The Role of Human Milk Lipids and Lipid Metabolites in Protecting the Infant against Non-Communicable Disease.

Non-communicable diseases continue to increase globally and have their origins early in life. Early life obesity tracks from childhood to adulthood, is associated with obesity, inflammation, and metabolic dysfunction, and predicts non-communicable disease risk in later life. There is mounting evidence that these factors are more prevalent in infants who are formula-fed compared to those who are breastfed. Human milk provides the infant with a complex formulation of lipids, many of which are not present in infant formula, or are present in markedly different concentrations, and the plasma lipidome of breastfed infants differs significantly from that of formula-fed infants. With this knowledge, and the knowledge that lipids have critical implications in human health, the lipid composition of human milk is a promising approach to understanding how breastfeeding protects against obesity, inflammation, and subsequent cardiovascular disease risk. Here we review bioactive human milk lipids and lipid metabolites that may play a protective role against obesity and inflammation in later life. We identify key knowledge gaps and highlight priorities for future research.

Shark liver oil supplementation enriches endogenous plasmalogens and reduces markers of dyslipidemia and inflammation.

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.

T helper 2-driven immune dysfunction in chronic arsenic-exposed individuals and its link to the features of allergic asthma.

Limited information is available regarding the effects of arsenic exposure on immune function. We have recently reported that chronic exposure to As was associated asthma, as determined by spirometry and respiratory symptoms. Because T helper 2 (Th2)-driven immune responses are implicated in the pathogenesis of allergic diseases, including asthma, we studied the associations of serum Th1 and Th2 mediators with the As exposure markers and the features of asthma among individuals exposed to As. A total of 553 blood samples were selected from the same study subjects recruited in our previous asthma study. Serum levels of Th1 and Th2 cytokines were analyzed by immunoassay. Subjects' arsenic exposure levels (drinking water, hair and nail arsenic concentrations) were determined by inductively coupled plasma mass spectroscopy. Arsenic exposure levels of the subjects showed significant positive associations with serum Th2-mediators- interleukin (IL)-4, IL-5, IL-13, and eotaxin without any significant changes in Th1 mediators- interferon-γ and tumor necrosis factor-α. The ratios of Th2 to Th1 mediators were significantly increased with increasing exposure to As. Notably, most of the Th2 mediators were positively associated with serum levels of total immunoglobulin E and eotaxin. The serum levels of Th2 mediators were significantly higher in the subjects with asthma than those without asthma. The results of our study suggest that the exacerbated Th2-driven immune responses are involved in the increased susceptibility to allergic asthma among individuals chronically exposed to As.

Generation of tumor antigen-specific murine CD8+ T cells with enhanced anti-tumor activity via highly efficient CRISPR/Cas9 genome editing.

Immunotherapies have led to the successful development of novel therapies for cancer. However, there is increasing concern regarding the adverse effects caused by non-tumor-specific immune responses. Here, we report an effective strategy to generate high-avidity tumor-antigen-specific CTLs, using Cas9/single-guide RNA (sgRNA) ribonucleoprotein (RNP) delivery. As a proof-of-principle demonstration, we selected the gp100 melanoma-associated tumor antigen, and cloned the gp100-specific high-avidity TCR from gp100-immunized mice. To enable rapid structural dissection of the TCR, we developed a 3D protein structure modeling system for the TCR/antigen-major histocompatibility complex (pMHC) interaction. Combining these technologies, we efficiently generated gp100-specific PD-1(-) CD8+ T cells, and demonstrated that the genetically engineered CD8+ T cells have high avidity against melanoma cells both in vitro and in vivo. Our methodology offers computational prediction of the TCR response, and enables efficient generation of tumor antigen-specific CD8+ T cells that can neutralize tumor-induced immune suppression leading to a potentially powerful cancer therapeutic.

Chronic exposure to arsenic, LINE-1 hypomethylation, and blood pressure: a cross-sectional study in Bangladesh.

