Hasil untuk "Nutritional diseases. Deficiency diseases"

Peter Lyon, V. Strippoli, Byron Fang et al.

Vitamins B9 (folate) and B12 are essential water-soluble vitamins that play a crucial role in the maintenance of one-carbon metabolism: a set of interconnected biochemical pathways driven by folate and methionine to generate methyl groups for use in DNA synthesis, amino acid homeostasis, antioxidant generation, and epigenetic regulation. Dietary deficiencies in B9 and B12, or genetic polymorphisms that influence the activity of enzymes involved in the folate or methionine cycles, are known to cause developmental defects, impair cognitive function, or block normal blood production. Nutritional deficiencies have historically been treated with dietary supplementation or high-dose parenteral administration that can reverse symptoms in the majority of cases. Elevated levels of these vitamins have more recently been shown to correlate with immune dysfunction, cancer, and increased mortality. Therapies that specifically target one-carbon metabolism are therefore currently being explored for the treatment of immune disorders and cancer. In this review, we will highlight recent studies aimed at elucidating the role of folate, B12, and methionine in one-carbon metabolism during normal cellular processes and in the context of disease progression.

Zaldy Pagaduan, Jason Occidental, Nathaniel Duro et al.

Smallholder cacao producers often rely on outdated farming techniques and face significant challenges from pests and diseases, unlike larger plantations with more resources and expertise. In the Philippines, cacao farmers have limited access to data, information, and good agricultural practices. This study addresses these issues by developing a mobile application for cacao disease identification and management that functions offline, enabling use in remote areas where farms are mostly located. The core of the system is a deep learning model trained to identify cacao diseases accurately. The trained model is integrated into the mobile app to support farmers in field diagnosis. The disease identification model achieved a validation accuracy of 96.93% while the model for detecting cacao black pod infection levels achieved 79.49% validation accuracy. Field testing of the application showed an agreement rate of 84.2% compared with expert cacao technician assessments. This approach empowers smallholder farmers by providing accessible, technology-enabled tools to improve cacao crop health and productivity.

Anas Ali, Hina Arshad, Sameer Ibrahim et al.

Background: Food–drug interactions (FDIs) significantly influence drug effectiveness and patient safety by altering drug absorption, metabolism, distribution, or excretion, potentially resulting in therapeutic failure or adverse drug reactions. Nurses, as primary medication administrators, must possess adequate knowledge of FDIs to ensure safe and effective patient care. Objective: To assess the level of awareness regarding food–drug interactions among staff nurses working in a clinical setting. Methods: A cross-sectional descriptive study was conducted at Fatima Memorial Hospital, Lahore. A total of 115 registered nurses were selected through convenient sampling. Data were collected using a structured questionnaire comprising demographic variables and knowledge-related items on FDIs, including general awareness, drug–drug interactions, and timing of drug administration. Data were analyzed using SPSS version 21.0 with descriptive statistics and Pearson correlation tests. Results: The findings showed that 68% of nurses had moderate awareness, 12% demonstrated good awareness, and 20% had poor knowledge of FDIs. The mean awareness score was 17.68 ± 2.98, with a mean percentage score of 57.05%. No significant association was found between awareness level and demographic variables such as education and professional experience. Conclusion: Most nurses demonstrated only moderate awareness of food–drug interactions, with notable knowledge gaps in drug-specific interactions and timing of administration. Targeted educational interventions are recommended to enhance nurses’ knowledge and promote safer, evidence-based medication practices, ultimately improving patient outcomes and reducing adverse effects.

Stella Proikaki, Nikolaos Georgiadis, Theodoros N. Sergentanis et al.

