The "Virtual Digital Twins" Concept in Precision Nutrition.
Ontology highlight
ABSTRACT:
SUBMITTER: Gkouskou K
PROVIDER: S-EPMC7666894 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeSpecialized robotic and surgical tools are increasing the complexity of operating rooms (ORs), requiring elaborate preparation especially when techniques or devices are to be used for the first time. Spatial planning can improve efficiency and identify procedural obstacles ahead of time, but real ORs offer little availability to optimize space utilization. Methods for creating reconstructions of physical setups, i.e., digital twins, are needed to enable immersive spatial planning of such complex environments in virtual reality.MethodsWe present a neural rendering-based method to create immersive digital twins of complex medical environments and devices from casual video capture that enables spatial planning of surgical scenarios. To evaluate our approach we recreate two operating rooms and ten objects through neural reconstruction, then conduct a user study with 21 graduate students carrying out planning tasks in the resulting virtual environment. We analyze task load, presence, perceived utility, plus exploration and interaction behavior compared to low visual complexity versions of the same environments.ResultsResults show significantly increased perceived utility and presence using the neural reconstruction-based environments, combined with higher perceived workload and exploratory behavior. There's no significant difference in interactivity.ConclusionWe explore the feasibility of using modern reconstruction techniques to create digital twins of complex medical environments and objects. Without requiring expert knowledge or specialized hardware, users can create, explore and interact with objects in virtual environments. Results indicate benefits like high perceived utility while being technically approachable, which may indicate promise of this approach for spatial planning and beyond.
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Project description:INTRODUCTION:The objective of this study was to examine changes in hemoglobin A1c (HbA1c), anti-diabetic medication use, insulin resistance, and other ambulatory glucose profile metrics between baseline and after 90 days of participation in the Twin Precision Nutrition (TPN) Program enabled by Digital Twin Technology. METHODS:This was a retrospective study of patients with type 2 diabetes who participated in the TPN Program and had at least 3 months of follow-up. The TPN machine learning algorithm used daily continuous glucose monitor (CGM) and food intake data to provide guidelines that would enable individual patients to avoid foods that cause blood glucose spikes and to replace them with foods that do not produce spikes. Physicians with access to daily CGM data titrated medications and monitored patient conditions. RESULTS:Of the 89 patients who initially enrolled in the TPN Program, 64 patients remained in the program and adhered to it for at least 90 days; all analyses were performed on these 64 patients. At the 90-day follow-up assessment, mean (±?standard deviation) HbA1c had decreased from 8.8?±?2.2% at baseline by 1.9 to 6.9?±?1.1%, mean weight had decreased from 79.0?±?16.2 kg at baseline to 74.2?±?14.7 kg, and mean fasting blood glucose had fallen from 151.2 ±?45.0 mg/dl at baseline to 129.1?±?36.7 mg/dl. Homeostatic model assessment of insulin resistance (HOMA-IR) had decreased by 56.9% from 7.4?±?3.5 to 3.2?±?2.8. At the 90-day follow-up assessment, all 12 patients who were on insulin had stopped taking this medication; 38 of the 56 patients taking metformin had stopped metformin; 26 of the 28 patients on dipeptidyl peptidase-4 (DPP-4) inhibitors discontinued DPP-4 inhibitors; all 13 patients on alpha-glucosidase inhibitors discontinued these inhibitors; all 34 patients on sulfonylureas were able to stop taking these medications; two patients stopped taking pioglitazone; all ten patients on sodium-glucose cotransporter-2 (SGLT2) inhibitors stopped taking SGLT2 inhibitors; and one patient stopped taking glucagon-like peptide-1 analogues. CONCLUSION:The results provide evidence that daily precision nutrition guidance based on CGM, food intake data, and machine learning algorithms can benefit patients with type 2 diabetes. Adherence for 3 months to the TPN Program resulted in patients achieving a 1.9 percentage point decrease in HbA1c, a 6.1% drop in weight, a 56.9% reduction in HOMA-IR, a significant decline in glucose time below range, and, in most patients, the elimination of diabetes medication use.
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Project description:The use of digital twins (DTs) has proliferated across various fields and industries, with a recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds great promise to revolutionize the entire healthcare system, including management and delivery, disease treatment and prevention, and health well-being maintenance, ultimately improving human life. The rapid growth of big data and continuous advancement in data science (DS) and artificial intelligence (AI) have the potential to significantly expedite DT research and development by providing scientific expertise, essential data, and robust cybertechnology infrastructure. Although various DT initiatives have been underway in the industry, government, and military, DT4H is still in its early stages. This paper presents an overview of the current applications of DTs in healthcare, examines consortium research centers and their limitations, and surveys the current landscape of emerging research and development opportunities in healthcare. We envision the emergence of a collaborative global effort among stakeholders to enhance healthcare and improve the quality of life for millions of individuals worldwide through pioneering research and development in the realm of DT technology.
| S-EPMC10960047 | biostudies-literature
Project description:This review provides a literature overview of new findings relating nutritional genomics and bariatric surgery. It also describes the importance of nutritional genomics concepts in personalized bariatric management. It includes a discussion of the potential role bariatric surgery plays in altering the three pillars of nutritional genomics: nutrigenetics, nutrigenomics, and epigenetics. We present studies that show the effect of each patient's genetic and epigenetic variables on the response to surgical weight loss treatment. We include investigations that demonstrate the association of single nucleotide polymorphisms with obesity phenotypes and their influence on weight loss after bariatric surgery. We also present reports on how significant weight loss induced by bariatric surgery impacts telomere length, and we discuss studies on the existence of an epigenetic signature associated with surgery outcomes and specific gene methylation profile, which may help to predict weight loss after a surgical procedure. Finally, we show articles which evidence that bariatric surgery may affect expression of numerous genes involved in different metabolic pathways and consequently induce functional and taxonomic changes in gut microbial communities. The role nutritional genomics plays in responses to weight loss after bariatric surgery is evident. Better understanding of the molecular pathways involved in this process is necessary for successful weight management and maintenance.
| S-EPMC5622734 | biostudies-literature
Project description:Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM's applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.
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Project description:"Precision nutrition" is an emerging area of nutrition research that focuses on understanding metabolic variability within and between individuals and helps develop customized dietary plans and interventions to maintain optimal individual health. It encompasses nutritional genomic (gene-nutrient interactions), epigenetic, microbiome, and environmental factors. Obesity is a complex disease that is affected by genetic and environmental factors and thus a relevant target of precision nutrition-based approaches. Recent studies have shown significant associations between obesity phenotypes (body weight, body mass index, waist circumference, and central and regional adiposity) and genetic variants, epigenetic factors (DNA methylation and noncoding RNA), microbial species, and environment (sociodemographics and physical activity). Additionally, studies have also shown that the interactions between genetic variants, microbial metabolites, and epigenetic factors affect energy balance and adiposity. These include variants in FTO, MC4R, PPAR, APOA, and FADS genes, DNA methylation in CpG island regions, and specific miRNAs and microbial species such as Firmicutes, Bacteriodes, Clostridiales, etc. Similarly, studies have shown that microbial metabolites, folate, B-vitamins, and short-chain fatty acids interact with miRNAs to influence obesity phenotypes. With the advent of next-generation sequencing and analytical approaches, the advances in precision nutrition have the potential to lead to new paradigms, which can further lead to interventions or customized treatments specific to individuals or susceptible groups of individuals. This review highlights the recent advances in precision nutrition as applied to obesity and projects the importance of precision nutrition in obesity and weight management.
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