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1.
Neuroimage ; 269: 119898, 2023 04 01.
Artículo en Inglés | MEDLINE | ID: mdl-36702211

RESUMEN

Generative adversarial networks (GANs) are one powerful type of deep learning models that have been successfully utilized in numerous fields. They belong to the broader family of generative methods, which learn to generate realistic data with a probabilistic model by learning distributions from real samples. In the clinical context, GANs have shown enhanced capabilities in capturing spatially complex, nonlinear, and potentially subtle disease effects compared to traditional generative methods. This review critically appraises the existing literature on the applications of GANs in imaging studies of various neurological conditions, including Alzheimer's disease, brain tumors, brain aging, and multiple sclerosis. We provide an intuitive explanation of various GAN methods for each application and further discuss the main challenges, open questions, and promising future directions of leveraging GANs in neuroimaging. We aim to bridge the gap between advanced deep learning methods and neurology research by highlighting how GANs can be leveraged to support clinical decision making and contribute to a better understanding of the structural and functional patterns of brain diseases.


Asunto(s)
Enfermedad de Alzheimer , Neurociencias , Humanos , Neuroimagen , Envejecimiento , Encéfalo
2.
J Ultrasound Med ; 42(10): 2183-2213, 2023 Oct.
Artículo en Inglés | MEDLINE | ID: mdl-37148467

RESUMEN

Non-invasive ultrasound (US) imaging enables the assessment of the properties of superficial blood vessels. Various modes can be used for vascular characteristics analysis, ranging from radiofrequency (RF) data, Doppler- and standard B/M-mode imaging, to more recent ultra-high frequency and ultrafast techniques. The aim of the present work was to provide an overview of the current state-of-the-art non-invasive US technologies and corresponding vascular ageing characteristics from a technological perspective. Following an introduction about the basic concepts of the US technique, the characteristics considered in this review are clustered into: 1) vessel wall structure; 2) dynamic elastic properties, and 3) reactive vessel properties. The overview shows that ultrasound is a versatile, non-invasive, and safe imaging technique that can be adopted for obtaining information about function, structure, and reactivity in superficial arteries. The most suitable setting for a specific application must be selected according to spatial and temporal resolution requirements. The usefulness of standardization in the validation process and performance metric adoption emerges. Computer-based techniques should always be preferred to manual measures, as long as the algorithms and learning procedures are transparent and well described, and the performance leads to better results. Identification of a minimal clinically important difference is a crucial point for drawing conclusions regarding robustness of the techniques and for the translation into practice of any biomarker.


Asunto(s)
Arterias , Ultrasonografía Doppler , Humanos , Ultrasonografía/métodos , Arterias/diagnóstico por imagen , Algoritmos , Tecnología
3.
J Med Internet Res ; 25: e42519, 2023 02 06.
Artículo en Inglés | MEDLINE | ID: mdl-36745490

RESUMEN

BACKGROUND: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. OBJECTIVE: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. METHODS: The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. RESULTS: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). CONCLUSIONS: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics.


Asunto(s)
Enfermedades Transmisibles , Gripe Humana , Medios de Comunicación Sociales , Humanos , Gripe Humana/epidemiología , Memoria a Corto Plazo , Reproducibilidad de los Resultados , Tiempo (Meteorología)
4.
Sensors (Basel) ; 22(15)2022 Aug 04.
Artículo en Inglés | MEDLINE | ID: mdl-35957374

RESUMEN

Patients usually deviate from prescribed medication schedules and show reduced adherence. Even when the adherence is sufficient, there are conditions where the medication schedule should be modified. Crucial drug-drug, food-drug, and supplement-drug interactions can lead to treatment failure. We present the development of an internet of medical things (IoMT) platform to improve medication adherence and enable remote treatment modifications. Based on photos of food and supplements provided by the patient, using a camera integrated to a portable 3D-printed low-power pillbox, dangerous interactions with treatment medicines can be detected and prevented. We compare the medication adherence of 14 participants following a complex medication schedule using a functional prototype that automatically receives remote adjustments, to a dummy pillbox where the adjustments are sent with text messages. The system usability scale (SUS) score was 86.79, which denotes excellent user acceptance. Total errors (wrong/no pill) between the functional prototype and the dummy pillbox did not demonstrate any statistically significant difference (p = 0.57), but the total delay of the intake time was higher (p = 0.03) during dummy pillbox use. Thus, the proposed low-cost IoMT pillbox improves medication adherence even with a complex regimen while supporting remote dose adjustment.


