Hasil untuk "Nutritional diseases. Deficiency diseases"

K. Norman, U. Haß, M. Pirlich

Malnutrition in older adults has been recognised as a challenging health concern associated with not only increased mortality and morbidity, but also with physical decline, which has wide ranging acute implications for activities of daily living and quality of life in general. Malnutrition is common and may also contribute to the development of the geriatric syndromes in older adults. Malnutrition in the old is reflected by either involuntary weight loss or low body mass index, but hidden deficiencies such as micronutrient deficiencies are more difficult to assess and therefore frequently overlooked in the community-dwelling old. In developed countries, the most cited cause of malnutrition is disease, as both acute and chronic disorders have the potential to result in or aggravate malnutrition. Therefore, as higher age is one risk factor for developing disease, older adults have the highest risk of being at nutritional risk or becoming malnourished. However, the aetiology of malnutrition is complex and multifactorial, and the development of malnutrition in the old is most likely also facilitated by ageing processes. This comprehensive narrative review summarizes current evidence on the prevalence and determinants of malnutrition in old adults spanning from age-related changes to disease-associated risk factors, and outlines remaining challenges in the understanding, identification as well as treatment of malnutrition, which in some cases may include targeted supplementation of macro- and/or micronutrients, when diet alone is not sufficient to meet age-specific requirements.

C. Mccay, M. F. Crowell, L. Maynard

M. Muckenthaler, B. Galy, M. Hentze

Maria C Canino, Stefan M Pasiakos, Joseph J Knapik et al.

Ariana DeLong Bailey, Derek C Miketinas, Elisha London et al.

Ananya Joshi, George Khoury, Christodoulas Floudas

Alzheimer's disease is characterized by dangerous amyloid plaques formed by deposits of the protein Beta-Amyloid aggregates in the brain. The specific amino acid sequence that is responsible for the aggregates of Beta-Amyloid is lys-leu-val-phe-phe (KLVFF). KLVFF aggregation inhibitors, which we design in this paper, prevent KLVFF from binding with itself to form oligomers or fibrils (and eventually plaques) that cause neuronal death. Our binder-blocker peptides are designed such that, on one side, they bind strongly to KLVFF, and on the other side, they disrupt critical interactions, thus preventing aggregation. Our methods use optimization techniques and molecular simulations and identify 10 candidate sequences for trial of the 3.2 million possible sequences. This approach for inhibitor identification can be generalized to other diseases characterized by protein aggregation, such as Parkinson's, Huntington's, and prion diseases.

Catarina Canastra, Cátia Pesquita

Discovery gene-disease links is important in biology and medicine areas, enabling disease identification and drug repurposing. Machine learning approaches accelerate this process by leveraging biological knowledge represented in ontologies and the structure of knowledge graphs. Still, many existing works overlook ontologies explicitly representing diseases, missing causal and semantic relationships between them. The gene-disease association problem naturally frames itself as a link prediction task, where embedding algorithms directly predict associations by exploring the structure and properties of the knowledge graph. Some works frame it as a node-pair classification task, combining embedding algorithms with traditional machine learning algorithms. This strategy aligns with the logic of a machine learning pipeline. However, the use of negative examples and the lack of validated gene-disease associations to train embedding models may constrain its effectiveness. This work introduces a novel framework for comparing the performance of link prediction versus node-pair classification tasks, analyses the performance of state of the art gene-disease association approaches, and compares the different order-based formalizations of gene-disease association prediction. It also evaluates the impact of the semantic richness through a disease-specific ontology and additional links between ontologies. The framework involves five steps: data splitting, knowledge graph integration, embedding, modeling and prediction, and method evaluation. Results show that enriching the semantic representation of diseases slightly improves performance, while additional links generate a greater impact. Link prediction methods better explore the semantic richness encoded in knowledge graphs. Although node-pair classification methods identify all true positives, link prediction methods outperform overall.

Jing Li, Jun‐Xia Liu, Xiao‐Li Shen et al.

