Optical coherence tomography-derived texture-based radiomics features identify eyes with intraocular inflammation in the HAWK clinical trial.

Background and objective: Intravitreal injection of anti-VEGF agents is the first-line treatment for patients with neovascular-age related macular degeneration (nAMD). One unique serious adverse event that may be associated with these agents is intraocular inflammation (IOI). The main purpose of this analysis was to evaluate the potential presence of texture-based radiomics features characterizing heterogeneity within the vitreous compartment of spectral domain optical coherence tomography (SD-OCT) images that may precede or develop in association with IOI and might serve as OCT biomarkers for IOI. Methods: This is a post-hoc analysis of a subset of cases (N = 67) involving IOI, endophthalmitis, and/or retinal vascular occlusion in the phase 3 HAWK trial. These were investigator determined diagnoses that were also confirmed by the safety review committee. Intraocular inflammation was any signs of inflammation within the eye, endophthalmitis was inflammation associated with presumed infection, and retinal vascular occlusions consisted of intraocular inflammation with concurrent vascular occlusions/vasculitis. Out of 67 eyes, 34 belonged to the Safety group with an IOI event and 33 were propensity-matched Controls. A total of 481 texture-based radiomics features were extracted from the vitreous compartment of the SD-OCT scans at pre-IOI time point (i.e., much earlier than the actual event). Most discriminating five features, selected by the Wilcoxon Rank Sum feature selection were evaluated using Random Forest (RF) classifier on the training set ( S t r , N = 47) to differentiate between the two patient groups. Classifier performance was subsequently validated on the independent test set ( S t , N = 20). Additionally, the classifier performance in discriminating the Control and Safety group was also validated on S t at the IOI event timepoint. Results: The RF classifier yielded area under the Receiver Operating Characteristics curve (AUC) of 0.76 and 0.81 on S t using texture-based radiomics features at pre-IOI and event time-point, respectively. Conclusions: In this analysis, the presence of a pre-IOI safety signal was detected in the form of textural heterogeneity within the vitreous compartment even prior to the actual event being identified by the investigator. This finding may help the clinicians to assess for underlying posterior inflammation.

Combination of optical coherence tomography-derived shape and texture features are associated with development of sub-foveal geographic atrophy in dry AMD.

Geographic atrophy (GA) is an advanced form of dry age-related macular degeneration (AMD) that leads to progressive and irreversible vision loss. Identifying patients with greatest risk of GA progression is important for targeted utilization of emerging therapies. This study aimed to comprehensively evaluate the role of shape-based fractal dimension features ( F fd ) of sub-retinal pigment epithelium (sub-RPE) compartment and texture-based radiomics features ( F t ) of Ellipsoid Zone (EZ)-RPE and sub-RPE compartments for risk stratification for subfoveal GA (sfGA) progression. This was a retrospective study of 137 dry AMD subjects with a 5-year follow-up. Based on sfGA status at year 5, eyes were categorized as Progressors and Non-progressors. A total of 15 shape-based F fd of sub-RPE surface and 494 F t from each of sub-RPE and EZ-RPE compartments were extracted from baseline spectral domain-optical coherence tomography scans. The top nine features were identified from F fd and F t feature pool separately using minimum Redundancy maximum Relevance feature selection and used to train a Random Forest (RF) classifier independently using three-fold cross validation on the training set ( S t , N = 90) to distinguish between sfGA Progressors and Non-progressors. Combined F fd and F t was also evaluated in predicting risk of sfGA progression. The RF classifier yielded AUC of 0.85, 0.79 and 0.89 on independent test set ( S v , N = 47) using F fd , F t , and their combination, respectively. Using combined F fd and F t , the improvement in AUC was statistically significant on S v with p-values of 0.032 and 0.04 compared to using only F fd and only F t , respectively. Combined F fd and F t appears to identify high-risk patients. Our results show that FD and texture features could be potentially used for predicting risk of sfGA progression and future therapeutic response.

Texture-Based Radiomic SD-OCT Features Associated With Response to Anti-VEGF Therapy in a Phase III Neovascular AMD Clinical Trial.

