Comparative evaluation of the STANDARD M10 and Xpert C. difficile assays for detection of toxigenic Clostridioides difficile in stool specimens.

This study compared the performance of two commercial molecular assays, the STANDARD M10 Clostridioides difficile assay (M10) and the Xpert C. difficile assay (Xpert), for detecting toxigenic C. difficile in stool specimens. A total of 487 consecutive stool specimens submitted for routine C. difficile testing between June and November 2023 were included. Following routine testing using C. DIFF QUIK CHEK COMPLETE (QCC), M10 and Xpert were tested in parallel, alongside toxigenic culture (reference standard). Additionally, two-step algorithms, using QCC on the first step and either M10 or Xpert on the second step, were assessed. Both M10 and Xpert demonstrated a sensitivity and negative predictive value (NPV) of 100%. M10 exhibited significantly higher specificity and positive predictive value (PPV; 91.9% and 64.2%, respectively) than Xpert (90.3% and 59.8%, respectively). Both two-step algorithms showed a sensitivity and NPV of 98.4% and 99.8%, respectively. The specificity and PPV of the two-step algorithm using M10 (95.2% and 75.0%, respectively) were slightly higher than those of the one using Xpert (94.8% and 73.2%, respectively), without statistical significance. Receiver operating characteristic curve analysis, assessing the predictive ability of cycle threshold (Ct) values for the detection of free toxin, exhibited an area under the curve of 0.825 for M10 and 0.843 for Xpert. This indicates the utility of Ct values as predictors for the detection of free toxin in both assays. In conclusion, M10 proves to be an effective diagnostic tool with performance comparable to Xpert, whether utilized independently or as part of a two-step algorithm.

Precision engineered peptide targeting leukocyte extracellular traps mitigate acute kidney injury in Crush syndrome.

Crush syndrome induced by skeletal muscle compression causes fatal rhabdomyolysis-induced acute kidney injury (RIAKI) that requires intensive care, including hemodialysis. However, access to crucial medical supplies is highly limited while treating earthquake victims trapped under fallen buildings, lowering their chances of survival. Developing a compact, portable, and simple treatment method for RIAKI remains an important challenge. Based on our previous finding that RIAKI depends on leukocyte extracellular traps (ETs), we aimed to develop a novel medium-molecular-weight peptide to provide clinical treatment of Crush syndrome. We conducted a structure-activity relationship study to develop a new therapeutic peptide. Using human peripheral polymorphonuclear neutrophils, we identified a 12-amino acid peptide sequence (FK-12) that strongly inhibited neutrophil extracellular trap (NET) release in vitro and further modified it by alanine scanning to construct multiple peptide analogs that were screened for their NET inhibition ability. The clinical applicability and renal-protective effects of these analogs were evaluated in vivo using the rhabdomyolysis-induced AKI mouse model. One candidate drug [M10Hse(Me)], wherein the sulfur of Met10 is substituted by oxygen, exhibited excellent renal-protective effects and completely inhibited fatality in the RIAKI mouse model. Furthermore, we observed that both therapeutic and prophylactic administration of M10Hse(Me) markedly protected the renal function during the acute and chronic phases of RIAKI. In conclusion, we developed a novel medium-molecular-weight peptide that could potentially treat patients with rhabdomyolysis and protect their renal function, thereby increasing the survival rate of victims affected by Crush syndrome.

Clinical performance of the STANDARD M10 SARS-CoV-2 rapid RT-PCR assay in patients visiting an emergency department.

