Search Results (35)

Background High-sensitivity C-reactive protein (hs-CRP) is a protein that indicates inflammation and the risk of cardiovascular diseases. The intake of dietary amino acids can influence immune and inflammatory reactions. However, studies on the relationship between dietary amino acids and hs-CRP, especially in children, remain scarce. Methods This cross-sectional study analyzed data from the Nutrition and China Children and Lactating Women Nutrition and Health Survey (2016–2019), focusing on 3514 children (724 with elevated hs-CRP ≥ 3 mg/L and 2790 with normal levels). Dietary information was gathered via a food frequency questionnaire, and hs-CRP levels were obtained from blood samples. Boruta algorithm and propensity scores were used to select and match dietary factors and sample sizes. Machine learning (ML) algorithms and logistic regression models assessed the link between amino acid intake and elevated hs-CRP risk, adjusting for age, sex, BMI, and lifestyle factors. Results The odds ratios (ORs) for elevated hs-CRP were significant for several amino acids, including Ile, Leu, Lys, Ser, Cys, Tyr, His, Pro, SAA, and AAA, with values ranging from 1.10 to 2.07. The LightGBM algorithm was the most effective in predicting elevated hs-CRP risk, achieving an AUC of 0.927. Tyrosine, methionine, cysteine, and proline were identified as important features by SHAP analysis and logistic regression. The intake of Ser, Cys, Tyr, and Pro showed a linear increase in the risk of elevated hs-CRP, especially in individuals with low protein intake and normal weight (p < 0.1). Conclusions Intake of amino acids like Ser, Cys, Tyr, and Pro significantly impacts hs-CRP levels in children, indicating that regulating these could help prevent inflammation-related diseases. This study supports future dietary and health management strategies. This is first large-scale ML study linking amino acids to pediatric inflammation in China. The main limitations are the cross-section design and the use of self-reported dietary data. Full article

Background/Objectives: Major adverse cardiovascular events (MACE)—including heart attacks and strokes—are the leading cause of death in patients with peripheral artery disease (PAD), yet biomarker research for MACE prediction in PAD patients remains limited. Inflammatory proteins play a key role in the progression of atherosclerosis and may serve as useful prognostic indicators for systemic cardiovascular risk in PAD. The objective of this study was to evaluate a broad panel of circulating inflammatory proteins to identify those independently associated with 2-year MACE in patients with PAD. Methods: We conducted a prospective cohort study involving 465 patients with PAD. Plasma concentrations of 15 inflammatory proteins were measured at baseline using validated immunoassays. Patients were followed over a two-year period for the development of MACE, defined as a composite endpoint of myocardial infarction, stroke, or mortality. Protein levels were compared between patients with and without MACE using the Mann–Whitney U test. Cox proportional hazards regression was used to determine the independent association of each protein with MACE after adjusting for baseline demographic and clinical variables, including existing coronary and cerebrovascular disease. To validate the findings, a random forest machine learning model was developed to assess the relative importance of each protein for predicting 2-year MACE. Results: The mean age of the cohort was 71 years (SD 10), and 145 participants (31.1%) were female. Over the two-year follow-up, 84 patients (18.1%) experienced MACE. Six proteins were significantly elevated in PAD patients who developed MACE: interferon gamma (IFN-γ; 42.55 [SD 15.11] vs. 33.85 [SD 12.46] pg/mL, p < 0.001), tumor necrosis factor alpha (TNF-α; 9.00 [SD 5.00] vs. 4.65 [SD 4.29] pg/mL, p < 0.001), chemokine (C-X-C motif) ligand 9 (CXCL9; 75.99 [SD 65.14] vs. 5.38 [SD 64.18] pg/mL, p = 0.002), macrophage inflammatory protein-1 beta (MIP-1β; 20.88 [SD 18.10] vs. 15.67 [SD 16.93] pg/mL, p = 0.009), MIP-1δ (25.29 [SD 4.22] vs. 17.98 [SD 4.01] pg/mL, p = 0.026), and interleukin-6 (IL-6; 12.50 [SD 40.00] vs. 6.72 [SD 38.98] pg/mL, p = 0.035). After adjusting for all baseline covariates, only two proteins—TNF-α (adjusted HR 1.66, 95% CI 1.28–2.33, p = 0.001) and IFN-γ (adjusted HR 1.25, 95% CI 1.12–2.29, p = 0.033)—remained significantly and independently associated with 2-year MACE. These findings were corroborated by the random forest model, where TNF-α and IFN-γ received the highest importance scores for predicting 2-year MACE: (TNF-α: 0.15 [95% CI 0.13–0.18], p = 0.002; IFN-γ: 0.19 [95% CI 0.17–0.21], p = 0.001). Conclusions: From a panel of 15 proteins, TNF-α and IFN-γ emerged as inflammatory biomarkers associated with 2-year MACE in PAD patients. Their measurement may aid in cardiovascular risk stratification, helping to identify high-risk individuals who could benefit from early multidisciplinary referrals to cardiology, neurology, and/or vascular medicine specialists to provide intensified medical therapy. Incorporating these biomarkers into PAD management may improve systemic cardiovascular outcomes through more personalized and targeted treatment approaches. Full article

