Quantifying gaps in the tuberculosis care cascade in Brazil: A mathematical model study using national program data.

BACKGROUND: In Brazil, many individuals with tuberculosis (TB) do not receive appropriate care due to delayed or missed diagnosis, ineffective treatment regimens, or loss-to-follow-up. This study aimed to estimate the health losses and TB program costs attributable to each gap in the care cascade for TB disease in Brazil. METHODS AND FINDINGS: We constructed a Markov model simulating the TB care cascade and lifetime health outcomes (e.g., death, cure, postinfectious sequelae) for individuals developing TB disease in Brazil. We stratified the model by age, human immunodeficiency virus (HIV) status, drug resistance, state of residence, and disease severity, and developed a parallel model for individuals without TB that receive a false-positive TB diagnosis. Models were fit to data (adult and pediatric) from Brazil's Notifiable Diseases Information System (SINAN) and Mortality Information System (SIM) for 2018. Using these models, we assessed current program performance and simulated hypothetical scenarios that eliminated specific gaps in the care cascade, in order to quantify incremental health losses and TB diagnosis and treatment costs along the care cascade. TB-attributable disability-adjusted life years (DALYs) were calculated by comparing changes in survival and nonfatal disability to a no-TB counterfactual scenario. We estimated that 90.0% (95% uncertainty interval [UI]: 85.2 to 93.4) of individuals with TB disease initiated treatment and 10.0% (95% UI: 7.6 to 12.5) died with TB. The average number of TB-attributable DALYs per incident TB case varied across Brazil, ranging from 2.9 (95% UI: 2.3 to 3.6) DALYs in Acre to 4.0 (95% UI: 3.3 to 4.7) DALYs in Rio Grande do Sul (national average 3.5 [95% UI: 2.8 to 4.1]). Delayed diagnosis contributed the largest health losses along the care cascade, followed by post-TB sequelae and loss to follow up from TB treatment, with TB DALYs reduced by 71% (95% UI: 65 to 76), 41% (95% UI: 36 to 49), and 10% (95% UI: 7 to 16), respectively, when these factors were eliminated. Total health system costs were largely unaffected by improvements in the care cascade, with elimination of treatment failure reducing attributable costs by 3.1% (95% UI: 1.5 to 5.4). TB diagnosis and treatment of false-positive individuals accounted for 10.2% (95% UI: 3.9 to 21.7) of total programmatic costs but contributed minimally to health losses. Several assumptions were required to interpret programmatic data for the analysis, and we were unable to estimate the contribution of social factors to care cascade outcomes. CONCLUSIONS: In this study, we observed that delays to diagnosis, post-disease sequelae and treatment loss to follow-up were primary contributors to the TB burden of disease in Brazil. Reducing delays to diagnosis, improving healthcare after TB cure, and reducing treatment loss to follow-up should be prioritized to improve the burden of TB disease in Brazil.

Critically reading machine learning literature in neurosurgery: a reader's guide and checklist for appraising prediction models.

OBJECTIVE: Machine learning (ML) has become an increasingly popular tool for use in neurosurgical research. The number of publications and interest in the field have recently seen significant expansion in both quantity and complexity. However, this also places a commensurate burden on the general neurosurgical readership to appraise this literature and decide if these algorithms can be effectively translated into practice. To this end, the authors sought to review the burgeoning neurosurgical ML literature and to develop a checklist to help readers critically review and digest this work. METHODS: The authors performed a literature search of recent ML papers in the PubMed database with the terms "neurosurgery" AND "machine learning," with additional modifiers "trauma," "cancer," "pediatric," and "spine" also used to ensure a diverse selection of relevant papers within the field. Papers were reviewed for their ML methodology, including the formulation of the clinical problem, data acquisition, data preprocessing, model development, model validation, model performance, and model deployment. RESULTS: The resulting checklist consists of 14 key questions for critically appraising ML models and development techniques; these are organized according to their timing along the standard ML workflow. In addition, the authors provide an overview of the ML development process, as well as a review of key terms, models, and concepts referenced in the literature. CONCLUSIONS: ML is poised to become an increasingly important part of neurosurgical research and clinical care. The authors hope that dissemination of education on ML techniques will help neurosurgeons to critically review new research better and more effectively integrate this technology into their practices.

