Genetic Regulation of Physical Fitness in Children: A Twin Study of 15 Tests from Eurofit and Fitnessgram Test Batteries.

PURPOSE: This study aimed to analyze the shared genetic background of physical fitness tests in children. METHODS: Physical fitness was assessed in 198 Portuguese twin pairs (6-18 yr old, 40% monozygotic) through 15 tests from the Eurofit and Fitnessgram test batteries. Genetic twin modeling was used to estimate the heritability of each test and the genetic correlations between them. RESULTS: Girls performed better than boys in flexibility, whereas boys performed better than girls in cardiorespiratory endurance and muscular strength. No sex differences were found in the influence of genetic factors on the physical fitness tests or their mutual correlations. Genetic factors explained 52% (standing long jump) to 79% (sit and reach) of the individual variation in motor performance, whereas individual-specific environmental factors explained the remaining variation. Most of the tests showed modest to moderate genetic correlations. Out of all 105 genetic correlations, 65% ranged from 0.2 to 0.6 indicating that they shared from 4% to 36% of genetic variation. The correlations between individual-specific environmental factors were mostly negligible. CONCLUSIONS: Tests measuring the strength of different muscle groups showed only modest correlations, but moderate correlations were found between tests measuring explosive strength, running speed/agility, and cardiorespiratory endurance. Genetic factors explained a major portion of the variation in tests included in the Eurofit and Fitnessgram test batteries and explained the correlations between them. The modest to moderate genetic correlations indicated that there is little redundancy of tests in either Eurofit or Fitnessgram test batteries.

Interrelationships among skeletal age, growth status and motor performances in female athletes 10-15 years.

BACKGROUND: Motor performances of youth are related to growth and maturity status, among other factors. AIM: To estimate the contribution of skeletal maturity status per se to the motor performances of female athletes aged 10-15 years and the mediation effects of growth status on the relationships. SUBJECTS AND METHODS: Skeletal age (TW3 RUS SA), body size, proportions, estimated fat-free mass (FFM), motor performances, training history and participation motivation were assessed in 80 non-skeletally mature female participants in several sports. Hierarchical and regression-based statistical mediation analyses were used. RESULTS: SA per se explained a maximum of 1.8% and 5.8% of the variance in motor performances of athletes aged 10-12 and 13-15 years, respectively, over and above that explained by covariates. Body size, proportions, and hours per week of training and participation motivation explained, respectively, a maximum of 40.7%, 18.8%, and 22.6% of the variance in performances. Mediation analysis indicated specific indirect effects of SA through stature and body mass, alone or in conjunction with FFM on performances. CONCLUSION: SA per se accounted for small and non-significant amounts of variance in several motor performances of female youth athletes; rather, SA influenced performances indirectly through effects on stature, body mass and estimated FFM.

Novel scFv against Notch Ligand JAG1 Suitable for Development of Cell Therapies toward JAG1-Positive Tumors.

The Notch signaling ligand JAG1 is overexpressed in various aggressive tumors and is associated with poor clinical prognosis. Hence, therapies targeting oncogenic JAG1 hold great potential for the treatment of certain tumors. Here, we report the identification of specific anti-JAG1 single-chain variable fragments (scFvs), one of them endowing chimeric antigen receptor (CAR) T cells with cytotoxicity against JAG1-positive cells. Anti-JAG1 scFvs were identified from human phage display libraries, reformatted into full-length monoclonal antibodies (Abs), and produced in mammalian cells. The characterization of these Abs identified two specific anti-JAG1 Abs (J1.B5 and J1.F1) with nanomolar affinities. Cloning the respective scFv sequences in our second- and third-generation CAR backbones resulted in six anti-JAG1 CAR constructs, which were screened for JAG1-mediated T-cell activation in Jurkat T cells in coculture assays with JAG1-positive cell lines. Studies in primary T cells demonstrated that one CAR harboring the J1.B5 scFv significantly induced effective T-cell activation in the presence of JAG1-positive, but not in JAG1-knockout, cancer cells, and enabled specific killing of JAG1-positive cells. Thus, this new anti-JAG1 scFv represents a promising candidate for the development of cell therapies against JAG1-positive tumors.

Genetic regulation of body size and morphology in children: a twin study of 22 anthropometric traits.

