Valorization of non-edible fruit seeds into valuable products: A sustainable approach towards circular bioeconomy

Global imperatives have recently shown a paradigm shift in the prevailing resource utilization model from a linear approach to a circular bioeconomy. The primary goal of the circular bioeconomy model is to minimize waste by effective re-usage of organic waste and efficient nutrient recycling. In essence, circular bioeconomy integrates the fundamental concept of circular economy, which strives to offer sustainable goods and services by leveraging biological resources and processes. Notably, the circular bioeconomy differs from conventional waste recycling by prioritizing the safeguarding and restoration of production ecosystems, focusing on harnessing renewable biological resources and their associated waste streams to produce value-added products like food, animal feed, and bioenergy. Amidst these sustainability efforts, fruit seeds are getting considerable attention, which were previously overlooked and commonly discarded but were known to comprise diverse chemicals with significant industrial applications, not limited to cosmetics and pharmaceutical industries. While, polyphenols in these seeds offer extensive health benefits, the inadequate conversion of fruit waste into valuable products poses substantial environmental challenges and resource wastage. This review aims to comprehend the known information about the application of non-edible fruit seeds for synthesising metallic nanoparticles, carbon dots, biochar, biosorbent, and biodiesel. Further, this review sheds light on the potential use of these seeds as functional foods and feed ingredients; it also comprehends the safety aspects associated with their utilization. Overall, this review aims to provide a roadmap for harnessing the potential of non-edible fruit seeds by adhering to the principles of a sustainable circular bioeconomy.

Participatory development of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED)

BACKGROUND

The escalating global prevalence of type 2 diabetes and prediabetes presents a major public health challenge. Physical activity plays a critical role in managing (pre)diabetes; however, adherence to physical activity recommendations remains low. The ENERGISED trial was designed to address these challenges by integrating mHealth tools into the routine practice of general practitioners, aiming for a significant, scalable impact in (pre)diabetes patient care through increased physical activity and reduced sedentary behaviour.

METHODS

The mHealth intervention for the ENERGISED trial was developed according to the mHealth development and evaluation framework, which includes the active participation of (pre)diabetes patients. This iterative process encompasses four sequential phases: (a) conceptualisation to identify key aspects of the intervention; (b) formative research including two focus groups with (pre)diabetes patients (n = 14) to tailor the intervention to the needs and preferences of the target population; (c) pre-testing using think-aloud patient interviews (n = 7) to optimise the intervention components; and (d) piloting (n = 10) to refine the intervention to its final form.

RESULTS

The final intervention comprises six types of text messages, each embodying different behaviour change techniques. Some of the messages, such as those providing interim reviews of the patients' weekly step goal or feedback on their weekly performance, are delivered at fixed times of the week. Others are triggered just in time by specific physical behaviour events as detected by the Fitbit activity tracker: for example, prompts to increase walking pace are triggered after 5 min of continuous walking; and prompts to interrupt sitting following 30 min of uninterrupted sitting. For patients without a smartphone or reliable internet connection, the intervention is adapted to ensure inclusivity. Patients receive on average three to six messages per week for 12 months. During the first six months, the text messaging is supplemented with monthly phone counselling to enable personalisation of the intervention, assistance with technical issues, and enhancement of adherence.

CONCLUSIONS

The participatory development of the ENERGISED mHealth intervention, incorporating just-in-time prompts, has the potential to significantly enhance the capacity of general practitioners for personalised behavioural counselling on physical activity in (pre)diabetes patients, with implications for broader applications in primary care.

Multiple Perspectives on the Adoption of SMART Technologies for Improving Care of Older People: Mixed Methods Study

BACKGROUND

Despite the ever-increasing offering of SMART technologies (ie, computer-controlled devices acting intelligently and capable of monitoring, analyzing or reporting), a wide gap exists between the development of new technological innovations and their adoption in everyday care for older adults.

OBJECTIVE

This study aims to explore the barriers and concerns related to the adoption of SMART technologies among different groups of stakeholders.