Periplasmic stress contributes to a trade-off between protein secretion and cell growth in Escherichia coli Nissle 1917.

Maximizing protein secretion is an important target in the design of engineered living systems. In this paper, we characterize a trade-off between cell growth and per-cell protein secretion in the curli biofilm secretion system of Escherichia coli Nissle 1917. Initial characterization using 24-h continuous growth and protein production monitoring confirms decreased growth rates at high induction, leading to a local maximum in total protein production at intermediate induction. Propidium iodide (PI) staining at the endpoint indicates that cellular death is a dominant cause of growth reduction. Assaying variants with combinatorial constructs of inner and outer membrane secretion tags, we find that diminished growth at high production is specific to secretory variants associated with periplasmic stress mediated by outer membrane secretion and periplasmic accumulation of protein containing the outer membrane transport tag. RNA sequencing experiments indicate upregulation of known periplasmic stress response genes in the highly secreting variant, further implicating periplasmic stress in the growth-secretion trade-off. Overall, these results motivate additional strategies for optimizing total protein production and longevity of secretory engineered living systems Graphical Abstract.

Plasmid Vectors for in Vivo Selection-Free Use with the Probiotic E. coli Nissle 1917.

Escherichia coli Nissle 1917 (EcN) is a probiotic bacterium, commonly employed to treat certain gastrointestinal disorders. It is fast emerging as an important target for the development of therapeutic engineered bacteria, benefiting from the wealth of knowledge of E. coli biology and ease of manipulation. Bacterial synthetic biology projects commonly utilize engineered plasmid vectors, which are simple to engineer and can reliably achieve high levels of protein expression. However, plasmids typically require antibiotics for maintenance, and the administration of an antibiotic is often incompatible with in vivo experimentation or treatment. EcN natively contains plasmids pMUT1 and pMUT2, which have no known function but are stable within the bacteria. Here, we describe the development of the pMUT plasmids into a robust platform for engineering EcN for in vivo experimentation, alongside a CRISPR-Cas9 system to remove the native plasmids. We systematically engineered both pMUT plasmids to contain selection markers, fluorescent markers, temperature sensitive expression, and curli secretion systems to export a customizable functional material into the extracellular space. We then demonstrate that the engineered plasmids were maintained in bacteria as the engineered bacteria pass through the mouse GI tract without selection, and that the secretion system remains functional, exporting functionalized curli proteins into the gut. Our plasmid system presents a platform for the rapid development of therapeutic EcN bacteria.

Simulation training improves team dynamics and performance in a low-resource cardiac intensive care unit.

INTRODUCTION: Although simulation training has been utilized quite extensively in highincome medical environments, its feasibility and effect on team performance in lowresource pediatric Cardiac Intensive Care Unit (CICU) environments has not been demonstrated. We hypothesized that lowfidelity simulationbased crisis resource management training would lead to improvements in team performance in such settings. METHODS: In this prospective observational study, the effect of simulation on team dynamics and performance was assessed in 23 healthcare providers in a pediatric CICU in Southeast Asia. A 5day training program was utilized consisting of various didactic sessions and simulation training exercises. Improvements in team dynamics were assessed using participant questionnaires, expert evaluations, and video analysis of time to intervention and frequency of closedloop communication. RESULTS: In subjective questionnaires, participants noted significant (P < 0.05) improvement in team dynamics and performance over the training period. Video analysis revealed a decrease in time to intervention and significant (P < 0.05) increase in frequency of closedloop communication because of simulation training. CONCLUSIONS: This study demonstrates the feasibility and effectiveness of simulationbased training in improving team dynamics and performance in lowresource pediatric CICU environments, indicating its potential role in eliminating communication barriers in these settings.