Hasil untuk "Nutritional diseases. Deficiency diseases"

Guosong Jiang, Licheng Feng, Xiaoxiao Qu et al.

Eric Bosa Nobela, Romance Dissieka, Marieke Vossenaar et al.

Ruonan Zhao, Xu Zhao, Rongqiang Zhang

Yan Su, Qiulin Wu, Weizhen Li et al.

The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.

Ke Feng, Martin Haenggi

Physical contact or proximity is often a necessary condition for the spread of infectious diseases. Common destinations, typically referred to as hubs or points of interest, are arguably the most effective spots for the type of disease spread via airborne transmission. In this work, we model the locations of individuals (agents) and common destinations (hubs) by random spatial point processes in $\mathbb{R}^d$ and focus on disease propagation through agents visiting common hubs. The probability of an agent visiting a hub depends on their distance through a connection function $f$. The system is represented by a random bipartite geometric (RBG) graph. We study the degrees and percolation of the RBG graph for general connection functions. We show that the critical density of hubs for percolation is dictated by the support of the connection function $f$, which reveals the critical role of long-distance travel (or its restrictions) in disease spreading.

Maral Bishehkolaei, Yashwant Pathak

According to the World Health Organization (WHO), cardiovascular disease (CVD) is the number one cause of death globally. According to the Centers for Disease Control and Prevention (CDC), in 2022, nearly 8 in 10 individuals who suffered from a stroke showed a history of hypertension, and over 60 % of those with Diabetes have hypertension with high triglycerides and low-density lipoprotein (LDL, bad cholesterol). Both high LDL and Diabetes double the threat of CVD incidence, with the probability of all the previous risk factors being higher in adults who are overweight and obese. The n-6/n-3 polyunsaturated fatty acid (PUFA) ratio is critical to developing metabolic disorders that increase the risk of cardiovascular disease. The elaboration of the mechanisms by which n-6 and n-3 polyunsaturated fatty acids operate and convert to the essential fatty acids in the body will allow us to clearly understand the significance of the optimum ratio of the two. According to research, the human body can maintain optimum health with an intake ratio of n-6/n-3 of 5:1; however, the current ratio of n-6/n-3 PUFA intake is 20:1 in the Western diet. As the intake of n-6 PUFA heavy diet increases, we notice an incline in the incidence rate of metabolic syndromes through activating the inflammatory pathways. Omega 6 and omega 3 compete for the same enzyme binding site, and depending on which is bound, the resulting essential fatty acid signals a cascade of pro-inflammatory or anti-inflammatory factors. This review discusses the importance of the n-6/n-3 polyunsaturated fatty acid (PUFA) ratio in preventing, developing, and progressing cardiovascular disease.

Cathal Mills, Tarek Alrefae, William S. Hart et al.

During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle

Ali Al-kassab-Córdova, David Villarreal-Zegarra, Guido Bendezu-Quispe et al.

Abstract Objective: To evaluate the factors associated with food insecurity (FI) among Venezuelan migrants residing in Peru. Secondarily, to evaluate the psychometric properties of the Food Insecurity Experience Scale (FIES). Design: A cross-sectional study based on secondary data analysis of the 2022 Venezuelan Population Residing in Peru Survey (ENPOVE-2022, from the Spanish acronym) was conducted. FI was measured with the FIES, whose properties were tested using the Rasch model. Multinomial logistic regression was performed to estimate relative prevalence ratios with their corresponding 95 % confidence intervals. Setting: This survey was conducted in February and March 2022 in the eight cities most populated by Venezuelan migrants and refugees in Peru. Participants: Venezuelan migrants and refugees over the age of 18 years living in Peru. Results: A total of 7727 participants were included. Rasch reliability was adequate (0·73). The prevalence of mild, moderate and severe FI was 36·71 %, 31·14 % and 10·48 %, respectively. Being aged 25–34 and 35–44 years, unemployed, uninsured, having no formal education or secondary, illegal status, living in a dwelling with 2–4 and more than 4 people, presenting one or more than one chronic disease, residing in Peru for 0–6 months and perceived discrimination were associated with a higher probability of moderate FI. Furthermore, having secondary education, being unemployed, uninsured, never married, illegal, residing in Tumbes, presenting one or more than one chronic disease and perceived discrimination were significantly associated with severe FI. Conclusion: Four out of ten Venezuelan migrants residing in Peru presented moderate to severe FI. The FIES showed adequate psychometric properties. Differences in the socio-demographic, health and migratory factors associated with FI levels were found. Inter-sectoral and multi-sectoral interventions are needed and should be focused on addressing the determinants of FI.

