The Gap Junction Inhibitor Octanol Decreases Proliferation and Increases Glial Differentiation of Postnatal Neural Progenitor Cells.

Neural precursor cells (NPCs) that persist in the postnatal/adult subventricular zone (SVZ) express connexins that form hemichannels and gap junctions. Gap junctional communication plays a role in NPC proliferation and differentiation during development, but its relevance on postnatal age remains to be elucidated. In this work we aimed to evaluate the effect of the blockade of gap junctional communication on proliferation and cell fate of NPCs obtained from the SVZ of postnatal rats. NPCs were isolated and expanded in culture as neurospheres. Electron microscopy revealed the existence of gap junctions among neurosphere cells. Treatment of cultures with octanol, a broad-spectrum gap junction blocker, or with Gap27, a specific blocker for gap junctions formed by connexin43, produced a significant decrease in bromodeoxyuridine incorporation. Octanol treatment also exerted a dose-dependent antiproliferative effect on glioblastoma cells. To analyze possible actions on NPC fate, cells were seeded in the absence of mitogens. Treatment with octanol led to an increase in the percentage of astrocytes and oligodendrocyte precursors, whereas the percentage of neurons remained unchanged. Gap27 treatment, in contrast, did not modify the differentiation pattern of SVZ NPCs. Our results indicate that general blockade of gap junctions with octanol induces significant effects on the behavior of postnatal SVZ NPCs, by reducing proliferation and promoting glial differentiation.

Characterization of optical fibers doped with nanoparticles for distributed displacement sensing.

High-scattering optical fibers have emerged as a key component in distributed sensing systems, primarily due to their capacity to enhance signal-to-noise ratio. This paper presents an experimental characterization of optical fibers doped with oxide nanoparticles for displacement sensing. They were manufactured using the phase-separation technique and different doping compounds, including calcium, strontium, lanthanum and magnesium. The Rayleigh backscattering (RBS) signatures in time and frequency domains were acquired using an Optical Backscatter Reflectometer (OBR). The maximum representative length, backscattering gain and strain sensitivity were evaluated. The results indicate that the fiber co-doped with magnesium and erbium chlorides offered the best compromise between strain sensitivity (0.96 pm/µ ϵ) and maximum length (17 m). For conditions of single and multiple perturbations, strain saturation was reached at ≥7000 µm and <1500 µm, respectively. In addition, the results reveal that, under a condition of variable temperature (30-60 °C), the sensor response becomes significantly nonlinear over length, requiring a technique for temperature cross-sensitivity mitigation that accounts for nonlinearities in sensitivity and hysteresis.

Data-Driven Approach for Upper Limb Fatigue Estimation Based on Wearable Sensors.

Muscle fatigue is defined as a reduced ability to maintain maximal strength during voluntary contraction. It is associated with musculoskeletal disorders that affect workers performing repetitive activities, affecting their performance and well-being. Although electromyography remains the gold standard for measuring muscle fatigue, its limitations in long-term work motivate the use of wearable devices. This article proposes a computational model for estimating muscle fatigue using wearable and non-invasive devices, such as Optical Fiber Sensors (OFSs) and Inertial Measurement Units (IMUs) along the subjective Borg scale. Electromyography (EMG) sensors are used to observe their importance in estimating muscle fatigue and comparing performance in different sensor combinations. This study involves 30 subjects performing a repetitive lifting activity with their dominant arm until reaching muscle fatigue. Muscle activity, elbow angles, and angular and linear velocities, among others, are measured to extract multiple features. Different machine learning algorithms obtain a model that estimates three fatigue states (low, moderate and high). Results showed that between the machine learning classifiers, the LightGBM presented an accuracy of 96.2% in the classification task using all of the sensors with 33 features and 95.4% using only OFS and IMU sensors with 13 features. This demonstrates that elbow angles, wrist velocities, acceleration variations, and compensatory neck movements are essential for estimating muscle fatigue. In conclusion, the resulting model can be used to estimate fatigue during heavy lifting in work environments, having the potential to monitor and prevent muscle fatigue during long working shifts.

