Arbitrary Threshold Research Articles

Correlation of clinical decision-making with probability of disease: A web-based study among general practitioners.

Medical decision-making relies partly on the probability of disease. Current recommendations for the management of common diseases are based increasingly on scores that use arbitrary probability thresholds. To assess decision-making in pharyngitis and appendicitis using a set of clinical vignettes, and the extent to which management is congruent with the true probability of having the disease. We developed twenty-four clinical vignettes with clinical presentations corresponding to specific probabilities of having disease defined by McIsaac (pharyngitis) or Alvarado (appendicitis) scores. Each participant answered four randomly selected web-based vignettes. General practitioners (GP) working in primary care structures in Switzerland and the USA. A comparison between the GP's management decision according to the true probability of having the disease and to the GP's estimated probability, investigating the GP's ability to estimate probability of disease. The mean age of the GPs was 48 years (SD 12) and 66% were men. The correlation between the GP's clinical management decision based on the vignette and the recommendations was stronger for appendicitis than pharyngitis (kw = 0.74, 95% CI 0.70-0.78 vs. kw = 0.66, 95% CI 0.62-0.71). On the other hand, the association between the clinical management decision and the probability of disease estimated by GPs was more congruent with recommendations for pharyngitis than appendicitis (kw = 0.70, 95% CI 0.66-0.73 vs. 0.61, 95% CI 0.56-0.66). Only a minority of GPs correctly estimated the probability of disease (29% for appendicitis and 39% for pharyngitis). Despite the fact that general practitioners often misestimate the probability of disease, their management decisions are usually in line with recommendations. This means that they use other approaches, perhaps more subjective, to make decisions, such as clinical judgment or reasoning that integrate factors other than just the risk of the disease.

ScatLay: utilizing transcriptome-wide noise for identifying and visualizing differentially expressed genes

Differential expressed (DE) genes analysis is valuable for understanding comparative transcriptomics between cells, conditions or time evolution. However, the predominant way of identifying DE genes is to use arbitrary threshold fold or expression changes as cutoff. Here, we developed a more objective method, Scatter Overlay or ScatLay, to extract and graphically visualize DE genes across any two samples by utilizing their pair-wise scatter or transcriptome-wide noise, while factoring replicate variabilities. We tested ScatLay for 3 cell types: between time points for Escherichia coli aerobiosis and Saccharomyces cerevisiae hypoxia, and between untreated and Etomoxir treated Mus Musculus embryonic stem cell. As a result, we obtain 1194, 2061 and 2932 DE genes, respectively. Next, we compared these data with two widely used current approaches (DESeq2 and NOISeq) with typical twofold expression changes threshold, and show that ScatLay reveals significantly larger number of DE genes. Hence, our method provides a wider coverage of DE genes, and will likely pave way for finding more novel regulatory genes in future works.

S0578 Adherence to Best Practices in the Detection and Characterisation of Premalignant Upper Gastrointestinal Lesions Among Endoscopists in India: A Survey

INTRODUCTION: Gastric and esophageal cancers are associated with the high morbidity and mortality in India. Detection of these cancers at an early stage can reduce the high morbidity. In the absence of formal population based screening for upper gastrointestinal cancers in India, it is important that all elective Esophago-Gastro-Duodenoscopies (EGDs), irrespective of the primary indication, be viewed as an opportunity to screen for pre-malignant lesions. With this premise, we aimed to assess the adherence to best practices with regard to detection of pre-malignant upper gastro-intestinal lesions (PMUGIL) among endoscopists in India. METHODS: We conducted a cross-sectional survey among current practicing endoscopists in India using an online platform (SurveyMonkeyTM). The survey questionnaire contained 28 questions pertaining to demographic details, best practices (as defined by Asian Consensus on standards and ESGE guidelines for performance measures), training and facilities available to endoscopists. The survey link was circulated amongst GI endoscopists working in India through email and WhatsApp. The responses were collected from online submission to the survey link. The primary objectivewas to assess adherence to best practices regarding detection and characterization of PMUGILin India. An arbitrary threshold of 90% adherence was considered adequate. The secondary objectiveswere – assessment of the differences between teaching and non-teaching centers, adequacy of training, and availability of appropriate facilities with the endoscopists. RESULTS: A total of 422 eligible responses were obtained. The adherence to best practices assessed were lower than the set threshold in all except one metric - the reporting of Barrett's (Table 1) in both teaching centers and non-teaching centers. None of the respondents adhered to all the best practices surveyed. Among responding endoscopists, 58.5% (247/422) reported to having been trained in the detection of PMUGIL. Endoscopy equipment with facility of appropriate electronic Image Enhanced Endoscopy (IEE) for evaluating PMUGILwere available to 54.4% (223/410) of responding endoscopists. CONCLUSION: Training interventions are necessary to improve practices in the evaluation of PMUGIL. Centers lacking appropriate IEE facilities should be encouraged to refer patients to appropriate centers for evaluation and follow up.Table 1

