Toward a Common Coordinate Framework for the Human Body.

Understanding the genetic and molecular drivers of phenotypic heterogeneity across individuals is central to biology. As new technologies enable fine-grained and spatially resolved molecular profiling, we need new computational approaches to integrate data from the same organ across different individuals into a consistent reference and to construct maps of molecular and cellular organization at histological and anatomical scales. Here, we review previous efforts and discuss challenges involved in establishing such a common coordinate framework, the underlying map of tissues and organs. We focus on strategies to handle anatomical variation across individuals and highlight the need for new technologies and analytical methods spanning multiple hierarchical scales of spatial resolution.

Using the kinetics of C-reactive protein response to improve the differential diagnosis between acute bacterial and viral infections.

PURPOSE: Differential diagnosis between acute viral and bacterial infection is an emerging common challenge for a physician in the emergency department. Serum C-reactive protein (CRP) is used to support diagnosis of bacterial infection, but in patients admitted with low CRP, its ability to discriminate between viral and bacterial infections is limited. We aimed to use two consecutive CRP measurements in order to improve differential diagnosis between bacterial and viral infection. METHODS: A single-center retrospective cohort (n = 1629) study of adult patients admitted to the emergency department with a subsequent microbiological confirmation of either viral or bacterial infection. Trend of CRP was defined as the absolute difference between the first two measurements of CRP divided by the time between them, and we investigated the ability of this parameter to differentiate between viral and bacterial infection. RESULTS: In patients with relatively low initial CRP concentration (< 60 mg/L, n = 634 patients), where the uncertainty regarding the type of infection is the highest, the trend improved diagnosis accuracy (AUC 0.83 compared to 0.57 for the first CRP measurement). Trend values above 3.47 mg/L/h discriminated bacterial from viral infection with 93.8% specificity and 50% sensitivity. CONCLUSIONS: The proposed approach for using the kinetics of CRP in patients whose first CRP measurement is low can assist in differential diagnosis between acute bacterial and viral infection.

Characteristics and Symptoms of App Users Seeking COVID-19-Related Digital Health Information and Remote Services: Retrospective Cohort Study.

BACKGROUND: Patient-facing digital health tools have been promoted to help patients manage concerns related to COVID-19 and to enable remote care and self-care during the COVID-19 pandemic. It has also been suggested that these tools can help further our understanding of the clinical characteristics of this new disease. However, there is limited information on the characteristics and use patterns of these tools in practice. OBJECTIVE: The aims of this study are to describe the characteristics of people who use digital health tools to address COVID-19-related concerns; explore their self-reported symptoms and characterize the association of these symptoms with COVID-19; and characterize the recommendations provided by digital health tools. METHODS: This study used data from three digital health tools on the K Health app: a protocol-based COVID-19 self-assessment, an artificial intelligence (AI)-driven symptom checker, and communication with remote physicians. Deidentified data were extracted on the demographic and clinical characteristics of adults seeking COVID-19-related health information between April 8 and June 20, 2020. Analyses included exploring features associated with COVID-19 positivity and features associated with the choice to communicate with a remote physician. RESULTS: During the period assessed, 71,619 individuals completed the COVID-19 self-assessment, 41,425 also used the AI-driven symptom checker, and 2523 consulted with remote physicians. Individuals who used the COVID-19 self-assessment were predominantly female (51,845/71,619, 72.4%), with a mean age of 34.5 years (SD 13.9). Testing for COVID-19 was reported by 2901 users, of whom 433 (14.9%) reported testing positive. Users who tested positive for COVID-19 were more likely to have reported loss of smell or taste (relative rate [RR] 6.66, 95% CI 5.53-7.94) and other established COVID-19 symptoms as well as ocular symptoms. Users communicating with a remote physician were more likely to have been recommended by the self-assessment to undergo immediate medical evaluation due to the presence of severe symptoms (RR 1.19, 95% CI 1.02-1.32). Most consultations with remote physicians (1940/2523, 76.9%) were resolved without need for referral to an in-person visit or to the emergency department. CONCLUSIONS: Our results suggest that digital health tools can help support remote care and self-management of COVID-19 and that self-reported symptoms from digital interactions can extend our understanding of the symptoms associated with COVID-19.

Increase of body mass index and waist circumference predicts development of metabolic syndrome criteria in apparently healthy individuals with 2 and 5 years follow-up.

