenotypes (p < 0.033).

CONCLUSION

Distinct fatty acid patterns were identified which were related to demographics, dietary habits, and metabolic profile. A pattern higher in VLCSFA and lower in ALA was associated with healthier metabolic outcome.

EPIG-Seq: extracting patterns and identifying co-expressed genes from RNA-Seq data

Background

RNA sequencing (RNA-Seq) measures genome-wide gene expression. RNA-Seq data is count-based rendering normal distribution models for analysis inappropriate. Normalization of RNA-Seq data to transform the data has limitations which can adversely impact the analysis. Furthermore, there are a few count-based methods for analysis of RNA-Seq data but they are essentially for pairwise analysis of treatment groups or multiclasses but not pattern-based to identify co-expressed genes.

Results

We adapted our extracting patterns and identifying genes methodology for RNA-Seq (EPIG-Seq) count data. The software uses count-based correlation to measure similarity between genes, quasi-Poisson modelling to estimate dispersion in the data and a location parameter to indicate magnitude of differential expression. EPIG-Seq is different than any other software currently available for pattern analysis of RNA-Seq data in that EPIG-Seq 1) uses count level data and supports cases of inflated zeros, 2) identifies statistically significant clusters of genes that are co-expressed across experimental conditions, 3) takes into account dispersion in the replicate data and 4) provides reliable results even with small sample sizes. EPIG-Seq operates in two steps: 1) extract the pattern profiles from data as seeds for clustering co-expressed genes and 2) cluster the genes to the pattern seeds and compute statistical significance of the pattern of co-expressed genes. EPIG-Seq provides a table of the genes with bootstrapped p-values and profile plots of the patterns of co-expressed genes. In addition, EPIG-Seq provides a heat map and principal component dimension reduction plot of the clustered genes as visual aids. We demonstrate the utility of EPIG-Seq through the analysis of toxicogenomics and cancer data sets to identify biologically relevant co-expressed genes. EPIG-Seq is available at: sourceforge.net/projects/epig-seq.

Conclusions

EPIG-Seq is unlike any other software currently available for pattern analysis of RNA-Seq count level data across experimental groups. Using the EPIG-Seq software to analyze RNA-Seq count data across biological conditions permits the ability to extract biologically meaningful co-expressed genes associated with coordinated regulation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2584-7) contains supplementary material, which is available to authorized users.

Mid-level image representations for real-time heart view plane classification of echocardiograms

In this paper, we explore mid-level image representations for real-time heart view plane classification of 2D echocardiogram ultrasound images. The proposed representations rely on bags of visual words, successfully used by the computer vision community in visual recognition problems. An important element of the proposed representations is the image sampling with large regions, drastically reducing the execution time of the image characterization procedure. Throughout an extensive set of experiments, we evaluate the proposed approach against different image descriptors for classifying four heart view planes. The results show that our approach is effective and efficient for the target problem, making it suitable for use in real-time setups. The proposed representations are also robust to different image transformations, e.g., downsampling, noise filtering, and different machine learning classifiers, keeping classification accuracy above 90%. Feature extraction can be performed in 30 fps or 60 fps in some cases. This paper also includes an in-depth review of the literature in the area of automatic echocardiogram view classification giving the reader a through comprehension of this field of study.

Decoding letter position in word reading

A fundamental computation underlying visual word recognition is the ability to transform a set of letters into a visual word form. Neuropsychological data suggest that letter position within a word may be independently affected by brain damage, resulting in a dissociable subtype of peripheral dyslexia. Here we used functional magnetic resonance imaging and supervised machine learning techniques to classify letter position based on activation patterns evoked during reading Hebrew words. Across the entire brain, activity patterns in the left intraparietal sulcus provided the best classification accuracy (80%) with respect to letter position. Importantly, the same set of voxels that showed highest classification performance of letter position using one letter-of-interest also showed highest classification performance using a different letter-of-interest. A functional connectivity analysis revealed that activity in these voxels co-varied with activity in the Visual Word Form Area, confirming cross-talk between these regions during covert reading. The results converge with reports of patients with acquired letter position dyslexia, who suffer from left occipito-parietal lesions. These findings provide direct and novel evidence for the role of left IPS within the reading network in processing relative letter positions.