Novelty detection in an auditory oddball task on freely moving rats.

The relative importance or saliency of sensory inputs depend on the animal's environmental context and the behavioural responses to these same inputs can vary over time. Here we show how freely moving rats, trained to discriminate between deviant tones embedded in a regular pattern of repeating stimuli and different variations of the classic oddball paradigm, can detect deviant tones, and this discriminability resembles the properties that are typical of neuronal adaptation described in previous studies. Moreover, the auditory brainstem response (ABR) latency decreases after training, a finding consistent with the notion that animals develop a type of plasticity to auditory stimuli. Our study suggests the existence of a form of long-term memory that may modulate the level of neuronal adaptation according to its behavioural relevance, and sets the ground for future experiments that will help to disentangle the functional mechanisms that govern behavioural habituation and its relation to neuronal adaptation.

Antiproliferative effect of boldine on neural progenitor cells and on glioblastoma cells.

Introduction: The subventricular zone (SVZ) is a brain region that contains neural stem cells and progenitor cells (NSCs/NPCs) from which new neurons and glial cells are formed during adulthood in mammals. Recent data indicate that SVZ NSCs are the cell type that acquires the initial tumorigenic mutation in glioblastoma (GBM), the most aggressive form of malignant glioma. NSCs/NPCs of the SVZ present hemichannel activity whose function has not yet been fully elucidated. In this work, we aimed to analyze whether hemichannel-mediated communication affects proliferation of SVZ NPCs and GBM cells. Methods and Results: For that purpose, we used boldine, an alkaloid derived from the boldo tree (Peumus boldus), that inhibits connexin and pannexin hemichannels, but without affecting gap junctional communication. Boldine treatment (50 µM) of rat SVZ NPCs grown as neurospheres effectively inhibited dye uptake through hemichannels and induced a significant reduction in neurosphere diameter and in bromodeoxyuridine (BrdU) incorporation. However, the differentiation pattern was not modified by the treatment. Experiments with specific blockers for hemichannels formed by connexin subunits (D4) or pannexin 1 (probenecid) revealed that probenecid, but not D4, produced a decrease in BrdU incorporation similar to that obtained with boldine. These results suggest that inhibition of pannexin 1 hemichannels could be partially responsible for the antiproliferative effect of boldine on SVZ NPCs. Analysis of the effect of boldine (25-600 µM) on different types of primary human GBM cells (GBM59, GBM96, and U87-MG) showed a concentration-dependent decrease in GBM cell growth. Boldine treatment also induced a significant inhibition of hemichannel activity in GBM cells. Discussion: Altogether, we provide evidence of an antimitotic action of boldine in SVZ NPCs and in GBM cells which may be due, at least in part, to its hemichannel blocking function. These results could be of relevance for future possible strategies in GBM aimed to suppress the proliferation of mutated NSCs or glioma stem cells that might remain in the brain after tumor resection.

A fabric-based soft hand exoskeleton for assistance: the ExHand Exoskeleton.

Introduction: The rise of soft robotics has driven the development of devices for assistance in activities of daily living (ADL). Likewise, different types of actuation have been developed for safer human interaction. Recently, textile-based pneumatic actuation has been introduced in hand exoskeletons for features such as biocompatibility, flexibility, and durability. These devices have demonstrated their potential use in assisting ADLs, such as the degrees of freedom assisted, the force exerted, or the inclusion of sensors. However, performing ADLs requires the use of different objects, so exoskeletons must provide the ability to grasp and maintain stable contact with a variety of objects to lead to the successful development of ADLs. Although textile-based exoskeletons have demonstrated significant advancements, the ability of these devices to maintain stable contact with a variety of objects commonly used in ADLs has yet to be fully evaluated. Materials and methods: This paper presents the development and experimental validation in healthy users of a fabric-based soft hand exoskeleton through a grasping performance test using The Anthropomorphic Hand Assessment Protocol (AHAP), which assesses eight types of grasping with 24 objects of different shapes, sizes, textures, weights, and rigidities, and two standardized tests used in the rehabilitation processes of post- stroke patients. Results and discussion: A total of 10 healthy users (45.50 ± 14.93 years old) participated in this study. The results indicate that the device can assist in developing ADLs by evaluating the eight types of grasps of the AHAP. A score of 95.76 ± 2.90% out of 100% was obtained for the Maintaining Score, indicating that the ExHand Exoskeleton can maintain stable contact with various daily living objects. In addition, the results of the user satisfaction questionnaire indicated a positive mean score of 4.27 ± 0.34 on a Likert scale ranging from 1 to 5.

