Hasil untuk "Nutritional diseases. Deficiency diseases"

C. Chaparro, P. Suchdev

Anemia affects a third of the world's population and contributes to increased morbidity and mortality, decreased work productivity, and impaired neurological development. Understanding anemia's varied and complex etiology is crucial for developing effective interventions that address the context‐specific causes of anemia and for monitoring anemia control programs. We outline definitions and classifications of anemia, describe the biological mechanisms through which anemia develops, and review the variety of conditions that contribute to anemia development. We emphasize the risk factors most prevalent in low‐ and middle‐income countries, including nutritional deficiencies, infection/inflammation, and genetic hemoglobin disorders. Recent work has furthered our understanding of anemia's complex etiology, including the proportion of anemia caused by iron deficiency (ID) and the role of inflammation and infection. Accumulating evidence indicates that the proportion of anemia due to ID differs by population group, geographical setting, infectious disease burden, and the prevalence of other anemia causes. Further research is needed to explore the role of additional nutritional deficiencies, the contribution of infectious and chronic disease, as well as the importance of genetic hemoglobin disorders in certain populations.

R. Fielding, B. Vellas, W. Evans et al.

P. Trumbo, A. Yates, S. Schlicker et al.

C. Aring, T. Spies

M. Berger, A. Shenkin, K. Amrein et al.

BACKGROUND Trace elements and vitamins, named together micronutrients (MNs), are essential for human metabolism. Recent research has shown the importance of MNs in common pathologies, with significant deficiencies impacting the outcome. OBJECTIVE This guideline aims to provide information for daily clinical nutrition practice regarding assessment of MN status, monitoring, and prescription. It proposes a consensus terminology, since many words are used imprecisely, resulting in confusion. This is particularly true for the words "deficiency", "repletion", "complement", and "supplement". METHODS The expert group attempted to apply the 2015 standard operating procedures (SOP) for ESPEN which focuses on disease. However, this approach could not be applied due to the multiple diseases requiring clinical nutrition resulting in one text for each MN, rather than for diseases. An extensive search of the literature was conducted in the databases Medline, PubMed, Cochrane, Google Scholar, and CINAHL. The search focused on physiological data, historical evidence (published before PubMed release in 1996), and observational and/or randomized trials. For each MN, the main functions, optimal analytical methods, impact of inflammation, potential toxicity, and provision during enteral or parenteral nutrition were addressed. The SOP wording was applied for strength of recommendations. RESULTS There was a limited number of interventional trials, preventing meta-analysis and leading to a low level of evidence. The recommendations underwent a consensus process, which resulted in a percentage of agreement (%): strong consensus required of >90% of votes. Altogether the guideline proposes sets of recommendations for 26 MNs, resulting in 170 single recommendations. Critical MNs were identified with deficiencies being present in numerous acute and chronic diseases. Monitoring and management strategies are proposed. CONCLUSION This guideline should enable addressing suboptimal and deficient status of a bundle of MNs in at-risk diseases. In particular, it offers practical advice on MN provision and monitoring during nutritional support.

D. Ting, C. S. Ho, Rashmi Deshmukh et al.

Corneal opacity is the 5th leading cause of blindness and visual impairment globally, affecting ~6 million of the world population. In addition, it is responsible for 1.5–2.0 million new cases of monocular blindness per year, highlighting an ongoing uncurbed burden on human health. Among all aetiologies such as infection, trauma, inflammation, degeneration and nutritional deficiency, infectious keratitis (IK) represents the leading cause of corneal blindness in both developed and developing countries, with an estimated incidence ranging from 2.5 to 799 per 100,000 population-year. IK can be caused by a wide range of microorganisms, including bacteria, fungi, virus, parasites and polymicrobial infection. Subject to the geographical and temporal variations, bacteria and fungi have been shown to be the most common causative microorganisms for corneal infection. Although viral and Acanthamoeba keratitis are less common, they represent important causes for corneal blindness in the developed countries. Contact lens wear, trauma, ocular surface diseases, lid diseases, and post-ocular surgery have been shown to be the major risk factors for IK. Broad-spectrum topical antimicrobial treatment is the current mainstay of treatment for IK, though its effectiveness is being challenged by the emergence of antimicrobial resistance, including multidrug resistance, in some parts of the world. In this review, we aim to provide an updated review on IK, encompassing the epidemiology, causative microorganisms, major risk factors and the impact of antimicrobial resistance.

