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Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as "extracellular lipid droplets", which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients. [ABSTRACT FROM AUTHOR]

Longitudinal tumour volume data from head-and-neck cancer patients show that tumours of comparable pre-treatment size and stage may respond very differently to the same radiotherapy fractionation protocol. Mathematical models are often proposed to predict treatment outcome in this context, and have the potential to guide clinical decision-making and inform personalised fractionation protocols. Hindering effective use of models in this context is the sparsity of clinical measurements juxtaposed with the model complexity required to produce the full range of possible patient responses. In this work, we present a compartment model of tumour volume and tumour composition, which, despite relative simplicity, is capable of producing a wide range of patient responses. We then develop novel statistical methodology and leverage a cohort of existing clinical data to produce a predictive model of both tumour volume progression and the associated level of uncertainty that evolves throughout a patient’s course of treatment. To capture inter-patient variability, all model parameters are patient specific, with a bootstrap particle filter-like Bayesian approach developed to model a set of training data as prior knowledge. We validate our approach against a subset of unseen data, and demonstrate both the predictive ability of our trained model and its limitations. [ABSTRACT FROM AUTHOR]

Adipose tissue is a dynamic regulatory organ that has profound effects on the overall health of patients. Unfortunately, inconsistencies in human adipose tissues are extensive and multifactorial, including large variability in cellular sizes, lipid content, inflammation, extracellular matrix components, mechanics, and cytokines secreted. Given the high human variability, and since much of what is known about adipose tissue is from animal models, we sought to establish correlations and patterns between biological, mechanical, and epidemiological properties of human adipose tissues. To do this, twenty-six independent variables were cataloged for twenty patients, which included patient demographics and factors that drive health, obesity, and fibrosis. A factorial analysis for mixed data (FAMD) was used to analyze patterns in the dataset (with BMI > 25), and a correlation matrix was used to identify interactions between quantitative variables. Vascular endothelial growth factor A (VEGFA) and actin alpha 2, smooth muscle (ACTA2) gene expression were the highest loadings in the first two dimensions of the FAMD. The number of adipocytes was also a key driver of patient-related differences, where a decrease in the density of adipocytes was associated with aging. Aging was also correlated with a decrease in overall lipid percentage of subcutaneous tissue, with lipid deposition being favored extracellularly, an increase in transforming growth factor-β1 (TGFβ1), and an increase in M1 macrophage polarization. An important finding was that self-identified race contributed to variance between patients in this study, where Black patients had significantly lower gene expression levels of TGFβ1 and ACTA2. This finding supports the urgent need to account for patient ancestry in biomedical research to develop better therapeutic strategies for all patients. Another important finding was that TGFβ induced factor homeobox 1 (TGIF1), an understudied signaling molecule, which is highly correlated with leptin signaling, was correlated with metabolic inflammation. Furthermore, this study draws attention to what we define as “extracellular lipid droplets”, which were consistently found in collagen-rich regions of the obese adipose tissues evaluated here. Reduced levels of TGIF1 were correlated with higher numbers of extracellular lipid droplets and an inability to suppress fibrotic changes in adipose tissue. Finally, this study indicated that M1 and M2 macrophage markers were correlated with each other and leptin in patients with a BMI > 25. This finding supports growing evidence that macrophage polarization in obesity involves a complex, interconnecting network system rather than a full switch in activation patterns from M2 to M1 with increasing body mass. Overall, this study reinforces key findings in animal studies and identifies important areas for future research, where human and animal studies are divergent. Understanding key drivers of human patient variability is required to unravel the complex metabolic health of unique patients.

Abstract Background Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. Methods Patients’ parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). Results To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. Conclusion This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models.