Optical fiber sensors for posture monitoring, ulcer detection and control in a wheelchair: a state-of-the-art.

BACKGROUND: In the last ten years, the design and implementation of Optical Fiber Sensors (OFS) in biomedical applications have been discussed, with a focus on different subareas, such as body parameter monitoring and control of assistive devices. MATERIALS AND METHODS: A scoping review was performed including scientific literature (PubMed/Scopus, IEEE and Web of Science), patents (WIPO/Google Scholar), and commercial information. RESULTS: The main applications of OFS in the rehabilitation field for preventing future postural diseases and applying them in device controllers were discussed in this review. Physical characteristics of OFS, different uses, and applications of Polymer Optical Fiber pressure sensors are mentioned. The main postures used for posture monitoring analysis when the user is sitting are normal position, crooked back, high lumbar pressure, sitting on the edge of the chair, and crooked back, left position, and right position. Additionally, it is possible to use Machine Learning (ML) algorithms for posture classification, and device control such as Support Vector Machine, k-Nearest Neighbors, etc., obtaining accuracies above 97%. Moreover, the literature mentions wheelchair controllers and Graphical User Interfaces using pressure maps to provide feedback to the user. CONCLUSIONS: OFS have been used in several healthcare applications as well as postural and preventive applications. The literature showed an effort to implement and design accessible devices for people with disabilities and people with specific diseases. Alternatively, ML algorithms are widely used in this direction, leaving the door open for further studies that allow the application of real-time systems for posture monitoring and wheelchairs control.

IMPLICATIONS FOR REHABILITATIONPosture monitoring and ulcer detection systems are very useful to prevent or treat diseases related to bedsores or pressure ulcers using a different kind of electronic or optic instrumentation to improve the user's quality of life.The system characteristics using optical fiber sensors discussed in this review set an important precedent in the fabrication of low-cost systems for biomedical applications.

Localized Surface Plasmon Resonance-Based Nanosensor for Rapid Detection of Glyphosate in Food Samples.

In this study, we developed a biosensor based on the localized surface plasmon resonance (LSPR) phenomenon of gold nanoparticles (AuNPs) to detect the widely used herbicide glyphosate in food samples. To do so, either cysteamine or a specific antibody for glyphosate were conjugated to the surface of the nanoparticles. AuNPs were synthesized using the sodium citrate reduction method and had their concentration determined via inductively plasma coupled mass spectrometry. Their optical properties were analyzed using UV-vis spectroscopy, X-ray diffraction, and transmission electron microscopy. Functionalized AuNPs were further characterized via Fourier-transform infrared spectroscopy, Raman scattering, Zeta potential, and dynamic light scattering. Both conjugates succeeded in detecting the presence of glyphosate in the colloid, although nanoparticles functionalized with cysteamine tended to aggregate at high concentrations of the herbicide. On the other hand, AuNPs functionalized with anti-glyphosate functioned at a broad concentration range and successfully identified the presence of the herbicide in non-organic coffee samples and when it was added to an organic coffee sample. This study demonstrates the potential of AuNP-based biosensors to detect glyphosate in food samples. The low-cost and specificity of these biosensors make them a viable alternative to current methods for detecting glyphosate in foodstuffs.

Soft-Sensor System for Grasp Type Recognition in Underactuated Hand Prostheses.