Asunto(s)
Internet , Cumplimiento de la Medicación , Humanos
5.
Sensors (Basel) ; 22(7)2022 Mar 23.
Artículo en Inglés | MEDLINE | ID: mdl-35408088

RESUMEN

In this article, an unobtrusive and affordable sensor-based multimodal approach for real time recognition of engagement in serious games (SGs) for health is presented. This approach aims to achieve individualization in SGs that promote self-health management. The feasibility of the proposed approach was investigated by designing and implementing an experimental process focusing on real time recognition of engagement. Twenty-six participants were recruited and engaged in sessions with a SG that promotes food and nutrition literacy. Data were collected during play from a heart rate sensor, a smart chair, and in-game metrics. Perceived engagement, as an approximation to the ground truth, was annotated continuously by participants. An additional group of six participants were recruited for smart chair calibration purposes. The analysis was conducted in two directions, firstly investigating associations between identified sitting postures and perceived engagement, and secondly evaluating the predictive capacity of features extracted from the multitude of sources towards the ground truth. The results demonstrate significant associations and predictive capacity from all investigated sources, with a multimodal feature combination displaying superiority over unimodal features. These results advocate for the feasibility of real time recognition of engagement in adaptive serious games for health by using the presented approach.


Asunto(s)
Juegos de Video , Humanos , Postura
6.
Europace ; 23(1): 99-103, 2021 01 27.
Artículo en Inglés | MEDLINE | ID: mdl-33038213

RESUMEN

AIMS: Cardiac implantable electronic devices (CIEDs) are susceptible to electromagnetic interference (EMI). Smartwatches and their chargers could be a possible source of EMI. We sought to assess whether the latest generation smartwatches and their chargers interfere with proper CIED function. METHODS AND RESULTS: We included consecutive CIED recipients in two centres. We tested two latest generation smartwatches (Apple Watch and Samsung Galaxy Watch) and their charging cables for potential EMI. The testing was performed under continuous electrocardiogram recording and real-time device telemetry, with nominal and 'worst-case' settings. In vitro magnetic field measurements were performed to assess the emissions from the tested devices, initially in contact with the probe and then at a distance of 10 cm and 20 cm. In total, 171 patients with CIEDs (71.3% pacemakers-28.7% implantable cardioverter-defibrillators) from five manufacturers were enrolled (63.2% males, 74.8 ± 11.4 years), resulting in 684 EMI tests. No EMI was identified in any patient either under nominal or 'worst-case scenario' programming. The peak magnetic flux density emitted by the smartwatches was similar to the background noise level (0.81 µT) even when in contact with the measuring probe. The respective values for the chargers were 4.696 µΤ and 4.299 µΤ for the Samsung and Apple chargers, respectively, which fell at the background noise level when placed at 20 cm and 10 cm, respectively. CONCLUSION: Two latest generation smartwatches and their chargers resulted in no EMI in CIED recipients. The absence of EMI in conjunction with the extremely low intensity of magnetic fields emitted by these devices support the safety of their use by CIED patients.


Asunto(s)
Desfibriladores Implantables , Marcapaso Artificial , Suministros de Energía Eléctrica , Campos Electromagnéticos/efectos adversos , Electrónica , Femenino , Humanos , Campos Magnéticos , Masculino
7.
BMC Med Inform Decis Mak ; 19(1): 163, 2019 08 16.
Artículo en Inglés | MEDLINE | ID: mdl-31419982

RESUMEN

BACKGROUND: To understand user needs, system requirements and organizational conditions towards successful design and adoption of Clinical Decision Support Systems for Type 2 Diabetes (T2D) care built on top of computerized risk models. METHODS: The holistic and evidence-based CEHRES Roadmap, used to create eHealth solutions through participatory development approach, persuasive design techniques and business modelling, was adopted in the MOSAIC project to define the sequence of multidisciplinary methods organized in three phases, user needs, implementation and evaluation. The research was qualitative, the total number of participants was ninety, about five-seventeen involved in each round of experiment. RESULTS: Prediction models for the onset of T2D are built on clinical studies, while for T2D care are derived from healthcare registries. Accordingly, two set of DSSs were defined: the first, T2D Screening, introduces a novel routine; in the second case, T2D Care, DSSs can support managers at population level, and daily practitioners at individual level. In the user needs phase, T2D Screening and solution T2D Care at population level share similar priorities, as both deal with risk-stratification. End-users of T2D Screening and solution T2D Care at individual level prioritize easiness of use and satisfaction, while managers prefer the tools to be available every time and everywhere. In the implementation phase, three Use Cases were defined for T2D Screening, adapting the tool to different settings and granularity of information. Two Use Cases were defined around solutions T2D Care at population and T2D Care at individual, to be used in primary or secondary care. Suitable filtering options were equipped with "attractive" visual analytics to focus the attention of end-users on specific parameters and events. In the evaluation phase, good levels of user experience versus bad level of usability suggest that end-users of T2D Screening perceived the potential, but they are worried about complexity. Usability and user experience were above acceptable thresholds for T2D Care at population and T2D Care at individual. CONCLUSIONS: By using a holistic approach, we have been able to understand user needs, behaviours and interactions and give new insights in the definition of effective Decision Support Systems to deal with the complexity of T2D care.


