Hasil untuk "Nutritional diseases. Deficiency diseases"

Zewen Liu, Zhangpin Ren, Jun Zhang et al.

The overproduction of reactive oxygen species (ROS) has been implicated in the development of various chronic and degenerative diseases such as cancer, respiratory, neurodegenerative, and digestive diseases. Under physiological conditions, the concentrations of ROS are subtlety regulated by antioxidants, which can be either generated endogenously or externally supplemented. A combination of antioxidant-deficiency and malnutrition may render individuals more vulnerable to oxidative stress, thereby increasing the risk of cancer occurrence. In addition, antioxidant defense can be overwhelmed during sustained inflammation such as in chronic obstructive pulmonary diseases, inflammatory bowel disease, and neurodegenerative disorders, cardiovascular diseases, and aging. Certain antioxidant vitamins, such as vitamin D, are essential in regulating biochemical pathways that lead to the proper functioning of the organs. Antioxidant supplementation has been shown to attenuate endogenous antioxidant depletion thus alleviating associated oxidative damage in some clinical research. However, some results indicate that antioxidants exert no favorable effects on disease control. Thus, more studies are warranted to investigate the complicated interactions between ROS and different types of antioxidants for restoration of the redox balance under pathologic conditions. This review highlights the potential roles of ROS and nutritional antioxidants in the pathogenesis of several redox imbalance-related diseases and the attenuation of oxidative stress-induced damages.

J. Parrott, L. Frank, Rebecca Rabena et al.

S. Kumar, Shanvanth R Arnipalli, P. Mehta et al.

Iron deficiency anemia (IDA) has reached epidemic proportions in developing countries and has become a major global public health problem, affecting mainly 0–5-year-old children and young women of childbearing age, especially during pregnancy. Iron deficiency can lead to life-threatening loss of red blood cells, muscle function, and energy production. Therefore, the pathogenic features associated with IDA are weakness and impaired growth, motor, and cognitive performance. IDA affects the well-being of the young generation and the economic advancement of developing countries, such as India. The imbalance between iron intake/absorption/storage and iron utilization/loss culminates into IDA. However, numerous strategic programs aimed to increase iron intake have shown that improvement of iron intake alone has not been sufficient to mitigate IDA. Emerging critical risk factors for IDA include a composition of cultural diets, infections, genetics, inflammatory conditions, metabolic diseases, dysbiosis, and socioeconomic parameters. In this review, we discuss numerous IDA mitigation programs in India and their limitations. The new multifactorial mechanism of IDA pathogenesis opens perspectives for the improvement of mitigation programs and relief of IDA in India and worldwide.

P. Bhattacharya, S. Misra, Mohsina Hussain

Human body requires certain essential elements in small quantities and their absence or excess may result in severe malfunctioning of the body and even death in extreme cases because these essential trace elements directly influence the metabolic and physiologic processes of the organism. Rapid urbanization and economic development have resulted in drastic changes in diets with developing preference towards refined diet and nutritionally deprived junk food. Poor nutrition can lead to reduced immunity, augmented vulnerability to various oral and systemic diseases, impaired physical and mental growth, and reduced efficiency. Diet and nutrition affect oral health in a variety of ways with influence on craniofacial development and growth and maintenance of dental and oral soft tissues. Oral potentially malignant disorders (OPMD) are treated with antioxidants containing essential trace elements like selenium but even increased dietary intake of trace elements like copper could lead to oral submucous fibrosis. The deficiency or excess of other trace elements like iodine, iron, zinc, and so forth has a profound effect on the body and such conditions are often diagnosed through their early oral manifestations. This review appraises the biological functions of significant trace elements and their role in preservation of oral health and progression of various oral diseases.

Ashwani Kumar, Vidisha Tomer, A. Kaur et al.

World is facing agrarian as well as nutritional challenges. Agricultural lands with irrigation facilities have been exploited to maximum, and hence we need to focus on dry lands to further increase grain production. Owing to low fertility, utilization of dry lands to produce sufficient quality grains is a big challenge. Millets as climate change compliant crops score highly over other grains like wheat and rice in terms of marginal growing conditions and high nutritional value. These nutri-cereals abode vitamins, minerals, essential fatty acids, phyto-chemicals and antioxidants that can help to eradicate the plethora of nutritional deficiency diseases. Millets cultivation can keep dry lands productive and ensure future food and nutritional security.