In emergency departments, rapid screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was important for arranging limited isolation resources and patient care during the coronavirus disease 2019 (COVID-19) pandemic. STANDARD M10 SARS-CoV-2 (SD Biosensor) is a recently developed cartridge-based RT-PCR that provides a turnaround time of 1 h, which is shorter than that for conventional RT-PCR. This study evaluated the clinical performance of STANDARD M10 in patients visiting an emergency department. From March to June 2022, two specimens were collected from patients visiting an emergency department. Each specimen comprised one nasopharyngeal and one oropharyngeal swab. Respective specimens underwent rapid RT-PCR using STANDARD M10 and conventional RT-PCR using Allplex SARS-CoV-2 (Seegene). When discordant results occurred, specimens undergoing the STANDARD M10 were retested with the Allplex to exclude specimen variations. Retest results replaced initial results of the Allplex. Clinical performance of STANDARD M10 was compared with Allplex. The study enrolled 1971 patients. COVID-19 prevalence was 6.2% based on the Allplex. Compared with the Allplex, overall agreement, positive percent agreement, and negative percent agreement of STANDARD M10 were 99.5% (95% CI: 99.1%-99.8%), 95.9% (95% CI: 90.8%-98.3%), and 99.8% (95% CI: 99.4%-99.9%), respectively. Nine discordant results were all positive on droplet digital PCR, except for one specimen that was positive with STANDARD M10. The STANDARD M10 showed reliable diagnostic performance for detecting SARS-CoV-2 from patients visiting in emergency departments and is a useful tool in emergency healthcare systems because of its easy-to-use cartridge-based assay and short resulting time for detecting SARS-CoV-2.

Under-reported time-varying MINAR(1) process for modeling multivariate count series.

A time-varying multivariate integer-valued autoregressive of order one (tvMINAR(1)) model is introduced for the non-stationary time series of correlated counts when under-reporting is likely present. A non-diagonal autoregression probability network is structured to preserve the cross-correlation of multivariate series, provide a necessary condition to ease model-fittings computations, and derive the full likelihood using the Viterbi algorithm. The motivating construction applies to fully under-reported counts that rely on a mixture presentation of the random thinning operator. Simulation studies are conducted to examine the proposed model, and the analysis of COVID-19 daily cases is accomplished to highlight its usefulness in applications. Finally, the comparison of models is presented using the posterior predictive checking method.

Api m 6 and Api m 10 as Major Allergens in Patients With Honeybee Venom Allergy.

BACKGROUND: Component-resolved diagnosis plays a key role in the diagnosis and treatment of honeybee venom allergy (HVA). Our aim was to study whether any of the allergens not included in the usual diagnostic platforms are relevant in our population. MATERIAL AND METHODS: The allergenic sensitization profile of Spanish patients who experienced a systemic reaction after a honeybee sting and were diagnosed with HVA was studied by immunoblotting based on raw autochthonous Apis mellifera venom characterized using SDS-PAGE and mass spectrometry and a commercial assay (ImmunoCAP). RESULTS: Allergens in the International Union of Immunological Societies database were detected in the raw A mellifera venom extract used, except Api m 12. Sera from 51 patients with a median (IQR) age of 46.2 years (35.6-54.6) were analyzed. ImmunoCAP revealed Api m 1 and Api m 10 to be major allergens (88.2% and 74.5%, respectively). Moreover, Api m 6 (85.4%) was detected by immunoblotting. CONCLUSION: Api m 1, Api m 6, and Api m 10 are major A mellifera venom allergens in our population.

Comparative Pharmacokinetics and Tissue Distribution of M10 and Its Metabolite Myricetin in Normal and Dextran-Sodium-Sulfate-Induced Colitis Mice.

M10, a novel myricetin derivative, is an anti-inflammatory agent designed for treatment of colitis. Here, we aim to investigate its pharmacokinetic behavior and tissue distribution in a mouse model with colitis. Pharmacokinetics and tissue distribution of M10 and its metabolite myricetin were compared in normal mice and in dextran-sodium-sulfate (DSS)-induced colitis mice. The role of fecal microbiota was also analyzed during metabolism of M10 in vitro. After oral administration, M10 was very low in the plasma of both normal and diseased mice. However, both M10 and myricetin were mainly distributed in the gastrointestinal tract, including the stomach, colon and small intestine, in physiological and pathological conditions. Significantly, M10 and myricetin were found in higher levels in gastrointestinal tracts with inflamed tissues than in normal tissues of mice. An in vitro assay revealed that 80% of M10 was metabolized to myricetin via fecal microbiota. After oral administration, M10 was not absorbed into circulation but mainly distributed in the inflamed submucosal tissues of colitic mice, where it was metabolized into myricetin to prevent colitis development.

