Hasil untuk "Nutritional diseases. Deficiency diseases"

Yalda Rahbar Saadat, Abolfazl Barzegari, Zahra Saadatian et al.

Abstract Aging induces structural and functional alterations in the kidneys, including changes in renal morphology and progressive decline in renal function. During aging, the gut microbiota undergoes profound shifts in composition and activity, transitioning from predominantly commensal to more pathogenic communities. Renal dysfunction further exacerbates this process by reducing toxin clearance and promoting the accumulation of uremic metabolites, which disrupt gut microbial balance. In turn, gut dysbiosis impairs kidney function, creating a self-perpetuating cycle of microbial imbalance and renal damage. Hence, breaking this vicious cycle of dysbiosis and kidney damage is important. This review sheds light on the bidirectional relationship between gut microbiota and kidney aging. It also highlights the potential of microbiota-targeted interventions to restore microbial balance and delay the onset of age-related issues.

Ruth Salway, Danielle House, Simona Kent-Saisch et al.

Abstract Background In a stepped wedge design, schools are randomised to a sequence of measurements, with each sequence transitioning to intervention status at a different time. There are several advantages to such designs, including increased statistical power, logistical benefits and the ability to explore change over time. However, stepped wedge designs have not previously been used to evaluate school-based physical activity interventions in children. This paper aimed to explore the feasibility of this design, by identifying school constraints, balancing these with statistical considerations and exploring the power of this chosen design under different scenarios. Methods We conducted three interlinked studies, with the results from one informing the next. Study 1 was a qualitative study to identify school constraints that inform the choice of stepped wedge configuration. Study 2 used simulation to choose a configuration that balanced these school constraints and statistical properties. Study 3 explored the statistical power for the chosen design for different school and pupil sample sizes, using an open cohort design (a mixture of new and repeated pupils). Results School staff considered the proposed data collection feasible, and supported a maximum of 3–4 measurements per year and an implementation period of one school term. Study 2 therefore considered incomplete stepped wedge designs with five steps. Statistically, the best designs had a mix of control and intervention measurements in terms 2–4 and a spread of measurements across the whole study duration. Power depended on a combination of the overall recruitment rate and the retention rate. For 20 schools with an eligible class size of 30 pupils, we would be able to detect a 6 min difference in average weekday moderate-to-vigorous physical activity with 80% power, provided there were > 50% of pupils measured per school at each time. A similarly powered cluster randomised controlled trial would require 42 schools. Conclusion Stepped wedge trials are a viable design for evaluating school-based physical activity interventions. Incomplete designs, where not all schools are measured at each point, offer the flexibility to work around practical constraints.

Iqra Farooq, Sara Atito, Ayse Demirkan et al.

Traditional GWAS has advanced our understanding of complex diseases but often misses nonlinear genetic interactions. Deep learning offers new opportunities to capture complex genomic patterns, yet existing methods mostly depend on feature selection strategies that either constrain analysis to known pathways or risk data leakage when applied across the full dataset. Further, covariates can inflate predictive performance without reflecting true genetic signals. We explore different deep learning architecture choices for GWAS and demonstrate that careful architectural choices can outperform existing methods under strict no-leakage conditions. Building on this, we extend our approach to a multi-label framework that jointly models five diseases, leveraging shared genetic architecture for improved efficiency and discovery. Applied to five million SNPs across 37,000 samples, our method achieves competitive predictive performance (AUC 0.68-0.96), offering a scalable, leakage-free, and biologically meaningful approach for multi-disease GWAS analysis.

Xiang Liu, Zhaoxiang Liu, Huan Hu et al.

