Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia.

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.

Mobile Robot + IoT: Project of Sustainable Technology for Sanitizing Broiler Poultry Litter.

The traditional aviary decontamination process involves farmers applying pesticides to the aviary's ground. These agricultural defenses are easily dispersed in the air, making the farmers susceptible to chronic diseases related to recurrent exposure. Industry 5.0 raises new pillars of research and innovation in transitioning to more sustainable, human-centric, and resilient companies. Based on these concepts, this paper presents a new aviary decontamination process that uses IoT and a robotic platform coupled with ozonizer (O3) and ultraviolet light (UVL). These clean technologies can successfully decontaminate poultry farms against pathogenic microorganisms, insects, and mites. Also, they can degrade toxic compounds used to control living organisms. This new decontamination process uses physicochemical information from the poultry litter through sensors installed in the environment, which allows accurate and safe disinfection. Different experimental tests were conducted to construct the system. First, tests related to measuring soil moisture, temperature, and pH were carried out, establishing the range of use and the confidence interval of the measurements. The robot's navigation uses a back-and-forth motion that parallels the aviary's longest side because it reduces the number of turns, reducing energy consumption. This task becomes more accessible because of the aviaries' standardized geometry. Furthermore, the prototype was tested in a real aviary to confirm the innovation, safety, and effectiveness of the proposal. Tests have shown that the UV + ozone combination is sufficient to disinfect this environment.

Enrichment of sweat-derived extracellular vesicles of human and bacterial origin for biomarker identification.

Sweat contains biomarkers for real-time non-invasive health monitoring, but only a few relevant analytes are currently used in clinical practice. In the present study, we investigated whether sweat-derived extracellular vesicles (EVs) can be used as a source of potential protein biomarkers of human and bacterial origin. Methods: By using ExoView platform, electron microscopy, nanoparticle tracking analysis and Western blotting we characterized EVs in the sweat of eight volunteers performing rigorous exercise. We compared the presence of EV markers as well as general protein composition of total sweat, EV-enriched sweat and sweat samples collected in alginate skin patches. Results: We identified 1209 unique human proteins in EV-enriched sweat, of which approximately 20% were present in every individual sample investigated. Sweat derived EVs shared 846 human proteins (70%) with total sweat, while 368 proteins (30%) were captured by medical grade alginate skin patch and such EVs contained the typical exosome marker CD63. The majority of identified proteins are known to be carried by EVs found in other biofluids, mostly urine. Besides human proteins, EV-enriched sweat samples contained 1594 proteins of bacterial origin. Bacterial protein profiles in EV-enriched sweat were characterized by high interindividual variability, that reflected differences in total sweat composition. Alginate-based sweat patch accumulated only 5% proteins of bacterial origin. Conclusion: We showed that sweat-derived EVs provide a rich source of potential biomarkers of human and bacterial origin. Use of commercially available alginate skin patches selectively enrich for human derived material with very little microbial material collected.

Modeling intrinsic factors of inclusive engagement in citizen science: Insights from the participants' survey analysis of CSI-COP.