Abstract Iodine, an essential trace element for the human body, plays a pivotal role in sustaining health. Malnutrition has emerged as a pressing public health concern, posing a significant threat to human well‐being. Iodine deficiency poses a substantial threat to the development of children, potentially leading to neurological developmental disorders and mental retardation. Conversely, excessive iodine intake can result in structural and functional abnormalities in the thyroid gland. In this study, we selected children aged 3–6 years through a stratified cluster sampling approach in six regions across China to explore the correlation between iodine nutrition and their physical growth. A total of 5920 preschool children participated in this study, with a median urinary iodine concentration (UIC) of 177.33 [107.06, 269.92] μg/L. Among these children, 250 (4.2%) exhibited stunting, 180 (3.0%) were underweight, 198 (3.3%) experienced wasting, 787 (3.3%) were overweight and 414 (7.0%) were classified as obese. The multivariate linear regression revealed that UIC exhibited a positive correlation with body mass index z‐Score (BMIZ) in overweight children (β = 0.038; 95% CI: 0.001, 0.075). In normally growing children, the associations between UIC and height‐for‐age z‐score, weight‐for‐age z‐score and BMIZ displayed nonlinear patterns. Our findings suggest that iodine nutrition is adequate for Chinese children aged 3–6 years. Furthermore, iodine nutrition is intricately linked to the growth and development of these children. Consequently, it is imperative to implement decisive measures to prevent both iodine deficiency and excess.

Sara Mazidi, Mohammad-Hadi Eskandari, Mehrdad Niakosari et al.

Background: Tiri bread is one of the oldest known bread types in the Middle East. It is single layer, unleavened, soft, and flat traditional bread baked at home. Dry bread has a long shelf life if stored appropriately at room temperature, but a fresh bread gets moldy with off-flavor 3-4 days after baking. This study has assessed physicochemical properties and microbial storage stability of Tiri bread as traditional flat bread in Iran.Methods: Twenty samples of fresh home-baked Tiri bread were examined for their physicochemical characteristics and shelf life stability at 4°C and 25°C. Their most common spoiling factors, morphological and molecular attributes were investigated. The breads were assumed unhealthy for consumption when the first sign of mold strains appeared.Results: The thickness (0.4-0.9 mm), water activity (0.82-0.90), moisture content (18.08-24.13%), salt content (1.59-3.24%), pH (5.75-5.95), and total titrable acidity (2.00-2.90 mL;0.1 N NaOH) were determined. The shelf life of fresh Tiri bread was 10 and 4 days at 4°C and 25°C, respectively. The main factors limiting the shelf life of these breads were appearance of mold as well as development of an off-odor. The most common isolated species were Aspergillus niger (31.38%), A. flavus (16.12%), A. tubingensis (15.12%), A. awamori (12.10%), A. ochraceus (10.14%), and Penicilliumcorylophilum (16.26%).Conclusion: As some types of fungi produce harmful toxins which may trigger allergic reactions and can cause harmful infections, it is vital to set out principles concerning safety and health during production and storage of these breads to pay attention to the production and storage conditions of Tiri bread to inhibit mold growth.

Giulio D’Anna, Lorenzo Lucherini Angeletti, Lara Allegrini et al.

Abstract Purpose To provide a prospective 2 year follow-up of a previously enrolled adolescent high school sample, regarding body image and eating concerns, and patterns of sports type and physical activity. Methods Sports type, weekly time devoted to it, and psychopathological self-reports (Eating Disorders Examination Questionnaire 6.0, Body Uneasiness Test, and Muscle Dysmorphia Disorder Inventory) were evaluated longitudinally in a general population sample enrolled in a previous study, testing prospective variations in an observational setting. Results At follow-up, girls expressed increased dietary restraint and body uneasiness as compared to baseline, whereas boys expressed increased body uneasiness—and specifically in the avoidance, depersonalisation and compulsive self-monitoring dimensions. Among both sexes, a significant shift towards individual activities or lack of activity was observed after 2 years, with a reduction in team sports involvement. Among girls, time devoted to exercise significantly decreased at follow-up. Conclusions The present findings indirectly confirm an increased vulnerability to dietary, bodily and appearance-based concerns among adolescents. The role of different patterns of physical activity and exercise time may interact bidirectionally with these problematic areas, considering that previous studies point out increased concerns among inactive subjects, and among those who choose individual activities. Level of evidence: Level IV-longitudinal observational study.

