The Risk of Postoperative Complications After Major Elective Surgery in Active or Resolved COVID-19 in the United States.

OBJECTIVE: To assess the association between the timing of surgery relative to the development of Covid-19 and the risks of postoperative complications. SUMMARY BACKGROUND DATA: It is unknown whether patients who recovered from Covid-19 and then underwent a major elective operation have an increased risk of developing postoperative complications. METHODS: The risk of postoperative complications for patients with Covid-19 undergoing 18 major types of elective operations in the Covid-19 Research Database was evaluated using multivariable logistic regression. Patients were grouped by time of surgery relative to SARS-CoV-2 infection; that is, surgery performed: (1) before January 1, 2020 ("pre-Covid-19"), (2) 0 to 4 weeks after SARS-CoV-2 infection ("peri-Covid-19"), (3) 4 to 8 weeks after infection ("early post-Covid-19"), and (4) ≥8 weeks after infection ("late post-Covid-19"). RESULTS: Of the 5479 patients who met study criteria, patients with peri-Covid-19 had an elevated risk of developing postoperative pneumonia [adjusted odds ratio (aOR), 6.46; 95% confidence interval (CI): 4.06-10.27], respiratory failure (aOR, 3.36; 95% CI: 2.22-5.10), pulmonary embolism (aOR, 2.73; 95% CI: 1.35-5.53), and sepsis (aOR, 3.67; 95% CI: 2.18-6.16) when compared to pre-Covid-19 patients. Early post-Covid-19 patients had an increased risk of developing postoperative pneumonia when compared to pre-Covid-19 patients (aOR, 2.44; 95% CI: 1.20-4.96). Late post-Covid-19 patients did not have an increased risk of postoperative complications when compared to pre-Covid-19 patients. CONCLUSIONS: Major, elective surgery 0 to 4 weeks after SARS-CoV-2 infection is associated with an increased risk of postoperative complications. Surgery performed 4 to 8 weeks after SARS-CoV-2 infection is still associated with an increased risk of postoperative pneumonia, whereas surgery 8 weeks after Covid-19 diagnosis is not associated with increased complications.

Sustained Inhibition of NF-κB Activity Mitigates Retinal Vasculopathy in Diabetes.

This study investigated the effects of long-term NF-κB inhibition in mitigating retinal vasculopathy in a type 1 diabetic mouse model (Akita, Ins2Akita). Akita and wild-type (C57BL/6J) male mice, 24 to 26 weeks old, were treated with or without a selective inhibitor of NF-κB, 4-methyl-N1-(3-phenyl-propyl) benzene-1,2-diamine (JSH-23), for 4 weeks. Treatment was given when the mice were at least 24 weeks old. Metabolic parameters, key inflammatory mediators, blood-retinal barrier junction molecules, retinal structure, and function were measured. JSH-23 significantly lowered basal glucose levels and intraocular pressure in Akita. It also mitigated vascular remodeling and microaneurysms significantly. Optical coherence tomography of untreated Akita showed thinning of retinal layers; however, treatment with JSH-23 could prevent it. Electroretinogram demonstrated that A- and B-waves in Akita were significantly smaller than in wild type mice, indicating that JSH-23 intervention prevented loss of retinal function. Protein levels and gene expression of key inflammatory mediators, such as NOD-like receptor family pyrin domain-containing 3, intercellular adhesion molecule-1, inducible nitric oxide synthase, and cyclooxygenase-2, were decreased after JSH-23 treatment. At the same time, connexin-43 and occludin were maintained. Vision-guided behavior also improved significantly. The results show that reducing inflammation could protect the diabetic retina and its vasculature. Findings appear to have broader implications in treating not only ocular conditions but also other vasculopathies.

Virus Dynamics and Decay in Evaporating Human Saliva Droplets on Fomites.