Purpose: The goal of this study was to evaluate the role of texture-based baseline radiomic features (Fr) and dynamic radiomics alterations (delta, FΔr) within multiple targeted compartments on optical coherence tomography (OCT) scans to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration (nAMD). Methods: HAWK is a phase 3 clinical trial data set of active nAMD patients (N = 1082) comparing brolucizumab and aflibercept. This analysis included patients receiving 6 mg brolucizumab or 2 mg aflibercept and categorized as complete responders (n = 280) and incomplete responders (n = 239) based on whether or not the eyes achieved/maintained fluid resolution on OCT. A total of 481 Fr were extracted from each of the fluid, subretinal hyperreflective material (SHRM), retinal tissue, and sub-retinal pigment epithelium (RPE) compartments. Most discriminating eight baseline features, selected by the minimum redundancy, maximum relevance feature selection, were evaluated using a quadratic discriminant analysis (QDA) classifier on the training set (Str, n = 363) to differentiate between the two patient groups. Classifier performance was subsequently validated on independent test set (St, n = 156). Results: In total, 519 participants were included in this analysis from the HAWK phase 3 study. There were 280 complete responders and 219 incomplete responders. Compartmental analysis of radiomics featured identified the sub-RPE and SHRM compartments as the most distinguishing between the two response groups. The QDA classifier yielded areas under the curve of 0.78, 0.79, and 0.84, respectively, using Fr, FΔr, and combined Fr, FΔr, and Fc on St. Conclusions: Utilizing compartmental static and dynamic radiomics features, unique differences were identified between eyes that respond differently to anti-VEGF therapy in a large phase 3 trial that may provide important predictive value. Translational Relevance: Imaging biomarkers, such as radiomics features identified in this analysis, for predicting treatment response are needed to enhanced precision medicine in the management of nAMD.

Novel Fractal-Based Sub-RPE Compartment OCT Radiomics Biomarkers Are Associated With Subfoveal Geographic Atrophy in Dry AMD.

OBJECTIVE: The purpose of this study was to quantitatively characterize the shape of the sub-retinal pigment epithelium (sub-RPE, i.e., space bounded by RPE and Bruch's membrane) compartment on SD-OCT using fractal dimension (FD) features and evaluate their impact on risk of subfoveal geographic atrophy (sfGA) progression. METHODS: This was an IRB-approved retrospective study of 137 subjects with dry age-related macular degeneration (AMD) with subfoveal GA. Based on sfGA status at year five, eyes were categorized as "Progressors" and "Non-progressors". FD analysis allows quantification of the degree of shape complexity and architectural disorder associated with a structure. To characterize the structural irregularities along the sub-RPE surface between the two groups of patients, a total of 15 shape descriptors of FD were extracted from the sub-RPE compartment of baseline OCT scans. The top four features were identified using minimum Redundancy maximum Relevance (mRmR) feature selection method and evaluated with Random Forest (RF) classifier using three-fold cross validation from the training set (N = 90). Classifier performance was subsequently validated on the independent test set (N = 47). RESULTS: Using the top four FD features, a RF classifier yielded an AUC of 0.85 on the independent test set. Mean fractal entropy (p-value = 4.8e-05) was identified as the most significant biomarker; higher values of entropy being associated with greater shape disorder and risk for sfGA progression. CONCLUSIONS: FD assessment holds promise for identifying high-risk eyes for GA progression. SIGNIFICANCE: With further validation, FD features could be potentially used for clinical trial enrichment and assessments for therapeutic response in dry AMD patients.

Proteomic profiling of cell line-derived extracellular vesicles to identify candidate circulatory markers for detection of gallbladder cancer.

Gallbladder cancer (GBC) is the sixth most common gastrointestinal tract cancer with a very low overall survival and poor prognosis. Profiling of cancer-derived extracellular vesicles (EVs) is an emerging strategy for identification of candidate biomarkers for the detection and prognosis of the disease. The aim of the study was to analyse the protein content from GBC cell line- derived EVs with emphasis on proteins which could be used as candidate biomarkers for the detection of GBC. NOZ and OCUG-1 cell lines were cultured and EVs were isolated from conditioned media. LC-MS/MS analysis of total EV proteins led to the identification of a total of 268 proteins in both the cell lines. Of these, 110 proteins were identified with ≥2 unique peptides with ≥2 PSMs in at least two experimental and technical replicate runs. STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) database was used to perform bioinformatics analysis of 110 proteins which showed 'cell adhesion molecule binding', 'integrin binding', 'cadherin binding' among the top molecular functions and 'focal adhesion' to be among the top pathways associated with the EV proteins. A total of 42 proteins including haptoglobin (HP), pyruvate kinase (PKM), annexin A2 (ANXA2), thrombospondin 1 (THBS1), were reported to be differentially abundant in GBC tissue. Of these, 16 proteins were reported to be differentially abundant in plasma and plasma-derived EVs. We infer these proteins to be highly important to be considered as potential circulatory biomarkers for the detection of GBC. To check the validity of this hypothesis, one of the proteins, haptoglobin (HP) as a representative case, was analysed in plasma by quantitative Enzyme- linked immunosorbent assay (ELISA) and we observed its increased levels in GBC in comparison to controls (p value= 0.0063). Receiver operating characteristic (ROC) curve analysis for GBC vs controls showed an Area under the ROC Curve (AUC) of 0.8264 for HP with 22% sensitivity against 100% specificity. We propose that HP along with other candidate proteins may be further explored for their clinical application.

