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The integration of Artificial Intelligence (AI) and Wearable Internet of Things (WIoT) for mental health detection is a promising area of research with the potential to revolutionize mental health monitoring and diagnosis. Since early detection of mental diseases, i.e., depression, is of great importance for diagnosis and treatment, a fast and convenient way is urgently needed. Traditional diagnostic methods are time-consuming, laborious, over-subjective, and easily lead to misdiagnosis. The advance in information techniques and wearable devices brings innovation to mental disease detection. Therefore, this article first compares intelligent depression detection methods and traditional methods to illustrate the significance and then analyzes the opportunities of the wearable device. Then we provide specific psychophysiological data measured by wearable devices and introduce relevant datasets for depression detection. An illustrative example of depression detection with sleep data is presented and discussed and our proposed ensemble method has improved nearly 10% to baselines. Analytical results demonstrate the great potential of using wearable device-measured psychophysiological data to detect depression intelligently.

Abstract This special issue editorial introduces the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and security and privacy. In this special issue, nine manuscripts are published related to advanced learning and computing systems for biomedical engineering.

Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

BackgroundWith the ever-increasing demand on acute healthcare, the hospital discharge process and delayed discharges are considered relevant in achieving optimal performance in clinical settings. The purpose of this paper is to review the literature to identify conceptual and operational definitions of delayed discharges, identify causes and effects of delayed discharges, and also to explore the literature for interventions aimed at decreasing the impact (in terms of reducing the number/rate of delays) of delayed discharges in acute healthcare settings.MethodsAn extensive literature search yielded a total of 26 248 records. Sixty-four research articles were included in the scoping review after considering inclusion/exclusion criteria and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) search strategy. The following databases were utilized: Cochrane, EBSCO, PubMed, PubMed Central, Medline, and Web of Science. The search was carried out between January 2017 and March 2020 and covered literature ranging from 1990 to 2019. Results were reviewed by authors for duplicates and filtered using the inclusion/exclusion criteria. Tables were created to classify the chosen articles (n = 64), allowing us to organise findings and results.ResultsConceptual and operational definitions were analysed. In turn, causes and effects of delayed discharges were extracted and represented in diagrammatic format, together with specific interventions used in acute healthcare settings to lessen the effect of delayed discharges. Operational definitions of delayed discharges were found to be more difficult to establish, particularly in the light of the vast number of different scenarios and workplace interventions uncovered in the literature. The main causes of delayed discharges were faulty organisational management, inadequate discharge planning, transfer of care problems, and age. The main effects were bed-blocking, A&E (Accident & Emergency) overcrowding, and financial implications. The main interventions included ‘discharge before noon’ initiative, ‘discharge facilitation tools,’ ‘discharge delay tracking’ mechanisms, and the role of general practitioners and social care staff.ConclusionThis paper fills a gap in the fragmented literature on delayed inpatient discharges by providing a research-based perspective on conceptual and operational definitions, causes and effects, as well as interventions to minimize their impact. The findings and definitions are intended as points of reference for future research.

BackgroundQuality of health service delivery data remains sub-optimal in many Low and middle-income countries (LMICs) despite over a decade of progress in digitization and Health Management Information Systems (HMIS) improvements. Identifying everyone residing in a country utilizing universal civil registration and/or national unique identification number systems especially for vulnerable patients seeking care within the care continuum is an essential part of pursuing universal health coverage (UHC). Many different strategies or candidate digital technologies exist for uniquely identifying and tracking patients within a health system, and the different strategies also have their advantages and trade-offs. The recent approval of Decentralized identifier (DID) core specification by World Wide Web Consortium (W3C) heralds the search for consensus on standard interoperable DID methods.ObjectiveThis paper aims to: (1) assess how candidate Patient Identification Systems fit the digital Patient ID desirable attributes framework in literature; and (2) use insights from Nigeria to propose the scale-up of an offline, interoperable decentralized Patient ID generation and a matching model for addressing network reliability challenges of centralized electronic registries in LMICs.MethodsWe combined: (i) systematic review of the literature to identify the characteristics of leading candidates for Patient ID systems, with (ii) review of policies and (iii) quantitative survey of 14 general hospitals in Nigeria's Federal Capital Territory to understand the model(s) of patient ID strategies currently implemented by public hospitals.ResultsEvidence from the literature review and quantitative survey showed that no current Patient ID strategy in Nigeria simultaneously meets the six attributes of uniqueness, unchanging, uncontroversial, inexpensive, ubiquitous, and uncomplicated required for ensuring the reliability of unique patient identification systems and of the HMIS more generally.ConclusionsThe findings are used to propose a model of algorithms for universal-offline Patient ID generation and matching models that is cost effective and can be easily scaled-up throughout Nigeria. The prototype has promise for generating and validating a universally unique Patient ID given a set of patient characteristics without a central rigid authority. The model can also help to fast-track the implementation of a Master Patient Index (MPI) and interoperability of existing digital health platforms in LMICs.

