Cadmium is a cumulative nephrotoxicant that is absorbed into the body from dietary sources and cigarette smoking. The levels of Cd in organs such as liver and kidney cortex increase with age because of the lack of an active biochemical process for its elimination coupled with renal reabsorption. Recent research has provided evidence linking Cd-related kidney dysfunction and decreases in bone mineral density in nonoccupationally exposed populations who showed no signs of nutritional deficiency. This challenges the previous view that the concurrent kidney and bone damage seen in Japanese itai-itai disease patients was the result of Cd toxicity in combination with nutritional deficiencies, notably, of zinc and calcium. Further, such Cd-linked bone and kidney toxicities were observed in people whose dietary Cd intakes were well within the provisional tolerable weekly intake (PTWI) set by the Joint Food and Agriculture Organization/World Health Organization Expert Committee on Food Additives of 1 μg/kg body weight/day or 70 μg/day. This evidence points to the much-needed revision of the current PTWI for Cd. Also, evidence for the carcinogenic risk of chronic Cd exposure is accumulating and Cd effects on reproductive outcomes have begun to emerge.
Carnitine is a conditionally essential nutrient that plays a vital role in energy production and fatty acid metabolism. Vegetarians possess a greater bioavailability than meat eaters. Distinct deficiencies arise either from genetic mutation of carnitine transporters or in association with other disorders such as liver or kidney disease. Carnitine deficiency occurs in aberrations of carnitine regulation in disorders such as diabetes, sepsis, cardiomyopathy, malnutrition, cirrhosis, endocrine disorders and with aging. Nutritional supplementation of L-carnitine, the biologically active form of carnitine, is ameliorative for uremic patients, and can improve nerve conduction, neuropathic pain and immune function in diabetes patients while it is life-saving for patients suffering primary carnitine deficiency. Clinical application of carnitine holds much promise in a range of neural disorders such as Alzheimer's disease, hepatic encephalopathy and other painful neuropathies. Topical application in dry eye offers osmoprotection and modulates immune and inflammatory responses. Carnitine has been recognized as a nutritional supplement in cardiovascular disease and there is increasing evidence that carnitine supplementation may be beneficial in treating obesity, improving glucose intolerance and total energy expenditure.
Motivation: Predicting gene-disease associations (GDAs) is the problem to determine which gene is associated with a disease. GDA prediction can be framed as a ranking problem where genes are ranked for a query disease, based on features such as phenotypic similarity. By describing phenotypes using phenotype ontologies, ontology-based semantic similarity measures can be used. However, traditional semantic similarity measures use only the ontology taxonomy. Recent methods based on ontology embeddings compare phenotypes in latent space; these methods can use all ontology axioms as well as a supervised signal, but are inherently transductive, i.e., query entities must already be known at the time of learning embeddings, and therefore these methods do not generalize to novel diseases (sets of phenotypes) at inference time. Results: We developed INDIGENA, an inductive disease-gene association method for ranking genes based on a set of phenotypes. Our method first uses a graph projection to map axioms from phenotype ontologies to a graph structure, and then uses graph embeddings to create latent representations of phenotypes. We use an explicit aggregation strategy to combine phenotype embeddings into representations of genes or diseases, allowing us to generalize to novel sets of phenotypes. We also develop a method to make the phenotype embeddings and the similarity measure task-specific by including a supervised signal from known gene-disease associations. We apply our method to mouse models of human disease and demonstrate that we can significantly improve over the inductive semantic similarity baseline measures, and reach a performance similar to transductive methods for predicting gene-disease associations while being more general. Availability and Implementation: https://github.com/bio-ontology-research-group/indigena
Evidence for the importance of zinc for all immune cells and for mounting an efficient and balanced immune response to various environmental stressors has been accumulating in recent years. This article describes the role of zinc in fundamental biological processes and summarizes our current knowledge of zinc's effect on hematopoiesis, including differentiation into immune cell subtypes. In addition, the important role of zinc during activation and function of immune cells is detailed and associated with the specific immune responses to bacteria, parasites, and viruses. The association of zinc with autoimmune reactions and cancers as diseases with increased or decreased immune responses is also discussed. This article provides a broad overview of the manifold roles that zinc, or its deficiency, plays in physiology and during various diseases. Consequently, we discuss why zinc supplementation should be considered, especially for people at risk of deficiency. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
Milena Kobylińska, K. Antosik, Agnieszka Decyk
et al.