OCT-Derived Radiomic Features Predict Anti-VEGF Response and Durability in Neovascular Age-Related Macular Degeneration.

Purpose: No established biomarkers currently exist for therapeutic efficacy and durability of anti-VEGF therapy in neovascular age-related macular degeneration (nAMD). This study evaluated radiomic-based quantitative OCT biomarkers that may be predictive of anti-VEGF treatment response and durability. Design: Assessment of baseline biomarkers using machine learning (ML) classifiers to predict tolerance to anti-VEGF therapy. Participants: Eighty-one participants with treatment-naïve nAMD from the OSPREY study, including 15 super responders (patients who achieved and maintained retinal fluid resolution) and 66 non-super responders (patients who did not achieve or maintain retinal fluid resolution). Methods: A total of 962 texture-based radiomic features were extracted from fluid, subretinal hyperreflective material (SHRM), and different retinal tissue compartments of OCT scans. The top 8 features, chosen by the minimum redundancy maximum relevance feature selection method, were evaluated using 4 ML classifiers in a cross-validated approach to distinguish between the 2 patient groups. Longitudinal assessment of changes in different texture-based radiomic descriptors (delta-texture features) between baseline and month 3 also was performed to evaluate their association with treatment response. Additionally, 8 baseline clinical parameters and a combination of baseline OCT, delta-texture features, and the clinical parameters were evaluated in a cross-validated approach in terms of association with therapeutic response. Main Outcome Measures: The cross-validated area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to validate the classifier performance. Results: The cross-validated AUC by the quadratic discriminant analysis classifier was 0.75 ± 0.09 using texture-based baseline OCT features. The delta-texture features within different OCT compartments between baseline and month 3 yielded an AUC of 0.78 ± 0.08. The baseline clinical parameters sub-retinal pigment epithelium volume and intraretinal fluid volume yielded an AUC of 0.62 ± 0.07. When all the baseline, delta, and clinical features were combined, a statistically significant improvement in the classifier performance (AUC, 0.81 ± 0.07) was obtained. Conclusions: Radiomic-based quantitative assessment of OCT images was shown to distinguish between super responders and non-super responders to anti-VEGF therapy in nAMD. The baseline fluid and SHRM delta-texture features were found to be most discriminating across groups.

Computational Imaging Biomarker Correlation with Intraocular Cytokine Expression in Diabetic Macular Edema: Radiomics Insights from the IMAGINE Study.

Purpose: Various pathways and cytokines are implicated in pathogenesis of diabetic macular edema (DME). Computational imaging biomarkers (CIBs) of vessel tortuosity from ultra-widefield fluorescein angiography (UWFA) and texture patterns from OCT images have been associated with anti-vascular endothelial growth factor (VEGF) therapy treatment response in DME. This analysis was a radiogenomic assessment of the association between underlying cytokines, UWFA, and OCT-based DME CIBs. Design: Biclustering analysis based on UWFA and OCT CIBs to identify a common imaging phenotype across patients with subsequent assessment of underlying cytokine signatures and treatment response attributes. Participants: The IMAGINE DME study was a post hoc study of cytokine expressions that included 24 eyes with sufficient baseline aqueous humor samples and an in-depth assessment of the imaging studies obtained during the phase I/II DmeAntiVEgf study (DAVE) that measured different cytokine expressions. Methods: A total of 151 graph or morphologic features quantifying leakage shape, size, density, interobject distance, and architecture of leakage spots and 5 vessel tortuosity features were extracted from the baseline UWFA scans, and 494 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images. Biclustering enables simultaneous clustering of patients and features and was used to aggregate patients in terms of their commonality of phenotypes (based on similar imaging attributes) and to identify commonality in terms of cytokine expression and treatment response to anti-VEGF therapy. Main Outcome Measures: Identification of eyes with similar imaging phenotypes to evaluate commonalities of patterns and underlying cytokine expression. Results: Strong correlations between VEGF and 7 UWFA leakage morphologic features (Pearson correlation coefficient [PCC], 0.45-0.51; P < 0.05), 1 vascular tortuosity-based UWFA feature (PCC, 0.45; P = 0.00016), and 2 OCT-derived intraretinal fluid texture features (PCC, 0.58-0.63; P < 0.05) were identified. Strong correlation between intraretinal fluid features and other cytokines (PCC, 0.41-0.59; P < 0.05) were also observed. Conclusions: This study identified groups of eyes with similar imaging phenotypes as defined by UWFA and OCT CIBs that demonstrated similar treatment response patterns and cytokine expression, including a strong association between VEGF with UWFA-derived leakage morphologic and vessel tortuosity features.