Background The energy-constrained heterogeneous nodes are the most challenging wireless sensor networks (WSNs) for developing energy-aware clustering schemes. Although various clustering approaches are proven to minimise energy consumption and delay and extend the network lifetime by selecting optimum cluster heads (CHs), it is still a crucial challenge. Methods This article proposes a genetic algorithm-based energy-aware multi-hop clustering (GA-EMC) scheme for heterogeneous WSNs (HWSNs). In HWSNs, all the nodes have varying initial energy and typically have an energy consumption restriction. A genetic algorithm determines the optimal CHs and their positions in the network. The fitness of chromosomes is calculated in terms of distance, optimal CHs, and the node's residual energy. Multi-hop communication improves energy efficiency in HWSNs. The areas near the sink are deployed with more supernodes far away from the sink to solve the hot spot problem in WSNs near the sink node. Results Simulation results proclaim that the GA-EMC scheme achieves a more extended network lifetime network stability and minimises delay than existing approaches in heterogeneous nature.

BackgroundReferral linkages are crucial for efficient functioning of primary health care (PHC) systems. Fast Healthcare Interoperability Resource (FHIR) is an open global standard that facilitates structuring of health information for coordinated exchange among stakeholders. ObjectiveThe objective of this study is to design FHIR profiles and present methodology and the profiled FHIR resource for Maternal and Child Health referral use cases in Ebonyi state, Nigeria—a typical low- and middle-income country (LMIC) setting. MethodsPracticing doctors, midwives, and nurses were purposefully sampled and surveyed. Different referral forms were reviewed. The union of data sets from surveys and forms was aggregated and mapped to base patient FHIR resource elements, and extensions were created for data sets not in the core FHIR specification. This study also introduced FHIR and its relation to the World Health Organization’s (WHO’s) International Classification of Diseases. ResultsWe found many different data elements from the referral forms and survey responses even in urban settings. The resulting FHIR standard profile is published on GitHub for adaptation or adoption as necessary to aid alignment with WHO recommendations. Understanding data sets used in health care and clinical practice for information sharing is crucial in properly standardizing information sharing, particularly during the management of COVID-19 and other infectious diseases. Development organizations and governments can use this methodology and profile to fast-track FHIR standards adoption for paper and electronic information sharing at PHC systems in LMICs. ConclusionsWe presented our methodology for profiling the referral resource crucial for the standardized exchange of new and expectant moms’ information. Using data from frontline providers and mapping to the FHIR profile helped contextualize the standardized profile.

BackgroundThe government and partners have invested heavily in the health information system (HIS) for service delivery, surveillance, reporting, and monitoring. Sierra Leone’s government launched its first digital health strategy in 2018. In 2019, a broader national innovation and digital strategy was launched. The health pillar direction will use big data and artificial intelligence (AI) to improve health care in general and maternal and child health in particular. Understanding the number, distribution, and interoperability of digital health solutions is crucial for successful implementation strategies. ObjectiveThis paper presents the state of digital health solutions in Sierra Leone and how these solutions currently interoperate. This study further presents opportunities for big data and AI applications. MethodsAll the district health management teams, all digital health implementing organizations, and a stratified sample of 72 (out of 1284) health facilities were purposefully selected from all health districts and surveyed. ResultsThe National Health Management Information System’s (NHMIS’s) aggregate reporting solution populated by health facility forms HF1 to HF9 was, by far, the most used tool. A health facility–based weekly aggregate electronic integrated disease surveillance and response solution was also widely used. Half of the health facilities had more than 2 digital health solutions in use. The different digital health software solutions do not share data among one another, though aggregate reporting data were sent as necessary. None of the respondents use any of the health care registries for patient, provider, health facility, or terminology identification. ConclusionsMany digital health solutions are currently used at health facilities in Sierra Leone. The government can leverage current investment in HIS from surveillance and reporting for using big data and AI for care. The vision of using big data for health care is achievable if stakeholders prioritize individualized and longitudinal patient data exchange using agreed use cases from national strategies. This study has shown evidence of distribution, types, and scale of digital health solutions in health facilities and opportunities for leveraging big data to fill critical gaps necessary to achieve the national digital health vision.

