Employing Siamese Networks as Quantitative Biomarker for Assessing the Effect of Dolphin-Assisted Therapy on Pediatric Cerebral Palsy.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Spastic Cerebral Palsy (SCP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with SCP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from SCP patients undergoing DAT sessions. The study employed Event-Related Potential (ERP) and Fast Fourier Transform (FFT) analyses to examine cerebral activity and brain rhythms, proposing the use of SNN to compare electroencephalographic (EEG) signals of children with cerebral palsy before and after Dolphin-Assisted Therapy. Testing on samples from four children yielded a high average similarity index of 0.9150, indicating consistent similarity metrics before and after therapy. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network's architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of SCP post-DAT. Confirming these observations will require large, longitudinal studies but such findings would suggest that the Siamese Network could have utility as a biomarker in monitoring treatment responses for children with SCP who undergo DAT and offer them more objective as well as quantifiable manners of assessing therapeutic interventions. Great discrepancies in neuronal voltage perturbations, 7.9825 dB on average at the specific samples compared to the whole dataset (6.2838 dB), imply a noted deviation from resting activity. These findings indicate that Dolphin-Assisted Therapy activates particular brain regions specifically during the intervention.

Efficacy of pain management strategies in adults with Amyotrophic Lateral Sclerosis (ALS): A Systematic Review.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive muscle weakness. Presence of pain in ALS patients is heterogeneously reported in studies, and mostly underrepresented in symptom scales. The aim of this study is to evaluate the efficacy of pharmacological and non-pharmacological therapeutic modalities for pain management in patients with ALS. A systematic review was conducted in four databases; PubMed, Scopus, Clinicaltrials.gov, and Cochrane-Ovid. Five randomized controlled clinical trials were included regarding pharmacological and non-pharmacological pain management interventions in adult patients with confirmed diagnosis of ALS in whom pain was objectively evaluated. Risk of bias assessment was evaluated using the RoB2.0 tool. Eligible studies were reported as a descriptive analysis. This systematic review was registered with PROSPERO ID: CRD42024495009. Five clinical trials regarding pain management strategies in ALS were eligible for analysis. Two out of five were non-pharmacological approaches whilst the remaining three provided pharmacological therapies. Of these, Mexiletine was efficient in terms of pain relief, particularly between 600 and 900 mg per day, whereas Mecasin showed no pain relief at both, high and low doses. Non-pharmacological therapies, such as exercise and osteopathic manual treatment also lacked efficacy in regard to pain management. Clinical trials focusing on pain management strategies for ALS patients are limited. Medical professionals, understandably focused on immediate life-threatening aspects, may inadvertently sideline the nuanced and intricate dimension of pain experienced by patients with ALS.

DS-CNN: Deep Convolutional Neural Networks for Facial Emotion Detection in Children with Down Syndrome during Dolphin-Assisted Therapy.

In Mexico, according to data from the General Directorate of Health Information (2018), there is an annual incidence of 689 newborns with Trisomy 21, well-known as Down Syndrome. Worldwide, this incidence is estimated between 1 in every 1000 newborns, approximately. That is why this work focuses on the detection and analysis of facial emotions in children with Down Syndrome in order to predict their emotions throughout a dolphin-assisted therapy. In this work, two databases are used: Exploratory Data Analysis, with a total of 20,214 images, and the Down's Syndrome Dataset database, with 1445 images for training, validation, and testing of the neural network models. The construction of two architectures based on a Deep Convolutional Neural Network manages an efficiency of 79%, when these architectures are tested with a large reference image database. Then, the architecture that achieves better results is trained, validated, and tested in a small-image database with the facial emotions of children with Down Syndrome, obtaining an efficiency of 72%. However, this increases by 9% when the brain activity of the child is included in the training, resulting in an average precision of 81%. Using electroencephalogram (EEG) signals in a Convolutional Neural Network (CNN) along with the Down's Syndrome Dataset (DSDS) has promising advantages in the field of brain-computer interfaces. EEG provides direct access to the electrical activity of the brain, allowing for real-time monitoring and analysis of cognitive states. Integrating EEG signals into a CNN architecture can enhance learning and decision-making capabilities. It is important to note that this work has the primary objective of addressing a doubly vulnerable population, as these children also have a disability.