This paper presents the development of an intelligent soft-sensor system to add haptic perception to the underactuated hand prosthesis PrHand. Two sensors based on optical fiber were constructed, one for finger joint angles and the other for fingertips' contact force. Three sensor fabrications were tested for the angle sensor by axially rotating the sensors in four positions. The configuration with the most similar response in the four rotations was chosen. The chosen sensors presented a polynomial response with R2 higher than 92%. The tactile force sensors tracked the force made over the objects. Almost all sensors presented a polynomial response with R2 higher than 94%. The system monitored the prosthesis activity by recognizing grasp types. Six machine learning algorithms were tested: linear regression, k-nearest neighbor, support vector machine, decision tree, k-means clustering, and hierarchical clustering. To validate the algorithms, a k-fold test was used with a k = 10, and the accuracy result for k-nearest neighbor was 98.5%, while that for decision tree was 93.3%, enabling the classification of the eight grip types.

Towards Multimodal Equipment to Help in the Diagnosis of COVID-19 Using Machine Learning Algorithms.

COVID-19 occurs due to infection through respiratory droplets containing the SARS-CoV-2 virus, which are released when someone sneezes, coughs, or talks. The gold-standard exam to detect the virus is Real-Time Polymerase Chain Reaction (RT-PCR); however, this is an expensive test and may require up to 3 days after infection for a reliable result, and if there is high demand, the labs could be overwhelmed, which can cause significant delays in providing results. Biomedical data (oxygen saturation level-SpO2, body temperature, heart rate, and cough) are acquired from individuals and are used to help infer infection by COVID-19, using machine learning algorithms. The goal of this study is to introduce the Integrated Portable Medical Assistant (IPMA), which is a multimodal piece of equipment that can collect biomedical data, such as oxygen saturation level, body temperature, heart rate, and cough sound, and helps infer the diagnosis of COVID-19 through machine learning algorithms. The IPMA has the capacity to store the biomedical data for continuous studies and can be used to infer other respiratory diseases. Quadratic kernel-free non-linear Support Vector Machine (QSVM) and Decision Tree (DT) were applied on three datasets with data of cough, speech, body temperature, heart rate, and SpO2, obtaining an Accuracy rate (ACC) and Area Under the Curve (AUC) of approximately up to 88.0% and 0.85, respectively, as well as an ACC up to 99% and AUC = 0.94, respectively, for COVID-19 infection inference. When applied to the data acquired with the IMPA, these algorithms achieved 100% accuracy. Regarding the easiness of using the equipment, 36 volunteers reported that the IPMA has a high usability, according to results from two metrics used for evaluation: System Usability Scale (SUS) and Post Study System Usability Questionnaire (PSSUQ), with scores of 85.5 and 1.41, respectively. In light of the worldwide needs for smart equipment to help fight the COVID-19 pandemic, this new equipment may help with the screening of COVID-19 through data collected from biomedical signals and cough sounds, as well as the use of machine learning algorithms.

Fiber-Optic Hydrophone Based on Michelson's Interferometer with Active Stabilization for Liquid Volume Measurement.

Sensing technologies using optical fibers have been studied and applied since the 1970s in oil and gas, industrial, medical, aerospace, and civil areas. Detecting ultrasound acoustic waves through fiber-optic hydrophone (FOH) sensors can be one solution for continuous measurement of volumes inside production tanks used by these industries. This work presents an FOH system composed of two optical fiber coils made with commercial single mode fiber (SMF) working in the sensor head of a Michelson's interferometer (MI) supported by an active stabilization mechanism that drives another optical coil wound around a piezoelectric actuator (PZT) in the reference arm to mitigate external mechanical and thermal noise from the environment. A 1000 mL glass graduated cylinder filled with water is used as a test tank, inside which the sensor head and an ultrasound source are placed. For detection, amplitudes and phases are measured, and machine learning algorithms predict their respective liquid volumes. The acoustic waves create patterns electronically detected with resolution of 1 mL and sensitivity of 340 mrad/mL and 70 mvolts/mL. The nonlinear behavior of both measurands requires classification, distance metrics, and regression algorithms to define an adequate model. The results show the system can determine liquid volumes with an accuracy of 99.4% using a k-nearest neighbors (k-NN) classification with one neighbor and Manhattan's distance. Moreover, Gaussian process regression using rational quadratic metrics presented a root mean squared error (RMSE) of 0.211 mL.

