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A presença do invasor mexilhão dourado em águas brasileiras acarreta em impactos ecológicos ou econômicos. Uma alternativa de controle populacional é promissora: a imersão de uma espécie geneticamente modificada com o objetivo de, em contato com o molusco selvagem, produzir gerações inférteis. Uma proposta de modelo matemático que contemple tal competição torna-se importante, uma vez que pode fornecer informações para nortear futuros experimentos. Contudo, o modelo de competição necessita de uma dinâmica de crescimento bem estabelecida. Este trabalho apresenta dois modelos para a dinâmica do mexilhão selvagem, estudados de forma analítica. O estudo expõe as soluções de equilíbrio, sua estabilidade local nos pontos estacionários e uma análise de sensibilidade do números de reprodução basal (R_0's). Com isso, define-se o modelo mais adequado para receber as parcelas que modelem a competição. São realizadas simulações numéricas a fim de aferir possíveis desfechos da competição, utilizando o método de Runge-Kutta de quarta ordem. O estudo é capaz de apresentar dois pontos conclusivos importantes: que através da competição planejada a erradicação pode ser atingida e que há necessidade de vantagens competitivas para que a estratégia funcione. Este estudo ressalta a necessidade de vantagens competitivas para o mexilhão modificado. Das possíveis vantagens, o trabalho indica a eficiência reprodutiva e a taxa de mortalidade do mexilhão modificado como as mais promissoras. À vista de um tópico tão importante, este trabalho é necessário para que essa estratégia de controle populacional seja estudada e testada para auxiliar no desenvolvimento da espécie modificada de forma a respeitar a ecologia.

Background: Remote ischemic conditioning (RIC), exposure of body parts to brief periods of circulatory occlusion and reperfusion, has been shown to improve cardiovascular responses to exercise in healthy individuals but its effects in people with MS are unknown. Objective: This study aimed to assess the effect of RIC on heart rate responses to walking in people with MS. Design: Double blind randomized controlled trial Setting: Multiple sclerosis clinic of tertiary care center teaching hospital in the United Kingdom. Methods: Three cycles of RIC were delivered by occluding the upper arm with a blood pressure cuff inflated to a pressure of 30 mmHg above the systolic blood pressure. In the sham group, the blood pressure cuff was inflated to 30 mmHg below diastolic blood pressure. Heart rate responses to the 6-minute walk test (6MWT), the tolerability of RIC using a numerical rating scale for discomfort (0-10), and adverse events were studied. Results: Seventy-five participants (RIC -38 and Sham-37) completed the study. RIC was well tolerated. Compared to sham, RIC significantly decreased the rise in heart rate (P = 0.04) and percentage of predicted maximum heart rate (P = 0.016) after the 6MWT. Conclusion: RIC was well tolerated and improved the heart rate response to walking in people with MS. Further studies on RIC in the management of MS are needed.

This work shows the construction of a simple, functional and real-time application to estimate the population dynamics of the Golden Mussel, an invasive species that has been causing great ecological and economic damage, particularly to the electricity sector by obstructing water collection routes of hydroelectric plant equipment. The tool Golden Mussel App, developed with Shiny package of the R©, programming language, aims to enable professionals engaged in harm reduction in the energy generation sector to follow the temporal evolution of populations, even without advanced knowledge about the mathematical modeling of the problem. In this way, the application configures itself as an auxiliary tool in the planning of actions to control the Golden Mussel

Degenerative neurological diseases, although common in the population, are difficult to diagnose. However, it is known that most of them are directly related to the so-called oxidative stress. Understanding and evaluating how this process takes place is of great interest and, in this sense, this work extends the existing mathematical and computational models for the oxidative stress process (REIS, 2005; REIS et al., 2006; VIANNA, 2005; VIANNA; REIS; CARVALHO, 2012), incorporating an aspect not previously evaluated, the influence of homocysteine indices on the oxidants present in this process. The results indicate that hyperhomocysteinemia can in fact cause oxidative stress and consequent neuronal death, leading to the appearance of neurodegenerative diseases.

Abstract The current COVID-19 pandemic is affecting different countries in different ways. The assortment of reporting techniques alongside other issues, such as underreporting and budgetary constraints, makes predicting the spread and lethality of the virus a challenging task. This work attempts to gain a better understanding of how COVID-19 will affect one of the least studied countries, namely Brazil. Currently, several Brazilian states are in a state of lock-down. However, there is political pressure for this type of measures to be lifted. This work considers the impact that such a termination would have on how the virus evolves locally. This was done by extending the SEIR model with an on / off strategy. Given the simplicity of SEIR we also attempted to gain more insight by developing a neural regressor. We chose to employ features that current clinical studies have pinpointed has having a connection to the lethality of COVID-19. We discuss how this data can be processed in order to obtain a robust assessment.