BACKGROUND: Chronic exposure to arsenic is associated with cancer and hypertension. Growing evidence suggests that altered methylation in long interspersed nuclear element-1 (LINE-1) is involved in many types of disorders, including cardiovascular disease. Here we evaluated the association between arsenic exposure and LINE-1 methylation levels, especially in relation to blood pressure (BP). METHODS: A total of 236 subjects (175 from arsenic-endemic areas and 61 from a non-endemic area) in rural Bangladesh were recruited. The subjects' arsenic exposure levels (i.e., drinking water, hair and nail arsenic concentrations) were measured by inductively coupled plasma mass spectroscopy. The subjects' LINE-1 methylation levels were determined by pyrosequencing. RESULTS: The average LINE-1 methylation levels of the subjects living in the arsenic-endemic areas were significantly (p < 0.01) lower than those of the subjects living in the non-endemic area. In a sex-stratified analysis, the arsenic exposure levels in female but not male subjects showed a significant inverse association with LINE-1 methylation levels before (water arsenic: p < 0.01, hair arsenic: p < 0.05, nail arsenic: p < 0.001) and after (water arsenic: p < 0.01, hair arsenic: p < 0.05, nail arsenic: p < 0.001) adjustment for age, body mass index and smoking. Analyses examining interactions among arsenic levels, BP and LINE-1 methylation showed that arsenic-related elevated levels of BP were associated with LINE-1 hypomethylation. CONCLUSIONS: Our findings demonstrated that chronic exposure to arsenic was inversely associated with LINE-1 methylation levels in blood leukocyte DNA and this was more pronounced in females than males; in addition, the decreased levels of LINE-1 methylation might be involved in the arsenic-induced elevation of BP.

Protective mechanism of turmeric (Curcuma longa) on carbofuran-induced hematological and hepatic toxicities in a rat model.

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.

Physicochemical Properties, Minerals, Trace Elements, and Heavy Metals in Honey of Different Origins: A Comprehensive Review.

Honey is a popular natural food product with a very complex composition mainly consisting of both organic and inorganic constituents. The composition of honey is strongly influenced by both natural and anthropogenic factors, which vary based on its botanical and geographical origins. Although minerals and heavy metals are minor constituents of honey, they play vital role in determining its quality. There are several different analytical methods used to determine the chemical elements in honey. These methods are typically based on spectroscopy or spectrometry techniques (including atomic absorption spectrometry, atomic emission spectrometry, inductively coupled plasma mass spectrometry, and inductively coupled plasma optical emission spectrometry). This review compiles available scientific information on minerals and heavy metals in honey reported from all over the world. To date, 54 chemical elements in various types of honey have been identified and can be divided into 3 groups: major or macroelements (Na, K, Ca, Mg, P, S, Cl), minor or trace elements (Al, Cu, Pb, Zn, Mn, Cd, Tl, Co, Ni, Rb, Ba, Be, Bi, U, V, Fe, Pt, Pd, Te, Hf, Mo, Sn, Sb, La, I, Sm, Tb, Dy, Sd, Th, Pr, Nd, Tm, Yb, Lu, Gd, Ho, Er, Ce, Cr, As, B, Br, Cd, Hg, Se, Sr), and heavy metals (trace elements that have a specific gravity at least 5 times higher than that of water and inorganic sources). Chemical elements in honey samples throughout the world vary in terms of concentrations and are also influenced by environmental pollution.

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion.

BACKGROUND: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot. OBJECTIVE: In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other dataset. METHODS: In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated. RESULTS: 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy. CONCLUSION: The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement for TCIA and BRaTS datasets respectively.

Importance of health history analysis in Parkinson's disease.

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).

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

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.

Retrotransposons in Werner syndrome-derived macrophages trigger type I interferon-dependent inflammation in an atherosclerosis model.

The underlying mechanisms of atherosclerosis, the second leading cause of death among Werner syndrome (WS) patients, are not fully understood. Here, we establish an in vitro co-culture system using macrophages (iMφs), vascular endothelial cells (iVECs), and vascular smooth muscle cells (iVSMCs) derived from induced pluripotent stem cells. In co-culture, WS-iMφs induces endothelial dysfunction in WS-iVECs and characteristics of the synthetic phenotype in WS-iVSMCs. Transcriptomics and open chromatin analysis reveal accelerated activation of type I interferon signaling and reduced chromatin accessibility of several transcriptional binding sites required for cellular homeostasis in WS-iMφs. Furthermore, the H3K9me3 levels show an inverse correlation with retrotransposable elements, and retrotransposable element-derived double-stranded RNA activates the DExH-box helicase 58 (DHX58)-dependent cytoplasmic RNA sensing pathway in WS-iMφs. Conversely, silencing type I interferon signaling in WS-iMφs rescues cell proliferation and suppresses cellular senescence and inflammation. These findings suggest that Mφ-specific inhibition of type I interferon signaling could be targeted to treat atherosclerosis in WS patients.

A let-7 microRNA-RALB axis links the immune properties of iPSC-derived megakaryocytes with platelet producibility.

