Drug repurposing: identification of SARS-CoV-2 potential inhibitors by virtual screening and pharmacokinetics strategies.

INTRODUCTION: The coronavirus disease 2019 (COVID-19) pandemic caused global health, economic, and population loss. Variants of the coronavirus contributed to the severity of the disease and persistent rise in infections. This study aimed to identify potential drug candidates from fifteen approved antiviral drugs against SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike protein (6M0J) using virtual screening and pharmacokinetics to gain insights into COVID-19 therapeutics. METHODOLOGY: We employed drug repurposing approach to analyze binding performance of fifteen clinically approved antiviral drugs against the main protease of SARS-CoV-2 (6LU7), SARS-CoV (5B6O), and SARS-CoV-2 spike proteins bound to ACE-2 receptor (6M0J), to provide an insight into the therapeutics of COVID-19. AutoDock Vina was used for docking studies. The binding affinities were calculated, and 2-3D structures of protein-ligand interactions were drawn. RESULTS: Rutin, hesperidin, and nelfinavir are clinically approved antiviral drugs with high binding affinity to proteins 6LU7, 5B6O, and 6M0J. These ligands have excellent pharmacokinetics, ensuring efficient absorption, metabolism, excretion, and digestibility. Hesperidin showed the most potent interaction with spike protein 6M0J, forming four H-bonds. Nelfinavir had a high human intestinal absorption (HIA) score of 0.93, indicating maximum absorption in the body and promising interactions with 6LU7. CONCLUSIONS: Our results indicated that rutin, hesperidin, and nelfinavir had the highest binding results against the proposed drug targets. The computational approach effectively identified SARS-CoV-2 inhibitors. COVID-19 is still a recurrent threat globally and predictive analysis using natural compounds might serve as a starting point for new drug development against SARS-CoV-2 and related viruses.

LDMRes-Net: A Lightweight Neural Network for Efficient Medical Image Segmentation on IoT and Edge Devices.

In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based convolutional neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. In this study, we present the Lightweight Dual Multiscale Residual Block-based Convolutional Neural Network (LDMRes-Net), which is specifically designed to overcome these difficulties. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multiscale residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on the segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning.

Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature.

Ginger ring compounds as an inhibitor of spike binding protein of alpha, beta, gamma and delta variants of SARS-CoV-2: An in-silico study.

The available drugs against coronavirus disease 2019 (COVOD-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), are limited. This study aimed to identify ginger-derived compounds that might neutralize SARS-CoV-2 and prevent its entry into host cells. Ring compounds of ginger were screened against spike (S) protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The S protein FASTA sequence was retrieved from Global Initiative on Sharing Avian Influenza Data (GISAID) and converted into ".pdb" format using Open Babel tool. A total of 306 compounds were identified from ginger through food and phyto-databases. Out of those, 38 ring compounds were subjected to docking analysis using CB Dock online program which implies AutoDock Vina for docking. The Vina score was recorded, which reflects the affinity between ligands and receptors. Further, the Protein Ligand Interaction Profiler (PLIP) program for detecting the type of interaction between ligand-receptor was used. SwissADME was used to compute druglikeness parameters and pharmacokinetics characteristics. Furthermore, energy minimization was performed by using Swiss PDB Viewer (SPDBV) and energy after minimization was recorded. Molecular dynamic simulation was performed to find the stability of protein-ligand complex and root-mean- square deviation (RMSD) as well as root-mean-square fluctuation (RMSF) were calculated and recorded by using myPresto v5.0. Our study suggested that 17 out of 38 ring compounds of ginger were very likely to bind the S protein of SARS-CoV-2. Seventeen out of 38 ring compounds showed high affinity of binding with S protein of alpha, beta, gamma, and delta variants of SARS-CoV-2. The RMSD showed the stability of the complex was parallel to the S protein monomer. These computer-aided predictions give an insight into the possibility of ginger ring compounds as potential anti-SARS-CoV-2 worthy of in vitro investigations.

Leveraging image complexity in macro-level neural network design for medical image segmentation.

Recent progress in encoder-decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth or size to meet computer memory constraints. In this paper, we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our illustrative experiments, we use DeepLabV3+ (deep large-size), M2U-Net (deep lightweight), U-Net (shallow large-size), and U-Net Lite (shallow lightweight). Our results suggest that median frequency is the best complexity measure when deciding on an acceptable input downsampling factor and using a deep versus shallow, large-size versus lightweight network. For high-complexity datasets, a lightweight network running on the original images may yield better segmentation results than a large-size network running on downsampled images, whereas the opposite may be the case for low-complexity images.