While conversational generative AI has shown considerable potential in enhancing decision-making for agricultural professionals, its exploration has predominantly been anchored in text-based interactions. The evolution of multimodal conversational AI, leveraging vast amounts of image-text data from diverse sources, marks a significant stride forward. However, the application of such advanced vision-language models in the agricultural domain, particularly for crop disease diagnosis, remains underexplored. In this work, we present the crop disease domain multimodal (CDDM) dataset, a pioneering resource designed to advance the field of agricultural research through the application of multimodal learning techniques. The dataset comprises 137,000 images of various crop diseases, accompanied by 1 million question-answer pairs that span a broad spectrum of agricultural knowledge, from disease identification to management practices. By integrating visual and textual data, CDDM facilitates the development of sophisticated question-answering systems capable of providing precise, useful advice to farmers and agricultural professionals. We demonstrate the utility of the dataset by finetuning state-of-the-art multimodal models, showcasing significant improvements in crop disease diagnosis. Specifically, we employed a novel finetuning strategy that utilizes low-rank adaptation (LoRA) to finetune the visual encoder, adapter and language model simultaneously. Our contributions include not only the dataset but also a finetuning strategy and a benchmark to stimulate further research in agricultural technology, aiming to bridge the gap between advanced AI techniques and practical agricultural applications. The dataset is available at https: //github.com/UnicomAI/UnicomBenchmark/tree/main/CDDMBench.

Alavikunhu Panthakkan, Zubair Medammal, S M Anzar et al.

Falconry, a revered tradition involving the training and hunting with falcons, requires meticulous health surveillance to ensure the health and safety of these prized birds, particularly in hunting scenarios. This paper presents an innovative method employing a hybrid of ConvNeXt and EfficientNet AI models for the classification of falcon diseases. The study focuses on accurately identifying three conditions: Normal, Liver Disease and 'Aspergillosis'. A substantial dataset was utilized for training and validating the model, with an emphasis on key performance metrics such as accuracy, precision, recall, and F1-score. Extensive testing and analysis have shown that our concatenated AI model outperforms traditional diagnostic methods and individual model architectures. The successful implementation of this hybrid AI model marks a significant step forward in precise falcon disease detection and paves the way for future developments in AI-powered avian healthcare solutions.

K. A. Muthukumar, Dhruva Nandi, Priya Ranjan et al.

Cardiovascular diseases (CVD) are a predominant health concern globally, emphasizing the need for advanced diagnostic techniques. In our research, we present an avant-garde methodology that synergistically integrates ECG readings and retinal fundus images to facilitate the early disease tagging as well as triaging of the CVDs in the order of disease priority. Recognizing the intricate vascular network of the retina as a reflection of the cardiovascular system, alongwith the dynamic cardiac insights from ECG, we sought to provide a holistic diagnostic perspective. Initially, a Fast Fourier Transform (FFT) was applied to both the ECG and fundus images, transforming the data into the frequency domain. Subsequently, the Earth Mover's Distance (EMD) was computed for the frequency-domain features of both modalities. These EMD values were then concatenated, forming a comprehensive feature set that was fed into a Neural Network classifier. This approach, leveraging the FFT's spectral insights and EMD's capability to capture nuanced data differences, offers a robust representation for CVD classification. Preliminary tests yielded a commendable accuracy of 84 percent, underscoring the potential of this combined diagnostic strategy. As we continue our research, we anticipate refining and validating the model further to enhance its clinical applicability in resource limited healthcare ecosystems prevalent across the Indian sub-continent and also the world at large.

O. A. Shidlovskaya, S. B. Artemyeva, E. D. Belousova et al.

We present a case of a rare disease, aromatic L-amino acid decarboxylase deficiency (AADCD), with delayed diagnosis even after a pathogenic mutation indicative of AADCD was found. In most cases, AADCD causes marked impairment of motor and psycho- speech development and is accompanied by severe episodes of dystonia – oculogyric crises. The careful attention of neurologists, pediatricians, geneticists, gastroenterologists, and pulmonologists in cases of a complex set of diverse symptoms determines the success of early diagnosis and the earliest possible prescription of modern gene replacement therapy for AADCD.

Onyedika Gabriel Ani, Isaac Duah Boateng

Diet-related diseases and mortalities are assuming epidemic proportions globally. It is more worrisome in the Global South, especially in Africa, where the healthcare system is not resilient to the public health burden. There is a growing effort around the world to foster urban food system policies that would checkmate the failing health of the public and ensure improved quality of life. However, these efforts seem non-existent in many African regions. Therefore, there is a need for heightened efforts in these areas to address the food system and ensure a global healthy society. This study identified Nnewi, Nigeria, in sub-Saharan Africa, a typical urban area in Nigeria, and analyzed the public health challenges attributed to the non-existent food system policy and poor nutritional practices. The Milan Urban Food Policy Pact model, which has been successfully implemented in many cities, was adopted to propose a sustainable food system policy for Nnewi. Key policies proposed include autonomous local government power, government-assisted programs, clean and sustainable amenities, agricultural reforms, nutrition education, and reductions in food wastage to achieve a circular economy. An evaluation tool for implementing the food system policy was also developed. Overall, implementing the food system policies proposed herein would improve the quality of life of Nnewi residents. Other urban areas could also adopt similar food system policies to achieve the Sustainable Development Goals of a healthy and resilient global society.