The transmission of most respiratory pathogens, including SARS-CoV-2, occurs via virus-containing respiratory droplets, and thus, factors that affect virus viability in droplet residues on surfaces are of critical medical and public health importance. Relative humidity (RH) is known to play a role in virus survival, with a U-shaped relationship between RH and virus viability. The mechanisms affecting virus viability in droplet residues, however, are unclear. This study examines the structure and evaporation dynamics of virus-containing saliva droplets on fomites and their impact on virus viability using four model viruses: vesicular stomatitis virus, herpes simplex virus 1, Newcastle disease virus, and coronavirus HCoV-OC43. The results support the hypothesis that the direct contact of antiviral proteins and virions within the "coffee ring" region of the droplet residue gives rise to the observed U-shaped relationship between virus viability and RH. Viruses survive much better at low and high RH, and their viability is substantially reduced at intermediate RH. A phenomenological theory explaining this phenomenon and a quantitative model analyzing and correlating the experimentally measured virus survivability are developed on the basis of the observations. The mechanisms by which RH affects virus viability are explored. At intermediate RH, antiviral proteins have optimal influence on virions because of their largest contact time and overlap area, which leads to the lowest level of virus activity.

Long-Term Follow-Up of Outer Retinal Layers in Patients with Epiretinal Membranes.

PURPOSE: This study aimed to investigate the long-term effect of observed epiretinal membranes on the outer retinal layers and visual acuity. METHODS: It is a retrospective observational study. Subjects with an epiretinal membrane and consecutive optical coherence tomography scans were followed for changes in visual acuity, central macular thickness, ellipsoid zone loss, and outer foveal thickness (OFT). RESULTS: The study consisted of 24 eyes of 22 patients, with a mean follow-up of 5 ± 1.6 years. The mean visual acuity was slightly worse at the last follow-up (0.22 ± 0.36 LogMAR [20/33] vs. 0.27 ± 0.36 LogMAR [20/36], p = 0.05). Ellipsoid zone loss was found in 37.5% of eyes. Vision loss was associated with initial size of ellipsoid disruption (p = 0.048) and age (p = 0.027). A decrease in OFT was associated with an initially larger zone of ellipsoid disruption (p = 0.006) and an initially thicker OFT (p = 0.011). An epiretinal membrane associated with vitreomacular adhesion within 1,000 µm of the foveal center at baseline was associated with ellipsoid zone loss (p = 0.012) but not with a change in visual acuity. CONCLUSIONS: Ellipsoid zone changes were common in this study and tended to enlarge over time. Epiretinal membranes associated with vitreomacular adhesion within 1,000 µm of the foveal center may be a risk factor for ellipsoid zone loss.

A strategy to identify event specific hospitalizations in large health claims databases.

BACKGROUND: Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. METHODS: In this paper, we propose a framework for classifying hospitalizations due to a specific event. We then tested this framework in a private health insurance claims database (Symphony) with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age. RESULTS: Across all ages (18 +) the total percentage of Symphony patients who met our definition of hospitalized due to COVID-19 was 7.3% which was similar to the CDC's estimate of 7.5%. By age group, defined by the CDC, our estimates vs. the CDC's estimates were 18-49: 2.7% vs. 3%, 50-64: 8.2% vs. 9.2%, and 65 + : 14.6% vs. 28.1%. CONCLUSIONS: The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.

Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques.

Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.

The Role of Different Retinal Imaging Modalities in Predicting Progression of Diabetic Retinopathy: A Survey.

Diabetic retinopathy (DR) is a devastating condition caused by progressive changes in the retinal microvasculature. It is a leading cause of retinal blindness in people with diabetes. Long periods of uncontrolled blood sugar levels result in endothelial damage, leading to macular edema, altered retinal permeability, retinal ischemia, and neovascularization. In order to facilitate rapid screening and diagnosing, as well as grading of DR, different retinal modalities are utilized. Typically, a computer-aided diagnostic system (CAD) uses retinal images to aid the ophthalmologists in the diagnosis process. These CAD systems use a combination of machine learning (ML) models (e.g., deep learning (DL) approaches) to speed up the diagnosis and grading of DR. In this way, this survey provides a comprehensive overview of different imaging modalities used with ML/DL approaches in the DR diagnosis process. The four imaging modalities that we focused on are fluorescein angiography, fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). In addition, we discuss limitations of the literature that utilizes such modalities for DR diagnosis. In addition, we introduce research gaps and provide suggested solutions for the researchers to resolve. Lastly, we provide a thorough discussion about the challenges and future directions of the current state-of-the-art DL/ML approaches. We also elaborate on how integrating different imaging modalities with the clinical information and demographic data will lead to promising results for the scientists when diagnosing and grading DR. As a result of this article's comparative analysis and discussion, it remains necessary to use DL methods over existing ML models to detect DR in multiple modalities.

