Review of Alternatives to Incarceration Efforts Worldwide

Despite global interest in treating substance use disorders as a health issue, many countries choose a criminal justice response instead. The goal of this project was to research the readiness of countries to establish or expand alternatives to incarceration (ATIs) for persons with substance use disorders (SUDs) in countries around the globe. This report gathers, compiles and analyzes information on alternatives to incarceration for persons involved in the criminal justice with substance use disorders, worldwide (193 UN Member States plus Greenland, Kosovo, Palestine and Taiwan). As such, this report presents the first attempt to compile this information globally and completely. A video of the webinar meeting with the presentation of results is available at: https://www.youtube.com/watch?v=p3_h6hMOvTc

Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the host’s inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 94 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.84). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.92, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues

Host gene expression classifiers diagnose acute respiratory illness etiology.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P \u3c 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance

Host gene expression classifiers diagnose acute respiratory illness etiology

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational, cohort study determined whether host gene expression patterns discriminate non-infectious from infectious illness, and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or non-infectious illness as well as 44 healthy controls was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a non-infectious cause of illness (26 probes). Overall accuracy was 87% (238/273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, p<0.03) and three published classifiers of bacterial vs. viral infection (78-83%). The classifiers developed here externally validated in five publicly available datasets (AUC 0.90-0.99). A sixth publically available dataset included twenty-five patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI; viral ARI; co-infection; and neither a bacterial nor viral response. These findings create an opportunity to develop and utilize host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance

Projecting the mouse <i>S. aureus</i> classifier onto human subjects.

<p>The murine <i>S. aureus</i> classifier identifies humans with <i>S. aureus</i> BSI, but does not differentiate <i>S. aureus</i> from <i>E. coli</i> BSI.</p

Validation in an independent human cohort [18].

<p>(A) The murine <i>S. aureus</i> classifier differentiates between <i>S. aureus</i> infection and healthy. (B) The human <i>S. aureus</i> classifier differentiates between <i>S. aureus</i> infection and healthy.</p

The murine <i>S. aureus</i> classifier differentiates <i>S. aureus</i> from <i>E. coli</i> infection.

<p>(A) Inbred mice were tested under three conditions: uninfected controls (black circles), <i>S. aureus</i> infected (red “x”), and <i>E. coli</i> infected (blue triangles). The y-axis represents the predicted probability that a given animal was infected with <i>S. aureus</i>. (B) The murine <i>S. aureus</i> classifier is validated in outbred CD-1 mice where it differentiates <i>S. aureus</i> infection from <i>E. coli</i> infection and uninfected controls.</p

Schematic of derivation and validation cohorts.

<p>The Murine Derivation Cohort includes <i>S. aureus</i> infection (n = 83), healthy control mice (n = 54), and <i>E. coli</i> infection (n = 50). It served as a validation cohort to assess Mouse Strain Effect, <i>S. aureus</i> Genetic Background Effect, Time Course, and to compare <i>S. aureus</i> vs. <i>E. coli</i> and <i>E. coli</i> vs. Healthy. The murine <i>S. aureus</i> classifier was externally validated in Outbred Mice (n = 30) and the CAPSOD Human Cohort. The CAPSOD Human Cohort includes <i>S. aureus</i> BSI (n = 32), healthy volunteers (n = 43), and <i>E. coli</i> BSI (n = 19). It served as a validation cohort to compare <i>S. aureus</i> vs. Healthy, <i>S. aureus</i> vs. <i>E. coli</i>, and <i>E. coli</i> vs. Healthy. Model derivation and validation using the entire cohort of animals or humans is depicted by the blue outline and arrows. An independent classifier was generated using only subjects with <i>S. aureus</i> or <i>E. coli</i> BSI (green outline). This classifier was validated using leave one out cross validation (green arrow). The Human Pediatric Cohort (n = 46 <i>S. aureus</i>, 10 Healthy) used for external validation does not include patients with <i>E. coli</i> infection. Therefore, <i>S. aureus</i> classifiers were generated from the murine and CAPSOD cohorts that excluded <i>E. coli</i> data (red outline and thick red arrow). The Human Pediatric Cohort was used to derive a Human <i>S. aureus</i> vs. Healthy classifier which was validated in the <i>S. aureus</i>-infected and Healthy populations within the murine and CAPSOD human cohorts (thin red arrow).</p