Recent advances in loop-mediated isothermal amplification (LAMP) for rapid and efficient detection of pathogens.

Loop-mediated isothermal amplification (LAMP) method has been demonstrated to bea reliable and robust method for detection and identification of viral and microbial pathogens. LAMP method of amplification, coupled with techniques for easy detection of amplicons, makes a simple-to-operate and easy-to-read molecular diagnostic tool for both laboratory and on-field settings. Several LAMP-based diagnostic kits and assays have been developed that are specifically targeted against a variety of pathogens. With the growing needs of the demanding molecular diagnostic industry, many technical advances have been made over the years by combining the basic LAMP principle with several other molecular approaches like real-time detection, multiplex methods, chip-based assays.This has resulted in enhancing thethe sensitivity and accuracy of LAMP for more rigorous and wide-ranging pathogen detection applications. This review summarizes the current developments in LAMP technique and their applicability in present and future disease diagnosis.

Quantitative tissue proteome profile reveals neutrophil degranulation and remodeling of extracellular matrix proteins in early stage gallbladder cancer.

Gallbladder cancer (GBC) is an aggressive malignancy of the gastrointestinal tract with a poor prognosis. It is important to understand the molecular processes associated with the pathogenesis of early stage GBC and identify proteins useful for diagnostic and therapeutic strategies. Here, we have carried out an iTRAQ-based quantitative proteomic analysis of tumor tissues from early stage GBC cases (stage I, n=7 and stage II, n=5) and non-tumor controls (n=6) from gallstone disease (GSD). We identified 357 differentially expressed proteins (DEPs) based on ≥ 2 unique peptides and ≥ 2 fold change with p value < 0.05. Pathway analysis using the STRING database showed, 'neutrophil degranulation' to be the major upregulated pathway that includes proteins such as MPO, PRTN3, S100A8, MMP9, DEFA1, AZU, and 'ECM organization' to be the major downregulated pathway that includes proteins such as COL14A1, COL1A2, COL6A1, COL6A2, COL6A3, BGN, DCN. Western blot and/or IHC analysis confirmed the elevated expression of MPO, PRTN3 and S100A8 in early stage of the disease. Based on the above results, we hypothesize that there is an increased neutrophil infiltration in tumor tissue and neutrophil degranulation leading to degradation of extracellular matrix (ECM) proteins promoting cancer cell invasion in the early stage GBC. Some of the proteins (MPO, MMP9, DEFA1) associated with 'neutrophil degranulation' showed the presence of 'signal sequence' suggesting their potential as circulatory markers for early detection of GBC. Overall, the study presents a protein dataset associated with early stage GBC.

Evaluating the utility of deep learning for predicting therapeutic response in diabetic eye disease.