Yumeng Shi, Wei Zhou

Abstract Background No research report has been conducted to investigate the impact of oxidation balance score (OBS) on the estimated pulse wave velocity(ePWV).We aimed to examine the association between OBS and ePWV. Method We evaluated data for 13,073 patients from the National Health and Nutrition Examination Survey (NHANES). The exposure variable was OBS. The outcome variables was combination of ePWV and arterial stiffness. Results We observed a significant negative correlation between OBS (Per 1SD increase) and ePWV in the gradually adjusted models. Based on the aforementioned results, a two-piecewise logistic regression adjusted model was subsequently employed to establish the association between OBS and elevated ePWV, and the inflection point was determined as 5. The increased risk of elevated ePWV (OR:0.70; 95%CI:0.51–0.94) gradually decreases with the increase of OBS on the left side of the inflection point; however, when OBS exceeds 5, this decrease in risk of elevated ePWV(OR:1.00; 95%CI:0.96–1.04) is no longer observed (P for log likelihood ratio test = 0.028). Conclusions There exists a significant association between OBS and ePWV in the context of American adults. Specifically, OBS exhibits a negative correlation with ePWV; however, when considering an elevated ePWV, a saturation effect is observed in relation to OBS.

Jana Olšovská, Lenka Straková, Vladimír Nesvadba et al.

The well-known hop variety, Saaz, which gives the Pilsner lager beer its characteristic hop aroma, may be threatened by climate change in the future. Therefore, new Saaz-related hop varieties, including Saaz Late, Saaz Brilliant, Saaz Comfort, and Saaz Shine, were recently bred. A comparison study was carried out to evaluate whether these varieties are acceptable for traditional lagers. For this purpose, sensorial and chemical analyses of hops and related beers, namely, an analysis of hop resins and oils, were performed. Sensory profiles of Saaz varieties are very similar (fine, hoppy aroma; floral; herbal), except for Saaz Comfort, which has a slightly higher aroma intensity, and Saaz Shine, which has the most noticeable fruity scent, with traces of citrus. The chemical profiles are also very similar, with α-humulene, β-pinene, (E)-β-farnesene, β-caryophyllene, and myrcene being the most abundant. Decoction mashing and kettle hopping technology with bottom fermentation show that the compared varieties result in very similar lager beers with hoppy, floral, herbal, fruity, and spicy aromas. Typical hop oils include farnesol, linalool, methyl geranate, β-pinene, and limonene. The high concentration of farnesol in beer correlates with the concentrations of (E)-β-farnesene and farnesol in hops. New Saaz varieties are widely used to produce Pilsner lager without affecting the traditional sensory aroma of this widespread style. Varieties have a higher yield of approximately 25% and bitter acid concentrations of approximately 15%, with Saaz Comfort comprising approximately 100%. Furthermore, the concentration of hop oils is approximately 40% higher in Saaz Shine than a traditional Saaz variety. Moreover, Saaz Shine and Saaz Comfort have very good resistance to drought, which is an important property from a climate change perspective.

Jinli Mahe, Ao Xu, Li Liu et al.

