Streptococcus anginosus lung infection and empyema: A case report and review of the literature

Streptococcus milleri group (SMG) also referred to as the Streptococcus anginosus group. These are Gram-positive, variable hemolysis, catalase negative, microaerophilic, non-motile facultative anaerobes which have been known to cause abscesses in humans. We report a case of empyema caused by Streptococcus anginosus in a patient with an unresolved pneumonia for over a month. In early October 2018, the patient presented to an emergency room with the complaints of shortness of air, productive cough, chills, subjective fever and weight loss for 4 weeks. A chest X-ray revealed a left lower lobe pneumonia. He was treated with 250 mg of azithromycin for 4 days. During a follow-up visit in November 2018, he reported having persistent symptoms. The chest CT revealed a localized pleural fluid collection at the left lower chest highly suggestive of empyema. He was prescribed 100mg of doxycycline for a month. However, he was admitted to the hospital a week later due to worsening symptoms. The microbiological cultures for sputum and blood were negative; however, pleural fluid cultures grew Streptococcus anginosus resistant to clindamycin and erythromycin. The patient was treated with broad spectrum antimicrobial regimen in conjunction with surgical management. Initially, the patient underwent CT guided placement of chest tube with instillation of Tissue Plasminogen Activator (TPA) for the drainage of pleural fluid followed Video-assisted Thoracoscopy with lateral decortication and drainage of empyema of the left lung due to persistence of complicated effusion. There was remarkable improvement in his symptoms, and he recovered subsequently. Our case highlights the infections caused by the Streptococcus milleri group (SMG) in individuals with an unresolved pneumonia. Such patients should be diagnosed accurately and treated aggressively with rapid and effective interventions

Development of a Real-time PCR assay for Pneumocystis jirovecii on the Luminex ARIES® Platform

Pneumocystis pneumonia (PCP) is an opportunistic infection caused by the fungus Pneumocystis jirovecii. Infection with P. jirovecii can result in serious illness in patients with a weakened immune system, and can lead to death if it is not properly diagnosed and treated. Direct detection of P. jirovecii in lower respiratory tract specimens such as bronchoalveolar lavage (BAL) is preferred for rapid diagnosis, a laboratory service currently not available locally. We report here the development of a diagnostic real-time Polymerase Chain Reaction (PCR) assay using BAL specimens to detect P. jirovecii. By targeting the multi-copy mitochondrial large subunit ribosomal RNA gene (mtLSU rRNA) of P. jirovecii, assay sensitivity is increased. Primer pairs were designed to include a fluorescent reporter dye-labeled primer with a unique MultiCode® base pair isoC on the 5’end and one unlabeled primer. The performance characteristics were determined on the Luminex ARIES® instrument, combining DNA extraction, amplification and detection into a one-step process. The cassette contains the reagents needed to perform all of the steps including extraction, purification, amplification, and detection, plus a sample processing control. Accuracy, precision, sensitivity, specificity and stability studies were conducted to validate the assay to meet CLIA requirements. The analytical sensitivity was 89.1%, and the analytical specificity was 100%. The assay could reliably detect 200 organisms/mL, crossing thresholds (Ct) and melt temperatures (Tm) were consistent, and no cross-reactivity was observed with other pathogens known to cause respiratory infections. The results demonstrated that these primers are specific to Pneumocystis jirovecii. The real-time PCR method using the ARIES® system allowed for rapid and sensitive detection of Pneumocystis pneumonia infections with P. jirovecii using clinical respiratory specimens

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

As a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a \textit{stochastic satisfiability} (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure.Comment: 24 pages, 7 figures, 5 theorem

Preliminary Evaluation of an lytA PCR Assay for Detection of Streptococcus pneumoniae in Urine Specimens from Hospitalized Patients with Community-Acquired Pneumonia

Community acquired pneumonia (CAP) due to Streptococcus pneumoniae still occurs in at risk populations, despite the availability of effective vaccines. Laboratory confirmation of S. pneumoniae remains challenging in cases of CAP despite advances in blood culture techniques and the availability of nucleic acid amplification tests such as PCR-based methods. Urine specimens are an attractive sample type because they are non-invasive compared to bronchial washes or whole blood specimens for patients with CAP. While urine specimens have been used successfully in antigen detection assays, they have not been extensively evaluated for PCR-based assays. In this preliminary study, we evaluated the potential for a real-time PCR assay targeting the S. pneumoniae autolysin gene (lytA) to detect in archived urine samples from patients with CAP. Results indicate that the real time lytA PCR assay on the Luminex ARIES® system shows promise as a screening tool for patients with CAP based on comparison to urine antigen detection assay results

Implementing a Clinical Research Program in Long Term Care Facilities: Experiences from the University of Louisville Center Excellence for Research in Infectious Diseases [CERID]