METHODS

Data from 4 sources were used: semistructured in-person or internet-based interviews with professional caregivers (n=12), structured email interviews with experts in the area of aging (n=9), a web-based survey of older adults (>55 years) attending the Virtual University of the Third Age (n=369), and a case study on the adoption of new technology by an older adult care facility.

RESULTS

Although all stakeholders noted the potential of SMART technologies to improve older adult care, multiple barriers to their adoption were identified. Caregivers perceived older adults as disinterested or incompetent in using technology, reported preferring known strategies over new technologies, and noted own fears of using technology. Experts viewed technologies as essential but expressed concerns about cost, low digital competency of older adults, and lack of support or willingness to implement technologies in older adult care. Older adults reported few concerns overall, but among the mentioned concerns were lack of ability or interest, misuse of data, and limited usefulness (in specific subgroups or situations). In addition, older adults' ratings of the usefulness of different technologies correlated with their self-rating of digital competency (r=0.258; P<.001).

CONCLUSIONS

Older adults appeared to have more positive views of various technologies than professional caregivers; however, their concerns varied by the type of technology. Lack of competence and lack of support were among the common themes, suggesting that educationally oriented programs for both older adults and their caregivers should be pursued.

Concept and Evolution in 3-D Printing for Excellence in Healthcare

Three-dimensional printing (3DP) has gained popularity among scientists and researchers in every field due to its potential to drastically reduce energy costs for the production of customised products by utilising less energy-intensive machines as well as minimising material waste. The 3D printing technology is an additive manufacturing approach that uses material layer-by-layer fabrication to produce the digitally specified 3D model. The use of 3D printing technology in the pharmaceutical sector has the potential to revolutionise research and development by providing a quick and easy means to manufacture personalised one-off batches, each with unique dosages, distinct substances, shapes, and sizes, as well as variable release rates. This overview addresses the concept of 3D printing, its evolution, and its operation, as well as the most popular types of 3D printing processes utilised in the health care industry. It also discusses the application of these cutting-edge technologies to the pharmaceutical industry, advancements in various medical fields and medical equipment, 3D bioprinting, the most recent initiatives to combat COVID-19, regulatory frameworks, and the major challenges that this technology currently faces. In addition, we attempt to provide some futuristic approaches to 3DP applications.

Non-edible fruit seeds: nutritional profile, clinical aspects, and enrichment in functional foods and feeds

Nowadays, fruits are gaining high demand due to their promising advantages on human health. Astonishingly, their by-products, that is, seeds and peels, account for 10-35% of fruit weight and are usually thrown as waste after consumption or processing. But it is neglected that fruit seeds also have functional properties and nutritional value, and thus could be utilized for dietary and therapeutic purposes, ultimately reducing the waste burden on the environment. Owing to these benefits, researchers have started to assess the nutritional value of different fruits seeds, in addition to the chemical composition in various bioactive constituents, like carotenoids (lycopene), flavonoids, proteins (bioactive peptides), vitamins, etc., that have substantial health benefits and can be used in formulating different types of food products with noteworthy functional and nutraceutical potential. The current review aims to comprehend the known information of nutritional and phytochemical profiling of non-edible fruits seeds, viz. apple, apricot, avocado, cherry, date, jamun, litchi, longan, mango, and papaya. Additionally, clinical studies conducted on these selected non-edible fruit seed extracts, their safety issues and their enrichment in food products as well as animal feed has also been discussed. This review aims to highlight the potential applications of the non-edible fruit seeds in developing new food products and also provide a viable alternative to reduce the waste disposal issue faced by agro-based industries.

