Development of Continuous Assessment of Muscle Quality and Frailty in Older Patients Using Multiparametric Combinations of Ultrasound and Blood Biomarkers: Protocol for the ECOFRAIL Study.

BACKGROUND: Frailty resulting from the loss of muscle quality can potentially be delayed through early detection and physical exercise interventions. There is a demand for cost-effective tools for the objective evaluation of muscle quality, in both cross-sectional and longitudinal assessments. Literature suggests that quantitative analysis of ultrasound data captures morphometric, compositional, and microstructural muscle properties, while biological assays derived from blood samples are associated with functional information. OBJECTIVE: This study aims to assess multiparametric combinations of ultrasound and blood-based biomarkers to offer a cross-sectional evaluation of the patient frailty phenotype and to track changes in muscle quality associated with supervised exercise programs. METHODS: This prospective observational multicenter study will include patients aged 70 years and older who are capable of providing informed consent. We aim to recruit 100 patients from hospital environments and 100 from primary care facilities. Each patient will undergo at least two examinations (baseline and follow-up), totaling a minimum of 400 examinations. In hospital environments, 50 patients will be measured before/after a 16-week individualized and supervised exercise program, while another 50 patients will be followed up after the same period without intervention. Primary care patients will undergo a 1-year follow-up evaluation. The primary objective is to compare cross-sectional evaluations of physical performance, functional capacity, body composition, and derived scales of sarcopenia and frailty with biomarker combinations obtained from muscle ultrasound and blood-based assays. We will analyze ultrasound raw data obtained with a point-of-care device, along with a set of biomarkers previously associated with frailty, using quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay. Additionally, we will examine the sensitivity of these biomarkers to detect short-term muscle quality changes and functional improvement after a supervised exercise intervention compared with usual care. RESULTS: At the time of manuscript submission, the enrollment of volunteers is ongoing. Recruitment started on March 1, 2022, and ends on June 30, 2024. CONCLUSIONS: The outlined study protocol will integrate portable technologies, using quantitative muscle ultrasound and blood biomarkers, to facilitate an objective cross-sectional assessment of muscle quality in both hospital and primary care settings. The primary objective is to generate data that can be used to explore associations between biomarker combinations and the cross-sectional clinical assessment of frailty and sarcopenia. Additionally, the study aims to investigate musculoskeletal changes following multicomponent physical exercise programs. TRIAL REGISTRATION: ClinicalTrials.gov NCT05294757; https://clinicaltrials.gov/ct2/show/NCT05294757. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/50325.

Privacy-Preserving Automatic Collection of Acoustic Voiding Events.

Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention. This platform operates outside of human-audible frequencies, providing privacy-preserving, automatic uroflowmetries that can be conducted at home as part of daily routines. After evaluating several machine learning algorithms, we found that the Multi-layer Perceptron classifier performed exceptionally well, with a classification accuracy of 97.8% and a low false negative rate of 1.2%. Furthermore, even on lightweight SVM models, performance remains robust. Our results also showed that the voiding flow envelope, helpful for diagnosing underlying pathologies, remains intact even when using only inaudible frequencies.Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous embedded privacy-preserving audio capture devices with Edge AI capabilities.

Classification of the Physical Surface in Sound-based Uroflowmetry.

This work constitutes a first approach to automatically classify the urination medium for non-invasive sound based uroflowmetry tests. Often the voiding flow impacts the toilet wall (often made of ceramic) instead of the water. This causes a reduction in the amplitude of the recorded audio signal, and thus a reduction in the amplitude of the extracted envelope. Analysing the envelope alone, it is not possible to tell accurately if the reduction in the amplitude is due to a low voiding flow or an impact on the toilet walls. In this work, we carry out a study on the classification of sound uroflowmetry data depending on the medium where the urine impacts within the toilet: water or ceramic. In the analysis, a classification algorithm is proposed to identify the physical medium automatically based on the urination acoustics. The classification algorithm takes as input the frequency spectrum, the variance, and the kurtosis of the audio signal corresponding to a voiding event.Clinical relevance- Sound uroflowmetry has a strong correlation with the standard uroflowmetry. It is useful for the non-invasive detection of pathologies associated with the urinary tract as a support tool for information processing and screening. It consists of a characterization of the urinary flow patterns by capturing the sound generated when the urine stream impacts the water in the toilet. Identifying the medium which originates the sound is of paramount importance to better interpret the sound uroflowmetry.

UroSound: A Smartwatch-Based Platform to Perform Non-Intrusive Sound-Based Uroflowmetry.

