Rare Genetic Variants in LDLR, APOB, and PCSK9 are Associated with Aortic Stenosis.

Background: Despite a proposed causal role for low-density lipoprotein cholesterol (LDL-C) in aortic stenosis (AS), randomized controlled trials of lipid-lowering therapy failed to prevent severe AS. We aimed to assess the impact on AS and peak velocity across the aortic valve conferred by lifelong alterations in LDL-C levels mediated by protein-disrupting variants in three clinically significant genes for LDL metabolism (LDLR, APOB, PCSK9). Methods: We utilized sequencing data and electronic health records from UK Biobank (UKB) and All of Us and magnetic resonance imaging data from UKB. We identified predicted protein-disrupting variants with the LOFTEE and AlphaMissense algorithms and evaluated their associations with LDL-C and peak velocity across the aortic valve (UK Biobank), as well as diagnosed AS and aortic valve replacement (UK Biobank + All of Us). Results: We included 421,049 unrelated participants (5,621 with AS) in UKB and 195,519 unrelated participants (1,087 with AS) in All of Us. Carriers of protein-disrupting variants in LDLR had higher mean LDL-C (UKB: +42.6 mg/dl, P=4.4e-237) and greater risk of AS (meta-analysis: odds ratio [OR] =3.52 [95% CI 2.39-5.20], P=2.3e-10) and aortic valve replacement (meta-analysis: OR=3.78 [95% CI 2.26-6.32], P=4.0e-7). Carriers of protein-disrupting variants in APOB or PCSK9 had lower mean LDL-C (UKB: -32.3 mg/dl, P<5e-324) and lower risk of AS (meta-analysis: OR=0.49 [0.31-0.75], P=0.001) and aortic valve replacement (meta-analysis: OR=0.54 [0.30-0.97], P=0.04). Among 57,371 UKB imaging substudy participants, peak velocities across the aortic valve were greater in carriers of protein-disrupting variants in LDLR (+12.2cm/s, P=1.6e-5) and lower in carriers of protein-disrupting variants in PCSK9 (-6.9cm/s, P=0.022). Conclusions: Rare genetic variants that confer lifelong higher or lower LDL-C levels are associated with substantially increased and decreased risk of AS, respectively. Early and sustained lipid-lowering therapy may slow or prevent AS development.

Trajectories of sentiment in 11,816 psychoactive narratives.

OBJECTIVE: Can machine learning (ML) enable data-driven discovery of how changes in sentiment correlate with different psychoactive experiences? We investigate by training models directly on text testimonials from a diverse 52-drug pharmacopeia. METHODS: Using large language models (i.e. BERT) and 11,816 publicly-available testimonials, we predicted 28-dimensions of sentiment across each narrative, and then validated these predictions with adjudication by a clinical psychiatrist. BERT was then fine-tuned to predict biochemical and demographic information from these narratives. Lastly, canonical correlation analysis linked the drugs' receptor affinities with word usage, revealing 11 statistically-significant latent receptor-experience factors, each mapped to a 3D cortical Atlas. RESULTS: These methods elucidate a neurobiologically-informed, sequence-sensitive portrait of drug-induced subjective experiences. The models' results converged, revealing a pervasive distinction between the universal psychedelic heights of feeling in contrast to the grim, mundane, and personal experiences of addiction and mental illness. Notably, MDMA was linked to "Love", DMT and 5-MeO-DMT to "Mystical Experiences" and "Entities and Beings", and other tryptamines to "Surprise", "Curiosity" and "Realization". CONCLUSIONS: ML methods can create unified and robust quantifications of subjective experiences with many different psychoactive substances and timescales. The representations learned are evocative and mutually confirmatory, indicating great potential for ML in characterizing psychoactivity.

Lean and deep models for more accurate filtering of SNP and INDEL variant calls.