Background: The interaction between HIV infection, nutrition and immune system functioning is intricate, leading, in many cases, to a cycle of poor health outcomes. Despite the widespread use of highly active antiretroviral therapy (HAART) since the late 1990s and the concomitant increase in the life expectancy of people living with HIV (PLHIV), malnutrition and HIV-associated wasting continue to pose significant challenges, particularly in developing countries. Additionally, metabolic adverse effects associated with HAART, such as alterations in bone and lipid metabolism, as well as the impact on cardiovascular health, add further complexity to patient care. Methods: We conducted a comprehensive literature review of relevant studies involving adults diagnosed with HIV. The studies, published between 2000 and 2023, were identified using the Medline/PubMed, Scopus and Google Scholar databases. Results: Accumulating evidence in the literature indicates that careful monitoring and appropriate nutritional interventions can significantly enhance clinical outcomes in malnourished HIV-positive persons. The importance of addressing the prevalent deficiencies in certain micronutrients discussed in many of the studies is clearly underlined. However, challenges remain, particularly in low-income settings, where limited resources and infrastructure can impede effective implementation. Conclusions: There are critical research gaps with regard to the interaction between ART and nutrition, as well as the development of tailored nutritional approaches that aim to improve patient outcomes. Future research directions and policy strategies should focus on the development of sustainable programmes aimed at enhancing the quality of life for PLHIV.

Chanti Raju Mylay, Bobin Deng, Zhipeng Cai et al.

Crop diseases pose significant threats to global food security, agricultural productivity, and sustainable farming practices, directly affecting farmers' livelihoods and economic stability. To address the growing need for effective crop disease management, AI-based disease alerting systems have emerged as promising tools by providing early detection and actionable insights for timely intervention. However, existing systems often overlook critical aspects such as data privacy, market pricing power, and farmer-friendly usability, leaving farmers vulnerable to privacy breaches and economic exploitation. To bridge these gaps, we propose AgriSentinel, the first Privacy-Enhanced Embedded-LLM Crop Disease Alerting System. AgriSentinel incorporates a differential privacy mechanism to protect sensitive crop image data while maintaining classification accuracy. Its lightweight deep learning-based crop disease classification model is optimized for mobile devices, ensuring accessibility and usability for farmers. Additionally, the system includes a fine-tuned, on-device large language model (LLM) that leverages a curated knowledge pool to provide farmers with specific, actionable suggestions for managing crop diseases, going beyond simple alerting. Comprehensive experiments validate the effectiveness of AgriSentinel, demonstrating its ability to safeguard data privacy, maintain high classification performance, and deliver practical, actionable disease management strategies. AgriSentinel offers a robust, farmer-friendly solution for automating crop disease alerting and management, ultimately contributing to improved agricultural decision-making and enhanced crop productivity.

JunJie Wee, Faisal Suwayyid, Mushal Zia et al.

Genetic mutations can disrupt protein structure, stability, and solubility, contributing to a wide range of diseases. Existing predictive models often lack interpretability and fail to integrate physical and chemical interactions critical to molecular mechanisms. Moreover, current approaches treat disease association, stability changes, and solubility alterations as separate tasks, limiting model generalizability. In this study, we introduce a unified framework based on multiscale commutative algebra to capture intrinsic physical and chemical interactions for the first time. Leveraging Persistent Stanley-Reisner Theory, we extract multiscale algebraic invariants to build a Commutative Algebra neural Network (CANet). Integrated with transformer features and auxiliary physical features, we apply CANet to tackle three key domains for the first time: disease-associated mutations, mutation-induced protein stability changes, and solubility changes upon mutations. Across six benchmark tasks, CANet and its gradient boosting tree counterpart, CATree, consistently attain state-of-the-art performance, achieving up to 7.5% improvement in predictive accuracy. Our approach offers multiscale, mechanistic, interpretable,and generalizable models for predicting disease-mutation associations.

Ananya Raghu, Anisha Raghu, Alice S. Tang et al.

Background/Objectives: Age-related macular degeneration, glaucoma, diabetic retinopathy (DR), diabetic macular edema, and pathological myopia affect hundreds of millions of people worldwide. Early screening for these diseases is essential, yet access to medical care remains limited in low- and middle-income countries as well as in resource-limited settings. We develop InSight, an AI-based app that combines patient metadata with fundus images for accurate diagnosis of five common eye diseases to improve accessibility of screenings. Methods: InSight features a three-stage pipeline: real-time image quality assessment, disease diagnosis model, and a DR grading model to assess severity. Our disease diagnosis model incorporates three key innovations: (a) Multimodal fusion technique (MetaFusion) combining clinical metadata and images; (b) Pretraining method leveraging supervised and self-supervised loss functions; and (c) Multitask model to simultaneously predict 5 diseases. We make use of BRSET (lab-captured images) and mBRSET (smartphone-captured images) datasets, both of which also contain clinical metadata for model training/evaluation. Results: Trained on a dataset of BRSET and mBRSET images, the image quality checker achieves near-100% accuracy in filtering out low-quality fundus images. The multimodal pretrained disease diagnosis model outperforms models using only images by 6% in balanced accuracy for BRSET and 4% for mBRSET. Conclusions: The InSight pipeline demonstrates robustness across varied image conditions and has high diagnostic accuracy across all five diseases, generalizing to both smartphone and lab captured images. The multitask model contributes to the lightweight nature of the pipeline, making it five times computationally efficient compared to having five individual models corresponding to each disease.