A Technique for Estimating Liquid Droplet Diameter and Liquid Water Content in Stratocumulus Clouds Using Radar and Lidar Measurements

AbstractThis paper describes a technique for estimating the liquid water content (LWC) and a characteristic particle diameter in stratocumulus clouds using radar and lidar observations. The uncertainty in LWC estimate from radar and lidar measurements is significantly reduced once the characteristic particle diameter is known. The technique is independent of the drop size distribution. It is applicable for a broad range of W-band reflectivityZbetween −30 and 0 dBZand all values of lidar backscatterβobservations. No partitioning of cloud or drizzle is required on the basis of an arbitrary threshold ofZas in prior studies. A method for estimating droplet diameter and LWC was derived from the electromagnetic simulations of radar and lidar observations. In situ stratocumulus cloud and drizzle probe spectra were input to the electromagnetic simulation. The retrieved droplet diameter and LWC were validated using in situ measurements from the southeastern Pacific Ocean. The retrieval method was applied to radar and lidar measurements from the northeastern Pacific. Uncertainty in the retrieved droplet diameter and LWC that are due to the measurement errors in radar and lidar backscatter measurements are 7% and 14%, respectively. The retrieved LWC was validated using the concurrent G-band radiometer estimates of the liquid water path.

Thromboprophylaxis in arthroscopy: Survey of current practices in France and comparison with recommendations

IntroductionCurrent national and international recommendations for thromboprophylaxis after arthroscopy procedures are not very detailed and likely not well known by surgeons. The aim of this study was to compare the daily clinical practices of members of the Francophone Arthroscopy Society (SFA) with current national and international recommendations. HypothesisThe primary hypothesis was that the procedures adopted by more than 75% of SFA members conform to the national recommendations set out by the French Anesthesia and Intensive Care Society (SFAR) in effect at the time of this study. MethodsA questionnaire was sent to members of the SFA asking three questions to capture their protocol (decision maker, prescriber, indication by procedure). Descriptive statistics were generated for each question. The percentage of prescription protocols conforming to each recommendation was compared to the target percentage (75% arbitrary threshold) by a Chi2 test with a 5% threshold overall and for each type of procedure. ResultsThirty-five percent of the SFA members responded to the questionnaire. Overall, the compliance rate was 69%, which was significantly lower than the target (p<0.001). The compliance rate was significantly lower than the target for hip procedures, minor knee procedures and ankle procedures; each time, the prescription was excessive. The compliance rate was not different than the target for major knee procedures and upper limb procedures. The sole reason for non-compliance was a non-justified prescription. DiscussionLess than 75% of SFA members have adopted thromboprophylaxis protocols that comply with national SFAR recommendations in effect at the time of this study, with excessive prescription for procedures on the lower limb. Recent studies do not confirm that broadening of the prescription is justified. This large gap between published recommendations and current practices means that we should reconsider the relevance of current recommendations and re-evaluate the cost-benefit ratio of prescribing a drug-based thromboprophylaxis after arthroscopy surgery. Level of evidenceLevel IV.