Purpose: Deep learning (DL) is a technique explored within ophthalmology that requires large datasets to distinguish feature representations with high diagnostic performance. There is a need for developing DL approaches to predict therapeutic response, but completed clinical trial datasets are limited in size. Predicting treatment response is more complex than disease diagnosis, where hallmarks of treatment response are subtle. This study seeks to understand the utility of DL for clinical problems in ophthalmology such as predicting treatment response and where large sample sizes for model training are not available. Materials and Methods: Four DL architectures were trained using cross-validated transfer learning to classify ultra-widefield angiograms (UWFA) and fluid-compartmentalized optical coherence tomography (OCT) images from a completed clinical trial (PERMEATE) dataset (n=29) as tolerating or requiring extended interval Anti-VEGF dosing. UWFA images (n=217) from the Anti-VEGF study were divided into five increasingly larger subsets to evaluate the influence of dataset size on performance. Class activation maps (CAMs) were generated to identify regions of model attention. Results: The best performing DL model had a mean AUC of 0.507 ± 0.042 on UWFA images, and highest observed AUC of 0.503 for fluid-compartmentalized OCT images. DL had a best performing AUC of 0.634 when dataset size was incrementally increased. Resulting CAMs show inconsistent regions of interest. Conclusions: This study demonstrated the limitations of DL for predicting therapeutic response when large datasets were not available for model training. Our findings suggest the need for hand-crafted approaches for complex and data scarce prediction problems in ophthalmology.

Plasma-derived candidate biomarkers for detection of gallbladder carcinoma.

Gallbladder carcinoma (GBC) is a major cancer of the gastrointestinal tract with poor prognosis. Reliable and affordable biomarker-based assays with high sensitivity and specificity for the detection of this cancer are a clinical need. With the aim of studying the potential of the plasma-derived extracellular vesicles (EVs), we carried out quantitative proteomic analysis of the EV proteins, using three types of controls and various stages of the disease, which led to the identification of 86 proteins with altered abundance. These include 29 proteins unique to early stage, 44 unique to the advanced stage and 13 proteins being common to both the stages. Many proteins are functionally relevant to the tumor condition or have been also known to be differentially expressed in GBC tissues. Several of them are also present in the plasma in free state. Clinical verification of three tumor-associated proteins with elevated levels in comparison to all the three control types-5'-nucleotidase isoform 2 (NT5E), aminopeptidase N (ANPEP) and neprilysin (MME) was carried out using individual plasma samples from early or advanced stage GBC. Sensitivity and specificity assessment based on receiver operating characteristic (ROC) analysis indicated a significant association of NT5E and ANPEP with advanced stage GBC and MME with early stage GBC. These and other proteins identified in the study may be potentially useful for developing new diagnostics for GBC.

Quantitative Imaging Biomarkers in Age-Related Macular Degeneration and Diabetic Eye Disease: A Step Closer to Precision Medicine.

The management of retinal diseases relies heavily on digital imaging data, including optical coherence tomography (OCT) and fluorescein angiography (FA). Targeted feature extraction and the objective quantification of features provide important opportunities in biomarker discovery, disease burden assessment, and predicting treatment response. Additional important advantages include increased objectivity in interpretation, longitudinal tracking, and ability to incorporate computational models to create automated diagnostic and clinical decision support systems. Advances in computational technology, including deep learning and radiomics, open new doors for developing an imaging phenotype that may provide in-depth personalized disease characterization and enhance opportunities in precision medicine. In this review, we summarize current quantitative and radiomic imaging biomarkers described in the literature for age-related macular degeneration and diabetic eye disease using imaging modalities such as OCT, FA, and OCT angiography (OCTA). Various approaches used to identify and extract these biomarkers that utilize artificial intelligence and deep learning are also summarized in this review. These quantifiable biomarkers and automated approaches have unleashed new frontiers of personalized medicine where treatments are tailored, based on patient-specific longitudinally trackable biomarkers, and response monitoring can be achieved with a high degree of accuracy.

Multi-Compartment Spatially-Derived Radiomics From Optical Coherence Tomography Predict Anti-VEGF Treatment Durability in Macular Edema Secondary to Retinal Vascular Disease: Preliminary Findings.

OBJECTIVE: Diabetic macular edema (DME) and retinal vein occlusion (RVO) are the leading causes of visual impairments across the world. Vascular endothelial growth factor (VEGF) stimulates breakdown of blood-retinal barrier that causes accumulation of fluid within macula. Anti-VEGF therapy is the first-line treatment for both the diseases; however, the degree of response varies for individual patients. The main objective of this work was to identify the (i) texture-based radiomics features within individual fluid and retinal tissue compartments of baseline spectral-domain optical coherence tomography (SD-OCT) images and (ii) the specific spatial compartments that contribute most pertinent features for predicting therapeutic response. METHODS: A total of 962 texture-based radiomics features were extracted from each of the fluid and retinal tissue compartments of OCT images, obtained from the PERMEATE study. Top-performing features selected from the consensus of different feature selection methods were evaluated in conjunction with four different machine learning classifiers: Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF), and Support Vector Machine (SVM) in a cross-validated approach to distinguish eyes tolerating extended interval dosing (non-rebounders) and those requiring more frequent dosing (rebounders). RESULTS: Combination of fluid and retinal tissue features yielded a cross-validated area under receiver operating characteristic curve (AUC) of 0.78±0.08 in distinguishing rebounders from non-rebounders. CONCLUSIONS: This study revealed that the texture-based radiomics features pertaining to IRF subcompartment were most discriminating between rebounders and non-rebounders to anti-VEGF therapy. Clinical Impact: With further validation, OCT-based imaging biomarkers could be used for treatment management of DME patients.