Jiali Xia, Li Ding, Guoyan Liu

Abstract Metabolic syndrome (MetS) is a clinical syndrome associated with cardiovascular disease, diabetes, obesity, and dyslipidemia. Its primary features include dyslipidemia, hypertension, abdominal obesity, and insulin resistance (IR). Recently, research has revealed that MetS is not only a manifestation of internal metabolic disturbances but is also closely associated with various dermatological conditions, including inflammatory skin diseases, autoimmune skin diseases, and skin tumors. These studies have clarified the complex mechanisms underlying the interaction between MetS and these skin diseases, including IR, chronic inflammatory responses, and oxidative stress. This review summarizes the association between MetS and related dermatological conditions and their shared physiological mechanisms. It aims to provide clinicians with new therapeutic strategies and preventive measures to improve the treatment outcomes and quality of life of patients with skin conditions.

Peng Gu, Bowen Shi, Zheng Zhang et al.

Abstract Background Metabolic Score for Visceral Fat (METS-VF) is commonly used as an indicator for assessing visceral fat metabolism. However, the relationship between METS-VF, Bone Mineral Density (BMD), and osteoporosis remains unclear in the American population. Methods This study utilized cross-sectional data from the National Health and Nutrition Examination Survey (NHANES), including participants aged 20 years and older, from the survey cycles conducted between 2005 and 2010, 2013–2014, and 2017–2018. Multivariable weighted linear regression and logistic regression analyses were first applied to investigate the associations between the METS-VF, femoral BMD, and osteoporosis. In addition, subgroup interaction analyses were performed to evaluate the robustness of these associations. To address potential non-linear relationships, restricted cubic spline regression was employed. All statistical analyses were conducted using R software version 4.3.3. P values were two-tailed, with P < 0.05 considered statistically significant. Results After adjusting for all covariates, the positive correlations between METS-VF and BMD measurements at all sites remained statistically significant (p < 0.001 & p for trend < 0.001). Multivariable logistic regression analysis indicated that, after adjusting for covariates related to osteoporosis, each one-unit increase in METS-VF was associated with a 63.1% reduction in the risk of developing osteoporosis. Moreover, the direction of the associations between METS-VF and both BMD and osteoporosis remained consistent across all subgroups, while restricted cubic spline (RCS) analyses suggested nonlinear relationships. The 5.82–7.35 METS-VF range yielded a mean 51.9% osteoporosis risk reduction (sustained ≥ 30% peak efficacy in 66.7% of participants). Conclusions METS-VF demonstrated a nonlinear positive association with BMD and a nonlinear inverse relationship with osteoporosis risk. Future studies should establish optimal biological thresholds of METS-VF for skeletal health. Clinical trial number Not applicable.

Atul Gupta, Pritam Halder, Rachana Srivastava et al.

Abstract Introduction Large meal portions contribute to the rise in Body Mass Index (BMI) leading to higher burden of Non-Communicable Diseases (NCDs), especially among the youths. College students frequently exhibit poor dietary habits; thus, it is important to pay attention to their eating habits in order to lower their risk for NCD. Therefore, we conducted this study with objective to measure the effect of health promotion intervention for small portion size consumption behaviour on body mass index (BMI) among college students in Chandigarh, India. Methods We conducted this quasi-experimental research among college students aged 18–21 years from two colleges with co-education, having same streams considered as intervention and control from 2019 to 2020. Motivating group therapy, individual counselling, classroom training including power point presentations, lectures, and messaging via WhatsApp were implemented to the intervention group. No intervention was applied to the control group. Results Overall, the mean BMI decreased in the both the intervention (n = 149) and control groups (n = 142) between the baseline and 6 months post intervention follow up period. The difference in difference (DID) of mean using paired t-test analysis showed that the estimated difference between the two groups was statistically significant for BMI (0.21; 95% CI 0.117–0.244; p-value < 0.001) and for waist hip ratio (0.02; 95% CI 0.001–0.005; p-value 007). There was reduction of 8.1% (from 28.9 to 20.8%) in obese, while 2.7% (from 44.3 to 47.0%) increase in participants with normal BMI in intervention. On the contrary there was almost no change in the proportion of obese and overweight participants in control group after 6 months. Conclusion Considering the higher surge of obesity especially among the Indian youths, this study provided significant contribution in the field of public health in view of effective health promotion intervention to reduce BMI levels, by which get hold of the rising trend of this alarming problem. We recommend pragmatic community level large scale randomised control trials with frequent and controlled follow up to generate further evidence.

