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Background This review aims to explore advances in the field of cerebral palsy (CP) focusing on machine learning (ML) models. The objectives of this study is to analyze the advances in the application of ML models in the field of CP and to compare the performance of different ML algorithms in terms of their effectiveness in CP identification, classifying CP into its subtypes, prediction of abnormalities in CP, and its management. These objectives guide the review in examining how ML techniques are applied to CP and their potential impact on improving outcomes in CP research and treatment. Methodology A total of 20 studies were identified on ML for CP from 2013 to 2023. Search Engines used during the review included electronic databases like PubMed for accessing biomedical and life sciences, IEEE Xplore for technical literature in computer, Google Scholar for a broad range of academic publications, Scopus and Web of Science for multidisciplinary high impact journals. Inclusion criteria included articles containing keywords such as cerebral palsy, machine learning approaches, outcome response, identification, classification, diagnosis, and treatment prediction. Studies were included if they reported the application of ML techniques for CP patients. Peer reviewed articles from 2013 to 2023 were only included for the review. We selected full-text articles, clinical trials, randomized control trial, systematic reviews, narrative reviews, and meta-analyses published in English. Exclusion criteria for the review included studies not directly related to CP. Editorials, opinion pieces, and non-peer-reviewed articles were also excluded. To ensure the validity and reliability of the findings in this review, we thoroughly examined the study designs, focusing on the appropriateness of their methodologies and sample sizes. To synthesize and present the results, data were extracted and organized into tables for easy comparison. The results were presented through a combination of text, tables, and figures, with key findings emphasized in summary tables and relevant graphs. Results Random forest (RF) is mainly used for classifying movements and deformities due to CP. Support vector machine (SVM), decision tree (DT), RF, and K-nearest neighbors (KNN) show 100% accuracy in exercise evaluation. RF and DT show 94% accuracy in the classification of gait patterns, multilayer perceptron (MLP) shows 84% accuracy in the classification of CP children, Bayesian causal forests (BCF) have 74% accuracy in predicting the average treatment effect on various orthopedic and neurological conditions. Neural networks are 94.17% accurate in diagnosing CP using eye images. However, the studies varied significantly in their design, sample size, and quality of data, which limits the generalizability of the findings. Conclusion Clinical data are primarily used in ML models in the CP field, accounting for almost 47%. With the rise in popularity of machine learning techniques, there has been a rise in interest in developing automated and data-driven approaches to explore the use of ML in CP.

The objective of this research article is to investigate the impact of various health history factors on the risk of developing Parkinson's disease (PD). From the medical history we can identify PD Symptoms and this also help to detect the progression of PD symptoms. By conducting statistical analyses, the study seeks to identify independent risk and protective factors for Parkinson's disease (PD), considering variations in impact across genders and BMI categories. Introduction: In the diagnosis of PD the analysis of previous health history is very rare in practice while the main diagnosis have been done through the different motor and non-motor symptoms taking in consideration besides the cardinal symptoms of PD for identification and determination the stages of PD. Here we have analyzed the impact of 56 different diseases, symptoms, and surgeries which a subject may have experienced in their life before PD, considered as a health history. Methods: The behavioral impact for each types of health history have been analyzed statistically with 31,265 subjects including PD, and Control. In this analysis we have calculated the variation of impact for both the Male, and Female, as well as subjects BMI. Results: 98.12 % PD patients, where 97.63 % Male PD, and 98.71 % Female PD were found with at least one health history record. Coronary heart disease odds ratio (OR) 2.15 (1.85–2.51), Colon Cancer OR 2.11 (1.45–3.05), Cranial brain surgery OR 6.21 (5.11–7.56) have the higher risks to PD. Having the history of Asthma OR 0.66 (0.6–0.72), Anemia OR 0.56 (0.51–0.63), Cirrhosis in Liver OR 0.7 (0.57–0.86), Cosmetic surgery OR 0.7 (0.64–0.77), and Gastritis OR 0.78 (0.71–0.87) have been found to be protective to PD. The risk of developing PD varies between male, and female including subjects BMI for each individual health history types. The diseases which reduce the oxygen saturation in blood like, anemia, asthma, and thalassemia act as protective to PD. Conclusions: In this study we have analyzed fifty six diseases which include surgeries as a health history of PD patients. Study suggests that a thorough health history could greatly aid in understanding the onset and progression of Parkinson's disease (PD).