Abstract Background It is uncertain whether the weekend warrior pattern is associated with all-cause mortality among adults living with type 2 diabetes. This study explored how the ‘weekend warrior’ physical activity (PA) pattern was associated with all-cause mortality among adults living with type 2 diabetes. Methods This prospective cohort study investigated US adults living with type 2 diabetes in the National Health and Nutrition Examination Survey (NHANES). Mortality data was linked to the National Death Index. Based on self-reported leisure-time and occupational moderate-to-vigorous PA (MVPA), participants were categorized into 3 groups: physically inactive (< 150 min/week of MVPA), weekend warrior (≥ 150 min/week of MVPA in 1 or 2 sessions), and physically active (≥ 150 min/week of MVPA in 3 or more sessions). Results A total of 6067 participants living with type 2 diabetes [mean (SD) age, 61.4 (13.5) years; 48.0% females] were followed for a median of 6.1 years, during which 1206 deaths were recorded. Of leisure-time and occupational activity, compared with inactive individuals, hazard ratios (HRs) for all-cause mortality were 0.49 (95% CI 0.26–0.91) and 0.57 (95% CI 0.38–0.85) for weekend warrior individuals, and 0.55 (95% CI 0.45–0.67) and 0.64 (95% CI 0.53–0.76) for regularly active individuals, respectively. However, when compared leisure-time and occupational weekend warrior with regularly active participants, the HRs were 0.82 (95% CI 0.42–1.61) and 1.00 (95% CI 0.64–1.56) for all-cause mortality, respectively. Conclusions Weekend warrior PA pattern may have similar effects on lowering all-cause mortality as regularly active pattern among adults living with type 2 diabetes, regardless of leisure-time or occupational activity. Therefore, weekend warrior PA pattern may be sufficient to reduce all-cause mortality for adults living with type 2 diabetes.

Heiko Hahn, Manuel Friedel, Claudia Niessner et al.

Zihan Zhou, Ziyi Zeng, Wenhao Jiang et al.

As social changes accelerate, the incidence of psychosomatic disorders has significantly increased, becoming a major challenge in global health issues. This necessitates an innovative knowledge system and analytical methods to aid in diagnosis and treatment. Here, we establish the ontology model and entity types, using the BERT model and LoRA-tuned LLM for named entity recognition, constructing the knowledge graph with 9668 triples. Next, by analyzing the network distances between disease, symptom, and drug modules, it was found that closer network distances among diseases can predict greater similarities in their clinical manifestations, treatment approaches, and psychological mechanisms, and closer distances between symptoms indicate that they are more likely to co-occur. Lastly, by comparing the proximity d and proximity z score, it was shown that symptom-disease pairs in primary diagnostic relationships have a stronger association and are of higher referential value than those in diagnostic relationships. The research results revealed the potential connections between diseases, co-occurring symptoms, and similarities in treatment strategies, providing new perspectives for the diagnosis and treatment of psychosomatic disorders and valuable information for future mental health research and practice.

Nina Abrahams, Sahar Khodabakhsh, Zoi Toumpakari et al.

Abstract Background Community-based programmes [CBPs], targeting increased physical activity and/or healthier eating, have been used in the prevention and management of non-communicable diseases. However, CBPs are only useful, insofar as they can be scaled up and sustained in some meaningful way. Social networks—defined as “social structures that exists between actors, individuals or organizations”—may serve as an important tool to identify underlying mechanisms that contribute to this process. This scoping review aimed to map and collate literature on the role of social network research in scaling-up and sustaining physical activity and/or diet CBPs in low-and middle-income countries [LMICs]. Methods Arksey and O’Malley’s framework and its enhancement were followed. Inclusion criteria were peer-reviewed articles exploring the role of social networks in scaled-up and/or sustained physical activity and/or diet CBPs in adult populations, published in English since 2000, and based in a LMIC. Databases searched were PubMed, Cochrane, Scopus, Web of Science, CINAHL, SocIndex, International Bibliography of the Social Sciences, and Google Scholar. Books, conference abstracts, and programmes focused on children were excluded. Two reviewers independently selected and extracted eligible studies. Included publications were thematically analysed using the Framework Approach. Results Authors identified 12 articles for inclusion, covering 13 CBPs. Most were based in Latin America, with others in the Caribbean, the Pacific Islands, Iran, and India. All articles were published since 2009. Only three used social network analysis methods (with others using qualitative methods). Five main social network themes were identified: centralisation, cliques, leaders, quality over quantity, and shared goals. Contextual factors to be considered when scaling-up programmes in LMICs were also identified. Conclusions This review has shown that the evidence of the use of social network research in programme scale-up has not yet caught up to its theoretical possibilities. Programmes aiming to scale should consider conducting social network research with identified network themes in mind to help improve the evidence-base of what network mechanisms, in what contexts, might best support the strengthening of networks in physical activity and diet programmes. Importantly, the voice of individuals and communities in these networks should not be forgotten.