Asunto(s)
Sistemas de Apoyo a Decisiones Clínicas , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/etiología , Adulto , Anciano , Simulación por Computador , Femenino , Humanos , Masculino , Tamizaje Masivo , Persona de Mediana Edad , Medición de Riesgo , Programas Informáticos , Telemedicina
8.
Brief Bioinform ; 17(2): 322-35, 2016 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-26197808

RESUMEN

Alarming epidemiological features of Alzheimer's disease impose curative treatment rather than symptomatic relief. Drug repurposing, that is reappraisal of a substance's indications against other diseases, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. In this study, we have used gene signatures, where up- and down-regulated gene lists summarize a cell's gene expression perturbation from a drug or disease. To cope with the inherent biological and computational noise, we used an integrative approach on five disease-related microarray data sets of hippocampal origin with three different methods of evaluating differential gene expression and four drug repurposing tools. We found a list of 27 potential anti-Alzheimer agents that were additionally processed with regard to molecular similarity, pathway/ontology enrichment and network analysis. Protein kinase C, histone deacetylase, glycogen synthase kinase 3 and arginase inhibitors appear consistently in the resultant drug list and may exert their pharmacologic action in an epidermal growth factor receptor-mediated subpathway of Alzheimer's disease.


Asunto(s)
Enfermedad de Alzheimer/tratamiento farmacológico , Enfermedad de Alzheimer/metabolismo , Reposicionamiento de Medicamentos/métodos , Proteínas del Tejido Nervioso/metabolismo , Fármacos Neuroprotectores/farmacocinética , Fármacos Neuroprotectores/uso terapéutico , Biología Computacional/métodos , Hipocampo/efectos de los fármacos , Hipocampo/metabolismo , Humanos , Terapia Molecular Dirigida/métodos , Mapeo de Interacción de Proteínas/métodos
9.
Bioelectromagnetics ; 35(1): 1-15, 2014 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-24115132

RESUMEN

Wireless medical telemetry permits the measurement of physiological signals at a distance through wireless technologies. One of the latest applications is in the field of implantable and ingestible medical devices (IIMDs) with integrated antennas for wireless radiofrequency (RF) communication (telemetry) with exterior monitoring/control equipment. Implantable medical devices (MDs) perform an expanding variety of diagnostic and therapeutic functions, while ingestible MDs receive significant attention in gastrointestinal endoscopy. Design of such wireless IIMD telemetry systems is highly intriguing and deals with issues related to: operation frequency selection, electronics and powering, antenna design and performance, and modeling of the wireless channel. In this paper, we attempt to comparatively review the current status and challenges of IIMDs with wireless telemetry functionalities. Full solutions of commercial IIMDs are also recorded. The objective is to provide a comprehensive reference for scientists and developers in the field, while indicating directions for future research.


Asunto(s)
Telemetría/instrumentación , Tecnología Inalámbrica , Humanos , Prótesis e Implantes/efectos adversos , Telemetría/efectos adversos
10.
Sensors (Basel) ; 14(3): 4618-33, 2014 Mar 07.
Artículo en Inglés | MEDLINE | ID: mdl-24608005

RESUMEN

Parkinson's disease (PD) alters the motor performance of affected individuals. The dopaminergic denervation of the striatum, due to substantia nigra neuronal loss, compromises the speed, the automatism and smoothness of movements of PD patients. The development of a reliable tool for long-term monitoring of PD symptoms would allow the accurate assessment of the clinical status during the different PD stages and the evaluation of motor complications. Furthermore, it would be very useful both for routine clinical care as well as for testing novel therapies. Within this context we have validated the feasibility of using a Body Network Area (BAN) of wireless accelerometers to perform continuous at home gait monitoring of PD patients. The analysis addresses the assessment of the system performance working in real environments.