Central hubs prediction for bio networks by directed hypergraph - GA with validation to COVID-19 PPI.

Network structures have attracted much interest and have been rigorously studied in the past two decades. Researchers used many mathematical tools to represent these networks, and in recent days, hypergraphs play a vital role in this analysis. This paper presents an efficient technique to find the influential nodes using centrality measure of weighted directed hypergraph. Genetic Algorithm is exploited for tuning the weights of the node in the weighted directed hypergraph through which the characterization of the strength of the nodes, such as strong and weak ties by statistical measurements (mean, standard deviation, and quartiles) is identified effectively. Also, the proposed work is applied to various biological networks for identification of influential nodes and results shows the prominence the work over the existing measures. Furthermore, the technique has been applied to COVID-19 viral protein interactions. The proposed algorithm identified some critical human proteins that belong to the enzymes TMPRSS2, ACE2, and AT-II, which have a considerable role in hosting COVID-19 viral proteins and causes for various types of diseases. Hence these proteins can be targeted in drug design for an effective therapeutic against COVID-19.

A robust numerical two-level second-order explicit approach to predicting the spread of Covid-2019 pandemic with undetected infectious cases.

This paper develops a numerical two-level explicit approach for solving a mathematical model for the spread of Covid-19 pandemic with that includes the undetected infectious cases. The stability and convergence rate of the new numerical method are deeply analyzed in the L ∞ -norm. The proposed technique is less time consuming than a broad range of related numerical schemes. Furthermore, the method is stable, and at least second-order accurate and it can serve as a robust tool for the integration of general ODEs systems of initial-value problems. Some numerical experiments are provided which include the pandemic in Cameroon, and discussed.

Two-step functional screen on multiple proteinaceous substrates reveals temperature-robust proteases with a broad-substrate range.

To support the bio-based industry in development of environment-friendly processes and products, an optimal toolbox of biocatalysts is key. Although functional screen of (meta)genomic libraries may potentially contribute to identifying new enzymes, the discovery of new enzymes meeting industry compliance demands is still challenging. This is particularly noticeable in the case of proteases, for which the reports of metagenome-derived proteases with industrial applicability are surprisingly limited. Indeed, proteolytic clones have been typically assessed by its sole activity on casein or skim milk and limited to mild screening conditions. Here, we demonstrate the use of six industry-relevant animal and plant by-products, namely bone, feather, blood meals, gelatin, gluten, and zein, as complementary substrates in functional screens and show the utility of temperature as a screening parameter to potentially discover new broad-substrate range and robust proteases for the biorefinery industry. By targeting 340,000 clones from two libraries of pooled isolates of mesophilic and thermophilic marine bacteria and two libraries of microbial communities inhabiting marine environments, we identified proteases in four of eleven selected clones that showed activity against all substrates herein tested after prolonged incubation at 55 °C. Following sequencing, in silico analysis and recombinant expression in Escherichia coli, one functional protease, 58% identical at sequence level to previously reported homologs, was found to readily hydrolyze highly insoluble zein at temperatures up to 50 °C and pH 9-11. It is derived from a bacterial group whose ability to degrade zein was unknown. This study reports a two-step screen resulting in identification of a new marine metagenome-derived protease with zein-hydrolytic properties at common biomass processing temperatures that could be useful for the modern biorefinery industry. KEY POINTS: • A two-step multi-substrate strategy for discovery of robust proteases. • Feasible approach for shortening enzyme optimization to industrial demands. • A new temperature-tolerant protease efficiently hydrolyzes insoluble zein.

Perspectives on updates, clarifications and controversies in chromatographic assay guidance for bioanalytical method validation from major regulatory agencies and organizations.

Bioanalysis, a key supporting function for generating data for pre-clinical and clinical studies in drug development, is under the regulation of local agencies as well as global organizations to ensure the data integrity and quality in submission. As major regulatory agencies and organizations, the US Food and Drug Administration, the European Medicines Agency and the International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use have been updating their industry guidance for bioanalytical method validation, to keep up with the development new modalities, technologies and regulations. This article summarizes the recent updates and any clarifications and controversies triggered by those updates. Perspectives and recommendations are given based on our own experience as well as commonly accepted practice in the bioanalytical community.