Determining Attributes of Finite Fracture Networks in Low-Permeability Carbonate Reservoirs

SummaryCarbonate reservoirs usually consist of multiple units each with different degrees of layer-bound fracture density, height, length, and angular scatter. Some units may have finite fracture networks (FFNs), and some others may contain only isolated fractures. FFNs are islands of interconnected fractures within a sea of isolated fractures. The fracture volume can be estimated from the initial surge in production if a wellbore intersects only a single FFN. Combining with fracture density and aperture measurements from borehole images, FFN volume, drainage area, and other relevant attributes can be calculated. If, however, a slanted well intersects an unknown number of FFNs each in a different unit, it is not possible to estimate each FFN fracture volume from initial fracture production. Because the connectivity threshold for initiation of FFNs is not known or cannot be calculated, it is not possible to determine how many FFNs the borehole intersects. One way is to set an arbitrary threshold of fracture connectivity, λc.Based on this initial value, a fracture-flow model can be generated and adjusted until the inferred rate time and cumulative time curves converge to observed values. The adjustment is accomplished by changing the number of FFNs using different connectivity threshold λc and estimating their size by modifying unknown or uncertain parameters such as fracture aperture. The final model yields the mostly likely number and size of all FFNs and provides an approximate forecast for future production performance.The ability to match production performance without taking pressure into consideration shows that rate decrease in fractured reservoirs with FFNs is not only related to pressure drawdown but also, perhaps more importantly, to depletion of FFNs starting from the smallest size or the one with highest drainage efficiency.One drawback of the method is that different numbers of FFNs may have similar inferred production performance and cumulative production. This is an inevitable drawback inherent in most models that must rely on multiple uncertain or unknown parameters. The only way to decrease the range of possible solutions is to decrease the uncertainties in the underlying input parameters. In any case, alternative results may be presented as a range of possible solutions.

Urbanity as a Process and the Role of Relative Network Properties—A Case Study From the Early Iron Age

A manifold of concepts of urbanity have been discussed in the past and many criteria of towns have been developed. These criteria, e. g. size, population,legal aspects, way of life, structural and functional approaches are insufficient, because they cover just a part of the phenomenon and because they partly use fixed and arbitrary thresholds. We turn to an understanding of urbanity as a process which fills and shapes the scenery of the buildings and people and which involves complexity as one of the main drivers. In this sense we understand urbanity as a process of adaption to changed requirements or contexts in a complex settlement system, triggered by size, attracted by exemplary solutions and characterized by the emergence of new structures. With this paper we address the issues of relativity in the urbanity process. As adaption process, urbanity is relative in the sense, that other places may have gained better or worse adaption. Concerning networks, this means that network centrality measures have to be adapted to geographical networks. This paper applies this concept to the Princely Seats of the Early Iron Age with a special focus on the Heuneburg. A very limited part of the material culture is used to represent similarities and interaction between the different nodes. For this purpose we use fibulae which allow rather good dating and hence ensure a narrow time slice for the network analysis. Fibulae also provide a limitation to a certain social segment, which can be addressed as a middle class. This paper is intended to contribute to the problem of addressing the rather complex issue of urbanity using rather simple approaches such as network analysis. We pay attention to a tight integration of theory and method as well as to certain conceptual issues. This paper has two main results. First, we develop a consistent approach to apply social network centrality measures on geographical networks. Secondly, the role of a kind of middle class in the course of urbanity processes is enlightened.

