Modeling COVID-19 disease processes by remote elicitation of causal Bayesian networks from medical experts.

BACKGROUND: COVID-19 is a new multi-organ disease causing considerable worldwide morbidity and mortality. While many recognized pathophysiological mechanisms are involved, their exact causal relationships remain opaque. Better understanding is needed for predicting their progression, targeting therapeutic approaches, and improving patient outcomes. While many mathematical causal models describe COVID-19 epidemiology, none have described its pathophysiology. METHODS: In early 2020, we began developing such causal models. The SARS-CoV-2 virus's rapid and extensive spread made this particularly difficult: no large patient datasets were publicly available; the medical literature was flooded with sometimes conflicting pre-review reports; and clinicians in many countries had little time for academic consultations. We used Bayesian network (BN) models, which provide powerful calculation tools and directed acyclic graphs (DAGs) as comprehensible causal maps. Hence, they can incorporate both expert opinion and numerical data, and produce explainable, updatable results. To obtain the DAGs, we used extensive expert elicitation (exploiting Australia's exceptionally low COVID-19 burden) in structured online sessions. Groups of clinical and other specialists were enlisted to filter, interpret and discuss the literature and develop a current consensus. We encouraged inclusion of theoretically salient latent (unobservable) variables, likely mechanisms by extrapolation from other diseases, and documented supporting literature while noting controversies. Our method was iterative and incremental: systematically refining and validating the group output using one-on-one follow-up meetings with original and new experts. 35 experts contributed 126 hours face-to-face, and could review our products. RESULTS: We present two key models, for the initial infection of the respiratory tract and the possible progression to complications, as causal DAGs and BNs with corresponding verbal descriptions, dictionaries and sources. These are the first published causal models of COVID-19 pathophysiology. CONCLUSIONS: Our method demonstrates an improved procedure for developing BNs via expert elicitation, which other teams can implement to model emergent complex phenomena. Our results have three anticipated applications: (i) freely disseminating updatable expert knowledge; (ii) guiding design and analysis of observational and clinical studies; (iii) developing and validating automated tools for causal reasoning and decision support. We are developing such tools for the initial diagnosis, resource management, and prognosis of COVID-19, parameterized using the ISARIC and LEOSS databases.

Environmental contamination by Chlamydia trachomatis and Neisseria gonorrhoeae: is it time to change our infection control practices? Results of a regional study.

OBJECTIVES: Nucleic acid amplification tests (NAATs) are highly sensitive for the detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) DNA/ribosomal RNA (rRNA). Previous studies have demonstrated contamination of surfaces in sexual health clinics (SHCs) with CT/NG. False positive results can occur if patient samples are contaminated by environmental DNA/rRNA. This can have a dramatic impact on patients' lives and relationships. Previous attempts to reduce contamination, through staff training alone, have been unsuccessful. We aimed to investigate environmental contamination levels in SHCs and to assess a two-armed intervention aimed at reducing surface contamination. METHODS: Questionnaires were sent to 10 SHCs. Six clinics, with differing characteristics, were selected to participate in sample collection. Each clinic followed standardised instructions to sample surfaces using a CT/NG NAAT swab. Clinics were invited to introduce the two-armed intervention. The first arm was cleaning with a chlorine-based cleaning solution once daily. The second arm involved introducing clinic-specific changes to reduce contamination. RESULTS: 7/10 (70%) clinics completed the questionnaire. Overall, 88/263 (33%) swabs were positive for CT/NG. Clinics 1, 3 and 4 had high levels of contamination, with 28/64 (44%), 17/33 (52%) and 30/52 (58%) swabs testing positive, respectively. Clinics 2 and 6 had lower levels of contamination, with 7/46 (15%) and 6/35 (17%), respectively. 0/33 (0%) of swabs were positive at clinic 5 and this was the only clinic already using a chlorine-based solution to clean all surfaces and delivering twice-yearly clinic-specific infection control training. Following both intervention arms at clinic 1, 2/50 (4%, p<0.0001) swabs tested positive for CT/NG. Clinic 4 introduced each arm separately. After the first intervention, 13/52 (25%, p=0.003) swabs tested positive and following the second arm 4/50 (8%, p<0.0001) swabs were positive. CONCLUSIONS: Environmental contamination is a concern in SHCs. We recommend that all SHCs monitor contamination levels and, if necessary, consider using chlorine-based cleaning products and introduce clinic-specific changes to address environmental contamination.