Jenna A. Fryer, Elizabeth Tomasino

Wines produced from grapes exposed to wildfire smoke exhibit smoke-related flavors, such as smoky, burnt, and an ashy finish. While grapes are impacted on the vine, winemaking strategies can influence the perception of smoke-related properties in the resulting wine. This case study evaluated eight smoke-affected wines across three vintages from commercial production to assess how oak influences smoke-related flavors. Each vintage explored a different usage of oak, including oak origin, oak chips with carbon fining, and potential carryover of flavors through reused barrels. Wines were assessed using descriptive analysis, with intensity ratings collected for seven attributes representing smoke-related and typical wine flavors. Results showed that American oak reduced the perception of smoke-related flavors compared to French oak. The use of oak chips, both alone and with carbon fining, did not improve the flavor profile. This suggests that combining treatments should be approached with caution, especially when strategies target smoke taint mitigation through different mechanisms. Sensory results also indicated no evidence of smoke flavor carryover from using barrels that previously held smoke-affected wine. Overall, this work showed that oak can influence sensory profile of smoke-affected wines and consideration of different wine production practices can be beneficial when faced with a smoke-impacted vintage.

Menghua Tao, Chun-Hui Lin, Jialiang Liu et al.

Nour Zahi Gammoh, L. Rink

Zn is an essential trace element for all organisms. In human subjects body growth and development is strictly dependent on Zn. The nervous, reproductive and immune systems are particularly influenced by Zn deficiency, as well as by increased levels of Zn. The relationship between Zn and the immune system is complex, since there are four different types of influence associated with Zn. (1) The dietary intake and the resorption of Zn depends on the composition of the diet and also on age and disease status. (2) Zn is a cofactor in more than 300 enzymes influencing various organ functions having a secondary effect on the immune system. (3) Direct effects of Zn on the production, maturation and function of leucocytes. (4) Zn influences the function of immunostimulants used in the experimental systems. Here we summarize all four types of influence on the immune function. Nutritional aspects of Zn, the physiology of Zn, the influence of Zn on enzymes and cellular functions, direct effects of Zn on leucocytes at the cellular and molecular level, Zn-altered function of immunostimulants and the therapeutic use of Zn will be discussed in detail.

Zhoutao Chen, A. Karaplis, S. Ackerman et al.

Kohei Iwano, Shogo Shibuya, Satoshi Kagiwada et al.

Recently, object detection methods (OD; e.g., YOLO-based models) have been widely utilized in plant disease diagnosis. These methods demonstrate robustness to distance variations and excel at detecting small lesions compared to classification methods (CL; e.g., CNN models). However, there are issues such as low diagnostic performance for hard-to-detect diseases and high labeling costs. Additionally, since healthy cases cannot be explicitly trained, there is a risk of false positives. We propose the Hierarchical object detection and recognition framework (HODRF), a sophisticated and highly integrated two-stage system that combines the strengths of both OD and CL for plant disease diagnosis. In the first stage, HODRF uses OD to identify regions of interest (ROIs) without specifying the disease. In the second stage, CL diagnoses diseases surrounding the ROIs. HODRF offers several advantages: (1) Since OD detects only one type of ROI, HODRF can detect diseases with limited training images by leveraging its ability to identify other lesions. (2) While OD over-detects healthy cases, HODRF significantly reduces these errors by using CL in the second stage. (3) CL's accuracy improves in HODRF as it identifies diagnostic targets given as ROIs, making it less vulnerable to size changes. (4) HODRF benefits from CL's lower annotation costs, allowing it to learn from a larger number of images. We implemented HODRF using YOLOv7 for OD and EfficientNetV2 for CL and evaluated its performance on a large-scale dataset (4 crops, 20 diseased and healthy classes, 281K images). HODRF outperformed YOLOv7 alone by 5.8 to 21.5 points on healthy data and 0.6 to 7.5 points on macro F1 scores, and it improved macro F1 by 1.1 to 7.2 points over EfficientNetV2.

