Hasil untuk "Nutritional diseases. Deficiency diseases"

R. Goyer

J. Shenai

Yang Zhang, Yiwen Zhu, Mengying Li et al.

Abstract Objectives Oxymatrine (OMT), a quinolizidine alkaloid derived from Sophora flavescens Ait., has demonstrated therapeutic potential in type 2 diabetes mellitus (T2DM). This study aimed to investigate its effects on diabetic osteoporosis (DOP) and explore the underlying mechanisms involving gut microbiota and osteogenic regulation. Methods In a rat model of T2DM, intragastric Oxymatrine was used to study trabecular bone repair through bone microstructure and histopathology analyses. Changes in gut microbiota, especially Gram-negative bacteria releasing lipopolysaccharides (LPS), were assessed via 16S rRNA sequencing. miRNA sequencing on LPS-induced rat osteoblasts, with and without Oxymatrine, explored osteoblast proliferation, mineralization, and the miR-539-5p/OGN/Runx2 pathway. Results The administration of OMT resulted in an enhancement of diabetic osteopathy by reversing trabecular bone loss and modifying the composition of gut microbiota, specifically affecting Gram-negative bacteria that release LPS into the bloodstream. miRNA sequencing revealed that miR-539-5p, which was upregulated in LPS-induced ROBs, was downregulated following OMT treatment. Furthermore, OMT was found to promote osteoblast proliferation and mineralization under conditions of LPS exposure and modulate the miR-539-5p/OGN/Runx2 signaling pathway. Conclusions OMT improves diabetic osteoporosis by altering gut microbiota, decreasing LPS release, and enhancing osteoblast growth and differentiation through the miR-539-5p/OGN/Runx2 pathway, suggesting its potential as a treatment.

Peiyao Wang, Duo Zhou, Lingwei Hu et al.

Abstract Purpose This study aimed to explore metabolite pathways and identify residual metabolites during the post-neonatal intrahepatic cholestasis caused by citrin deficiency (post-NICCD) phase, while developing a predictive model for failure to thrive (FTT) using selected metabolites. Method A case-control study was conducted from October 2020 to July 2024, including 16 NICCD patients, 31 NICCD-matched controls, 34 post-NICCD patients, and 70 post-NICCD-matched controls. Post-NICCD patients were further stratified into two groups based on growth outcomes. Biomarkers for FTT were identified using Lasso regression and random forest analysis. A non-invasive predictive model was developed, visualized as a nomogram, and internally validated using the enhanced bootstrap method. The model’s performance was evaluated with receiver operating characteristic curves and calibration curves. Metabolite concentrations (amino acids, acylcarnitines, organic acids, and free fatty acids) were measured using liquid chromatography or ultra-performance liquid chromatography-tandem mass spectrometry. Results The biosynthesis of unsaturated fatty acids was identified as the most significantly altered pathway in post-NICCD patients. Twelve residual metabolites altered during both NICCD and post-NICCD phases were identified, including: 2-hydroxyisovaleric acid, alpha-ketoisovaleric acid, C5:1, 3-methyl-2-oxovaleric acid, C18:1OH, C20:4, myristic acid, eicosapentaenoic acid, carnosine, hydroxylysine, phenylpyruvic acid, and 2-methylcitric acid. Lasso regression and random forest analysis identified kynurenine, arginine, alanine, and aspartate as the optimal biomarkers for predicting FTT in post-NICCD patients. The predictive model constructed with these four biomarkers demonstrated an AUC of 0.947. Conclusion While post-NICCD patients recover clinically and biochemically, their metabolic profiles remain incompletely restored. The predictive model based on kynurenine, arginine, alanine, and aspartate provides robust diagnostic performance for detecting FTT in post-NICCD patients.

Chen Xinyan, Wu Yajie, He Shangfan et al.

