Evaluation of deep oscillation therapy for the treatment of lumbar pain syndrome using motion capture systems: A systematic review.

Low back pain is a painful disorder that prevents normal mobilization, increases muscle tension and whose first-line treatment is usually non-steroidal anti-inflammatory drugs, together with non-invasive manual therapies, such as deep oscillation therapy. This systematic review aims to investigate and examine the scientific evidence of the effectiveness of deep oscillation therapy in reducing pain and clinical symptomatology in patients with low back pain, through the use of motion capture technology. To carry out this systematic review, the guidelines of the PRISMA guide were followed. A literature search was performed from 2013 to March 2022 in the PubMed, Elsevier, Science Director, Cochrane Library, and Springer Link databases to collect information on low back pain, deep oscillation, and motion capture. The risk of bias of the articles was assessed using the Cochrane risk of bias tool. Finally, they were included 16 articles and 5 clinical trials which met the eligibility criteria. These articles discussed the effectiveness of deep oscillation therapy in reducing pain, eliminating inflammation, and increasing lumbar range of motion, as well as analyzing the use of motion capture systems in the analysis, diagnosis, and evaluation of a patient with low back pain before, during and after medical treatment. There is no strong scientific evidence that demonstrates the high effectiveness of deep oscillation therapy in patients with low back pain, using motion capture systems. This review outlines the background for future research directed at the use of deep oscillation therapy as a treatment for other types of musculoskeletal injuries.

The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN.

Melanoma is the most aggressive and prevalent form of skin cancer globally, with a higher incidence in men and individuals with fair skin. Early detection of melanoma is essential for the successful treatment and prevention of metastasis. In this context, deep learning methods, distinguished by their ability to perform automated and detailed analysis, extracting melanoma-specific features, have emerged. These approaches excel in performing large-scale analysis, optimizing time, and providing accurate diagnoses, contributing to timely treatments compared to conventional diagnostic methods. The present study offers a methodology to assess the effectiveness of an AlexNet-based convolutional neural network (CNN) in identifying early-stage melanomas. The model is trained on a balanced dataset of 10,605 dermoscopic images, and on modified datasets where hair, a potential obstructive factor, was detected and removed allowing for an assessment of how hair removal affects the model's overall performance. To perform hair removal, we propose a morphological algorithm combined with different filtering techniques for comparison: Fourier, Wavelet, average blur, and low-pass filters. The model is evaluated through 10-fold cross-validation and the metrics of accuracy, recall, precision, and the F1 score. The results demonstrate that the proposed model performs the best for the dataset where we implemented both a Wavelet filter and hair removal algorithm. It has an accuracy of 91.30%, a recall of 87%, a precision of 95.19%, and an F1 score of 90.91%.

Classification of the Pathological Range of Motion in Low Back Pain Using Wearable Sensors and Machine Learning.

Low back pain (LBP) is a highly common musculoskeletal condition and the leading cause of work absenteeism. This project aims to develop a medical test to help healthcare professionals decide on and assign physical treatment for patients with nonspecific LBP. The design uses machine learning (ML) models based on the classification of motion capture (MoCap) data obtained from the range of motion (ROM) exercises among healthy and clinically diagnosed patients with LBP from Imbabura-Ecuador. The following seven ML algorithms were tested for evaluation and comparison: logistic regression, decision tree, random forest, support vector machine (SVM), k-nearest neighbor (KNN), multilayer perceptron (MLP), and gradient boosting algorithms. All ML techniques obtained an accuracy above 80%, and three models (SVM, random forest, and MLP) obtained an accuracy of >90%. SVM was found to be the best-performing algorithm. This article aims to improve the applicability of inertial MoCap in healthcare by making use of precise spatiotemporal measurements with a data-driven treatment approach to improve the quality of life of people with chronic LBP.

BackMov: Individualized Motion Capture-Based Test to Assess Low Back Pain Mobility Recovery after Treatment.