SUMMARY: We investigate convolutional neural networks (CNNs) for filtering small genomic variants in short-read DNA sequence data. Errors created during sequencing and library preparation make variant calling a difficult task. Encoding the reference genome and aligned reads covering sites of genetic variation as numeric tensors allows us to leverage CNNs for variant filtration. Convolutions over these tensors learn to detect motifs useful for classifying variants. Variant filtering models are trained to classify variants as artifacts or real variation. Visualizing the learned weights of the CNN confirmed it detects familiar DNA motifs known to correlate with real variation, like homopolymers and short tandem repeats (STR). After confirmation of the biological plausibility of the learned features we compared our model to current state-of-the-art filtration methods like Gaussian Mixture Models, Random Forests and CNNs designed for image classification, like DeepVariant. We demonstrate improvements in both sensitivity and precision. The tensor encoding was carefully tailored for processing genomic data, respecting the qualitative differences in structure between DNA and natural images. Ablation tests quantitatively measured the benefits of our tensor encoding strategy. Bayesian hyper-parameter optimization confirmed our notion that architectures designed with DNA data in mind outperform off-the-shelf image classification models. Our cross-generalization analysis identified idiosyncrasies in truth resources pointing to the need for new methods to construct genomic truth data. Our results show that models trained on heterogenous data types and diverse truth resources generalize well to new datasets, negating the need to train separate models for each data type. AVAILABILITY AND IMPLEMENTATION: This work is available in the Genome Analysis Toolkit (GATK) with the tool name CNNScoreVariants (https://github.com/broadinstitute/gatk). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Utility of Point-of-Care Lung Ultrasonography for Evaluating Acute Chest Syndrome in Young Patients With Sickle Cell Disease.

STUDY OBJECTIVE: Acute chest syndrome is a leading cause of mortality in patients with sickle cell disease (SCD). Because early detection of acute chest syndrome is directly tied to prognosis, young patients with SCD undergo countless chest radiography screenings throughout their lifetime for commonly occurring acute chest syndrome risk factors such as fever, chest pain, or cough. Chest radiography is not an ideal screening method because it is associated with radiation exposure, which accumulates with repeated imaging. Point-of-care lung ultrasonography is a nonradiating imaging modality that has been used to identify other lung pathology and may have a role in SCD. The goal of this study was to determine the accuracy of point-of-care lung ultrasound to identify an infiltrate suggestive of acute chest syndrome in patients with SCD compared to chest radiography as the gold standard. METHODS: This was a prospective observational study in 2 urban pediatric emergency departments to evaluate the accuracy of point-of-care lung ultrasonography in identifying patients with SCD who were aged 0 to 21 years and had an infiltrate suggestive of acute chest syndrome compared with chest radiography. Clinicians and trainees with point-of-care lung ultrasonographic training obtained informed consent and performed investigational point-of-care lung ultrasonography to evaluate for lung consolidation. A blinded point-of-care lung ultrasonographic expert reviewed results for quality assurance and agreement. Accuracy, sensitivity, specificity, likelihood ratios, and positive and negative predictive value were calculated for point-of-care lung ultrasonography test performance characteristics, with chest radiography as a reference standard. RESULTS: Point-of-care lung ultrasonography was performed on 191 SCD patients with a mean age of 8 years; 41% were female patients, and there was a 17% prevalence of acute chest syndrome. Accuracy of point-of-care lung ultrasonography to detected acute chest syndrome was 92%, sensitivity was 88%, and specificity was 93% compared with that for chest radiography. CONCLUSION: Point-of-care lung ultrasonography is a feasible alternative to chest radiography for screening for acute chest syndrome in young patients with SCD. Further studies are needed to determine how this test performs within clinical practice.

'Like Skydiving without a Parachute': How Class Origin Shapes Occupational Trajectories in British Acting.

There is currently widespread concern that access to, and success within, the British acting profession is increasingly dominated by those from privileged class origins. This article seeks to empirically interrogate this claim using data on actors from the Great British Class Survey (N = 404) and 47 qualitative interviews. First, survey data demonstrate that actors from working-class origins are significantly underrepresented within the profession. Second, they indicate that even when those from working-class origins do enter the profession they do not have access to the same economic, cultural and social capital as those from privileged backgrounds. Third, and most significantly, qualitative interviews reveal how these capitals shape the way actors can respond to shared occupational challenges. In particular we demonstrate the profound occupational advantages afforded to actors who can draw upon familial economic resources, legitimate embodied markers of class origin (such as Received Pronunciation) and a favourable typecasting.