Gingivitis Ulcerativa Necrosante: Reporte de un Caso / Necrotizing Ulcerative Gingivitis: Case Report

La gingivitis ulcerosa necrotizante (GUN) es una forma distinta y específica de enfermedad periodontal. Es un proceso inflamatorio agudo de los tejidos gingivales que se caracteriza por síntomas clínicos específicos asociados a dolor (generalmente de inicio rápido), necrosis interdental y sangrado gingival. Si uno de estos tres síntomas está ausente, no se puede diagnosticar GUN.Este caso clínico describe el abordaje diagnóstico y el manejo conservador con buena evolución de la GUN en una paciente femenina de 14 años sin enfermedad sistémica.

Necrotizing ulcerative gingivitis (NEG) is a distinct and specific form of periodontal disease. This entity represents an acute inflammatory process of the gingival tissues characterized by specific clinical symptoms associated with pain (usually rapid onset), interdental necrosis, and gingival bleeding. If one of these three symptoms is absent GUN cannot be diagnosed. This clinical case describes the diagnostic approach and conservative management with good evolution of GUN in a 14-year-old female patient without systemic disease.

A Comprehensive Review on Smart Grids: Challenges and Opportunities.

Recently, the operation of distribution systems does not depend on the state or utility based on centralized procedures, but rather the decentralization of the decisions of the distribution companies whose objectives are the efficiency of interconnectivity. Therefore, distribution companies are exposed to greater risks, and due to this, the need to make decisions based on increasingly reliable models has grown up considerably. Therefore, we present a survey of key aspects, technologies, protocols, and case studies of the current and future trend of Smart Grids. This work proposes a taxonomy of a large number of technologies in Smart Grids and their applications in scenarios of Smart Networks, Neural Networks, Blockchain, Industrial Internet of Things, or Software-Defined Networks. Therefore, this work summarizes the main features of 94 research articles ranging the last four years. We classify these survey, according Smart Grid Network Topologies, because it can group as the main axis the sensors applied to Smart Grids, as it shows us the interconnection forms generalization of the Smart Networks with respect to the sensors found in a home or industry.

VirtualCPR: Virtual Reality Mobile Application for Training in Cardiopulmonary Resuscitation Techniques.

Deaths due to heart diseases are a leading cause of death in Mexico. Cardiovascular diseases are considered a public health problem because they produce cardiorespiratory arrests. During an arrest, cardiac and/or respiratory activity stops. A cardiorespiratory arrest is rapidly fatal without a quick and efficient intervention. As a response to this problem, the VirtualCPR system was designed in the present work. VirtualCPR is a mobile virtual reality application to support learning and practicing of basic techniques of cardiopulmonary resuscitation (CPR) for experts or non-experts in CPR. VirtualCPR implements an interactive virtual scenario with the user, which is visible by means of employment of virtual reality lenses. User's interactions, with our proposal, are by a portable force sensor for integration with training mannequins, whose development is based on an application for the Android platform. Furthermore, this proposal integrates medical knowledge in first aid, related to the basic CPR for adults using only the hands, as well as technological knowledge, related to development of simulations on a mobile virtual reality platform by three main processes: (i) force measurement and conversion, (ii) data transmission and (iii) simulation of a virtual scenario. An experiment by means of a multifactorial analysis of variance was designed considering four factors for a CPR session: (i) previous training in CPR, (ii) frequency of compressions, (iii) presence of auditory suggestions and (iv) presence of color indicator. Our findings point out that the more previous training in CPR a user of the VirtualCPR system has, the greater the percentage of correct compressions obtained from a virtual CPR session. Setting the rate to 100 or 150 compressions per minute, turning on or off the auditory suggestions and turning the color indicator on or off during the session have no significant effect on the results obtained by the user.

Non-Parametric Evaluation Methods of the Brain Activity of a Bottlenose Dolphin during an Assisted Therapy.