Improving the detection of shell alteration: Implications for sclerochronology

Sclerochronology makes use of (fossil) shell-archives to establish records allowing for investigation of high-resolution environmental dynamics. Nevertheless, this potential can often not be fully exploited due to the interplay between paleoenvironmental variability, vital effects and the potential diagenetic modification of skeletal materials, which often results in highly complex records. A novel dynamic approach, aiming to separate pristine from altered shell material for paleoclimate and paleoenvironmental reconstructions is proposed.Seventeen fossil bivalve shells (requieniid rudists, pectinids and chondrodonts) from two neighbouring Lower Cretaceous (Albian) shallow-water sections (Lusitanian Basin, western Portugal) were analysed for their major and trace elemental compositions using high-resolution quantitative μXRF line scans. Their complex records were subject to a novel statistical analysis protocol, which tested mono- and multi-species datasets, as well as comparing shells from both locations.Characteristic elemental associations reveal the differential impact of early and late diagenetic alteration processes. The incorporation of elements associated with detrital contribution (Fe, Si, Al) is attributed to syn-depositional bioerosion (shell-boring). In clear contrast, shell-portions showing a strong correlation between Fe and Mn are indicative of later diagenetic alteration. The influence of each process is different at each site, revealing local differential alteration pathways. Mono-specific comparisons provides identical geochemical responses, suggesting that intra-specific differences do not control the observed elemental patterns. In contrast, inter-species tests rendered a clear separation in the way elements are incorporated in the shells of pectinids and requieniids (e.g., as evidenced by differences in Mg or Sr content). Such differences can be linked to differential biomineralization pathways, easily detected by the applied method.We present a new, dynamic method for distinguishing pristine from altered shell material, not relying on arbitrary diagenetic thresholds for trace element content. By clearly identifying shell-alteration pathways, syn- to post-depositional processes are recognized. A progressive cleaning of the elemental dataset allows paleoenvironmental studies to be based on the most pristine data, contributing to unravelling the complex interplay between climate, environmental dynamics and their impact on biomineralization processes and sclerochronological archives.

Applying conventional agency theory to prediction of employee self-reporting performance behaviour

PurposeThe purpose of this study is to test various hypotheses regarding if managers' voluntarily prefer honesty in self-reported managerial performance (HPR).Design/methodology/approachThis study uses an experimental approach with a data set of 300 Ghanaian employees.FindingsThe results confirm that the current trend where employee contracts are underpinned by the classical agency theory (CAT) is problematic, ineffective and costly because it does not appropriately explain the observed behaviour of honesty and partial honesty in self-reported performance or the dishonesty in reporting performance when there is no financial reward to be gained by employees. Therefore MNCs may benefit from a consideration of wider and alternative perspectives. Additionally, stakeholders must consider a strategy of delaying performance-related bonuses (pay-offs) to improve HPR and avoid capping performance-related pay off with an arbitrary threshold. This is because the setting of arbitrary thresholds reduces the established relationship between effort and reward and introduces gaming into the managerial performance reporting process.Originality/valueUnlike other prior studies that rely on students as surrogates for employees, this study uses actual employees to test the experimental constructs. Aside from the comparatively large data set, this study is the first exploration of the differential effects of national characteristics on HPR in Ghana.

AmpliCI: a high-resolution model-based approach for denoising Illumina amplicon data.

MotivationNext-generation amplicon sequencing is a powerful tool for investigating microbial communities. A main challenge is to distinguish true biological variants from errors caused by amplification and sequencing. In traditional analyses, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false-positive rates. Recently developed ‘denoising’ methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low-frequency sequences, especially those near more frequent sequences, because they ignore the sequencing quality information.ResultsWe introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI considers the quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. AmpliCI has better performance than three popular denoising methods, with acceptable computation time and memory usage.Availability and implementationSource code is available at https://github.com/DormanLab/AmpliCI.Supplementary information Supplementary material are available at Bioinformatics online.

Price Discrimination and Focal Points for Tacit Collusion: Evidence from the Airline Industry

We use unique data sets with round-the-clock posted fares and a regression-discontinuity design to identify price discrimination in advance-purchase discounts. Price discrimination increases fares by 14% between two and one week before departure, and by 7.6% between three and two weeks to departure. While competition reduces price discrimination, it is unaffected by product variety for a multiproduct monopolist. The results show that the arbitrary thresholds of 7 and 14 days-in-advance serve as focal points for tacit collusion and to implement price discrimination in competitive markets. For round-trip tickets discrimination depends on the days-in-advance of both the outbound and inbound flights.