Summary: Background: Decreased levels of circulating ethanolamine plasmalogens [PE(P)], and a concurrent increase in phosphatidylethanolamine (PE) are consistently reported in various cardiometabolic conditions. Here we devised, a plasmalogen score (Pls Score) that mirrors a metabolic signal that encompasses the levels of PE(P) and PE and captures the natural variation in circulating plasmalogens and perturbations in their metabolism associated with disease, diet, and lifestyle. Methods: We utilised, plasma lipidomes from the Australian Obesity, Diabetes and Lifestyle study (AusDiab; n = 10,339, 55% women) a nationwide cohort, to devise the Pls Score and validated this in the Busselton Health Study (BHS; n = 4,492, 56% women, serum lipidome) and in a placebo-controlled crossover trial involving Shark Liver Oil (SLO) supplementation (n = 10, 100% men). We examined the association of the Pls Score with cardiometabolic risk factors, type 2 diabetes mellitus (T2DM), cardiovascular disease and all-cause mortality (over 17 years). Findings: In a model, adjusted for age, sex and BMI, individuals in the top quintile of the Pls Score (Q5) relative to Q1 had an OR of 0.31 (95% CI 0.21–0.43), 0.39 (95% CI 0.25–0.61) and 0.42 (95% CI 0.30–0.57) for prevalent T2DM, incident T2DM and prevalent cardiovascular disease respectively, and a 34% lower mortality risk (HR = 0.66; 95% CI 0.56–0.78). Significant associations between diet and lifestyle habits and Pls Score exist and these were validated through dietary supplementation of SLO that resulted in a marked change in the Pls Score. Interpretation: The Pls Score as a measure that captures the natural variation in circulating plasmalogens, was not only inversely related to cardiometabolic risk and all-cause mortality but also associate with diet and lifestyle. Our results support the potential utility of the Pls Score as a biomarker for metabolic health and its responsiveness to dietary interventions. Further research is warranted to explore the underlying mechanisms and optimise the practical implementation of the Pls Score in clinical and population settings. Funding: National Health and Medical Research Council (NHMRC grant 233200), National Health and Medical Research Council of Australia (Project grant APP1101320), Health Promotion Foundation of Western Australia, and National Health and Medical Research Council of Australia Senior Research Fellowship (#1042095).

Background and noveltyWhen RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.MethodologyAnnotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland–Altman, and (iv) Correlation plots.ResultsAmong the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann–Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p

Background: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. Methodology: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. Results: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. Conclusions: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

BackgroundBreastfed infants have lower disease risk compared to formula-fed infants, however, the mechanisms behind this protection are unknown. Human milk has a complex lipidome which may have many critical roles in health and disease risk. However, human milk lipidomics is challenging, and research is still required to fully understand the lipidome and to interpret and translate findings. This study aimed to address key human milk lipidome knowledge gaps and discuss possible implications for early life health.MethodsHuman milk samples from two birth cohorts, the Barwon Infant Study (n = 312) and University of Western Australia birth cohort (n = 342), were analysed using four liquid chromatography-mass spectrometry (LC–MS) methods (lipidome, triacylglycerol, total fatty acid, alkylglycerol). Bovine, goat, and soy-based infant formula, and bovine and goat milk were analysed for comparison. Composition was explored as concentrations, relative abundance, and infant lipid intake. Statistical analyses included principal component analysis, mixed effects modelling, and correlation, with false discovery rate correction, to explore human milk lipidome longitudinal trends and inter and intra-individual variation, differences between sample types, lipid intakes, and correlations between infant plasma and human milk lipids.ResultsLipidomics analysis identified 979 lipids. The human milk lipidome was distinct from that of infant formula and animal milk. Ether lipids were of particular interest, as they were significantly higher, in concentration and relative abundance, in human milk than in formula and animal milk, if present in the latter samples at all. Many ether lipids were highest in colostrum, and some changed significantly through lactation. Significant correlations were identified between human milk and infant circulating lipids (40% of which were ether lipids), and specific ether lipid intake by exclusively breastfed infants was 200-fold higher than that of an exclusively formula-fed infant.ConclusionThere are marked differences between the lipidomes of human milk, infant formula, and animal milk, with notable distinctions between ether lipids that are reflected in the infant plasma lipidome. These findings have potential implications for early life health, and may reveal why breast and formula-fed infants are not afforded the same protections. Comprehensive lipidomics studies with outcomes are required to understand the impacts on infant health and tailor translation.