ABSTRACT Golden mussel is an invasive species in Brazil which impacts local environments, dislocating native species and altering microecological conditions as well as affecting hydroelectric power plants and water treatment systems. The objective of this research is to establish a method that is both effective and efficient to quantify the population of the Golden mussel in hydroelectric power plant reservoirs, with a focus on population control measures. A two-dimensional mathematical model was developed combining hydrodynamics and populational dynamics to simulate the distribution of mussels in a reservoir. The results showed that dam’s region was progressively infested, and after 18 months of simulation it has reached around 80% of its carrying capacity. The method proved to be satisfactory and the generated map of cluster locations for the golden mussel corresponds to field observations. Furthermore, the result of the algae density simulation matched chlorophyll-a density map obtained from satellite images. The methodology can be further applied to new areas and could be expanded to predict population variations in order to guide environmental measures for preservation and recovery of impacted reservoirs, presenting another tool for hydroelectric operators who can use information together with field inspections to plan maintenance intervals before infestation damages equipment.

This paper introduces a mathematical model to describe the dynamics of the spread of H1N1 flu in a human population. The model is comprised of a system of ordinary differential equations that involve susceptible, exposed, infected and recovered/immune individuals. The distinguishing feature in the proposed model with respect to other models in the literature is that it takes into account the possibility of infection due to immunity loss over time. The acquired immunity comes from self-recovery or via vaccination. Furthermore, the proposed model strives to find an optimal vaccination strategy by means of an optimal control problem and Pontryagin’s Maximum Principle.

Objective: Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Methods: Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)). Results: Models accuracy ranged between 70.42-88.73% (AUC: 78.4-94.5%) with a sensitivity of 72.84-90.12% and a specificity of 60.3-86.89%. Signal-based digital gait characteristics independently gave 87.32% accuracy. The most influential characteristics in the classification models were related to root mean square values, power spectral density, step velocity and length, gait regularity and age. Conclusions: This study highlights the importance of signal-based gait characteristics in the development of tools to help classify PD in the early stages of the disease.

Introduction There are no disease-modifying treatments for Parkinson’s disease (PD). We undertook the first drug screen in PD patient tissue and idntified ursodeoxycholic acid (UDCA) as a promising mitochondrial rescue agent. The aims of this trial are to determine safety and tolerability of UDCA in PD at 30 mg/kg, confirm the target engagement of UDCA, apply a novel motion sensor-based approach to quantify disease progression objectively, and estimate the mean effect size and its variance on the change in motor severity.Methods and analysis This is a phase II, two-centre, double-blind, randomised, placebo-controlled trial of UDCA at a dose of 30 mg/kg in 30 participants with early PD. Treatment duration is 48 weeks, followed by an 8-week washout phase. Randomisation is 2:1, drug to placebo. Assessments are performed at baseline, week 12, 24, 36, 48 and 56. The primary outcome is safety and tolerability. Secondary outcomes will compare the change between baseline and week 48 using the following three approaches: the Movement Disorders Society Unified Parkinson’s Disease Rating Scale Part 3 in the practically defined ‘OFF’ medication state; confirmation of target engagement, applying 31Phosphorus MR Spectroscopy to assess the levels of ATP and relevant metabolites in the brain; and objective quantification of motor impairment, using a validated, motion sensor-based approach. The primary outcome will be reported using descriptive statistics and comparisons between treatment groups. For each secondary outcome, the change from baseline will be summarised within treatment groups using summary statistics and appropriate statistical tests assessing for significant differences. All outcomes will use an intention-to-treat analysis population.Ethics and dissemination This trial has been approved by the East of England – Cambridgeshire and Hertfordshire Research Ethics committee. Results will be disseminated in peer-reviewed journals, presentations at scientific meetings and to patients in a lay-summary format.Trial registration number NCT03840005.

Background Remote ischaemic preconditioning (RIPC) is the exposure of body parts to brief periods of circulatory occlusion and reperfusion. Recent studies have also shown that RIPC can improve exercise performance in healthy individuals.Objective This study aimed to assess the effect of RIPC on walking in people with multiple sclerosis (MS).Methods This was a double-blind randomised controlled clinical trial. We used three cycles of RIPC delivered by occluding the upper arm with a blood pressure (BP) cuff inflated to a pressure of 30 mm Hg above the systolic BP. In patients in the sham intervention group, the BP cuff was inflated only to 30 mm Hg below diastolic BP. Outcome measures included the Six-Minute Walk Test (6MWT), gait speed, the Borg rate of perceived exertion (RPE) scale, the tolerability of the RIPC using a Numerical Rating Scale for discomfort from 0 to 10, and adverse events. We identified responders meeting the minimal clinically important difference (MCID) established in the literature in each group.Results Seventy-five participants completed the study (RIPC: 38 and Sham: 37). The distance walked during the 6MWT improved by 1.9% in the sham group and 5.7% in the RIPC group (p=0.012). The number of responders meeting MCID criteria in the RIPC group was significantly greater compared with the sham intervention group. No serious adverse events occurred.Conclusion Single cycle of RIPC resulted in immediate improvement in walking distances during 6MWT in people with MS.Trial registration numbers NCT03153553

Abstract This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987.

