Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis

Purpose

To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.

Design

Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965).

Participants

Patients with type 1 DM and controls included in the progenitor study.

Methods

Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types.

Main Outcome Measures

Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types.

Results

A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets.

Conclusions

Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

Salivary microbiota may predict the presence of esophageal squamous cell carcinoma

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P < 0.05). The areas under the ROC curves for Streptococcus salivarius, Fusobacterium nucleatum, Porphyromonas gingivalis and the combination of the three bacteria for predicting patients with ESCC were 69%, 56.5%, 61.8% and 76.4%, respectively. The sensitivities corresponding to cutoff value were 69.3%, 22.7%, 35.2% and 86.4%, respectively, and the matched specificity were 78.4%, 96.1%, 90.2% and 58.8%, respectively. These highly expressed Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in the saliva, alone or in combination, indicate their predictive value for ESCC.

"Q-markers targeted screening" strategy for comprehensive qualitative and quantitative analysis in fingerprints of Angelica dahurica with chemometric methods

Angelica dahurica is a famous functional food and herb. To guarantee quality of A. dahurica, a strategy “Q-markers targeted screening” was successfully developed by sufficient extraction of compounds and the targeted screening of qualitative and quantitative markers calculated through chemometric methods based fingerprints. Accelerated solvent extraction was selected due to its prominent advantages exhibiting the maximum extraction yields and varieties of compounds and especially excellent reproducibility (RSD < 1). After extraction, the fingerprints of A. dahuricae samples were established. For the preliminary herb authenticity, the targeted screening of 23 quantitative markers were performed by similarity analysis and hierarchical cluster analysis based on the fingerprints, which were identified by liquid chromatography tandem mass spectrometry (LC-MS). Subsequently, for further quality control, the targeted screening of nine quantitative markers were done by similarity analysis & linear discriminant analysis, which were determined by LC. Lastly, the strategy was successfully applied to quality assessment of A. dahurica samples.

Validity and reliability of Raman spectroscopy for carotenoid assessment in cattle skin

Carotenoids are powerful antioxidants capable of helping to protect the skin from the damaging effects of exposure to sun by reducing the free radicals in skin produced by exposure to ultraviolet radiation, and they may also have a physical protective effect in human skin. Since carotenoids are lipophilic molecules which can be ingested with the diet, they can accumulate in significant quantities in the skin. Several studies on humans have been conducted to evaluate the protective function of carotenoids against various diseases, but there is very limited published information available to understand the mechanism of carotenoid bioavailability in animals. The current study was conducted to investigate the skin carotenoid level (SCL) in two cattle skin sets - weaners with an unknown feeding regime and New Generation Beef (NGB) cattle with monitored feed at three different ages. Rapid analytical and sensitive Raman spectroscopy has been shown to be of interest as a powerful technique for the detection of carotenoids in cattle skin due to the strong resonance enhancement with 532 nm laser excitation. The spectral difference of both types of skin were measured and quantified using univariate and linear discriminant analysis. SCL was higher in NGB cattle than weaners and there is a perfect classification accuracy between weaners and NGB cattle skin using carotenoid markers as a basis. Further work carried out on carotenoid rich NGB cattle skin of 8, 12 and 24 months of age identified an increasing trend in SCL with age. The present work validated the ability of Raman spectroscopy to determine the skin carotenoid level in cattle by comparing it with established HPLC methods. There is an excellent correlation of R2 = 0.96 between the two methods that could serve as a model for future application for larger population studies.

Novel compound FLZ alleviates rotenone-induced PD mouse model by suppressing TLR4/MyD88/NF-κB pathway through microbiota-gut-brain axis

Parkinson's disease (PD) is the second most common neurodegenerative disease, but none of the current treatments for PD can halt the progress of the disease due to the limited understanding of the pathogenesis. In PD development, the communication between the brain and the gastrointestinal system influenced by gut microbiota is known as microbiota-gut-brain axis. However, the explicit mechanisms of microbiota dysbiosis in PD development have not been well elucidated yet. FLZ, a novel squamosamide derivative, has been proved to be effective in many PD models and is undergoing the phase I clinical trial to treat PD in China. Moreover, our previous pharmacokinetic study revealed that gut microbiota could regulate the absorption of FLZ in vivo. The aims of our study were to assess the protective effects of FLZ treatment on PD and to further explore the underlying microbiota-related mechanisms of PD by using FLZ as a tool. In the current study, chronic oral administration of rotenone was utilized to induce a mouse model to mimic the pathological process of PD. Here we revealed that FLZ treatment alleviated gastrointestinal dysfunctions, motor symptoms, and dopaminergic neuron death in rotenone-challenged mice. 16S rRNA sequencing found that PD-related microbiota alterations induced by rotenone were reversed by FLZ treatment. Remarkably, FLZ administration attenuated intestinal inflammation and gut barrier destruction, which subsequently inhibited systemic inflammation. Eventually, FLZ treatment restored blood-brain barrier structure and suppressed neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra (SN). Further mechanistic research demonstrated that FLZ treatment suppressed the TLR4/MyD88/NF-κB pathway both in the SN and colon. Collectively, FLZ treatment ameliorates microbiota dysbiosis to protect the PD model via inhibiting TLR4 pathway, which contributes to one of the underlying mechanisms beneath its neuroprotective effects. Our research also supports the importance of microbiota-gut-brain axis in PD pathogenesis, suggesting its potential role as a novel therapeutic target for PD treatment.