Outcomes of Older Patients in the Recent Era of Heart Kidney Transplantation

Background and ObjectiveVariable age thresholds are often used at transplant centers for simultaneous heart and kidney transplantation (HKT). We hypothesize that selected older recipients enjoy comparable outcome to younger recipients in the current era of HKT. MethodsWe performed a retrospective analysis of HKT outcomes in the United Network for Organ Sharing (UNOS) registry from 2006 to 2018, classifying patients by age at transplant as ≥ 65 or < 65 years. The primary outcome was patient death. Secondary outcomes included all-cause kidney graft failure and death-censored kidney allograft failure. ResultsOf 973 patients, 774 (80%) were younger than 65 years (mean 52 ± 10 years) and 199 (20%) were 65 years or older (mean 67 ± 2 years). The older HKT cohort had fewer blacks (22% vs 35%, P = .01) and women (12 vs 18%, P = .04). Fewer older patients received dialysis (30% vs 54%, P < .001) and mechanical support (36% vs 45%, P = .03) before HKT. Older recipients received organs from slightly older donors. The median follow-up time was shorter for patients 65 years or older than for the younger group (2.3 vs 3.3 years, P < .001). Patient survival was similar between the groups (mean 8.8 vs 9.8 years, P = .3), with the most common causes of death being cardiovascular (29%) and infectious complications (28%). There was no difference in all-cause kidney graft survival (mean 8.7 vs 9.3 years, P = .8). Most commonly, recipients died with a functional renal allograft (59.8%), and this occurred more commonly in older patients (81.4% vs 54.8%, P = .001). Cox proportional hazard modeling showed that higher donor age (hazard ratio [HR] 1.015, P = .01; HR 1.022, P = .02) and use of pre-transplant dialysis (HR 1.5, P = .004; HR 1.8, P = .006) increased the risk for both all-cause and death-censored kidney allograft failure, respectively. ConclusionsOur study showed that carefully selected older patients have outcomes similar to those of a younger cohort and argues for comprehensive evaluation of the recipients with age as part of comorbidity assessment rather than use of an arbitrary age threshold for candidacy.

Continuous lactate monitoring in critically ill patients using microdialysis

IntroductionBlood lactate is a strong predictor of mortality in critically ill patients. Its monitoring implies repeated measurements. The EIRUS™ system (Maquet Critical Care AB, 17154, Solna, Sweden) is a new device allowing continuous lactate monitoring by intravascular micro dialysis. The present study aimed at assessing the accuracy of the EIRUS™ system in critically ill patients with circulatory failure. MethodsAn observational cohort study was conducted in Nîmes University Hospital. Eligible patients were those with circulatory failure in which a specific central venous access was put in place by the physician in charge, allowing continuous lactate measurement by the EIRUS™ system. Lactate measurements obtained by the system were compared to lactate from arterial blood samples at H4 and H8 from the calibration, during the first 48hours of shock. ResultsIn all, 28 patients were included providing 244 pairs of measures. The Bland-Altman analysis showed a bootstrapped mean bias at H4 of 0.05 and 95% limits of agreement of −0.9 to 1.0mmol/L. At H8 the mean bias was 0.06 and 95% limits of agreement −1.1 to 1.2mmol/L. The global trend agreement [95% CI] for a pre-specified arbitrary threshold of 1mmol/L, defining clinically significant variations, between H0 and H4 and H4 and H8 was 91.6% [85.1; 95.9] and 89.5% [82.3; 94.4], respectively. ConclusionThe EIRUS™ device provided an overall accurate measurement of lactate in critically ill patients with circulatory failure. Detection of lactate variations over time is less precise and technical issues may limit its clinical use.