The study of human movement and biomechanics forms an integral part of various clinical assessments and provides valuable information toward diagnosing neurodegenerative disorders where the motor symptoms predominate. Conventional gait and postural balance analysis techniques like force platforms, motion cameras, etc., are complex, expensive equipment requiring specialist operators, thereby posing a significant challenge toward translation to the clinics. The current manuscript presents an overview and relevant literature summarizing the umbrella of factors associated with neurodegenerative disorder management: from the pathogenesis and motor symptoms of commonly occurring disorders to current alternate practices toward its quantification and mitigation. This article reviews recent advances in technologies and methodologies for managing important neurodegenerative gait and balance disorders, emphasizing assessment and rehabilitation/assistance. The review predominantly focuses on the application of inertial sensors toward various facets of gait analysis, including event detection, spatiotemporal gait parameter measurement, estimation of joint kinematics, and postural balance analysis. In addition, the use of other sensing principles such as foot-force interaction measurement, electromyography techniques, electrogoniometers, force-myography, ultrasonic, piezoelectric, and microphone sensors has also been explored. The review also examined the commercially available wearable gait analysis systems. Additionally, a summary of recent progress in therapeutic approaches, viz., wearables, virtual reality (VR), and phytochemical compounds, has also been presented, explicitly targeting the neuro-motor and functional impairments associated with these disorders. Efforts toward therapeutic and functional rehabilitation through VR, wearables, and different phytochemical compounds are presented using recent examples of research across the commonly occurring neurodegenerative conditions [viz., Parkinson’s disease (PD), Alzheimer’s disease (AD), multiple sclerosis, Huntington’s disease (HD), and amyotrophic lateral sclerosis (ALS)]. Studies exploring the potential role of Phyto compounds in mitigating commonly associated neurodegenerative pathologies such as mitochondrial dysfunction, α-synuclein accumulation, imbalance of free radicals, etc., are also discussed in breadth. Parameters such as joint angles, plantar pressure, and muscle force can be measured using portable and wearable sensors like accelerometers, gyroscopes, footswitches, force sensors, etc. Kinetic foot insoles and inertial measurement tools are widely explored for studying kinematic and kinetic parameters associated with gait. With advanced correlation algorithms and extensive RCTs, such measurement techniques can be an effective clinical and home-based monitoring and rehabilitation tool for neuro-impaired gait. As evident from the present literature, although the vast majority of works reported are not clinically and extensively validated to derive a firm conclusion about the effectiveness of such techniques, wearable sensors present a promising impact toward dealing with neurodegenerative motor disorders.

ABSTRACT: Echinostomes (Trematoda: Echinostomatidae) are food-borne zoonotic flatworms that affect birds, animals and humans, and has been classified as neglected tropical diseases (NTDs) by the World Health Organization (WHO), which cause severe enteritis in poultry and hamper production. Here, we confirmed the species of echinostomes affecting chickens in Bangladesh along with their genetic analyses, pathology and vectors. We isolated and identified adult worms from chickens, cercariae from fresh water snails and metacerariae (MC) from some wild fishes. We recovered Echinostoma revolutum (10.3%) and Hypoderaeum conoideum (6.0%) from chickens. Zoonotic E. revolutum was confirmed by amplifying nad1 gene and subsequent sequencing. Several mutations were detected in nad1 gene and our isolates belonged to the Euro-Asian clade. We observed thickening of mucosal layer, hyperplasia of goblet cells, infiltration of eosinophils, lymphocytes and must cells in the infected intestine. About 5.3% snails were infected and the highest percentage of infection was found in Lymnaea luteola (12.1%). Echinostome infection in snails was the highest in November (9.6%) and lowest in February (3.1%) in Bangladesh. MC of echinostomes were identified from blue panchax (Aplocheilus panchax) and tank goby (Glossogobius giuris). In conclusion, echinostomiasis is a notable big problem in indigenous chickens in Bangladesh and people, especially, villagers are at risk.