Asunto(s)
Redes de Comunicación de Computadores , Marcha/fisiología , Enfermedad de Parkinson/fisiopatología , Telemetría/instrumentación , Telemetría/métodos , Tecnología Inalámbrica/instrumentación , Acelerometría , Anciano , Recolección de Datos , Estudios de Factibilidad , Femenino , Humanos , Masculino , Persona de Mediana Edad , Movimiento , Procesamiento de Señales Asistido por Computador
11.
J Neural Eng ; 2024 Jul 19.
Artículo en Inglés | MEDLINE | ID: mdl-39029490

RESUMEN

OBJECTIVE: Understanding the generative mechanism between Local Field Potentials (LFP) and neuronal spiking activity is a crucial step for understanding information processing in the brain. Up to now, most approaches have relied on simply quantifying the coupling between LFP and spikes. However, very few have managed to predict the exact timing of spike occurrence based on LFP variations. APPROACH: Here, we fill this gap by proposing novel spiking Laguerre-Volterra Network (sLVN) models to describe the dynamic LFP-spike relationship. Compared to conventional artificial neural networks, the sLVNs are interpretable models that provide explainable features of the underlying dynamics. MAIN RESULTS: The proposed networks were applied on extracellular microelectrode recordings of Parkinson's Disease (PD) patients during Deep Brain Stimulation (DBS) surgery. Based on the predictability of the LFP-spike pairs, we detected three neuronal populations with unique signal characteristics and sLVN model features. SIGNIFICANCE: These clusters were indirectly associated with motor score improvement following DBS surgery, warranting further investigation into the potential of spiking activity predictability as an intraoperative biomarker for optimal DBS lead placement.

12.
IEEE Rev Biomed Eng ; 17: 19-41, 2024.
Artículo en Inglés | MEDLINE | ID: mdl-37943654

RESUMEN

OBJECTIVE: Artificial intelligence and machine learning are transforming many fields including medicine. In diabetes, robust biosensing technologies and automated insulin delivery therapies have created a substantial opportunity to improve health. While the number of manuscripts addressing the topic of applying machine learning to diabetes has grown in recent years, there has been a lack of consistency in the methods, metrics, and data used to train and evaluate these algorithms. This manuscript provides consensus guidelines for machine learning practitioners in the field of diabetes, including best practice recommended approaches and warnings about pitfalls to avoid. METHODS: Algorithmic approaches are reviewed and benefits of different algorithms are discussed including importance of clinical accuracy, explainability, interpretability, and personalization. We review the most common features used in machine learning applications in diabetes glucose control and provide an open-source library of functions for calculating features, as well as a framework for specifying data sets using data sheets. A review of current data sets available for training algorithms is provided as well as an online repository of data sources. SIGNIFICANCE: These consensus guidelines are designed to improve performance and translatability of new machine learning algorithms developed in the field of diabetes for engineers and data scientists.


Asunto(s)
Inteligencia Artificial , Diabetes Mellitus , Humanos , Control Glucémico , Aprendizaje Automático , Diabetes Mellitus/tratamiento farmacológico , Algoritmos
13.
Children (Basel) ; 11(1)2024 Jan 15.
Artículo en Inglés | MEDLINE | ID: mdl-38255420

RESUMEN

Childhood obesity is a complex disease with multiple biological and psychosocial risk factors. Recently, novel digital programs were developed with growing evidence for their effectiveness in pediatric weight management studies. The ENDORSE platform consists of mobile applications, wearables, and serious games for the remote management of childhood obesity. The pilot studies included 50 mothers and their children aged 6-14 years and resulted in a clinically significant BMI z-score reduction over 4 to 5 months. This secondary analysis of the ENDORSE study focuses on parenting styles and psychosocial factors. METHODOLOGY: Semi-structured clinical interviews were conducted with all participating mothers pre-and post-intervention. The Parenting Styles and Dimensions Questionnaire (PSDQ) evaluated the mothers' parenting styles. The psychosocial functioning of the participating children was assessed with the parental version of the Strengths and Difficulties Questionnaire (SDQ). The relationship between parenting styles, psychosocial parameters, and weight outcomes was investigated using a linear regression analysis. RESULTS: Weight-related stigma at school (56%), body image concerns (66%), and difficulties in family relationships (48%) were the main concerns documented during the initial psychological interviews. According to the SDQ, there was a significant decrease in children's conduct problems during the study's initial phase (pre-pilot group). A decrease in maternal demandingness (i.e., strict parenting style) was associated with a decrease in BMI z-score (beta coefficient = 0.314, p-value = 0.003). CONCLUSION: Decreasing parental demandingness was associated with better weight outcomes, highlighting the importance of assessing parenting factors in pediatric weight management programs.