Merging microarray studies to identify a common gene expression signature to several structural heart diseases

BackgroundHeart disease is the leading cause of death worldwide. Knowing a gene expression signature in heart disease can lead to the development of more efficient diagnosis and treatments that may prevent premature deaths. A large amount of microarray data is available in public repositories and can be used to identify differentially expressed genes. However, most of the microarray datasets are composed of a reduced number of samples and to obtain more reliable results, several datasets have to be merged, which is a challenging task. The identification of differentially expressed genes is commonly done using statistical methods. Nonetheless, these methods are based on the definition of an arbitrary threshold to select the differentially expressed genes and there is no consensus on the values that should be used.ResultsNine publicly available microarray datasets from studies of different heart diseases were merged to form a dataset composed of 689 samples and 8354 features. Subsequently, the adjusted p-value and fold change were determined and by combining a set of adjusted p-values cutoffs with a list of different fold change thresholds, 12 sets of differentially expressed genes were obtained. To select the set of differentially expressed genes that has the best accuracy in classifying samples from patients with heart diseases and samples from patients with no heart condition, the random forest algorithm was used. A set of 62 differentially expressed genes having a classification accuracy of approximately 95% was identified.ConclusionsWe identified a gene expression signature common to different cardiac diseases and supported our findings by showing their involvement in the pathophysiology of the heart. The approach used in this study is suitable for the identification of gene expression signatures, and can be extended to different diseases.

A sEMG-Driven Soft ExoSuit Based on Twisted String Actuators for Elbow Assistive Applications

The scope of this letter is to show the applicability of the Twisted String Actuators (TSAs) for lightweight, wearable and assistive robotic applications. To this aim, we have developed a novel surface electromyography (sEMG)-driven soft ExoSuit using the TSAs to perform both single and dual-arm elbow assistive applications. The proposed ExoSuit, with an overall weight of 1650 g, uses a pair of TSAs mounted in the back of the user, connected via tendons to the user's forearms to actuate each arm independently for supporting external loads. We confirm this new light-weight and customizable wearable solution via multiple user studies based on the biceps and tricep' sEMG measurements. We demonstrate that user's muscles can automatically activate and regulate the TSAs and compensate for the user's effort: by using our controller based on a Double Threshold Strategy (DTS) with a standard PID regulator, we report that the system was able to limit the biceps' sEMG activity under an arbitrary target threshold, compensating a muscular activity equal to 220% (related to a single arm 3 kg load) and 110% (related to a dual arm 4 kg load) of the threshold value itself. Moreover, the triceps' sEMG signal detects the external load and, depending on the threshold, returns the system to the initial state where it requires no assistance from the ExoSuit. The experimental results show the proposed ExoSuit's capabilities in both single and dual-arm load compensation tasks. Therefore, the applicability of the TSAs is experimentally demonstrated for a real-case assistive device, fostering future studies and developments of this kind of actuation strategy for wearable robotic systems.

Measuring ontogenetic shifts in central-place foragers: A case study with honeybees.

Measuring time-activity budgets over the complete individual life span is now possible for many animals with the recent advances of life-long individual monitoring devices. Although analyses of changes in the patterns of time-activity budgets have revealed ontogenetic shifts in birds or mammals, no such technique has been applied to date on insects. We tested an automated breakpoint-based procedure to detect, assess and quantify shifts in the temporal pattern of the flight activities in honeybees. We assumed that the learning and foraging stages of honeybees will differ in several respects, to detect the age at onset of foraging (AOF). Using an extensive dataset covering the life-long monitoring of 1,167 individuals, we compared the AOF outputs with the more conventional approaches based on arbitrary thresholds. We further evaluated the robustness of the different methods comparing the foraging time-activity budget allocations between the presumed foragers and confirmed foragers. We revealed a clear-cut learning-foraging ontogenetic shift that differs in duration, frequency and time of occurrence of flights. Although AOF appeared to be highly plastic among bees, the breakpoint-based procedure seems better capable to detect it than arbitrary threshold-based methods that are unable to deal with inter-individual variation. We developed the aof r-package including a broad range of examples with both simulated and empirical datasets to illustrate the simplicity of use of the procedure. This simple procedure is generic enough to be derived from any individual life-long monitoring devices recording the time-activity budgets, and could propose new ecological applications of bio-logging to detect ontogenetic shifts in the behaviour of central-place foragers.

The Unrecognized Prevalence of Primary Aldosteronism: A Cross-sectional Study.