J. Im, Y. Je, J. Baek et al.

The relationship between immunity and nutrition is well known and its role in coronavirus disease 2019 (COVID-19) is also being paid great attention. However, the nutritional status of COVID-19 patients is unknown. Vitamin B1, B6, B12, vitamin D (25-hydroxyvitamin D), folate, selenium, and zinc levels were measured in 50 hospitalized patients with COVID-19. Overall, 76% of the patients were vitamin D deficient and 42% were selenium deficient. No significant increase in the incidence of deficiency was found for vitamins B1, B6, and B12, folate, and zinc in patients with COVID-19. The COVID-19 group showed significantly lower vitamin D values than the healthy control group (150 people, matched by age/sex). Severe vitamin D deficiency (based on a cut-off of ≤10 ng/dl) was found in 24.0% of the patients in the COVID-19 group and 7.3% in the control group. Among 12 patients with respiratory distress, 11 (91.7%) were deficient in at least one nutrient. However, patients without respiratory distress showed a deficiency in 30/38 cases (78.9%; p =  0.425). These results suggest that a deficiency of vitamin D or selenium may decrease the immune defenses against COVID-19 and cause progression to severe disease. However, more precise and large-scale studies are needed.

A. K. Kiani, K. Dhuli, Kevin Donato et al.

Summary Nutrition is the source of energy that is required to carry out all the processes of human body. A balanced diet is a combination of both macro- and micronutrients. “Nutritional inadequacy” involves an intake of nutrients that is lower than the estimated average requirement, whereas “nutritional deficiency” consists of severely reduced levels of one or more nutrients, making the body unable to normally perform its functions and thus leading to an increased risk of several diseases like cancer, diabetes, and heart disease. Malnutrition could be caused by environmental factors, like food scarcity, as well as disease conditions, like anorexia nervosa, fasting, swallowing inability, persistent vomiting, impaired digestion, intestinal malabsorption, or other chronic diseases. Nutritional biomarkers – like serum or plasma levels of nutrients such as folate, vitamin C, B vitamins, vitamin D, selenium, copper, zinc – could be used for the evaluation of nutrient intake and dietary exposure. Macronutrients deficiencies could cause kwashiorkor, marasmus, ketosis, growth retardation, wound healing, and increased infection susceptibility, whereas micronutrient – like iron, folate, zinc, iodine, and vitamin A – deficiencies lead to intellectual impairment, poor growth, perinatal complications, degenerative diseases associated with aging and higher morbidity and mortality. Preventing macro- and micronutrient deficiency is crucial and this could be achieved through supplementation and food-based approaches.

Guantong Qi, Jiasheng Wang, Mei Ling Chong et al.

Millions of children worldwide are affected by severe rare Mendelian disorders, yet exome and genome sequencing still fail to provide a definitive molecular diagnosis for a large fraction of patients, prolonging the diagnostic odyssey. Bridging this gap increasingly requires transitioning from DNA-only interpretation to multi-modal diagnostic reasoning that combines genomic data, transcriptomic sequencing (RNA-seq), and phenotype information; however, computational frameworks that coherently integrate these signals remain limited. Here we present RareCollab, an agentic diagnostic framework that pairs a stable quantitative Diagnostic Engine with Large Language Model (LLM)-based specialist modules that produce high-resolution, interpretable assessments from transcriptomic signals, phenotypes, variant databases, and the literature to prioritize potential diagnostic variants. In a rigorously curated benchmark of Undiagnosed Diseases Network (UDN) patients with paired genomic and transcriptomic data, RareCollab achieved 77% top-5 diagnostic accuracy and improved top-1 to top-5 accuracy by ~20% over widely used variant-prioritization approaches. RareCollab illustrates how modular artificial intelligence (AI) can operationalize multi-modal evidence for accurate, scalable rare disease diagnosis, offering a promising path toward reducing the diagnostic odyssey for affected families.

Tanvi Kale, Vijay Hegde, Nikhil V Dhurandhar

Cuimei Wei, Yaohui Huang, Ping Xi et al.

