Towards automated detection, semi-quantification and identification of microbial growth in clinical bacteriology: A proof of concept.
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ABSTRACT: Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies.Defined monomicrobial and clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and clinical samples.The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and clinical urine samples, despite the presence of multiple species in the clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested.The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.
<h4>Background</h4>Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, ...[more]
Project description:PurposeThe purpose of this paper was to develop a deep learning algorithm to detect retinal vascular leakage (leakage) in fluorescein angiography (FA) of patients with uveitis and use the trained algorithm to determine clinically notable leakage changes.MethodsAn algorithm was trained and tested to detect leakage on a set of 200 FA images (61 patients) and evaluated on a separate 50-image test set (21 patients). The ground truth was leakage segmentation by two clinicians. The Dice Similarity Coefficient (DSC) was used to measure concordance.ResultsDuring training, the algorithm achieved a best average DSC of 0.572 (95% confidence interval [CI] = 0.548-0.596). The trained algorithm achieved a DSC of 0.563 (95% CI = 0.543-0.582) when tested on an additional set of 50 images. The trained algorithm was then used to detect leakage on pairs of FA images from longitudinal patient visits. Longitudinal leakage follow-up showed a >2.21% change in the visible retina area covered by leakage (as detected by the algorithm) had a sensitivity and specificity of 90% (area under the curve [AUC] = 0.95) of detecting a clinically notable change compared to the gold standard, an expert clinician's assessment.ConclusionsThis deep learning algorithm showed modest concordance in identifying vascular leakage compared to ground truth but was able to aid in identifying vascular FA leakage changes over time.Translational relevanceThis is a proof-of-concept study that vascular leakage can be detected in a more standardized way and that tools can be developed to help clinicians more objectively compare vascular leakage between FAs.
Project description:The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a unified model to integrate disparate real-world data (RWD) sources. An integral part of the OMOP CDM is the Standardized Vocabularies (henceforth referred to as the OMOP vocabulary), which enables organization and standardization of medical concepts across various clinical domains of the OMOP CDM. For concepts with the same meaning from different source vocabularies, one is designated as the standard concept, while the others are specified as non-standard or source concepts and mapped to the standard one. However, due to the heterogeneity of source vocabularies, there may exist mapping issues such as erroneous mappings and missing mappings in the OMOP vocabulary, which could affect the results of downstream analyses with RWD. In this paper, we focus on quality assurance of vaccine concept mappings in the OMOP vocabulary, which is necessary to accurately harness the power of RWD on vaccines. We introduce a semi-automated lexical approach to audit vaccine mappings in the OMOP vocabulary. We generated two types of vaccine-pairs: mapped and unmapped, where mapped vaccine-pairs are pairs of vaccine concepts with a "Maps to" relationship, while unmapped vaccine-pairs are those without a "Maps to" relationship. We represented each vaccine concept name as a set of words, and derived term-difference pairs (i.e., name differences) for mapped and unmapped vaccine-pairs. If the same term-difference pair can be obtained by both mapped and unmapped vaccine-pairs, then this is considered as a potential mapping inconsistency. Applying this approach to the vaccine mappings in OMOP, a total of 2087 potentially mapping inconsistencies were obtained. A randomly selected 200 samples were evaluated by domain experts to identify, validate, and categorize the inconsistencies. Experts identified 95 cases revealing valid mapping issues. The remaining 105 cases were found to be invalid due to the external and/or contextual information used in the mappings that were not reflected in the concept names of vaccines. This indicates that our semi-automated approach shows promise in identifying mapping inconsistencies among vaccine concepts in the OMOP vocabulary.
Project description:Fingermarks have, for a long time, been vital in the forensic community for the identification of individuals, and a possibility to non-destructively date the fingermarks would of course be beneficial. Raman spectroscopy is, herein, evaluated for the purpose of estimating the age of fingermarks deposits. Well-resolved spectra were non-destructively acquired to reveal spectral uniqueness, resembling those of epidermis, and several molecular markers were identified that showed different decay kinetics: carotenoids > squalene > unsaturated fatty acids > proteins. The degradation rates were accelerated, less pronounced for proteins, when samples were stored under ambient light conditions, likely owing to photo-oxidation. It is hypothesized that fibrous proteins are present and that oxidation of amino acid side chains can be observed both through Raman and fluorescence spectroscopy. Clearly, Raman spectroscopy is a useful technique to non-destructively study the aging processes of fingermarks.