Primary aldosteronism is a nonsuppressible renin-independent aldosterone production that causes hypertension and cardiovascular disease. To characterize the prevalence of nonsuppressible renin-independent aldosterone production, as well as biochemically overt primary aldosteronism, in relation to blood pressure. Cross-sectional study. 4 U.S. academic medical centers. Participants with normotension (n= 289), stage 1 hypertension (n= 115), stage 2 hypertension (n= 203), and resistant hypertension (n= 408). Participants completed an oral sodium suppression test, regardless of aldosterone or renin levels, as a confirmatory diagnostic for primary aldosteronism and to quantify the magnitude of renin-independent aldosterone production. Urinary aldosterone was measured in participants in high sodium balance with suppressed renin activity. Biochemically overt primary aldosteronism was diagnosed when urinary aldosterone levels were higher than 12 μg/24 h. Every blood pressure category had a continuum of renin-independent aldosterone production, where greater severity of production was associated with higher blood pressure, kaliuresis, and lower serum potassium levels. Mean adjusted levels of urinary aldosterone were 6.5 μg/24 h (95% CI, 5.2 to 7.7 μg/24 h) in normotension, 7.3 μg/24 h (CI, 5.6 to 8.9 μg/24 h) in stage 1 hypertension, 9.5 μg/24 h (CI, 8.2 to 10.8 μg/24 h) in stage 2 hypertension, and 14.6 μg/24 h (CI, 12.9 to 16.2 μg/24 h) in resistant hypertension; corresponding adjusted prevalence estimates for biochemically overt primary aldosteronism were 11.3% (CI, 5.9% to 16.8%), 15.7% (CI, 8.6% to 22.9%), 21.6% (CI, 16.1% to 27.0%), and 22.0% (CI, 17.2% to 26.8%). The aldosterone-renin ratio had poor sensitivity and negative predictive value for detecting biochemically overt primary aldosteronism. Prevalence estimates rely on arbitrary and conventional thresholds, and the study population may not represent nationwide demographics. The prevalence of primary aldosteronism is high and largely unrecognized. Beyond this categorical definition of primary aldosteronism, there is a prevalent continuum of renin-independent aldosterone production that parallels the severity of hypertension. These findings redefine the primary aldosteronism syndrome and implicate it in the pathogenesis of "essential" hypertension. National Institutes of Health.

StanDep: Capturing transcriptomic variability improves context-specific metabolic models.

Diverse algorithms can integrate transcriptomics with genome-scale metabolic models (GEMs) to build context-specific metabolic models. These algorithms require identification of a list of high confidence (core) reactions from transcriptomics, but parameters related to identification of core reactions, such as thresholding of expression profiles, can significantly change model content. Importantly, current thresholding approaches are burdened with setting singular arbitrary thresholds for all genes; thus, resulting in removal of enzymes needed in small amounts and even many housekeeping genes. Here, we describe StanDep, a novel heuristic method for using transcriptomics to identify core reactions prior to building context-specific metabolic models. StanDep clusters gene expression data based on their expression pattern across different contexts and determines thresholds for each cluster using data-dependent statistics, specifically standard deviation and mean. To demonstrate the use of StanDep, we built hundreds of models for the NCI-60 cancer cell lines. These models successfully increased the inclusion of housekeeping reactions, which are often lost in models built using standard thresholding approaches. Further, StanDep also provided a transcriptomic explanation for inclusion of lowly expressed reactions that were otherwise only supported by model extraction methods. Our study also provides novel insights into how cells may deal with context-specific and ubiquitous functions. StanDep, as a MATLAB toolbox, is available at https://github.com/LewisLabUCSD/StanDep.