We recently achieved the first-in-human transfusion of induced pluripotent stem cell-derived platelets (iPSC-PLTs) as an alternative to standard transfusions, which are dependent on donors and therefore variable in supply. However, heterogeneity characterized by thrombopoiesis-biased or immune-biased megakaryocytes (MKs) continues to pose a bottleneck against the standardization of iPSC-PLT manufacturing. To address this problem, here we employ microRNA (miRNA) switch biotechnology to distinguish subpopulations of imMKCLs, the MK cell lines producing iPSC-PLTs. Upon miRNA switch-based screening, we find imMKCLs with lower let-7 activity exhibit an immune-skewed transcriptional signature. Notably, the low activity of let-7a-5p results in the upregulation of RAS like proto-oncogene B (RALB) expression, which is crucial for the lineage determination of immune-biased imMKCL subpopulations and leads to the activation of interferon-dependent signaling. The dysregulation of immune properties/subpopulations, along with the secretion of inflammatory cytokines, contributes to a decline in the quality of the whole imMKCL population.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When 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. Methodology: Annotations 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. Results: Among 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 < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Development and validation of a plasmalogen score as an independent modifiable marker of metabolic health: population based observational studies and a placebo-controlled cross-over study.

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).

A lipid atlas of human and mouse immune cells provides insights into ferroptosis susceptibility.

The cellular lipidome comprises thousands of unique lipid species. Here, using mass spectrometry-based targeted lipidomics, we characterize the lipid landscape of human and mouse immune cells ( www.cellularlipidatlas.com ). Using this resource, we show that immune cells have unique lipidomic signatures and that processes such as activation, maturation and development impact immune cell lipid composition. To demonstrate the potential of this resource to provide insights into immune cell biology, we determine how a cell-specific lipid trait-differences in the abundance of polyunsaturated fatty acid-containing glycerophospholipids (PUFA-PLs)-influences immune cell biology. First, we show that differences in PUFA-PL content underpin the differential susceptibility of immune cells to ferroptosis. Second, we show that low PUFA-PL content promotes resistance to ferroptosis in activated neutrophils. In summary, we show that the lipid landscape is a defining feature of immune cell identity and that cell-specific lipid phenotypes underpin aspects of immune cell physiology.

Ehf controls mammary alveolar lineage differentiation and is a putative suppressor of breast tumorigenesis.

The transcription factor EHF is highly expressed in the lactating mammary gland, but its role in mammary development and tumorigenesis is not fully understood. Utilizing a mouse model of Ehf deletion, herein, we demonstrate that loss of Ehf impairs mammary lobuloalveolar differentiation at late pregnancy, indicated by significantly reduced levels of milk genes and milk lipids, fewer differentiated alveolar cells, and an accumulation of alveolar progenitor cells. Further, deletion of Ehf increased proliferative capacity and attenuated prolactin-induced alveolar differentiation in mammary organoids. Ehf deletion also increased tumor incidence in the MMTV-PyMT mammary tumor model and increased the proliferative capacity of mammary tumor organoids, while low EHF expression was associated with higher tumor grade and poorer outcome in luminal A and basal human breast cancers. Collectively, these findings establish EHF as a non-redundant regulator of mammary alveolar differentiation and a putative suppressor of mammary tumorigenesis.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Effect of learning parameters on the performance of the U-Net architecture for cell nuclei segmentation from microscopic cell images.

Nuclei segmentation of cells is the preliminary and essential step of pathological image analysis. However, robust and accurate cell nuclei segmentation is challenging due to the enormous variability of staining, cell sizes, morphologies, cell adhesion or overlapping of the nucleus. The automation process to find the cell's nuclei is a giant leap in this direction and has an important step toward bioimage analysis using software tools. This article extensively analyzes deep U-Net architecture and has been applied to the Data Science Bowl dataset to segment the cell nuclei. The dataset undergoes various preprocessing tasks such as resizing, intensity normalization and data augmentation prior to segmentation. The complete dataset then undergoes the rigorous training and validation process to find the optimized hyperparameters and then the optimized model selection. The mean (m) ± standard deviation (SD) of Intersection over Union (IoU) and F1-score (Dice score) have been calculated along with accuracy during the training and validation process, respectively. The optimized U-Net model results in a training IoU of 0.94 ± 0.16 (m ± SD), an F1-score of 0.94 ± 0.17 (m ± SD), a training accuracy of 95.54 and validation accuracy of 95.45. With this model, we applied a completely independent test cohort of the dataset and obtained the mean IOU of 0.93, F1-score of 0.9311, and mean accuracy of 94.12, respectively to measure the segmentation performance.

Defining the lipid profiles of human milk, infant formula, and animal milk: implications for infant feeding.

Background: Breastfed 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. Methods: Human 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. Results: Lipidomics 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. Conclusion: There 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.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.