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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.
Antimicrobial usage among acutely ill hospitalised children aged 2‒23 months in sub-Saharan Africa and South Asia
Abstract Background Understanding patterns of antimicrobial use is critical to supporting antibiotic stewardship and limiting antimicrobial resistance (AMR). We aimed to describe antimicrobial prescribing in acutely ill hospitalised children aged 2-23 months across a range of rural and urban hospital settings in sub-Saharan Africa and South Asia. Methods The CHAIN cohort collected data daily throughout hospitalisation from children with acute illness aged 2-23 months admitted to nine hospitals from November 2016 to January 2019. We determined proportions of children receiving antimicrobials, inpatient-days receiving antimicrobials, antimicrobial classes, WHO Access, Watch, and Reserve (AWaRe) classifications, and examined factors associated with Watch antimicrobial use. Results Of 3101 admissions, 1422 (46%) received antimicrobials prior to hospitalization. 2816 (91%) children received antimicrobials during 19398/21807 (93%) inpatient child-days. 2477 (76%), 1092 (35%), and 12 (0.3%) children received Access, Watch, and Reserve antimicrobials, mostly <48 hours from admission. 341 (11%) of admissions received an antimicrobial without any indication. Prior admission, chronic illness, diagnoses of sepsis or meningitis, hypoglycemia and duration of admission were associated with receiving Watch antimicrobials, whilst WHO danger signs, severe malnutrition, HIV and receipt of prior antimicrobials were not, despite their known association with mortality and AMR. Conclusions Antimicrobial use was similar across sites with some overuse, and notably limited escalation and de-escalation, likely due to guideline adherence. Guidelines need updating for the absence of relevant antimicrobial sensitivities, to include risk-based antimicrobial prescribing considering mortality risk and prior exposure to antimicrobials and the hospital environment. Hence, clinical trials of risk-differentiated care are needed.
Dosing interval is a major factor determining the quality of T cells induced by SARS-CoV-2 mRNA and adenoviral vector vaccines
Functional T cell responses are crucial for protective immunity induced by COVID-19 vaccination, but factors influencing the quality of these responses are incompletely understood. We used an activation-induced marker (AIM) assay and single-cell transcriptomic sequencing to analyze SARS-CoV-2 spike-responsive T cells after mild SARS-CoV-2 infection or after one or two doses of mRNA–lipid nanoparticle (mRNA-LNP) or adenoviral-vectored COVID-19 vaccines. Our findings revealed broad functional and clonal heterogeneity in T cells generated by vaccination or infection, including multiple distinct effector populations. T cell function was largely conserved between COVID-19 vaccine platforms but was distinct compared with SARS-CoV-2 infection. Notably, the dosing interval greatly influenced the quality of T cells after two vaccine doses, particularly after mRNA-LNP vaccination, where a longer interval led to reduced inflammatory signaling and increased secondary proliferation. These insights enhance our understanding of SARS-CoV-2–specific T cells and inform the optimization of mRNA vaccination regimens.
MAIT and other innate-like T cells integrate adaptive immune responses to modulate interval-dependent reactogenicity to mRNA vaccines
Adenoviral (Ad) vectors and mRNA vaccines exhibit distinct patterns of immune responses and reactogenicity, but underpinning mechanisms remain unclear. We longitudinally compared homologous ChAdOx1 nCoV-19 and BNT162b2 vaccination, focusing on cytokine-responsive innate-like lymphocytes—mucosal-associated invariant T (MAIT) cells and Vδ2 + γδ T cells—which sense and tune innate-adaptive cross-talk. Ad priming elicited robust type I interferon (IFN)–mediated innate-like T cell activation, augmenting T cell responses (innate-to-adaptive signaling), which was dampened at boost by antivector immunity. Conversely, mRNA boosting enhanced innate-like responses, driven by prime-induced spike-specific memory T cell–derived IFN-γ (adaptive-to-innate signaling). Extending the dosing interval dampened inflammation at boost because of waning T cell memory. In a separate vaccine trial, preboost spike-specific T cells predicted severe mRNA reactogenicity regardless of the priming platform or interval. Overall, bidirectional innate-like and adaptive cross-talk, and IFN-γ–licensed innate-like T cells, orchestrate interval-dependent early vaccine responses, suggesting modifiable targets for safer, more effective regimens.