Ether lipids are a unique class of glycero- and glycerophospho-lipid that carry an ether or vinyl ether linked fatty alcohol at the sn-1 position of the glycerol backbone. These specialised lipids are important endogenous anti-oxidants with additional roles in regulating membrane fluidity and dynamics, intracellular signalling, immunomodulation and cholesterol metabolism. Lipidomic profiling of human population cohorts has identified new associations between reduced circulatory plasmalogen levels, an abundant and biologically active sub-class of ether lipids, with obesity and body-mass index. These findings align with the growing body of work exploring novel roles for ether lipids within adipose tissue. In this regard, ether lipids have now been linked to facilitating lipid droplet formation, regulating thermogenesis and mediating beiging of white adipose tissue in early life. This review will assess recent findings in both population studies and studies using cell and animal models to delineate the functional and protective roles of ether lipids in the setting of obesity. We will also discuss the therapeutic potential of ether lipid supplementation to attenuate diet-induced obesity.

Background: There is mounting evidence that in utero and early life exposures may predispose an individual to metabolic disorders in later life; and dysregulation of lipid metabolism is critical in such outcomes. However, there is limited knowledge about lipid metabolism and factors causing lipid dysregulation in early life that could result in adverse health outcomes in later life. We studied the effect of antenatal factors such as gestational age, birth weight, and mode of birth on lipid metabolism at birth; changes in the circulating lipidome in the first 4 years of life and the effect of breastfeeding in the first year of life. From this study, we aim to generate a framework for deeper understanding into factors effecting lipid metabolism in early life, to provide early interventions for those at risk of developing metabolic disorders including cardiovascular diseases. Methods: We performed comprehensive lipid profiling of 1074 mother-child dyads in the Barwon Infant Study (BIS), a population-based pre-birth cohort and measured 776 distinct lipid features across 39 lipid classes using ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS). We measured lipids in 1032 maternal serum samples at 28 weeks’ gestation, 893 cord serum samples at birth, 793, 735, and 511 plasma samples at 6, 12 months, and 4 years, respectively. Cord serum was enriched with long chain poly-unsaturated fatty acids (LC-PUFAs), and corresponding cholesteryl esters relative to the maternal serum. We performed regression analyses to investigate the associations of cord serum lipid species with antenatal factors: gestational age, birth weight, mode of birth and duration of labour. Results: The lipidome differed between mother and newborn and changed markedly with increasing child’s age. Alkenylphosphatidylethanolamine species containing LC-PUFAs increased with child’s age, whereas the corresponding lysophospholipids and triglycerides decreased. Majority of the cord serum lipids were strongly associated with gestational age and birth weight, with most lipids showing opposing associations. Each mode of birth showed an independent association with cord serum lipids. Breastfeeding had a significant impact on the plasma lipidome in the first year of life, with up to 17-fold increases in a few species of alkyldiaclylglycerols at 6 months of age. Conclusions: This study sheds light on lipid metabolism in infancy and early childhood and provide a framework to define the relationship between lipid metabolism and health outcomes in early childhood. Funding: This work was supported by the A*STAR-NHMRC joint call funding (1711624031).

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Plasmalogens are membrane glycerophospholipids with diverse biological functions. Reduced plasmalogen levels have been observed in metabolic diseases; hence, increasing their levels might be beneficial in ameliorating these conditions. Shark liver oil (SLO) is a rich source of alkylglycerols that can be metabolized into plasmalogens. This study was designed to evaluate the impact of SLO supplementation on endogenous plasmalogen levels in individuals with features of metabolic disease. In this randomized, double-blind, placebo-controlled cross-over study, the participants (10 overweight or obese males) received 4-g Alkyrol® (purified SLO) or placebo (methylcellulose) per day for 3 weeks followed by a 3-week washout phase and were then crossed over to 3 weeks of the alternate placebo/Alkyrol® treatment. SLO supplementation led to significant changes in plasma and circulatory white blood cell lipidomes, notably increased levels of plasmalogens and other ether lipids. In addition, SLO supplementation significantly decreased the plasma levels of total free cholesterol, triglycerides, and C-reactive protein. These findings suggest that SLO supplementation can enrich plasma and cellular plasmalogens and this enrichment may provide protection against obesity-related dyslipidemia and inflammation.