Background/Objectives: Lumbar disc herniation (LDH) is the most common condition associated with low back pain, and it adversely impacts individuals’ health. The interplay between energy metabolism and apoptosis is critical, as the loss of viable cells in the intervertebral disc (IVD) can lead to a cascade of degenerative changes. Efferocytosis is a key biological process that maintains homeostasis by removing apoptotic cells, resolving inflammation, and promoting tissue repair. Therefore, enhancing mitochondrial energy metabolism and efferocytosis function in IVD cells holds great promise as a potential therapeutic approach for LDH. Methods: In this study, energy metabolism and efferocytosis-related differentially expressed genes (EMERDEGs) were identified from the transcriptomic datasets of LDH. Machine learning approaches were used to identify key genes. Functional enrichment analyses were performed to elucidate the biological roles of these genes. The functions of the hub genes were validated by RT-qPCR. The CIBERSORT algorithm was used to compare immune infiltration between LDH and Control groups. Additionally, we used single-cell RNA sequencing dataset to analyze cell-specific expression of the hub genes. Results: By using bioinformatics methods, we identified six EMERDEGs hub genes (IL6R, TNF, MAPK13, ELANE, PLAUR, ABCA1) and verified them using RT-qPCR. Functional enrichment analysis revealed that these genes were primarily associated with inflammatory response, chemokine production, and cellular energy metabolism. Further, we identified candidate drugs as potential treatments for LDH. Additionally, in immune infiltration analysis, the abundance of activated dendritic cells, neutrophils, and gamma delta T cells varied significantly between the LDH group and Control group. The scRNA-seq analysis showed that these hub genes were mainly expressed in chondrocyte-like cells. Conclusions: The identified EMERDEG hub genes and pathways offer novel insights into the molecular mechanisms underlying LDH and suggest potential therapeutic targets. Full article

Sustainable informal learning environments are increasingly recognized as critical components of educational architecture, yet their environmental and behavioral dynamics remain underexplored. Informal learning spaces (ILS) support flexible, student-driven learning beyond formal classrooms. While prior research often isolates individual environmental factors, integrated multi-domain interactions and reciprocal occupant–space dynamics receive less attention. This study adopts a dual-perspective analytical framework, combining spatial analysis and student surveys (n = 1048) across 130 ILS in five academic buildings in China. The findings highlight several environmental dimensions influencing student experience. One extracted factor combines acoustic and thermal comfort with learning atmosphere—domains seldom grouped together—indicating their collective relevance to student experience. Additionally, spatial openness and natural connectivity further enhance student experience. Importantly, the results show that frequently used spaces receive lower physical quality ratings, group collaboration areas outperform individual study zones, and spontaneously formed spaces—informally appropriated, unplanned areas such as corridors or leftover corners—score lowest. These patterns may reflect mismatches between spatial supply and use intensity, institutional investment priorities, and differing levels of student autonomy and environmental control. This research extends conventional post-occupancy evaluations by introducing a comprehensive dual-perspective framework that links spatial characteristics with user-driven dynamics, and by identifying the combined effects of multi-domain physical environmental and supportive elements on student experience. The insights offer empirical grounding and actionable strategies for campus planners and architects, including prioritizing sensory comfort, enhancing spatial diversity, and supporting student-led adaptations to promote sustainable learning environments. Full article

