Search results
Found 13819 matches for
Kinetic Pattern Recognition in Home-Based Knee Rehabilitation Using Machine Learning Clustering Methods on the Slider Digital Physiotherapy Device: Prospective Observational Study
Background Recent advancements in rehabilitation sciences have progressively used computational techniques to improve diagnostic and treatment approaches. However, the analysis of high-dimensional, time-dependent data continues to pose a significant problem. Prior research has used clustering techniques on rehabilitation data to identify movement patterns and forecast recovery outcomes. Nonetheless, these initiatives have not yet used force or motion datasets obtained outside a clinical setting, thereby limiting the capacity for therapeutic decisions. Biomechanical data analysis has demonstrated considerable potential in bridging these gaps and improving clinical decision-making in rehabilitation settings. Objective This study presents a comprehensive clustering analysis of multidimensional movement datasets captured using a novel home exercise device, the “Slider”. The aim is to identify clinically relevant movement patterns and provide answers to open research questions for the first time to inform personalized rehabilitation protocols, predict individual recovery trajectories, and assess the risks of potential postoperative complications. Methods High-dimensional, time-dependent, bilateral knee kinetic datasets were independently analyzed from 32 participants using four unsupervised clustering techniques: k-means, hierarchical clustering, partition around medoids, and CLARA (Clustering Large Applications). The data comprised force, laser-measured distance, and optical tracker coordinates from lower limb activities. The optimal clusters identified through the unsupervised clustering methods were further evaluated and compared using silhouette analysis to quantify their performance. Key determinants of cluster membership were assessed, including demographic factors (eg, gender, BMI, and age) and pain levels, by using a logistic regression model with analysis of covariance adjustment. Results Three distinct, time-varying movement patterns or clusters were identified for each knee. Hierarchical clustering performed best for the right knee datasets (with an average silhouette score of 0.637), while CLARA was the most effective for the left knee datasets (with an average silhouette score of 0.598). Key predictors of the movement cluster membership were discovered for both knees. BMI was the most influential determinant of cluster membership for the right knee, where higher BMI decreased the odds of cluster-2 membership (odds ratio [OR] 0.95, 95% CI 0.94-0.96; P<.001) but increased the odds for cluster-3 assignment relative to cluster 1 (OR 1.05, 95% CI 1.03-1.06; P<.001). For the left knee, all predictors of cluster-2 membership were significant (.001≤P≤.008), whereas only BMI (P=.81) could not predict the likelihood of an individual belonging to cluster 3 compared to cluster 1. Gender was the strongest determinant for the left knee, with male participants significantly likely to belong to cluster 3 (OR 3.52, 95% CI 2.91-4.27; P<.001). Conclusions These kinetic patterns offer significant insights for creating personalized rehabilitation procedures, potentially improving patient outcomes. These findings underscore the efficacy of unsupervised clustering techniques in the analysis of biomechanical data for clinical rehabilitation applications.
Modelling the Transmission Dynamics of Tuberculosis in the Ashanti Region of Ghana
Mathematical models can aid in elucidating the spread of infectious disease dynamics within a given population over time. In an attempt to model tuberculosis (TB) dynamics among high-burden districts in the Ashanti Region of Ghana, the SEIR epidemic model with demography was employed within both deterministic and stochastic settings for comparison purposes. The deterministic model showed success in modelling TB infection in the region to the transmission dynamics of the stochastic SEIR model over time. It predicted tuberculosis dying out in ten of twelve high-burden districts in the Ashanti Region, but an outbreak in Obuasi municipal and Amansie West district. The effect of introducing treatment at the incubation stage of TB transmission was also investigated, and it was discovered that treatment introduced at the exposed stage decreased the spread of TB. Branching process approximation was used to derive explicit forms of relevant epidemiological quantities of the deterministic SEIR model for stability analysis of equilibrium points. Numerical simulations were performed to validate the overall infection rate, basic reproductive number, herd immunity threshold, and Malthusian parameter based on bootstrapping, jackknife, and Latin Hypercube sampling schemes. It was recommended that the Ghana Health Service should find a good mechanism to detect TB in the early stages of infection in the region. Public health attention must also be given to districts with a potentially higher risk of experiencing endemic TB even though the estimates of the overall epidemic thresholds from our SEIR model suggested that the Ashanti Region as a whole had herd immunity against TB infection.