Maria Amélia A Campos, Maria Lúcia R Oppermann, Maria Teresa V Sanseverino et al.

Abstract Objective To describe the frequency and types of congenital anomalies and associated risk factors in Brazilian women with type 2 diabetes. Methods In this retrospective cohort study between 2005 and 2021, we included all pregnant participants with type 2 diabetes from the two major public hospitals in southern Brazil. We collected data from the electronic hospital records. Congenital anomalies were classified by the 10th revised International Classification of Diseases, Q chapter, enhanced by the EUROCAT registry classification, and categorized by type and gravity. We used multiple Poisson regression with robust estimates to estimate risks. Results Among 648 participants, we excluded 19, and 62 were lost to follow-up; therefore, we included 567 participants. Overt diabetes arose in 191 participants (33.7%, 95% CI 30.0% – 38.0%). Less than 20% of the participants supplemented folate. Congenital anomalies occurred in 78 neonates (13.8%, CI 11.0 − 16.9%), 73 babies (93.6%) presented major anomalies, and 20 (10.5%) cases occurred in participants with overt diabetes. Cardiac anomalies were the most frequent (43 isolated and 12 combined). Pre-eclampsia was associated with an increased risk in the analyses including all women (adjusted RR 1.87 (95% CI 1.23–2.85), p = 0.003), but not in analyses including only women with an HbA1c measured up to the 14th gestational age. HbA1c, either measured at any time in pregnancy (adjusted RR 1.21 (95% CI 1.10–1.33), p < 0.001) or up to the first 14 weeks (adjusted RR 1.22, 95% CI 1.10–1.35, p < 0.001) was the only sustained risk factor. Risk factors such as maternal age, obesity, diabetes diagnosis, or use of antidiabetic medications were not associated with congenital anomalies. Conclusion We found a high frequency of congenital anomalies associated with poor maternal glycemic control and revealed an almost universal lack of preconception care. An urgent call to action is mandatory for the reversal of this gray scenario.

Michael H Green, Veronica Lopez-Teros, Joanne Balmer Green et al.

Background: Limited data are available on vitamin A kinetics and total body stores (TBS) in women. Such information can be obtained using compartmental modeling and retinol isotope dilution (RID). Objectives: Objectives were to apply population-based (“super-subject”) modeling to determine retinol kinetics in nonpregnant Ghanaian women of reproductive age and to use RID to predict TBS in the group and its individuals. Methods: Women (n = 89) ingested a dose of [2H6]retinyl acetate and blood samples (3/woman) were collected from 6 h to 91 d, with all participants sampled at 14 d, about half at either 21 or 28 d, and each at one other time. Composite data (plasma retinol fraction of dose; FDp) were analyzed using Simulation, Analysis and Modeling software to obtain kinetic parameters, TBS, and other state variables as well as model-derived values for the RID composite coefficient FaS. The latter were used in the RID equation TBS (μmol) = FaS × 1/SAp (where SAp is plasma retinol specific activity) to predict TBS at various times. Results: Model-predicted TBS was 973 μmol (n = 87). Geometric mean RID-predicted TBS was 965, 926, and 1006 μmol at 14, 21, and 28 d, respectively, with wide ranges [for example, 252–3848 μmol on day 14 (n = 86)]; TBS predictions were similar at later times. Participants had a mean 2 y of vitamin A in stores and estimated liver vitamin A concentrations in the normal range. Model-predicted vitamin A disposal rate was 1.3 μmol/d and plasma recycling number was 37. Conclusions: Super-subject modeling provides an estimate of group mean TBS as well as group-specific values for the RID coefficient FaS; the latter can be used to confidently predict TBS by RID for individual participants in the group under study or in similar individuals at 14 d or more after isotope ingestion. Trial registration number: Trial is registered (NCT04632771) at https://clinicaltrials.gov.