Coronary artery disease (CAD) remains a major cause of cardiovascular morbidity and mortality, with growing evidence linking immune dysregulation to its pathogenesis. Aortic stenosis often coexists with CAD (ASCAD), representing an advanced disease form. This study investigates immune pathways in isolated CAD (iCAD) and ASCAD. For this purpose, peripheral blood from 72 individuals (healthy donors, iCAD, and ASCAD patients) was analysed via flow cytometry to assess immune populations. Circulating cytokine levels were measured, and machine learning models identified predictive immune biomarkers. Our data showed that both iCAD and ASCAD patients exhibited immune dysregulation, with reduced dendritic cells, basophils, NK cells, B cells, and T cells, alongside lower frequencies of DCs, lymphocytes, CD8+CD28+ T cells, and CD57+ T cells. Elevated IL-15 and fractalkine, but reduced IL-8 and MCP-1, suggest impaired monocyte and neutrophil mobilisation due to immune cell sequestration in vascular lesions. Distinct immune features emerged between iCAD and ASCAD. iCAD patients showed heightened immune activation, with increased inflammatory CD14+CD16+ monocytes, higher Treg frequencies, and greater CD4+ T cell differentiation into TEM and TEMRA phenotypes. In contrast, ASCAD patients exhibited pronounced immunosenescence, with higher neutrophil counts, lymphopenia, and increased NK and T cell cytotoxicity. Our predictive model distinguished iCAD from ASCAD with high accuracy, identifying CD4+ T cell memory subsets and CD57 expression as key discriminators. This study reveals iCAD as being driven by immune activation and ASCAD by immunosenescence and cytotoxicity. These insights advance CAD immunopathology understanding and support immune-based classification, particularly for ASCAD, where treatment remains limited to surgical intervention. Full article

Depression and anxiety are mental health disorders that significantly impact global public health, affecting more than 280 million people with depression and 301 million with anxiety worldwide. These conditions impair individuals’ ability to engage in economic and personal activities and can lead to severe outcomes, such as suicide. Current research suggests that inflammatory cytokines, such as interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor (TNF), play crucial roles in the pathophysiology of these disorders, influencing neurotransmitters. Elevated cortisol levels, typically associated with anxiety, worsen these conditions through dysregulation of the hypothalamic–pituitary–adrenal (HPA) axis. Additionally, vitamin D deficiency has been linked to reduced production of dopamine and norepinephrine, hormones involved in depressive symptoms. This study utilized the Random Forest machine learning algorithm along with cross-validation to assess the importance of various biomarkers, including IL-1β, IL-6, IL-8, TNF, cortisol, vitamin D, NT-proBNP, CK-MB, troponin, myoglobin, and C-reactive protein (CRP), in volunteers of both sexes diagnosed with mental disorders. A single sample from each of the 96 participants was analyzed, consisting of 50 women and 46 men. The results revealed sex-specific differences in biomarker relevance, with vitamin D, CRP, and D-dimer being the most predictive for depression in men, while IL-6, CRP, and vitamin D were significant in women. For anxiety, vitamin D and myoglobin were important biomarkers in men, while IL-8 and vitamin D were key in women. The methodological strategy adopted, based on the use of Random Forest and cross-validation assessment, not only confirmed the robustness of the model but also reliably identified the most important biomarkers for the outcomes studied. Full article

In recent years, system intelligence has revolutionized various domains, including the automotive industry, which has fully incorporated intelligence through the emergence of Advanced Driver Assistance Systems (ADAS). Within this transformative context, Autonomous Parking Systems (APS) have emerged as a foundational component, revolutionizing the way vehicles navigate and park with precision and efficiency. This paper presents a comprehensive approach to autonomous parallel parking, leveraging advancements in Artificial Intelligence (AI). Three state-of-the-practice approaches—Imitation Learning (IL), deep Reinforcement Learning (deep RL), and a hybrid deep RL-IL method—are employed and evaluated through extensive experiments in the CARLA Simulator using randomly generated parallel parking scenarios. Results demonstrate that the hybrid deep RL-IL approach achieves a remarkable success rate of 98% in parking attempts, surpassing the individual IL and deep RL methods. Furthermore, the proposed hybrid model exhibits superior maneuvering efficiency and higher overall reward accumulation. These findings underscore the advantages of combining deep RL and IL, representing a significant advancement in APS technology. Full article