Abstract Objective The strong correlation between the ratio of residual cholesterol to high-density lipoprotein cholesterol (RC/HDL-c) and major cardiovascular events has been extensively studied. However, the role of this ratio in diabetic retinopathy (DR) has not been investigated. Hence, this present study aims to examine the association between the RC/HDL-c ratio and DR in patients diagnosed with type 2 diabetes mellitus (T2DM). Methods This study conducted a cross-sectional analysis involving a total of 1942 patients diagnosed with T2DM in two Taiwanese hospitals, spanning from April 2002 to November 2004. The primary objective was to explore the independent association between the RC/HDL-c ratio and the presence of DR, as well as proliferative diabetic retinopathy (PDR), using a binary logistic regression model. To accurately determine the shape of the association between these variables, we utilized a generalized additive model (GAM) and employed smooth curve fitting techniques. The data was downloaded from the website: https://journals.plos.org/plosone . Results Our study comprised participants with an average age of 64.06 ± 11.32 years, with males accounting for 43.05% of the total. Among the patients, 35.12% were found to have DR, while PDR was present in 18.23% of cases. The average RC/HDL-c ratio was calculated as 0.67 ± 0.39. Utilizing a fully adjusted logistic regression model, we investigated the potential association between the TC/HDL-c ratio and both DR and PDR. However, no statistically significant association was observed (DR: OR 1.060; 95% CI 0.707, 1.588; PDR: OR 1.258; 95% CI 0.773, 2.047). Interestingly, we did discover a non-linear association between the RC/HDL-c ratio and DR. Employing a two-piece logistic regression model and a recursive algorithm, we identified an inflection point at 0.460. When the RC/HDL-c ratio fell below 0.460, each 1-unit increase in the ratio was associated with an 11.8-fold increase in the adjusted odds of developing DR (OR = 12.824; 95% CI 3.583, 45.897). Moreover, a non-linear association between the RC/HDL-c ratio and PDR was observed, with an inflection point occurring at 0.90. When the RC/HDL-c ratio was below 0.90, a one-unit increase in the ratio was linked to a 1.46-fold increase in the adjusted odds of PDR (OR = 2.459; 95% CI: 1.245, 4.857). Conclusion This study contributes valuable insights into the intricate association between the RC/HDL-c ratio and both DR and PDR in individuals diagnosed with T2DM. By identifying a non-linear association, our findings enhance the existing knowledge surrounding the link between the RC/HDL-c ratio and the development of DR and PDR.

Ameni Abdi, Emna Gatri, Pasquale Filannino et al.

In the original publication [...]

Yuan Zhang, Ya Zhang, Jing Wang et al.

Abstract Objective By conducting TORCH screening and risk assessment analysis on childbearing-age women in different regions of China, the aim is to provide reference for reducing adverse pregnancy outcomes and improving the health status of childbearing-age women. Methods Between February and May 2021, in the eastern, central, and western regions of China (Beijing, Henan, Gansu), a total of 1,942 couples aged 18 to 49, from both urban and rural areas, were included in this cross-sectional study. TORCH screening was conducted on all these women of childbearing-age, and risk assessment was performed based on the examination results. Result In this study, toxoplasmosis, rubella, CMV, HSV, IgM positive rate were 0.2%, 0.4%, 0.3%, 0.3%, respectively, and CT, TP, HBsAg, HCV, HIV, positive rate were 0.1%, 0.2%, 2.1%, 0.3%, 0.0%, respectively. The total TORCH screening identified 63.4% of women as having potential risks, compared to 15.5% of routine ToRCH screening. The distribution of the risk population shows significant differences among provinces, ethnicities, education levels, and age groups (p ≤ 0.001). Trend chi-square tests revealed that as the level of education increased, the proportion of the risk population decreased. Conclusions The TORCH screening utilized in this study demonstrates advantages over ToRCH, as it can identify more women of childbearing age with potential risks before pregnancy, allowing for early interventions. Simultaneously, these findings underscore the necessity for targeted health education, especially for young women in economically underdeveloped areas and those with relatively lower education levels.

Manuela Daniela Danu, George Marica, Constantin Suciu et al.

The rapidly increasing volume of electronic health record (EHR) data underscores a pressing need to unlock biomedical knowledge from unstructured clinical texts to support advancements in data-driven clinical systems, including patient diagnosis, disease progression monitoring, treatment effects assessment, prediction of future clinical events, etc. While contextualized language models have demonstrated impressive performance improvements for named entity recognition (NER) systems in English corpora, there remains a scarcity of research focused on clinical texts in low-resource languages. To bridge this gap, our study aims to develop multiple deep contextual embedding models to enhance clinical NER in the cardiology domain, as part of the BioASQ MultiCardioNER shared task. We explore the effectiveness of different monolingual and multilingual BERT-based models, trained on general domain text, for extracting disease and medication mentions from clinical case reports written in English, Spanish, and Italian. We achieved an F1-score of 77.88% on Spanish Diseases Recognition (SDR), 92.09% on Spanish Medications Recognition (SMR), 91.74% on English Medications Recognition (EMR), and 88.9% on Italian Medications Recognition (IMR). These results outperform the mean and median F1 scores in the test leaderboard across all subtasks, with the mean/median values being: 69.61%/75.66% for SDR, 81.22%/90.18% for SMR, 89.2%/88.96% for EMR, and 82.8%/87.76% for IMR.