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Background and Motivation: Diagnosis of Parkinson’s disease (PD) is often based on medical attention and clinical signs. It is subjective and does not have a good prognosis. Artificial Intelligence (AI) has played a promising role in the diagnosis of PD. However, it introduces bias due to lack of sample size, poor validation, clinical evaluation, and lack of big data configuration. The purpose of this study is to compute the risk of bias (RoB) automatically. Method: The PRISMA search strategy was adopted to select the best 39 AI studies out of 85 PD studies closely associated with early diagnosis PD. The studies were used to compute 30 AI attributes (based on 6 AI clusters), using AP(ai)Bias 1.0 (AtheroPointTM, Roseville, CA, USA), and the mean aggregate score was computed. The studies were ranked and two cutoffs (Moderate-Low (ML) and High-Moderate (MH)) were determined to segregate the studies into three bins: low-, moderate-, and high-bias. Result: The ML and HM cutoffs were 3.50 and 2.33, respectively, which constituted 7, 13, and 6 for low-, moderate-, and high-bias studies. The best and worst architectures were “deep learning with sketches as outcomes” and “machine learning with Electroencephalography,” respectively. We recommend (i) the usage of power analysis in big data framework, (ii) that it must undergo scientific validation using unseen AI models, and (iii) that it should be taken towards clinical evaluation for reliability and stability tests. Conclusion: The AI is a vital component for the diagnosis of early PD and the recommendations must be followed to lower the RoB.

Context: Turmeric (Curcuma longa L. [Zingiberaceae]) is used in the treatment of a variety of conditions including pesticide-induced toxicity. Objective: The study reports the antioxidant properties and the protective effects of turmeric against carbofuran (CF)-induced toxicity in rats. Materials and methods: The antioxidant potential was determined by using free radicals scavenging activity and ferric reducing antioxidant power values. Male Wistar rats were randomly divided into four groups, designated as control, turmeric (100 mg/kg/day), CF (1 mg/kg/day) and turmeric (100 mg/kg/day) + CF (1 mg/kg/day) treatments. All of the doses were administered orally for 28 consecutive days. The biological activity of the turmeric and CF was determined by using several standard biochemical methods. Results: Turmeric contains high concentrations of polyphenols (8.97 ± 0.15 g GAEs), flavonoids (5.46 ± 0.29 g CEs), ascorbic acid (0.06 ± 0.00 mg AEs) and FRAP value (1972.66 ± 104.78 μM Fe2+) per 100 g of sample. Oral administration of CF caused significant changes in some of the blood indices, such as, mean corpuscular volume, corpuscular hemoglobin, white blood cell, platelet distribution width and induced severe hepatic injuries associated with oxidative stress, as observed by the significantly higher lipid peroxidation (LPO) levels when compared to control, while the activities of cellular antioxidant enzymes (including superoxide dismutase and glutathione peroxidase) were significantly suppressed in the liver tissue. Discussion and conclusion: Turmeric supplementation could protect against CF-induced hematological perturbations and hepatic injuries in rats, plausibly by the up-regulation of antioxidant enzymes and inhibition of LPO to confer the protective effect.

The aim of this study was to investigate the antioxidant potentials, subacute toxicity, and beneficiary effects of methanolic extract of pomelo (Citrus grandis L. Osbeck) in rats. Long Evans rats were divided into four groups of eight animals each. The rats were orally treated with three doses of pomelo (250, 500, and 1000 mg/kg) once daily for 21 days. Pomelo extract contained high concentrations of polyphenols, flavonoids, and ascorbic acid while exhibiting high 1,1-diphenyl-2-picrylhydrazyl radical scavenging activity and ferric reducing antioxidant power values. There was no significant change in the body weight, percentage water content, and relative organ weight at any administered doses. In addition, no significant alterations in the hematological parameters were also observed. However, rats which received 1000 mg/kg dose had a significant reduction in some serum parameters, including alanine transaminase (15.29%), alkaline phosphatase (2.5%), lactate dehydrogenase (15.5%), γ-glutamyltransferase (20%), creatinine (14.47%), urea (18.50%), uric acid (27.14%), total cholesterol (5.78%), triglyceride (21.44%), low-density lipoprotein cholesterol (40.74%), glucose (2.48%), and all atherogenic indices including cardiac risk ratio (24.30%), Castelli’s risk index-2 (45.71%), atherogenic coefficient (42%), and atherogenic index of plasma (25%) compared to control. In addition, the highest dose (1000 mg/kg) caused a significant increase in iron (12.07%) and high-density lipoprotein cholesterol (8.87%) levels. Histopathological findings of the vital organs did not indicate any pathological changes indicating that pomelo is nontoxic, safe, and serves as an important source of natural antioxidants. In addition, the fruit extract has the potential to ameliorate hepato- and nephrotoxicities and cardiovascular diseases as well as iron deficiency anemia.