Low back pain (LBP) is a common issue that negatively affects a person's quality of life and imposes substantial healthcare expenses. In this study, we introduce the (Back-pain Movement) BackMov test, using inertial motion capture (MoCap) to assess lumbar movement changes in LBP patients. The test includes flexion-extension, rotation, and lateralization movements focused on the lumbar spine. To validate its reproducibility, we conducted a test-retest involving 37 healthy volunteers, yielding results to build a minimal detectable change (MDC) graph map that would allow us to see if changes in certain variables of LBP patients are significant in relation to their recovery. Subsequently, we evaluated its applicability by having 30 LBP patients perform the movement's test before and after treatment (15 received deep oscillation therapy; 15 underwent conventional therapy) and compared the outcomes with a specialist's evaluations. The test-retest results demonstrated high reproducibility, especially in variables such as range of motion, flexion and extension ranges, as well as velocities of lumbar movements, which stand as the more important variables that are correlated with LBP disability, thus changes in them may be important for patient recovery. Among the 30 patients, the specialist's evaluations were confirmed using a low-back-specific Short Form (SF)-36 Physical Functioning scale, and agreement was observed, in which all patients improved their well-being after both treatments. The results from the specialist analysis coincided with changes exceeding MDC values in the expected variables. In conclusion, the BackMov test offers sensitive variables for tracking mobility recovery from LBP, enabling objective assessments of improvement. This test has the potential to enhance decision-making and personalized patient monitoring in LBP management.

Detection of Paramagnetic Spins with an Ultrathin van der Waals Quantum Sensor.

Detecting magnetic noise from small quantities of paramagnetic spins is a powerful capability for chemical, biochemical, and medical analysis. Quantum sensors based on optically addressable spin defects in bulk semiconductors are typically employed for such purposes, but the 3D crystal structure of the sensor inhibits sensitivity by limiting the proximity of the defects to the target spins. Here we demonstrate the detection of paramagnetic spins using spin defects hosted in hexagonal boron nitride (hBN), a van der Waals material that can be exfoliated into the 2D regime. We first create negatively charged boron vacancy (VB-) defects in a powder of ultrathin hBN nanoflakes (<10 atomic monolayers thick on average) and measure the longitudinal spin relaxation time (T1) of this system. We then decorate the dry hBN nanopowder with paramagnetic Gd3+ ions and observe a clear T1 quenching under ambient conditions, consistent with the added magnetic noise. Finally, we demonstrate the possibility of performing spin measurements, including T1 relaxometry using solution-suspended hBN nanopowder. Our results highlight the potential and versatility of the hBN quantum sensor for a range of sensing applications and make steps toward the realization of a truly 2D, ultrasensitive quantum sensor.

GUSignal: An Informatics Tool to Analyze Glucuronidase Gene Expression in Arabidopsis Thaliana Roots.

The uidA gene codifies for a glucuronidase (GUS) enzyme which has been used as a biotechnological tool during the last years. When uidA gene is fused to a gene's promotor region, it is possible to evaluate the activity of this one in response to a stimulus. Arabidopsis thaliana has served as the biological platform to elucidate molecular and regulatory signaling responses in plants. Transgenic lines of A. thaliana, tagged with the uidA gene, have allowed explaining how plants modify their hormonal pathways depending on the environmental conditions. Although the information extracted from microscopic images of these transgenic plants is often qualitative and in many publications is not subjected to quantification, in this paper we report the development of an informatics tool focused on computer vision for processing and analysis of digital images in order to analyze the expression of the GUS signal in A. thaliana roots, which is strongly correlated with the intensity of the grayscale images. This means that the presence of the GUS-induced color indicates where the gene has been actively expressed, such as our statistical analysis has demonstrated after treatment of A. thaliana DR5::GUS with naphtalen-acetic acid (0.0001 mM and 1 mM). GUSignal is a free informatics tool that aims to be fast and systematic during the image analysis since it executes specific and ordered instructions, to offer a segmented analysis by areas or regions of interest, providing quantitative results of the image intensity levels.

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network.

Background: Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. Methods: This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Results: Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. Conclusions: This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.

Welfare assessment of horses and mules used in recreational and muleteer work in the Colombian coffee region.