Anti-oxidant potential of plants and probiotic spp. in alleviating oxidative stress induced by H2O2

Cells produce reactive oxygen species (ROS) as a metabolic by-product. ROS molecules trigger oxidative stress as a feedback response that significantly initiates biological processes such as autophagy, apoptosis, and necrosis. Furthermore, extensive research has revealed that hydrogen peroxide (H2O2) is an important ROS entity and plays a crucial role in several physiological processes, including cell differentiation, cell signalling, and apoptosis. However, excessive production of H2O2 has been shown to disrupt biomolecules and cell organelles, leading to an inflammatory response and contributing to the development of health complications such as collagen deposition, aging, liver fibrosis, sepsis, ulcerative colitis, etc. Extracts of different plant species, phytochemicals, and Lactobacillus sp (probiotic) have been reported for their anti-oxidant potential. In this view, the researchers have gained significant interest in exploring the potential plants spp., their phytochemicals, and the potential of Lactobacillus sp. strains that exhibit anti-oxidant properties and health benefits. Thus, the current review focuses on comprehending the information related to the formation of H2O2, the factors influencing it, and their pathophysiology imposed on human health. Moreover, this review also discussed the anti-oxidant potential and role of different extract of plants, Lactobacillus sp. and their fermented products in curbing H2O2‑induced oxidative stress in both in-vitro and in-vivo models via boosting the anti-oxidative activity, inhibiting of important enzyme release and downregulation of cytochrome c, cleaved caspases-3, - 8, and - 9 expression. In particular, this knowledge will assist R&D sections in biopharmaceutical and food industries in developing herbal medicine and probiotics-based or derived food products that can effectively alleviate oxidative stress issues induced by H2O2 generation.

Classification of health deterioration by geometric invariants

BACKGROUND AND OBJECTIVES

Prediction of patient deterioration is essential in medical care, and its automation may reduce the risk of patient death. The precise monitoring of a patient's medical state requires devices placed on the body, which may cause discomfort. Our approach is based on the processing of long-term ballistocardiography data, which were measured using a sensory pad placed under the patient's mattress.

METHODS

The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).

RESULTS

For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.

mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED): rationale and study protocol for a pragmatic randomised controlled trial

BACKGROUND

The growing number of patients with type 2 diabetes and prediabetes is a major public health concern. Physical activity is a cornerstone of diabetes management and may prevent its onset in prediabetes patients. Despite this, many patients with (pre)diabetes remain physically inactive. Primary care physicians are well-situated to deliver interventions to increase their patients' physical activity levels. However, effective and sustainable physical activity interventions for (pre)diabetes patients that can be translated into routine primary care are lacking.

METHODS

We describe the rationale and protocol for a 12-month pragmatic, multicentre, randomised, controlled trial assessing the effectiveness of an mHealth intervention delivered in general practice to increase physical activity and reduce sedentary behaviour of patients with prediabetes and type 2 diabetes (ENERGISED). Twenty-one general practices will recruit 340 patients with (pre)diabetes during routine health check-ups. Patients allocated to the active control arm will receive a Fitbit activity tracker to self-monitor their daily steps and try to achieve the recommended step goal. Patients allocated to the intervention arm will additionally receive the mHealth intervention, including the delivery of several text messages per week, with some of them delivered just in time, based on data continuously collected by the Fitbit tracker. The trial consists of two phases, each lasting six months: the lead-in phase, when the mHealth intervention will be supported with human phone counselling, and the maintenance phase, when the intervention will be fully automated. The primary outcome, average ambulatory activity (steps/day) measured by a wrist-worn accelerometer, will be assessed at the end of the maintenance phase at 12 months.

DISCUSSION

The trial has several strengths, such as the choice of active control to isolate the net effect of the intervention beyond simple self-monitoring with an activity tracker, broad eligibility criteria allowing for the inclusion of patients without a smartphone, procedures to minimise selection bias, and involvement of a relatively large number of general practices. These design choices contribute to the trial's pragmatic character and ensure that the intervention, if effective, can be translated into routine primary care practice, allowing important public health benefits.

TRIAL REGISTRATION

ClinicalTrials.gov (NCT05351359, 28/04/2022).