Hossein Farhadnejad, Niloufar Saber, Asal Neshatbini Tehrani et al.

Type 2 diabetes (T2D) is known as a major public health problem with a noticeable adverse impact on quality of life and health expenditures worldwide. Despite using routine multiple pharmacological and nonpharmacological interventions, including diet therapy and increasing physical activity, controlling this chronic disease remains a challenging issue, and therapeutic goals are often not achieved. Therefore, recently, other therapeutic procedures, such as using herbal products and functional foods as complementary or alternative medicine (CAM), have received great attention as a new approach to managing T2D complications, according to the literature. We reviewed the existing evidence that supports using various fundamental medicinal herbs, including cinnamon, saffron, ginger, jujube, turmeric, and barberry, as CAM adjunctive therapeutic strategies for T2D patients. The current review addressed different aspects of the potential impact of the abovementioned herbal products in improving glycemic indices and lipid profiles, including the effect size reported in the studies, their effective dose, possible side effects, herbs-drug interactions, and their potential action mechanisms.

Luiza Lober, Kirstin O. Roster, Francisco A. Rodrigues

Supervised machine learning models and public surveillance data has been employed for infectious disease forecasting in many settings. These models leverage various data sources capturing drivers of disease spread, such as climate conditions or human behavior. However, few models have incorporated the organizational structure of different geographic locations for forecasting. Traveling waves of seasonal outbreaks have been reported for dengue, influenza, and other infectious diseases, and many of the drivers of infectious disease dynamics may be shared across different cities, either due to their geographic or socioeconomic proximity. In this study, we developed a machine learning model to predict case counts of four infectious diseases across Brazilian cities one week ahead by incorporating information from related cities. We compared selecting related cities using both geographic distance and GDP per capita. Incorporating information from geographically proximate cities improved predictive performance for two of the four diseases, specifically COVID-19 and Zika. We also discuss the impact on forecasts in the presence of anomalous contagion patterns and the limitations of the proposed methodology.

Suma K, Deepali Koppad, Preethi Kumar et al.

In recent years, advancements in deep learning techniques have considerably enhanced the efficiency and accuracy of medical diagnostics. In this work, a novel approach using multi-task learning (MTL) for the simultaneous classification of lung sounds and lung diseases is proposed. Our proposed model leverages MTL with four different deep learning models such as 2D CNN, ResNet50, MobileNet and Densenet to extract relevant features from the lung sound recordings. The ICBHI 2017 Respiratory Sound Database was employed in the current study. The MTL for MobileNet model performed better than the other models considered, with an accuracy of74\% for lung sound analysis and 91\% for lung diseases classification. Results of the experimentation demonstrate the efficacy of our approach in classifying both lung sounds and lung diseases concurrently. In this study,using the demographic data of the patients from the database, risk level computation for Chronic Obstructive Pulmonary Disease is also carried out. For this computation, three machine learning algorithms namely Logistic Regression, SVM and Random Forest classifierswere employed. Among these ML algorithms, the Random Forest classifier had the highest accuracy of 92\%.This work helps in considerably reducing the physician's burden of not just diagnosing the pathology but also effectively communicating to the patient about the possible causes or outcomes.

Paridhi Mundra, Manik Sharma, Yashwardhan Chaudhuri et al.

As respiratory illnesses become more common, it is crucial to quickly and accurately detect them to improve patient care. There is a need for improved diagnostic methods for immediate medical assessments for optimal patient outcomes. This paper introduces VoxMed, a UI-assisted one-step classifier that uses digital stethoscope recordings to diagnose respiratory diseases. It employs an Audio Spectrogram Transformer(AST) for feature extraction and a 1-D CNN-based architecture to classify respiratory diseases, offering professionals information regarding their patients respiratory health in seconds. We use the ICBHI dataset, which includes stethoscope recordings collected from patients in Greece and Portugal, to classify respiratory diseases. GitHub repository: https://github.com/Sample-User131001/VoxMed

Jie Chen, Lei Nie, Shiowjen Lee et al.

Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phenotypes and geneotypes within a disorder, and lack of appropriate surrogate endpoints to measure clinical benefits. The Real-World Evidence (RWE) Scientific Working Group of the American Statistical Association Biopharmaceutical Section has assembled a research team to assess the landscape including challenges and possible strategies to address these challenges and the role of real-world data (RWD) and RWE in rare disease drug development. This paper first reviews the current regulations by regulatory agencies worldwide and then discusses in more details the challenges from a statistical perspective in the design, conduct, and analysis of rare disease clinical trials. After outlining an overall development pathway for rare disease drugs, corresponding strategies to address the aforementioned challenges are presented. Other considerations are also discussed for generating relevant evidence for regulatory decision-making on drugs for rare diseases. The accompanying paper discusses how RWD and RWE can be used to improve the efficiency of rare disease drug development.

Felix Wagner, Wentian Xu, Pramit Saha et al.

Segmentation models for brain lesions in MRI are typically developed for a specific disease and trained on data with a predefined set of MRI modalities. Such models cannot segment the disease using data with a different set of MRI modalities, nor can they segment other types of diseases. Moreover, this training paradigm prevents a model from using the advantages of learning from heterogeneous databases that may contain scans and segmentation labels for different brain pathologies and diverse sets of MRI modalities. Additionally, the confidentiality of patient data often prevents central data aggregation, necessitating a decentralized approach. Is it feasible to use Federated Learning (FL) to train a single model on client databases that contain scans and labels of different brain pathologies and diverse sets of MRI modalities? We demonstrate promising results by combining appropriate, simple, and practical modifications to the model and training strategy: Designing a model with input channels that cover the whole set of modalities available across clients, training with random modality drop, and exploring the effects of feature normalization methods. Evaluation on 7 brain MRI databases with 5 different diseases shows that this FL framework can train a single model achieving very promising results in segmenting all disease types seen during training. Importantly, it can segment these diseases in new databases that contain sets of modalities different from those in training clients. These results demonstrate, for the first time, the feasibility and effectiveness of using FL to train a single 3D segmentation model on decentralised data with diverse brain diseases and MRI modalities, a necessary step towards leveraging heterogeneous real-world databases. Code: https://github.com/FelixWag/FedUniBrain

Zhi Chang Chen, W. Peng, Jian Li et al.

D. Christopher Taylor, Cortney L. Norris, Nelson A. Barber et al.

Purpose: Exploring antecedents of flawed wine purchase intention, this study attempts to assess consumer acceptance leading to the purchase or consumption of a flawed wine product as well as build a profile of flawed wine consumers. Design/Methodology/Approach: A survey, from Amazon Mechanical Turk (Mturk) with 260 valid survey responses collected. ANOVA with post hoc testing was used to analyze the data. Findings: Results reflect that attitude, subjective knowledge, perceived behavioral control, perceived risk, and sensory appeal all significantly influence intent to purchase a flawed wine product. Additionally, environmental attitude significantly influences their intent to purchase wines with flaws and their attitude toward flawed wine. Originality: To date, no research has explored consumer acceptance of flawed wines. This study attempted to fill a gap in the literature and add to the overall body of knowledge regarding flawed wines and consumer understanding/acceptance of flawed wines, as well as generating a profile of potential flawed wine consumers. Research Limitations/Implications: Consumer panel data is not as rich as an experimental study design; however, this work starts an academic conversation on flawed wine and provides a foundation for future research. Practical Implications: The results of this study offer practical opportunities, from educating consumers toward a richer understanding of wine flaws; promotional opportunities for wine producers with a product to be disposed of, enhancing revenue generation; and how sensory appeal and environmental concern are beneficial to furthering the understanding and predictability of consumer intentions to purchase flawed wines.