Development of a Loop-mediated isothermal amplification (LAMP) technique for specific and early detection of Mycobacterium leprae in clinical samples.

Leprosy, a progressive, mutilating and highly stigmatized disease caused by Mycobacterium leprae (ML), continues to prevail in the developing world. This is due to the absence of rapid, specific and sensitive diagnostic tools for its early detection since the disease gets notified only with the advent of physical scarring in patients. This study reports the development of a Loop-mediated isothermal amplification (LAMP) technique for fast, sensitive and specific amplification of 16S rRNA gene of ML DNA for early detection of leprosy in resource-limited areas. Various parameters were optimized to obtain robust and reliable amplification of ML DNA. Blind clinical validation studies were performed which showed that this technique had complete concurrence with conventional techniques. Total absence of amplification of negative control DNA confirmed the specificity of this test. Various visual detection methods viz. colorimetric, turbidity differentiation and bridge flocculation were standardized to establish easy-to-read and rapid diagnosis. This technique eliminates the lack of accuracy and sensitivity in skin smear tests in patients and the requirement for expensive lab equipments and trained technicians. The technique holds promise for further expansion and has the potential to cater to the unmet needs of society for a cheap, highly-sensitive and robust rapid diagnosis of ML.

Characterization of Ultra-Widefield Angiographic Vascular Features in Diabetic Retinopathy with Automated Severity Classification.

PURPOSE: To determine the association between diabetic retinopathy (DR) severity and quantitative retinal vascular features. DESIGN: Retrospective image analysis study. PARTICIPANTS: Eyes with DR and eyes with no posterior segment disease (normal eyes) that had undergone ultra-widefield fluorescein angiography (UWFA) with associated color fundus photography. Exclusion criteria were any previous laser photocoagulation, low image quality, intravitreal or periocular pharmacotherapy within 6 months of imaging, and any other significant retinal disease including posterior uveitis, retinal vein occlusion, and choroidal neovascularization. METHODS: The centered early mid-phase UWFA frame that captured the maximum vessel area was selected using automated custom software for each eye. Panretinal and zonal vascular features were extracted using a machine learning algorithm. Eyes with DR were graded for DR severity as mild nonproliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). Parameters of normal eyes were compared with age- and gender-matched patients with DR using the t test. Differences between severity groups were evaluated by the analysis of variance and Kruskal-Wallis tests, generalized linear mixed-effects models, and random forest regression models. MAIN OUTCOME MEASURES: Diabetic retinopathy severity and vascular features (panretinal and zonal vessel area, length and geodesic distance, panretinal area index, tortuosity measures, vascular density measures, and zero vessel density rate). RESULTS: Ninety-seven eyes from 60 patients with DR and 12 normal eyes from 12 patients that underwent UWFA for evaluation of fellow eye pathology had images of sufficient quality to be included in this analysis. The mean age was 60 ± 10 years in DR eyes and 46 ± 17 years in normal eyes. Panretinal vessel area, mean geodesic distance, skewness, and kurtosis of local vessel density was significantly higher in normal eyes compared with the age- and gender-matched eyes with DR (P < 0.05). Zero vessel density rate, skewness of vessel density, and mean mid-peripheral geodesic distance were among the most important features for distinguishing mild NPDR from advanced forms of DR and PDR versus eyes without PDR. CONCLUSIONS: Automated analysis of retinal vasculature demonstrated associations with DR severity and visual and subvisual vascular biomarkers. Further studies are needed to evaluate the clinical significance of these parameters for DR prognosis and therapeutic response.

Immunoproteomics approach revealed elevated autoantibody levels against ANXA1 in early stage gallbladder carcinoma.