Automatic seizure detection by convolutional neural networks with computational complexity analysis

BACKGROUND AND OBJECTIVES

Nowadays, an automated computer-aided diagnosis (CAD) is an approach that plays an important role in the detection of health issues. The main advantages should be in early diagnosis, including high accuracy and low computational complexity without loss of the model performance. One of these systems type is concerned with Electroencephalogram (EEG) signals and seizure detection. We designed a CAD system approach for seizure detection that optimizes the complexity of the required solution while also being reusable on different problems.

METHODS

The methodology is built-in deep data analysis for normalization. In comparison to previous research, the system does not necessitate a feature extraction process that optimizes and reduces system complexity. The data classification is provided by a designed 8-layer deep convolutional neural network.

RESULTS

Depending on used data, we have achieved the accuracy, specificity, and sensitivity of 98%, 98%, and 98.5% on the short-term Bonn EEG dataset, and 96.99%, 96.89%, and 97.06% on the long-term CHB-MIT EEG dataset.

CONCLUSIONS

Through the approach to detection, the system offers an optimized solution for seizure diagnosis health problems. The proposed solution should be implemented in all clinical or home environments for decision support.

Feasibility of Real-time Behavior Monitoring Via Mobile Technology in Czech Adults Aged 50 Years and Above: 12-Week Study With Ecological Momentary Assessment

Background

Czech older adults have lower rates of physical activity than the average population and lag behind in the use of digital technologies, compared with their peers from other European countries.

Objective

This study aims to assess the feasibility of intensive behavior monitoring through technology in Czech adults aged ≥50 years.

Methods

Participants (N=30; mean age 61.2 years, SD 6.8 years, range 50-74 years; 16/30, 53% male; 7/30, 23% retired) were monitored for 12 weeks while wearing a Fitbit Charge 2 monitor and completed three 8-day bursts of intensive data collection through surveys presented on a custom-made mobile app. Web-based surveys were also completed before and at the end of the 12-week period (along with poststudy focus groups) to evaluate participants’ perceptions of their experience in the study.

Results

All 30 participants completed the study. Across the three 8-day bursts, participants completed 1454 out of 1744 (83% compliance rate) surveys administered 3 times per day on a pseudorandom schedule, 451 out of 559 (81% compliance rate) end-of-day surveys, and 736 episodes of self-reported planned physical activity (with 29/736, 3.9% of the reports initiated but returned without data). The overall rating of using the mobile app and Fitbit was above average (74.5 out of 100 on the System Usability Scale). The majority reported that the Fitbit (27/30, 90%) and mobile app (25/30, 83%) were easy to use and rated their experience positively (25/30, 83%). Focus groups revealed that some surveys were missed owing to notifications not being noticed or that participants needed a longer time window for survey completion. Some found wearing the monitor in hot weather or at night uncomfortable, but overall, participants were highly motivated to complete the surveys and be compliant with the study procedures.

Conclusions

The use of a mobile survey app coupled with a wearable device appears feasible for use among Czech older adults. Participants in this study tolerated the intensive assessment schedule well, but lower compliance may be expected in studies of more diverse groups of older adults. Some difficulties were noted with the pairing and synchronization of devices on some types of smartphones, posing challenges for large-scale studies.

Application of mechanical trigger for unobtrusive detection of respiratory disorders from body recoil micro-movements

Background and Objectives Automatic detection of breathing disorders plays an important role in the early signalization of respiratory diseases. Measuring methods can be based on electrocardiogram (ECG), sound, oximetry, or respiratory analysis. However, these approaches require devices placed on the human body or they are prone to disturbance by environmental influences. To solve these problems, we proposed a heart contraction mechanical trigger for unobtrusive detection of respiratory disorders from the mechanical measurement of cardiac contractions. We designed a novel method to calculate this mechanical trigger purely from measured mechanical signals without the use of ECG. Methods The approach is a built-on calculation of the so-called euclidean arc length from the signals. In comparison to previous researches, this system does not require any equipment attached to a person. This is achieved by locating the tensometers on the bed. Data from sensors are fused by the Cartan curvatures method to beat-to-beat vector input for the Convolutional neural network (CNN) classifier. Results In sum, 2281 disordered and 5130 normal breathing samples was collected for analysis. The experiments with use of 10-fold cross validation show that accuracy, sensitivity, and specificity reach values of 96.37%, 92.46%, and 98.11% respectively. Conclusions By the approach for detection, the system offers a novel way for a completely unobtrusive diagnosis of breathing-related health problems. The proposed solution can effectively be deployed in all clinical or home environments.