Leveraging consumer technology such as smartwatches to objectively and remotely assess people with voiding dysfunction could capture unique features for prompt diagnosis of a disease. This paper presents the UroSound, the first platform that performs non-intrusive sound-based uroflowmetry with a smartwatch. We study the feasibility of using a smartwatch to assess how well the urinary tract functions by processing the sound generated when the urine stream hits the water level in the toilet bowl, which can be modelled through the sound envelope. Signal-based features related to the sound envelope were extracted from a smartwatch's built-in microphone. The constructed model achieves a good correlation between acoustic and standard uroflowmetry in terms of the voiding shape and it can extract relevant voiding parameters. This indicates that accurate and remote measurement of the ambulatory characteristics of voiding dysfunction can be achieved with smartwatch-based uroflowmetry. UroSound also facilitates the collection of a voiding diary by measuring multiple uroflows during daytime and nighttime. Finally, the performance of 6 commercial smartwatches was analysed while recording a voiding event. The results demonstrate that the presence of an automatic gain control in the smartwatch microphone has a negative impact on the signal envelope, and should be avoided. Overall, this work demonstrates the potential for the use of smartwatches in the assessment of voiding dysfunction, to deliver more personalized and effective health care at home with less waste of time and resources, in particular in rural or less developed areas where access to a urology specialist is more difficult.

Automatic Classification of Audio Uroflowmetry with a Smartwatch.

Prior work has shown the classification of voiding dysfunctions from uroflowmeter data using machine learning. We present the use of smartwatch audio, collected through the UroSound platform, in order to automatically classify voiding signals as normal or abnormal, using classical machine learning techniques. We train several classification models using classical machine learning and report a maximal test accuracy of 86.16% using an ensemble method classifier. Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous audio capture devices such as smartphones and smartwatches.

A Review of IoT Sensing Applications and Challenges Using RFID and Wireless Sensor Networks.

Radio frequency identification (RFID) and wireless sensors networks (WSNs) are two fundamental pillars that enable the Internet of Things (IoT). RFID systems are able to identify and track devices, whilst WSNs cooperate to gather and provide information from interconnected sensors. This involves challenges, for example, in transforming RFID systems with identification capabilities into sensing and computational platforms, as well as considering them as architectures of wirelessly connected sensing tags. This, together with the latest advances in WSNs and with the integration of both technologies, has resulted in the opportunity to develop novel IoT applications. This paper presents a review of these two technologies and the obstacles and challenges that need to be overcome. Some of these challenges are the efficiency of the energy harvesting, communication interference, fault tolerance, higher capacities to handling data processing, cost feasibility, and an appropriate integration of these factors. Additionally, two emerging trends in IoT are reviewed: the combination of RFID and WSNs in order to exploit their advantages and complement their limitations, and wearable sensors, which enable new promising IoT applications.

Dynamic Frame Update Policy for UHF RFID Sensor Tag Collisions.

The current growing demand for low-cost edge devices to bridge the physical-digital divide has triggered the growing scope of Radio Frequency Identification (RFID) technology research. Besides object identification, researchers have also examined the possibility of using RFID tags for low-power wireless sensing, localisation and activity inference. This paper focuses on passive UHF RFID sensing. An RFID system consists of a reader and various numbers of tags, which can incorporate different kinds of sensors. These sensor tags require fast anti-collision protocols to minimise the number of collisions with the other tags sharing the reader's interrogation zone. Therefore, RFID application developers must be mindful of anti-collision protocols. Dynamic Frame Slotted Aloha (DFSA) anti-collision protocols have been used extensively in the literature because EPCglobal Class 1 Generation 2 (EPC C1G2), which is the current communication protocol standard in RFID, employs this strategy. Protocols under this category are distinguished by their policy for updating the transmission frame size. This paper analyses the frame size update policy of DFSA strategies to survey and classify the main state-of-the-art of DFSA protocols according to their policy. Consequently, this paper proposes a novel policy to lower the time to read one sensor data packet compared to existing strategies. Next, the novel anti-collision protocol Fuzzy Frame Slotted Aloha (FFSA) is presented, which applies this novel DFSA policy. The results of our simulation confirm that FFSA significantly decreases the sensor tag read time for a wide range of tag populations when compared to earlier DFSA protocols thanks to the proposed frame size update policy.

Pumpkin Waste as Livestock Feed: Impact on Nutrition and Animal Health and on Quality of Meat, Milk, and Egg.

Meat, milk, and egg contribute positively to the nutrition and health of humans; however, livestock requires a large number of resources, including land for fodder and grains. Worldwide millions of tons of vegetable waste are produced without any further processing, causing pollution and health risks. Properly managed vegetable waste could provide a source of feed for livestock, thus reducing feeding costs. In this regard, pumpkin waste (Cucurbita sp.) is an alternative. Research on pumpkin waste on animal nutrition is scarce, however, it has potential as animal feed not only for its nutritional value but also for its antioxidants, pigments, and polysaccharides content that could enhance quality of meat, milk, and egg, as well animal health. In this review, we describe the environmental impact of livestock as a result of greater demand for food of animal origin, including the importance of the consumption of animal foods in human nutrition and health. Moreover, we emphasize the potential of plant residues and, particularly, on the characteristics of pumpkins and how their use as feedstuff for livestock could improve productivity and modify the composition of meat, milk, and egg.

