Dataset Information

Morphological and cytokine profiles as key parameters to distinguish between Gram-negative and Gram-positive bacterial keratitis.

ABSTRACT: Bacterial keratitis (BK) is an ocular disorder associated with poor visual prognosis. Quantification of the associated inflammatory response may provide insight into the pathogenesis of BK and guide treatment options. In this exploratory study, we evaluated 45 BK patients and 20 healthy controls by optical coherence tomography and pro-inflammatory tear cytokine analysis. The aim was to quantify the differential morphological and cytokine inflammatory response between Gram-negative and Gram-positive BK and to determine the diagnostic value of corneal thickness (CT) and infiltrate thickness (IT) in distinguishing Gram-ve BK in a clinical cohort. Greater CT and IT, at clinical presentation, were indicative of Gram-ve infection with values detected of ??950 ?m and ??450 ?m, respectively. Combination of these CT and IT values had a 100% sensitivity and 83.3% specificity as a diagnostic indicator of Gram-ve infection. Similarly, there were higher levels of IL-1?, IL-6 and IL-8 cytokines were quantified in keratitis caused by Gram-negative bacteria. Among the different tear cytokines analysed, a significant reduction after three days of treatment was detected for pro-inflammatory cytokines IL-1?, IL-2, IL-6, IL-8 and TNF-?, prior to starting with the administration of steroid drops. Overall, this study shows the potential value of serial OCT and tear cytokine measurements in the management of BK.

SUBMITTER: Konstantopoulos A

PROVIDER: S-EPMC7674500 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Morphological and cytokine profiles as key parameters to distinguish between Gram-negative and Gram-positive bacterial keratitis.

Konstantopoulos Aris A Cendra Maria Del Mar MDM Tsatsos Michael M Elabiary Mariam M Christodoulides Myron M Hossain Parwez P

Scientific reports 20201118 1

Bacterial keratitis (BK) is an ocular disorder associated with poor visual prognosis. Quantification of the associated inflammatory response may provide insight into the pathogenesis of BK and guide treatment options. In this exploratory study, we evaluated 45 BK patients and 20 healthy controls by optical coherence tomography and pro-inflammatory tear cytokine analysis. The aim was to quantify the differential morphological and cytokine inflammatory response between Gram-negative and Gram-posit ...[more]

PMID: 33208864

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Project description:Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria.

Project description:PurposeTo determine whether convolutional neural networks can detect morphological differences between images of microbiologically positive and negative corneal ulcers.MethodsA cross-sectional comparison of prospectively collected data consisting of bacterial and fungal cultures and smears from eyes with acute infectious keratitis at Aravind Eye Hospital. Two convolutional neural network architectures (DenseNet and MobileNet) were trained using images obtained from handheld cameras collected from culture-positive and negative images and smear-positive and -negative images. Each architecture was trained on two image sets: (1) one with labels assigned using only culture results and (2) one using culture and smear results. The outcome measure was area under the receiver operating characteristic curve for predicting whether an ulcer would be microbiologically positive or negative.ResultsThere were 1970 images from 886 patients were included. None of the models were better than random chance at predicting positive microbiologic results (area under the receiver operating characteristic curve ranged from 0.49 to 0.56; all confidence intervals included 0.5).ConclusionsThese two state-of-the-art deep convolutional neural network architectures could not reliably predict whether a corneal ulcer would be microbiologically positive or negative based on clinical photographs. This absence of detectable morphological differences informs the future development of computer vision models trained to predict the causative agent in infectious keratitis using corneal photography.Translational relevanceThese deep learning models were not able to identify morphological differences between microbiologically positive and negative corneal ulcers. This finding suggests that similar artificial intelligence models trained to identify the causative pathogen using only microbiologically positive cases may have potential to generalize well, including to cases with falsely negative microbiologic testing.

Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Project description:Objective: It is unclear whether the host response of gram-positive sepsis differs from gram-negative sepsis at a transcriptome level. Using microarray technology, we compared the gene-expression profiles of gram-positive sepsis and gram-negative sepsis in critically ill patients. Design: A prospective cross-sectional study. Setting: A 20-bed general intensive care unit of a tertiary referral hospital. Patients: Seventy-two patients admitted to the intensive care unit. Interventions: Intravenous blood was collected for leukocyte separation and RNA extraction. Microarray experiements were then performed examing the expression level of 19,232 genes in each sample. Measurements and Main Results: There was no difference in the expression profile between gram-positive and gram-negative sepsis. The finding remained unchanged even when genes with lower expression level were included or after statistical stringency was lowered. There were, however, ninety-four genes differentially expressed between sepsis and control patients. These genes included those involved in immune regulation, inflammation and mitochondrial function. Hierarchical cluster analysis confirmed that the difference in gene expression profile existed between sepsis and control patients, but not between gram-positive and gram-negative patients. Conclusion: Gram-positive and gram-negative sepsis share a common host response at a transcriptome level. These findings support the hypothesis that the septic response is non-specific and is designed to provide a more general response that can be elicited by a wide range of different micro-organisms. The study included seventy-two critically ill patients admitted to the intensive care unit (ICU) of Nepean Hospital, Sydney, Australia. Of these, fifty-five patients were diagnosed to have sepsis, as confirmed by microbiological culture. The remaining seventeen patients did not have sepsis and were therefore used as controls. The study was approved by the hospital ethics committee and informed consent was obtained from all patients or their relatives. Patient Samples. Whole blood was taken from each patient on admission to ICU. Neutrophils were separated from whole blood using density-gradient separation with Ficoll-PaqueP P(Amersham). Subsequent neutrophil RNA extraction was performed using guanidinium thiocyanate (Ambion). Microarray Experiment. The neutrophil RNA was converted to cDNA, fluorescently labeled and hybridized to its complimentary sequences on the microarray (Invitrogen). The fluorescent signals on each micrroarray were captured using the GenePix 4000B laser scanner (Axon Instruments). Expression level of each gene was represented by the intensity of its fluorescent signal. Data Extraction. All signal intensity values were processed using background-subtraction method. Prior to analysis, all values were log-transformed and normalized by fitting a print-tip group Lowess curve. Normalization minimizes bias due to dye chemistry, signal intensity or location of a gene on the array. It ensures the detection of genes that are truly differentially expressed, instead of those caused by experimental artifacts or variation in the hybridization process. After normalization, genes that had more than 50% of data missing were removed. We then selected genes that had at least 80% of the data showing two-fold changes from the geneâs median values. After filtering, 1617 genes were available for further analysis.

Dataset Information

Morphological and cytokine profiles as key parameters to distinguish between Gram-negative and Gram-positive bacterial keratitis.

Publications

Morphological and cytokine profiles as key parameters to distinguish between Gram-negative and Gram-positive bacterial keratitis.

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