Physical activity in an air-polluted environment: behavioral, psychological and neuroimaging protocol for a prospective cohort study (Healthy Aging in Industrial Environment study - Program 4)

Background

Air pollution has been linked to increased mortality and morbidity. The Program 4 of the Healthy Aging in Industrial Environment study investigates whether the health and wellbeing benefits of physical activity (PA) can be fully realized in individuals living in highly polluted environments. Herein, we introduce the behavioral, psychological and neuroimaging protocol of the study.

Methods

This is a prospective cohort study of N = 1500 individuals aged 18–65 years comparing: (1) individuals living in the highly polluted, industrial region surrounding the city of Ostrava (n = 750), and (2) controls from the comparison region with relative low pollution levels in Southern Bohemia (n = 750). Quota sampling is used to obtain samples balanced on age, gender, PA status (60% active runners vs. 40% insufficiently active). Participants are screened and complete baseline assessments through online questionnaires and in-person lab-based assessments of physiological, biomechanical, neuroimaging and cognitive function parameters. Prospective 12-month intensive monitoring of air pollution and behavioral parameters (PA, inactivity, and sleep) follows, with a focus on PA-related injuries and psychological factors through fitness trackers, smartphones, and mobile apps. Subsequently, there will be a 5-year follow-up of the study cohort.

Discussion

The design of the study will allow for (1) the assessment of both short-term variation and long-term change in behavioral parameters, (2) evaluation of the incidence of musculoskeletal injuries and psychological factors impacting behavior and injury recovery, and (3) the impact that air pollution status (and change) has on behavior, psychological resilience, and injury recovery. Furthermore, the integration of MRI techniques and cognitive assessment in combination with data on behavioral, biological and environmental variables will provide an opportunity to examine brain structure and cognitive function in relation to health behavior and air pollution, as well as other factors affecting resilience against and vulnerability to adverse changes in brain structure and cognitive aging. This study will help inform individuals about personal risk factors and decision-makers about the impact of environmental factors on negative health outcomes and potential underlying biological, behavioral and psychological mechanisms. Challenges and opportunities stemming from the timing of the study that coincided with the COVID-19 pandemic are also discussed.

Anticipated Social and Healthcare Economic Burden of People with Alzheimer's Disease in Two Selected Regions of the Czech Republic

Increasing life expectancy in modern society is undoubtedly due to improved healthcare, scientific advances in medicine, and the overall healthy lifestyle of the general population. However, this positive trend has led to an increase in the number of older people with a growing need for a sustainable system for the long-term care of this part of the population, which includes social and health services that are essential for a high quality of life. Longevity also brings challenges in the form of a polymorbid geriatric population that places financial pressure on healthcare systems. Regardless, one disease dominates the debate about financial sustainability due to the increasing numbers of people diagnosed, and that is Alzheimer’s disease (AD). The presented paper aims to demonstrate the economic burden of social and healthcare services. Data from two regions in the Czech Republic were selected to demonstrate the potential scope of the problem. The future costs connected with AD are calculated by a prediction model, which is based on a population model for predicting the number of people with AD between 2020 and 2070. Based on the presented data from the two regions in the Czech Republic and the prediction model, several trends emerged. There appears to be a significant difference in the annual direct costs per person diagnosed with AD depending on the region in which they reside. This may lead to a significant inequality of the services a person can acquire followed by subsequent social issues that can manifest as a lower quality of life. Furthermore, given the prediction of the growing AD population, the costs expressed in constant prices based on the year 2020 will increase almost threefold during the period 2020–2070. The predicted threefold increase will place additional financial pressure on all stakeholders responsible for social and healthcare services, as the current situation is already challenging.