Abstract Intermittent fasting (IF) as a dietary intervention with potential health benefits has garnered significant attention in recent years. This study investigated the effects of varying fasting intensities on skeletal muscle growth using mouse models. Compared to the normal-diet (ND) control group, short-term fasting induced feeding amount-dependent alterations in skeletal muscle autophagy markers, characterized by elevated LC3B expression, reduced p62 levels, and decreased p-mTOR/mTOR ratio. Notably, short-term mild fasting (sMF) significantly upregulated myogenic (MYH, MyoD) and adipogenic (LPL, PPARγ) differentiation markers, whereas short-term severe fasting (sSF) suppressed myogenic markers without significantly affecting adipogenic factors. Pharmacological modulation using 3-methyladenine (3-MA) and rapamycin (RAPA) confirmed the critical role of autophagy in myogenic and adipogenic processes. Multi-cycle IF studies revealed that intermittent mild fasting (IMF) enhanced metabolic efficiency (evidenced by increased feed conversion ratio), elevated organ indices of gastrocnemius and quadriceps femoris muscles, and reduced groin fat. IMF also promoted intramuscular adipogenesis and myofiber remodeling. In contrast, intermittent severe fasting (ISF) impaired glucose tolerance, decreased triglyceride levels and aspartate aminotransferase (AST) activity, inhibited myofiber growth, and exhibited no significant effect on intramuscular adipogenesis. Our findings demonstrate that IMF enhances skeletal muscle mass and reduces visceral adiposity through mTOR-autophagy axis, providing an optimized fasting regimen for metabolic health and body composition regulation.

Junfeng Ge, Lin Zhu, Sijie Jiang et al.

Abstract Background To investigate the association between the Dietary Inflammatory Index (DII), biological aging, and the staging and mortality of cardiovascular-kidney-metabolic (CKM) syndrome. Methods Data of 7,918 participants were derived from the National Health and Nutrition Examination Survey 2005–2018. Cross-sectional analyses using multivariable logistic regression were conducted to evaluate the relationship between DII and CKM staging. Cox proportional hazards models were employed to assess the impact of DII on mortality in CKM patients. Mediation analyses were performed to determine whether biological aging mediated DII-staging and DII-mortality association. Machine learning models were developed to classify CKM stages 3/4 and predict all-cause mortality, with SHapley Additive exPlanations (SHAP) used to interpret the contribution of DII components. Results Over a median follow-up of 9.3 years, 819 deaths were recorded. Higher DII were associated with an increased risk of advanced CKM stages [OR (95% CI): tertile 2, 1.39 (1.17, 1.65); tertile 3, 1.85 (1.56, 2.20)], and all-cause mortality [(HR (95% CI): tertile 2, 1.20 (1.01–1.43); tertile 3: 1.45 (1.21–1.73)]. The optimal risk stratification threshold for DII to predict all-cause mortality was 1.93. Mediation analyses revealed that biological aging accounted for 23% (95% CI: 18-28%) of the effect of DII on advanced CKM stages and 13% (95% CI: 8-22%) of the effect of DII on all-cause mortality. Furthermore, the Light Gradient Boosting Machine model showed strong performance in predicting advanced CKM staging (AUC: 0.896, 95% CI: 0.882–0.911), while Logistic regression performed better in predicting all-cause mortality (AUC: 0.857, 95% CI: 0.831–0.884). SHAP analysis revealed that intake of magnesium and n-3 fatty acid were associated with reduced risk of both advanced CKM stages and all-cause mortality. Conclusion DII, a marker of pro-inflammatory dietary patterns, was significantly linked to CKM syndrome progression and mortality, partly by influencing biological aging. This underscores the importance of diet quality in managing CKM staging and mortality risk.

Sandra L Lopez-Arana

Rachel M Cole, Stephan Zarich, Kelsey Fairchild et al.

Zheng Wang, Fachao Shi, Long Wang et al.

Abstract Objective The relationship between Glycated Hemoglobin Index (HGI) and cardiovascular disease (CVD) risk in individuals with diabetes or prediabetes remains unclear. Therefore, this study aims to investigate the relationship between baseline HGI and CVD risk in U.S. adults with diabetes or prediabetes. Methods This study analyzed data from 10,889 diabetic or prediabetic participants from the National Health and Nutrition Examination Survey (NHANES). Weighted multivariable regression analysis and subgroup analyses were employed to assess the relationship between HGI and CVD risk. Restricted cubic splines were used to explore nonlinear associations, along with threshold effect analysis and subgroup analyses. Results A total of 10,889 participants (mean age 52.82 years, 54.57% male) were included in this study. We observed a U-shaped relationship between HGI and the risk of cardiovascular disease (CVD) (P nonlinear < 0.0001), heart attack (P nonlinear = 0.0006), and congestive heart failure (CHF) (P nonlinear = 0.0001). The inflection points for HGI concerning CVD, heart attack, and CHF were − 0.140, -0.447, and − 0.140, respectively. When baseline HGI exceeded these thresholds, each unit increase in HGI was significantly associated with higher risks of CVD (OR: 1.34, 95% CI: 1.23–1.48), heart attack(OR: 1.34, 95% CI: 1.20–1.51), and CHF (OR: 1.39, 95% CI: 1.22–1.58).Subgroup analysis revealed significant differences in CHF risk associated with HGI across racial groups (interaction P = 0.03). Conclusion In individuals with diabetes and prediabetes, HGI displays a U-shaped relationship with CVD, heart attack, and CHF risks, with threshold values of -0.14, -0.45, and − 0.14, respectively. HGI may serve as a more effective indicator for identifying populations at early risk for cardiovascular disease.