Statistical Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana
Most mortality studies usually attribute death to single disease, while various other diseases could also act in the same individual or a population at large. Few works have been done by considering HIV, Tuberculosis (TB), and Hepatitis B (HB) as jointly acting in a population in spite of their high rate of infections in Ghana. This study applied competing risk methods on these three diseases by assuming they were the major risks in the study population. Among all opportunistic infections that could also act within HIV-infected individuals, TB has been asserted to be the most predominant. Other studies have also shown cases of HIV and Hepatitis B coinfections. The validity of these comorbidity assertions was statistically determined by exploring the conditional dependencies existing among HIV, TB, and HB through Bayesian networks or directed graphical model. Through Classification tree, sex and age group of individuals were found as significant demographic predictors that influence the prevalence of HIV and TB. Females were more likely to contract HIV, whereas males were prone to contracting TB.
Spatial and temporal parasite dynamics: microhabitat preferences and infection progression of two co-infecting gyrodactylids
AbstractBackgroundMathematical modelling of host-parasite systems has seen tremendous developments and broad applications in theoretical and applied ecology. The current study focuses on the infection dynamics of a gyrodactylid-fish system. Previous experimental studies have explored the infrapopulation dynamics of co-infecting ectoparasites,Gyrodactylus turnbulliandG. bullatarudis, on their fish host,Poecilia reticulata, but questions remain about parasite microhabitat preferences, host survival and parasite virulence over time. Here, we use more advanced statistics and a sophisticated mathematical model to investigate these questions based on empirical data to add to our understanding of this gyrodactylid-fish system.MethodsA rank-based multivariate Kruskal-Wallis test coupled with its post-hoc tests and graphical summaries were used to investigate the spatial and temporal parasite distribution of different gyrodactylid strains across different host populations. By adapting a multi-state Markov model that extends the standard survival models, we improved previous estimates of survival probabilities. Finally, we quantified parasite virulence of three different strains as a function of host mortality and recovery across different fish stocks and sexes.ResultsWe confirmed that the captive-bredG. turnbulliand wildG. bullatarudisstrains preferred the caudal and rostral regions respectively across different fish stocks; however, the wildG. turnbullistrain changed microhabitat preference over time, indicating microhabitat preference of gyrodactylids is host and time dependent. The average time of host infection before recovery or death was between 6 and 14 days. For this gyrodactylid-fish system, a longer period of host infection led to a higher chance of host recovery. Parasite-related mortalities are host, sex and time dependent, whereas fish size is confirmed to be the key determinant of host recovery.ConclusionFrom existing empirical data, we provided new insights into the gyrodactylid-fish system. This study could inform the modelling of other host-parasite interactions where the entire infection history of the host is of interest by adapting multi-state Markov models. Such models are under-utilised in parasitological studies and could be expanded to estimate relevant epidemiological traits concerning parasite virulence and host survival.Graphical Abstract
Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana
Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.
Determinants of durable humoral and T cell immunity in myeloma patients following COVID‐19 vaccination
AbstractObjectiveTo describe determinants of persisting humoral and cellular immune response to the second COVID‐19 vaccination among patients with myeloma.MethodsThis is a prospective, observational study utilising the RUDYstudy.org platform. Participants reported their second and third COVID‐19 vaccination dates. Myeloma patients had an Anti‐S antibody level sample taken at least 21 days after their second vaccination and a repeat sample before their third vaccination.Results60 patients provided samples at least 3 weeks (median 57.5 days) after their second vaccination and before their third vaccination (median 176.0 days after second vaccine dose). Low Anti‐S antibody levels (<50 IU/mL) doubled during this interval (p = .023) and, in the 47 participants with T‐spot data, there was a 25% increase negative T‐spot tests (p = .008). Low anti–S antibody levels prior to the third vaccination were predicted by lower Anti‐S antibody level and negative T‐spot status after the second vaccine. Independent determinants of a negative T‐spot included increasing age, previous COVID infection, high CD4 count and lower percentage change in Anti‐S antibody levels.ConclusionsNegative T‐spot results predict low Anti‐S antibody levels (<50 IU/mL) following a second COVID‐19 vaccination and a number of biomarkers predict T cell responses in myeloma patients.