Blockchain, a form of distributed ledger technology has attracted the interests of stakeholders across several sectors including healthcare. Its' potential in the multi-stakeholder operated sector like health has been responsible for several investments, studies, and implementations. Electronic Health Records (EHR) systems traditionally used for the exchange of health information amongst healthcare stakeholders have been criticised for centralising power, failures and attack-points with exchange data custodians. EHRs have struggled in the face of multi-stakeholder and system requirements while adhering to security, privacy, ethical and other regulatory constraints. Blockchain is promising amongst others to address the many EHR challenges, primarily trustless and secure exchange of health information amongst stakeholders. Many blockchain-in-healthcare frameworks have been proposed; some prototyped and/or implemented. This study leveraged the PRISMA framework to systematically search and evaluate the different models proposed; prototyped and/or implemented. The bibliometric and functional distribution of all 143 articles from this study were presented. This study evaluated 61 articles that discussed either prototypes or pilot or implementations. The technical and architectural analysis of these 61 articles for privacy, security, cost, and performance were detailed. Blockchain was found to solve the trust, security and privacy constraints of traditional EHRs often at significant performance, storage and cost trade-offs.

This study aims at understanding how a user's emotions fluctuate when undertaking certain tasks on a social media platform such as Facebook or other software products which may have emotional effects on its user. Specifically, we explored the difference in the usability aspect of Facebook concerning frequent and new Facebook users. The study involves a qualitative study on eighteen participants, nine of whom were Facebook users and nine non-Facebook users who had never used Facebook before participating in this study. During the testing procedure, users were asked to complete several tasks on Facebook, while the electrophysiological activity of their brain was recorded using an EEG (electroencephalogram) acquisition system. Certainly, this study can be applied to any software product, before its release, to improve its user interface by acquiring insight into how user-friendly it is for new users when compared to frequent users. Additionally, a correlation in user friendliness between new users and frequent users is investigated. Furthermore, the study will help us discern which parts of the brain had the most significant difference between groups and discuss the motives behind an individual's emotional state, concerning user experience. Based on the analysis of the power spectrum of the characteristic brain waves, this research establishes that there is a substantial statistical difference between new and frequent Facebook users. Also, it resulted that there is a significant difference between the central, temporal and occipital lobes of new and frequent users. These results will assist developers in creating optimal and user-friendly software products.

Automated digital contact tracing is effective and efficient, and one of the non-pharmaceutical complementary approaches to mitigate and manage epidemics like Coronavirus disease 2019 (COVID-19). Despite the advantages of digital contact tracing, it is not widely used in the western world, including the US and Europe, due to strict privacy regulations and patient rights. We categorized the current approaches for contact tracing, namely: mobile service-provider-application, mobile network operators' call detail, citizen-application, and IoT-based. Current measures for infection control and tracing do not include animals and moving objects like cars despite evidence that these moving objects can be infection carriers. In this article, we designed and presented a novel privacy anonymous IoT model. We presented an RFID proof-of-concept for this model. Our model leverages blockchain's trust-oriented decentralization for on-chain data logging and retrieval. Our model solution will allow moving objects to receive or send notifications when they are close to a flagged, probable, or confirmed diseased case, or flagged place or object. We implemented and presented three prototype blockchain smart contracts for our model. We then simulated contract deployments and execution of functions. We presented the cost differentials. Our simulation results show less than one-second deployment and call time for smart contracts, though, in real life, it can be up to 25 seconds on Ethereum public blockchain. Our simulation results also show that it costs an average of $1.95 to deploy our prototype smart contracts, and an average of $0.34 to call our functions. Our model will make it easy to identify clusters of infection contacts and help deliver a notification for mass isolation while preserving individual privacy. Furthermore, it can be used to understand better human connectivity, model similar other infection spread network, and develop public policies to control the spread of COVID-19 while preparing for future epidemics.