Mind the gap: financial London and the regional class pay gap.

The hidden barriers, or 'gender pay gap', preventing women from earning equivalent incomes to men is well documented. Yet recent research has uncovered that, in Britain, there is also a comparable class-origin pay gap in higher professional and managerial occupations. So far this analysis has only been conducted at the national level and it is not known whether there are regional differences within the UK. This paper uses pooled data from the 2014 and 2015 Labour Force Survey (N = 7,534) to stage a more spatially sensitive analysis that examines regional variation in the class pay gap. We find that this 'class ceiling' is not evenly spatially distributed. Instead it is particularly marked in Central London, where those in high-status occupations who are from working-class backgrounds earn, on average, £10,660 less per year than those whose parents were in higher professional and managerial employment. Finally, we inspect the Capital further to reveal that the class pay gap is largest within Central London's banking and finance sector. Challenging policy conceptions of London as the 'engine room' of social mobility, these findings suggest that class disadvantage within high-status occupations is particularly acute in the Capital. The findings also underline the value of investigating regional differences in social mobility, and demonstrate how such analysis can unravel important and previously unrecognized spatial dimensions of class inequality.

Consider this a WARNing.

Atrial fibrillation (AF) prediction can be valuable at many timescales and in many populations. In this issue of Patterns, Gavidia et al. train a model called WARN for short-term prediction of AF in the timescale of minutes in patients wearing 24-h continuous Holter electrocardiograms. The ability to predict near-term (e.g., 30 min) AF has the potential to enable preventive therapies with rapid mechanisms of action (e.g., oral anticoagulation, anti-arrhythmic drugs). In this way, efficient, continuous, and algorithmic monitoring of AF risk could reduce burden on healthcare workers and represents a valuable clinical pursuit.

Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities.

The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype-phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned de novo, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.

PARG inhibition induces nuclear aggregation of PARylated PARP1.

Poly (ADP-ribose) glycohydrolase (PARG) inhibitors are currently under clinical development for the treatment of DNA repair-deficient cancers; however, their precise mechanism of action is still unclear. Here, we report that PARG inhibition leads to excessive PARylated poly (ADP-ribose) polymerase 1 (PARP1) reducing the ability of PARP1 to properly localize to sites of DNA damage. Strikingly, the mis-localized PARP1 accumulates as aggregates throughout the nucleus. Abrogation of the catalytic activity of PARP1 prevents aggregate formation, indicating that PAR chains play a key role in this process. Finally, we find that PARP1 nuclear aggregates were highly persistent and were associated with cleaved cytoplasmic PARP1, ultimately leading to cell death. Overall, our data uncover an unexpected mechanism of PARG inhibitor cytotoxicity, which will shed light on the use of these drugs as anti-cancer therapeutics.

Deep learning-derived splenic radiomics, genomics, and coronary artery disease.

Background: Despite advances in managing traditional risk factors, coronary artery disease (CAD) remains the leading cause of mortality. Circulating hematopoietic cells influence risk for CAD, but the role of a key regulating organ, spleen, is unknown. The understudied spleen is a 3-dimensional structure of the hematopoietic system optimally suited for unbiased radiologic investigations toward novel mechanistic insights. Methods: Deep learning-based image segmentation and radiomics techniques were utilized to extract splenic radiomic features from abdominal MRIs of 42,059 UK Biobank participants. Regression analysis was used to identify splenic radiomics features associated with CAD. Genome-wide association analyses were applied to identify loci associated with these radiomics features. Overlap between loci associated with CAD and the splenic radiomics features was explored to understand the underlying genetic mechanisms of the role of the spleen in CAD. Results: We extracted 107 splenic radiomics features from abdominal MRIs, and of these, 10 features were associated with CAD. Genome-wide association analysis of CAD-associated features identified 219 loci, including 35 previously reported CAD loci, 7 of which were not associated with conventional CAD risk factors. Notably, variants at 9p21 were associated with splenic features such as run length non-uniformity. Conclusions: Our study, combining deep learning with genomics, presents a new framework to uncover the splenic axis of CAD. Notably, our study provides evidence for the underlying genetic connection between the spleen as a candidate causal tissue-type and CAD with insight into the mechanisms of 9p21, whose mechanism is still elusive despite its initial discovery in 2007. More broadly, our study provides a unique application of deep learning radiomics to non-invasively find associations between imaging, genetics, and clinical outcomes.