BACKGROUND: Early diagnosis is important for the timely treatment of gallbladder carcinoma (GBC) patients and may lead to increased survival outcomes. Here, we have applied serological proteome analysis (SERPA), an immunoproteomics approach, for the detection of 'tumor-associated antigens (TAAs) that elicit humoral response' in early stage GBC patients. METHODS: Total protein from pooled tumor tissue of GBC patients (n = 7) was resolved by two-dimensional gel electrophoresis (2-DE) followed by immunoblotting using pooled blood plasma from healthy volunteers (n = 11) or gallstone disease (GSD) cases (n = 11) or early stage GBC (Stage I and II) (n = 5) or GBC stage IIIA (n = 9). 2-D gel and immunoblot images were acquired and analyzed using PDQuest software to identify immunoreactive spots in GBC cases in comparison to controls. Proteins from immunoreactive spots were identified by liquid chromatography- tandem mass spectrometric analysis (LC-MS/MS). Autoantibody levels for two of the functionally relevant proteins were investigated in individual plasma samples (52 cases and 89 controls) by dot blot assay using recombinant proteins. RESULTS: Image analysis using PDQuest software identified 25 protein spots with significantly high or specific immunoreactivity in GBC cases. Mass spectrometric analysis of 8 corresponding protein spots showing intense immunoreactivity (based on densitometric analysis) in early stage GBC or GBC stage IIIA cases led to the identification of 27 proteins. Some of the identified proteins include ANXA1, HSPD1, CA1, CA2, ALDOA and CTSD. Among the two proteins, namely ANXA1 and HSPD1 verified using a cohort of samples, significantly elevated autoantibody levels against ANXA1 were observed in early stage GBC cases in comparison to healthy volunteers or GSD cases (unpaired t-test, p < 0.05). Receiver operating characteristic (ROC) curve analysis for ANXA1 showed an Area under the Curve (AUC) of 0.69, with 41.7% sensitivity against a specificity of 89.9% for early stage GBC. IHC analysis for ANXA1 protein showed 'high' expression levels in 72% of GBC cases whereas all the controls showed 'low' expression levels. CONCLUSIONS: The study suggests that the ANXA1 autoantibody levels against ANXA1 may be potentially employed for early stage detection of GBC patients. Other proteins could also be explored and verified in a large cohort of clinical samples.

Persistent peripheral presence of Staphylococcus aureus promotes histone H3 hypoacetylation and decreases tyrosine hydroxylase protein level in rat brain tissues.

OBJECTIVE: Growing evidences suggest systemic pathogen-induced neuroimmune interaction is a major risk factor for several neurological disorders. Our goal was to investigate whether asymptomatic peripheral carriage of Staphylococcus aureus, a widespread opportunistic pathogen, could modulate selective molecular features in brain tissues. METHODS: To address this, a peripheral infection model was developed by challenging Wistar rats repeatedly with a clinical strain of S. aureus. Animals infected with S. aureus (10 CFU for three times in 10 days) showed significant changes in acetylation profile of selective lysine (K) residues K9 (H3K9), K14 (H3K14) and K27 (H3K27) of histone H3 in the hippocampus and prefrontal cortex (PFC). RESULTS: Although S. aureus was restricted peripherally, the infection induced hypoacetylation of H3K9, H3K14 and H3K27 in the hippocampus and H3K27 in the PFC. Histone H3 hypoacetylation in the hippocampus and PFC was also detected when rats were challenged with an engineered invasive strain of E. coli K12, SK3842. This confirmed that modulation of epigenetic landscape in distal brain tissues may not be specific to S. aureus. Moreover, the tyrosine hydroxylase protein, the rate limiting enzyme in dopamine synthesis pathway whose gene-expression is regulated by H3 acetylation at the promoter, was remarkably reduced in the brain tissues of the infected hosts. CONCLUSION: The results indicate that commensals like S. aureus, in spite of being largely restricted to the peripheral tissues, could modulate the homeostasis of molecular features in brain tissues whose maintenance is critical for preserving normal neurological functions.

Automatic Detection of Retinal Lesions for Screening of Diabetic Retinopathy.