Background: The World Health Organization (WHO) classifies malnutrition as the biggest threat to public health worldwide, and this condition is observed in 20–60% of hospitalized patients. Malnutrition is a state of the body in which due to insufficient supply or incorrect absorption of essential nutrients, the body composition changes and the body’s functions are impaired. Malnutrition is associated not only with reduced body mass index but also with obesity. Summary: Obesity is defined as a paradoxical state of malnutrition, which despite excessive energy consumption is associated with a shortage of individual microelements. Deficiency or lack of homeostasis of essential micronutrients can significantly affect daily performance, intellectual and emotional state, but also the physical state of the body. Food deficiency can also contribute to further weight gain or the development of other metabolic diseases. Micronutrient deficiency may include not only incorrect dietary choices and insufficient access to nutrient-rich foods but also changes in the absorption, distribution or excretion of nutrients, and altered micronutrient metabolism resulting from systemic inflammation caused by obesity. An effective therapy method recommended for people with morbid obesity is bariatric surgery aimed at both weight loss and improving quality of life. Unfortunately, the effects of these treatments are often medium- and long-term complications associated with micronutrient deficiency as a result of reduced consumption or absorption. Therefore, the use of bariatric surgery in patients with extreme obesity can affect the metabolism of microelements and increase the risk of nutritional deficiencies. Key Messages: Studies by many authors indicate a higher incidence of food deficiency among people with excessive body weight, than in people with normal body weight of the same age and same sex. Monitoring the concentration of minerals and vitamins in blood serum is a good practice in the treatment of obesity. The proper nutritional status of the body affects not only the state of health but also the effectiveness of therapy. The aim of the review was to present the issue of malnutrition in the context of obesity.
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) known as coronavirus disease (COVID-19), emerged in Wuhan, China, in December 2019. On March 11, 2020, it was declared a global pandemic. As the world grapples with COVID-19 and the paucity of clinically meaningful therapies, attention has been shifted to modalities that may aid in immune system strengthening. Taking into consideration that the COVID-19 infection strongly affects the immune system via multiple inflammatory responses, pharmaceutical companies are working to develop targeted drugs and vaccines against SARS-CoV-2 COVID-19. A balanced nutritional diet may play an essential role in maintaining general wellbeing by controlling chronic infectious diseases. A balanced diet including vitamin A, B, C, D, E, and K, and some micronutrients such as zinc, sodium, potassium, calcium, chloride, and phosphorus may be beneficial in various infectious diseases. This study aimed to discuss and present recent data regarding the role of vitamins and minerals in the treatment of COVID-19. A deficiency of these vitamins and minerals in the plasma concentration may lead to a reduction in the good performance of the immune system, which is one of the constituents that lead to a poor immune state. This is a narrative review concerning the features of the COVID-19 and data related to the usage of vitamins and minerals as preventive measures to decrease the morbidity and mortality rate in patients with COVID-19.
Valentina Carbonari, Pierangelo Veltri, Pietro Hiram Guzzi
Recent advances in artificial intelligence, particularly large language models LLMs, have shown promising capabilities in transforming rare disease research. This survey paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies that leverage textual data to uncover insights and patterns critical for diagnosis, treatment, and patient care. While current research predominantly employs textual data, the potential for multimodal data integration combining genetic, imaging, and electronic health records stands as a promising frontier. We review foundational papers that demonstrate the application of LLMs in identifying and extracting relevant medical information, simulating intelligent conversational agents for patient interaction, and enabling the formulation of accurate and timely diagnoses. Furthermore, this paper discusses the challenges and ethical considerations inherent in deploying LLMs, including data privacy, model transparency, and the need for robust, inclusive data sets. As part of this exploration, we present a section on experimentation that utilizes multiple LLMs alongside structured questionnaires, specifically designed for diagnostic purposes in the context of different diseases. We conclude with future perspectives on the evolution of LLMs towards truly multimodal platforms, which would integrate diverse data types to provide a more comprehensive understanding of rare diseases, ultimately fostering better outcomes in clinical settings.
Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). For diseases such as type 2 diabetes, accurate progression modeling can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time progression dynamics from irregularly sampled clinical events amid patient heterogeneity (e.g., different progression rates and pathways). Existing mechanistic and data-driven methods either lack adaptability to learn from real-world data or fail to capture complex continuous-time dynamics on progression trajectories. To address these limitations, we propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework. TD-HNODE contains a learnable TD-Hypergraph Laplacian that captures the interdependency of disease complication markers within both intra- and inter-progression trajectories. Experiments on two real-world clinical datasets demonstrate that TD-HNODE outperforms multiple baselines in modeling the progression of type 2 diabetes and related cardiovascular diseases.
Tarek Kamal Motawi, Dina Sabry, Nancy Mamdouh Ahmed
et al.
Abstract Background/objectives Diabetic nephropathy (DN) is a prevalent microvascular diabetic complication that is not totally unveiled. In this study, we considered GAS6, AXL, GAS6-AS1, and GAS6-DT as possible early diagnostic biomarkers of DN. Subjects/methods The study included 70 patients with normoalbuminuria type 2 diabetes (DM), 70 patients with microalbuminuria type 2 diabetes (DN), and 60 apparently healthy controls. Fasting plasma glucose, glycosylated hemoglobin, and plasma creatinine were enzymatically assayed. Albuminuria, plasma GAS6, and AXL levels were determined using ELISA. Long non-coding RNAs GAS6-AS1 and GAS6-DT levels were determined in blood using qRT-PCR. Several bioinformatics databases were employed to suggest interactions with the studied biomolecules. Results GAS6 and AXL were downregulated in DM + DN compared to controls, being the lowest in DN (p < 0.0001). GAS6-DT was upregulated in DM + DN compared to controls, being the highest in DN (p < 0.0001). GAS6-AS1 was higher in DN than in controls (p = 0.013). GAS6, AXL, and GAS6-DT showed fair-to-moderate significant correlations with HbA1c, fasting glucose, creatinine, and albuminuria. ROC curves showed remarkable diagnostic power of GAS6, AXL, and GAS6-DT (AUC = 0.72–1.0), but not GAS6-AS1, in DN and DM, with moderate-to-excellent agreement with conventional diagnostics. Conclusions The current findings emphasize the significance of the GAS6/AXL pathway in DM and DN progression, where GAS6, AXL, and GAS6-DT showed significantly altered values in DM, and further in DN, with notable diagnostic power for both diseases. To date, this is the first study confirming the diagnostic power of AXL and GAS6-DT in DN and DM. Future studies are warranted to evaluate therapeutically targeting this pathway to manage DN.
In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come.
Abstract Background The triglyceride glucose (TyG) index is a novel and reliable alternative marker for insulin resistance. Previous studies have shown that TyG index is closely associated with cardiovascular outcomes in cardiovascular diseases and coronary revascularization. However, the relationship between TyG index and renal outcomes of coronary revascularization is unclear. The purpose of this study was to investigate the correlation between TyG index and the risk of acute kidney injury (AKI) in patients with coronary revascularization. Methods A retrospective cohort study was conducted to select eligible patients with coronary revascularization admitted to ICU in the medical information mart for intensive care IV (MIMIC-IV). According to the TyG index quartile, these patients were divided into four groups (Q1-Q4). The primary endpoint was the incidence of AKI, and secondary endpoints included 28-day mortality and the rate of renal replacement therapy (RRT) use in the AKI population. Multivariate Cox regression analysis and restricted cubic splines (RCS) were used to analyze TyG index association with AKI risk. Kaplan-Meier survival analysis was performed to assess the incidence of endpoints in the four groups. Results In this study, 790 patients who underwent coronary revascularization surgery were included, and the incidence of AKI was 30.13%. Kaplan-Meier analysis showed that patients with a high TyG index had a significantly increased incidence of AKI (Log-rank P = 0.0045). Multivariate Cox regression analysis showed that whether TyG index was a continuous variable (HR 1.42, 95% CI 1.06–1.92, P = 0.018) or a categorical variable (Q4: HR 1.89, 95% CI 1.12–3.17, P = 0.017), and there was an independent association between TyG index and AKI in patients with coronary revascularization. The RCS curve showed a linear relationship between higher TyG index and AKI in this particular population (P = 0.078). In addition, Kaplan-Meier analysis showed a significantly increased risk of RRT application in a subset of AKI patients based on quartiles of TyG index (P = 0.029). Conclusion TyG index was significantly associated with increased risk of AKI and adverse renal outcomes in patients with coronary revascularization. This finding suggests that the TyG index may be useful in identifying people at high risk for AKI and poor renal outcomes in patients with coronary revascularization.