Aisha Mahmood, Muhammad Naeem Faisal, Junaid Ali Khan et al.

Abstract Background The epithelial lining of the gut expresses intestinal fatty-acid binding proteins (I-FABPs), which increase in circulation and in plasma concentration during intestinal damage. From the perspective of obesity, the consumption of a diet rich in fat causes a disruption in the integrity of the gut barrier and an increase in its permeability. Hypothesis There is an association between the expression of I-FABP in the gut and various metabolic changes induced by a high-fat (HF) diet. Methods Wistar albino rats (n = 90) were divided into three groups (n = 30 per group), viz. One control and two HF diet groups (15 and 30%, respectively) were maintained for 6 weeks. Blood samples were thus collected to evaluate the lipid profile, blood glucose level and other biochemical tests. Tissue sampling was conducted to perform fat staining and immunohistochemistry. Results HF diet-fed rats developed adiposity, insulin resistance, leptin resistance, dyslipidemia, and increased expression of I-FABP in the small intestine compared to the control group. Increased I-FABP expression in the ileal region of the intestine is correlated significantly with higher fat contents in the diet, indicating that higher I-FABP expression occurs due to increased demand of enterocytes to transport lipids, leading to metabolic alterations. Conclusion In summary, there is an association between the expression of I-FABP and HF diet-induced metabolic alterations, indicating that I-FABP can be used as a biomarker for intestinal barrier dysfunction.

Tammie Jakstas, Berit Follong, Tamara Bucher et al.

Abstract Background Teachers form a large and essential workforce globally. Their wellbeing impacts personal health-related outcomes with flow on effects for the health, and wellbeing of their students. However, food and nutrition (FN) interventions that include teachers, typically neglect the impact of personal FN factors on a teachers’ ability to achieve optimal nutrition-related health and wellbeing, and successfully fulfil their professional FN roles as health promoters, gate keepers, educators’, and role models. The aim of this review was to scope FN constructs that have been studied internationally regarding teacher FN-related health and wellbeing. Methods Six databases were searched, and papers extracted in June/July 2021. Eligibility criteria guided by the population, concept, context mnemonic included studies published after 2000, in English language, with an aspect of personal FN-related health and wellbeing, among in-service (practising) and pre-service (training), primary, and secondary teachers. Screening studies for inclusion was completed by two independent researchers with data extraction piloted with the same reviewers and completed by lead author, along with complete descriptive and thematic analysis. Results Ten thousand six hundred seventy-seven unique articles were identified with 368 eligible for full text review and 105 included in final extraction and analysis. Sixty-nine descriptive studies were included, followed by 35 intervention studies, with the main data collection method used to assess both personal and professional FN constructs being questionnaires (n = 99 papers), with nutrition knowledge and dietary assessment among the most commonly assessed. Conclusion FN constructs are used within interventions and studies that include teachers, with diversity in constructs included and how these terms are defined. The evidence from this scoping review can be used to inform data collection and evaluation in future epidemiological and interventional research that addresses teacher FN-related health and wellbeing.

Jia-ni Wang, Miao Wang, Guo-wei Wu et al.

Abstract Purpose Bulimia nervosa (BN) is characterized by recurrent binge-eating episodes and inappropriate compensatory behaviors. This study investigated alterations in resting-state surface-based neural activity in BN patients and explored correlations between brain activity and eating behavior. Methods A total of 26 BN patients and 28 healthy controls were enrolled. Indirect measurement of cerebral cortical activity and functional connectivity (FC) analyses were performed in Surfstat. A principal component analysis (PCA) model was used to capture the commonalities within the behavioral questionnaires from the BN group. Results Compared with the healthy control group, the BN group showed decreased surface-based two-dimensional regional homogeneity in the right superior parietal lobule (SPL). Additionally, the BN group showed decreased FC between the right SPL and the bilateral lingual gyrus and increased FC between the right SPL and the left caudate nucleus and right putamen. In the FC–behavior association analysis, the second principal component (PC2) was negatively correlated with FC between the right SPL and the left caudate nucleus. The third principal component (PC3) was negatively correlated with FC between the right SPL and the left lingual gyrus and positively correlated with FC between the right SPL and the right lingual gyrus. Conclusion We revealed that the right SPL undergoes reorganization with respect to specific brain regions at the whole-brain level in BN. In addition, our results suggest a correlation between brain reorganization and maladaptive eating behavior. These findings may provide useful information to better understand the neural mechanisms of BN. Level of evidence V, descriptive study.