This study aimed to identify the cytokine expression profile in prostate cancer (PCa) patients compared to healthy individuals. Plasma samples from 75 PCa patients and 14 healthy controls were analyzed using Multiplex ELISA (Luminex) to measure the expression levels of 12 cytokines: IL-4, IL-5, IL-6, IL-10, IL-1β, IL-17A, IL-12p70, MCP-1/CCL2, MIP-1α/CCL3, MIP-1β/CCL4, TNF-α, and IFN-γ. Differences in cytokine expression levels were analyzed using the Mann–Whitney test, Wilcoxon’s rank-sum test, Spearman’s rank correlation, and K-means Clustering unsupervised machine learning to validate cytokine expression patterns. In PCa patients, MIP-1α/CCL3, MIP-1β/CCL4, IFN-γ, and interleukins exhibited significantly higher expression levels; conversely, TNF-α and MCP-1/CCL2 both had decreased expression compared to healthy individuals. The clustering analysis confirmed that PCa patients exclusively exhibit the highest associations with MIP-1α/CCL3, IFN- γ, IL-12p70, IL-4, and IL-5. Furthermore, specific correlations between IL-4 and MIP-1 beta, IL-4 and IFN-gamma, IL-5 and IL-12p70, and IL-5 and IFN-gamma in PCa patients did not occur in healthy individuals. Such results will guide forthcoming in vitro and in vivo human prostate cancer-drug treatment models, paving the way for exploration of future drug targets and candidates with potential to predict FDA-approved prostate cancer treatment responses by targeting cytokine levels and the oncogenesis pathways. Full article

Type 1 diabetes (T1D) is an autoimmune condition characterized by the destruction of insulin-producing pancreatic beta cells, leading to lifelong insulin dependence and significant complications. Early detection of T1D is essential to delay disease onset and improve outcomes. Recent advancements in artificial intelligence (AI) and machine learning (ML) have provided powerful tools for predicting and diagnosing T1D. This systematic review evaluates the current landscape of AI/ML-based approaches for early T1D detection. A comprehensive search across PubMed, EMBASE, Science Direct, and Scopus identified 1447 studies, of which 10 met the inclusion criteria for narrative synthesis after screening and full-text review. The studies utilized diverse ML models, including logistic regression, support vector machines, random forests, and artificial neural networks. The datasets encompassed clinical parameters, genetic risk markers, continuous glucose monitoring (CGM) data, and proteomic and metabolomic biomarkers. The included studies involved a total of 49,172 participants and employed case–control, retrospective cohort, and prospective cohort designs. Models integrating multimodal data achieved the highest predictive accuracy, with area under the curve (AUC) values reaching up to 0.993 in sex-specific models. CGM data and plasma biomarkers, such as CXCL10 and IL-1RA, also emerged as valuable tools for identifying at-risk individuals. While the results highlight the potential of AI/ML in revolutionizing T1D risk stratification and diagnosis, challenges remain. Data heterogeneity and limited model generalizability present barriers to widespread implementation. Future research should prioritize the development of universal frameworks and real-world validation to enhance the reliability and clinical integration of these tools. Ultimately, AI/ML technologies hold transformative potential for clinical practice by enabling earlier diagnosis, guiding targeted interventions, and improving long-term patient outcomes. These advancements could support clinicians in making more informed, timely decisions, thus reducing diagnostic delays and paving the way for personalized prevention strategies in both pediatric and adult populations. Full article