The welfare of working equids in developing countries is sometimes threatened due to the limited resources and/or knowledge of their owners. The objective of this study is to evaluate the welfare of creole horses and mules using a validated protocol that assesses animal-based indicators. A total of 160 horses and 40 mules from three municipalities in the Colombian coffee-growing region were evaluated by means of direct observation of health and behavioral parameters. A descriptive analysis of the variables expressed in proportions was performed. Interactions between the different measurements were examined using the Chi-squared test. Spearman correlations were used to relate the measurements. Horses and mules demonstrated friendly behavior in front of the evaluators (78.13 and 61.54%, respectively); apathetic or severely depressed behavior was low (10.7 and 17.5%, P > 0.05). Significant differences in body condition score (BCS) were observed between mules and horses (P < 0.05); eighty percent of the mules and 54.4% of the horses exhibited a healthy body condition score (3 or more on a scale of 1 to 5). Less than 15% of the animals had eye problems, limb deformities, and gait abnormalities. Injuries to the head, withers, spine, ribs/flank, hindquarters, and hind legs were observed in a frequency between 12.5 and 30.43% of the animals, with a higher frequency in horses (P < 0.05). Weak correlations (R2 coefficient < 0.5), although statistically significant, were observed between low body conditions and the presence of skin and deeper tissue lesions, systemic health abnormalities, and limb problems (P < 0.05). The results indicate that owners care for their animals. However, the presence of skin and deep tissue lesions, especially in horses, suggests that they are subjected to high workloads. Therefore, it is essential to train owners in aspects related to the importance of providing their equids with adequate rest periods to recover from work and develop actions to strengthen human-equine interaction.

NIFtHool: an informatics program for identification of NifH proteins using deep neural networks.

Atmospheric nitrogen fixation carried out by microorganisms has environmental and industrial importance, related to the increase of soil fertility and productivity. The present work proposes the development of a new high precision system that allows the recognition of amino acid sequences of the nitrogenase enzyme (NifH) as a promising way to improve the identification of diazotrophic bacteria. For this purpose, a database obtained from UniProt built a processed dataset formed by a set of 4911 and 4782 amino acid sequences of the NifH and non-NifH proteins respectively. Subsequently, the feature extraction was developed using two methodologies: (i) k-mers counting and (ii) embedding layers to obtain numerical vectors of the amino acid chains. Afterward, for the embedding layer, the data was crossed by an external trainable convolutional layer, which received a uniform matrix and applied convolution using filters to obtain the feature maps of the model. Finally, a deep neural network was used as the primary model to classify the amino acid sequences as NifH protein or not. Performance evaluation experiments were carried out, and the results revealed an accuracy of 96.4%, a sensitivity of 95.2%, and a specificity of 96.7%. Therefore, an amino acid sequence-based feature extraction method that uses a neural network to detect N-fixing organisms is proposed and implemented. NIFtHool is available from: https://nifthool.anvil.app/.

Wearable sensors in the diagnosis and study of Parkinson's disease symptoms: a systematic review.

Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.

The application of single cell gel electrophoresis or comet assay to human monitoring studies

Objetivo. En la búsqueda de nuevos marcadores genotóxicos aplicables a estudios de poblaciones humanas expuestos a xenobióticos, la utilización del ensayo de electroforesis en una sola célula se ha propuesto como un método sensible y una buena alternativa. Material y métodos. Esta técnica detecta rompimientos en el ADN de cadena sencilla, así como sitios álcali lábiles y sitios retardados de reparación. Resultados. En este trabajo, presentamos nuestra experiencia utilizando este ensayo en poblaciones humanas expuestas ocupacionalmente o ambientalmente a diferentes xenobióticos. Conclusiones. Se discute la posible utilidad de este ensayo como un biomarcador de efecto genotóxico

Policondrite recidivante com traqueobroncomalácia grave: a propósito de um caso clínico raro / Relapsing polychondritis with severe tracheobronchomalacia: case report

Os autores apresentam um caso clínico de um doente do sexo feminino de 59 anos de idade com poliartrite assimétrica, seronegativa, näo nodular, näo erosiva e näo deformante, com três anos de evoluçäo. Simultaneamente com o quadro articular, a doente apresentou condrite nasal e auricular bilateral. Em dezembro de 1994, foi internada por dispnéia intensa, näo tolerando o decúbito, e expectoraçäo mucosa. A tomografia computadorizada (TC) do tórax e a broncofibroscopia revelaram diminuiçäo do calibre da traquéia e dos brônquios principais, com colapso da sua parede posterior e ausência quase completa dos anéis da traquéia. A terapêutica com corticosteróide e com broncodilatadores näo foi eficaz, pelo que foi colocada prótese dinâmica de Freitag. Durante a colocaçäo da prótese a doente fez parada cardiorrespiratória e faleceu. Näo se encontra descrita uma traqueobroncomalácia täo marcada como neste caso. Os casos descritos referem-se apenas a traqueomalácia sem envolvimento brônquio explícito, o que acentua mais a gravidade e raridade deste caso clínico