Yauheni Shastak, Wolf Pelletier

Vitamin A (retinol) is an essential micronutrient with a crucial role in the immune system of non-ruminant animals, such as swine and poultry. It includes three chemical compounds with distinct properties and functions in the body: retinol, retinal, and retinoic acid. In monogastric feed, vitamin A is primarily present in the form of retinyl esters. The metabolism of dietary vitamin A esters involves their conversion to retinol, which is then transported to different tissues and cells for further metabolism into active forms such as retinoic acid. These active forms of vitamin A have been found to play a crucial role in regulating both innate and adaptive immune responses. Specifically, they are involved in the differentiation, proliferation, and function of immune cells such as T and B lymphocytes, as well as dendritic cells. Vitamin A deficiency can lead to impaired cellular immunity, reduced antibody production, and consequently an increased susceptibility to infections. In swine and poultry, hypovitaminosis A can also affect gut-associated lymphoid tissues, leading to gut-related health problems and compromised growth performance. On the other hand, vitamin A supplementation has been shown to have immunomodulatory effects on non-ruminant immune responses. By administering or supplementing retinol, immune cell proliferation, antibody production, and cytokine secretion can be enhanced, which can ultimately result in improved immune function and disease resistance. Therefore, vitamin A has potential applications as an immuno-micronutrient for improving health and preventing diseases in swine and poultry. However, the optimal dosage and timing of vitamin A supplementation need to be carefully determined based on the specific requirements of different non-ruminant species and their production stages. Overall, a better understanding of the role of vitamin A in non-ruminant nutritional immunology could have significant implications for animal health and productivity and could inform the development of effective dietary strategies to optimize immune function and prevent diseases in swine and domestic fowl. This review paper aims to offer valuable insights into the role of vitamin A in the nutritional immunology of non-ruminants while also emphasizing the current gaps in knowledge and potential areas for further research.

Simone Farías-Antúnez, Márcia Maria Tavares Machado, Luciano Lima Correia et al.

Abstract Purpose To assess the prevalence of food insecurity (FI) among families with infants born during the COVID-19 pandemic and its associated factors in Fortaleza, the fifth largest city in Brazil. Methods Data from two survey rounds of the Iracema-COVID cohort study collected at 12 (n = 325) and 18 months (n = 331) after birth. FI was measured using the Brazilian Household Food Insecurity Scale. FI levels were described according to potential predictors. Crude and adjusted logistic regressions with robust variance were used to assess factors associated with FI. Results In the 12- and 18-month follow-ups interviews, there was a 66.5% and 57.1% prevalence of FI, respectively. Over the study period, 3.5% of the families persisted in severe FI and 27.4% in mild/moderate FI. Households headed by mothers, with more children, low education and income, sustained maternal common mental disorders, and that were beneficiaries of cash transfer programs were the most affected by persistent FI. Conclusions Although the prevalence of FI decreased in our sample, almost 60% of families in Fortaleza still have no regular access to enough and/or nutritionally appropriate food. We have identified the groups at higher FI risk, which can guide governmental policies.