Periprosthetic joint infections are still a challenge in orthopedics and traumatology. Nowadays, genomics comes to the aid of diagnosis and treatment, in addition to traditional methods. Recently, a key role of the intestinal microbiota has been postulated, and great efforts are aimed at discovering its interconnection, which shows to be at different levels. Firstly, the gut microbiome influences the immune system through the gut-associated lymphoid tissue (GALT). A balanced microbiome promotes a strong immune response, which is essential to prevent all local and systemic infections, including PJI. Thus, a dysbiosis, i.e., the disruption of this system, leads to an imbalance between the various strains of microorganisms co-existing in the gut microbiome, which can result in a weakened immune system, increasing susceptibility to infections, including PJI. Additionally, the dysbiosis can result in the production of pro-inflammatory mediators that enter the systemic circulation, creating a state of chronic inflammation that can compromise the immune system’s ability to fend off infections. Furthermore, the microbiome maintains the integrity of the gut barrier, preventing the translocation of harmful bacteria and endotoxins into the bloodstream; dysbiosis can compromise this protective “wall”. In addition, the gut microbiome may harbor antibiotic-resistance genes; during antibiotic treatment for other infections or prophylaxis, these genes may be transferred to pathogenic bacteria, making the treatment of PJI more difficult. In this complex landscape, next-generation sequencing (NGS) technology can play a key role; indeed, it has revolutionized the study of the microbiome, allowing for detailed and comprehensive analysis of microbial communities. It offers insights into the functional potential and metabolic capabilities of the microbiome, studies the collective genome of the microbiome directly from environmental samples sequencing DNA without isolating individual organisms, analyzes the RNA transcripts to understand gene expression and functional activity of the microbiome, analyzes the RNA transcripts to understand gene expression and functional activity of the microbiome, investigates the metabolites produced by the microbiome and studies the entire set of proteins produced by the microbiome. NGS technology, the study of the micromyoma and its implications in the field of orthopedic trauma are innovative topics on which few publications are yet to be found in the international scientific literature. The costs are still high, the focus of research is maximum, and it will certainly change our approach to infections. Our study is an up-to-date review of the hot topic application of NGS in the study and investigation of periprosthetic infections and the microbiome.

BackgroundWrist-worn inertial sensors are used in digital health for evaluating mobility in real-world environments. Preceding the estimation of spatiotemporal gait parameters within long-term recordings, gait detection is an important step to identify regions of interest where gait occurs, which requires robust algorithms due to the complexity of arm movements. While algorithms exist for other sensor positions, a comparative validation of algorithms applied to the wrist position on real-world data sets across different disease populations is missing. Furthermore, gait detection performance differences between the wrist and lower back position have not yet been explored but could yield valuable information regarding sensor position choice in clinical studies. ObjectiveThe aim of this study was to validate gait sequence (GS) detection algorithms developed for the wrist position against reference data acquired in a real-world context. In addition, this study aimed to compare the performance of algorithms applied to the wrist position to those applied to lower back–worn inertial sensors. MethodsParticipants with Parkinson disease, multiple sclerosis, proximal femoral fracture (hip fracture recovery), chronic obstructive pulmonary disease, and congestive heart failure and healthy older adults (N=83) were monitored for 2.5 hours in the real-world using inertial sensors on the wrist, lower back, and feet including pressure insoles and infrared distance sensors as reference. In total, 10 algorithms for wrist-based gait detection were validated against a multisensor reference system and compared to gait detection performance using lower back–worn inertial sensors. ResultsThe best-performing GS detection algorithm for the wrist showed a mean (per disease group) sensitivity ranging between 0.55 (SD 0.29) and 0.81 (SD 0.09) and a mean (per disease group) specificity ranging between 0.95 (SD 0.06) and 0.98 (SD 0.02). The mean relative absolute error of estimated walking time ranged between 8.9% (SD 7.1%) and 32.7% (SD 19.2%) per disease group for this algorithm as compared to the reference system. Gait detection performance from the best algorithm applied to the wrist inertial sensors was lower than for the best algorithms applied to the lower back, which yielded mean sensitivity between 0.71 (SD 0.12) and 0.91 (SD 0.04), mean specificity between 0.96 (SD 0.03) and 0.99 (SD 0.01), and a mean relative absolute error of estimated walking time between 6.3% (SD 5.4%) and 23.5% (SD 13%). Performance was lower in disease groups with major gait impairments (eg, patients recovering from hip fracture) and for patients using bilateral walking aids. ConclusionsAlgorithms applied to the wrist position can detect GSs with high performance in real-world environments. Those periods of interest in real-world recordings can facilitate gait parameter extraction and allow the quantification of gait duration distribution in everyday life. Our findings allow taking informed decisions on alternative positions for gait recording in clinical studies and public health. Trial RegistrationISRCTN Registry 12246987; https://www.isrctn.com/ISRCTN12246987 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-050785