Edouard Charlebois, K. Pantopoulos

Dietary iron assimilation is critical for health and essential to prevent iron-deficient states and related comorbidities, such as anemia. The bioavailability of iron is generally low, while its absorption and metabolism are tightly controlled to satisfy metabolic needs and prevent toxicity of excessive iron accumulation. Iron entry into the bloodstream is limited by hepcidin, the iron regulatory hormone. Hepcidin deficiency due to loss-of-function mutations in upstream gene regulators causes hereditary hemochromatosis, an endocrine disorder of iron overload characterized by chronic hyperabsorption of dietary iron, with deleterious clinical complications if untreated. The impact of high dietary iron intake and elevated body iron stores in the general population is not well understood. Herein, we summarize epidemiological data suggesting that a high intake of heme iron, which is abundant in meat products, poses a risk factor for metabolic syndrome pathologies, cardiovascular diseases, and some cancers. We discuss the clinical relevance and potential limitations of data from cohort studies, as well as the need to establish causality and elucidate molecular mechanisms.

E. Mantadakis

Iron deficiency and iron-deficiency anemia (IDA) affects approximately two billion people worldwide, and most of them reside in low- and middle-income countries. In these nations, additional causes of anemia include parasitic infections like malaria, other nutritional deficiencies, chronic diseases, hemoglobinopathies, and lead poisoning. Maternal anemia in resource-poor nations is associated with low birth weight, increased perinatal mortality, and decreased work productivity. Maintaining a normal iron balance in these settings is challenging, as iron-rich foods with good bioavailability are of animal origin and either expensive and/or available in short supply. Apart from infrequent consumption of meat, inadequate vitamin C intake, and diets rich in inhibitors of iron absorption are additional important risk factors for IDA in low-income countries. In-home iron fortification of complementary foods with micronutrient powders has been shown to effectively reduce the risk of iron deficiency and IDA in infants and young children in developing countries but is associated with unfavorable changes in gut flora and induction of intestinal inflammation that may lead to diarrhea and hospitalization. In developed countries, iron deficiency is the only frequent micronutrient deficiency. In the industrialized world, IDA is more common in infants beyond the sixth month of life, in adolescent females with heavy menstrual bleeding, in women of childbearing age and older people. Other special at-risk populations for IDA in developed countries are regular blood donors, endurance athletes, and vegetarians. Several medicinal ferrous or ferric oral iron products exist, and their use is not associated with harmful effects on the overall incidence of infectious illnesses in sideropenic and/or anemic subjects. However, further research is needed to clarify the risks and benefits of supplemental iron for children exposed to parasitic infections in low-income countries, and for children genetically predisposed to iron overload.

Magdalena Sevilla-González, Maria Fernanda Garibay-Gutiérrez, Arsenio Vargas-Vázquez et al.

Background: A risk haplotype in SLC16A11 characterized by alterations in fatty acid metabolism emerged as a genetic risk factor associated with increased susceptibility to type 2 diabetes (T2D) in Mexican population. Its role on treatment responses is not well understood. Objectives: We aimed to determine the impact of the risk haplotype on the metabolomic profile during a lifestyle intervention (LSI). Methods: We recruited Mexican-mestizo individuals with ≥1 prediabetes criteria according to the American Diabetes Association with a body mass index between 25 and 45 kg/m2. We conducted a 24-wk quasiexperimental LSI study for diabetes prevention. Here, we compared longitudinal plasma liquid chromatography/mass spectrometry metabolomic changes between carriers and noncarriers. We analyzed the association of risk haplotype with metabolites leveraging repeated assessments using multivariable-adjusted linear mixed models. Results: Before the intervention, carriers (N = 21) showed higher concentrations of hippurate, C16 carnitine, glycine, and cinnamoylglycine. After 24 wk of LSI, carriers exhibited a deleterious metabolomic profile. This profile was characterized by increased concentrations of hippurate, cinnamoglycine, xanthosine, N-acetylputrescine, L-acetylcarnitine, ceramide (d18:1/24:1), and decreased concentrations of citrulline and phosphatidylethanolamine. These metabolites were associated with higher concentrations of total cholesterol, triglycerides, and low density lipoprotein cholesterol. The effect of LSI on the risk haplotype was notably more pronounced in its impact on 2 metabolites: methylmalonylcarnitine (β: −0.56; P-interaction = 0.014) and betaine (β: −0.64; P-interaction = 0.017). Interestingly, lower consumption across visits of polyunsaturated (β: −0.038; P = 0.017) fatty acids were associated with higher concentrations of methylmalonylcarnitine. Covariates for adjustment across models included age, sex, genetic ancestry principal components, and body mass index. Conclusions: Our study highlights the persistence of deleterious metabolomic patterns associated with the risk haplotype before and during a 24-wk LSI. We also emphasize the potential regulatory role of polyunsaturated fatty acids on methylmalonylcarnitine concentrations suggesting a route for improving interventions for individuals with high-genetic risk.