The detrimental effect of COVID-19 nationwide quarantine on accelerometer-assessed physical activity of heart failure patients

AIMS

A reduction of habitual physical activity due to prolonged COVID-19 quarantine can have serious consequences for patients with cardiovascular diseases, such as heart failure. This study aimed to explore the effect of COVID-19 nationwide quarantine on accelerometer-assessed physical activity of heart failure patients.

METHODS AND RESULTS

We analysed the daily number of steps in 26 heart failure patients during a 6-week period that included 3 weeks immediately preceding the onset of the quarantine and the first 3 weeks of the quarantine. The daily number of steps was assessed using a wrist-worn accelerometer worn by the patients as part of an ongoing randomized controlled trial. Multilevel modelling was used to explore the effect of the quarantine on the daily step count adjusted for weather conditions. As compared with the 3 weeks before the onset of the quarantine, the step count was significantly lower during each of the first 3 weeks of the quarantine (P < 0.05). When the daily step count was averaged across the 3 weeks before and during the quarantine, the decrease amounted to 1134 (SE 189) steps per day (P < 0.001), which translated to a 16.2% decrease.

CONCLUSIONS

The introduction of the nationwide quarantine due to COVID-19 had a detrimental effect on the level of habitual physical activity in heart failure patients, leading to an abrupt decrease of daily step count that lasted for at least the 3-week study period. Staying active and maintaining sufficient levels of physical activity during the COVID-19 pandemic are essential despite the unfavourable circumstances of quarantine.

Numeric, Agent-based or System Dynamics Model? Which Modeling Approach is the Best for Vast Population Simulation?

BACKGROUND

Alzheimer's disease is one of the most common mental illnesses. It is posited that more than 25% of the population is affected by some mental disease during their lifetime. Treatment of each patient draws resources from the economy concerned. Therefore, it is important to quantify the potential economic impact.

METHODS

Agent-based, system dynamics and numerical approaches to dynamic modeling of the population of the European Union and its patients with Alzheimer's disease are presented in this article. Simulations, their characteristics, and the results from different modeling tools are compared.

RESULTS

The results of these approaches are compared with EU population growth predictions from the statistical office of the EU by Eurostat. The methodology of a creation of the models is described and all three modeling approaches are compared. The suitability of each modeling approach for the population modeling is discussed.

CONCLUSION

In this case study, all three approaches gave us the results corresponding with the EU population prediction. Moreover, we were able to predict the number of patients with AD and, based on the modeling method, we were also able to monitor different characteristics of the population.

Prediction of population with Alzheimer's disease in the European Union using a system dynamics model

INTRODUCTION

Alzheimer's disease (AD) is a slowly progressing neurodegenerative brain disease with irreversible brain effects; it is the most common cause of dementia. With increasing age, the probability of suffering from AD increases. In this research, population growth of the European Union (EU) until the year 2080 and the number of patients with AD are modeled.

AIM

The aim of this research is to predict the spread of AD in the EU population until year 2080 using a computer simulation.

METHODS

For the simulation of the EU population and the occurrence of AD in this population, a system dynamics modeling approach has been used. System dynamics is a useful and effective method for the investigation of complex social systems. Over the past decades, its applicability has been demonstrated in a wide variety of applications. In this research, this method has been used to investigate the growth of the EU population and predict the number of patients with AD. The model has been calibrated on the population prediction data created by Eurostat.

RESULTS

Based on data from Eurostat, the EU population until year 2080 has been modeled. In 2013, the population of the EU was 508 million and the number of patients with AD was 7.5 million. Based on the prediction, in 2040, the population of the EU will be 524 million and the number of patients with AD will be 13.1 million. By the year 2080, the EU population will be 520 million and the number of patients with AD will be 13.7 million.

CONCLUSION

System dynamics modeling approach has been used for the prediction of the number of patients with AD in the EU population till the year 2080. These results can be used to determine the economic burden of the treatment of these patients. With different input data, the simulation can be used also for the different regions as well as for different noncontagious disease predictions.