Dolphin-Assisted Therapies (DAT) are alternative therapies aimed to reduce anxiety levels, stress relief and physical benefits. This paper is focused on measuring and analyzing dolphins brain activity when DAT is taking place in order to identify if there is any differences in female dolphin's neuronal signal when it is interacting with control or intervention subjects, performing our research in Delfiniti, Ixtapa, Mexico facilities. We designed a wireless and portable electroencephalographic single-channel signal capture sensor to acquire and monitor the brain activity of a female bottle-nose dolphin. This EEG sensor was able to show that dolphin activity at rest is characterized by high spectral power at slow-frequencies bands. When the dolphin participated in DAT, a 23.53% increment in the 12-30 Hz frequency band was observed, but this only occurred for patients with some disease or disorder, given that 0.5-4 Hz band keeps it at 17.91% when there is a control patient. Regarding the fractal or Self-Affine Analysis, we found for all samples studied that at the beginning the dolphin's brain activity behaved as a self-affine fractal described by a power-law until the fluctuations of voltage reached the crossovers, and after the crossovers these fluctuations left this scaling behavior. Hence, our findings validate the hypothesis that the participation in a DAT of a Patient with a certain disease or disorder modifies the usual behavior of a female bottle-nose dolphin.

Defining a no-reference image quality assessment by means of the self-affine analysis.

In this paper we propose a novel Blind Image Quality Assessment via Self-Affine Analysis (BIQSAA) method by considering the wavelet transform as a linear operation that decomposes a complex signal into elementary blocks at different scales or resolutions. BIQSAA decomposes a distorted image into a set of wavelet planes ω λ, ϕ of different spatial frequencies λ and spatial orientations ϕ, and it transforms these wavelet planes into one-dimension vector Ω using a Hilbert scanning. From the vector Ω there were obtained their wavelet coefficient fluctuations estimated by the inverse of the Hurst exponent in decibels, whose scaling-law or fractal behavior was obtained by applying Fractal Geometry or Self-Affine Analysis. The scaling exponents calculated for the coefficient fluctuation behavior of Image Lena at 24bpp, at 1.375bpp, and at 0.50bpp were H 24b p p = 0.0395, H 1.375b p p = 0.0551, and H 0.50b p p = 0.0612, respectively. Our experiments show that BIQSAA algorithm improves in 14.36% the Human Visual System correlation, respect to the four state-of-the-art No-Reference Image Quality Assessments.

Biomedical Signal Acquisition Using Sensors under the Paradigm of Parallel Computing.

There are several pathologies attacking the central nervous system and diverse therapies for each specific disease. These therapies seek as far as possible to minimize or offset the consequences caused by these types of pathologies and disorders in the patient. Therefore, comprehensive neurological care has been performed by neurorehabilitation therapies, to improve the patients' life quality and facilitating their performance in society. One way to know how the neurorehabilitation therapies contribute to help patients is by measuring changes in their brain activity by means of electroencephalograms (EEG). EEG data-processing applications have been used in neuroscience research to be highly computing- and data-intensive. Our proposal is an integrated system of Electroencephalographic, Electrocardiographic, Bioacoustic, and Digital Image Acquisition Analysis to provide neuroscience experts with tools to estimate the efficiency of a great variety of therapies. The three main axes of this proposal are: parallel or distributed capture, filtering and adaptation of biomedical signals, and synchronization in real epochs of sampling. Thus, the present proposal underlies a general system, whose main objective is to be a wireless benchmark in the field. In this way, this proposal could acquire and give some analysis tools for biomedical signals used for measuring brain interactions when it is stimulated by an external system during therapies, for example. Therefore, this system supports extreme environmental conditions, when necessary, which broadens the spectrum of its applications. In addition, in this proposal sensors could be added or eliminated depending on the needs of the research, generating a wide range of configuration limited by the number of CPU cores, i.e., the more biosensors, the more CPU cores will be required. To validate the proposed integrated system, it is used in a Dolphin-Assisted Therapy in patients with Infantile Cerebral Palsy and Obsessive-Compulsive Disorder, as well as with a neurotypical one. Event synchronization of sample periods helped isolate the same therapy stimulus and allowed it to be analyzed by tools such as the Power Spectrum or the Fractal Geometry.

Neurodynamics of Patients during a Dolphin-Assisted Therapy by Means of a Fractal Intraneural Analysis.