Regulation of the parental gene GRM4 by circGrm4 RNA transcript and glutamate-mediated neurovascular toxicity in eyes.

Epigenetic memory plays crucial roles in gene regulation. It not only modulates the expression of specific genes but also has ripple effects on transcription as well as translation of other genes. Very often an alteration in expression occurs either via methylation or demethylation. In this context, "1-carbon metabolism" assumes a special significance since its dysregulation by higher levels of homocysteine; Hcy (known as hyperhomocysteinemia; HHcy), a byproduct of "1-Carbon Metabolism" during methionine biosynthesis leads to serious implications in cardiovascular, renal, cerebrovascular systems, and a host of other conditions. Currently, the circular RNAs (circRNAs) generated via non-canonical back-splicing events from the pre-mRNA molecules are at the center stage for their essential roles in diseases via their epigenetic manifestations. We recently identified a circular RNA transcript (circGRM4) that is significantly upregulated in the eye of cystathionine ß-synthase-deficient mice. We also discovered a concurrent over-expression of the mGLUR4 receptor in the eyes of these mice. In brief, circGRM4 is selectively transcribed from its parental mGLUR4 receptor gene (GRM4) functions as a "molecular-sponge" for the miRNAs and results into excessive turnover of the mGLUR4 receptor in the eye in response to extremely high circulating glutamate concentration. We opine that this epigenetic manifestation potentially predisposes HHcy people to retinovascular malfunctioning.

An Automated CAD System for Accurate Grading of Uveitis Using Optical Coherence Tomography Images.

Uveitis is one of the leading causes of severe vision loss that can lead to blindness worldwide. Clinical records show that early and accurate detection of vitreous inflammation can potentially reduce the blindness rate. In this paper, a novel framework is proposed for automatic quantification of the vitreous on optical coherence tomography (OCT) with particular application for use in the grading of vitreous inflammation. The proposed pipeline consists of two stages, vitreous region segmentation followed by a neural network classifier. In the first stage, the vitreous region is automatically segmented using a U-net convolutional neural network (U-CNN). For the input of U-CNN, we utilized three novel image descriptors to account for the visual appearance similarity of the vitreous region and other tissues. Namely, we developed an adaptive appearance-based approach that utilizes a prior shape information, which consisted of a labeled dataset of the manually segmented images. This image descriptor is adaptively updated during segmentation and is integrated with the original greyscale image and a distance map image descriptor to construct an input fused image for the U-net segmentation stage. In the second stage, a fully connected neural network (FCNN) is proposed as a classifier to assess the vitreous inflammation severity. To achieve this task, a novel discriminatory feature of the segmented vitreous region is extracted. Namely, the signal intensities of the vitreous are represented by a cumulative distribution function (CDF). The constructed CDFs are then used to train and test the FCNN classifier for grading (grade from 0 to 3). The performance of the proposed pipeline is evaluated on a dataset of 200 OCT images. Our segmentation approach documented a higher performance than related methods, as evidenced by the Dice coefficient of 0.988 ± 0.01 and Hausdorff distance of 0.0003 mm ± 0.001 mm. On the other hand, the FCNN classification is evidenced by its average accuracy of 86%, which supports the benefits of the proposed pipeline as an aid for early and objective diagnosis of uvea inflammation.

Clinical, radiographic, and intraoperative risk factors for retinal detachment after open globe injury.