BACKGROUND: The metabolic syndrome (MetS) is associated with overweight and abdominal obesity. Our aim was to use longitudinal measurements to provide clinically relevant information on the relative influence of changes in body mass index (BMI), waist circumference (WC), and weekly physical exercise duration on the development of each of the MetS components. METHODS: We analyzed data collected at the Tel-Aviv Medical Center Inflammation Survey (TAMCIS). Apparently healthy individuals with two consecutive visits that were not treated for any metabolic criteria were included in this study. We analyzed the influence of changes in BMI, WC, and time engaged in physical exercise on the change in each of the components of the metabolic syndrome using linear regressions. RESULTS: Included were 7532 individuals (5431 men, 2101 women) with 2 years follow-up. Participants who gained two BMI points, had the mean number of criteria increase from 1.07 to 1.52, while participants who lost two BMI points, decreased from 1.64 to 1.16. A long-term analysis over 5 years showed similar results. Furthermore, an increase in WC was independently associated with increased severity of each of the other components, when controlling for increase in BMI. Increase in weekly exercise duration had a small but statistically significant favorable effect on blood triglycerides and HDL levels, but not on blood pressure or HbA1C. CONCLUSIONS: Changes in BMI and WC are highly associative with the likelihood and severity of the MetS independently of the baseline levels, suggesting that obese individuals can substantially improve their MetS prognosis by losing both body weight and abdominal fat.

Metabolic syndrome is associated to high-sensitivity cardiac troponin T elevation.

INTRODUCTION: Metabolic syndrome (MetS) and high-sensitivity cardiac troponin T (hs-TnT) are associated with higher risk for cardiovascular diseases (CVD). Our aim was to assess the relation between hs-TnT elevation and MetS in a general population sample. MATERIALS AND METHODS: Individuals participating in an annual health survey program between 2010 and 2016 were included in the study. Blood samples including hs-TnT levels were collected. The study population was divided into three groups based on hs-TnT levels - undetectable (<5 ng/L), intermediate (5-14 ng/L) and elevated (>14 ng/L). RESULTS: A total of 5994 subjects were included in the study, the mean age was 48.5 and 4336 (72%) were males. Compared with subjects with undetectable hs-TnT the prevalence of MetS was higher in those with detectable and elevated levels - 392 (10%) vs. 270 (15%) and 51 (33%), respectively (p < 0.001). In a multivariate model adjusted for age, gender and multiple co-morbidities, the number of MetS components and presence of MetS were significantly associated with an increased risk for detectable hs-TnT levels (OR = 1.02 {for each component}; 95% CI [1.00-1.05], p = 0.04) and (OR = 1.13; 95% CI [1.07-1.2], p < 0.001) respectively. Only the waist, glucose and hypertension components of the MetS were significantly associated with elevated troponin. CONCLUSIONS: The MetS and its distinct components have a cumulative impact on hs-TnT levels in apparently healthy subjects.

Total serum cholinesterase activity predicts hemodynamic changes during exercise and associates with cardiac troponin detection in a sex-dependent manner.

BACKGROUND: Imbalanced autonomic nervous system (ANS) activity is associated with poor cardiovascular outcome. However, clinically validated biomarkers to assess parasympathetic function are not yet available. We sought to evaluate parasympathetic dysfunction by measuring serum cholinesterase activity and to determine its relationship to high sensitive cardiac troponin T (hs-cTnT) as well as traditional non-invasive parameters of ANS function during exercise in apparently healthy individuals. METHODS: We enrolled 1526 individuals (mean age 49 ± 11 yr., 75% men) from the Tel Aviv Medical Center Inflammation Survey (TAMCIS). We used the acetylcholine (ACh) analog acetylthiocholine (ATCh) as a substrate that is hydrolyzed by both ACh degrading enzymes and reflects the total serum capacity for acetylcholine hydrolysis, referred to as cholinergic status (CS). All subjects performed a cardiac stress test reviewed on the spot by a cardiologist and multiple physiological and metabolic parameters including hs-cTnT were measured. RESULTS: CS values at rest predicted multiple exercise-hemodynamic changes. Heart rate recovery after exercise was inversely correlated to CS values (p < 0.01 and p = 0.03 for women and men respectively), and a hypertensive reaction during exercise was associated with elevated CS levels in women. Most importantly, women with detectable hs-cTnT (> 5 ng/L) presented with elevated CS levels compared to women with undetectable levels; 1423 ± 272.5 vs 1347 ± 297.9 (p = 0.02). An opposite trend was observed in men. Metabolic dysfunction parameters were also associated with CS elevation in both men and women. CONCLUSIONS: Parasympathetic dysfunction assessed by total serum cholinesterase activity predicts hemodynamic changes during exercise. CS is also associated with hs-cTnT detection in women and inversely so in men. Future studies to assess the potential clinical use of this new sex-specific biomarker in cardiovascular disease risk stratification are warranted.

Elevated high-sensitive troponin T in negative stress test individuals.