Inclusive citizen science, an emerging field, has seen extensive research. Prior studies primarily concentrated on creating theoretical models and practical strategies for diversifying citizen science (CS) projects. These studies relied on ethical frameworks or post-project empirical observations. Few examined active participants' socio-demographic and behavioral data. Notably, none, to our knowledge, explored prospective citizen scientists' traits as intrinsic factors to enhance diversity and engagement in CS. This paper presents a new inclusive CS engagement model based on quantitative analysis of surveys administered to 540 participants of the dedicated free informal education MOOC (Massive Open Online Course) 'Your Right to Privacy Online' from eight countries in the EU funded project, CSI-COP (Citizen Scientists Investigating Cookies and App GDPR compliance). The surveys were filled out just after completing the training stage and before joining the project as active CSs. Out of the 540 participants who completed the surveys analyzed in this study, only 170 (32%) individuals actively participated as CSs in the project. Therefore, the study attempted to understand what characterizes these participants compared to those who decided to refrain from joining the project after the training stage. The study employed descriptive analysis and advanced statistical tests to explore the correlations among different research variables. The findings revealed several important relationships and predictors for becoming a citizen scientist based on the surveys analysis, such as age, gender, culture, education, Internet accessibility and apps usage, as well as the satisfaction with the MOOC, the mode of training and initial intentions for becoming a CS. These findings lead to the development of the empirical model for inclusive engagement in CS and enhance the understanding of the internal factors that influence individuals' intention and actual participation as CSs. The devised model offers valuable insights and key implications for future CS initiatives. It emphasizes the necessity of targeted recruitment strategies, focusing on underrepresented groups and overcoming accessibility barriers. Positive learning experiences, especially through MOOCs, are crucial; enhancing training programs and making educational materials accessible and culturally diverse can boost participant motivation. Acknowledging varying technological proficiency and providing necessary resources enhances active engagement. Addressing the intention-engagement gap is vital; understanding underlying factors and creating supportive environments can transform intentions into active involvement. Embracing cultural diversity through language-specific strategies ensures an inclusive environment for effective contributions.

Clinical-Grade Patches as a Medium for Enrichment of Sweat-Extracellular Vesicles and Facilitating Their Metabolic Analysis.

Cell-secreted extracellular vesicles (EVs), carrying components such as RNA, DNA, proteins, and metabolites, serve as candidates for developing non-invasive solutions for monitoring health and disease, owing to their capacity to cross various biological barriers and to become integrated into human sweat. However, the evidence for sweat-associated EVs providing clinically relevant information to use in disease diagnostics has not been reported. Developing cost-effective, easy, and reliable methodologies to investigate EVs' molecular load and composition in the sweat may help to validate their relevance in clinical diagnosis. We used clinical-grade dressing patches, with the aim being to accumulate, purify and characterize sweat EVs from healthy participants exposed to transient heat. The skin patch-based protocol described in this paper enables the enrichment of sweat EVs that express EV markers, such as CD63. A targeted metabolomics study of the sweat EVs identified 24 components. These are associated with amino acids, glutamate, glutathione, fatty acids, TCA, and glycolysis pathways. Furthermore, as a proof-of-concept, when comparing the metabolites' levels in sweat EVs isolated from healthy individuals with those of participants with Type 2 diabetes following heat exposure, our findings revealed that the metabolic patterns of sweat EVs may be linked with metabolic changes. Moreover, the concentration of these metabolites may reflect correlations with blood glucose and BMI. Together our data revealed that sweat EVs can be purified using routinely used clinical patches, setting the foundations for larger-scale clinical cohort work. Furthermore, the metabolites identified in sweat EVs also offer a realistic means to identify relevant disease biomarkers. This study thus provides a proof-of-concept towards a novel methodology that will focus on the use of the sweat EVs and their metabolites as a non-invasive approach, in order to monitor wellbeing and changes in diseases.

Predicting Emotion with Biosignals: A Comparison of Classification and Regression Models for Estimating Valence and Arousal Level Using Wearable Sensors.

This study aims to predict emotions using biosignals collected via wrist-worn sensor and evaluate the performance of different prediction models. Two dimensions of emotions were considered: valence and arousal. The data collected by the sensor were used in conjunction with target values obtained from questionnaires. A variety of classification and regression models were compared, including Long Short-Term Memory (LSTM) models. Additionally, the effects of different normalization methods and the impact of using different sensors were studied, and the way in which the results differed between the study subjects was analyzed. The results revealed that regression models generally performed better than classification models, with LSTM regression models achieving the best results. The normalization method called baseline reduction was found to be the most effective, and when used with an LSTM-based regression model it achieved high accuracy in detecting valence (mean square error = 0.43 and R2-score = 0.71) and arousal (mean square error = 0.59 and R2-score = 0.81). Moreover, it was found that even if all biosignals were not used in the training phase, reliable models could be obtained; in fact, for certain study subjects the best results were obtained using only a few of the sensors.