NAPRT Silencing in FH-Deficient Renal Cell Carcinoma Confers Therapeutic Vulnerabilities via NAD+ Depletion.

Hereditary leiomyomatosis and renal cell carcinoma (HLRCC) is caused by loss of function mutations in fumarate hydratase (FH) and results in an aggressive subtype of renal cell carcinoma with limited treatment options. Loss of FH leads to accumulation of fumarate, an oncometabolite that disrupts multiple cellular processes and drives tumor progression. High levels of fumarate inhibit alpha ketoglutarate-dependent dioxygenases, including the ten-eleven translocation (TET) enzymes, and can lead to global DNA hypermethylation. Here, we report patterns of hypermethylation in FH-mutant cell lines and tumor samples are associated with the silencing of nicotinate phosphoribosyl transferase (NAPRT), a rate-limiting enzyme in the Preiss-Handler pathway of NAD+ biosynthesis, in a subset of HLRCC cases. NAPRT is hypermethylated at a CpG island in the promoter in cell line models and patient samples, resulting in loss of NAPRT expression. We find that FH-deficient RCC models with loss of NAPRT expression, as well as other oncometabolite-producing cancer models that silence NAPRT, are extremely sensitive to nicotinamide phosphoribosyl transferase inhibitors (NAMPTi). NAPRT silencing was also associated with synergistic tumor cell killing with PARP inhibitors and NAMPTis, which was associated with effects on PAR-mediated DNA repair. Overall, our findings indicate that NAPRT silencing can be targeted in oncometabolite-producing cancers and elucidates how oncometabolite-associated hypermethylation can impact diverse cellular processes and lead to therapeutically relevant vulnerabilities in cancer cells. Implications: NAPRT is a novel biomarker for targeting NAD+ metabolism in FH-deficient HLRCCs with NAMPTis alone and targeting DNA repair processes with the combination of NAMPTis and PARP inhibitors.

Migration narratives: expanding methods to examine the interaction of person and environment among aging gay men.

As they age, gay and bisexual men are embedded in multiple environments and communities. This article reanalyzes data collected as part of a larger qualitative study of crystal methamphetamine use in New York City. Focusing on the migration narratives of 30 racially/ethnically diverse men, age 40 years old and older, recruited from multiple venues several key areas emerged: ostracization, lack of affirmation as well as movement activities. Interactively they transformed social practices and increased spaces to explore sexuality, build community engagements and exchange resources. This study suggests that assessment of gay men (and other marginalized groups) may be enhanced through application of migration narratives.

COVID-19-Related Changes to Drug-Selling Networks and Their Effects on People Who Use Illicit Opioids.

OBJECTIVE: The COVID-19 pandemic has significantly affected people's ability to buy, sell, and obtain items that they use in their daily lives. It may have had a particularly negative effect on the ability of people who use illicit opioids to obtain them because the networks they relied on are illicit and not part of the formal economy. Our objective in this research was to examine if, and how, disruptions related to COVID-19 of illicit opioid markets have affected people who use illicit opioids. METHOD: We collected 300 posts--including replies to posts--related to the intersection of COVID-19 and opioid use from Reddit.com, a forum that has several discussion threads (i.e., subreddits) dedicated to opioids. We then coded posts from the two most popular opioid subreddits during the early pandemic period (March 5, 2020-May 13, 2020) using an inductive/deductive approach. RESULTS: We found two themes related to active opioid use during the early pandemic: (a) changes in drug supply and difficulty obtaining opioids, and (b) buying less-trustworthy drugs from lesser-known sources. CONCLUSIONS: Our findings suggest that COVID-19 has created market conditions that place people who use opioids at risk of adverse outcomes, such as fatal overdose.