Predicting postpartum haemorrhage: A systematic review of prognostic models.

BACKGROUND: Postpartum haemorrhage (PPH) remains a leading cause of maternal mortality and morbidity worldwide, and the rate is increasing. Using a reliable predictive model could identify those at risk, support management and treatment, and improve maternal outcomes. AIMS: To systematically identify and appraise existing prognostic models for PPH and ascertain suitability for clinical use. MATERIALS AND METHODS: MEDLINE, CINAHL, Embase, and the Cochrane Library were searched using combinations of terms and synonyms, including 'postpartum haemorrhage', 'prognostic model', and 'risk factors'. Observational or experimental studies describing a prognostic model for risk of PPH, published in English, were included. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies checklist informed data extraction and the Prediction Model Risk of Bias Assessment Tool guided analysis. RESULTS: Sixteen studies met the inclusion criteria after screening 1612 records. All studies were hospital settings from eight different countries. Models were developed for women who experienced vaginal birth (n = 7), caesarean birth (n = 2), any type of birth (n = 2), hypertensive disorders (n = 1) and those with placental abnormalities (n = 4). All studies were at high risk of bias due to use of inappropriate analysis methods or omission of important statistical considerations or suboptimal validation. CONCLUSIONS: No existing prognostic models for PPH are ready for clinical application. Future research is needed to externally validate existing models and potentially develop a new model that is reliable and applicable to clinical practice.

BARD: A Structured Technique for Group Elicitation of Bayesian Networks to Support Analytic Reasoning.

In many complex, real-world situations, problem solving and decision making require effective reasoning about causation and uncertainty. However, human reasoning in these cases is prone to confusion and error. Bayesian networks (BNs) are an artificial intelligence technology that models uncertain situations, supporting better probabilistic and causal reasoning and decision making. However, to date, BN methodologies and software require (but do not include) substantial upfront training, do not provide much guidance on either the model building process or on using the model for reasoning and reporting, and provide no support for building BNs collaboratively. Here, we contribute a detailed description and motivation for our new methodology and application, Bayesian ARgumentation via Delphi (BARD). BARD utilizes BNs and addresses these shortcomings by integrating (1) short, high-quality e-courses, tips, and help on demand; (2) a stepwise, iterative, and incremental BN construction process; (3) report templates and an automated explanation tool; and (4) a multiuser web-based software platform and Delphi-style social processes. The result is an end-to-end online platform, with associated online training, for groups without prior BN expertise to understand and analyze a problem, build a model of its underlying probabilistic causal structure, validate and reason with the causal model, and (optionally) use it to produce a written analytic report. Initial experiments demonstrate that, for suitable problems, BARD aids in reasoning and reporting. Comparing their effect sizes also suggests BARD's BN-building and collaboration combine beneficially and cumulatively.

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology.

BACKGROUND: ASD and ADHD are prevalent neurodevelopmental disorders that frequently co-occur and have strong evidence for a degree of shared genetic aetiology. Behavioural and neurocognitive heterogeneity in ASD and ADHD has hampered attempts to map the underlying genetics and neurobiology, predict intervention response, and improve diagnostic accuracy. Moving away from categorical conceptualisations of psychopathology to a dimensional approach is anticipated to facilitate discovery of data-driven clusters and enhance our understanding of the neurobiological and genetic aetiology of these conditions. The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project is one of the first large-scale, family-based studies to take a truly transdiagnostic approach to ASD and ADHD. Using a comprehensive phenotyping protocol capturing dimensional traits central to ASD and ADHD, the MAGNET project aims to identify data-driven clusters across ADHD-ASD spectra using deep phenotyping of symptoms and behaviours; investigate the degree of familiality for different dimensional ASD-ADHD phenotypes and clusters; and map the neurocognitive, brain imaging, and genetic correlates of these data-driven symptom-based clusters. METHODS: The MAGNET project will recruit 1,200 families with children who are either typically developing, or who display elevated ASD, ADHD, or ASD-ADHD traits, in addition to affected and unaffected biological siblings of probands, and parents. All children will be comprehensively phenotyped for behavioural symptoms, comorbidities, neurocognitive and neuroimaging traits and genetics. CONCLUSION: The MAGNET project will be the first large-scale family study to take a transdiagnostic approach to ASD-ADHD, utilising deep phenotyping across behavioural, neurocognitive, brain imaging and genetic measures.