Data-Driven Identification of Long-Term Glycemia Clusters and Their Individualized Predictors in Finnish Patients with Type 2 Diabetes.

Purpose: To gain an understanding of the heterogeneous group of type 2 diabetes (T2D) patients, we aimed to identify patients with the homogenous long-term HbA1c trajectories and to predict the trajectory membership for each patient using explainable machine learning methods and different clinical-, treatment-, and socio-economic-related predictors. Patients and Methods: Electronic health records data covering primary and specialized healthcare on 9631 patients having T2D diagnosis were extracted from the North Karelia region, Finland. Six-year HbA1c trajectories were examined with growth mixture models. Linear discriminant analysis and neural networks were applied to predict the trajectory membership individually. Results: Three HbA1c trajectories were distinguished over six years: "stable, adequate" (86.5%), "improving, but inadequate" (7.3%), and "fluctuating, inadequate" (6.2%) glycemic control. Prior glucose levels, duration of T2D, use of insulin only, use of insulin together with some oral antidiabetic medications, and use of only metformin were the most important predictors for the long-term treatment balance. The prediction model had a balanced accuracy of 85% and a receiving operating characteristic area under the curve of 91%, indicating high performance. Moreover, the results based on SHAP (Shapley additive explanations) values show that it is possible to explain the outcomes of machine learning methods at the population and individual levels. Conclusion: Heterogeneity in long-term glycemic control can be predicted with confidence by utilizing information from previous HbA1c levels, fasting plasma glucose, duration of T2D, and use of antidiabetic medications. In future, the expected development of HbA1c could be predicted based on the patient's unique risk factors offering a practical tool for clinicians to support treatment planning.

Detection of intra-family coronavirus genome sequences through graphical representation and artificial neural network.

In this study, chaos game representation (CGR) is introduced for investigating the pattern of genome sequences. It is an image representation of the genome for the overall visualization of the sequence. The CGR representation is a mapping technique that assigns each sequence base into the respective position in the two-dimension plane to portray the DNA sequence. Importantly, CGR provides one to one mapping to nucleotides as well as sequence. A coordinate of the CGR plane can tell the corresponding base and its location in the original genome. Therefore, the whole nucleotide sequence (until the current nucleotide) can be restored from the one point of the CGR. In this study, CGR coupled with artificial neural network (ANN) is introduced as a new way to represent the genome and to classify intra-coronavirus sequences. A hierarchy clustering study is done to validate the approach and found to be more than 90% accurate while comparing the result with the phylogenetic tree of the corresponding genomes. Interestingly, the method makes the genome sequence significantly shorter (more than 99% compressed) saving the data space while preserving the genome features.

ClinFlow - An Interactive Application for Clinical Data Mining.

Analyzing clinical data comes with many challenges. Medical expertise combined with statistical and programming knowledge must go hand-in-hand when applying data mining methods on clinical datasets. This work aims at bridging the gap between clinical expertise and computer science knowledge by providing an application for clinical data analysis with no requirement for statistical programming knowledge. Our tool allows clinical researchers to conduct data processing and visualization in an interactive environment, thus providing an assisting tool for clinical studies. The application was experimentally evaluated with an analysis of Type 1 Diabetes clinical data. The results obtained with the tool are in line with the domain literature, demonstrating the value of our application in data exploration and hypothesis testing.

Clustering and classification of virus sequence through music communication protocol and wavelet transform.

The coronavirus pandemic became a major risk in global public health. The outbreak is caused by SARS-CoV-2, a member of the coronavirus family. Though the images of the virus are familiar to us, in the present study, an attempt is made to hear the coronavirus by translating its protein spike into audio sequences. The musical features such as pitch, timbre, volume and duration are mapped based on the coronavirus protein sequence. Three different viruses Influenza, Ebola and Coronavirus were studied and compared through their auditory virus sequences by implementing Haar wavelet transform. The sonification of the coronavirus benefits in understanding the protein structures by enhancing the hidden features. Further, it makes a clear difference in the representation of coronavirus compared with other viruses, which will help in various research works related to virus sequence. This evolves as a simplified and novel way of representing the conventional computational methods.