Plasmalogens or alkenylphospholipids are a sub-class of glycerophospholipids with numerous biological functions and are thought to have protective effects against metabolic disease. Dietary supplementation with alkylglycerols (AKGs) has been shown to increase endogenous plasmalogen levels, however effective modulation of different molecular plasmalogen species has not yet been demonstrated. In this study, the effects of an orally-administered AKG mix (a mixture of chimyl, batyl and selachyl alcohol at a 1:1:1 ratio) on plasma and tissue lipids, including plasmalogens, was evaluated. Mice on a Western-type diet were treated with either an AKG mix or vehicle (lecithin) for 1, 2, 4, 8 and 12 weeks. Treatment with the AKG mix significantly increased the total plasmalogen content of plasma, liver and adipose tissue as a result of elevations in multiple plasmalogen species with different alkenyl chains. Alkylphospholipids, the endogenous precursors of plasmalogens, showed a rapid and significant increase in plasma, adipose tissue, liver and skeletal muscle. A significant accumulation of alkyl-diacylglycerol and lyso-ether phospholipids was also observed in plasma and tissues. Additionally, the dynamics of plasmalogen-level changes following AKG mix supplementation differed between tissues. These findings indicate that oral supplementation with an AKG mix is capable of upregulating and maintaining stable expression of multiple molecular plasmalogen species in circulation and tissues.

Statins are the first-line lipid-lowering therapy for reducing cardiovascular disease (CVD) risk. A plasma lipid ratio of two phospholipids, PI(36:2) and PC(18:0_20:4), was previously identified to explain 58% of the relative CVD risk reduction associated with pravastatin, independent of a change in low-density lipoprotein-cholesterol. This ratio may be a potential biomarker for the treatment effect of statins; however, the underlying mechanisms linking this ratio to CVD risk remain unclear. In this study, we investigated the effect of altered cholesterol conditions on the lipidome of cultured human liver cells (Hep3B). Hep3B cells were treated with simvastatin (5 μM), cyclodextrin (20 mg/mL) or cholesterol-loaded cyclodextrin (20 mg/mL) for 48 h and their lipidomes were examined. Induction of a low-cholesterol environment via simvastatin or cyclodextrin was associated with elevated levels of lipids containing arachidonic acid and decreases in phosphatidylinositol species and the PI(36:2)/PC(18:0_20:4) ratio. Conversely, increasing cholesterol levels via cholesterol-loaded cyclodextrin resulted in reciprocal regulation of these lipid parameters. Expression of genes involved in cholesterol and fatty acid synthesis supported the lipidomics data. These findings demonstrate that the PI(36:2)/PC(18:0_20:4) ratio responds to changes in intracellular cholesterol abundance per se, likely through a flux of the n-6 fatty acid pathway and altered phosphatidylinositol synthesis. These findings support this ratio as a potential marker for CVD risk reduction and may be useful in monitoring treatment response.

Prostate cancer (PCa) is the most common malignancy in men and the leading cause of death for men all over the world. Early diagnosis is the key to start treatment at an early stage of PCa and to reduce the death toll. Generally, PCa expresses characteristic morphologic features and serum biomarkers; however, early diagnosis is challenging due to its heterogeneity and long-term indolent phase in the early stage. Following positive diagnosis, PCa patients receive conventional treatments including surgery, radiation therapy, androgen deprivation therapy, focal therapy, and chemotherapy to enhance survival time and alleviate PCa-related complications. However, these treatment strategies have both short and long-term side effects, notably impotence, urinary incontinence, erectile dysfunctions, and recurrence of cancer. These limitations warrant the quest for novel PCa theranostic agents with robust diagnostic and therapeutic potentials to lessen the burden of PCa-related suffering. Iron oxide nanoparticles (IONPs) have recently drawn attention for their symmetrical usage in the diagnosis and treatment of several cancer types. Here, we performed a systematic search in four popular online databases (PubMed, Google Scholar, Scopus, and Web of Science) for the articles regarding PCa and IONPs. Published literature confirmed that the surface modification of IONPs with biopolymers and diagnostic biomarkers improved the early diagnosis of PCa, even in the metastatic stage with reliable accuracy and sensitivity. Furthermore, fine-tuning of IONPs with biopolymers, nucleic acids, anticancer drugs, and bioactive compounds can improve the therapeutic efficacy of these anticancer agents against PCa. This review covers the symmetrical use of IONPs in the diagnosis and treatment of PCa, investigates their biocompatibility, and examines their potential as PCa theranostic agents.

