Preventive healthcare | EPFL Graph Search

Preventive healthcare, or prophylaxis is the application of healthcare measures to prevent diseases. Disease and disability are affected by environmental factors, genetic predisposition, disease agents, and lifestyle choices, and are dynamic processes which begin before individuals realize they are affected. Disease prevention relies on anticipatory actions that can be categorized as primal, primary, secondary, and tertiary prevention. Each year, millions of people die of preventable deaths. A 2004 study showed that about half of all deaths in the United States in 2000 were due to preventable behaviors and exposures. Leading causes included cardiovascular disease, chronic respiratory disease, unintentional injuries, diabetes, and certain infectious diseases. This same study estimates that 400,000 people die each year in the United States due to poor diet and a sedentary lifestyle. According to estimates made by the World Heal

Identification of genes required for host-cell adhesion and antibiotic persistence of uropathogenic Escherichia coli via high-content screening

Thomas Marie Simonet

Uropathogenic Escherichia coli (UPEC) are the most common bacterial pathogens causing urinary tract infections (UTIs). Driven by the development of antibiotic resistant UPEC strains, UTIs have become a major public health issue and generate substantial healthcare costs. Even in the absence of antibiotic resistance, UTIs are notoriously difficult to treat and have high rates of recurrence, mostly due to: 1. the ability of UPEC to adhere to and invade bladder epithelial cells, which protects them from the action of antimicrobial agents and the immune system 2. the presence in antibiotic-susceptible UPEC populations of a small fraction of so-called persister cells, which are killed at a slower rate than the bulk of the population and can therefore survive prolonged antibiotic exposureThese two features allow some bacteria to remain viable in a protected niche for the whole duration of the antibiotic treatment, potentially leading to infection relapse when the treatment is terminated.High-content screening (HCS), which uses microscopy as a screening tool, allows the visualization of bacterial phenotypes at the single-cell level and can therefore provide a powerful and unbiased tool to understand the genetic basis of these phenotypes. The present thesis describes two complementary high-content screening approaches aimed at identifying UPEC mutants with altered adhesion to bladder cells and with altered antibiotic persistence respectively.In a first chapter, we report preparatory steps that led to the construction of a transposon insertion library of UPEC mutants that is suitable for high-content imaging, using the CFT073 clinical UTI isolate as background strain.In the second chapter, we performed fluorescence microscopy-based high-content screening to identify mutants with defects in early adhesion to bladder epithelial cells. We recovered 82 mutants with decreased adhesion and 54 mutants with increased adhesion. Unexpectedly, nine low-adhesion ÂÂhitsÂÂ mapped to the two P pili operons encoded by CFT073, which are thought to mediate adhesion to kidney cells rather than bladder cells. Additionally, six high-adhesion hits mapped to the operon coding for F1C pili, and we show that this phenotype is linked to increased P pili synthesis. These results therefore reveal a critical role for P pili in UPEC adhesion to bladder epithelial cells, which may inform the development of novel anti-adhesion therapies to prevent UTI recurrence.In the third chapter, we combined microfluidics with time-lapse microscopy to identify mutants with altered persistence to fosfomycin (FOS) âÂÂ a cell-wall inhibitor used as first-line agent in the treatment of UTIs âÂÂ in synthetic human urine (SHU), which mimics the physiology of UPEC in real urine. We recovered four mutants with decreased persistence, three of which had defects in lipopolysaccharide (LPS) synthesis. These results identify the outer membrane of UPEC as a key component for survival to FOS treatment, thus opening up new avenues in the fight against antibiotic persistence.

EPFL2022

Statistical physics of vaccination

Marcel Salathé, Zhen Wang

Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination one of the most important preventive measures of modern times is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research. (C) 2016 Elsevier B.V. All rights reserved.

Elsevier Science Bv2016

Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning

Nahal Mansouri, Sahand Jamal Rahi

Background: Early prediction of symptoms and mortality risks for COVID-19 patients would improve healthcare outcomes, allow for the appropriate distribution of healthcare resources, reduce healthcare costs, aid in vaccine prioritization and self-isolation strategies, and thus reduce the prevalence of the disease. Such publicly accessible prediction models are lacking, however. Methods: Based on a comprehensive evaluation of existing machine learning (ML) methods, we created two models based solely on the age, gender, and medical histories of 23,749 hospital-confirmed COVID-19 patients from February to September 2020: a symptom prediction model (SPM) and a mortality prediction model (MPM). The SPM predicts 12 symptom groups for each patient: respiratory distress, consciousness disorders, chest pain, paresis or paralysis, cough, fever or chill, gastrointestinal symptoms, sore throat, headache, vertigo, loss of smell or taste, and muscular pain or fatigue. The MPM predicts the death of COVID-19-positive individuals. Results: The SPM yielded ROC-AUCs of 0.53-0.78 for symptoms. The most accurate prediction was for consciousness disorders at a sensitivity of 74% and a specificity of 70%. 2,440 deaths were observed in the study population. MPM had a ROC-AUC of 0.79 and could predict mortality with a sensitivity of 75% and a specificity of 70%. About 90% of deaths occurred in the top 21 percentile of risk groups. To allow patients and clinicians to use these models easily, we created a freely accessible online interface at www. aicovid.net. Conclusion: The ML models predict COVID-19-related symptoms and mortality using information that is readily available to patients as well as clinicians. Thus, both can rapidly estimate the severity of the disease, allowing shared and better healthcare decisions with regard to hospitalization, self-isolation strategy, and COVID-19 vaccine prioritization in the coming months.