Synovial tissue atlas in juvenile idiopathic arthritis reveals pathogenic niches associated with disease severity
Precision application of targeted therapies is urgently needed to improve long-term clinical outcomes for children affected by inflammatory arthritis, known as juvenile idiopathic arthritis (JIA). Progress has been hampered by our limited understanding of the cellular basis of inflammation in the target tissue of the disease, the synovial membrane. Here, we analyzed biopsies from the inflamed joints of treatment-naïve children with JIA, early in the course of their disease, using single-cell RNA sequencing, multiplexed immunofluorescence, and spatial transcriptomics to establish a cellular atlas of the JIA synovium. We identified distinct spatial tissue niches, composed of specific stromal and immune cell populations. In addition, we localized genes linked to arthritis severity and disease risk to effector cell populations, including tissue resident SPP1 + macrophages and fibrin-associated myeloid cells. Combined analyses of synovial fluid and peripheral blood from matched individuals revealed differences in cellular composition, signaling pathways, and transcriptional programs across these distinct anatomical compartments. Furthermore, our analysis revealed several pathogenic cell populations that are shared with adult-onset inflammatory arthritis, as well as age-associated differences in tissue vascularity, prominence of innate immunity, and enrichment of TGF-β–responsive stromal subsets that up-regulate expression of disease risk–associated genes. Overall, our findings demonstrate the need for age-specific analyses of synovial tissue pathology to guide targeted treatment strategies in JIA.
Proposed framework for triage of putative germline variants detected via tumour genomic testing in UK oncology practice.
In the UK, most patients receive publicly funded medical care through the National Health Service (NHS), which funds tumour and/or germline testing for eligible patients with cancer to inform clinical management.Testing on tumour-derived DNA may identify putative heritable variants, with implications for the proband and their wider family, but is not a reliable substitute for germline genetic testing when hereditary cancer predisposition is suspected.The likelihood that a variant identified through tumour testing is of germline origin depends on multiple clinical and technical factors. Certain genotypes significantly influence a patient's cancer risk, and intervention in those carriers may facilitate cancer prevention or early detection, while other genotypes are associated with lower cancer risk, and associated intervention in such cases have limited clinical utility.We convened a national meeting of clinical cancer genetics and scientific leads to rationalise germline follow-up testing of variants identified through tumour-based testing. After contrasting potential approaches, implementation of an NHS-contextualised 'intermediate conservative' approach was agreed and refined by the authors, with the final pathway recirculated to the UK clinical and scientific community for consensus agreement and publication.We outline relevant patient, genetic and technical considerations informing likely origin of variants, a review of current relevant guidance and NHS laboratory practices and a workflow for laboratory and clinical teams to triage tumour-detected variants requiring onward germline follow-up. This approach aims to direct limited resources towards identifying germline variants associated with the greatest potential clinical impact, with a view to supporting more efficient and equitable delivery of genomic medicine in oncology.
Estimates of HIV-1 within-host recombination rates across the whole genome.
Recombination plays a pivotal role in generating within-host diversity and enabling HIV's evolutionary success, particularly in evading the host immune response. Despite this, the variability in recombination rates across different settings and the underlying factors that drive these differences remain poorly understood. In this study, we analysed a large dataset encompassing hundreds of untreated, longitudinally sampled infections using both whole-genome long-read and short-read sequencing datasets. By quantifying recombination rates, we uncover substantial variation across subtypes, viral loads, and stages of infection. We also map recombination hot and cold spots across the genome using a sliding window approach, finding that previously reported inter-subtype regions of high or low recombination are replicated at the within-host level. Importantly, our findings reveal the significant influence of selection on recombination, showing that the presence and success of recombinant genomes is strongly interconnected with the fitness landscape. These results offer valuable insights into the contribution of recombination to evolutionary dynamics and demonstrate the enhanced resolution that long-read sequencing offers for studying viral evolution.