Predicting the causative pathogen among children with osteomyelitis using Bayesian networks - improving antibiotic selection in clinical practice.

Infection of bone, osteomyelitis (OM), is a serious bacterial infection in children requiring urgent antibiotic therapy. While biological specimens are often obtained and cultured to guide antibiotic selection, culture results may take several days, are often falsely negative, and may be falsely positive because of contamination by non-causative bacteria. This poses a dilemma for clinicians when choosing the most suitable antibiotic. Selecting an antibiotic which is too narrow in spectrum risks treatment failure; selecting an antibiotic which is too broad risks toxicity and promotes antibiotic resistance. We have developed a Bayesian Network (BN) model that can be used to guide individually targeted antibiotic therapy at point-of-care, by predicting the most likely causative pathogen in children with OM and the antibiotic with optimal expected utility. The BN explicitly models the complex relationship between the unobserved infecting pathogen, observed culture results, and clinical and demographic variables, and integrates data with critical expert knowledge under a causal inference framework. Development of this tool resulted from a multidisciplinary approach, involving experts in infectious diseases, modelling, paediatrics, microbiology, computer science and statistics. The model-predicted prevalence of causative pathogens among children with osteomyelitis were 56 % for Staphylococcus aureus, 17 % for 'other' culturable bacteria (like Streptococcus pyogenes), and 27 % for bacterial pathogens that are not culturable using routine methods (like Kingella kingae). Log loss cross-validation suggests that the model performance is robust, with the best fit to culture results achieved when data and expert knowledge were combined during parameterisation. AUC values of 0.68 - 0.77 were achieved for predicting culture results of different types of specimens. BN-recommended antibiotics were rated optimal or adequate by experts in 82-98% of 81 cases sampled from the cohort. We have demonstrated the potential use of BNs in improving antibiotic selection for children with OM, which we believe to be generalisable in the development of a broader range of decision support tools. With appropriate validation, such tools might be effectively deployed for real-time clinical decision support, to promote a shift in clinical practice from generic to individually-targeted antibiotic therapy, and ultimately improve the management and outcomes for a range of serious bacterial infections.

Individuals vs. BARD: Experimental Evaluation of an Online System for Structured, Collaborative Bayesian Reasoning.

US intelligence analysts must weigh up relevant evidence to assess the probability of their conclusions, and express this reasoning clearly in written reports for decision-makers. Typically, they work alone with no special analytic tools, and sometimes succumb to common probabilistic and causal reasoning errors. So, the US government funded a major research program (CREATE) for four large academic teams to develop new structured, collaborative, software-based methods that might achieve better results. Our team's method (BARD) is the first to combine two key techniques: constructing causal Bayesian network models (BNs) to represent analyst knowledge, and small-group collaboration via the Delphi technique. BARD also incorporates compressed, high-quality online training allowing novices to use it, and checklist-inspired report templates with a rudimentary AI tool for generating text explanations from analysts' BNs. In two prior experiments, our team showed BARD's BN-building assists probabilistic reasoning when used by individuals, with a large effect (Glass' Δ 0.8) (Cruz et al., 2020), and even minimal Delphi-style interactions improve the BN structures individuals produce, with medium to very large effects (Glass' Δ 0.5-1.3) (Bolger et al., 2020). This experiment is the critical test of BARD as an integrated system and possible alternative to business-as-usual for intelligence analysis. Participants were asked to solve three probabilistic reasoning problems spread over 5 weeks, developed by our team to test both quantitative accuracy and susceptibility to tempting qualitative fallacies. Our 256 participants were randomly assigned to form 25 teams of 6-9 using BARD and 58 individuals using Google Suite and (if desired) the best pen-and-paper techniques. For each problem, BARD outperformed this control with very large to huge effects (Glass' Δ 1.4-2.2), greatly exceeding CREATE's initial target. We conclude that, for suitable problems, BARD already offers significant advantages over both business-as-usual and existing BN software. Our effect sizes also suggest BARD's BN-building and collaboration combined beneficially and cumulatively, although implementation differences decreased performances compared to Cruz et al. (2020), so interaction may have contributed. BARD has enormous potential for further development and testing of specific components and on more complex problems, and many potential applications beyond intelligence analysis.