Ranga Baminiwatte, Kazi Jewel Rana, Aaron J. Masino

Understanding disease similarity is critical for advancing diagnostics, drug discovery, and personalized treatment strategies. We present PhenoGnet, a novel graph-based contrastive learning framework designed to predict disease similarity by integrating gene functional interaction networks with the Human Phenotype Ontology (HPO). PhenoGnet comprises two key components: an intra-view model that separately encodes gene and phenotype graphs using Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), and a cross view model implemented as a shared weight multilayer perceptron (MLP) that aligns gene and phenotype embeddings through contrastive learning. The model is trained using known gene phenotype associations as positive pairs and randomly sampled unrelated pairs as negatives. Diseases are represented by the mean embeddings of their associated genes and/or phenotypes, and pairwise similarity is computed via cosine similarity. Evaluation on a curated benchmark of 1,100 similar and 866 dissimilar disease pairs demonstrates strong performance, with gene based embeddings achieving an AUCPR of 0.9012 and AUROC of 0.8764, outperforming existing state of the art methods. Notably, PhenoGnet captures latent biological relationships beyond direct overlap, offering a scalable and interpretable solution for disease similarity prediction. These results underscore its potential for enabling downstream applications in rare disease research and precision medicine.

Idris Ochijenu, Monday Abutu Idakwo, Sani Felix

Like many countries, Nigeria is naturally endowed with fertile agricultural soil that supports large-scale tomato production. However, the prevalence of disease causing pathogens poses a significant threat to tomato health, often leading to reduced yields and, in severe cases, the extinction of certain species. These diseases jeopardise both the quality and quantity of tomato harvests, contributing to food insecurity. Fortunately, tomato diseases can often be visually identified through distinct forms, appearances, or textures, typically first visible on leaves and fruits. This study presents an enhanced Capsule-YOLO network architecture designed to automatically segment overlapping and occluded tomato leaf images from complex backgrounds using the YOLO framework. It identifies disease symptoms with impressive performance metrics: 99.31% accuracy, 98.78% recall, and 99.09% precision, and a 98.93% F1-score representing improvements of 2.91%, 1.84%, 5.64%, and 4.12% over existing state-of-the-art methods. Additionally, a user-friendly interface was developed to allow farmers and users to upload images of affected tomato plants and detect early disease symptoms. The system also provides recommendations for appropriate diagnosis and treatment. The effectiveness of this approach promises significant benefits for the agricultural sector by enhancing crop yields and strengthening food security.

Natalia Glazman, Jyoti Mangal, Pedro Borges et al.

The application of causal discovery to diseases like Alzheimer's (AD) is limited by the static graph assumptions of most methods; such models cannot account for an evolving pathophysiology, modulated by a latent disease pseudotime. We propose to apply an existing latent variable model to real-world AD data, inferring a pseudotime that orders patients along a data-driven disease trajectory independent of chronological age, then learning how causal relationships evolve. Pseudotime outperformed age in predicting diagnosis (AUC 0.82 vs 0.59). Incorporating minimal, disease-agnostic background knowledge substantially improved graph accuracy and orientation. Our framework reveals dynamic interactions between novel (NfL, GFAP) and established AD markers, enabling practical causal discovery despite violated assumptions.

Lasse Hyldig Hansen, Nikolaj Andersen, Jack Gallifant et al.

Background Advancements in Large Language Models (LLMs) hold transformative potential in healthcare, however, recent work has raised concern about the tendency of these models to produce outputs that display racial or gender biases. Although training data is a likely source of such biases, exploration of disease and demographic associations in text data at scale has been limited. Methods We conducted a large-scale textual analysis using a dataset comprising diverse web sources, including Arxiv, Wikipedia, and Common Crawl. The study analyzed the context in which various diseases are discussed alongside markers of race and gender. Given that LLMs are pre-trained on similar datasets, this approach allowed us to examine the potential biases that LLMs may learn and internalize. We compared these findings with actual demographic disease prevalence as well as GPT-4 outputs in order to evaluate the extent of bias representation. Results Our findings indicate that demographic terms are disproportionately associated with specific disease concepts in online texts. gender terms are prominently associated with disease concepts, while racial terms are much less frequently associated. We find widespread disparities in the associations of specific racial and gender terms with the 18 diseases analyzed. Most prominently, we see an overall significant overrepresentation of Black race mentions in comparison to population proportions. Conclusions Our results highlight the need for critical examination and transparent reporting of biases in LLM pretraining datasets. Our study suggests the need to develop mitigation strategies to counteract the influence of biased training data in LLMs, particularly in sensitive domains such as healthcare.