Recent trends and advances in fundus image analysis: A review.

Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.

A region growing and local adaptive thresholding-based optic disc detection.

Automatic optic disc (OD) localization and segmentation is not a simple process as the OD appearance and size may significantly vary from person to person. This paper presents a novel approach for OD localization and segmentation which is fast as well as robust. In the proposed method, the image is first enhanced by de-hazing and then cropped around the OD region. The cropped image is converted to HSV domain and then V channel is used for OD detection. The vessels are extracted from the Green channel in the cropped region by multi-scale line detector and then removed by the Laplace Transform. Local adaptive thresholding and region growing are applied for binarization. Furthermore, two region properties, eccentricity, and area are then used to detect the true OD region. Finally, ellipse fitting is used to fill the region. Several datasets are used for testing the proposed method. Test results show that the accuracy and sensitivity of the proposed method are much higher than the existing state-of-the-art methods.

Recent Developments in the Treatment of Spinal Epidural Abscesses.

Spinal epidural abscess (SEA) is a serious condition that can be challenging to diagnose due to nonspecific symptomology and delayed presentation. Despite this, it requires prompt recognition and management in order to prevent permanent neurologic sequelae. Several recent studies have improved our understanding of SEA. Herein, we summarize the recent literature from the past 10 years relevant to SEA diagnosis, management and outcome. While surgical care remains the mainstay of treatment, a select subset of SEA patients may be managed without operative intervention. Multidisciplinary management involves internal medicine, infectious disease, critical care, and spine surgeons in order to optimize care.

Is the Readability of Spine-Related Patient Education Material Improving?: An Assessment of Subspecialty Websites.

STUDY DESIGN: Analysis of spine-related patient education materials (PEMs) from subspecialty websites. OBJECTIVE: The aim of this study was to assess the readability of spine-related PEMs and compare to readability data from 2008. SUMMARY OF BACKGROUND DATA: Many spine patients use the Internet for health information. Several agencies recommend that the readability of online PEMs should be no greater than a sixth-grade reading level, as health literacy predicts health-related quality of life outcomes. This study evaluated whether the North American Spine Society (NASS), American Association of Neurological Surgeons (AANS), and American Academy of Orthopaedic Surgeons (AAOS) online PEMs meet recommended readability guidelines for medical information. METHODS: All publicly accessible spine-related entries within the patient education section of the NASS, AANS, and AAOS websites were analyzed for grade level readability using the Flesch-Kincaid formula. Readability scores were also compared with a similar 2008 analysis. Comparative statistics were performed. RESULTS: A total of 125 entries from the subspecialty websites were analyzed. The average (SD) readability of the online articles was grade level 10.7 (2.3). Of the articles, 117 (93.6%) had a readability score above the sixth-grade level. The readability of the articles exceeded the maximum recommended level by an average of 4.7 grade levels (95% CI, 4.292-5.103; P < 0.001). Compared with 2008, the three societies published more spine-related patient education articles (61 vs. 125, P = 0.045) and the average readability level improved from 11.5 to 10.7 (P = 0.018). Of three examined societies, only one showed significant improvement over time. CONCLUSION: Our findings suggest that the spine-related PEMs on the NASS, AAOS, and AANS websites have readability levels that may make comprehension difficult for a substantial portion of the patient population. Although some progress has been made in the readability of PEMs over the past 7 years, additional improvement is necessary. LEVEL OF EVIDENCE: 2.

Readability of Invasive Procedure Consent Forms.

BACKGROUND: Informed consent is a pillar of ethical medicine which requires patients to fully comprehend relevant issues including the risks, benefits, and alternatives of an intervention. Given the average reading skill of US adults is at the 8th grade level, the American Medical Association (AMA) and the National Institutes of Health (NIH) recommend patient information materials should not exceed a 6th grade reading level. We hypothesized that text provided in invasive procedure consent forms would exceed recommended readability guidelines for medical information. MATERIALS AND METHODS: To test this hypothesis, we gathered procedure consent forms from all surgical inpatient hospitals in the state of Rhode Island. For each consent form, readability analysis was measured with the following measures: Flesch Reading Ease Formula, Flesch-Kincaid Grade Level, Fog Scale, SMOG Index, Coleman-Liau Index, Automated Readability Index, and Linsear Write Formula. These readability scores were used to calculate a composite Text Readability Consensus Grade Level. RESULTS: Invasive procedure consent forms were found to be written at an average of 15th grade level (i.e., third year of college), which is significantly higher than the average US adult reading level of 8th grade (p < 0.0001) and the AMA/NIH recommended readability guidelines for patient materials of 6th grade (p < 0.0001). CONCLUSION: Invasive procedure consent forms have readability levels which makes comprehension difficult or impossible for many patients. Efforts to improve the readability of procedural consent forms should improve patient understanding regarding their healthcare decisions.