Background/Objectives: Aging and immune mechanisms play a key role in the development of cardiovascular disease (CVD), especially in the context of chronic inflammation. Therefore, in order to detect early aging in the elderly, we have developed a prognostic model based on clinical and immunological markers using machine learning. Methods: This paper analyzes the relationships between immunological markers, clinical parameters, and lifestyle factors in individuals over 60 years of age. A machine learning (ML) model including random forest, logistic regression, k-nearest neighbors, and XGBoost was developed to predict the aging rate and risk of CVD. Correlation anal is revealed significant associations between immune markers (CD14+, HLA-DR, IL-10, CD8+), clinical parameters (BMI, coronary heart disease, hypertension, diabetes), and behavioral factors (physical activity, smoking, alcohol). Results: The results of the study confirm that systemic inflammation, as reflected by markers such as CD14+, HLA-DR, and IL-10, plays a central role in the pathogenesis of aging and related diseases. CD14+ shows a moderate positive correlation with post-infarction cardiosclerosis, accounting for 37%. HLA-DR correlates with body mass index at 39%. A negative association between IL-10 level and BMI was also found, where the correlation reaches 52% (r = −0.52). The level of CD8+ cells shows a negative correlation with smoking and their number, being 40%. Training was performed on clinical and immunological data and models were evaluated using accuracy, ROC-AUC, and F1-score metrics. Among all the trained models, the XGBoost model performed best, achieving an accuracy of 91% and an area under the ROC curve (AUC) of 0.8333. Conclusions: The study reveals significant correlations between immunological markers and clinical parameters, which allows the assessment of individual risks of premature cardiovascular aging. R (version 4.3.0) and specialized libraries for correlation matrix construction and visualization were used for data analysis, and Python (version 3.11.11) was used for model development and training. Full article

The incidence of cognitive disorders is increasing globally, with a reported prevalence of over 50 million individuals affected, and current interventions offer limited efficacy. This study investigates the effects of sunflower oil fortified with sunflower lecithin, vitamin D, and vitamin A on scopolamine-induced cognitive dysfunction in mice and explores the underlying mechanisms. The incidence of cognitive disorders, such as Alzheimer’s disease, is increasing yearly, and current interventions offer limited efficacy. Therefore, this research aims to evaluate the cognitive improvement effects of the three added functional factors on mice with learning and memory impairments, along with the associated molecular mechanisms. Behavioral tests, biochemical assays, and real-time quantitative polymerase chain reaction (RT-qPCR) were utilized to examine the intervention effects of these functional factors on scopolamine-induced cognitive impairment in mice. The results revealed that the groups treated with sunflower lecithin and vitamin D significantly enhanced the mice’s exploratory behavior, working memory, and spatial memory, with increases of 1.6 times and 4.5 times, respectively, in the open field and novel object recognition tests (VD group). Additionally, these treatments reduced levels of inflammatory markers and IL-6, increased antioxidant GSH levels, and decreased oxidative stress marker MDA levels, with all effects showing significant differences (p < 0.01). The effects were further enhanced when vitamin A was combined with these treatments. Transcriptomic analysis demonstrated that the intervention groups had markedly improved learning and memory abilities through upregulation of key gene expression levels in the PI3K-AKT signaling pathway, cholinergic pathway, and folate biosynthesis pathway. These findings provide a theoretical basis for the development of nutritionally fortified edible oils with added sunflower lecithin, vitamin D, and vitamin A, which may help prevent and ameliorate cognitive disorders. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) are neurodevelopmental disorders marked by challenges in social interaction, communication, and repetitive behaviors. People with ASD may exhibit repetitive behaviors, unique ways of learning, and different ways of interacting with the world. The term “spectrum” reflects the wide variability in how ASD manifests in individuals, including differences in abilities, symptoms, and support needs, and conditions characterized by difficulties in social interactions, communication, restricted interests, and repetitive behaviors. Inflammation plays a crucial role in the pathophysiology, with increased pro-inflammatory cytokines in cerebrospinal fluid. Previous studies with transcranial magnetic stimulation have shown promising results, suggesting nervous system susceptibility to electromagnetic fields, with evidence indicating that extremely low-frequency electromagnetic field (ELF-EMF) treatment may modulate inflammatory responses through multiple pathways, including the reduction of pro-inflammatory cytokines like IL-6 and TNF-α, and the enhancement of anti-inflammatory mediators. Methods: This pilot study included 20 children (ages 2–13) with a confirmed diagnosis of ASD. A 15-week protocol involved ELF-EMF treatments using the SEQEX device, with specific day and night programs. Assessment was conducted through standardized pre- and post-treatment tests: Achenbach Child Behavior Checklist, Peabody Picture Vocabulary Test-4, Expressive One Word Picture Vocabulary Test-4, and Conner’s 3GI. Results: Statistically significant improvements were observed in receptive language (PPVT-4: from 74.07 to 90.40, p = 0.002) and expressive language (EOWPVT-4: from 84.17 to 90.50, p = 0.041). Notable reductions, with statistical significance, were found in externalizing problems across both age groups (1.5–5 years: p = 0.028; 6–18 years: p = 0.027), with particular improvement in attention and behavioral problems. The results were observed over a short period of 15 weeks, therefore excluding the possibility of coincidental age-related gains, that would typically occur during a normal developmental timeframe. Parent evaluations showed significant reduction in ASD symptoms, particularly in the 1.5–5 years group (p = 0.046). Conclusions: ELF-EMF treatment demonstrated a high safety profile and efficacy in mitigating ASD-related symptoms. The observed improvements suggest both direct effects on central and autonomic nervous systems and indirect effects through inflammatory response modulation. Further studies are needed to confirm these promising results through broader demographics and randomized control designs. Full article