Ronak Talebi Qormik, Hadi Alipour, Reza Darvishzadeh

Extended Abstract Background: In arid and semi-arid regions, biotic and abiotic stresses can directly or indirectly lead to restrictions and decreased growth of different plants. In these areas, salinity stress is one of the major challenges facing agriculture and crop production that causes huge damage to crop yields annually. The amount of salinity in the soil results in plant growth limitation, and increased soil salinity disrupts water and essential nutrient absorption for the plant and reduces plant growth, which can then lead to plant death. Reduced root growth and development, decreased nutrient absorption, increased likelihood of allergies to diseases and pests, decreased yield, and final product quality (e.g., nutrient deficiency), and increased toxic elements, are among the negative effects of salinity on plants. Various factors are involved in the creation of salinity, the most important of which can be climate change, source rock weathering, improper irrigation, drought, excessive consumption of fertilizers, and reduced seawater levels. Following climate change, these damages are on the rise every year. Due to the increase in population growth, demand for food production is increasing day by day. Wheat is known as the major grain in the supply of nutritional needs in the world, hence its sustainable production is of paramount importance. Salinity is recognized as an important factor in reducing wheat yield, and it may increase the accumulation of harmful salts in the plant tissue, which can lead to physiological damage and decreased plant growth. The effects of soil salinity vary depending on the amount of salinity, the type of salinity, and the type of wheat. One way to prevent the negative effects of salinity is to use salinity-resistant wheat cultivars. The range of diversity in relation to salt stress tolerance in different plants, especially the wheat plant, depends on various factors such as plant genotype, duration of stress, and plant growth stage. The seedling stage in wheat is one of the important stages regarding tolerance to salt stress. This study aims to investigate the response of spring wheat cultivars in the seedling stage to salinity stress. Methods: In the present study, the reaction of 64 Iranian spring wheat genotypes at the seedling stage under normal conditions and 12 dS/m salinity stress was investigated in two replications in a simple lattice design at the research greenhouse of the Faculty of Agriculture, Urmia University in 2021-2022. In this study, in the four-leaf stage, salinity stress was applied gradually for two days. The measured traits were chlorophyll (SPAD), canopy temperature, shoot length (SL), root length (RL), seedling length (PL), shoot potassium content (KS) ), root potassium content (KR), shoot sodium content (NaS), root sodium content (NaR), shoot potassium to sodium ratio (KNaS), root potassium to sodium ratio (KNaR), root volume (RV), leaf area index (LAI), radicle fresh weight (FWR), radicle dry weight (DWR), relative leaf water content (RWC), shoot fresh weight (FWS), shoot dry weight (DWS), seedling fresh weight (FWP), weight dry matter of seedlings (DWP). The data of the studied traits were obtained in a random complete block design. PROC GLM was used for the analysis of variance (ANOVA) in SAS 9.4 software. The correlation was examined using PROC CORR and decomposition into factors using PROC FACTOR. The figures were grouped using the gplots software package and the biplot diagram was drawn with the factoextra software package in the R 4.1 environment. The MANOVA statement in PROC GLM was used in SAS 9.4 software for multivariate variance analysis. Results: Based on the results of ANOVA, statistically significant differences were observed between the tested cultivars based on the traits studied in the seedling stage, including FWP, DWP, FWR, DWR, FWS, RWS, and (PL). In both normal and salt stress conditions, DWP showed the most significant correlation with FWP, DWS, and DWR. Under the salinity stress conditions, FWS was significantly correlated with DWS, FWP, and DWP. Based on factor analysis, the studied traits in both normal and salinity stress conditions were grouped into seven factors, which explained 77.93% and 76.44% of the total changes in normal and salinity stress conditions, respectively. Using cluster analysis, cultivars under both normal and salt stress conditions were grouped into three clusters. Conclusion: Based on the biplot results of factor analysis and cluster analysis, Maron, Darya, Shiroodi, Moghan 3, Darab 2, Roshan, Pishgam, and Pishtaz cultivars are introduced as favorable cultivars. Chamran, Bam, Alborz, and Maroodasht cultivars are categorized as unfavorable cultivars that can be used in further wheat breeding programs.