Harold Costales, Arpee Callejo-Arruejo, Noel Rafanan

Rice is the number one staple food in the country, as this serves as the primary livelihood for thousands of Filipino households. However, as the tradition continues, farmers are not familiar with the different types of rice leaf diseases that might compromise the entire rice crop. The need to address the common bacterial leaf blight in rice is a serious disease that can lead to reduced yields and even crop loss of up to 75%. This paper is a design and development of a rice leaf disease detection mobile application prototype using an algorithm used for image analysis. The researchers also used the Rice Disease Image Dataset by Huy Minh Do available at https://www.kaggle.com/ to train state-of-the-art convolutional neural networks using transfer learning. Moreover, we used image augmentation to increase the number of image samples and the accuracy of the neural networks as well

Affan Yasin, Rubia Fatima

The research introduces a novel plant disease detection model based on Convolutional Neural Networks (CNN) for plant image classification, marking a significant contribution to image categorization. The innovative training approach enables a streamlined and efficient system implementation. The model classifies two distinct plant diseases into four categories, presenting a novel technique for plant disease identification. In Experiment 1, Inception-V3, Dense-Net-121, ResNet-101-V2, and Xception models were employed for CNN training. The newly created plant disease image dataset includes 1963 tomato plant images and 7316 corn plant images from the PlantVillage dataset. Of these, 1374 tomato images and 5121 corn images were used for training, while 589 tomato images and 2195 corn images were used for testing/validation. Results indicate that the Xception model outperforms the other three models, yielding val_accuracy values of 95.08% and 92.21% for the tomato and corn datasets, with corresponding val_loss values of 0.3108 and 0.4204, respectively. In Experiment 2, CNN with Batch Normalization achieved disease detection rates of approximately 99.89% in the training set and val_accuracy values exceeding 97.52%, accompanied by a val_loss of 0.103. Experiment 3 employed a CNN architecture as the base model, introducing additional layers in Model 2, skip connections in Model 3, and regularizations in Model 4. Detailed experiment results and model efficiency are outlined in the paper's sub-section 1.5. Experiment 4 involved combining all corn and tomato images, utilizing various models, including MobileNet (val_accuracy=86.73%), EfficientNetB0 (val_accuracy=93.973%), Xception (val_accuracy=74.91%), InceptionResNetV2 (val_accuracy=31.03%), and CNN (59.79%). Additionally, our proposed model achieved a val_accuracy of 84.42%.

Gongshu Wang, Ning Jiang, Yunxiao Ma et al.

Structural magnetic resonance imaging (sMRI) has shown great clinical value and has been widely used in deep learning (DL) based computer-aided brain disease diagnosis. Previous approaches focused on local shapes and textures in sMRI that may be significant only within a particular domain. The learned representations are likely to contain spurious information and have a poor generalization ability in other diseases and datasets. To facilitate capturing meaningful and robust features, it is necessary to first comprehensively understand the intrinsic pattern of the brain that is not restricted within a single data/task domain. Considering that the brain is a complex connectome of interlinked neurons, the connectional properties in the brain have strong biological significance, which is shared across multiple domains and covers most pathological information. In this work, we propose a connectional style contextual representation learning model (CS-CRL) to capture the intrinsic pattern of the brain, used for multiple brain disease diagnosis. Specifically, it has a vision transformer (ViT) encoder and leverages mask reconstruction as the proxy task and Gram matrices to guide the representation of connectional information. It facilitates the capture of global context and the aggregation of features with biological plausibility. The results indicate that CS-CRL achieves superior accuracy in multiple brain disease diagnosis tasks across six datasets and three diseases and outperforms state-of-the-art models. Furthermore, we demonstrate that CS-CRL captures more brain-network-like properties, better aggregates features, is easier to optimize and is more robust to noise, which explains its superiority in theory. Our source code will be released soon.