PURPOSE: Retinal detachment (RD) is a vision-threatening complication of open globe injuries (OGI). This study sought to assess clinical, radiographic, and intraoperative risk factors for RD after OGI. A secondary goal was to test the retinal detachment after open globe injury (RD-OGI) score. METHODS: Records of patients undergoing OGI repair at a single trauma center over 3 years were reviewed using a retrospective case series design. Eyes that were enucleated or lost to follow up within 30 days of OGI without evidence of RD were excluded. Potential risk factors for RD development were assessed by logistic regression or chi-square tests were appropriate and were entered into a multivariate logistic regression if significant on univariate analysis. Risk of RD for each eye was categorized by its RD-OGI score. RESULTS: Seventy-three eyes (72 patients) were included. In univariate analysis, afferent pupillary defect, worse visual acuity, posterior injury, vitreous hemorrhage, and posterior segment volume loss (PSVL) on CT were strong predictors of RD. In multivariate analysis, only PSVL on CT (adjusted OR 10.8, P = 0.025) maintained a statistically significant association with RD risk. At 1 year, 5% of low-risk eyes, 20% of moderate-risk eyes, and 67% of high-risk eyes developed RD. These rates were not significantly different from the RD-OGI derivation or validation cohorts (P = 0.90 and P = 0.67, respectively). CONCLUSION: PSVL on CT increases the risk of RD after OGI. The RD-OGI Score was a good prognostic tool for assessing RD risk after OGI in this population.

Circular RNAs constitute an inherent gene regulatory axis in the mammalian eye and brain 1.

Circular RNAs (circRNAs) are being hailed as a newly rediscovered class of covalently closed transcripts that are produced via alternative, noncanonical pre-mRNA back-splicing events. These single-stranded RNA molecules have been identified in organisms ranging from the worm (Cortés-López et al. 2018. BMC Genomics, 19: 8; Ivanov et al. 2015. Cell Rep. 10: 170-177) to higher eukaryotes (Yang et al. 2017. Cell Res. 27: 626-641) to plants (Li et al. 2017. Biochem. Biophys. Res. Commun. 488: 382-386). At present, research on circRNAs is an active area because of their diverse roles in development, health, and diseases. Partly because their circularity makes them resistant to degradation, they hold great promise as unique biomarkers for ocular and central nervous system (CNS) disorders. We believe that further work on their applications could help in developing them as "first-in-class" diagnostics, therapeutics, and prognostic targets for numerous eye conditions. Interestingly, many circRNAs play key roles in transcriptional regulation by acting as miRNAs sponges, meaning that they serve as master regulators of RNA and protein expression. Since the retina is an extension of the brain and is part of the CNS, we highlight the current state of circRNA biogenesis, properties, and function and we review the crucial roles that they play in the eye and the brain. We also discuss their regulatory roles as miRNA sponges, regulation of their parental genes or linear mRNAs, translation into micropeptides or proteins, and responses to cellular stress. We posit that future advances will provide newer insights into the fields of RNA metabolism in general and diseases of the aging eye and brain in particular. Furthermore, in keeping pace with the rapidly evolving discipline of RNA"omics"-centered metabolism and to achieve uniformity among researchers, we recently introduced the term "cromics" (circular ribonucleic acids based omics) (Singh et al. 2018. Exp. Eye Res. 174: 80-92).

FREQUENT SUBCLINICAL MACULAR CHANGES IN COMBINED BRAF/MEK INHIBITION WITH HIGH-DOSE HYDROXYCHLOROQUINE AS TREATMENT FOR ADVANCED METASTATIC BRAF MUTANT MELANOMA: Preliminary Results From a Phase I/II Clinical Treatment Trial.

PURPOSE: To assess the potential ocular toxicity of a combined BRAF inhibition (BRAFi) + MEK inhibition (MEKi) + hydroxychloroquine (HCQ) regime used to treat metastatic BRAF mutant melanoma. METHODS: Patients with stage IV metastatic melanoma and BRAF V600E mutations (n = 11, 31-68 years of age) were included. Treatment was with oral dabrafenib, 150 mg bid, trametinib, 2 mg/day, and HCQ, 400 mg to 600 mg bid. An ophthalmic examination, spectral domain optical coherence tomography, near-infrared and short-wavelength fundus autofluorescence, and static perimetry were performed at baseline, 1 month, and q/6 months after treatment. RESULTS: There were no clinically significant ocular events; there was no ocular inflammation. The only medication-related change was a separation of the photoreceptor outer segment tip from the apical retinal pigment epithelium that could be traced from the fovea to the perifoveal retina noted in 9/11 (82%) of the patients. There were no changes in retinal pigment epithelium melanization or lipofuscin content by near-infrared fundus autofluorescence and short-wavelength fundus autofluorescence, respectively. There were no inner retinal or outer nuclear layer changes. Visual acuities and sensitivities were unchanged. CONCLUSION: BRAFi (trametinib) + MEKi (dabrafenib) + HCQ causes very frequent, subclinical separation of the photoreceptor outer segment from the apical retinal pigment epithelium without inner retinal changes or signs of inflammation. The changes suggest interference with the maintenance of the outer retinal barrier and/or phagocytic/pump functions of the retinal pigment epithelium by effective MEK inhibition.