The recent proliferation of sensor technology applications in therapies for children's disabilities to promote positive behavior among such children has produced optimistic results in developing a variety of skills and abilities in them. Dolphin-Assisted Therapy (DAT) has also become a topic of public and research interest for these disorders' intervention and treatment. This work exposes the development of a system that controls brain-computer interaction when a patient with different abilities undergoes a DAT. To develop the proposed system, TGAM1, i.e., ThinkGear-AM1 series of NeuroSky company, was used, connecting it to an isolated Bluetooth 4.0 communication protocol from a brackish and humid environment, and a Notch Filter was applied to reduce the input noise. In this way, at Definiti Ixtapa-Mexico facilities, we explored the behavior of three children with Infantile Spastic Cerebral Palsy (Experiment 1), as well as the behavior of Obsessive Compulsive Disorder and neurotypic children (Experiment 2). This was done applying the Power Spectrum Density (PSD) and the Self-Affine Analysis (SSA) from Electroencephalogram (EEG) biosignals. The EEG Raw data were time series showing the cerebral brain activity (voltage versus time) before and during DAT for the Experiment 1, and before, during DAT and after for the Experiment 2. Likewise, the EEW RAW data were recorded by the first frontopolar electrode (FP1) by means of an EEG biosensor TGAM1 Module. From the PSD we found that in all child patients a huge increment of brain activity during DAT regarding the before and after therapy periods around 376.28%. Moreover, from the SSA we found that the structure function of the all five child patients displayed an antipersistent behavior, characterized by σ ∝ δ t H , for before, during DAT and after. Nonetheless, we propose that one way to assess whether a DAT is being efficient to the child patients is to increase the during DAT time when the samples are collected, supposing the data fitting by a power law will raise the time, displaying a persistent behavior or positive correlations, until a crossover appears and the curve tends to be horizontal, pointing out that our system has reached a stationary state.

Energy-Efficient Industrial Internet of Things Software-Defined Network by Means of the Peano Fractal.

The Industrial Internet of Things (IIoT) network generates great economic benefits in processes, system installation, maintenance, reliability, scalability, and interoperability. Wireless sensor networks (WSNs) allow the IIoT network to collect, process, and share data of different parameters among Industrial IoT sense Node (IISN). ESP8266 are IISNs connected to the Internet by means of a hub to share their information. In this article, a light-diffusion algorithm in WSN to connect all the IISNs is designed, based on the Peano fractal and swarm intelligence, i.e., without using a hub, simply sharing parameters with two adjacent IINSs, assuming that any IISN knows the parameters of the rest of these devices, even if they are not adjacent. We simulated the performance of our algorithm and compared it with other state-of-the-art protocols, finding that our proposal generates a longer lifetime of the IIoT network when few IISNs were connected. Thus, there is a saving-energy of approximately 5% but with 64 nodes there is a saving of more than 20%, because the IIoT network can grow in a 3 n way and the proposed topology does not impact in a linear way but log 3 , which balances energy consumption throughout the IIoT network.

Fractal Characterization of Stochastic Series Fluctuations of Children with Reading Disorders.

Reading is an emerging process from human brain activity. This process sometimes is subject to disorders which has been studied from the performance of studies that provide data that are treated with qualitative and quantitative linear tools to obtain the average behavior determined and the causality of it. This research focuses on the nonlinear quantitative study of reading disorder and in this way fractal geometry and roughness interface growth theory approach were selected to be used in the processing of brain wave quantification (EEG). From the EEG of children with and without reading disorders in the State of Mexico (experimental and control group) were built time series of standard deviation for each of the 19 channels distributed in cerebral cortex. The self-affinity of these time series (treated as interfaces in motion) is studied by the scaling behavior of their structure functions. It was found that the behavior of the time series of children with reading problems (experimental group) and without them (control group) is similar to the Family-Vicsek scaling dynamic for a kinetic roughening of moving interface.

Is peak expiratory flow an accurate sarcopenia screening tool in older patients referred to respiratory rehabilitation?