BACKGROUND: The exercise ECG stress test (EST) is still the first step of work-up in intermediate risk patients in many clinical scenarios. High-sensitive cardiac troponin T (hs-cTnT) elevation is related to future cardiovascular events in the general population and in patients with ischaemic heart disease. The relation between these 2 tests is not well described. MATERIALS AND METHODS: A total of 2780 participants from the Tel-Aviv Medical Center Inflammation Survey cohort (mean age 49 years, 79% men) were analysed. Multiple physiologic and metabolic parameters including hs-cTnT were collected. All participants completed an EST manually reviewed by a cardiologist. RESULTS: A positive EST was documented in 224 subjects (8%). The majority (91%) of participants with hs-cTnT levels of 5-14 ng/L had a negative EST as well as 89.3% of subjects with levels >14 ng/L. The proportion of subjects with a positive EST and detectable hs-cTnT levels (>5 ng/L) was not significantly greater compared to those with a negative EST (53.1% vs 46.2%, respectively, P = .09). CONCLUSION: Among subjects referred for EST as part of an annual health survey, we found no significant association between EST results to hs-cTnT detection.

What is Interpretability?

We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence: (1) Are networks explainable, and if so, what does it mean to explain the output of a network? And (2) what does it mean for a network to be interpretable? We argue that accounts of "explanation" tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on "explainability," "understandability" and "interpretability." To remedy this, we distinguish between these notions, and answer (2) by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation: Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of "interpretability" is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems.

ctDNA monitoring using patient-specific sequencing and integration of variant reads.

Circulating tumor-derived DNA (ctDNA) can be used to monitor cancer dynamics noninvasively. Detection of ctDNA can be challenging in patients with low-volume or residual disease, where plasma contains very few tumor-derived DNA fragments. We show that sensitivity for ctDNA detection in plasma can be improved by analyzing hundreds to thousands of mutations that are first identified by tumor genotyping. We describe the INtegration of VAriant Reads (INVAR) pipeline, which combines custom error-suppression methods and signal-enrichment approaches based on biological features of ctDNA. With this approach, the detection limit in each sample can be estimated independently based on the number of informative reads sequenced across multiple patient-specific loci. We applied INVAR to custom hybrid-capture sequencing data from 176 plasma samples from 105 patients with melanoma, lung, renal, glioma, and breast cancer across both early and advanced disease. By integrating signal across a median of >105 informative reads, ctDNA was routinely quantified to 1 mutant molecule per 100,000, and in some cases with high tumor mutation burden and/or plasma input material, to parts per million. This resulted in median area under the curve (AUC) values of 0.98 in advanced cancers and 0.80 in early-stage and challenging settings for ctDNA detection. We generalized this method to whole-exome and whole-genome sequencing, showing that INVAR may be applied without requiring personalized sequencing panels so long as a tumor mutation list is available. As tumor sequencing becomes increasingly performed, such methods for personalized cancer monitoring may enhance the sensitivity of cancer liquid biopsies.

Using Stochastic Approximation Techniques to Efficiently Construct Confidence Intervals for Heritability.

Estimation of heritability is an important task in genetics. The use of linear mixed models (LMMs) to determine narrow-sense single-nucleotide polymorphism (SNP)-heritability and related quantities has received much recent attention, due of its ability to account for variants with small effect sizes. Typically, heritability estimation under LMMs uses the restricted maximum likelihood (REML) approach. The common way to report the uncertainty in REML estimation uses standard errors (SEs), which rely on asymptotic properties. However, these assumptions are often violated because of the bounded parameter space, statistical dependencies, and limited sample size, leading to biased estimates and inflated or deflated confidence intervals (CIs). In addition, for larger data sets (e.g., tens of thousands of individuals), the construction of SEs itself may require considerable time, as it requires expensive matrix inversions and multiplications. Here, we present FIESTA (Fast confidence IntErvals using STochastic Approximation), a method for constructing accurate CIs. FIESTA is based on parametric bootstrap sampling, and, therefore, avoids unjustified assumptions on the distribution of the heritability estimator. FIESTA uses stochastic approximation techniques, which accelerate the construction of CIs by several orders of magnitude, compared with previous approaches as well as to the analytical approximation used by SEs. FIESTA builds accurate CIs rapidly, for example, requiring only several seconds for data sets of tens of thousands of individuals, making FIESTA a very fast solution to the problem of building accurate CIs for heritability for all data set sizes.

Detecting heritable phenotypes without a model using fast permutation testing for heritability and set-tests.

Testing for association between a set of genetic markers and a phenotype is a fundamental task in genetic studies. Standard approaches for heritability and set testing strongly rely on parametric models that make specific assumptions regarding phenotypic variability. Here, we show that resulting p-values may be inflated by up to 15 orders of magnitude, in a heritability study of methylation measurements, and in a heritability and expression quantitative trait loci analysis of gene expression profiles. We propose FEATHER, a method for fast permutation-based testing of marker sets and of heritability, which properly controls for false-positive results. FEATHER eliminated 47% of methylation sites found to be heritable by the parametric test, suggesting a substantial inflation of false-positive findings by alternative methods. Our approach can rapidly identify heritable phenotypes out of millions of phenotypes acquired via high-throughput technologies, does not suffer from model misspecification and is highly efficient.