Technical assessment of small-scale wind power for residential use in Mexico: A Bayesian intelligence approach.

Nowadays, the global energy system is in a transition phase, in which the integration of renewable energy is among the main requirements for attenuating climate change. Wind power is a major alternative to supply clean energy; hence, its widespread penetration is being pursued in all end-use sectors. In particular, it is currently noteworthy to analyze the feasibility of deploying small-scale wind power technology to provide cleaner and cheaper energy in the residential sector. As a first step, a technical assessment must be carried out to provide crucial information to intensive energy consumers, providers of small-scale wind power technology, electric energy distribution utilities, and any other party, to help them decide whether or not to deploy small-scale wind turbines. With this aim, we propose to perform such an analysis using a suitable probabilistic paradigm to solve complex decision-making problems with uncertainty, namely Bayesian Intelligence, since wind resources and energy demands are intermittent variables, properly characterized by probability distribution functions. Then, the problem of determining the technical feasibility can be formulated as an investigation into whether or not small-scale wind turbine technology can produce enough energy to cover the excess demand of intensive energy residential consumers to get off high-priced tariffs. For this purpose, we introduce a novel model based on probabilistic reasoning to assess the suitability of small-scale wind turbine technology to produce the said energy, taking into consideration the availability of wind resources and the energy pricing structure. To demonstrate the usefulness and performance of the proposed model, we consider a case study of deploying 5 and 10 kW wind turbines and analyze the feasibility of their implementation in Mexico, where the energy pricing structure and scattered wind resource availability pose difficult challenges.

Verbal probabilities: Very likely to be somewhat more confusing than numbers.

People interpret verbal expressions of probabilities (e.g. 'very likely') in different ways, yet words are commonly preferred to numbers when communicating uncertainty. Simply providing numerical translations alongside reports or text containing verbal probabilities should encourage consistency, but these guidelines are often ignored. In an online experiment with 924 participants, we compared four different formats for presenting verbal probabilities with the numerical guidelines used in the US Intelligence Community Directive (ICD) 203 to see whether any could improve the correspondence between the intended meaning and participants' interpretation ('in-context'). This extends previous work in the domain of climate science. The four experimental conditions we tested were: 1. numerical guidelines bracketed in text, e.g. X is very unlikely (05-20%), 2. click to see the full guidelines table in a new window, 3. numerical guidelines appear in a mouse over tool tip, and 4. no guidelines provided (control). Results indicate that correspondence with the ICD 203 standard is substantially improved only when numerical guidelines are bracketed in text. For this condition, average correspondence was 66%, compared with 32% in the control. We also elicited 'context-free' numerical judgements from participants for each of the seven verbal probability expressions contained in ICD 203 (i.e., we asked participants what range of numbers they, personally, would assign to those expressions), and constructed 'evidence-based lexicons' based on two methods from similar research, 'membership functions' and 'peak values', that reflect our large sample's intuitive translations of the terms. Better aligning the intended and assumed meaning of fuzzy words like 'unlikely' can reduce communication problems between the reporter and receiver of probabilistic information. In turn, this can improve decision making under uncertainty.

HetFHMM: A Novel Approach to Infer Tumor Heterogeneity Using Factorial Hidden Markov Models.

Cancer arises from successive rounds of mutations, resulting in tumor cells with different somatic mutations known as clones. Drug responsiveness and therapeutics of cancer depend on the accurate detection of clones in a tumor sample. Recent research has considered inferring clonal composition of a tumor sample using computational models based on short read data of the sample generated using next-generation sequencing (NGS) technology. Short reads (segmented DNA parts of different tumor cells) are noisy; therefore, inferring the clones and their mutations from the data is a difficult and complex problem. We develop a new model called HetFHMM, based on factorial hidden Markov models, to infer clones and their proportions from noisy NGS data. In our model, each hidden chain represents the genomic signature of a clone, and a mixture of chains results in the observed data. We make use of Gibbs sampling and exponentiated gradient algorithms to infer the hidden variables and mixing proportions. We compare our model with strong models from previous work (PyClone and PhyloSub) based on both synthetic data and real cancer data on acute myeloid leukemia. Empirical results confirm that HetFHMM infers clonal composition of a tumor sample more accurately than previous work.

