Publications by authors named "Arun S Moorthy"

Article Synopsis
  • The study introduces a mathematical model to analyze the gut microbiota, focusing on how different types of carbohydrates (glycans) like dietary fiber and mucins affect mucin-degrading bacteria levels.
  • It distinguishes between a glycan generalist that can degrade both types of glycans and a mucin specialist that only degrades mucins, emphasizing the importance of a healthy mucus barrier for gut health.
  • The model incorporates a two-compartment system representing the gut and uses machine learning and sensitivity analysis to explore how variations in dietary inputs influence gut microbiota composition and health outcomes.
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The performance of three algorithms for predicting nominal molecular mass from an analyte's electron ionization mass spectrum is presented. The Peak Interpretation Method (PIM) attempts to quantify the likelihood that a molecular ion peak is contained in the mass spectrum, whereas the Simple Search Hitlist Method (SS-HM) and iterative Hybrid Search Hitlist Method (iHS-HM) leverage results from mass spectral library searching. These predictions can be employed in combination (recommended) or independently.

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Fentanyl analogs are a class of designer drugs that are particularly challenging to unambiguously identify due to the mass spectral and retention time similarities of unique compounds. In this paper, we use agglomerative hierarchical clustering to explore the measurement diversity of fentanyl analogs and better understand the challenge of unambiguous identifications using analytical techniques traditionally available to drug chemists. We consider four measurements in particular: gas chromatography retention indices, electron ionization mass spectra, electrospray ionization tandem mass spectra, and direct analysis in real time mass spectra.

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The standard reference libraries and associated custom software provided by the National Institute of Standards and Technology's Mass Spectrometry Data Center (NIST MSDC) are described with a focus on assisting the seized drug analyst with the identification of fentanyl-related substances (FRS). These tools are particularly useful when encountering novel substances when no certified sample is available. The MSDC provides three standard reference mass spectral libraries, as well as six software packages for mass spectral analysis, reference library searching, data interpretation, and measurement uncertainty estimation.

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Mass spectra are an important signature by which compounds can be identified. We recently formulated a mathematical approach for incorporating measurement variability when comparing sets of high-resolution mass spectra. Leveraging replicate mass spectra, we construct high-dimensional consensus mass spectra-representing each of the compared analytes-and compute the similarity between these data structures.

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Chromatographic-less mass spectrometry techniques like direct analysis in real-time mass spectrometry (DART-MS) are steadily being employed as seized drug screening tools. However, these newer analytical platforms require new computational methods to best make use of the collected data. The inverted library search algorithm (ILSA) is a recently developed method designed specifically for working with mass spectra of mixtures collected with DART-MS and has been implemented as a function in the NIST/NIJ DART-MS data interpretation tool (DIT).

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Identifying mixture components is a well-known challenge in analytical chemistry. The Inverted Library Search Algorithm is a recently proposed method for identifying mixture components using in-source collision induced dissociation (is-CID) mass spectra of a query mixture and a reference library of pure compound is-CID mass spectra ( 2021, 32 (7), 1725-1734). This article presents several subtle but important advances to the algorithm, including updated compound matching strategies that improve result explainability and spectral filtering to better handle noisy mass spectra as is often observed with real-world samples such as seized drug evidence.

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Deciding whether the mass spectra of seized drug evidence and a reference standard are measurements of two different compounds is a central challenge in forensic chemistry. Normally, an analyst will collect mass spectra from the sample and a reference standard under identical conditions, compute a mass spectral similarity score, and make a judgment about the sample using both the similarity score and their visual interpretation of the spectra. This approach is inherently subjective and not ideal when a rapid assessment of several samples is necessary.

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As seized drug casework becomes increasingly complex due to the continued prevalence of emerging drugs, laboratories are often looking for new analytical approaches including developing methods for the analysis of specific compounds classes. Recent efforts have focused on the development of targeted gas chromatography mass spectrometry (GC-MS) confirmation methods to compliment the information-rich screening results produced by techniques like direct analysis in real time mass spectrometry (DART-MS). In this work, a method for the confirmation of synthetic opioids and related compounds was developed and evaluated.

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To address challenges associated with the increased prevalence of novel psychoactive substances (NPSs), laboratories often adopt new techniques or new methods with the goal of obtaining more detailed chemical information with a higher level of confidence. To demonstrate how new methods applied to existing techniques can be a viable approach, a targeted gas chromatography mass spectrometry (GC-MS) method for synthetic cathinones was developed. To create the method, a range of GC-MS parameters were first investigated using a seven-component test solution with the goal of minimizing compounds with overlapping acceptance windows by maximizing retention time differences within a reasonable runtime.

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With the increased presence of novel psychoactive substances (NPSs) in casework, drug analysis has become more challenging. To address these challenges, new screening technologies with improved specificity are being implemented, allowing for the creation and adoption of targeted confirmatory analyses that produce more conclusive results. This paper outlines a six-step, data-driven, framework to develop and evaluate gas chromatography mass spectrometry (GC-MS) methods for targeted classes of drugs.

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Forensic analysis of seized drug evidence often involves determining whether the components of an unknown mixture are illicit compounds. One approach to this task is to screen the evidence using direct analysis in real time mass spectrometry (DART-MS) to make presumptive identifications. This manuscript introduces a new library-search algorithm that enhances presumptive identifications of mixture components using a series of in-source collision-induced dissociation mass spectra collected through DART-MS.

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Facing increasing caseloads and an everchanging drug landscape, forensic laboratories have been implementing new analytical tools. Direct analysis in real time mass spectrometry (DART-MS) is often one of these tools because it provides a wealth of information from a rapid, simple analysis. The data produced by these systems, while extremely useful, can be difficult to interpret, especially in the case of complex mixtures, and therefore, mass spectral databases are often used to assist in interpretation of data.

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Rapid identification of new or emerging psychoactive substances remains a critical challenge in forensic drug chemistry laboratories. Current analytical protocols are well-designed for confirmation of known substances yet struggle when new compounds are encountered. Many laboratories initially attempt to classify new compounds using gas chromatography-electron ionization-mass spectrometry (GC-EI-MS).

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A mass spectral library search algorithm that identifies compounds that differ from library compounds by a single "inert" structural component is described. This algorithm, the Hybrid Similarity Search, generates a similarity score based on matching both fragment ions and neutral losses. It employs the parameter DeltaMass, defined as the mass difference between query and library compounds, to shift neutral loss peaks in the library spectrum to match corresponding neutral loss peaks in the query spectrum.

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A spatially continuous mathematical model of transport processes, anaerobic digestion and microbial complexity as would be expected in the human colon is presented. The model is a system of first-order partial differential equations with context determined number of dependent variables, and stiff, non-linear source terms. Numerical simulation of the model is used to elucidate information about the colon-microbiota complex.

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Fermentation reactor systems are a key platform in studying intestinal microflora, specifically with respect to questions surrounding the effects of diet. In this study, we develop computational representations of colon fermentation reactor systems as a way to assess the influence of three design elements (number of reactors, emptying mechanism, and inclusion of microbial immobilization) on three performance measures (total biomass density, biomass composition, and fibre digestion efficiency) using a fractional-factorial experimental design. It was determined that the choice of emptying mechanism showed no effect on any of the performance measures.

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