Polychlorinated biphenyls altered gut microbiome in CAR and PXR knockout mice exhibiting toxicant-associated steatohepatitis

Polychlorinated biphenyls (PCBs) are persistent organic pollutants associated with non-alcoholic fatty liver disease (NAFLD). Previously, we demonstrated that the PCB mixture, Aroclor1260, exacerbated NAFLD, reflective of toxicant-associated steatohepatitis, in diet-induced obese mice, in part through pregnane-xenobiotic receptor (PXR) and constitutive androstane receptor (CAR) activation. Recent studies have also reported PCB-induced changes in the gut microbiome that consequently impact NAFLD. Therefore, the objective of this study is to examine PCB effects on the gut-liver axis and characterize the role of CAR and PXR in microbiome alterations. C57Bl/6 (wildtype, WT), CAR and PXR knockout mice were fed a high fat diet and exposed to Aroclor1260 (20 mg/kg, oral gavage, 12 weeks). Metagenomics analysis of cecal samples revealed that CAR and/or PXR ablation increased bacterial alpha diversity regardless of exposure status. CAR and PXR ablation also increased bacterial composition (beta diversity) versus WT; Aroclor1260 altered beta diversity only in WT and CAR knockouts. Distinct changes in bacterial abundance at different taxonomic levels were observed between WT and knockout groups; however Aroclor1260 had modest effects on bacterial abundance within each genotype. Notably, both knockout groups displayed increased Actinobacteria and Verrucomicrobia abundance. In spite of improved bacterial diversity, the knockout groups however failed to show protection from PCB-induced hepato- and intestinal- toxicity including decreased mRNA levels of ileal permeability markers (occludin, claudin3). In summary, CAR and PXR ablation significantly altered gut microbiome in diet-induced obesity while Aroclor1260 compromised intestinal integrity in knockout mice, implicating interactions between PCBs and CAR, PXR on the gut-liver axis.

Systemic iron reduction by venesection alters the gut microbiome in patients with haemochromatosis

Background & Aims

Iron reduction by venesection has been the cornerstone of treatment for haemochromatosis for decades, and its reported health benefits are many. Repeated phlebotomy can lead to a compensatory increase in intestinal iron absorption, reducing intestinal iron availability. Given that most gut bacteria are highly dependent on iron for survival, we postulated that, by reducing gut iron levels, venesection could alter the gut microbiota.

Methods

Clinical parameters, faecal bacterial composition and metabolomes were assessed before and during treatment in a group of patients with haemochromatosis undergoing iron reduction therapy.

Results

Systemic iron reduction was associated with an alteration of the gut microbiome, with changes evident in those who experienced reduced faecal iron availability with venesection. For example, levels of Faecalibacterium prausnitzii, a bacterium associated with improved colonic health, were increased in response to faecal iron reduction. Similarly, metabolomic changes were seen in association with reduced faecal iron levels.

Conclusion

These findings highlight a significant shift in the gut microbiome of patients who experience reduced colonic iron during venesection. Targeted depletion of faecal iron could represent a novel therapy for metabolic and inflammatory diseases, meriting further investigation.

Lay summary

Iron depletion by repeated venesection is the mainstay of treatment for haemochromatosis, an iron-overload disorder. Venesection has been associated with several health benefits, including improvements in liver function tests, reversal of liver scarring, and reduced risk of liver cancer. During iron depletion, iron absorption from the gastrointestinal (GI) tract increases to compensate for iron lost with treatment. Iron availability is limited in the GI tract and is crucial to the growth and function of many gut bacteria. In this study we show that reduced iron availability in the colon following venesection treatment leads to a change in the composition of the gut bacteria, a finding that, to date, has not been studied in patients with haemochromatosis.

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Venesection is the cornerstone of haemochromatosis treatment.

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Venesection leads to a compensatory increase in intestinal iron absorption.