Circular RNAs profiling in the cystathionine-ß-synthase mutant mouse reveals novel gene targets for hyperhomocysteinemia induced ocular disorders.

Cystathionine-ß-synthase (CBS) gene encodes L-serine hydrolyase which catalyzes ß-reaction to condense serine with homocysteine (Hcy) by pyridoxal-5'-phosphate helps to form cystathionine which in turn is converted to cysteine. CBS resides at the intersection of transmethylation, transsulfuration, and remethylation pathways, thus lack of CBS fundamentally blocks Hcy degradation; an essential step in glutathione synthesis. Redox homeostasis, free-radical detoxification and one-carbon metabolism (Methionine-Hcy-Folate cycle) require CBS and its deficiency leads to hyperhomocysteinemia (HHcy) causing retinovascular thromboembolism and eye-lens dislocation along with vascular cognitive impairment and dementia. HHcy results in retinovascular, coronary, cerebral and peripheral vessels' dysfunction and how it causes metabolic dysregulation predisposing patients to serious eye conditions remains unknown. HHcy orchestrates inflammation and redox imbalance via epigenetic remodeling leading to neurovascular pathologies. Although circular RNAs (circRNAs) are dominant players regulating their parental genes' expression dynamics, their importance in ocular biology has not been appreciated. Progress in gene-centered analytics via improved microarray and bioinformatics are enabling dissection of genomic pathways however there is an acute under-representation of circular RNAs in ocular disorders. This study undertook circRNAs' analysis in the eyes of CBS deficient mice identifying a pool of 12532 circRNAs, 74 exhibited differential expression profile, ∼27% were down-regulated while most were up-regulated (∼73%). Findings also revealed several microRNAs that are specific to each circRNA suggesting their roles in HHcy induced ocular disorders. Further analysis of circRNAs helped identify novel parental genes that seem to influence certain eye disease phenotypes.

Evaluation of lipid peroxidation and antioxidant status on fenvalerate, nitrate and their co-exposure in Bubalus bubalis.

The toxic effects of pesticides and minerals have been explored in different species, but still there is paucity of information regarding their combined toxicological effects. The present investigation reports oxidative stress induced by oral subacute exposure to fenvalerate (1 mg/kg) and sodium nitrate (20 mg/kg) alone, as well as in combination daily for 21 days in buffalo calves. Fenvalerate exposure produced significant elevation in lipid peroxidation (LPO), glutathione peroxidase (GPx), while it produced significant decline in blood glutathione (GSH) levels, superoxide dismutase (SOD) and catalase (CAT). No significant alteration was evidenced in nitric oxide (NOx) levels. Oral exposure to sodium nitrate produced significant inclination in LPO and NOx, while on the other hand significant depreciation in SOD and CAT with no significant change in GPx activity. Combined exposure to fenvalerate and sodium nitrate produced severe effects with an appreciably more prominent elevation in extent of LPO and decline in blood GSH levels.

Alterations in Hematological Profile of Experimentally Induced Subchronic Thiacloprid Toxicosis in Gallus domesticus.