The sports environment plays a crucial role in shaping the physical and mental well-being of individuals engaged in sports activities. Understanding how environmental factors and emotional experiences influence sports perceptions is essential for advancing public health research and guiding optimal design interventions. However, existing studies in this field often rely on subjective evaluations, lack objective validation, and fail to provide practical insights for design applications. To address these gaps, this study adopts a data-driven approach. Quantitative data were collected to explore the visual environment of badminton courts using eye-tracking technology and a semantic differential questionnaire. The relationships between environmental factors—such as illuminance (IL), height (Ht), roof saturation (RSa), roof slope (RS), backwall saturation (BSa), and natural materials proportion on the backwall (BN)—and sports perception (W) were analyzed. Furthermore, this study identifies the best-performing machine learning model for predicting sports perception, which is subsequently integrated with a genetic algorithm to optimize environmental design thresholds. These findings provide actionable insights for creating sports environments that enhance user experience and support public health objectives. Full article

Physical activity is effective for preventing and treating type 2 diabetes, but some individuals do not achieve metabolic benefits from exercise (“non-responders”). We investigated non-responders in terms of insulin sensitivity changes following a 12-week supervised strength and endurance exercise program. We used a hyperinsulinaemic euglycaemic clamp to measure insulin sensitivity among 26 men aged 40–65, categorizing them into non-responders or responders based on their insulin sensitivity change scores. The exercise regimen included VO₂max, muscle strength, whole-body MRI scans, muscle and fat biopsies, and serum samples. mRNA sequencing was performed on biopsies and Olink proteomics on serum samples. Non-responders showed more visceral and intramuscular fat and signs of dyslipidaemia and low-grade inflammation at baseline and did not improve in insulin sensitivity following exercise, although they showed gains in VO₂max and muscle strength. Impaired IL6-JAK-STAT3 signalling in non-responders was suggested by serum proteomics analysis, and a baseline serum proteomic machine learning (ML) algorithm predicted insulin sensitivity responses with high accuracy, validated across two independent exercise cohorts. The ML model identified 30 serum proteins that could forecast exercise-induced insulin sensitivity changes. Full article

Context/Objective: In order to better understand which metabolic differences are related to insulin resistance in metabolic syndrome (MetSyn), we used hyperinsulinemic–euglycemic (HE) clamps in individuals with MetSyn and related peripheral insulin resistance to circulating biomarkers. Design/Methods: In this cross-sectional study, HE-clamps were performed in treatment-naive men (n = 97) with MetSyn. Subjects were defined as insulin-resistant based on the rate of disappearance (Rd). Machine learning models and conventional statistics were used to identify biomarkers of insulin resistance. Findings were replicated in a cohort with n = 282 obese men and women with (n = 156) and without (n = 126) MetSyn. In addition to this, the relation between biomarkers and adipose tissue was assessed by nuclear magnetic resonance imaging. Results: Peripheral insulin resistance is marked by changes in proteins related to inflammatory processes such as IL-1 and TNF-receptor and superfamily members. These proteins can distinguish between insulin-resistant and insulin-sensitive individuals (AUC = 0.72 ± 0.10) with MetSyn. These proteins were also associated with IFG, liver fat (rho 0.36, p = 1.79 × 10⁻⁹) and visceral adipose tissue (rho = 0.35, p = 6.80 × 10⁻⁹). Interestingly, these proteins had the strongest association in the MetSyn subgroup compared to individuals without MetSyn. Conclusions: MetSyn associated with insulin resistance is characterized by protein changes related to body fat content, insulin signaling and pro-inflammatory processes. These findings provide novel targets for intervention studies and should be the focus of future in vitro and in vivo studies. Full article