M. Marvá, E. Venturino, M. C. Vera

This work presents a model combining the simplest communicable and non-communicable disease models. The latter is, by far, the leading cause of sickness and death in the World, and introduces basal heterogeneity in populations where communicable diseases evolve. The model can be interpreted as a risk-structured model, another way of accounting for population heterogeneity. Our results show that considering the non-communicable disease (in the end, heterogeneous populations) allows the communicable disease to become endemic even if the basic reproduction number is less than $1$. This feature is known as subcritical bifurcation. Furthermore, ignoring the non-communicable disease dynamics results in overestimating the reproduction number and, thus, giving wrong information about the actual number of infected individuals. We calculate sensitivity indices and derive interesting epidemic-control information.

Mutong Liu, Yang Liu, Jiming Liu

Infectious diseases, either emerging or long-lasting, place numerous people at risk and bring heavy public health burdens worldwide. In the process against infectious diseases, predicting the epidemic risk by modeling the disease transmission plays an essential role in assisting with preventing and controlling disease transmission in a more effective way. In this paper, we systematically describe how machine learning can play an essential role in quantitatively characterizing disease transmission patterns and accurately predicting infectious disease risks. First, we introduce the background and motivation of using machine learning for infectious disease risk prediction. Next, we describe the development and components of various machine learning models for infectious disease risk prediction. Specifically, existing models fall into three categories: Statistical prediction, data-driven machine learning, and epidemiology-inspired machine learning. Subsequently, we discuss challenges encountered when dealing with model inputs, designing task-oriented objectives, and conducting performance evaluation. Finally, we conclude with a discussion of open questions and future directions.

Asim Khan, Umair Nawaz, Lochan Kshetrimayum et al.

Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.

Francesca Patrignani, Gabriella Siesto, Davide Gottardi et al.

The present research is aimed at investigating the potential of two commercial <i>Saccharomyces cerevisiae</i> strains (EC1118 and AWRI796) to generate wine-specific volatile molecule fingerprinting in relation to the initial must applied. To eliminate the effects of all the process variables and obtain more reliable results, comparative fermentations on interlaboratory scale of five different regional red grape musts were carried out by five different research units (RUs). For this purpose, the two <i>S. cerevisiae</i> strains were inoculated separately at the same level and under the same operating conditions. The wines were analyzed by means of SPME-GC/MS. Quali-quantitative multivariate approaches (two-way joining, MANOVA and PCA) were used to explain the contribution of strain, must, and their interaction to the final wine volatile fingerprinting. Our results showed that the five wines analyzed for volatile compounds, although characterized by a specific aromatic profile, were mainly affected by the grape used, in interaction with the inoculated <i>Saccharomyces</i> strain. In particular, the AWRI796 strain generally exerted a greater influence on the aromatic component resulting in a higher level of alcohols and esters. This study highlighted that the variable strain could have a different weight, with some musts experiencing a different trend depending on the strain (i.e., Negroamaro or Magliocco musts).

Kush Vora, Dishant Padalia

Apple diseases, if not diagnosed early, can lead to massive resource loss and pose a serious threat to humans and animals who consume the infected apples. Hence, it is critical to diagnose these diseases early in order to manage plant health and minimize the risks associated with them. However, the conventional approach of monitoring plant diseases entails manual scouting and analyzing the features, texture, color, and shape of the plant leaves, resulting in delayed diagnosis and misjudgments. Our work proposes an ensembled system of Xception, InceptionResNet, and MobileNet architectures to detect 5 different types of apple plant diseases. The model has been trained on the publicly available Plant Pathology 2021 dataset and can classify multiple diseases in a given plant leaf. The system has achieved outstanding results in multi-class and multi-label classification and can be used in a real-time setting to monitor large apple plantations to aid the farmers manage their yields effectively.

Xu Ye, Meng Xiao, Zhiyuan Ning et al.