Towards an End-to-End Method for High-Variance SERS Spectra Classification and Quantification

Surface enhanced Raman spectroscopy (SERS) is a subfield of Raman spectroscopy where trace compounds can be detected with the help of plasmonic materials. They enhance the Raman signal from molecules in proximity. Our work exploits the low number of molecules with enhanced signal by taking sequences of short integration spectra. This enable us to see events where a Raman active molecule shortly adsorb to the surface in an enhancing spot and gives us strong, but anisotropic signal. Because of this, conventional classification algorithms are not suited for efficient classification.The novel data analysis method presented here is based on active spectrum detection, where spectra are kept based on how many Raman peaks they are composed of. The end goal is the characterization and quantification of common drugs and cutting agents in a short time frame. A reusable microfluidic SERS sensor is used in a batch flow way, such as a technician could inject a diluted drug sample in it, analyse it for a few minutes and get result on the screen.The current architecture of the classification of the spectra can be represented with the flowchart as seen on figure 1, where the preprocessing and active spectrum detection are both classical algorithms, in the sense that they are not being optimized by training like machine learning ones, and simply apply the same rules for each and every spectrum. This preprocessing exploits a Savitzky-Golay smoothing, a baseline generation with airPLS, and a peak finding function to select valid peaks and count them and determine if a spectrum is active or not. Afterwards, the active spectra are processed with a multiclass CNN.To create a streamlined quantification pipeline, two quantification algorithms are compared with drug spectra: a CNN that is processing “stitchings” of multiple random spectra and a custom network based on modular neural networks. Both are also compared with various active spectrum detection and classifier methods.The current active spectrum detection is based on peak detection, with an arbitrary threshold set in a manner that if a peak is over its height, it is an active peak, and a spectrum would need an arbitrary number of active peaks to be considered active. This method can sort good spectra from the inactive and weakly active one, but the weak one can be filled with information that could be processed by the classification network. The proposed quantification pipeline forces the next network to be dependent of the previous ones, and such, parallel optimization is possible during training. By setting a quality score based on how active the spectra are, we can let the network optimize the trade-off between how many spectra will be considered for classification and the accuracy of the classification, because weakly active spectra can lower the accuracy when they don’t have enough information to be correctly classified.As mentioned earlier, a multiclass CNN is used. This type of classifier uses a All vs. All (AVA) strategy. This implies that all classes are tested against each other, and that one of the classes must be chosen. In the case of physiological spectra classification, this is problematic. The active spectrum of an unknown molecule would reach the classifier and be forced into one of the learned classes, and the result would be inherently false.To avoid this problem, two different type of classification architecture are tested with the quantification network: 1. An ensemble of binary CNN classifiers will be tested with a One vs. All (OVA) strategy, where there are only 2 outcomes for each classifier.2. An ensemble of one-class classifiers (OCC) where the outcome possible is either Yes or No for one class each. They might look the same at first hand but the difference is vast. This is also called Concept Learning and Anomaly Detection. Figure 1

Association of Specific Comorbidities with Monosodium Urate Crystal Deposition in Urate-Lowering Therapy-Naive Gout Patients: A Cross-Sectional Dual-Energy Computed Tomography Study.

(1) Background: To determine which factors are associated with the volume of monosodium urate (MSU) crystal deposition quantified by dual-energy computed tomography (DECT) in urate-lowering therapy (ULT)-naive gout patients. (2) Methods: In this multicenter cross-sectional study, DECT scans of knees and feet/ankles were prospectively obtained from ULT-naive gout patients. Demographic, clinical (including gout history and comorbidities), and biological data were collected, and their association with DECT MSU crystal volume was analyzed using bivariate and multivariate analyses. A second bivariate analysis was performed by splitting the dataset depending on an arbitrary threshold of DECT MSU volume (1 cm3). (3) Results: A total of 91 patients were included. In the bivariate analysis, age (p = 0.03), gout duration (p = 0.003), subcutaneous tophi (p = 0.004), hypertension (p = 0.02), diabetes mellitus (p = 0.05), and chronic heart failure (p = 0.03) were associated with the total DECT volume of MSU crystal deposition. In the multivariate analysis, factors associated with DECT MSU volumes ≥1 cm3 were gout duration (odds ratio (OR) for each 10-year increase 3.15 (1.60; 7.63)), diabetes mellitus (OR 4.75 (1.58; 15.63)), and chronic heart failure (OR 7.82 (2.29; 31.38)). (4) Conclusion: Specific comorbidities, particularly chronic heart failure and diabetes mellitus, are more strongly associated with increased MSU crystal deposition in knees and feet/ankles than gout duration, regardless of serum urate level.