Yaara Goldschmidt

Paradoxical Hypersusceptibility of Drug-resistant Mycobacteriumtuberculosis to β-lactam Antibiotics

Mycobacterium tuberculosis (M. tuberculosis) is considered innately resistant to β-lactam antibiotics. However, there is evidence that susceptibility to β-lactam antibiotics in combination with β–lactamase inhibitors is variable among clinical isolates, and these may present therapeutic options for drug-resistant cases. Here we report our investigation of susceptibility to β-lactam/β–lactamase inhibitor combinations among clinical isolates of M. tuberculosis, and the use of comparative genomics to understand the observed heterogeneity in susceptibility. Eighty-nine South African clinical isolates of varying first and second-line drug susceptibility patterns and two reference strains of M. tuberculosis underwent minimum inhibitory concentration (MIC) determination to two β-lactams: amoxicillin and meropenem, both alone and in combination with clavulanate, a β–lactamase inhibitor. 41/91 (45%) of tested isolates were found to be hypersusceptible to amoxicillin/clavulanate relative to reference strains, including 14/24 (58%) of multiple drug-resistant (MDR) and 22/38 (58%) of extensively drug-resistant (XDR) isolates. Genome-wide polymorphisms identified using whole-genome sequencing were used in a phylogenetically-aware linear mixed model to identify polymorphisms associated with amoxicillin/clavulanate susceptibility. Susceptibility to amoxicillin/clavulanate was over-represented among isolates within a specific clade (LAM4), in particular among XDR strains. Twelve sets of polymorphisms were identified as putative markers of amoxicillin/clavulanate susceptibility, five of which were confined solely to LAM4. Within the LAM4 clade, ‘paradoxical hypersusceptibility’ to amoxicillin/clavulanate has evolved in parallel to first and second-line drug resistance. Given the high prevalence of LAM4 among XDR TB in South Africa, our data support an expanded role for β-lactam/β-lactamase inhibitor combinations for treatment of drug-resistant M. tuberculosis.

Smarter log analysis

Modern computer systems generate an enormous number of logs. IBM Mining Effectively Large Output Data Yield (MELODY) is a unique and innovative solution for handling these logs and filtering out the anomalies and failures. MELODY can detect system errors early on and avoid subsequent crashes by identifying the root causes of such errors. By analyzing the logs leading up to a problem, MELODY can pinpoint when and where things went wrong and visually present them to the user, ensuring that corrections are accurately and effectively done. We present the MELODY solution and describe its architecture, algorithmic components, functions, and benefits. After being trained on a large portion of relevant data, MELODY provides alerts of abnormalities in newly arriving log files or in streams of logs. The solution is being used by IBM services groups that support IBM xSeries® servers on a regular basis. MELODY was recently tested with ten large IBM customers who use zSeries® machines and was found to be extremely useful for the information technology experts in those companies. They found that the solutions ability to reduce extensively large log data to manageable sets of highlighted messages saved them time and helped them make better use of the data.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Integrated multisystem analysis in a mental health and criminal justice ecosystem

BackgroundPatients with a serious mental illness often receive care that is fragmented due to reduced availability of or access to resources, and inadequate, discontinuous, and uncoordinated care across health, social services, and criminal justice organizations. This article describes the creation of a multisystem analysis that derives insights from an integrated dataset including patient access to case management services, medical services, and interactions with the criminal justice system.MethodsData were combined from electronic systems within a US mental health ecosystem that included mental health and substance abuse services, as well as data from the criminal justice system. Cox models were applied to test the associations between delivery of services and re-incarceration. Additionally, machine learning was used to train and validate a predictive model to examine effects of non-modifiable risk factors (age, past arrests, mental health diagnosis) and modifiable risk factors (outpatient, medical and case management services, and use of a jail diversion program) on re-arrest outcome.ResultsAn association was found between past arrests and admission to crisis stabilization services in this population (N = 10,307). Delivery of case management or medical services provided after release from jail was associated with a reduced risk for re-arrest. Predictive models linked non-modifiable and modifiable risk factors and outcomes and predicted the probability of re-arrests with fair accuracy (area under the receiver operating characteristic curve of 0.67).ConclusionsBy modeling the complex interactions between risk factors, service delivery, and outcomes, systems of care might be better enabled to meet patient needs and improve outcomes.

Analytics for resiliency in the mainframe

The IBM System z® mainframe computer is a direct descendant of the IBM System/360 family of computing systems initially made available in 1965. With each release of an IBM mainframe, its developers include new reliability, availability, and serviceability (RAS) functions. This paper describes recent technology added to the System z and z/OS® operating system to enhance the ability to provide high levels of availability. Specifically, we discuss the use of machine-learning algorithms to analyze log messages. We also describe combining temporal and textual information for system log-file analysis. Finally, we discuss the results of this analysis and the assistance it provides to mainframe users.

Characterizing Subpopulations with Better Response to Treatment Using Observational Data - an Epilepsy Case Study

Electronic health records and health insurance claims, providing observational data on millions of patients, offer great opportunities, and challenges, for population health studies. The objective of this study is identifying subpopulations that are likely to benefit from a given treatment using observational data. We refer to these subpopulations as “better responders” and focus on characterizing these using linear scores with a limited number of variables. Building upon well-established causal inference techniques for analyzing observational data, we propose two algorithms that generate such scores for identifying better responders, as well as methods for evaluating and comparing these scores. We applied our methodology to a large dataset of ~135,000 epilepsy patients derived from claims data. Out of this sample, 85,000 were used to characterize subpopulations with better response to next-generation (“Newer”) anti-epileptic drugs (AEDs), compared to an alternative treatment by first-generation (“Older”) AEDs. The remaining 50,000 epilepsy patients were then used to evaluate our scores. Our results demonstrate the ability of our scores to identify large subpopulations of epilepsy patients with significantly better response to newer AEDs.

Estimating the effects of second-line therapy for type 2 diabetes mellitus: retrospective cohort study

Objective Metformin is the recommended initial drug treatment in type 2 diabetes mellitus, but there is no clearly preferred choice for an additional drug when indicated. We compare the counterfactual drug effectiveness in lowering glycated hemoglobin (HbA1c) levels and effect on body mass index (BMI) of four diabetes second-line drug classes using electronic health records. Study design and setting Retrospective analysis of electronic health records of US-based patients in the Explorys database using causal inference methodology to adjust for patient censoring and confounders. Participants and exposures Our cohort consisted of more than 40 000 patients with type 2 diabetes, prescribed metformin along with a drug out of four second-line drug classes—sulfonylureas, thiazolidinediones, dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide-1 agonists—during the years 2000–2015. Roughly, 17 000 of these patients were followed for 12 months after being prescribed a second-line drug. Main outcome measures HbA1c and BMI of these patients after 6 and 12 months following treatment. Results We demonstrate that all four drug classes reduce HbA1c levels, but the effect of sulfonylureas after 6 and 12 months of treatment is less pronounced compared with other classes. We also estimate that DPP-4 inhibitors decrease body weight significantly more than sulfonylureas and thiazolidinediones. Conclusion Our results are in line with current knowledge on second-line drug effectiveness and effect on BMI. They demonstrate that causal inference from electronic health records is an effective way for conducting multitreatment causal inference studies.