I. Parzanese, Dorina Qehajaj, Federica Patrinicola
et al.
Celiac disease, also known as “celiac sprue”, is a chronic inflammatory disorder of the small intestine, produced by the ingestion of dietary gluten products in susceptible people. It is a multifactorial disease, including genetic and environmental factors. Environmental trigger is represented by gluten while the genetic predisposition has been identified in the major histocompatibility complex region. Celiac disease is not a rare disorder like previously thought, with a global prevalence around 1%. The reason of its under-recognition is mainly referable to the fact that about half of affected people do not have the classic gastrointestinal symptoms, but they present nonspecific manifestations of nutritional deficiency or have no symptoms at all. Here we review the most recent data concerning epidemiology, pathogenesis, clinical presentation, available diagnostic tests and therapeutic management of celiac disease.
Abstract The pathogenesis of inflammatory bowel disease (IBD) is related to genetic susceptibility, enteric dysbiosis, and uncontrolled, chronic inflammatory responses that lead to colonic tissue damage and impaired intestinal absorption. As a consequence, patients with IBD are prone to nutrition deficits after each episode of disease resurgence. Nutritional supplementation, especially for protein components, is often implemented during the remission phase of IBD. Notably, ingested nutrients could affect the progression of IBD and the prognostic outcome of patients; therefore, they should be cautiously evaluated prior to being used for IBD intervention. Arginine (Arg) is a semi-essential amino acid required for protein synthesis and intimately associated with gut pathophysiology. To help optimize arginine-based nutritional intervention strategies, the present work summarizes that during the process of IBD, patients manifest colonic Arg deficiency and the turbulence of Arg metabolic pathways. The roles of Arg–nitric oxide (catalyzed by inducible nitric oxide synthase) and Arg–urea (catalyzed by arginases) pathways in IBD are debatable; the Arg–polyamine and Arg–creatine pathways are mainly protective. Overall, supplementation with Arg is a promising therapeutic strategy for IBD; however, the dosage of Arg may need to be carefully tailored for different individuals at different disease stages. Additionally, the combination of Arg supplementation with inhibitors of Arg metabolic pathways as well as other treatment options is worthy of further exploration.
Transition metals are required cofactors for many proteins that are critical for life, and their concentration within cells is carefully maintained to avoid both deficiency and toxicity. To defend against bacterial pathogens, vertebrate immune proteins sequester metals, in particular zinc, iron, and manganese, as a strategy to limit bacterial acquisition of these necessary nutrients in a process termed "nutritional immunity." In response, bacteria have evolved elegant strategies to access metals and counteract this host defense. In mammals, metal abundance can drastically shift due to changes in dietary intake or absorption from the intestinal tract, disrupting the balance between host and pathogen in the fight for metals and altering susceptibility to disease. This review describes the current understanding of how dietary metals modulate host-microbe interactions and the subsequent impact on the outcome of disease.