OBJECTIVE: Diabetic retinopathy (DR) is characterized by the progressive deterioration of retina with the appearance of different types of lesions that include microaneurysms, hemorrhages, exudates, etc. Detection of these lesions plays a significant role for early diagnosis of DR. METHODS: To this aim, this paper proposes a novel and automated lesion detection scheme, which consists of the four main steps: vessel extraction and optic disc removal, preprocessing, candidate lesion detection, and postprocessing. The optic disc and the blood vessels are suppressed first to facilitate further processing. Curvelet-based edge enhancement is done to separate out the dark lesions from the poorly illuminated retinal background, while the contrast between the bright lesions and the background is enhanced through an optimally designed wideband bandpass filter. The mutual information of the maximum matched filter response and the maximum Laplacian of Gaussian response are then jointly maximized. Differential evolution algorithm is used to determine the optimal values for the parameters of the fuzzy functions that determine the thresholds of segmenting the candidate regions. Morphology-based postprocessing is finally applied to exclude the falsely detected candidate pixels. RESULTS AND CONCLUSIONS: Extensive simulations on different publicly available databases highlight an improved performance over the existing methods with an average accuracy of and robustness in detecting the various types of DR lesions irrespective of their intrinsic properties.

Melatonin pre-treatment mitigates SHSY-5Y cells against oxaliplatin induced mitochondrial stress and apoptotic cell death.

Oxaliplatin (Oxa) treatment to SH-SY5Y human neuroblastoma cells has been shown by previous studies to induce oxidative stress, which in turn modulates intracellular signaling cascades resulting in cell death. While this phenomenon of Oxa-induced neurotoxicity is known, the underlying mechanisms involved in this cell death cascade must be clarified. Moreover, there is still little known regarding the roles of neuronal mitochondria and cytosolic compartments in mediating Oxa-induced neurotoxicity. With a better grasp of the mechanisms driving neurotoxicity in Oxa-treated SH-SY5Y cells, we can then identify certain pathways to target in protecting against neurotoxic cell damage. Therefore, the purpose of this study was to determine whether one such agent, melatonin (Mel), could confer protection against Oxa-induced neurotoxicity in SH-SY5Y cells. Results from the present study found Oxa to significantly reduce SH-SY5Y cell viability in a dose-dependent manner. Alternatively, we found Mel pre-treatment to SH-SY5Y cells to attenuate Oxa-induced toxicity, resulting in a markedly increased cell viability. Mel exerted its protective effects by regulating reactive oxygen species (ROS) production and reducing superoxide radicals inside Oxa-exposed. In addition, we observed pre-treatment with Mel to rescue Oxa-treated cells by protecting mitochondria. As Oxa-treatment alone decreases mitochondrial membrane potential (Δψm), resulting in an altered Bcl-2/Bax ratio and release of sequestered cytochrome c, so Mel was shown to inhibit these pathways. Mel was also found to inhibit proteolytic activation of caspase 3, inactivation of Poly (ADP Ribose) polymerase, and DNA damage, thereby allowing SH-SY5Y cells to resist apoptotic cell death. Collectively, our results suggest a role for melatonin in reducing Oxa induced neurotoxicity. Further studies exploring melatonin's protective effects may prove successful in eliciting pathways to further alter the neurotoxic pathways of platinum compounds in cancer treatment.

Induction of intestinal stemness and tumorigenicity by aberrant internalization of commensal non-pathogenic E. coli.

Commensal Escherichia coli has been identified as a major protagonist of microbe-induced colorectal oncogenesis. Its tumour-promoting attribute is linked to the expression of DNA-damaging genotoxins. Using a constitutively invasive variant of non-pathogenic E. coli, we demonstrate that chronic presence of internalized E. coli leads to enhanced oncogenicity in colon cancer cells. Instead of genomic damage, the tumorigenic effect is mediated through an expansion of the cancer stem cell (CSC) population, likely through dedifferentiation of lineage-committed intestinal epithelial cells. Stemness-linked intestinal tumorigenicity is directly correlated to absence of microbial virulence factor expression and is specific for intestinal cells. The enriched CSC fraction remains stable in the absence of the instigating bacteria and can foster stemness traits in unexposed cells through secreted factors. Mechanistically, aberrant host invasion leads to realignment of multiple host signal transduction cascades, notably mutually re-enforcing NF-κB and ß-catenin activation, through reciprocal modulation of microbe sensing pathways Nod1/Rip2 and TLR/MyD88. The expanded tumorigenic CSC population is marked by enhanced malignancy traits, long-term self-renewal capacity and robust tumorigenic ability, both in vitro and in vivo. Our study shows that microbe-induced oncogenicity is not a strict correlate of commensal virulence and can be invoked by even non-pathogenic E. coli by engendering tumorigenic stemness in host cells.