Mathematical Modelling of Parasite Dynamics: A Stochastic Simulation-Based Approach and Parameter Estimation via Modified Sequential-Type Approximate Bayesian Computation
AbstractThe development of mathematical models for studying newly emerging and re-emerging infectious diseases has gained momentum due to global events. The gyrodactylid-fish system, like many host-parasite systems, serves as a valuable resource for ecological, evolutionary, and epidemiological investigations owing to its ease of experimental manipulation and long-term monitoring. Although this system has an existing individual-based model, it falls short in capturing information about species-specific microhabitat preferences and other biological details for different Gyrodactylus strains across diverse fish populations. This current study introduces a new individual-based stochastic simulation model that uses a hybrid $$\tau $$ τ -leaping algorithm to incorporate this essential data, enhancing our understanding of the complexity of the gyrodactylid-fish system. We compare the infection dynamics of three gyrodactylid strains across three host populations. A modified sequential-type approximate Bayesian computation (ABC) method, based on sequential Monte Carlo and sequential importance sampling, is developed. Additionally, we establish two penalised local-linear regression methods (based on L1 and L2 regularisations) for ABC post-processing analysis to fit our model using existing empirical data. With the support of experimental data and the fitted mathematical model, we address open biological questions for the first time and propose directions for future studies on the gyrodactylid-fish system. The adaptability of the mathematical model extends beyond the gyrodactylid-fish system to other host-parasite systems. Furthermore, the modified ABC methodologies provide efficient calibration for other multi-parameter models characterised by a large set of correlated or independent summary statistics.
Predictors of starting and stopping chemsex in men who have sex with men in England: findings from the AURAH2 prospective study
BackgroundChemsex (the use of psychoactive drugs in sexual contexts) has been associated with HIV acquisition and other STIs, so there is benefit in identifying those most likely to start chemsex to offer risk reduction interventions such as pre-exposure prophylaxis (PrEP). To date, there have been no data from a longitudinal study analysing factors most associated with starting and stopping chemsex.MethodsThe prospective cohort study, Attitudes to and Understanding Risk of Acquisition of HIV over Time (AURAH2), collected 4 monthly and annual online questionnaire data from men who have sex with men (MSM) from 2015 to 2018. We investigate the association of sociodemographic factors, sexual behaviours and drug use with starting and stopping chemsex among 622 men who completed at least one follow-up questionnaire. Poisson models with generalised estimating equations were used to produce risk ratios (RRs) accounting for multiple starting or stopping episodes from the same individual. Multivariable analysis was adjusted for age group, ethnicity, sexual identity and university education.FindingsIn the multivariable analysis, the under 40 age group was significantly more likely to start chemsex by the next assessment (RR 1.79, 95% CI 1.12 to 2.86). Other factors which showed significant association with starting chemsex were unemployment (RR 2.10, 95% CI 1.02 to 4.35), smoking (RR 2.49, 95% CI 1.63 to 3.79), recent condomless sex (CLS), recent STI and postexposure prophylaxis (PEP) use in the past year (RR 2.10, 95% CI 1.33 to 3.30). Age over 40 (RR 0.71, 95% CI 0.51 to 0.99), CLS, and use of PEP (RR 0.64, 95% CI 0.47 to 0.86) and PrEP (RR 0.47, 95% CI 0.29 to 0.78) were associated with lower likelihood of stopping chemsex by the next assessment.InterpretationKnowledge of these results allows us to identify men most likely to start chemsex, thus providing an opportunity for sexual health services to intervene with a package of risk mitigation measures, especially PrEP use.