CSI: a nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data.

Here we introduce the causal structure identification (CSI) package, a Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data. The standard CSI approach infers a single GRN via joint learning from multiple time series datasets; the hierarchical approach (HCSI) infers a separate GRN for each dataset, albeit with the networks constrained to favor similar structures, allowing for the identification of context specific networks. The software is implemented in MATLAB and includes a graphical user interface (GUI) for user friendly inference. Finally the GUI can be connected to high performance computer clusters to facilitate analysis of large genomic datasets.

Exploring risk judgments in a trade dispute using Bayesian networks.

Bayesian networks (BNs) are graphical modeling tools that are generally recommended for exploring what-if scenarios, visualizing systems and problems, and for communication between stakeholders during decision making. In this article, we investigate their potential for exploring different perspectives in trade disputes. To do so, we draw on a specific case study that was arbitrated by the World Trade Organization (WTO): the Australia-New Zealand apples dispute. The dispute centered on disagreement about judgments contained within Australia's 2006 import risk analysis (IRA). We built a range of BNs of increasing complexity that modeled various approaches to undertaking IRAs, from the basic qualitative and semi-quantitative risk analyses routinely performed in government agencies, to the more complex quantitative simulation undertaken by Australia in the apples dispute. We found the BNs useful for exploring disagreements under uncertainty because they are probabilistic and transparently represent steps in the analysis. Different scenarios and evidence can easily be entered. Specifically, we explore the sensitivity of the risk output to different judgments (particularly volume of trade). Thus, we explore how BNs could usefully aid WTO dispute settlement. We conclude that BNs are preferable to basic qualitative and semi-quantitative risk analyses because they offer an accessible interface and are mathematically sound. However, most current BN modeling tools are limited compared with complex simulations, as was used in the 2006 apples IRA. Although complex simulations may be more accurate, they are a black box for stakeholders. BNs have the potential to be a transparent aid to complex decision making, but they are currently computationally limited. Recent technological software developments are promising.

Bayesian networks for clinical decision support in lung cancer care.

Survival prediction and treatment selection in lung cancer care are characterised by high levels of uncertainty. Bayesian Networks (BNs), which naturally reason with uncertain domain knowledge, can be applied to aid lung cancer experts by providing personalised survival estimates and treatment selection recommendations. Based on the English Lung Cancer Database (LUCADA), we evaluate the feasibility of BNs for these two tasks, while comparing the performances of various causal discovery approaches to uncover the most feasible network structure from expert knowledge and data. We show first that the BN structure elicited from clinicians achieves a disappointing area under the ROC curve of 0.75 (± 0.03), whereas a structure learned by the CAMML hybrid causal discovery algorithm, which adheres with the temporal restrictions, achieves 0.81 (± 0.03). Second, our causal intervention results reveal that BN treatment recommendations, based on prescribing the treatment plan that maximises survival, can only predict the recorded treatment plan 29% of the time. However, this percentage rises to 76% when partial matches are included.

Incorporating expert knowledge when learning Bayesian network structure: a medical case study.

OBJECTIVES: Bayesian networks (BNs) are rapidly becoming a leading technology in applied Artificial Intelligence, with many applications in medicine. Both automated learning of BNs and expert elicitation have been used to build these networks, but the potentially more useful combination of these two methods remains underexplored. In this paper we examine a number of approaches to their combination when learning structure and present new techniques for assessing their results. METHODS AND MATERIALS: Using public-domain medical data, we run an automated causal discovery system, CaMML, which allows the incorporation of multiple kinds of prior expert knowledge into its search, to test and compare unbiased discovery with discovery biased with different kinds of expert opinion. We use adjacency matrices enhanced with numerical and colour labels to assist with the interpretation of the results. We present an algorithm for generating a single BN from a set of learned BNs that incorporates user preferences regarding complexity vs completeness. These techniques are presented as part of the first detailed workflow for hybrid structure learning within the broader knowledge engineering process. RESULTS: The detailed knowledge engineering workflow is shown to be useful for structuring a complex iterative BN development process. The adjacency matrices make it clear that for our medical case study using the IOWA dataset, the simplest kind of prior information (partially sorting variables into tiers) was more effective in aiding model discovery than either using no prior information or using more sophisticated and detailed expert priors. The method for generating a single BN captures relationships that would be overlooked by other approaches in the literature. CONCLUSION: Hybrid causal learning of BNs is an important emerging technology. We present methods for incorporating it into the knowledge engineering process, including visualisation and analysis of the learned networks.