BackgroundYears of health information system investment in many countries have facilitated service delivery, surveillance, reporting, and monitoring. Electricity, computing hardware, and internet networks are vital for health facility–based information systems. Availability of these infrastructures at health facilities is crucial for achieving national digital health visions. ObjectiveThe aim of this study was to gain insight into the state of computing hardware, electricity, and connectivity infrastructure at health facilities in Sierra Leone using a representative sample. MethodsStratified sampling of 72 (out of 1284) health facilities distributed in all districts of Sierra Leone was performed, factoring in the rural-urban divide, digital health activity, health facility type, and health facility ownership. Enumerators visited each health facility over a 2-week period. ResultsAmong the 72 surveyed health facilities, 59 (82%) do not have institutionally provided internet. Among the 15 Maternal and Child Health Posts, as a type of primary health care unit (PHU), 9 (60%) use solar energy as their only electricity source and the other 6 (40%) have no electricity source. Similarly, among the 13 hospitals, 5 (38%) use a generator as a primary electricity source. All hospitals have at least one functional computer, although only 7 of the 13 hospitals have four or more functional computers. Similarly, only 2 of the 59 (3%) PHUs have one computer each, and 37 (63%) of the PHUs have one tablet device each. We consider this health care computing infrastructure mapping to be representative with a 95% confidence level within an 11% margin of error. Two-thirds of the PHUs have only alternate solar electricity, only 10 of the 72 surveyed health facilities have functional official internet, and most use suboptimal computing hardware. Overall, 43% of the surveyed health facilities believe that inadequate electricity is the biggest threat to digitization. Similarly, 16 (22%) of the 72 respondents stated that device theft is a primary hindrance to digitization. ConclusionsElectricity provision for off-electricity-grid health facilities using alternative and renewable energy sources is emerging. The current trend where GSM (Global System for Mobile Communication) service providers provide the internet to all health facilities may change to other promising alternatives. This study provides evidence of the critical infrastructure gaps in health facilities in Sierra Leone.

BackgroundTeenage pregnancy remains high with low contraceptive prevalence among adolescents (aged 15-19 years) in Sierra Leone. Stakeholders leverage multiple strategies to address the challenge. Mobile technology is pervasive and presents an opportunity to reach young people with critical sexual reproductive health and family planning messages. ObjectiveThe objectives of this research study are to understand how mobile health (mHealth) is used for family planning, understand phone use habits among young people in Sierra Leone, and recommend strategies for mobile-enabled dissemination of family planning information at scale. MethodsThis formative research study was conducted using a systematic literature review and focus group discussions (FGDs). The literature survey assessed similar but existing interventions through a systematic search of 6 scholarly databases. Cross-sections of young people of both sexes and their support groups were engaged in 9 FGDs in an urban and a rural district in Sierra Leone. The FGD data were qualitatively analyzed using MAXQDA software (VERBI Software GmbH) to determine appropriate technology channels, content, and format for different user segments. ResultsOur systematic search results were categorized using Grading of Recommended Assessment and Evaluation (GRADE) into communication channels, audiovisual messaging format, purpose of the intervention, and message direction. The majority of reviewed articles report on SMS-based interventions. At the same time, most intervention purposes are for awareness and as helpful resources. Our survey did not find documented use of custom mHealth apps for family planning information dissemination. From the FGDs, more young people in Sierra Leone own basic mobile phones than those that have feature capablilities or are smartphone. Young people with smartphones use them mostly for WhatsApp and Facebook. Young people widely subscribe to the social media–only internet bundle, with the cost ranging from 1000 leones (US $0.11) to 1500 leones (US $0.16) daily. Pupils in both districts top-up their voice call and SMS credit every day between 1000 leones (US $0.11) and 5000 leones (US $0.52). ConclusionsmHealth has facilitated family planning information dissemination for demand creation around the world. Despite the widespread use of social and new media, SMS is the scalable channel to reach literate and semiliterate young people. We have cataloged mHealth for contraceptive research to show SMS followed by call center as widely used channels. Jingles are popular for audiovisual message formats, mostly delivered as either push or pull only message directions (not both). Interactive voice response and automated calls are best suited to reach nonliterate young people at scale.