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Reduced faecal iron availability leads to shifts in human colonic microbial composition.

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Changes in the human colonic metabolome occur with reduced faecal iron availability.

Prebiotics enhance the biotransformation and bioavailability of ginsenosides in rats by modulating gut microbiota

Background

Gut microbiota mainly function in the biotransformation of primary ginsenosides into bioactive metabolites. Herein, we investigated the effects of three prebiotic fibers by targeting gut microbiota on the metabolism of ginsenoside Rb1 in vivo.

Methods

Sprague Dawley rats were administered with ginsenoside Rb1 after a two-week prebiotic intervention of fructooligosaccharide, galactooligosaccharide, and fibersol-2, respectively. Pharmacokinetic analysis of ginsenoside Rb1 and its metabolites was performed, whilst the microbial composition and metabolic function of gut microbiota were examined by 16S rRNA gene amplicon and metagenomic shotgun sequencing.

Results

The results showed that peak plasma concentration and area under concentration time curve of ginsenoside Rb1 and its intermediate metabolites, ginsenoside Rd, F2, and compound K (CK), in the prebiotic intervention groups were increased at various degrees compared with those in the control group. Gut microbiota dramatically responded to the prebiotic treatment at both taxonomical and functional levels. The abundance of Prevotella, which possesses potential function to hydrolyze ginsenoside Rb1 into CK, was significantly elevated in the three prebiotic groups (P < 0.05). The gut metagenomic analysis also revealed the functional gene enrichment for terpenoid/polyketide metabolism, glycolysis, gluconeogenesis, propanoate metabolism, etc.

Conclusion

These findings imply that prebiotics may selectively promote the proliferation of certain bacterial stains with glycoside hydrolysis capacity, thereby, subsequently improving the biotransformation and bioavailability of primary ginsenosides in vivo.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26^th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26^th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26^th week of gestation.

Physico-elemental analysis of roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico using X-ray micro-computed tomography and external beam particle induced X-ray emission

The physico-elemental profiles of commercially attained and roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico were compared using light microscopy, X-ray micro-computed tomography, and external beam particle induced X-ray emission. External beam PIXE analysis detected P, S, Cl, K, Ca, Ti, Mn, Fe, Cu, Zn, Br, Rb, and Sr in samples. Linear discriminant analysis showed that there was no strong association between elemental data and production region, whilst a heatmap combined with hierarchical clustering showed that soil-plant physico-chemical properties may influence regional elemental signatures. Physical trait data showed that Mexican coffee beans weighed significantly more than beans from other regions, whilst Honduras beans had the highest width. X-ray micro-computed tomography qualitative data showed heterogeneous microstructural features within and between beans representing different regions. In conclusion, such multi-dimensional analysis may present a promising tool in assessing the nutritional content and qualitative characteristics of food products such as coffee.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, β-secretase, monoamine oxidase B, glycogen synthase kinase 3β, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Metagenomic Characterization of Microbial Communities In Situ Within the Deeper Layers of the Ileum in Crohn's Disease

BACKGROUND & AIMS

Microbial dysbiosis and aberrant host-microbe interactions in the gut are believed to contribute to the development and progression of Crohn's disease (CD). Microbiome studies in CD typically have focused on microbiota in feces or superficial mucosal layers of the colon because accessing DNA from deeper layers of the bowel is challenging. In this study, we analyzed the deep tissue microbiome in patients who underwent surgical resection of the small intestine.

METHODS

Paraffin blocks were obtained from 12 CD patients undergoing ileocecal resection, and healthy ileum samples (inflammatory bowel disease-free controls) were obtained from 12 patients undergoing surgery for right-sided colon cancer. Diseased and healthy-appearing ileum was identified using microscopy, and paraffin blocks were macrodissected using a core needle to specifically isolate DNA. Illumina Whole Genome Sequencing was used for microbial sequence identification and subsequent taxonomic classification using the PathSeq tool.

RESULTS

We observed significant differences between the microbiome of CD samples vs inflammatory bowel disease-free controls, including depletion of Bacteroidetes and Clostridia. Notably, microbial composition at the phyla level did not differ markedly between healthy and diseased areas of CD patients. However, we observed enrichment of potentially pathogenic organisms at the species level.

CONCLUSIONS

Our study showed dysbiosis within deeper layers of the ileum of CD patients, specifically enrichment of enterotoxigenic Staphylococcus aureus and an environmental Mycobacterium species not described previously. Future studies with larger cohort sizes are warranted to confirm these findings. Studies would benefit from effective microbial DNA extraction methods from paraffin sections and host nucleic acid depletion approaches to increase microbial read coverage.

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.