G. Genchi, G. Lauria, Alessia Catalano et al.

Selenium (Se) is a naturally occurring metalloid element essential to human and animal health in trace amounts but it is harmful in excess. Se plays a substantial role in the functioning of the human organism. It is incorporated into selenoproteins, thus supporting antioxidant defense systems. Selenoproteins participate in the metabolism of thyroid hormones, control reproductive functions and exert neuroprotective effects. Among the elements, Se has one of the narrowest ranges between dietary deficiency and toxic levels. Its level of toxicity may depend on chemical form, as inorganic and organic species have distinct biological properties. Over the last decades, optimization of population Se intake for the prevention of diseases related to Se deficiency or excess has been recognized as a pressing issue in modern healthcare worldwide. Low selenium status has been associated with an increased risk of mortality, poor immune function, cognitive decline, and thyroid dysfunction. On the other hand, Se concentrations slightly above its nutritional levels have been shown to have adverse effects on a broad spectrum of neurological functions and to increase the risk of type-2 diabetes. Comprehension of the selenium biochemical pathways under normal physiological conditions is therefore an important issue to elucidate its effect on human diseases. This review gives an overview of the role of Se in human health highlighting the effects of its deficiency and excess in the body. The biological activity of Se, mainly performed through selenoproteins, and its epigenetic effect is discussed. Moreover, a brief overview of selenium phytoremediation and rhizofiltration approaches is reported.

S. Massironi, C. Viganò, Andrea Palermo et al.

Inflammatory bowel disease (IBD), which includes Crohn's disease and ulcerative colitis, has become increasingly prevalent worldwide in the past decade. The nutritional status of patients with IBD is often impaired, with malnutrition presenting as imbalanced energy or nutrient intake, including protein-energy malnutrition, disease-related malnutrition, sarcopenia, and micronutrient deficiency. Additionally, malnutrition can manifest as overweight, obesity, and sarcopenic obesity. Malnutrition can lead to disturbances in gut microbiome composition that might alter homoeostasis and cause a dysbiotic state, potentially triggering inflammatory responses. Despite the clear link between IBD and malnutrition, little is known about the pathophysiological mechanisms beyond protein-energy malnutrition and micronutrient deficiencies that could promote inflammation through malnutrition, and vice versa. This Review focuses on potential mechanisms that trigger a vicious cycle between malnutrition and inflammation, and their clinical and therapeutic implications.

Garazi Retegui, María Dolores Ugarte, Jaione Etxeberria et al.

To date, we have seen the emergence of a large literature on multivariate disease mapping. That is, incidence of (or mortality from) multiple diseases is recorded at the scale of areal units where incidence (mortality) across the diseases is expected to manifest dependence. The modeling involves a hierarchical structure: a Poisson model for disease counts (conditioning on the rates) at the first stage, and a specification of a function of the rates using spatial random effects at the second stage. These random effects are specified as a prior and introduce spatial smoothing to the rate (or risk) estimates. What we see in the literature is the amount of smoothing induced under a given prior across areal units compared with the observed/empirical risks. Our contribution here extends previous research on smoothing in univariate areal data models. Specifically, for three different choices of multivariate prior, we investigate both within prior smoothing according to hyperparameters and across prior smoothing. Its benefit to the user is to illuminate the expected nature of departure from perfect fit associated with these priors since model performance is not a question of goodness of fit. We propose both theoretical and empirical metrics for our investigation and illustrate with both simulated and real data.