BACKGROUND: Anthropometric measures show high heritability, and genetic correlations have been found between obesity-related traits. However, we lack a comprehensive analysis of the genetic background of human body morphology using detailed anthropometric measures. METHODS: Height, weight, 7 skinfold thicknesses, 7 body circumferences and 4 body diameters (skeletal breaths) were measured in 214 pairs of twin children aged 3-18 years (87 monozygotic pairs) in the Autonomous Region of Madeira, Portugal. Factor analysis (Varimax rotation) was used to analyze the underlying structure of body physique. Genetic twin modeling was used to estimate genetic and environmental contributions to the variation and co-variation of the anthropometric traits. RESULTS: Together, two factors explained 80% of the variation of all 22 anthropometric traits in boys and 73% in girls. Obesity measures (body mass index, skinfold thickness measures, as well as waist and hip circumferences) and limb circumferences loaded most strongly on the first factor, whereas height and body diameters loaded especially on the second factor. These factors as well as all anthropometric measures showed high heritability (80% or more for most of the traits), whereas the rest of the variation was explained by environmental factors not shared by co-twins. Obesity measures showed high genetic correlations (0.75-0.98). Height showed the highest genetic correlations with body diameter measures (0.58-0.76). Correlations between environmental factors not shared by co-twins were weaker than the genetic correlations but still substantial. The correlation patterns were roughly similar in boys and girls. CONCLUSIONS: Our results show high genetic correlations underlying the human body physique, suggesting that there are sets of genes widely affecting anthropometric traits. Better knowledge of these genetic variants can help to understand the development of obesity and other features of the human physique.

Connected Mental Health Solutions: Global Attitudes, Preferences, and Concerns.

Background: Connected mental health (CMH) presents several technology-based solutions, which can help overcome many mental care delivery barriers. However, attitudes toward the use of CMH are diverse and differ from a cohort to another. Objective: The purpose of this study is to investigate the global attitudes toward CMH use and assess the use of technology for mental care. Methods: This study presents a synthesis of literature available in Scopus, Science Direct, and PubMed digital libraries, investigating attitudes toward CMH in different cohorts from different countries, based on a systematic review of relevant publications. This study also analyzes technology use patterns of the cohorts investigated, the reported preferred criteria that should be considered in CMH, and issues and concerns regarding CMH use. Results: One hundred and one publications were selected and analyzed. These publications were originated from different countries, with the majority (n = 23) being conducted in Australia. These studies reported positive attitudes of investigated cohorts toward CMH use and high technology use and ownership. Several preferred criteria were reported, mainly revolving around providing blended care functionalities, educational content, and mental health professionals (MHPs) support. Whereas concerns and issues related to CMH use addressed technical problems related to access to technology and to CMH solutions, the digital divide, lack of knowledge and use of CMH, and general reservations to use CMH. Concerns related to institutional and work barriers were also identified. Conclusions: Attitudes toward CMH show promising results from users and MHP views. However, factors such as providing blended care options and considering technical concerns should be taken into consideration for the successful adoption of CMH.

Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review.

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Chest X-ray analysis empowered with deep learning: A systematic review.

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.

First Documentation of Exophiala spp. Isolation in Psittaciformes.

Several fungi species are reported to act as opportunistic agents of infection in avian species. After the isolation of Exophiala spp., a dematiaceous fungal pathogen associated with a mucosal lesion in a military macaw (Ara militar), samples were collected from another 24 birds of the order Psittaciformes to study the possibility of Exophiala spp. being part of the commensal microbiota of these animals or its possible association with other clinical conditions. Swab samples were collected from the trachea and/or choanae of the birds and inoculated in Sabouraud chloramphenicol agar for fungal isolation. After incubation, fungal species were identified through their macroscopic and microscopic morphology. The presence of Exophiala spp. was identified in 15 of the 25 birds sampled and no statistical association was found between the clinical record of the birds and the fungal isolation. Our results suggest that Exophiala spp. can colonize the upper respiratory airways of psittaciform birds and has a low pathogenic potential in these animals. To the authors' knowledge, this is the first report of Exophiala spp. isolation from samples of the upper respiratory tract of Psittaciformes.

Impacts of ocean warming and acidification on the energy budget of three commercially important fish species.