Project description:We present a feasibility study of sodium quantification in a multicompartment model of the brain using sodium (23Na) magnetic resonance imaging. The proposed method is based on a multipulse sequence acquisition and simulation at 7 T, which allows to differentiate the 23Na signals emanating from three compartments in human brain in vivo: intracellular (compartment 1), extracellular (compartment 2), and cerebrospinal fluid (compartment 3). The intracellular sodium concentration C 1 and the volume fractions α 1, α 2, and α 3 of all respective three brain compartments can be estimated. Simulations of the sodium spin 3/2 dynamics during a 15-pulse sequence were used to optimize the acquisition sequence by minimizing the correlation between the signal evolutions from the three compartments. The method was first tested on a three-compartment phantom as proof-of-concept. Average values of the 23Na quantifications in four healthy volunteer brains were α 1 = 0.54 ± 0.01, α 2 = 0.23 ± 0.01, α 3 = 1.03 ± 0.01, and C 1 = 23 ± 3 mM, which are comparable to the expected physiological values [Formula: see text] ∼ 0.6, [Formula: see text] ∼ 0.2, [Formula: see text] ∼ 1, and [Formula: see text] ∼ 10-30 mM. The proposed method may allow a quantitative assessment of the metabolic role of sodium ions in cellular processes and their malfunctions in brain in vivo.
Project description:Infection-associated inflammatory stress during pregnancy is the most common cause of fetal growth restriction. Treatment strategies for protection of at-risk mothers are limited. Employing mouse models, we demonstrate that oral treatment during pregnancy with a microbial-derived immunomodulator (OM85), markedly reduces risk for fetal loss/growth restriction resulting from maternal challenge with bacterial LPS or influenza. Focusing on LPS exposure, we demonstrate that the key molecular indices of maternal inflammatory stress (RANTES, MIP-1a, CCL2, KC, G-CSF) in gestational tissues/serum, are abrogated by OM85 pretreatment. Systems-level analyses of RNASeq data revealed that OM85 pretreatment selectively tunes LPS-induced activation in maternal gestational tissues for attenuated expression of TNF-, IL1-, and IFNg- driven proinflammatory networks, without constraining Type1-IFN-associated networks central to first-line anti-microbial defense. This study suggests that broad-spectrum protection-of-pregnancy against infection-associated inflammatory stress, without compromising capacity for efficient pathogen eradication, represents an achievable therapeutic goal.
Project description:Subcellular structures exhibit diverse behaviors in different cellular processes, including changes in morphology, abundance, and relative spatial distribution. Faithfully tracking and quantifying these changes are essential to understand their functions. However, most freely accessible methods lack integrated features for tracking multiple objects in different spectral channels simultaneously. To overcome these limitations, we have developed TRACES (Tracking of Active Cellular Structures), a customizable and open-source pipeline capable of detecting, tracking, and quantifying fluorescently labeled cellular structures in up to three spectral channels simultaneously at single-cell level. Here, we detail step-by-step instructions for performing the TRACES pipeline, including image acquisition and segmentation, object identification and tracking, and data quantification and visualization. We believe that TRACES will be a valuable tool for cell biologists, enabling them to track and measure the spatiotemporal dynamics of subcellular structures in a robust and semi-automated manner.
Project description:BackgroundOur group studies the interactions between cells of the brain and the neurotropic parasite Toxoplasma gondii. Using an in vivo system that allows us to permanently mark and identify brain cells injected with Toxoplasma protein, we have identified that Toxoplasma-injected neurons (TINs) are heterogeneously distributed throughout the brain. Unfortunately, standard methods to quantify and map heterogeneous cell populations onto a reference brain atlas are time consuming and prone to user bias.New methodWe developed a novel MATLAB-based semi-automated quantification and mapping program to allow the rapid and consistent mapping of heterogeneously distributed cells on to the Allen Institute Mouse Brain Atlas. The system uses two-threshold background subtraction to identify and quantify cells of interest.ResultsWe demonstrate that we reliably quantify and neuroanatomically localize TINs with low intra- or inter-observer variability. In a follow up experiment, we show that specific regions of the mouse brain are enriched with TINs.Comparison with existing methodsThe procedure we use takes advantage of simple immunohistochemistry labeling techniques, use of a standard microscope with a motorized stage, and low cost computing that can be readily obtained at a research institute. To our knowledge there is no other program that uses such readily available techniques and equipment for mapping heterogeneous populations of cells across the whole mouse brain.ConclusionThe quantification method described here allows reliable visualization, quantification, and mapping of heterogeneous cell populations in immunolabeled sections across whole mouse brains.