A. K. Chan, Y. C. Tsang, Chloe Meng Jiang et al.

Diet, nutrition, and oral health are closely linked. Malnutrition is a challenging health concern in older adults that is associated with physical decline affecting their daily activities and quality of life. The aim of this review is to provide an evidence-based summary of the relationship between diet and nutrition and oral health in older adults and its implications. The World Health Organization has declared healthy ageing a priority of its work on ageing. The American Dental Association confirmed the bidirectional relationship between diet and nutrition and oral health. The literature shows that diet and nutrition are related to oral diseases, including dental caries, periodontal diseases, tooth wear, and even oral cancer. Insufficient nutritional intake and poor dietary habits increase the risk of oral diseases, such as dental caries, in older adults. On the other hand, in older adults, poor oral conditions such as periodontal disease may induce pain, infection, and tooth loss, affecting nutritional intake. Surveys have shown that older adults, in particular, those in disadvantaged communities, suffered from nutritional deficiencies or imbalances affecting their oral health. In addition, the current literature shows that malnutrition is associated with frailty, hospitalization, mortality, and morbidity. Good oral health and functional dentition are essential to maintain sufficient nutritional intake among older adults and reduce the risk of malnutrition. Therefore, integrating oral health into general health care service in older adults is imperative to improve their nutritional and oral health status to achieve healthy ageing.

Srinivas Kanakala, Sneha Ningappa

Plant diseases pose a serious challenge to agriculture by reducing crop yield and affecting food quality. Early detection and classification of these diseases are essential for minimising losses and improving crop management practices. This study applies Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to classify plant leaf diseases using a dataset containing 70,295 training images and 17,572 validation images across 38 disease classes. The CNN model was trained using the Adam optimiser with a learning rate of 0.0001 and categorical cross-entropy as the loss function. After 10 training epochs, the model achieved a training accuracy of 99.1% and a validation accuracy of 96.4%. The LSTM model reached a validation accuracy of 93.43%. Performance was evaluated using precision, recall, F1-score, and confusion matrix, confirming the reliability of the CNN-based approach. The results suggest that deep learning models, particularly CNN, enable an effective solution for accurate and scalable plant disease classification, supporting practical applications in agricultural monitoring.

Yang Liu

This paper conducts research on the established model and presents the main conclusions . Firstly, by separately considering the infectivity of each of the two infectious diseases and the infectivity of the population simultaneously infected with the two infectious diseases, the existence of three types of boundary equilibrium points is determined, as well as the existence of the interior equilibrium point when the parameters are under specific conditions. Then, the stability of the equilibrium points is analyzed. It is concluded that under different parameter conditions, the stability of the disease free equilibrium point can exhibit various scenarios, such as a stable node or a saddle-node, etc. For the boundary equilibrium points, the situation is more intricate,and a cusp may occur. The stability of the interior equilibrium point under specific conditions is also presented. Finally,the degeneracy of the equilibrium points is studied through the bifurcation theory.Mainly, the saddle-node bifurcation occurring at the interior equilibrium point is obtained, and when the infection rate of the first infectious disease, the infection rate of the second infectious disease, and the infection rate of the co-infected population to other populations are selected as bifurcation parameters, a codimension 3 B-T bifurcation is obtained.

Hongtao Hao, Joseph L. Austerweil

Chronic diseases often progress differently across patients. Rather than randomly varying, there are typically a small number of subtypes for how a disease progresses across patients. To capture this structured heterogeneity, the Subtype and Stage Inference Event-Based Model (SuStaIn) estimates the number of subtypes, the order of disease progression for each subtype, and assigns each patient to a subtype from primarily cross-sectional data. It has been widely applied to uncover the subtypes of many diseases and inform our understanding of them. But how robust is its performance? In this paper, we develop a principled Bayesian subtype variant of the event-based model (BEBMS) and compare its performance to SuStaIn in a variety of synthetic data experiments with varied levels of model misspecification. BEBMS substantially outperforms SuStaIn across ordering, staging, and subtype assignment tasks. Further, we apply BEBMS and SuStaIn to a real-world Alzheimer's data set. We find BEBMS has results that are more consistent with the scientific consensus of Alzheimer's disease progression than SuStaIn.