The problem of hospital patients’ delayed discharge or ‘bed blocking’ has long been a challenge for healthcare managers and policymakers. It negatively affects the hospital performance metrics and has other severe consequences for the healthcare system, such as affecting patients’ health. In our previous work, we proposed the phase-type survival tree (PHTST)-based analysis to cluster patients into clinically meaningful patient groups and an extension of this approach to examine the relationship between the length of stay in hospitals and the destination on discharge. This paper describes how PHTST-based clustering can be used for modelling delayed discharge and its effects in a stroke care unit, especially the extra beds required, additional cost, and bed blocking. The PHTST length of stay distribution of each group of patients (each PHTST node) is modelled separately as a finite state continuous-time Markov chain using Coxian-phase-type distributions. Delayed discharge patients waiting for discharge are modelled as the Markov chain, called the ‘blocking state’ in a special state. We can use the model to recognise the association between demographic factors and discharge delays and their effects and identify groups of patients who require attention to resolve the most common delays and prevent them from happening again. The approach is illustrated using five years of retrospective data of patients admitted to the Belfast City Hospital with a stroke diagnosis.

Background of the Article: Hepatocellular carcinoma (HCC) is one of the most common human malignancies worldwide. Radiofrequency ablation (RFA) is considered curative option in selected patients; efficacy is severely limited by lesion size and lesions bordering a large vessel. On the other hand, transarterial chemoembolization (TACE) is not limited by lesion size and arterial occlusion of the tumor feeding vessels leads to increase the volume of the ablative zone. Combination treatments using both intraarterial liver-directed therapy and percutaneous ablation seek to overcome the disadvantages of the individual treatments alone, theoretically improving response to therapy and survival. Material and Methods: This is a single-center retrospectively study in which patients who received TACE plus RFA for HCC were evaluated for technical success, local tumor progression rates, distant intra and extrahepatic recurrences and survival. Results: The study included 22 patients, 21 patients had a solitary HCC of size 3–7 cm and one patient had three target lesions. Technical success achieved after first session of combination treatment was 100% (24/24). At 1 and 3 months follow-up 100% patients (24 target lesions) had complete response and at 6 months; 21 (87.5%) had complete response, one (4.2%) had local tumor progression and two patients (8.3%) developed progressive disease. No major difference in complication was noted. The event-free survival as shown by Kaplan–Meier graph analysis at 6 and 12 months were 90.7% and 66.4% with mean time to event-free survival was 11.1 months. Conclusion: The combined use of TACE and RFA is a safe and effective option in the treatment of patients with single large or multinodular HCC when surgical resection is not feasible and this approach provides better results than RFA or TACE alone.

The development of new lighting sources, such as light emitting diode (LED), induction, and plasma, presented more possible cost effective ways for roadway lighting. A study was therefore conducted for the Indiana Department of Transportation (INDOT) to evaluate the performance and effectiveness of some selected new lighting devices in roadway lighting. This paper describes the field evaluation process and presents the evaluation results. A number of LEDs, plasma and induction luminaires from various manufacturers were selected to replace the existing high pressure sodium (HPS) lamps in conventional and high mast lightings. Illuminance values were measured over a period of 12 months on the existing and new light sources. Light performance metrics, including illuminance level and uniformity ratios, were calculated to make quantitative comparisons of the HPS and new types of light devices. Based on the evaluation in terms of lighting performance and life cycle costs, it was concluded that LED luminaires should be utilized in roadway lighting in place of HPS luminaires. The results of this study will be useful to state highway and city street agencies in making decisions on their lighting policies and developing technical specifications for use of the new lighting technologies in roadway and street lightings. The study provides a basis for manufacturers to improve their luminaire design and integration to better fit the needs of roadway and street lightings. Keywords: Roadway lighting, Luminaires, Illuminance, Field tests, LED, HPS