BCI-Based Control for Ankle Exoskeleton T-FLEX: Comparison of Visual and Haptic Stimuli with Stroke Survivors.

Brain-computer interface (BCI) remains an emerging tool that seeks to improve the patient interaction with the therapeutic mechanisms and to generate neuroplasticity progressively through neuromotor abilities. Motor imagery (MI) analysis is the most used paradigm based on the motor cortex's electrical activity to detect movement intention. It has been shown that motor imagery mental practice with movement-associated stimuli may offer an effective strategy to facilitate motor recovery in brain injury patients. In this sense, this study aims to present the BCI associated with visual and haptic stimuli to facilitate MI generation and control the T-FLEX ankle exoskeleton. To achieve this, five post-stroke patients (55-63 years) were subjected to three different strategies using T-FLEX: stationary therapy (ST) without motor imagination, motor imagination with visual stimulation (MIV), and motor imagination with visual-haptic inducement (MIVH). The quantitative characterization of both BCI stimuli strategies was made through the motor imagery accuracy rate, the electroencephalographic (EEG) analysis during the MI active periods, the statistical analysis, and a subjective patient's perception. The preliminary results demonstrated the viability of the BCI-controlled ankle exoskeleton system with the beta rebound, in terms of patient's performance during MI active periods and satisfaction outcomes. Accuracy differences employing haptic stimulus were detected with an average of 68% compared with the 50.7% over only visual stimulus. However, the power spectral density (PSD) did not present changes in prominent activation of the MI band but presented significant variations in terms of laterality. In this way, visual and haptic stimuli improved the subject's MI accuracy but did not generate differential brain activity over the affected hemisphere. Hence, long-term sessions with a more extensive sample and a more robust algorithm should be carried out to evaluate the impact of the proposed system on neuronal and motor evolution after stroke.

An Optimized Self-Compensated Solution for Temperature and Strain Cross-Sensitivity in FBG Interrogators Based on Edge Filter.

Optical fiber sensors based on fiber Bragg gratings (FBGs) are prone to measurement errors if the cross-sensitivity between temperature and strain is not properly considered. This paper describes a self-compensated technique for canceling the undesired influence of temperature in strain measurement. An edge-filter-based interrogator is proposed and the central peaks of two FBGs (sensor and reference) are matched with the positive and negative slopes of a Fabry-Perot interferometer that acts as an optical filter. A tuning process performed by the grey wolf optimizer (GWO) algorithm is required to determine the optimal spectral characteristics of each FBG. The interrogation range is not compromised by the proposed technique, being determined by the spectral characteristics of the optical filter in accordance with the traditional edge-filtering interrogation. Simulations show that, by employing FBGs with optimal characteristics, temperature variations of 30 °C led to an average relative error of 3.4% for strain measurements up to 700µÏµ. The proposed technique was experimentally tested under non-ideal conditions: two FBGs with spectral characteristics different from the optimized results were used. The temperature sensibility decreased by 50.8% as compared to a temperature uncompensated interrogation system based on an edge filter. The non-ideal experimental conditions were simulated and the maximum error between theoretical and experimental data was 5.79%, proving that the results from simulation and experimentation are compatible.

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise.

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients' condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject's physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.

Deviance detection in physiologically identified cell types in the rat auditory cortex.