Cross-modal autoencoder framework learns holistic representations of cardiovascular state.

A fundamental challenge in diagnostics is integrating multiple modalities to develop a joint characterization of physiological state. Using the heart as a model system, we develop a cross-modal autoencoder framework for integrating distinct data modalities and constructing a holistic representation of cardiovascular state. In particular, we use our framework to construct such cross-modal representations from cardiac magnetic resonance images (MRIs), containing structural information, and electrocardiograms (ECGs), containing myoelectric information. We leverage the learned cross-modal representation to (1) improve phenotype prediction from a single, accessible phenotype such as ECGs; (2) enable imputation of hard-to-acquire cardiac MRIs from easy-to-acquire ECGs; and (3) develop a framework for performing genome-wide association studies in an unsupervised manner. Our results systematically integrate distinct diagnostic modalities into a common representation that better characterizes physiologic state.

Artificial intelligence-enabled classification of hypertrophic heart diseases using electrocardiograms.

Background: Differentiating among cardiac diseases associated with left ventricular hypertrophy (LVH) informs diagnosis and clinical care. Objective: To evaluate if artificial intelligence-enabled analysis of the 12-lead electrocardiogram (ECG) facilitates automated detection and classification of LVH. Methods: We used a pretrained convolutional neural network to derive numerical representations of 12-lead ECG waveforms from patients in a multi-institutional healthcare system who had cardiac diseases associated with LVH (n = 50,709), including cardiac amyloidosis (n = 304), hypertrophic cardiomyopathy (n = 1056), hypertension (n = 20,802), aortic stenosis (n = 446), and other causes (n = 4766). We then regressed LVH etiologies relative to no LVH on age, sex, and the numerical 12-lead representations using logistic regression ("LVH-Net"). To assess deep learning model performance on single-lead data analogous to mobile ECGs, we also developed 2 single-lead deep learning models by training models on lead I ("LVH-Net Lead I") or lead II ("LVH-Net Lead II") from the 12-lead ECG. We compared the performance of the LVH-Net models to alternative models fit on (1) age, sex, and standard ECG measures, and (2) clinical ECG-based rules for diagnosing LVH. Results: The areas under the receiver operator characteristic curve of LVH-Net by specific LVH etiology were cardiac amyloidosis 0.95 [95% CI, 0.93-0.97], hypertrophic cardiomyopathy 0.92 [95% CI, 0.90-0.94], aortic stenosis LVH 0.90 [95% CI, 0.88-0.92], hypertensive LVH 0.76 [95% CI, 0.76-0.77], and other LVH 0.69 [95% CI 0.68-0.71]. The single-lead models also discriminated LVH etiologies well. Conclusion: An artificial intelligence-enabled ECG model is favorable for detection and classification of LVH and outperforms clinical ECG-based rules.

Targeting Krebs-cycle-deficient renal cell carcinoma with Poly ADP-ribose polymerase inhibitors and low-dose alkylating chemotherapy.

Loss-of-function mutations in genes encoding the Krebs cycle enzymes Fumarate Hydratase (FH) and Succinate Dehydrogenase (SDH) induce accumulation of fumarate and succinate, respectively and predispose patients to hereditary cancer syndromes including the development of aggressive renal cell carcinoma (RCC). Fumarate and succinate competitively inhibit αKG-dependent dioxygenases, including Lysine-specific demethylase 4A/B (KDM4A/B), leading to suppression of the homologous recombination (HR) DNA repair pathway. In this study, we have developed new syngeneic Fh1- and Sdhb-deficient murine models of RCC, which demonstrate the expected accumulation of fumarate and succinate, alterations in the transcriptomic and methylation profile, and an increase in unresolved DNA double-strand breaks (DSBs). The efficacy of poly ADP-ribose polymerase inhibitors (PARPis) and temozolomide (TMZ), alone and in combination, was evaluated both in vitro and in vivo. Combination treatment with PARPi and TMZ results in marked in vitro cytotoxicity in Fh1- and Sdhb-deficient cells. In vivo, treatment with standard dosing of the PARP inhibitor BGB-290 and low-dose TMZ significantly inhibits tumor growth without a significant increase in toxicity. These findings provide the basis for a novel therapeutic strategy exploiting HR deficiency in FH and SDH-deficient RCC with combined PARP inhibition and low-dose alkylating chemotherapy.