P. N. Karthikayan, Yoga Sri Varshan, Hitesh Gupta Kattamuri et al.

This paper presents dilated Residual Network (ResNet) models for disease classification from retinal fundus images. Dilated convolution filters are used to replace normal convolution filters in the higher layers of the ResNet model (dilated ResNet) in order to improve the receptive field compared to the normal ResNet model for disease classification. This study introduces computer-assisted diagnostic tools that employ deep learning, enhanced with explainable AI techniques. These techniques aim to make the tool's decision-making process transparent, thereby enabling medical professionals to understand and trust the AI's diagnostic decision. They are particularly relevant in today's healthcare landscape, where there is a growing demand for transparency in AI applications to ensure their reliability and ethical use. The dilated ResNet is used as a replacement for the normal ResNet to enhance the classification accuracy of retinal eye diseases and reduce the required computing time. The dataset used in this work is the Ocular Disease Intelligent Recognition (ODIR) dataset which is a structured ophthalmic database with eight classes covering most of the common retinal eye diseases. The evaluation metrics used in this work include precision, recall, accuracy, and F1 score. In this work, a comparative study has been made between normal ResNet models and dilated ResNet models on five variants namely ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152. The dilated ResNet model shows promising results as compared to normal ResNet with an average F1 score of 0.71, 0.70, 0.69, 0.67, and 0.70 respectively for the above respective variants in ODIR multiclass disease classification.

Yuan Yin, Jennifer A. Flegg, Mark B. Flegg

In the infectious disease literature, significant effort has been devoted to studying dynamics at a single scale. For example, compartmental models describing population-level dynamics are often formulated using differential equations. In cases where small numbers or noise play a crucial role, these differential equations are replaced with memoryless Markovian models, where discrete individuals can be members of a compartment and transition stochastically. Classic stochastic simulation algorithms, such as the next reaction method, can be employed to solve these Markovian models exactly. The intricate coupling between models at different scales underscores the importance of multiscale modelling in infectious diseases. However, several computational challenges arise when the multiscale model becomes non-Markovian. In this paper, we address these challenges by developing a novel exact stochastic simulation algorithm. We apply it to a showcase multiscale system where all individuals share the same deterministic within-host model while the population-level dynamics are governed by a stochastic formulation. We demonstrate that as long as the within-host information is harvested at a reasonable resolution, the novel algorithm will always be accurate. Furthermore, our implementation is still efficient even at finer resolutions. Beyond infectious disease modelling, the algorithm is widely applicable to other multiscale systems, providing a versatile, accurate, and computationally efficient framework.

Dipak Patra

An understanding of the disease spreading phenomenon based on a mathematical model is extremely needed for the implication of the correct policy measures to contain the disease propagation. Here, we report a new model namely the Ising-SIR model describing contagious disease spreading phenomena including both airborne and direct contact disease transformations. In the airborne case, a susceptible agent can catch the disease either from the environment or its infected neighbors whereas in the second case, the agent can be infected only through close contact with its infected neighbors. We have performed Monte Carlo simulations on a square lattice using periodic boundary conditions to investigate the dynamics of disease spread. The simulations demonstrate that the mechanism of disease spreading plays a significant role in the growth dynamics and leads to different growth exponent. In the direct contact disease spreading mechanism, the growth exponent is nearly equal to two for some model parameters which agrees with earlier empirical observations. In addition, the model predicts various types of spatiotemporal patterns that can be observed in nature.

Xiaoliang Shao, Yaqi Liu, Mingge Zhou et al.