Background: According to the US Census Bureau International Report, in 2015, almost nine percent of the world’s population was aged 65 and over. As the worldwide population ages, there is a need to understand how to best care for those individuals. Developing clinical research programs focusing on long term care (LTC) will be critical to defining best practice. Objectives: The objectives of this manuscript are to: 1) outline the challenges identified in performing clinical research in long term care facilities (LTCF), and 2) offer solutions for future clinical research in the LTC environment based upon our experiences. Methods: A research feasibility study was performed in 14 LTCFs in Louisville, Kentucky during 2018. Research questions involving identification of LTCF residents experiencing diarrhea were used as the basis for determining challenges and abilities to perform research in the LTC environment. Results: Challenges to performing clinical research involving an infectious disease were gathered throughout the twenty-week feasibility assessment period and organized into eight distinct yet inter-related areas. These included: 1) facility recruitment; 2) engagement of facility leadership; 3) engagement of facility personnel; 4) identification of research candidates; 5) consenting processes; 6) management of clinical samples; 7) navigating the medical record systems; and 8) study team workflow. Conclusions: This feasibility assessment found that conducting research in LTCFs was very different in almost every aspect from research conducted in the hospital setting. Results from this feasibility assessment will be used as a basis to determine a more comprehensive population-based incidence of C. difficile infection through the City of Louisville Diarrhea (CLOUD) study

Methane leak detection and sizing over long distances using dual frequency comb laser spectroscopy and a bootstrap inversion technique

Advances in natural gas extraction technology have led to increased activity in the production and transport sectors in the United States, and, as a consequence, an increased need for reliable monitoring of methane leaks to the atmosphere. We present a statistical methodology in combination with an observing system for the detection and attribution of fugitive emissions of methane from distributed potential source location landscapes such as natural gas production sites. We measure long (&gt;500 m), integrated open path concentrations of atmospheric methane using a dual frequency comb spectrometer and combine measurements with an atmospheric transport model to infer leak locations and strengths using a novel statistical method, the non-zero minimum bootstrap (NZMB). The new statistical method allows us to determine whether the empirical distribution of possible source strengths for a given location excludes zero. Using this information, we identify leaking source locations (i.e., natural gas wells) through rejection of the null hypothesis that the source is not leaking. The method is tested with a series of synthetic data inversions with varying measurement density and varying levels of model-data mismatch. It is also tested with field observations of 1) a non-leaking source location and 2) a source location where a controlled emission of 2.1 E-5 kg s-1 of methane gas is released over a period of several hours. This series of synthetic data tests and outdoor field observations using a controlled methane release demonstrate the viability of the approach for the detection and sizing of very small (&lt;2 g m-1 ) leaks of methane across large distances (4+ km2 in synthetic tests). The field tests demonstrate the ability to attribute small atmospheric enhancements of 18 ppb to the emitting source location against a background of combined atmospheric (e.g., background methane variability) and measurement uncertainty of 6 ppb (1-sigma), when measurements are averaged over 2 minutes. The results of the synthetic and field data testing show that the new observing system and statistical approach greatly decreases the incidence of false alarms (that is, wrongly identifying a well site to be leaking) compared with the same tests that don&rsquo;t use the NZMB approach, and therefore offers increased leak detection and sizing capabilities.</p

An innate pathogen sensing strategy involving ubiquitination of bacterial surface proteins.

Sensing of pathogens by ubiquitination is a critical arm of cellular immunity. However, universal ubiquitination targets on microbes remain unidentified. Here, using in vitro, ex vivo, and in vivo studies, we identify the first protein-based ubiquitination substrates on phylogenetically diverse bacteria by unveiling a strategy that uses recognition of degron-like motifs. Such motifs form a new class of intra-cytosolic pathogen-associated molecular patterns (PAMPs). Their incorporation enabled recognition of nonubiquitin targets by host ubiquitin ligases. We find that SCFFBW7 E3 ligase, supported by the regulatory kinase, glycogen synthase kinase 3β, is crucial for effective pathogen detection and clearance. This provides a mechanistic explanation for enhanced risk of infections in patients with chronic lymphocytic leukemia bearing mutations in F-box and WD repeat domain containing 7 protein. We conclude that exploitation of this generic pathogen sensing strategy allows conservation of host resources and boosts antimicrobial immunity

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

International audienceAs a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure

Justicia: A Stochastic SAT Approach to Formally Verify Fairness

International audienceAs a technology ML is oblivious to societal good or bad, and thus, the field of fair machine learning has stepped up to propose multiple mathematical definitions, algorithms, and systems to ensure different notions of fairness in ML applications. Given the multitude of propositions, it has become imperative to formally verify the fairness metrics satisfied by different algorithms on different datasets. In this paper, we propose a stochastic satisfiability (SSAT) framework, Justicia, that formally verifies different fairness measures of supervised learning algorithms with respect to the underlying data distribution. We instantiate Justicia on multiple classification and bias mitigation algorithms, and datasets to verify different fairness metrics, such as disparate impact, statistical parity, and equalized odds. Justicia is scalable, accurate, and operates on non-Boolean and compound sensitive attributes unlike existing distribution-based verifiers, such as FairSquare and VeriFair. Being distribution-based by design, Justicia is more robust than the verifiers, such as AIF360, that operate on specific test samples. We also theoretically bound the finite-sample error of the verified fairness measure