Forecasting COVID-19 pandemic using optimal singular spectrum analysis.

Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.

WEAK SINDy: GALERKIN-BASED DATA-DRIVEN MODEL SELECTION.

We present a novel weak formulation and discretization for discovering governing equations from noisy measurement data. This method of learning differential equations from data fits into a new class of algorithms that replace pointwise derivative approximations with linear transformations and variance reduction techniques. Compared to the standard SINDy algorithm presented in [S. L. Brunton, J. L. Proctor, and J. N. Kutz, Proc. Natl. Acad. Sci. USA, 113 (2016), pp. 3932-3937], our so-called weak SINDy (WSINDy) algorithm allows for reliable model identification from data with large noise (often with ratios greater than 0.1) and reduces the error in the recovered coefficients to enable accurate prediction. Moreover, the coefficient error scales linearly with the noise level, leading to high-accuracy recovery in the low-noise regime. Altogether, WSINDy combines the simplicity and efficiency of the SINDy algorithm with the natural noise reduction of integration, as demonstrated in [H. Schaeffer and S. G. McCalla, Phys. Rev. E, 96 (2017), 023302], to arrive at a robust and accurate method of sparse recovery.

A grouped beta process model for multivariate resting-state EEG microstate analysis on twins.

EEG microstate analysis investigates the collection of distinct temporal blocks that characterize the electrical activity of the brain. Brain activity within each microstate is stable, but activity switches rapidly between different microstates in a nonrandom way. We propose a Bayesian nonparametric model that concurrently estimates the number of microstates and their underlying behaviour. We use a Markov switching vector autoregressive (VAR) framework, where a hidden Markov model (HMM) controls the nonrandom state switching dynamics of the EEG activity and a VAR model defines the behaviour of all time points within a given state. We analyze the resting-state EEG data from twin pairs collected through the Minnesota Twin Family Study, consisting of 70 epochs per participant, where each epoch corresponds to 2 s of EEG data. We fit our model at the twin pair level, sharing information within epochs from the same participant and within epochs from the same twin pair. We capture within twin-pair similarity, using an Indian buffet process, to consider an infinite library of microstates, allowing each participant to select a finite number of states from this library. The state spaces of highly similar twins may completely overlap while dissimilar twins could select distinct state spaces. In this way, our Bayesian nonparametric model defines a sparse set of states that describe the EEG data. All epochs from a single participant use the same set of states and are assumed to adhere to the same state switching dynamics in the HMM model, enforcing within-participant similarity.

L'analyse des micro-états d'un électroencéphalogramme (EEG) porte sur une collection de différents blocs temporels caractérisant l'activité électrique du cerveau. L'activité cérébrale est stable à l'intérieur de chaque bloc, mais elle varie rapidement entre les différents micro-états de façon non aléatoire. Les auteurs proposent un modèle bayésien non paramétrique qui estime simultanément le nombre de micro-états et leur comportement sous-jacent. Ils utilisent le cadre de vecteurs autorégressifs (VAR) markoviens commutants où un modèle de Markov caché (MMC) contrôle les dynamiques de commutations non aléatoires de l'activité de l'EEG et le modèle de VAR définit le comportement à travers le temps pour un état donné. Ils analysent des données d'EEG au repos de paires de jumeaux collectées dans l'étude des jumeaux du Minnesota comportant 70 époques de deux secondes d'EEG chacune pour chaque participant. Les auteurs ajustent leur modèle au niveau des paires de jumeaux, partageant les informations d'un participant et de son jumeau pour une même époque. Ils capturent les similarités dans la paire de jumeaux avec un processus du buffet indien afin de constituer une bibliothèque infinie de micro-états et de permettre à chaque participant de choisir un ensemble fini d'états provenant de celle-ci. L'espace d'états de jumeaux très semblables peut se chevaucher entièrement alors que des jumeaux différents pourraient avoir des espaces distincts. Le modèle bayésien non paramétrique des auteurs définit ainsi un ensemble creux d'états qui décrivent les données d'EEG. Toutes les époques d'un même participant utilisent le même ensemble d'états, et elles doivent adhérer au même régime de changement d'état pour leur dynamique de commutation selon le MMC, forçant ainsi une similarité intra-participant.