Clostridium perfringens epsilon toxin (Etx) is categorized as the third most lethal bioterrorism agent by the Centers for Disease Control and Prevention (CDC), with no therapeutic counter measures available for humans. Here, we have developed a high-affinity inhibitory compound by synthesizing and evaluating the structure activity relationship (SAR) of a library of diverse glycosides (numbered 1-12). SAR of glycoside-Etx heptamers revealed exceptionally strong H-bond interactions of glycoside-4 with a druggable pocket in the oligomerization and β-hairpin region of Etx. Analysis of its structure suggested that glycoside-4 might self-aggregate to form a robust micelle-like supra-molecular complex due to its linear side-chain architecture, which was authenticated by fluorescence spectroscopy. Further, this micelle hinders the Etx monomer-monomer interaction required for oligomerization, validated by both surface plasmon resonance (SPR) and immunoblotting. This phenomenon in turn leads to blockage of pore formation. Downstream evaluation revealed that glycoside-4 effectively blocked cell death of Etx-treated cultured primary cells and maintained cellular homeostasis via disrupting oligomerization, blocking pore formation, restoring calcium homeostasis, stabilizing the mitochondrial membrane and impairing high mobility group box 1 (HMGB1) translocation from nucleus to cytoplasm. Furthermore, a single dosage of glycoside-4 protected the Etx-challenged mice and restored normal function to multiple organs. This work reports for the first time a potent, nontoxic glycoside with strong ability to occlude toxin lethality, representing it as a bio-arm therapeutic against Etx-based biological threat.

Access to standard and economical health care delivery, availability of significant health information are viewed as some of the most functional public health interventions in present-day history. Despite that, current information obtained from the WHO regarding Nigeria’s health condition shows that the average existence expectancy is at 54 years and maternal mortality at 608 per 100,000 live births as a result of poor health care services. Several aspects of health informatics have been applied to solve these challenges such as the transformation of records from manual to electronic. Among these are the telemedicine and socialized healthcare, which have been barely adopted in developing nations. This work thus proposes an architectural framework for a cloud-supported socialized healthcare system. In order to achieve this; a webbased application software was designed and implemented through the use of cloud computing technology platforms and server side scripting tools. This study proves that socialized healthcare will really go a long way in defeating barriers of viable human access and delivery.

We report an interesting case of coronary cameral fistula with associated aneurysmal dilatation of coronary artery. The complete evaluation including anatomical relationships with surrounding vascular and non-vascular structures can be achieved with ECG gated multi-detector computed tomography (MDCT). MDCT has many advantages over echocardiography and digital subtraction catheter angiography, because of its ability to demonstrate the fistula separate from surrounding cardiovascular structures along with any aneurysm or obstruction in its course. Thus, MDCT is emerging as the initial non-invasive imaging technique for comprehensive preoperative evaluation of these rare congenital anomalies for cardiovascular surgeons to achieve better operative assessibity and outcome.

Wheat bran, and especially wheat aleurone fraction, are concentrated sources of a wide range of components which may contribute to the health benefits associated with higher consumption of whole-grain foods. This study used NMR metabolomics to evaluate urine samples from baseline at one and two hours postprandially, following the consumption of minimally processed bran, aleurone or control by 14 participants (7 Females; 7 Males) in a randomized crossover trial. The methodology discriminated between the urinary responses of control, and bran and aleurone, but not between the two fractions. Compared to control, consumption of aleurone or bran led to significantly and substantially higher urinary concentrations of lactate, alanine, N-acetylaspartate acid and N-acetylaspartylglutamate and significantly and substantially lower urinary betaine concentrations at one and two hours postprandially. There were sex related differences in urinary metabolite profiles with generally higher hippurate and citrate and lower betaine in females compared to males. Overall, this postprandial study suggests that acute consumption of bran or aleurone is associated with a number of physiological effects that may impact on energy metabolism and which are consistent with longer term human and animal metabolomic studies that used whole-grain wheat diets or wheat fractions.