Abstract Purpose To use 18 F-FDG microPET dynamic imaging to preliminarily identify the changes of myocardial glucose metabolism corresponding to different functional phenotypes of diabetic cardiomyopathy (DCM) in mice and elucidate their relationships. Methods Left ventricular function was measured by echocardiography in C57BL/KsJ-db/db (db/db) mice and their controls at 8, 12, 16, and 20 weeks of age to divide DCM stages and functional phenotypes. Myocardial histopathology was used to verify the staging accuracy and list-mode microPET dynamic imaging was conducted. The myocardial metabolic rate of glucose (MRglu) and the glucose uptake rate constant (Ki) were derived via Patlak graphical analysis, and the differences in myocardial glucose metabolism levels in different DCM stages were compared. The key proteins involved in myocardial glucose metabolism signaling pathway were analyzed by Western blotting to elucidate the underlying mechanism of abnormal glucose metabolism in DCM. Results Compared with the controls, the ratio of early diastolic transmitral flow velocity to early diastolic mitral annular tissue velocity (E/e’) of db/db mice was significantly increased from the age of 12 weeks, while the left ventricular ejection fraction (LVEF) was significantly decreased from the age of 16 weeks (all P < 0.05). Based on the staging criteria, 8 and 12 weeks (8/12w) db/db mice were in DCM stage 1 (diastolic dysfunction with normal LVEF), and 16 and 20 weeks (16/20w) db/db mice were in DCM stage 2/3 (diastolic and systolic dysfunction). The degree of myocardial fibrosis, glycogen deposition and ultrastructural damage in 16/20w db/db mice were more obvious than those in 8/12w group. The myocardial MRglu, Ki of db/db mice in 8/12w group or 16/20w group were significantly lower than those in the control group (all P < 0.05), while the myocardial standard uptake value (SUV) was not significantly reduced in the 8/12w group compared with the control group (P > 0.05). MRglu and SUV were moderately negatively correlated with the E/e’ ratio (r=-0.539 and − 0.512, P = 0.007 and 0.011), which were not significantly correlated with LVEF (P > 0.05). Meanwhile, Ki was not significantly correlated with LVEF or E/e’ ratio. The decreased expression of glucose transporter (GLUT) -4 in db/db mice preceded GLUT-1 and was accompanied by decreased phosphorylated AMP-activated protein kinase (p-AMPK) expression. Myocardial MRglu, Ki and SUV were significantly positively correlated with the expression of GLUT-4 (MRglu: r = 0.537; Ki: r = 0.818; SUV: r = 0.491; P = 0.000 ~ 0.046), but there was no significant correlation with GLUT-1 expression (P = 0.238 ~ 0.780). Conclusions During the progression of DCM, with the changes of left ventricular functional phenotype, abnormal and dynamic changes of myocardial glucose metabolism can occur in the early stage.

Rebecca M. Leech, Alison C. Spence, Kathleen E. Lacy et al.

Irina Timm, Markus Reichert, Ulrich W. Ebner-Priemer et al.

Abstract Background Physical behavior (PB) is a key lifestyle factor in regulating and preventing diseases across the lifespan. Researchers identified affective, cognitive, and contextual factors like weather conditions, as significant contributors in determining if individuals are physically active. However, there is scarce empirical evidence about potential associations between PB and affective states influenced by weather conditions in daily life. Therefore, we explored if weather conditions moderated the within-subject association between momentary affective states and subsequent PB. Methods Utilizing ambulatory assessment, 79 participants completed electronic diaries about their affective states (i.e., valence, energetic arousal, and calmness) up to six times a day over five days, and their PB (i.e., physical activity and sedentariness) was simultaneously recorded via accelerometers. Weather conditions (i.e., temperature and precipitation) recorded near participants’ locations served as moderators in the multilevel analyses. Results We confirmed earlier findings associating affective states with PB. Increased valence and energetic arousal were positively associated with physical activity (β = 0.007; p < .001), whereas calmness predicted lower levels of physical activity (β = -0.006; p < .001). Higher levels of calmness showed a positive association with sedentary behavior (β = 0.054; p = .003). In addition, we revealed a significant positive association between temperature, as a momentary weather condition, and physical activity (β = 0.025; p = .015). Furthermore, we showed that the association of affective states and physical activity was moderated by temperature. Higher temperatures enhanced the positive effects of valence on physical activity (β = .001, p = .023) and attenuated the negative effects of calmness on physical activity (β = .001, p = .021). Moreover, higher temperatures enhanced the positive effects of valence on reduced sedentary behavior (β = -0.011, p = .043). Conclusions Temperature alterations appeared to have an impact on subsequent physical activity. Furthermore, temperature alterations moderated the influence of affective states on conducted physical activity. This might offer the opportunity for just-in-time adaptive interventions to intervene in individually appropriate environmental conditions for promoting physical activity.