Comparison of Regression and Classification Models for User-Independent and Personal Stress Detection.

In this article, regression and classification models are compared for stress detection. Both personal and user-independent models are experimented. The article is based on publicly open dataset called AffectiveROAD, which contains data gathered using Empatica E4 sensor and unlike most of the other stress detection datasets, it contains continuous target variables. The used classification model is Random Forest and the regression model is Bagged tree based ensemble. Based on experiments, regression models outperform classification models, when classifying observations as stressed or not-stressed. The best user-independent results are obtained using a combination of blood volume pulse and skin temperature features, and using these the average balanced accuracy was 74.1% with classification model and 82.3% using regression model. In addition, regression models can be used to estimate the level of the stress. Moreover, the results based on models trained using personal data are not encouraging showing that biosignals have a lot of variation not only between the study subjects but also between the session gathered from the same person. On the other hand, it is shown that with subject-wise feature selection for user-independent model, it is possible to improve recognition models more than by using personal training data to build personal models. In fact, it is shown that with subject-wise feature selection, the average detection rate can be improved as much as 4%-units, and it is especially useful to reduce the variance in the recognition rates between the study subjects.

Incremental Learning to Personalize Human Activity Recognition Models: The Importance of Human AI Collaboration.

This study presents incremental learning based methods to personalize human activity recognition models. Initially, a user-independent model is used in the recognition process. When a new user starts to use the human activity recognition application, personal streaming data can be gathered. Of course, this data does not have labels. However, there are three different ways to obtain this data: non-supervised, semi-supervised, and supervised. The non-supervised approach relies purely on predicted labels, the supervised approach uses only human intelligence to label the data, and the proposed method for semi-supervised learning is a combination of these two: It uses artificial intelligence (AI) in most cases to label the data but in uncertain cases it relies on human intelligence. After labels are obtained, the personalization process continues by using the streaming data and these labels to update the incremental learning based model, which in this case is Learn++. Learn++ is an ensemble method that can use any classifier as a base classifier, and this study compares three base classifiers: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and classification and regression tree (CART). Moreover, three datasets are used in the experiment to show how well the presented method generalizes on different datasets. The results show that personalized models are much more accurate than user-independent models. On average, the recognition rates are: 87.0% using the user-independent model, 89.1% using the non-supervised personalization approach, 94.0% using the semi-supervised personalization approach, and 96.5% using the supervised personalization approach. This means that by relying on predicted labels with high confidence, and asking the user to label only uncertain observations (6.6% of the observations when using LDA, 7.7% when using QDA, and 18.3% using CART), almost as low error rates can be achieved as by using the supervised approach, in which labeling is fully based on human intelligence.

Lower Limb Pulse Rise Time as a Marker of Peripheral Arterial Disease.

OBJECTIVE: The aim of the study was to show if pulse rise times (PRTs) extracted from photoplethysmographic (PPG) pulse waves (PWs) have an association with peripheral arterial disease (PAD) or its endovascular treatment, percutanoeus transluminal angioplasty (PTA) of the superficial femoral artery. METHODS: Lower and upper limb PPG PWs were recorded and analyzed from 24 patients who suffered from PAD. The measurements were conducted before and after the treatment, and one month later by using transmission-mode PPG-probes placed in the index finger and second toe. Ankle-to-brachial pressure index and toe pressures were used as references in clinical patient measurements. PRTs, i.e., the time from the foot point to the peak point of the PW, were extracted from the PWs and compared bilaterally. The results from the PAD patients were also compared with 31 same-aged and 34 younger control subjects. RESULTS: Statistically significant differences were found between the pretreatment PRTs of the treated limb of the PAD patients and the same-aged control subjects ( , Mann-Whitney U-test). The changes in the PRT of the treated lower limb were observed immediately after the PTA ( , Student's t-test), and after one month ( ), whereas the PRTs of the non-treated lower limb and upper limb did not indicate changes between different examinations. CONCLUSION: Results show that a PRT greater than 240 ms indicates PAD-lesions in the lower limb. SIGNIFICANCE: This proof-of-concept study suggests that the PRT could be an effective and easy-to-use indicator for PAD and monitoring the effectiveness of its treatment.