Predictors of depressive symptoms post-acute coronary syndrome.

OBJECTIVE: We examined the influence of gender on the prevalence of acute coronary syndrome (ACS) and the severity of depressive symptoms post-ACS. METHODS: Patients received a Zung self-assessment questionnaire at hospital discharge for unstable angina (UA) or acute myocardial infarction (AMI) and returned it by mail. Major depressive symptoms were diagnosed based on a summed depressive symptoms (SDS) score of >50. Depressive symptomatology was modeled by stepwise multivariable logistic regression with the following predictors: gender, age, hypertension, diabetes mellitus, history of smoking, hypercholesterolemia, peripheral vascular disease, prior stroke, prior myocardial infarction (MI), and prior percutaneous coronary intervention or coronary artery bypass graft surgery. We also modeled severity of depressive symptoms via stepwise multiple linear regression with the same predictor variables. RESULTS: A total of 944 patients were surveyed: 716 men and 228 women, mean (SD) age, 67 (13) years and 71 (12) years, respectively. Of these patients, 250 (35%) men and 103 (45%) women had depressive symptoms (P = 0.005). No significant difference was observed between men and women in rates of cardiac catheterization; severity of coronary artery disease; treatment with antiplatelet agents, beta-blockers, angiotensin-converting enzyme inhibitors, or statins; or percutaneous or surgical revascularization rates during or post-ACS. Significant predictors of the presence of depressive symptoms were female gender (odds ratio [OR] = 1.64; 95% CI, 1.19-1.28), diabetes mellitus (OR = 1.42; 95% CI, 1.03-1.97), prior MI (OR = 1.56; 95% CI, 1.15-2.20), and smoking (OR = 1.41; 95% CI, 1.01-1.97). Variables significantly associated with a higher severity of depressive symptoms were female gender, prior MI, smoking, and stroke. Men with prior MI had significantly higher mean (SD) SDS scores than did men without prior MI in all age groups (48.4 [11] vs 44.6 [11], respectively; P < 0.001). In addition, significantly more men with prior MI had depressive symptoms compared with those without prior MI (45% vs 32%; P = 0.001). However, prior MI did not appear to affect SDS scores in women (49.1 [12] for prior MI vs 48.5 [12] for no prior MI; P = NS), and there was no significant difference in the percentage of women who had depressive symptoms with or without a history of prior MI. Depressive symptoms were much more severe in women with UA (SDS = 49.0 [12]) compared with women with AMI (SDS = 45.0 [12]; P = NS), or men with AMI (45.0 [12]; P = 0.004) or UA (46.0 [11]; P = 0.007) (analysis of variance, P = 0.003). CONCLUSIONS: Female gender is a significant independent predictor of depressive symptoms and their severity post-UA and post-AMI. History of prior MI is associated with a higher frequency and severity of depressive symptoms in men. These findings call for routine screening for depressive symptoms in men with prior MI and in women who present with ACS.

Gender differences in the link between depression and cardiovascular disease.

OBJECTIVES: Cardiovascular disease is the leading cause of mortality in women costing more than 500,000 lives each year in the United States alone. Major depression in healthy subjects increases cardiovascular mortality in both men and women. The presence of major depression in patients with recent acute myocardial infarction (AMI) or unstable angina more than doubles the risk of cardiac death in both men and women. In the presence of depression, lack of social integration has an additive effect on cardiac events. Depression is more prevalent in women with coronary heart disease (CHD) than in men. Psychologic counseling as well as cognitive behavioral treatment in women post-AMI seems to adversely affect prognosis, whereas it has neutral effects in men. Pharmacologic treatment of depression with serotonin reuptake inhibitors is safe in men and women post-AMI and is particularly effective in patients with recurrent depression. Whether effective treatment of depression lowers cardiac mortality remains to be proven.