Effect of extremely low frequency electromagnetic field parameters on the proliferation of human breast cancer.

Extremely low-frequency electromagnetic field (ELF-EMF) exposures influence many biological systems. These effects are mainly related to the intensity, duration, frequency, and pattern of the ELF-EMF. Our intent was to characterize the effect of specific pulsed electromagnetic fields on the in vitro proliferation of MCF-7 adenocarcinoma and MDA-MB-231 breast cancer cell lines and one non-cancerous M10 breast epithelial cell line. The following four important parameters of ELF-EMF were examined: frequencies (7.83 ± 0.3, 23.49 ± 0.3, and 39.15 ± 0.3 Hz), flux density (0.5 and 1 mT), exposure duration (12, 24, and 48 h), and the exposure methodology (continuous exposure versus switching exposure). The viability of MDA-MB-231 cells exposed to the optimized ELF-EMF pattern (7.83 ± 0.3 Hz, 1 mT, and 6 h switching exposure) was 40.1%. By contrast, the optimized ELF-EMF parameters that were most cytotoxic to breast cancer MDA-MB-231 cells were not damaging to normal M10 cells. In vitro studies also showed that exposure of MDA-MB-231 cells to the optimized ELF-EMF pattern promoted Ca2+ influx and resulted in apoptosis. These data confirm that exposure to this specific ELF-EMF pattern can influence cellular processes and inhibit cancer cell growth. The specific ELF-EMF pattern determined in this study may provide a potential anti-cancer treatment in the future.

The Honeybee Venom Major Allergen Api m 10 (Icarapin) and Its Role in Diagnostics and Treatment of Hymenoptera Venom Allergy.

PURPOSE OF REVIEW: In Hymenoptera venom allergy, the research focus has moved from whole venoms to individual allergenic molecules. Api m 10 (icarapin) has been described as a major allergen of honeybee venom (HBV) with potentially high relevance for diagnostics and therapy of venom allergy. Here, we review recent studies on Api m 10 characteristics as well as its role in component-resolved diagnostics and potential implications for venom-specific immunotherapy (VIT). RECENT FINDINGS: Api m 10 is a major allergen of low abundance in HBV. It is an obviously unstable protein of unknown function that exhibits homologs in other insect species. Despite its low abundance in HBV, 35 to 72% of HBV-allergic patients show relevant sensitization to this allergen. Api m 10 is a marker allergen for HBV sensitization, which in many cases can help to identify primary sensitization to HBV and, hence, to discriminate between genuine sensitization and cross-reactivity. Moreover, Api m 10 might support personalized risk stratification in VIT, as dominant sensitization to Api m 10 has been identified as risk factor for treatment failure. This might be of particular importance since Api m 10 is strongly underrepresented in some therapeutic preparations commonly used for VIT. Although the role of Api m 10 in HBV allergy and tolerance induction during VIT is not fully understood, it certainly is a useful tool to unravel primary sensitization and individual sensitization profiles in component-resolved diagnostics (CRD). Moreover, a potential of Api m 10 to contribute to personalized treatment strategies in HBV allergy is emerging.

Conley Index Approach to Sampled Dynamics.

The topological method for the reconstruction of dynamics from time series [K. Mischaikow et al., Phys. Rev. Lett., 82 (1999), pp. 1144-1147] is reshaped to improve its range of applicability, particularly in the presence of sparse data and strong expansion. The improvement is based on a multivalued map representation of the data. However, unlike the previous approach, it is not required that the representation has a continuous selector. Instead of a selector, a recently developed new version of Conley index theory for multivalued maps [B. Batko, SIAM J. Appl. Dyn. Syst., 16 (2017), pp. 1587-1617; B. Batko and M. Mrozek, SIAM J. Appl. Dyn. Syst., 15 (2016), pp. 1143-1162] is used in computations. The existence of a continuous, single valued generator of the relevant dynamics is guaranteed in the vicinity of the graph of the multivalued map constructed from data. Some numerical examples based on time series derived from the iteration of Hénon-type maps are presented.