Using Sleep Time Data from Wearable Sensors for Early Detection of Migraine Attacks.

The migraine is a chronic, incapacitating neurovascular disorder, characterized by attacks of severe headache and autonomic nervous system dysfunction. Among the working age population, the costs of migraine are 111 billion euros in Europe alone. The early detection of migraine attacks would reduce these costs, as it would shorten the migraine attack by enabling correct timing when taking preventive medication. In this article, whether it is possible to detect migraine attacks beforehand using wearable sensors is studied, and t preliminary results about how accurate the recognition can be are provided. The data for the study were collected from seven study subjects using a wrist-worn Empatica E4 sensor, which measures acceleration, galvanic skin response, blood volume pulse, heart rate and heart rate variability, and temperature. Only sleep time data were used in this study. A novel method to increase the number of training samples is introduced, and the results show that, using personal recognition models and quadratic discriminant analysis as a classifier, balanced accuracy for detecting attacks one night prior is over 84%. While this detection rate is high, the results also show that balance accuracy varies greatly between study subjects, which shows how complicated the problem actually is. However, at this point, the results are preliminary as the data set contains only seven study subjects, so these do not cover all migraine types. If the findings of this article can be confirmed in a larger population, it may potentially contribute to early diagnosis of migraine attacks.

Renal carcinoma/kidney progenitor cell chimera organoid as a novel tumorigenesis gene discovery model.

Three-dimensional (3D) organoids provide a new way to model various diseases, including cancer. We made use of recently developed kidney-organ-primordia tissue-engineering technologies to create novel renal organoids for cancer gene discovery. We then tested whether our novel assays can be used to examine kidney cancer development. First, we identified the transcriptomic profiles of quiescent embryonic mouse metanephric mesenchyme (MM) and of MM in which the nephrogenesis program had been induced ex vivo The transcriptome profiles were then compared to the profiles of tumor biopsies from renal cell carcinoma (RCC) patients, and control samples from the same kidneys. Certain signature genes were identified that correlated in the developmentally induced MM and RCC, including components of the caveolar-mediated endocytosis signaling pathway. An efficient siRNA-mediated knockdown (KD) of Bnip3, Gsn, Lgals3, Pax8, Cav1, Egfr or Itgb2 gene expression was achieved in mouse RCC (Renca) cells. The live-cell imaging analysis revealed inhibition of cell migration and cell viability in the gene-KD Renca cells in comparison to Renca controls. Upon siRNA treatment, the transwell invasion capacity of Renca cells was also inhibited. Finally, we mixed E11.5 MM with yellow fluorescent protein (YFP)-expressing Renca cells to establish chimera organoids. Strikingly, we found that the Bnip3-, Cav1- and Gsn-KD Renca-YFP+ cells as a chimera with the MM in 3D organoid rescued, in part, the RCC-mediated inhibition of the nephrogenesis program during epithelial tubules formation. Altogether, our research indicates that comparing renal ontogenesis control genes to the genes involved in kidney cancer may provide new growth-associated gene screens and that 3D RCC-MM chimera organoids can serve as a novel model with which to investigate the behavioral roles of cancer cells within the context of emergent complex tissue structures.

Feasibility of Gamified Mobile Service Aimed at Physical Activation in Young Men: Population-Based Randomized Controlled Study (MOPO).