Auditory deviance detection is a function of the auditory system that allows reduction of the processing demand for repetitive stimuli while stressing unpredictable ones, which are potentially more informative. Deviance detection has been extensively studied in humans using the oddball paradigm, which evokes an event-related potential known as mismatch negativity (MMN). The same stimulation paradigms are used in animal studies that aim to elucidate the neuronal mechanisms underlying deviance detection. In order to understand the circuitry responsible for deviance detection in the auditory cortex (AC), it is necessary to determine the properties of excitatory and inhibitory neurons separately. Measuring the spike widths of neurons recorded extracellularly from the anaesthetized rat AC, we classified them as fast spiking or regular spiking units. These two neuron types are generally considered as putative inhibitory or excitatory, respectively. In response to an oddball paradigm, we found that both types of units showed similar amounts of deviance detection overall. When considering each AC field separately, we found that only in A1 fast spiking neurons showed higher deviance detection levels than regular spiking neurons, while in the rest of the fields there was no such distinction. Interpreting these responses in the context of the predictive coding framework, we found that the responses of both types of units reflect mainly prediction error signaling (i.e., genuine deviance detection) rather than repetition suppression.

Polymer Optical Fiber-Based Integrated Instrumentation in a Robot-Assisted Rehabilitation Smart Environment: A Proof of Concept.

Advances in robotic systems for rehabilitation purposes have led to the development of specialized robot-assisted rehabilitation clinics. In addition, advantageous features of polymer optical fiber (POF) sensors such as light weight, multiplexing capabilities, electromagnetic field immunity and flexibility have resulted in the widespread use of POF sensors in many areas. Considering this background, this paper presents an integrated POF intensity variation-based sensor system for the instrumentation of different devices. We consider different scenarios for physical rehabilitation, resembling a clinic for robot-assisted rehabilitation. Thus, a multiplexing technique for POF intensity variation-based sensors was applied in which an orthosis for flexion/extension movement, a modular exoskeleton for gait assistance and a treadmill were instrumented with POF angle and force sensors, where all the sensors were integrated in the same POF system. In addition, wearable sensors for gait analysis and physiological parameter monitoring were also proposed and applied in gait exercises. The results show the feasibility of the sensors and methods proposed, where, after the characterization of each sensor, the system was implemented with three volunteers: one for the orthosis on the flexion/extension movements, one for the exoskeleton for gait assistance and the other for the free gait analysis using the proposed wearable POF sensors. To the authors' best knowledge, this is the first time that optical fiber sensors have been used as a multiplexed and integrated solution for the simultaneous assessment of different robotic devices and rehabilitation protocols, where such an approach results in a compact, fully integrated and low-cost system, which can be readily employed in any clinical environment.

Maquetas como estrategia didáctica en estudiantes de la salud / Models as didactic strategy in health students

En el proceso de aprendizaje de la fisiología humana, la apropiación de conceptos y procesos fisiológicos que pueden ser abstractos para los estudiantes que se especializan en la salud es importante. Por eso es necesario asignar un rol más dinámico y participativo a los estudiantes. Con este objetivo se utilizaron metodologías de aprendizaje activo en clase, específicamente la construcción de modelos. Estas metodologías favorecieron el desarrollo de pensamiento reflexivo y crítico de los estudiantes y un intercambio de experiencias y opiniones a través del aprendizaje entre iguales, junto con el trabajo en equipo. Esta investigación tuvo como objetivo llevar a cabo una representación física o modelo de un proceso funcional asociado con el funcionamiento sistémico normal del ser humano, por grupos de estudiantes de 2 programas de pregrado de salud tales como Medicina y Nutrición y dietética. Por lo tanto, los estudiantes pudieron presentar de manera práctica la imagen física de su propio proceso de aprendizaje a medida que avanzaba su conocimiento. Se utilizó un estudio de metodología mixta como elementos de análisis cuantitativos y cualitativos. Los resultados se obtuvieron de una encuesta on-line. El objetivo fue determinar la percepción o satisfacción de 77 estudiantes de la Facultad de Medicina de la Universidad Católica de la Santísima Concepción. Se realizaron análisis de estadística descriptiva, distribución de frecuencia y estimación de análisis de confiabilidad Alfa de Cronbach. Además, se complementó con el desarrollo de focus group con estudiantes de Nutrición y dietética y una entrevista con 3 estudiantes del programa de Medicina. Finalmente, es posible enfatizar que para lograr un aprendizaje significativo, se deben tener en cuenta los estilos de aprendizaje de los estudiantes