With the development of natural language processing techniques(NLP), automatic diagnosis of eye diseases using ophthalmology electronic medical records (OEMR) has become possible. It aims to evaluate the condition of both eyes of a patient respectively, and we formulate it as a particular multi-label classification task in this paper. Although there are a few related studies in other diseases, automatic diagnosis of eye diseases exhibits unique characteristics. First, descriptions of both eyes are mixed up in OEMR documents, with both free text and templated asymptomatic descriptions, resulting in sparsity and clutter of information. Second, OEMR documents contain multiple parts of descriptions and have long document lengths. Third, it is critical to provide explainability to the disease diagnosis model. To overcome those challenges, we present an effective automatic eye disease diagnosis framework, NEEDED. In this framework, a preprocessing module is integrated to improve the density and quality of information. Then, we design a hierarchical transformer structure for learning the contextualized representations of each sentence in the OEMR document. For the diagnosis part, we propose an attention-based predictor that enables traceable diagnosis by obtaining disease-specific information. Experiments on the real dataset and comparison with several baseline models show the advantage and explainability of our framework.

Neha Khandpur, Fernanda Paranhos Quinta, Patricia Constante Jaime

Abstract Background Implementation science has scant evidence of how dietary guidelines can be developed into actionable behavior-change messages and even less evidence on their motivating potential and perceived effect on behavior. This may explain the widening gap between nutrition science and individual behavior and the low uptake of dietary recommendations by the population for which they are intended. This study aimed to: (i) assess participant receptivity and acceptance of behavior-change messages; (ii) determine if the behavior-change strategies used in the messages and the main theme they relayed influenced participant evaluation of the messages; (iii) explore if evaluations varied by participants’ stage of behavior-change; and (iv) elucidate reasons for non-compliance with the messages. Methods An online survey was used to test the face validity and participant receptivity of 28 behavior-change messages, among a diverse sample of 2400 adult Brazilians. Participants’ understanding of the messages, message likeability and convincingness, and the probability that participants would change behavior in accordance with the message were measured, along with reasons for non-compliance. Results The mean overall scores suggested that participants liked the messages, understood them, and found them convincing. As expected, the probability of complying with the messages scored lower compared to other study outcomes. Messages about shopping practices, cooking practices, and planning and organization performed better than those on other themes. Participants were more receptive to messages that included behavior-change strategies like goals, social identity, and pleasure, however, the probability of compliance was higher for messages with constructs that emphasized health and cost consequences. Participants trying to change their diet or seeking resources to support healthier dietary choices had greater engagement with and receptivity to the messages. Time and effort, and high costs associated with making healthy changes, were barriers to compliance. Conclusions Messages may help improve individual understanding, stimulate interest in a topic and get participants engaged, particularly if messages are goal-oriented and highlight the pleasure and collective identity of these food-related behaviors. However, messages stop short of addressing the structural, social, and economic barriers to healthy diets. These aspects will need to be targeted through legislative action for sustainable behavior change.

Gang Yu, Zhongzhi Yu, Yemin Shi et al.

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients' arrival. In pediatrics, the patients' limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors' limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819.

Maria V. Budzinskaya, Dmitry V. Lipatov, Vladislav G. Pavlov et al.

A data analysis on the actual direction of biomedicine, the study of biomarkers in diabetic retinopathy (DR), was done. Biomarkers identification is important for screening, diagnosis, monitoring, prevention and prediction of the clinical response of the patient to the treatment. In addition, studying the biomarkers allows increase of the effectiveness and safety of using various treatment options. The review examines two main groups of biomarkers, molecular and visualised, which shows the current state of the problem and the prospects for studying biomarkers in the context of the DR treatment. Nowadays, searching for and finding new biomarkers is important and will allow us to develop individual treatment regimens for DR and personalised medicine in an interdisciplinary aspect: ophthalmology and endocrinology.

Xavier Bardina, Marco Ferrante, Carles Rovira

To model the evolution of diseases with extended latency periods and the presence of asymptomatic patients like COVID-19, we define a simple discrete time stochastic SIR-type epidemic model. We include both latent periods as well as the presence of quarantine areas, to capture the evolutionary dynamics of such diseases.