BACKGROUND: The majority of young people do not meet the recommendations on physical activity for health. New innovative ways to motivate young people to adopt a physically active lifestyle are needed. OBJECTIVE: The study aimed to study the feasibility of an automated, gamified, tailored Web-based mobile service aimed at physical and social activation among young men. METHODS: A population-based sample of 496 young men (mean age 17.8 years [standard deviation 0.6]) participated in a 6-month randomized controlled trial (MOPO study). Participants were randomized to an intervention (n=250) and a control group (n=246). The intervention group was given a wrist-worn physical activity monitor (Polar Active) with physical activity feedback and access to a gamified Web-based mobile service, providing fitness guidelines, tailored health information, advice of youth services, social networking, and feedback on physical activity. Through the trial, the physical activity of the men in the control group was measured continuously with an otherwise similar monitor but providing only the time of day and no feedback. The primary outcome was the feasibility of the service based on log data and questionnaires. Among completers, we also analyzed the change in anthropometry and fitness between baseline and 6 months and the change over time in weekly time spent in moderate to vigorous physical activity. RESULTS: Mobile service users considered the various functionalities related to physical activity important. However, compliance of the service was limited, with 161 (64.4%, 161/250) participants visiting the service, 118 (47.2%, 118/250) logging in more than once, and 41 (16.4%, 41/250) more than 5 times. Baseline sedentary time was higher in those who uploaded physical activity data until the end of the trial (P=.02). A total of 187 (74.8%, 187/250) participants in the intervention and 167 (67.9%, 167/246) in the control group participated in the final measurements. There were no differences in the change in anthropometry and fitness from baseline between the groups, whereas waist circumference was reduced in the most inactive men within the intervention group (P=.01). Among completers with valid physical activity data (n=167), there was a borderline difference in the change in mean daily time spent in moderate to vigorous physical activity between the groups (11.9 min vs -9.1 min, P=.055, linear mixed model). Within the intervention group (n=87), baseline vigorous physical activity was inversely associated with change in moderate to vigorous physical activity during the trial (R=-.382, P=.01). CONCLUSIONS: The various functionalities related to physical activity of the gamified tailored mobile service were considered important. However, the compliance was limited. Within the current setup, the mobile service had no effect on anthropometry or fitness, except reduced waist circumference in the most inactive men. Among completers with valid physical activity data, the trial had a borderline positive effect on moderate to vigorous physical activity. Further development is needed to improve the feasibility and adherence of an integrated multifunctional service. TRIAL REGISTRATION: Clinicaltrials.gov NCT01376986; http://clinicaltrials.gov/ct2/show/NCT01376986 (Archived by WebCite at http://www.webcitation.org/6tjdmIroA).

Combining finger and toe photoplethysmograms for the detection of atherosclerosis.

In this study, we propose and analyze a noninvasive method for detecting the atherosclerotic changes of vasculature based on the analysis of photoplethysmographic (PPG) signals. METHODS: the proposed method is called finger-toe (FT)-plot analysis that utilizes both finger and toe PPG signals. For the features extracted from the FT-plots, we implemented different linear discriminant analysis based classifiers and analyzed seven promising ones in detail. We used the signals recorded from altogether 75 test subjects (categorized as 27 atherosclerotic patients and 48 control subjects based on ankle brachial pressure index, symptoms and disease history) in the training, and testing of the method. Besides leave one out cross validation, we tested the method by using training data independent signals recorded with two different PPG devices. The performance of the FT-plot is compared with other indicators related to the risk of cardiovascular diseases. RESULTS: we found an average area under ROC (receiver operating characteristic) curve of [Formula: see text] (mean ± standard deviation based on different datasets), sensitivity of [Formula: see text], specificity of [Formula: see text], accuracy of [Formula: see text], performance of [Formula: see text] and positive and negative predictive values of [Formula: see text] and [Formula: see text], respectively, for the different tested classifiers. CONCLUSIONS: the study shows that the FT-plot analysis could be a useful additional tool for detecting atherosclerotic changes. Our findings provide evidence for the utility of multi-channel pulse wave measurements and analysis for the detection of atherosclerosis. This may facilitate development of novel early diagnostic approaches and preventive strategies.