Protocol for Streaming Data from an RFID Sensor Network †.

Currently, there is an increasing interest in the use of Radio Frequency Identification (RFID) tags which incorporate passive or battery-less sensors. These systems are known as computational RFID (CRFID). Several CRFID tags together with a reader set up an RFID sensor network. The reader powers up the tags' microcontroller and their attached sensor using radio frequency waves, and tags backscatter, not only their EPC code but also the value of those sensors. The current standard for interrogating these CRFID tags is the EPC global Class 1 Generation 2 (EPC C1G2). When several tags are located inside the reader interrogation area, the EPC C1G2 results in very poor performance to obtain sensor data values. To solve this problem, a novel protocol called Sensor Frmed Slotted Aloha (sFSA) for streaming sensor data dealing with the tag collisions is presented. The proposed protocol increases the Sensor Read Rate (SRR), defined as the number of sensor data reads per second, compared to the standard. Additionally, this paper presents a prototype of an RFID sensor network to compare the proposed sFSA with the standard, increasing the SRR by more than five times on average. Additionally, the proposed protocol keeps a constant sensor sampling frequency for a suitable streaming of these tag sensors.

Energy-Aware RFID Anti-Collision Protocol.

The growing interest in mobile devices is transforming wireless identification technologies. Mobile and battery-powered Radio Frequency Identification (RFID) readers, such as hand readers and smart phones, are are becoming increasingly attractive. These RFID readers require energy-efficient anti-collision protocols to minimize the tag collisions and to expand the reader's battery life. Furthermore, there is an increasing interest in RFID sensor networks with a growing number of RFID sensor tags. Thus, RFID application developers must be mindful of tag anti-collision protocols. Energy-efficient protocols involve a low reader energy consumption per tag. This work presents a thorough study of the reader energy consumption per tag and analyzes the main factor that affects this metric: the frame size update strategy. Using the conclusion of this analysis, the anti-collision protocol Energy-Aware Slotted Aloha (EASA) is presented to decrease the energy consumption per tag. The frame size update strategy of EASA is configured to minimize the energy consumption per tag. As a result, EASA presents an energy-aware frame. The performance of the proposed protocol is evaluated and compared with several state of the art Aloha-based anti-collision protocols based on the current RFID standard. Simulation results show that EASA, with an average of 15 mJ consumed per tag identified, achieves a 6% average improvement in the energy consumption per tag in relation to the strategies of the comparison.

Influence of the Distribution of Tag IDs on RFID Memoryless Anti-Collision Protocols.

In recent years, Radio Frequency Identification (RFID) has become very popular. The main feature of this technology is that RFID tags do not require close handling and no line of sight is required between the reader and the tags. RFID is a technology that uses radio frequencies in order to identify tags, which do not need to be positioned accurately relative to the reader. Tags share the communication channel, increasing the likelihood of causing a problem, viz., a message collision. Tree based protocols can resolve these collisions, but require a uniform tag ID distribution. This means they are very dependent of the distribution of the IDs of the tags. Tag IDs are written in the tag and contain a predefined bit string of data. A study of the influence of the tag ID distribution on the protocols' behaviour is proposed here. A new protocol, called the Flexible Query window Tree (FQwT) is presented to estimate the tag ID distribution, taking into consideration the type of distribution. The aim is to create a flexible anti-collision protocol in order to identify a set of tags that constitute an ID distribution. As a result, the reader classifies tags into groups determined by using a distribution estimator. Simulations show that the FQwT protocol contributes to significant reductions in identification time and energy consumption regardless of the type of ID distribution.

Caso clínico: síndrome opsoclonus-mioclonus asociado a un neuroblastoma abdominal / No disponible

Opsoclono-ataxia, también llamada “dancing eye sindrome”, es un trastorno neurológico de importancia que a menudo se presenta como una manifestación paraneoplásica de neuroblastoma oculto en la primera infancia. Aunque la supervivencia con tratamiento antitumoral y terapia inmunosupresora es elevada, el resultado general indica importantes secuelas en el desarrollo y en la conducta. Presentamos un caso de una niña diagnosticada a los nueve meses de edad, tratada en nuestra Unidad y que fue derivada a un programa de rehabilitación interdisciplinar y neuropsicológica cuando la niña tenía 4 años de edad (AU)

Opsoclonus-ataxia, also called “dancing eye syndrome,” is a serious neurologic condition that is often a paraneoplastic manifestation of occult neuroblastoma in early childhood. Although survival is high with anti-tumoral treatment and immunosuppressive therapy, outcome generally includes significant developmental and behavioral sequelae. We report a case diagnosed at nine month of age, treated in our Unit and refered for a multidisciplinary neuropsychological rehabilitation when she was 4 year old (AU)