In the process of human physiology learning, the appropriation of concepts and physiological processes that can be abstract for students majoring in health is important. That is why, it is necessary to assign a more dynamic and participatory role to students. With this objective active learning methodologies were used in class, specifically the construction of models. These methodologies favored the students' development of critical, reflective thinking and an exchange of experiences and opinions through peer learning, along with teamwork. This research aimed to carry out a physical representation or model of a functional process associated with the normal systemic working of the human being, by groups of students from two Health undergraduate programmes such as Medicine and Nutrition and Dietetics. Thus, the students were able to present in a practical way the physical image of their own learning process as their knowledge progressed. A mixed methodology study was used as elements of quantitative and qualitative analysis were employed. The results were obtained from an online survey. The objective was to determine the perception or satisfaction of 77 students of the Faculty of Medicine of the Catholic University of the Holy Conception. Analyses of descriptive statistics and frequency distribution, estimating reliability analysis Cronbach's alpha were conducted. In addition, it was complemented by the development of a focus group with students of Nutrition and Dietetics and an interview with three students from the Medicine programme. Finally, it is possible to emphasize that to achieve meaningful learning, the learning styles of students should be taken into consideration

Primary cutaneous osteosarcoma of the scalp in an immunosuppressed individual: A case report and review of the literature.

We report a case of a 76-year-old man presenting with a 12-month history of a solitary lesion on his scalp. The histopathology was consistent with a grade 2/3 osteosarcoma extending to the subcutis. Full-body imaging excluded any involvement of the underlying bony tissue or solid organ malignancy, thus a diagnosis of primary cutaneous osteosarcoma (PCO) was made. Given the exceedingly rare nature of PCO, we discuss the clinico-pathological features of this case and those previously reported in the literature.

3D-Printing Techniques on the Development of Multiparameter Sensors Using One FBG.

We report the development of a fiber Bragg grating (FBG) sensor for multiparameter sensing using only one FBG. The FBG was half-embedded in a 3D-printed structure, which resulted in a division of the grating spectrum creating two peaks with different sensitivities with respect to different physical parameters. A numerical analysis of the proposed technique was performed using the coupled-mode theory with modified transfer matrix formulation. Then, experimental analyses were performed as function of temperature, strain and force, where the peaks showed different sensitivities in all analyzed cases. Such results enable the application of a technique for simultaneous measurement of multiple physical parameters using both peaks and the full width half maximum of the FBG embedded in a 3D structure. In the simultaneous multiparameter assessment, the proposed sensor system was able to estimate the three tested parameters (strain, temperature and force) with relative errors as low as 4%.

POF Smart Carpet: A Multiplexed Polymer Optical Fiber-Embedded Smart Carpet for Gait Analysis.

This paper presents the development of a smart carpet based on polymer optical fiber (POF) for ground reaction force (GRF) and spatio-temporal gait parameter assessment. The proposed carpet has 20 intensity variation-based sensors on one fiber with two photodetectors for acquisition, each one for the response of 10 closer sensors. The used multiplexing technique is based on side-coupling between the light sources and POF lateral sections in which one light-emitting diode (LED) is activated at a time, sequentially. Three tests were performed, two for sensor characterization and one for validation of the smart carpet, where the first test consisted of the application of calibrated weights on the top of each sensor for force characterization. In the second test, the foot was positioned on predefined points distributed on the carpet, where a mean relative error of 2.9% was obtained. Results of the walking tests on the proposed POF-embedded smart carpet showed the possibility of estimating the GRF and spatio-temporal gait parameters (step and stride lengths, cadence, and stance duration). The obtained results make possible the identification of gait events (stance and swing phases) as well as the stance duration and double support periods. The proposed carpet is a low-cost and reliable tool for gait analysis in different applications.