OBJECTIVES: Thiacloprid, a novel neonicotinoid insecticide is chiefly used as a crop protectant therefore it is likely to cause indirect exposure to poultry through contaminated feed and water because this species is occasionally supplied with feed that is, declared unfit for human consumption. The current study was performed to explore the nonlethal toxic effects of thiacloprid in Gallus domesticus on hematological parameters. MATERIALS AND METHODS: Fifty-two birds were randomly divided into nine groups. Groups I to IV of four birds each were kept as healthy control. The Groups V, VI, VII, VIII, IX, and X contained six birds each and were administered thiacloprid at 1 mg/kg/day for 15, 30, 45, 60, 75, and 90 days, respectively. RESULTS: Thiacloprid caused variable changes in the hematological parameters. There was a significant decline in the packed cell volume (PCV), hemoglobin (Hb) concentration, and total erythrocyte count (TEC). The PCV declined to the extent of 23.33 ± 0.76% on day 90 from the 0 day value of 29.75 ± 1.26% of experiment. The Hb concentration decreased from 9.93 ± 0.57 g/dl (0 day) to 7.52 ± 0.62 g/dl (90 days). The TEC declined from the 0 day value of 2.41 ± 0.08 × 10(6)/mm(3) to 90 days value of 2.08 ± 0.05 × 10(6)/mm(3). The total leukocyte count on 0 day was 12.50 ± 0.76 × 10(3)/mm(3) and it showed a significant increase from day 45 (17.80 ± 2.67 × 10(3)/mm(3)) to day 90 (21.33 ± 1.48 × 10(3)/mm(3)) of thiacloprid treatment. There was a significant rise in value of erythrocyte sedimentation rate to 19.25 ± 1.22 mm/24 h on day 90 of treatment from the 14.42 ± 1.09 mm/24 h on 0 day. The long-term oral administration of thiacloprid produced no significant alterations in the values of erythrocytic indices. CONCLUSIONS: The repeated oral toxicity on thiacloprid in present investigation suggested that it has an adverse effect on health of birds and is moderately risk insecticide in G. domesticus.

A non-invasive AI-based system for precise grading of anosmia in COVID-19 using neuroimaging.

COVID-19 (Coronavirus), an acute respiratory disorder, is caused by SARS-CoV-2 (coronavirus severe acute respiratory syndrome). The high prevalence of COVID-19 infection has drawn attention to a frequent illness symptom: olfactory and gustatory dysfunction. The primary purpose of this manuscript is to create a Computer-Assisted Diagnostic (CAD) system to determine whether a COVID-19 patient has normal, mild, or severe anosmia. To achieve this goal, we used fluid-attenuated inversion recovery (FLAIR) Magnetic Resonance Imaging (FLAIR-MRI) and Diffusion Tensor Imaging (DTI) to extract the appearance, morphological, and diffusivity markers from the olfactory nerve. The proposed system begins with the identification of the olfactory nerve, which is performed by a skilled expert or radiologist. It then proceeds to carry out the subsequent primary steps: (i) extract appearance markers (i.e., 1 s t and 2 n d order markers), morphology/shape markers (i.e., spherical harmonics), and diffusivity markers (i.e., Fractional Anisotropy (FA) & Mean Diffusivity (MD)), (ii) apply markers fusion based on the integrated markers, and (iii) determine the decision and corresponding performance metrics based on the most-promising classifier. The current study is unusual in that it ensemble bags the learned and fine-tuned ML classifiers and diagnoses olfactory bulb (OB) anosmia using majority voting. In the 5-fold approach, it achieved an accuracy of 94.1%, a balanced accuracy (BAC) of 92.18%, precision of 91.6%, recall of 90.61%, specificity of 93.75%, F1 score of 89.82%, and Intersection over Union (IoU) of 82.62%. In the 10-fold approach, stacking continued to demonstrate impressive results with an accuracy of 94.43%, BAC of 93.0%, precision of 92.03%, recall of 91.39%, specificity of 94.61%, F1 score of 91.23%, and IoU of 84.56%. In the leave-one-subject-out (LOSO) approach, the model continues to exhibit notable outcomes, achieving an accuracy of 91.6%, BAC of 90.27%, precision of 88.55%, recall of 87.96%, specificity of 92.59%, F1 score of 87.94%, and IoU of 78.69%. These results indicate that stacking and majority voting are crucial components of the CAD system, contributing significantly to the overall performance improvements. The proposed technology can help doctors assess which patients need more intensive clinical care.

Advanced OCTA imaging segmentation: Unsupervised, non-linear retinal vessel detection using modified self-organizing maps and joint MGRF modeling.

