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Steps toward a digital twin for functional food production with increased health benefits.

ABSTRACT: Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were developed using historical data from ten bioprocesses. These ANNs were designed to predict the biomass in batch bioprocesses with different media compositions, predict biomass in fed-batch bioprocesses, and predict the growth rate in fed-batch bioprocesses. The immunomodulatory effect of the L. rhamnosus samples was examined and found to elicit an anti-inflammatory response as evidenced by the inhibition of IL-6 and TNF-α secretion. Overall, the findings of this study reinforce the potential of ANN modelling for bioprocess optimization aimed at improved control for maximising the volumetric productivity of L. rhamnosus as an immunomodulatory agent with applications in the functional food industry.

SUBMITTER: Sorensen HM

PROVIDER: S-EPMC10543970 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Steps toward a digital twin for functional food production with increased health benefits.

Sørensen Helena Mylise HM Cunningham David D Balakrishnan Rengesh R Maye Susan S MacLeod George G Brabazon Dermot D Loscher Christine C Freeland Brian B

Current research in food science 20230926

Lactobacillus rhamnosus (L. rhamnosus) is a commensal bacterium with health-promoting properties and with a wide range of applications within the food industry. To improve and optimize the control of L. rhamnosus biomass production in batch and fed-batch bioprocesses, this study proposes the application of artificial neural network (ANN) modelling to improve process control and monitoring, with potential future implementation as a basis for a digital twin. Three ANNs were de ...[more]

PMID: 37790857

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Project description:BackgroundMobile health (mHealth) offers significant benefits for patients with stroke, facilitating remote monitoring and personalized health care solutions beyond traditional settings. However, there is a dearth of comprehensive data, particularly qualitative insights, on the barriers to mHealth access. Understanding these barriers is crucial for devising strategies to enhance mHealth use among patients with stroke.ObjectiveThis study aims to examine the recent literature focusing on barriers to mHealth access among patients with stroke.MethodsA systematic search of PubMed, MEDLINE, Web of Science, and CINAHL Plus Full Text was conducted for literature published between 2017 and 2023. Abstracts and full texts were independently screened based on predetermined inclusion and exclusion criteria. Data synthesis was performed using the convergent integrated analysis framework recommended by the Joanna Briggs Institute.ResultsA total of 12 studies met the inclusion criteria. The majority were qualitative studies (about 42%), followed by mixed methods (25%), pilot studies (about 17%), nonrandomized controlled trials (about 8%), and observational studies (about 8%). Participants included patients with stroke, caregivers, and various health care professionals. The most common mHealth practices were home-based telerehabilitation (30%) and poststroke mHealth and telecare services (20%). Identified barriers were categorized into two primary themes: (1) at the patient level and (2) at the health provider-patient-device interaction level. The first theme includes 2 subthemes: health-related issues and patient acceptability. The second theme encompassed 3 subthemes: infrastructure challenges (including software, networking, and hardware), support system deficiencies, and time constraints.ConclusionsThis systematic review underscores significant barriers to mHealth adoption among patients with stroke. Addressing these barriers in future research is imperative to ensure that mHealth solutions effectively meet patients' needs.

Dataset Information

Steps toward a digital twin for functional food production with increased health benefits.

Publications

Steps toward a digital twin for functional food production with increased health benefits.

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