14.
JAMA Psychiatry ; 81(5): 456-467, 2024 May 01.
Artículo en Inglés | MEDLINE | ID: mdl-38353984

RESUMEN

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.


Asunto(s)
Envejecimiento , Encéfalo , Humanos , Anciano , Femenino , Masculino , Persona de Mediana Edad , Anciano de 80 o más Años , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Envejecimiento/genética , Envejecimiento/fisiología , Disfunción Cognitiva/genética , Disfunción Cognitiva/fisiopatología , Disfunción Cognitiva/diagnóstico por imagen , Imagen por Resonancia Magnética , Estudios de Cohortes , Aprendizaje Profundo
15.
Bioelectromagnetics ; 34(3): 167-79, 2013 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-22948753

RESUMEN

We numerically assess the effects of head properties (anatomy and dielectric parameters) on the performance of a scalp-implantable antenna for telemetry in the Medical Implant Communications Service band (402.0-405.0 MHz). Safety issues and performance (resonance, radiation) are analyzed for an experimentally validated implantable antenna (volume of 203.6 mm(3) ), considering five head models (3- and 5-layer spherical, 6-, 10-, and 13-tissue anatomical) and seven scenarios (variations ± 20% in the reference permittivity and conductivity values). Simulations are carried out at 403.5 MHz using the finite-difference time-domain method. Anatomy of the head model around the implantation site is found to mainly affect antenna performance, whereas overall tissue anatomy and dielectric parameters are less significant. Compared to the reference dielectric parameter scenario within the 3-layer spherical head, maximum variations of -19.9%, +3.7%, -55.1%, and -39.2% are computed in the maximum allowable net input power imposed by the IEEE Std C95.1-1999 and Std C95.1-2005 safety guidelines, return loss, and maximum far-field gain, respectively. Compliance with the recent IEEE Std C95.1-2005 is found to be almost insensitive to head properties, in contrast with IEEE Std C95.1-1999. Taking tissue property uncertainties into account is highlighted as crucial for implantable antenna design and performance assessment. Bioelectromagnetics 34:167-179, 2013. © 2012 Wiley Periodicals, Inc.


Asunto(s)
Cabeza/anatomía & histología , Prótesis e Implantes , Telemetría/instrumentación , Simulación por Computador , Fenómenos Electrofisiológicos , Cabeza/fisiología , Humanos , Miniaturización , Fantasmas de Imagen , Cuero Cabelludo
16.
Nucleic Acids Res ; 39(Web Server issue): W381-4, 2011 Jul.
Artículo en Inglés | MEDLINE | ID: mdl-21572105

RESUMEN

Peptides, either as protein fragments or as naturally occurring entities are characterized by their sequence and function features. Many times the researchers need to massively manage peptide lists concerning protein identification, biomarker discovery, bioactivity, immune response or other functionalities. We present a web server that manages peptide lists in terms of feature analysis as well as interactive clustering and visualization of the given peptides. PepServe is a useful tool in the understanding of the peptide feature distribution among a group of peptides. The PepServe web application is freely available at http://bioserver-1.bioacademy.gr/Bioserver/PepServe/.


Asunto(s)
Péptidos/química , Programas Informáticos , Análisis por Conglomerados , Gráficos por Computador , Humanos , Internet , Análisis de Secuencia de Proteína
17.
Sci Data ; 10(1): 770, 2023 11 06.
Artículo en Inglés | MEDLINE | ID: mdl-37932314

RESUMEN

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.


Asunto(s)
Inteligencia Artificial , COVID-19 , Humanos , Tos , Análisis de Datos , Bases del Conocimiento , Pandemias
18.
Nutrients ; 15(6)2023 Mar 17.
Artículo en Inglés | MEDLINE | ID: mdl-36986180

RESUMEN

Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent-child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The heterogeneous dataset gathered through the interaction of the end-users with the platform composes the unique user profile. Part of it feeds an AI-based model that enables personalized messages. A feasibility pilot trial was conducted involving 50 overweight and obese children (mean age 10.5 years, 52% girls, 58% pubertal, median baseline BMI z-score 2.85) in a 3-month intervention. Adherence was measured by means of frequency of usage based on the data records. Overall, a clinically and statistically significant BMI z-score reduction was achieved (mean BMI z-score reduction -0.21 ± 0.26, p-value < 0.001). A statistically significant correlation was revealed between the level of activity tracker usage and the improvement of BMI z-score (-0.355, p = 0.017), highlighting the potential of the ENDORSE platform.