Monitoring arterial pulse waves with synchronous body sensor network.

A wireless body sensor network for arterial pulse wave (PW) measurements is presented and tested with ten subjects. The system is capable of recording both mechanical PW contours with sensors made of a low-cost polypropylene-based material called electromechanical film (EMFi) and volume pulse signal with photoplethysmographic transducers. By using both types of sensors, the PW contours can be recorded from various locations. The system combined with automatic analysis methods enables to easily analyze the PW contours in order to obtain a more comprehensive view on the vascular health. To demonstrate this, two parameters used in literature, reflection index and radial augmentation index were calculated for the test subjects as a function of time. The results show that these parameter values may vary more than 20% in a period of 100 s, which suggests that a large number of PWs should be analyzed before making conclusions based on the calculated indices. In addition, the effects of the static bias force to the mechanical PW signal recorded with the EMFi sensors were studied. The PW signal with the maximum amplitude is obtained when the pressure caused by the static bias force corresponds to the contact pressure between typical systolic and diastolic blood pressures. The EMFi sensors used in the proposed system are a potential low-cost alternative for tonometric sensors in collecting data in the PW analysis for arterial screening.

Gamified physical activation of young men--a Multidisciplinary Population-Based Randomized Controlled Trial (MOPO study).

BACKGROUND: Inactive and unhealthy lifestyles are common among adolescent men. The planned intervention examines the effectiveness of an interactive, gamified activation method, based on tailored health information, peer networks and participation, on physical activity, health and wellbeing in young men. We hypothesize that following the intervention the physical activation group will have an improved physical activity, as well as self-determined and measured health compared with the controls. METHODS/DESIGN: Conscription-aged men (18 years) attending compulsory annual call-ups for military service in the city of Oulu in Finland (n = 1500) will be randomized to a 6-months intervention (n = 640) or a control group (n = 640) during the fall 2013. A questionnaire on health, health behaviour, diet and wellbeing is administered in the beginning and end of the intervention. In addition, anthropometric measures (height, weight and waist circumference), body composition, grip strength, heart rate variability and aerobic fitness will be measured. The activation group utilizes an online gamified activation method in combination with communal youth services, objective physical activity measurement, social networking, tailored health information and exercise programs according to baseline activity level and the readiness of changes of each individual. Daily physical activity of the participants is monitored in both the activation and control groups. The activation service rewards improvements in physical activity or reductions in sedentary behaviour. The performance and completion of the military service of the participants will also be followed. DISCUSSION: The study will provide new information of physical activity, health and health behaviour of young men. Furthermore, a novel model including methods for increasing physical activity among young people is developed and its effects tested through an intervention. This unique gamified service for activating young men can provide a translational model for community use. It can also be utilized as such or tailored to other selected populations or age groups. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01376986.

Comparison of clinical and computerized image analyses in the assessment of skin ageing in smokers and non-smokers.

Tobacco smoke and UV radiation are extrinsic risk factors for accelerated skin ageing. In this study the effects of smoking on wrinkling and ageing were assessed in males living in Northern Finland, where cumulative sun exposure is low. Smoking habits, age and facial wrinkling were estimated from facial photographs of 41 smokers and 48 non-smokers by eight panellists, using a blinded standardized assessment. Wrinkling of 26 smokers and 31 non-smokers was also assessed by computerized image analysis. The panellists identified 68% of the smokers correctly as being smokers and the smokers were estimated as being an average of 2.1 years older than their age by the panellists, whereas the non-smokers were estimated as being an average of 0.7 years younger than their age (p < 0.05). No significant difference in skin wrinkling was found between the groups by either clinical assessment or by computerized image analysis. In conclusion, even in the absence of increased wrinkling, the smokers looked older than their age and a majority of them could be identified as smokers by their facial features alone.