Multi-Channel neurodegenerative pattern analysis and its application in Alzheimer's disease characterization

Neuroimaging has played an important role in non-invasive diagnosis and differentiation of neurodegenerative disorders, such as Alzheimer's disease and Mild Cognitive Impairment. Various features have been extracted from the neuroimaging data to characterize the disorders, and these features can be roughly divided into global and local features. Recent studies show a tendency of using local features in disease characterization, since they are capable of identifying the subtle disease-specific patterns associated with the effects of the disease on human brain. However, problems arise if the neuroimaging database involved multiple disorders or progressive disorders, as disorders of different types or at different progressive stages might exhibit different degenerative patterns. It is difficult for the researchers to reach consensus on what brain regions could effectively distinguish multiple disorders or multiple progression stages. In this study we proposed a Multi-Channel pattern analysis approach to identify the most discriminative local brain metabolism features for neurodegenerative disorder characterization. We compared our method to global methods and other pattern analysis methods based on clinical expertise or statistics tests. The preliminary results suggested that the proposed Multi-Channel pattern analysis method outperformed other approaches in Alzheimer's disease characterization, and meanwhile provided important insights into the underlying pathology of Alzheimer's disease and Mild Cognitive Impairment.

Pattern analysis of Epstein-Barr virus viremia and its significance in the evaluation of organ transplant patients suspected of having posttransplant lymphoproliferative disorders

OBJECTIVES

To explore posttransplant lymphoproliferative disorder (PTLD) cases associated with Epstein-Barr virus (EBV).

METHODS

We retrospectively reviewed the EBV DNA quantitation data of 9,779 blood samples from 740 transplant patients and their associations with PTLD.

RESULTS

EBV viremia occurred more frequently in patients with PTLD (85.4%) in comparison with patients without PTLD (38.3%; P < .0001). Patients with PTLD demonstrated significantly higher first positive results, higher peak levels, and a higher rate of increase in EBV viral load compared with patients without PTLD (P = .002, P < .0001, and P < .0001, respectively). However, in the multivariate analysis, only the peak level was associated with the development of PTLD. In particular, within hematopoietic stem cell recipients, the peak level able to diagnose PTLD was an area under the receiver operating characteristic curve of 0.806.

CONCLUSIONS

We believe that pattern analysis of EBV DNA quantitation results could lead to the early diagnosis and timely treatment of PTLD.

Metabolic network modeling approaches for investigating the "hungry cancer"

Metabolism is the functional phenotype of a cell, at a given condition, resulting from an intricate interplay of various regulatory processes. The study of these dynamic metabolic processes and their capabilities help to identify the fundamental properties of living systems. Metabolic deregulation is an emerging hallmark of cancer cells. This deregulation results in rewiring of the metabolic circuitry conferring an exploitative metabolic advantage for the tumor cells which leads to a distinct benefit in survival and lays the basis for unbound progression. Metabolism can be considered as a thermodynamic open-system in which source substrates of high value are being processed through a well established interconnected biochemical conversion system, strictly obeying physiochemical principles, generating useful intermediates and finally resulting in the release of byproducts. Based on this basic principle of an input-output balance, various models have been developed to interrogate metabolism elucidating its underlying functional properties. However, only a few modeling approaches have proved computationally feasible in elucidating the metabolic nature of cancer at a systems level. Besides this, statistical approaches have been set up to identify biochemical pathways being more relevant for specific types of tumor cells. In this review, we are briefly introducing the basic statistical approaches followed by the major modeling concepts. We have put an emphasis on the methods and their applications that have been used to a greater extent in understanding the metabolic remodeling of cancer.

Non-rigid Registration for Large Sets of Microscopic Images on Graphics Processors

Microscopic imaging is an important tool for characterizing tissue morphology and pathology. 3D reconstruction and visualization of large sample tissue structure requires registration of large sets of high-resolution images. However, the scale of this problem presents a challenge for automatic registration methods. In this paper we present a novel method for efficient automatic registration using graphics processing units (GPUs) and parallel programming. Comparing a C++ CPU implementation with Compute Unified Device Architecture (CUDA) libraries and pthreads running on GPU we achieve a speed-up factor of up to 4.11× with a single GPU and 6.68× with a GPU pair. We present execution times for a benchmark composed of two sets of large-scale images: mouse placenta (16K × 16K pixels) and breast cancer tumors (23K × 62K pixels). It takes more than 12 hours for the genetic case in C++ to register a typical sample composed of 500 consecutive slides, which was reduced to less than 2 hours using two GPUs, in addition to a very promising scalability for extending those gains easily on a large number of GPUs in a distributed system.