A mechanistic model based on Dynamic Energy Budget (DEB) theory was developed to predict the combined effects of ocean warming, acidification and decreased food availability on growth and reproduction of three commercially important marine fish species: white seabream (Diplodus sargus), zebra seabream (Diplodus cervinus) and Senegalese sole (Solea senegalensis). Model simulations used a parameter set for each species, estimated by the Add-my-Pet method using data from laboratory experiments complemented with bibliographic sources. An acidification stress factor was added as a modifier of the somatic maintenance costs and estimated for each species to quantify the effect of a decrease in pH from 8.0 to 7.4 (white seabream) or 7.7 (zebra seabream and Senegalese sole). The model was used to project total length of individuals along their usual lifespan and number of eggs produced by an adult individual within one year, under different climate change scenarios for the end of the 21st century. For the Intergovernmental Panel on Climate Change SSP5-8.5, ocean warming led to higher growth rates during the first years of development, as well as an increase of 32-34% in egg production, for the three species. Ocean acidification contributed to reduced growth for white seabream and Senegalese sole and a small increase for zebra seabream, as well as a decrease in egg production of 48-52% and 14-33% for white seabream and Senegalese sole, respectively, and an increase of 4-5% for zebra seabream. The combined effect of ocean warming and acidification is strongly dependent on the decrease of food availability, which leads to significant reduction in growth and egg production. This is the first study to assess the combined effects of ocean warming and acidification using DEB models on fish, therefore, further research is needed for a better understanding of these climate change-related effects among different taxonomic groups and species.

Comparison of Machine Learning Algorithms in the Prediction of Hospitalized Patients with Schizophrenia.

New computational methods have emerged through science and technology to support the diagnosis of mental health disorders. Predictive models developed from machine learning algorithms can identify disorders such as schizophrenia and support clinical decision making. This research aims to compare the performance of machine learning algorithms: Decision Tree, AdaBoost, Random Forest, Naïve Bayes, Support Vector Machine, and k-Nearest Neighbor in the prediction of hospitalized patients with schizophrenia. The data set used in the study contains a total of 11,884 electronic admission records corresponding to 6933 patients with various mental health disorders; these records belong to the acute units of 11 public hospitals in a region of Spain. Of the total, 5968 records correspond to patients diagnosed with schizophrenia (3002 patients) and 5916 records correspond to patients with other mental health disorders (3931 patients). The results recommend Random Forest with the best accuracy of 72.7%. Furthermore, this algorithm presents 79.6%, 72.8%, 72.7%, and 72.7% for AUC, precision, F1-Score, and recall, respectively. The results obtained suggest that the use of machine learning algorithms can classify hospitalized patients with schizophrenia in this population and help in the hospital management of this type of disorder, to reduce the costs associated with hospitalization.

An ensemble-based approach for automated medical diagnosis of malaria using EfficientNet.

Each year, more than 400,000 people die of malaria. Malaria is a mosquito-borne transmissible infection that affects humans and other animals. According to World Health Organization (WHO), 1.5 billion malaria cases and 7.6 million related deaths have been prevented from 2000 to 2019. Malaria is a disease that can be treated if early detected. We propose a support decision system for detecting malaria from microscopic peripheral blood cells images through convolutional neural networks (CNN). The proposed model is based on EfficientNetB0 architecture. The results are validated with 10-fold stratified cross-validation. This paper presents the classification findings using images from malaria patients and normal patients. The proposed approach is compared and outperforms the related work. Furthermore, the proposed ensemble method shows a recall value of 98.82%, a precision value of 97.74%, an F1-score of 98.28% and a ROC value of 99.76%. This work suggests that EfficientNet is a reliable architecture for automatic medical diagnostics of malaria. Supplementary Information: The online version contains supplementary material available at 10.1007/s11042-022-12624-6.

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

Telemedicine solutions for patients with mental disorders: a Delphi study and review of mobile applications in virtual stores.

Mental disorders are a critical public health challenge since they profoundly affected people lifestyle. Mental healthcare treatments aim to promote a higher quality of life of the patients. These procedures include interventions for prolonged mental illness which can be supported by telemedicine technologies. This paper presents a comprehensive analysis of mobile applications selected to address the most critical needs of people with mental problems. Needs include areas of the patient's life, such as basic activities, behavioral changes, and daily life tasks. This work has two main objectives; (1) identify critical needs for patients with mental disorders and (2) identify and analyze apps that can meet the identified critical needs. A Delphi methodology survey was carried with a group of thirteen volunteers, including nurses, assistants, and psychiatrists who are working in Zamora and Valladolid, Spain. This survey has recommended different needs for patients with mental disorders and address objective 1. Google Play and Apple Store have been assessed to select the most relevant mobile applications that were recommended in the Delphi study to address the essential needs of these patients according to objective 2. The results of the Delphi survey show 24 different needs for patients with mental disorders. This study has analyzed 62 mobile applications which address the essential needs recommended in the Delphi study. The selected mobile applications represent 31 applications with feedback (50%); 15 informative applications (24%), and 16 independent applications (26%). On the one hand, applications with feedback request can address 13 recommended needs (54%). On the other hand, informative applications can address 7 needs (29%). Finally, the independent applications are only able to respond to 4 of the 24 recommend needs (17%). Mobile health applications present effective technologies to support the needs of patients with mental disorders. However, this study suggests a critical limitation of mobile applications for mental health since the majority of the applications require user activity. Therefore, future research initiatives on the design and development of mobile apps for people who have mental disorders should focus on independent applications.