A functional single-nucleotide polymorphism in the TRPC6 gene promoter associated with idiopathic pulmonary arterial hypertension.

BACKGROUND: Excessive proliferation of pulmonary artery smooth muscle cells (PASMCs) plays an important role in the development of idiopathic pulmonary arterial hypertension (IPAH), whereas a rise in cytosolic Ca2+ concentration triggers PASMC contraction and stimulates PASMC proliferation. Recently, we demonstrated that upregulation of the TRPC6 channel contributes to proliferation of PASMCs isolated from IPAH patients. This study sought to identify single-nucleotide polymorphisms (SNPs) in the TRPC6 gene promoter that are associated with IPAH and have functional significance in regulating TRPC6 activity in PASMCs. METHODS AND RESULTS: Genomic DNA was isolated from blood samples of 237 normal subjects and 268 IPAH patients. Three biallelic SNPs, -361 (A/T), -254(C/G), and -218 (C/T), were identified in the 2000-bp sequence upstream of the transcriptional start site of TRPC6. Although the allele frequencies of the -361 and -218 SNPs were not different between the groups, the allele frequency of the -254(C-->G) SNP in IPAH patients (12%) was significantly higher than in normal subjects (6%; P<0.01). Genotype data showed that the percentage of -254G/G homozygotes in IPAH patients was 2.85 times that of normal subjects. Moreover, the -254(C-->G) SNP creates a binding sequence for nuclear factor-kappaB. Functional analyses revealed that the -254(C-->G) SNP enhanced nuclear factor-kappaB-mediated promoter activity and stimulated TRPC6 expression in PASMCs. Inhibition of nuclear factor-kappaB activity attenuated TRPC6 expression and decreased agonist-activated Ca2+ influx in PASMCs of IPAH patients harboring the -254G allele. CONCLUSIONS: These results suggest that the -254(C-->G) SNP may predispose individuals to an increased risk of IPAH by linking abnormal TRPC6 transcription to nuclear factor-kappaB, an inflammatory transcription factor.

Function of Kv1.5 channels and genetic variations of KCNA5 in patients with idiopathic pulmonary arterial hypertension.

The pore-forming alpha-subunit, Kv1.5, forms functional voltage-gated K(+) (Kv) channels in human pulmonary artery smooth muscle cells (PASMC) and plays an important role in regulating membrane potential, vascular tone, and PASMC proliferation and apoptosis. Inhibited Kv channel expression and function have been implicated in PASMC from patients with idiopathic pulmonary arterial hypertension (IPAH). Here, we report that overexpression of the Kv1.5 channel gene (KCNA5) in human PASMC and other cell lines produced a 15-pS single channel current and a large whole cell current that was sensitive to 4-aminopyridine. Extracellular application of nicotine, bepridil, correolide, and endothelin-1 (ET-1) all significantly and reversibly reduced the Kv1.5 currents, while nicotine and bepridil also accelerated the inactivation kinetics of the currents. Furthermore, we sequenced KCNA5 from IPAH patients and identified 17 single-nucleotide polymorphisms (SNPs); 7 are novel SNPs. There are 12 SNPs in the upstream 5' region, 2 of which may alter transcription factor binding sites in the promoter, 2 nonsynonymous SNPs in the coding region, 2 SNPs in the 3'-untranslated region, and 1 SNP in the 3'-flanking region. Two SNPs may correlate with the nitric oxide-mediated decrease in pulmonary arterial pressure. Allele frequency of two other SNPs in patients with a history of fenfluramine and phentermine use was significantly different from patients who have never taken the anorexigens. These results suggest that 1) Kv1.5 channels are modulated by various agonists (e.g., nicotine and ET-1); 2) novel SNPs in KCNA5 are present in IPAH patients; and 3) SNPs in the promoter and translated regions of KCNA5 may underlie the altered expression and/or function of Kv1.5 channels in PASMC from IPAH patients.