Computational approaches could help in identifying deleterious non-synonymous single nucleotide polymorphisms (nsSNPs) in a disease related gene which is a difficult and laborious task through laboratory experiments. In the present study, we analyzed the impacts of nsSNPs on structure and function of Paraxonase 1 (PON1) using different bioinformatics tools. The human PON1 protein sequence and its corresponding gene's SNP information were collected from UniProt and dbSNP databases, respectively. We utilized SIFT, Polyphen, I-Mutant 2.0, MutPred, SNP and GO, PhD-SNP and PANTHER tools in order to examine the total 39 nsSNPs occurring in the PON1 coding region. We filtered the most pathological mutations by combining the scores of the aforementioned servers and found 8 SNPs (G344C, S302L, W281C, D279Y, H134R, F120S, L90P, C42R) as deleterious and disease causing. The PDB structure of PON1 protein was obtained from RCSB Protein Data Bank (PDB ID: 1V04). The deleterious SNPs in native PON1 were introduced using Swiss-PDB Viewer package and changes in free energy were observed for six out of eight mutant structures. Two SNPs, S302L (substitution of serine to leucine at 302 position in amino acid sequence) and L90P (substitution of leucine to proline at 90 position in amino acid sequence) caused the highest energy increase amongst all. The findings implicate that these nsSNPs would be analyzed further in detail to enumerate their possible association with the protein deteriorating and disease causal potentialities.

Mycoplasma genitalium, a Gram-positive sexually transmitted pathogen, has been associated with urethritis in men and several inflammatory reproductive tract syndromes in women including cervicitis, pelvic inflammatory disease, and infertility. The complete sequence of the M. genitalium G37 genome revealed that it consists of 94 hypothetical proteins with unknown function in addition to functional proteins. In the present study, the MG_377 hypothetical protein of M. genitalium was selected for analyzing and modeling by different bioinformatics tools and databases. According to primary and secondary structure analyses, MG_377 is a stable hydrophilic protein containing a significant proportion of α-helices; besides, it is a cytoplasmic protein based on subcellular localization predictions. Homology modeling method was applied to generate its 3D structure using SWISS-MODEL server where the template PDB 1ZXJ with 84.4% sequence identity with the hypothetical protein was exploited. Several evaluations of quality assessment and validation parameters specified the generated protein model as reliable with fairly good quality. Functional genomics analysis carried out by InterProScan, Pfam and NCBI-CDD suggested that the hypothetical protein may contain Trigger factor/SurA domain. Moreover, comparative genomics analysis recommended MG_377 as a non-homologous protein essential for the organism. Further experimental validation would help to identify the actual function of MG_377 as well as to confirm the utility of the protein as drug targets.

The main pathophysiology of cerebral ischemia is the structural alteration in the neurovascular unit, coinciding with neurovascular matrix degradation. Resveratrol has been reported to be one of the most potent chemopreventive agents that can inhibit cellular processes associated with ischemic stroke. Matrix metalloproteinases (MMPs) has been considered as a potential drug target for the treatment of cerebral ischemia. To explore this, we tried to investigate the interaction of resveratrol with MMPs through molecular docking studies. At 30 minutes before and 2 hours after cerebral ischemia/reperfusion induced by occlusion of the middle cerebral artery, 40 mg/kg resveratrol was intraperitoneally administered. After resveratrol administration, neurological function and brain edema were significantly alleviated, cerebral infarct volume was significantly reduced, and nitrite and malondialdehyde levels in the cortical and striatal regions were significantly decreased. The molecular docking study of resveratrol and MMPs revealed that resveratrol occupied the active site of MMP-2 and MMP-9. The binding energy of the complexes was -37.848672 kJ/mol and -36.6345 kJ/mol for MMP-2 and MMP-9, respectively. In case of MMP-2, Leu 164, Ala 165 and Thr 227 were engaged in H-Bonding with resveratrol and in case of MMP-9, H-bonding was found with Glu 402, Ala 417 and Arg 424 residues. These findings collectively reveal that resveratrol exhibits neuroprotective effects on cerebral ischemia through inhibiting MMP-2 and MMP-9 activity.