Yuehua You, Hongbin Ni, Qin Ma et al.

Abstract Background & Aims Ketogenic diet (KD) is recognized as an effective lifestyle intervention for managing metabolic dysfunction-associated steatotic liver disease (MASLD). This research aimed to assess the impact of KD on metabolic parameters in MASLD mice and elucidate the underlying mechanism. Methods High-fat diet (HFD)-induced MASLD mice were subjected to KD for 2 weeks. Researchers measured hepatic fat, plasma Alanine Aminotransferase (ALT), and Aspartate Aminotransferase (AST) levels to assess metabolic changes. Hepatic mitochondrial dynamics were examined using transmission electron microscopy and Western blot. Mitochondrial functions were evaluated through Quantitative Polymerase Chain Reaction (qPCR) and measurement of ATP content. In vitro, HepG2 cells were treated with palmitate (PA), β-hydroxybutyric acid (β-OHB), and/or the mitochondrial fusion inhibitor MFI8 to study mitochondrial morphology, function, and lipid deposition. Results KD feeding partially improved the MASLD phenotype and reduced Fission 1 protein (Fis1) and Dynamin-related protein 1 (Drp1) levels in the livers of MASLD mice. Additionally, KD ameliorated HFD-stimulated mitochondrial dysfunctions, as evidenced by elevated ATP levels and upregulation of key genes responsible for fatty-acid-oxidation. β-OHB mitigated PA-stimulated mitochondrial dysfunction and fission in HepG2 cells. Furthermore, β-OHB attenuated PA-stimulated lipid deposition, with this effect being counteracted by MFI8. Conclusions Our study suggests that a 2-week KD partially alleviates lipid deposition, restores mitochondrial dynamics balance, and improves mitochondrial dysfunctions in the livers of MASLD mice.

Mark Cannon, Elizabeth Dempsey, Ashlee Cosantino et al.

The present study used a syngeneic mouse model of malignant melanoma to evaluate the inhibitory efficacy of continuous xylitol administration via a subcutaneous osmotic minipump. The B16F10 syngeneic model for malignant melanoma consisted of 6–8-week-old C57BL/6 male mice subcutaneously injected with 5 × 10⁵ B16F10 cells suspended in 100 μL PBS in the right flank. The mice were randomly assigned to two groups: Group 1 was the treatment group, which received 10% <i>w</i>/<i>v</i> xylitol in saline-loaded pumps (<i>n</i> = 10), while Group 2 was the control group, which received saline-loaded pumps (<i>n</i> = 10). ALZET 2004 minipumps were implanted subcutaneously in the left flank of B16F10-injected mice once more than 50% of all mice developed palpable tumors. After pump implantation surgery, the mice were monitored daily and weighed 2–3× times per week. Tumor sizes were measured with calipers 2–3× per week, and all mice were euthanized when their tumors became too large (20 mm on any axis or 2000 mm³). The tumor size growth was reduced by approximately 35% by volume in the xylitol-treated group which was not statistically significant. The xylitol group had a longer survival time, but this was not statistically significant (Kaplan–Meier), as was the case with the survival analysis by the Cox proportional hazards model. The metabolomic analysis suggests that xylitol significantly alters the tumor’s metabolism, potentially affecting the host immune response.

B. Bal, Frederick C. Finelli, T. Shope et al.

Khandoker Nosiba Arifin, Sayma Akter Rupa, Md Musfique Anwar et al.