Ezgi Bellikci-Koyu, Yasemin Karaağaç, Armağan Aytuğ Yürük

Abstract Purpose This study aimed to investigate the potential relationships between the use of different section of food label, and healthy and pathological aspects of orthorexia among adults. Methods This cross-sectional study was conducted using an online survey (n = 1326). Inclusion criteria were being 19–64 years and graduated from at least primary school. Pregnant and lactating women were excluded. Data were collected using questionnaire including socio-demographic variables, lifestyle factors, body weight and height, frequency of reading different sections of food label (“always”, “when buying a food for the first time”, “when comparing similar packaged foods”, “rarely”, “never”), food label literacy, and Teruel Orthorexia Scale. Participants were categorized as nutrition facts panel-users, ingredients list-users or claim-users if they read at least one item from the relevant parts. Results The proportions of nutrition facts, ingredients list, and claims sections users were 72.3%, 76.3%, and 79.9%, respectively. Both healthy and pathological aspects of orthorexia were associated with reading food labels. The healthy orthorexia had the strongest association with using the ingredients list (OR 1.76, 95% CI 1.41–2.20), whereas the orthorexia nervosa showed the highest association with using nutrition facts panel (OR 1.48, 95% CI 1.20–1.81). While women, physically active participants and those with higher food label literacy were more likely to use all sections of food labels; older age, having children, and chronic disease increased the likelihood of using claims and ingredients list (p < 0.05). Besides, following a diet was associated with higher use of nutrition facts and ingredients list (p < 0.05). Conclusions The study demonstrates that food label users have higher orthorexia tendencies compared to non-users. Of the food label sections, healthy orthorexia showed the strongest association with use of the list of ingredients, while pathological orthorexia showed the strongest association with use of the nutrition facts panel. Level of evidence Level V, cross-sectional study.

Samantha Morgillo, Lucinda K Bell, Claire Gardner et al.

Abstract Objective: To determine the reach, adoption, implementation and effectiveness of an intervention to increase children’s vegetable intake in long day care (LDC). Design: A 12-week pragmatic cluster randomised controlled trial, informed by the multiphase optimisation strategy (MOST), targeting the mealtime environment and curriculum. Children’s vegetable intake and variety was measured at follow-up using a modified Short Food Survey for early childhood education and care and analysed using a two-part mixed model for non-vegetable and vegetable consumers. Outcome measures were based on the RE-AIM framework. Setting: Australian LDC centres. Participants: Thirty-nine centres, 120 educators and 719 children at follow-up. Results: There was no difference between intervention and waitlist control groups in the likelihood of consuming any vegetables when compared with non-vegetable consumers for intake (OR = 0·70, (95 % CI 0·34–1·43), P = 0·32) or variety (OR = 0·73 (95 % CI 0·40–1·32), P = 0·29). Among vegetable consumers (n 652), there was no difference between groups in vegetable variety (exp(b): 1·07 (95 % CI:0·88–1·32, P = 0·49) or vegetable intake (exp(b): 1·06 (95 % CI: 0·78, 1·43)), P = 0·71) with an average of 1·51 (95 % CI 1·20–1·82) and 1·40 (95 % CI 1·08–1·72) serves of vegetables per day in the intervention and control group, respectively. Intervention educators reported higher skills for promoting vegetables at mealtimes, and knowledge and skills for teaching the curriculum, than control (all P < 0·001). Intervention fidelity was moderate (n 16/20 and n 15/16 centres used the Mealtime environment and Curriculum, respectively) with good acceptability among educators. The intervention reached 307/8556 centres nationally and was adopted by 22 % eligible centres. Conclusions: The pragmatic self-delivered online intervention positively impacted educator’s knowledge and skills and was considered acceptable and feasible. Intervention adaptations, using the MOST cyclic approach, could improve intervention impact on children’ vegetable intake.

G. Ram, J. Chinen