Asunto(s)
Obesidad Infantil , Telemedicina , Niño , Femenino , Humanos , Masculino , Inteligencia Artificial , Índice de Masa Corporal , Ecosistema , Estudios de Factibilidad , Obesidad Infantil/terapia , Proyectos Piloto
19.
Nutrients ; 15(7)2023 Apr 05.
Artículo en Inglés | MEDLINE | ID: mdl-37049618

RESUMEN

Childhood obesity is a serious public health problem worldwide. The ENDORSE platform is an innovative software ecosystem based on Artificial Intelligence which consists of mobile applications for parents and health professionals, activity trackers, and mobile games for children. This study explores the impact of the ENDORSE platform on metabolic parameters associated with pediatric obesity and on the food parenting practices of the participating mothers. Therefore, the metabolic parameters of the 45 children (mean age: 10.42 years, 53% girls, 58% pubertal, mean baseline BMI z-score 2.83) who completed the ENDORSE study were evaluated. The Comprehensive Feeding Practices Questionnaire was used for the assessment of food parenting practices. Furthermore, regression analysis was used to investigate possible associations between BMI z-score changes and changes in metabolic parameters and food parenting practices. Overall, there was a statistically significant reduction in glycated hemoglobin (mean change = -0.10, p = 0.013), SGOT (mean change = -1.84, p = 0.011), and SGPT (mean change = -2.95, p = 0.022). Emotional feeding/food as reward decreased (mean change -0.21, p = 0.007) and healthy eating guidance increased (mean change = 0.11, p = 0.051). Linear regression analysis revealed that BMI z-score change had a robust and significant correlation with important metabolic parameters: HOMA-IR change (beta coefficient = 3.60, p-value = 0.046), SGPT change (beta coefficient = 11.90, p-value = 0.037), and cortisol change (beta coefficient = 9.96, p-value = 0.008). Furthermore, healthy eating guidance change had a robust negative relationship with BMI z-score change (beta coefficient = -0.29, p-value = 0.007). Conclusions: The Endorse digital weight management program improved several metabolic parameters and food parenting practices.


Asunto(s)
Aplicaciones Móviles , Obesidad Infantil , Juegos de Video , Programas de Reducción de Peso , Femenino , Humanos , Niño , Adolescente , Masculino , Sobrepeso/terapia , Obesidad Infantil/terapia , Responsabilidad Parental/psicología , Alanina Transaminasa , Inteligencia Artificial , Ecosistema , Conducta Alimentaria/psicología , Encuestas y Cuestionarios , Metaboloma , Índice de Masa Corporal
20.
Ultrasound Med Biol ; 48(1): 78-90, 2022 01.
Artículo en Inglés | MEDLINE | ID: mdl-34666918

RESUMEN

The curvelet transform, which represents images in terms of their geometric and textural characteristics, was investigated toward revealing differences between moderate (50%-69%, n = 11) and severe (70%-100%, n = 14) stenosis asymptomatic plaque from B-mode ultrasound. Texture features were estimated in original and curvelet transformed images of atheromatous plaque (PL), the adjacent arterial wall (intima-media [IM]) and the plaque shoulder (SH) (i.e., the boundary between plaque and wall), separately at end systole and end diastole. Seventeen features derived from the original images were significantly different between the two groups (4 for IM, 3 for PL and 10 for SH; 9 for end diastole and 8 for end systole); 19 of 234 features (2 for IM and 17 for SH; 8 for end systole and 11 for end diastole) derived from curvelet transformed images were significantly higher in the patients with severe stenosis, indicating higher magnitude, variation and randomness of image gray levels. In these patients, lower body height and higher serum creatinine concentration were observed. Our findings suggest that (a) moderate and severe plaque have similar curvelet-based texture properties, and (b) IM and SH provide useful information about arterial wall pathophysiology, complementary to PL itself. The curvelet transform is promising for identifying novel indices of cardiovascular risk and warrants further investigation in larger cohorts.


Asunto(s)
Enfermedades de las Arterias Carótidas , Estenosis Carotídea , Placa Aterosclerótica , Arterias Carótidas/diagnóstico por imagen , Estenosis Carotídea/diagnóstico por imagen , Constricción Patológica , Humanos , Masculino , Placa Aterosclerótica/diagnóstico por imagen , Ultrasonografía
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