Moez Dawood, Ben Heavner, Marsha M. Wheeler et al.

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis. The Genomics Research to Elucidate the Genetics of Rare Diseases (GREGoR) Consortium was initiated to study thousands of challenging rare disease cases and families and apply, standardize, and evaluate emerging genomics technologies and analytics to accelerate their adoption in clinical practice. Further, all data generated, currently representing ~7500 individuals from ~3000 families, is rapidly made available to researchers worldwide via the Genomic Data Science Analysis, Visualization, and Informatics Lab-space (AnVIL) to catalyze global efforts to develop approaches for genetic diagnoses in rare diseases (https://gregorconsortium.org/data). The majority of these families have undergone prior clinical genetic testing but remained unsolved, with most being exome-negative. Here, we describe the collaborative research framework, datasets, and discoveries comprising GREGoR that will provide foundational resources and substrates for the future of rare disease genomics.

Asish Bera, Debotosh Bhattacharjee, Ondrej Krejcar

Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. A GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs. The best classification performances are: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40X: 95.50%, and BreakHis 100X: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, PND-Net achieves improved performances using five-fold cross validation.

Dirk Douwes-Schultz, Alexandra M. Schmidt, Laís Picinini Freitas et al.

Univariate zero-inflated models are increasingly being used to account for excess zeros in spatio-temporal infectious disease counts. However, the multivariate case is challenging due to the need to account for correlations across space, time and disease in both the count and zero-inflated components of the model. We are interested in comparing the transmission dynamics of several co-circulating infectious diseases across space and time, where some of the diseases can be absent for long periods. We first assume there is a baseline disease that is well-established and always present in the region. The other diseases switch between periods of presence and absence in each area through a series of coupled Markov chains, which account for long periods of disease absence, disease interactions and disease spread from neighboring areas. Since we are mainly interested in comparing the diseases, we assume the cases of the present diseases in an area jointly follow an autoregressive multinomial model. We use the multinomial model to investigate whether there are associations between certain factors, such as temperature, and differences in the transmission intensity of the diseases. Inference is performed using efficient Bayesian Markov chain Monte Carlo methods based on jointly sampling all unknown presence indicators. We apply the model to spatio-temporal counts of dengue, Zika, and chikungunya cases in Rio de Janeiro, during the first triple epidemic there.

Forkan Uddin Ahmed, Annesha Das, Md Zubair

The development of an intelligent agricultural decision-supporting system for crop selection and disease forecasting in Bangladesh is the main objective of this work. The economy of the nation depends heavily on agriculture. However, choosing crops with better production rates and efficiently controlling crop disease are obstacles that farmers have to face. These issues are addressed in this research by utilizing machine learning methods and real-world datasets. The recommended approach uses a variety of datasets on the production of crops, soil conditions, agro-meteorological regions, crop disease, and meteorological factors. These datasets offer insightful information on disease trends, soil nutrition demand of crops, and agricultural production history. By incorporating this knowledge, the model first recommends the list of primarily selected crops based on the soil nutrition of a particular user location. Then the predictions of meteorological variables like temperature, rainfall, and humidity are made using SARIMAX models. These weather predictions are then used to forecast the possibilities of diseases for the primary crops list by utilizing the support vector classifier. Finally, the developed model makes use of the decision tree regression model to forecast crop yield and provides a final crop list along with associated possible disease forecast. Utilizing the outcome of the model, farmers may choose the best productive crops as well as prevent crop diseases and reduce output losses by taking preventive actions. Consequently, planning and decision-making processes are supported and farmers can predict possible crop yields. Overall, by offering a detailed decision support system for crop selection and disease prediction, this work can play a vital role in advancing agricultural practices in Bangladesh.