Sexual contexts and the process of risk reduction.

Understanding the dynamics of sexual risks for HIV among men who have sex with men has been one of the ongoing challenges of HIV prevention. While the majority of HIV-prevention interventions target individual behaviour and decision making, multiple studies point to the importance of social context in shaping risk behaviour. Analysis of qualitative data from a study of men who have sex with men, drug use and sex found that sexual encounters were made up multiple contextual and interpersonal elements, which interacted to shape sexual practices and risk reduction strategies. Semi-structured interviews were conducted with 60 racially diverse men who have sex with men in NYC, recruited from multiple venues. The majority of respondents were gay-identified and half were 40 or older. Respondents described risk assessment and risk-reduction processes that develop throughout a sexual encounter, embedded in ongoing negotiations of sexual practices. Strategies of risk assessment and reduction draw on probability-based approaches to HIV prevention, presenting a challenge to health education.

Drug use, community action, and public health: gay men and crystal meth in NYC.

In 2004, GLBT and HIV/AIDS service providers in NYC mobilized against use of crystal methamphetamine among gay men. Both drug use and mobilization were shaped by the history of HIV, particularly the institutions, action repertoires, and social networks forged in earlier AIDS work. This paper is based on qualitative research conducted from 2007 to 2009 with advocates, service providers, and men who have sex with men recruited from diverse venues in NYC gay communities. The crystal use epidemic among gay men in NYC indicates the importance of social and historical context in shaping drug use and antidrug mobilization, including the potential for public health responses to drug use.

The cultural currency of a 'good' sense of humour: British comedy and new forms of distinction.

Traditionally considered lowbrow art par excellence, British comedy has grown steadily in legitimacy since the 'Alternative Comedy Movement' of the early 1980s. Yet while there might be evidence of a transformation in British comic production, there is little understanding of how this has been reflected in patterns of consumption. Indeed, there is a remarkable absence of studies probing comedy taste in British cultural sociology, most notably in Bennett et al's (2009) recent and otherwise exhaustive mapping of cultural taste and participation. This paper aims to plug this gap in the literature by examining contemporary comedy taste cultures in Britain. Drawing on a large-scale survey and in-depth interviews carried out at the Edinburgh Festival Fringe, it argues that comedy now represents an emerging field for the culturally privileged to activate their cultural capital resources. However, unlike previous studies on cultural capital and taste, this research finds that field-specific 'comic cultural capital' is mobilized less through taste for certain legitimate 'objects' of comedy and more through the expression of rarefied and somewhat 'disinterested'styles of comic appreciation. In short, it is 'embodied' rather than 'objectified' forms of cultural capital that largely distinguish the privileged in the field of comedy.

Application of Distributed Agent-based Modeling to Investigate Opioid Use Outcomes in Justice Involved Populations.

Criminal justice involved (CJI) individuals with a history of opioid use disorder (OUD) are at high risk of overdose and death in the weeks following release from jail. We developed the Justice-Community Circulation Model (JCCM) to investigate OUD/CJI dynamics post-release and the effects of interventions on overdose deaths. The JCCM uses a synthetic agent-based model population of approximately 150,000 unique individuals that is generated using demographic information collected from multiple Chicago-area studies and data sets. We use a high-performance computing (HPC) workflow to implement a sequential approximate Bayesian computation algorithm for calibrating the JCCM. The calibration results in the simulated joint posterior distribution of the JCCM input parameters. The calibrated model is used to investigate the effects of a naloxone intervention for a mass jail release. The simulation results show the degree to which a targeted intervention focusing on recently released jail inmates can help reduce the risk of death from opioid overdose.