Jamun leaf diseases pose a significant threat to agricultural productivity, negatively impacting both yield and quality in the jamun industry. The advent of machine learning has opened up new avenues for tackling these diseases effectively. Early detection and diagnosis are essential for successful crop management. While no automated systems have yet been developed specifically for jamun leaf disease detection, various automated systems have been implemented for similar types of disease detection using image processing techniques. This paper presents a comprehensive review of machine learning methodologies employed for diagnosing plant leaf diseases through image classification, which can be adapted for jamun leaf disease detection. It meticulously assesses the strengths and limitations of various Vision Transformer models, including Transfer learning model and vision transformer (TLMViT), SLViT, SE-ViT, IterationViT, Tiny-LeViT, IEM-ViT, GreenViT, and PMViT. Additionally, the paper reviews models such as Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet)-50V2, EfficientNet, Ensemble model, Convolutional Neural Network (CNN), and Locally Reversible Transformer. These machine-learning models have been evaluated on various datasets, demonstrating their real-world applicability. This review not only sheds light on current advancements in the field but also provides valuable insights for future research directions in machine learning-based jamun leaf disease detection and classification.
We numerically study disease dynamics that lead to the disease switching from one host species to another, resulting in diseases gaining the ability to infect, e.g., humans. Unlike previous studies that focused on branching processes starting with the first infected humans, we begin by considering a disease pathogen that initially cannot infect humans. We model the entire process, starting from an infection in the animal population, including mutations that eventually enable the disease to cause an epidemic outbreak in the human population. We use an SIR model on a network consisting of 132 dog and 1320 human nodes, with a single parameter representing the gene of the pathogen. We use numerical large-deviation techniques, specifically the $1/t$ Wang-Landau algorithm, to calculate the potentially very small probability of the host switching event. With this approach we are able to resolve probabilities as small as $10^{-120}$. Additionally the $1/t$ Wang-Landau algorithm allows us to obtain the complete probability density function $P(C)$ of the cumulative fraction $C$ of infected humans, which is an indicator for the severity of the disease in the human population. We also calculate correlations of $C$ with selected quantities $q$ that characterize the outbreak. Due to the application of the rare-event algorithm, this is possible for the entire range of $C$ values.
Gauri Naik, Nandini Narvekar, Dimple Agarwal
et al.
Eye diseases have posed significant challenges for decades, but advancements in technology have opened new avenues for their detection and treatment. Machine learning and deep learning algorithms have become instrumental in this domain, particularly when combined with Optical Coherent Technology (OCT) imaging. We propose a novel method for efficient detection of eye diseases from OCT images. Our technique enables the classification of patients into disease free (normal eyes) or affected by specific conditions such as Choroidal Neovascularization (CNV), Diabetic Macular Edema (DME), or Drusen. In this work, we introduce an end to end web application that utilizes machine learning and deep learning techniques for efficient eye disease prediction. The application allows patients to submit their raw OCT scanned images, which undergo segmentation using a trained custom UNet model. The segmented images are then fed into an ensemble model, comprising InceptionV3 and Xception networks, enhanced with a self attention layer. This self attention approach leverages the feature maps of individual models to achieve improved classification accuracy. The ensemble model's output is aggregated to predict and classify various eye diseases. Extensive experimentation and optimization have been conducted to ensure the application's efficiency and optimal performance. Our results demonstrate the effectiveness of the proposed approach in accurate eye disease prediction. The developed web application holds significant potential for early detection and timely intervention, thereby contributing to improved eye healthcare outcomes.
Rather than the prophylactic vaccination, any effective synthetic, natural, or nutritional therapy or regimen that may cure or remedy, albeit partially, the complications of SARS-CoV-2 should be highly acknowledged. Here, we reviewed and discussed possible beneficial biological effects of pomegranate juice in such diseased condition of viral infection based on the current published evidence (direct and indirect) and owing to the robust evidence that fresh pomegranate juice is highly rich with unique bioactive compounds that are approved in various occasions to be effective in several chronic diseased conditions. All related references that serve our aim are accessed through available electronic databases, particularly PubMed and Scopus. In summary, there is accepted evidence that pomegranate juice may be beneficial in SARS-CoV-2 infection conditions, especially for patients with the clinical history of chronic diseases such as hypertension, cardiovascular disease, diabetes, and cancer. However, the interventional studies that directly probe and confirm the effectiveness of fresh pomegranate juice in the management of SARS-CoV-2 infection are mandatory.