BACKGROUND AND OBJECTIVE: This paper proposes a fully automated and unsupervised stochastic segmentation approach using two-level joint Markov-Gibbs Random Field (MGRF) to detect the vascular system from retinal Optical Coherence Tomography Angiography (OCTA) images, which is a critical step in developing Computer-Aided Diagnosis (CAD) systems for detecting retinal diseases. METHODS: Using a new probabilistic model based on a Linear Combination of Discrete Gaussian (LCDG), the first level models the appearance of OCTA images and their spatially smoothed images. The parameters of the LCDG model are estimated using a modified Expectation Maximization (EM) algorithm. The second level models the maps of OCTA images, including the vascular system and other retina tissues, using MGRF with analytically estimated parameters from the input images. The proposed segmentation approach employs modified self-organizing maps as a MAP-based optimizer maximizing the joint likelihood and handles the Joint MGRF model in a new, unsupervised way. This approach deviates from traditional stochastic optimization approaches and leverages non-linear optimization to achieve more accurate segmentation results. RESULTS: The proposed segmentation framework is evaluated quantitatively on a dataset of 204 subjects. Achieving 0.92 ± 0.03 Dice similarity coefficient, 0.69 ± 0.25 95-percentile bidirectional Hausdorff distance, and 0.93 ± 0.03 accuracy, confirms the superior performance of the proposed approach. CONCLUSIONS: The conclusions drawn from the study highlight the superior performance of the proposed unsupervised and fully automated segmentation approach in detecting the vascular system from OCTA images. This approach not only deviates from traditional methods but also achieves more accurate segmentation results, demonstrating its potential in aiding the development of CAD systems for detecting retinal diseases.

Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.

Accurate noninvasive diagnosis of retinal disorders is required for appropriate treatment or precision medicine. This work proposes a multi-stage classification network built on a multi-scale (pyramidal) feature ensemble architecture for retinal image classification using optical coherence tomography (OCT) images. First, a scale-adaptive neural network is developed to produce multi-scale inputs for feature extraction and ensemble learning. The larger input sizes yield more global information, while the smaller input sizes focus on local details. Then, a feature-rich pyramidal architecture is designed to extract multi-scale features as inputs using DenseNet as the backbone. The advantage of the hierarchical structure is that it allows the system to extract multi-scale, information-rich features for the accurate classification of retinal disorders. Evaluation on two public OCT datasets containing normal and abnormal retinas (e.g., diabetic macular edema (DME), choroidal neovascularization (CNV), age-related macular degeneration (AMD), and Drusen) and comparison against recent networks demonstrates the advantages of the proposed architecture's ability to produce feature-rich classification with average accuracy of 97.78%, 96.83%, and 94.26% for the first (binary) stage, second (three-class) stage, and all-at-once (four-class) classification, respectively, using cross-validation experiments using the first dataset. In the second dataset, our system showed an overall accuracy, sensitivity, and specificity of 99.69%, 99.71%, and 99.87%, respectively. Overall, the tangible advantages of the proposed network for enhanced feature learning might be used in various medical image classification tasks where scale-invariant features are crucial for precise diagnosis.

Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images.

Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient's condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively.

Outpatient medications associated with protection from COVID-19 hospitalization.

The COVID-19 pandemic remains the pre-eminent global health problem, and yet after more than three years there is still no prophylactic agent against the disease aside from vaccines. The objective of this study was to evaluate whether pre-existing, outpatient medications approved by the US Food and Drug Administration (FDA) reduce the risk of hospitalization due to COVID-19. This was a retrospective cohort study of patients from across the United States infected with COVID-19 in the year 2020. The main outcome was adjusted odds of hospitalization for COVID-19 amongst those positive for the infection. Outcomes were adjusted for known risk factors for severe disease. 3,974,272 patients aged 18 or older with a diagnosis of COVID-19 in 2020 met our inclusion criteria and were included in the analysis. Mean age was 50.7 (SD 18). Of this group, 290,348 patients (7.3%) were hospitalized due to COVID-19, similar to the CDC's reported estimate (7.5%). Four drugs showed protective effects against COVID-19 hospitalization: rosuvastatin (aOR 0.91, p = 0.00000024), empagliflozin-metformin (aOR 0.69, p = 0.003), metformin (aOR 0.97, p = 0.017), and enoxaparin (aOR 0.88, p = 0.0048). Several pre-existing medications for outpatient use may reduce severity of disease and protect against COVID-19 hospitalization. Well-designed clinical trials are needed to assess the efficacy of these agents in a therapeutic or prophylactic setting.