PURPOSE: To assess the performance of peak expiratory flow (PEF) for sarcopenia screening in patients with chronic obstructive pulmonary disease (COPD), using the revised European Working Group on Sarcopenia in Older People (EWGSOP-2) criteria as the reference standard in pulmonary rehabilitation patients; and second, to study the factors associated with low PEF in this population. METHODS: Diagnostic accuracy study conducted in consecutive community-dwelling COPD rehabilitation patients. Sensitivity, specificity, accuracy, likelihood ratios, predictive values, and area under the Receiver-Operating Characteristic curve were retrospectively calculated for PEF (index test) and compared with EWGSOP-2 criteria (reference standard). RESULTS: Of 151 potentially eligible patients, 79 (67.5 ± 7.1 years; 78.8% men) fulfilled inclusion criteria and 10 (12.7%) had a diagnosis of sarcopenia. The PEF cut-off with highest accuracy (65.8%) was PEF ≤ 200 L/min (sensitivity 90%, specificity 62.3%, and positive and negative likelihood ratios 2.39 and 0.16, respectively). Airway obstruction and muscle mass were significantly associated with PEF ≤ 200 L/min. CONCLUSIONS: Considering the EWGSOP-2 criteria as the reference standard, a cut-off of PEF ≤ 200 L/min showed only fair validity for detecting sarcopenia, so it cannot be recommended as a stand-alone screening tool in older rehabilitation patients with COPD.

Long-term results of total knee arthroplasty after failed high tibial osteotomy.

PURPOSE: Sometimes, total knee replacement (TKR) is performed after a tibial osteotomy. It is not known if the osteotomy influences the clinical process of a TKR. The aim of this study was to look at the clinical outcome of a TKR after high tibial osteotomy (HTO) compared to a TKR with no HTO. METHODS: Between 1995 and 2005, 41 post-HTO TKRs (group A) with a minimum follow-up of ten years were selected. In parallel, it was selected a control group undergoing simple TKR, consisting of 41 patients (group B) matched with group A. The Knee Society Knee Score (KS), Knee Score Functional Score (FS), the Oxford Knee Score (OKS), and the range of motion (ROM) were used for evaluation at one, five and ten years after surgery. The Insall-Salvatti ratio (ISR), the posterior tibial slope (PTS), and the joint height line (JHL) were also measured both pre-operatively and post-operatively. RESULTS: No differences were found between both groups in KS, FS, OKS, and ROM at the pre-operative stage or at any of the post-operative controls. Higher patellar height was found in group A, both pre-operatively and post-operatively (p < 0.005), and the same for PTS pre-operative (p < 0.001) and post-operative (p < 0.05). No differences were found regarding the JHL. Post-operative differences were found among the post-HTO subgroups at the FS (p < 0.02), the OKS (p < 0.05), and the ROM (p < 0.01) as a function of whether there was a varus or a valgus deformity before the TKR. CONCLUSION: A HTO was not determinant for having a worse outcome at the TKR, even when there were radiographic differences. The coronal alignment with valgus deformity pre-TKR after a HTO showed better functional results than in the case of varus deformity. Level of evidence III.

La biotransformación de la esparteína en amerindios cunas de Panamá / Biotransformation of sparteine in Amerindians cunas of Panamá

Reportamos, por vez primera, un estudio de oxidación metabólica en un grupo amerindio: los cunas de Panamá. Estudios genéticos indican un escaso mestizaje de menos del 1% de mezcla racial y establecen la identidad genética del grupo cuna con respecto a otros grupos amerindios que se remonta, a lo menos, a 5,000 años. Ciento nueve voluntarios recibieron 50 mg de esparteína y recogieron sus orinas por 12 horas, para determinar la cantidad que contenían de esparteína y de sus dos productos de oxidación. El porcentaje del medicamento intacto recuperado de la orina, así como de sus metabolitos, fue menor que el encontrado en otros grupos étnicos y está normalmente distribuido. La razón metabólica, que es un índice de eficiencia de la actividad de la isozima involucrada en la biotransformación de la esparteína, indica que no existen metabolizadores deficientes en este grupo y que la frecuencia de los metabolizadores extensos muestra una distribución unimodal. Por esas razones concluimos diciendo que en el grupo cuna, a diferencia de los grupos caucasoides, no existe polimorfismo en la oxidación de la esparteína. Además la ausencia de metabolizadores deficientes entre los cunas indica que este grupo, en comparación con los caucasoides, se encuentra en menor riesgo de desarollar reacciones tóxicas al usar medicamentos que siguen la ruta de la esparteína