Lemons and oranges, both are the most economically significant citrus fruits globally. The production of lemons and oranges is severely affected due to diseases in its growth stages. Fruit quality has degraded due to the presence of flaws. Thus, it is necessary to diagnose the disease accurately so that we can avoid major loss of lemons and oranges. To improve citrus farming, we proposed a disease classification approach for lemons and oranges. This approach would enable early disease detection and intervention, reduce yield losses, and optimize resource allocation. For the initial modeling of disease classification, the research uses innovative deep learning architectures such as VGG16, VGG19 and ResNet50. In addition, for achieving better accuracy, the basic machine learning algorithms used for classification problems include Random Forest, Naive Bayes, K-Nearest Neighbors (KNN) and Logistic Regression. The lemon and orange fruits diseases are classified more accurately (95.0% for lemon and 99.69% for orange) by the model. The model's base features were extracted from the ResNet50 pre-trained model and the diseases are classified by the Logistic Regression which beats the performance given by VGG16 and VGG19 for other classifiers. Experimental outcomes show that the proposed model also outperforms existing models in which most of them classified the diseases using the Softmax classifier without using any individual classifiers.

Avneet Kaur, Rebecca Tyson, Iain Moyles

We analyze a disease transmission model that allows individuals to acquire fear and change their behaviour to reduce transmission. Fear is acquired through contact with infected individuals and through the influence of fearful individuals. We analyze the model in two limits: First, an Established Disease Limit (EDL), where the spread of the disease is much faster than the spread of fear, and second, a Novel Disease Limit (NDL), where the spread of the disease is comparable to that of fear. For the EDL, we show that the relative rate of fear acquisition to disease transmission controls the size of the fearful population at the end of a disease outbreak, and that the fear-induced contact reduction behaviour has very little impact on disease burden. Conversely, we show that in the NDL, disease burden can be controlled by fear-induced behaviour depending on the rate of fear loss. Specifically, fear-induced behaviour introduces a contact parameter $p$, which if too large prevents the contact reduction from effectively managing the epidemic. We analytically identify a critical prophylactic behaviour parameter $p=p_c$ where this happens leading to a discontinuity in epidemic prevalence. We show that this change in disease burden introduces delayed epidemic waves.

Yaojun Hu, Jintai Chen, Lianting Hu et al.

Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals' anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development.

Hujian Hong, Yijiang He, Zhiqiang Gong et al.

Abstract Background The relationship between the NHHR and kidney stone risk remains unknown. The purpose of this study was to evaluate the association between adult NHHR and kidney stone occurrence in USA. Methods This study used a variety of statistical techniques such as threshold effects, subgroup analysis, smooth curve fitting, multivariate logistic regression, and data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014. We aimed to clarify the relationship between the NHHR and kidney stone risk. Results The average age of the 21,058 individuals in this research was 49.70 ± 17.64 years. The mean NHHR was 3.00 ± 1.47, and the overall prevalence of kidney stone occurrence was 9.05%. The prevalence within the quartile ranges (Q1–Q4) was 7.01%, 8.71%, 9.98%, and 10.49%, respectively. The overall average recurrence rate of kidney stones was 3.05%, demonstrating a significant increase with increasing NHHR (Q1: 1.92%, Q2: 2.92%, Q3: 3.35%, Q4: 4.00%, P < 0.01). The occurrence of kidney stones increased by 4% (95% CI: 1.00-1.08, P = 0.0373) and the chance of recurrence increased by 9% (95% CI: 1.03–1.14, P < 0.01) with each unit increase in NHHR. The interaction analysis results demonstrated that the relationship between the NHHR and the risk of kidney stones was not significantly impacted by the following factors: sex, body mass index, poverty income ratio, diabetes, or hypertension. Curve fitting and threshold effect analysis also demonstrated a non-linear association, with a breakpoint found at 3.17, between the NHHR and the risk of kidney stones. Conclusions In adults in the USA, there is a substantial correlation between elevated NHHR levels and a higher probability of kidney stones developing and recurring. Timely intervention and management of NHHR may effectively mitigate the occurrence and recurrence of kidney stones.