Evolution of Api m10 Specific IgE and IgG4 After One Year of Bee Venom Immunotherapy.

Summary: Background. Bee-venom (BV) anaphylaxis can be life-threatening, requiring treatment with BV immunotherapy (bVIT). Different molecular profiles may be associated with different outcomes after bVIT. Methods. In 19 patients with BV anaphylaxis, sensitized both to Api m1 and Api m10, we evaluated sIgE and sIgG4 Api m1 and Api m10 levels before and after 1 year bVIT.Results.7 patients (37%) had higher baseline Api m10 than Api m1 sIgE levels (Api m10 predominant). bVIT reduced sIgE to both components but sIgG4 levels were increased only for Api m1. 5 patients (2 in the Api m10 predominant group) were re-stung without anaphylaxis. Conclusions. Although there was no increase in Api m10 sIgG4 levels after 1 year bVIT, we did not observe relevant differences in other outcomes between patients with predominant Api m1 or Api m10 sensitization.

M10 peptide attenuates silica-induced pulmonary fibrosis by inhibiting Smad2 phosphorylation.

Silica-induced pulmonary fibrosis is a kind of worldwide occupational disease, and there is no effective treatment at present. Peptide therapy has attracted significant attention due to its simple structure, high selectiveness, strong bioactivity, relative safety, and high patient tolerance. In this study, we first confirmed that M10, a 10 amino acid peptide, has anti-fibrotic effects during the early and late stages of silica-induced fibrosis in mouse models and then partly explored the underlying mechanisms in vitro. M10 was detected in both the cell cytoplasm and nuclei. M10 showed no cytotoxicity to pulmonary epithelial cells and fibroblasts at the given concentrations. Functionally, M10 can reverse the silica-induced EMT process in epithelial cells and decrease TGF-ß1-stimulated fibroblast activation. Further mechanism investigations supported that M10 can block TGF-ß1 signalling by inhibiting phosphorylation of Smad2 protein in vitro and in vivo. All of the results indicate that M10 peptide may be a new method for the treatment of silica-induced pulmonary fibrosis.

Generalizing Koopman Theory to Allow for Inputs and Control.

We develop a new generalization of Koopman operator theory that incorporates the e ects of inputs and control. Koopman spectral analysis is a theoretical tool for the analysis of nonlinear dynamical systems. Moreover, Koopman is intimately connected to dynamic mode decomposition (DMD), a method that discovers coherent, spatio-temporal modes from data, connects local-linear analysis to nonlinear operator theory, and importantly creates an equation-free architecture for the study of complex systems. For actuated systems, standard Koopman analysis and DMD are incapable of producing input-output models; moreover, the dynamics and the modes will be corrupted by external forcing. Our new theoretical developments extend Koopman operator theory to allow for systems with nonlinear input-output characteristics. We show how this generalization is rigorously connected to a recent development called dynamic mode decomposition with control. We demonstrate this new theory on nonlinear dynamical systems, including a standard susceptible-infectious-recovered model with relevance to the analysis of infectious disease data with mass vaccination (actuation).

Estimation of the linear mixed integrated Ornstein-Uhlenbeck model.

The linear mixed model with an added integrated Ornstein-Uhlenbeck (IOU) process (linear mixed IOU model) allows for serial correlation and estimation of the degree of derivative tracking. It is rarely used, partly due to the lack of available software. We implemented the linear mixed IOU model in Stata and using simulations we assessed the feasibility of fitting the model by restricted maximum likelihood when applied to balanced and unbalanced data. We compared different (1) optimization algorithms, (2) parameterizations of the IOU process, (3) data structures and (4) random-effects structures. Fitting the model was practical and feasible when applied to large and moderately sized balanced datasets (20,000 and 500 observations), and large unbalanced datasets with (non-informative) dropout and intermittent missingness. Analysis of a real dataset showed that the linear mixed IOU model was a better fit to the data than the standard linear mixed model (i.e. independent within-subject errors with constant variance).