Project description:Nanoparticles have been making their way in biomedical applications and personalized medicine, allowing for the coupling of diagnostics and therapeutics into a single nanomaterial-nanotheranostics. Gold nanoparticles, in particular, have unique features that make them excellent nanomaterials for theranostics, enabling the integration of targeting, imaging and therapeutics in a single platform, with proven applicability in the management of heterogeneous diseases, such as cancer. In this review, we focus on gold nanoparticle-based theranostics at the lab bench, through pre-clinical and clinical stages. With few products facing clinical trials, much remains to be done to effectively assess the real benefits of nanotheranostics at the clinical level. Hence, we also discuss the efforts currently being made to translate nanotheranostics into the market, as well as their commercial impact.
Project description:BackgroundThis study aims to evaluate the use of a computer-aided, semi-quantification approach to [18F]F-DOPA positron emission tomography (PET) in pediatric-type diffuse gliomas (PDGs) to calculate the tumor-to-background ratio.MethodsA total of 18 pediatric patients with PDGs underwent magnetic resonance imaging and [18F]F-DOPA PET, which were analyzed using both manual and automated procedures. The former provided a tumor-to-normal-tissue ratio (TN) and tumor-to-striatal-tissue ratio (TS), while the latter provided analogous scores (tn, ts). We tested the correlation, consistency, and ability to stratify grading and survival between these methods.ResultsHigh Pearson correlation coefficients resulted between the ratios calculated with the two approaches: ρ = 0.93 (p < 10-4) and ρ = 0.814 (p < 10-4). The analysis of the residuals suggested that tn and ts were more consistent than TN and TS. Similarly to TN and TS, the automatically computed scores showed significant differences between low- and high-grade gliomas (p ≤ 10-4, t-test) and the overall survival was significantly shorter in patients with higher values when compared to those with lower ones (p < 10-3, log-rank test).ConclusionsThis study suggested that the proposed computer-aided approach could yield similar results to the manual procedure in terms of diagnostic and prognostic information.
Project description:The mouse utricle model system is the best-characterized ex vivo preparation for studies of mature mammalian hair cells (HCs). Despite the many advantages of this model system, efficient and reliable quantification of HCs from cultured utricles has been a persistent challenge with this model system. Utricular HCs are commonly quantified by counting immunolabeled HCs in regions of interest (ROIs) placed over an image of the utricle. Our data indicate that the accuracy of HC counts obtained using this method can be impacted by variability in HC density across different regions of the utricle. In addition, the commonly used HC marker myosin 7a results in a diffuse cytoplasmic stain that is not conducive to automated quantification and must be quantified manually, a labor-intensive task. Furthermore, myosin 7a immunoreactivity is retained in dead HCs, resulting in inaccurate quantification of live HCs using this marker. Here we have developed a method for semi-automated quantification of surviving HCs that combines immunoreactivity for the HC-specific transcription factor Pou4f3 with labeling of activated caspase 3/7 (AC3/7) to detect apoptotic HCs. The discrete nuclear Pou4f3 signal allowed us to utilize the binary or threshold function within ImageJ to automate HC quantification. To further streamline this process, we created an ImageJ macro that automates the process from raw image loading to a final quantified image that can be immediately evaluated for accuracy. Within this quantified image, the user can manually correct the quantification via an image overlay indicating the counted HC nuclei. Pou4f3-positive HCs that also express AC3/7 are subtracted to yield accurate counts of surviving HCs. Overall, we present a semi-automated method that is faster than manual HC quantification and identifies surviving HCs with high accuracy.