Transfer Learning for Alzheimer's Disease through Neuroimaging Biomarkers: A Systematic Review.

Alzheimer's disease (AD) is a remarkable challenge for healthcare in the 21st century. Since 2017, deep learning models with transfer learning approaches have been gaining recognition in AD detection, and progression prediction by using neuroimaging biomarkers. This paper presents a systematic review of the current state of early AD detection by using deep learning models with transfer learning and neuroimaging biomarkers. Five databases were used and the results before screening report 215 studies published between 2010 and 2020. After screening, 13 studies met the inclusion criteria. We noted that the maximum accuracy achieved to date for AD classification is 98.20% by using the combination of 3D convolutional networks and local transfer learning, and that for the prognostic prediction of AD is 87.78% by using pre-trained 3D convolutional network-based architectures. The results show that transfer learning helps researchers in developing a more accurate system for the early diagnosis of AD. However, there is a need to consider some points in future research, such as improving the accuracy of the prognostic prediction of AD, exploring additional biomarkers such as tau-PET and amyloid-PET to understand highly discriminative feature representation to separate similar brain patterns, managing the size of the datasets due to the limited availability.

Machine Learning in Medical Emergencies: a Systematic Review and Analysis.

Despite the increasing demand for artificial intelligence research in medicine, the functionalities of his methods in health emergency remain unclear. Therefore, the authors have conducted this systematic review and a global overview study which aims to identify, analyse, and evaluate the research available on different platforms, and its implementations in healthcare emergencies. The methodology applied for the identification and selection of the scientific studies and the different applications consist of two methods. On the one hand, the PRISMA methodology was carried out in Google Scholar, IEEE Xplore, PubMed ScienceDirect, and Scopus. On the other hand, a review of commercial applications found in the best-known commercial platforms (Android and iOS). A total of 20 studies were included in this review. Most of the included studies were of clinical decisions (n = 4, 20%) or medical services or emergency services (n = 4, 20%). Only 2 were focused on m-health (n = 2, 10%). On the other hand, 12 apps were chosen for full testing on different devices. These apps dealt with pre-hospital medical care (n = 3, 25%) or clinical decision support (n = 3, 25%). In total, half of these apps are based on machine learning based on natural language processing. Machine learning is increasingly applicable to healthcare and offers solutions to improve the efficiency and quality of healthcare. With the emergence of mobile health devices and applications that can use data and assess a patient's real-time health, machine learning is a growing trend in the healthcare industry.

Comparison of machine learning techniques for the identification of human activities from inertial sensors available in a mobile device after the application of data imputation techniques.

Human activity recognition (HAR) is a significant research area due to its wide range of applications in intelligent health systems, security, and entertainment games. Over the past few years, many studies have recognized human daily living activities using different machine learning approaches. However, the performance of a machine learning algorithm varies based on the sensing device type, the number of sensors in that device, and the position of the underlying sensing device. Moreover, the incomplete activities (i.e., data captures) in a dataset also play a crucial role in the performance of machine learning algorithms. Therefore, we perform a comparative analysis of eight commonly used machine learning algorithms in different sensor combinations in this work. We used a publicly available mobile sensors dataset and applied the k-Nearest Neighbors (KNN) data imputation technique for extrapolating the missing samples. Afterward, we performed a couple of experiments to figure out which algorithm performs best at which sensors' data combination. The experimental analysis reveals that the AdaBoost algorithm outperformed all machine learning algorithms for recognizing five different human daily living activities with both single and multi-sensor combinations. Furthermore, the experimental results show that AdaBoost is capable to correctly identify all the activities presented in the dataset with 100% classification accuracy.