The study reports on major and trace elements as well as antioxidant properties of honey samples from Bangladesh. Four major cationic elements, seven trace elements, and three heavy metals were determined in the 12 honey samples using atomic absorption spectrophotometer. Nutritional values in these honey samples were further investigated according to their antioxidant properties. The content of major elements was in the range of 62.75–616.58, 579.48–2219.43, 69.42–632.25, and 0.13–1.20 mg/kg for sodium, potassium, magnesium, and calcium, respectively. The trace elements varied in the range of 0.41–28, 0.12–3.54, 1.54–2.85, 0.29–0.59, 0.02–0.35, and 0.01–0.06 mg/kg for iron, zinc, copper, nickel, cobalt, and cadmium, respectively. Among the heavy metals, only lead (0.17–2.19 mg/kg) was detected. The results of antioxidant analysis based on phenolics, flavonoids, ascorbic acid, reducing sugar, and proteins (as nonphenolic antioxidants) revealed that multifloral raw honey samples contain significantly higher levels of reducing agents than monofloral and commercial brand honeys. The study provides a useful insight on the minerals, heavy metals, and antioxidant properties of honey samples commonly consumed in Bangladesh and found to be rich source of antioxidants and minerals. Some samples might pose some risk to the health due to lead contamination.

Our previous studies indicated an increase in extracellular γ-aminobutyric acid (GABA) in rodent′s ischemic brain after Piroxicam administration, leading to alleviation of glutamate mediated excitotoxicity through activation of type A GABA receptor (GABAA). This study was to investigate if GABAA activation by Piroxicam affects extracellular 5-hydroxytryptamine or not. High performance liquid chromatography revealed that there was a significant decrease in extracellular 5-hydroxytryptamine release in ischemic cerebral cortex and striatum in Piroxicam pre-treated rat brains. This suggests a probable role of Piroxicam in reducing extracellular 5-hydroxytryptamine release in ischemic cerebral cortex and striatum possibly due to the GABAA activation by Piroxicam.

Aquaporin-4(AQP4) is an abundant water channel protein in brain that regulates water transport to maintain homeostasis. Cerebral edema resulting from AQP4 over expression is considered to be one of the major determinants for progressive neuronal insult during cerebral ischemia. Although, both upregulation and downregulation of AQP4 expression is associated with brain pathology, over expression of AQP4 is one of the chief contributors of water imbalance in brain during ischemic pathology. We have found that Piroxicam binds to AQP4 with optimal binding energy value. Thus, we hypothesized that Piroxicam is neuroprotective in the rodent cerebral ischemic model by mitigating cerebral edema via AQP4 regulation.Rats were treated with Piroxicam OR placebo at 30 min prior, 2 h post and 4 h post 60 minutes of MCAO followed by 24 hour reperfusion. Rats were evaluated for neurological deficits and motor function just before sacrifice. Brains were harvested for infarct size estimation, water content measurement, biochemical analysis, RT-PCR and western blot experiments.Piroxicam pretreatment thirty minutes prior to ischemia and four hour post reperfusion afforded neuroprotection as evident through significant reduction in cerebral infarct volume, improvement in motor behavior, neurological deficit and reduction in brain edema. Furthermore, ischemia induced surge in levels of nitrite and malondialdehyde were also found to be significantly reduced in ischemic brain regions in treated animals. This neuroprotection was found to be associated with inhibition of acid mediated rise in intracellular calcium levels and also downregulated AQP4 expression.Findings of the present study provide significant evidence that Piroxicam acts as a potent AQP4 regulator and renders neuroprotection in focal cerebral ischemia. Piroxicam could be clinically exploited for the treatment of brain stroke along with other anti-stroke therapeutics in future.