The use of posaconazole delayed-release tablets in the successful treatment of suspected mucormycosis in a bottlenose dolphin (Tursiops truncatus) calf.

Mucorales infections in cetaceans have a high mortality rate. This case report refers to a bottlenose dolphin calf with suspected mucormycosis treated with posaconazole. This antifungal agent was discontinued after 96 days of therapy, however, the infection relapsed. Posaconazole was then resumed for a total of 255 days, with no signs of disease reactivation. The retrospective analysis of posaconazole serum levels in this successful case showed concentrations varying between 5.18 and 11.63 mg/L.

Radiological Scouting, Monitoring and Inspection Using Drones.

Human populations and natural ecosystems are bound to be exposed to ionizing radiation from the deposition of artificial radionuclides resulting from nuclear accidents, nuclear devices or radiological dispersive devices ("dirty bombs"). On the other hand, Naturally Occurring Radioactive Material industries such as phosphate production or uranium mining, contribute to the on site storage of residuals with enhanced concentrations of natural radionuclides. Therefore, in the context of the European agreements concerning nuclear energy, namely the European Atomic Energy Community Treaty, monitoring is an essential feature of the environmental radiological surveillance. In this work, we obtain 3D maps from outdoor scenarios, and complete such maps with measured radiation levels and with its radionuclide signature. In such scenarios, we face challenges such as unknown and rough terrain, limited number of sampled locations and the need for different sensors and therefore different tasks. We propose a radiological solution for scouting, monitoring and inspecting an area of interest, using a fleet of drones and a controlling ground station. First, we scout an area with a Light Detection and Ranging sensor onboard a drone to accurately 3D-map the area. Then, we monitor that area with a Geiger-Müller Counter at a low-vertical distance from the ground to produce a radiological (heat)map that is overlaid on the 3D map of the scenario. Next, we identify the hotspots of radiation, and inspect them in detail using a drone by landing on them, to reveal its radionuclide signature using a Cadmium-Zinc-Telluride detector. We present the algorithms used to implement such tasks both at the ground station and on the drones. The three mission phases were validated using actual experiments in three different outdoor scenarios. We conclude that drones can not only perform the mission efficiently, but in general they are faster and as reliable as personnel on the ground.

Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review.

COVID-19 had led to severe clinical manifestations. In the current scenario, 98 794 942 people are infected, and it has responsible for 2 124 193 deaths around the world as reported by World Health Organization on 25 January 2021. Telemedicine has become a critical technology for providing medical care to patients by trying to reduce transmission of the virus among patients, families, and doctors. The economic consequences of coronavirus have affected the entire world and disrupted daily life in many countries. The development of telemedicine applications and eHealth services can significantly help to manage pandemic worldwide better. Consequently, the main objective of this paper is to present a systematic review of the implementation of telemedicine and e-health systems in the combat to COVID-19. The main contribution is to present a comprehensive description of the state of the art considering the domain areas, organizations, funding agencies, researcher units and authors involved. The results show that the United States and China have the most significant number of studies representing 42.11% and 31.58%, respectively. Furthermore, 35 different research units and 9 funding agencies are involved in the application of telemedicine systems to combat COVID-19.

Quantifying energetic and fitness consequences of seasonal heterothermy in an Arctic ungulate.

Animals have adapted behavioral and physiological strategies to conserve energy during periods of adverse conditions. Heterothermy is one such adaptation used by endotherms. While heterothermy-fluctuations in body temperature and metabolic rate-has been shown in large vertebrates, little is known of the costs and benefits of this strategy, both in terms of energy and in terms of fitness. Hence, our objective was to model the energetics of seasonal heterothermy in the largest Arctic ungulate, the muskox (Ovibos moschatus), using an individual-based energy budget model of metabolic physiology. We found that the empirically based drop in body temperature (winter max ~-0.8°C) overwinter in adult females resulted in substantial fitness benefits in terms of reduced daily energy expenditure and body mass loss. Body mass and energy reserves were 8.98% and 14.46% greater in modeled heterotherms compared to normotherms by end of winter. Based on environmental simulations, we show that seasonal heterothermy can, to some extent, buffer the negative consequences of poor prewinter body condition or reduced winter food accessibility, leading to greater winter survival (+20%-30%) and spring energy reserves (+10%-30%), and thus increased probability of future reproductive success. These results indicate substantial adaptive short-term benefits of seasonal heterothermy at the individual level, with potential implications for long-term population dynamics in highly seasonal environments.