Background: Mathematical models provide an understanding of the dynamics of a Plasmodium falciparum blood-stage infection (within-host models), and can predict the impact of control strategies that affect the blood-stage of malaria. However, the dynamics of P. falciparum blood-stage infections are highly variable between individuals. Within-host models use different techniques to capture this inter-individual variation. This struggle may be unnecessary because patients can be clustered according to similar key within-host dynamics. This study aimed to identify clusters of patients with similar parasitaemia profiles so that future mathematical models can include an improved understanding of within-host variation. Methods: Patients' parasitaemia data were analyzed to identify (i) clusters of patients (from 35 patients) that have a similar overall parasitaemia profile and (ii) clusters of patients (from 100 patients) that have a similar first wave of parasitaemia. For each cluster analysis, patients were clustered based on key features which previous models used to summarize parasitaemia dynamics. The clustering analyses were performed using a finite mixture model. The centroid values of the clusters were used to parameterize two established within-host models to generate parasitaemia profiles. These profiles (that used the novel centroid parameterization) were compared with profiles that used individual-specific parameterization (as in the original models), as well as profiles that ignored individual variation (using overall means for parameterization). Results: To capture the variation of within-host dynamics, when studying the overall parasitaemia profile, two clusters efficiently grouped patients based on their infection length and the height of the first parasitaemia peak. When studying the first wave of parasitaemia, five clusters efficiently grouped patients based on the height of the peak and the speed of the clearance following the peak of parasitaemia. The clusters were based on features that summarize the strength of patient innate and adaptive immune responses. Parameterizing previous within host-models based on cluster centroid values accurately predict individual patient parasitaemia profiles. Conclusion: This study confirms that patients have personalized immune responses, which explains the variation of parasitaemia dynamics. Clustering can guide the optimal inclusion of within-host variation in future studies, and inform the design and parameterization of population-based models. [ABSTRACT FROM AUTHOR]

Candida albicans is a major fungal pathogen of humans. It exists as a commensal in the oral cavity, gut or genital tract of most individuals, constrained by the local microbiota, epithelial barriers and immune defences. Their perturbation can lead to fungal outgrowth and the development of mucosal infections such as oropharyngeal or vulvovaginal candidiasis, and patients with compromised immunity are susceptible to life-threatening systemic infections. The importance of the interplay between fungus, host and microbiota in driving the transition from C. albicans commensalism to pathogenicity is widely appreciated. However, the complexity of these interactions, and the significant impact of fungal, host and microbiota variability upon disease severity and outcome, are less well understood. Therefore, we summarise the features of the fungus that promote infection, and how genetic variation between clinical isolates influences pathogenicity. We discuss antifungal immunity, how this differs between mucosae, and how individual variation influences a person's susceptibility to infection. Also, we describe factors that influence the composition of gut, oral and vaginal microbiotas, and how these affect fungal colonisation and antifungal immunity. We argue that a detailed understanding of these variables, which underlie fungal-host-microbiota interactions, will present opportunities for directed antifungal therapies that benefit vulnerable patients. [ABSTRACT FROM AUTHOR]

Aseptic loosening is the most common indication for revision of cementless acetabular cups and often depends on the primary stability achieved following surgery. Cup designs must be capable of achieving primary stability for a wide variety of individuals and surgical conditions to be successful. Typically, preclinical finite element (FE) testing of cups involves assessing the performance in a single patient and under a limited set of idealized conditions. The aim of this study was to assess the effect of patient and surgical parameters on the primary stability of an acetabular cup design in a set of subject‐specific FE models. Interference fit was varied in a representative set of 12 patient‐specific models of the implanted hemipelvis. Linear mixed models showed a significant association with micromotion for interference fit (P <.0001), acetabular bone elastic modulus (P <.001), native acetabular diameter (P =.03), and the interference fit‐elastic modulus interaction (P =.01). There were no significant associations between the polar gap and any of the parameters considered. The significant interference fit‐elastic modulus interaction suggests that increasing the interference fit in patients with low bone quality leads to a greater reduction in micromotion than in patients with higher bone quality. However, the significant association between percentage bone yielding and interference fit (P <.0001) suggests a higher periacetabular fracture risk at higher interference fits. This work supports the development of preclinical testing of cup designs for the broad range patients and surgical conditions a cup may face following surgery. [ABSTRACT FROM AUTHOR]

ABSTRACT: For cementless femoral stems, there is debate as to whether a collar enhances primary stability and load transfer compared to collarless designs. Finite Element (FE) analysis has the potential to compare stem designs within the same cohort, allowing for subtle performance differences to be identified, if present. Subject‐specific FE models of intact and implanted femora were run for a diverse cohort (21 males, 20 females; BMI 16.4–41.2 kg/m2, age 50–80 yrs). Collared and collarless versions of Corail® (DePuy Synthes, Warsaw, IN) were sized and positioned using an automated algorithm that aligns the femoral/stem axes, preserves the head‐center location, and maximizes metaphyseal fit. Joint contact and muscle forces simulating peak forces in level gait and stair climbing and were scaled to the body mass and applied to each subject. Three failure scenarios were assessed: Potential for peri‐prosthetic fibrous tissue formation (stem micromotion), potential for peri‐prosthetic bone damage (equivalent strains), and calcar bone remodeling (changes in strain‐energy density). Comparisons were performed using paired t‐tests. Only subtle differences were found (mean 90th percentile micromotion: Collared = 86 µm, collarless = 92.5 µm, mean 90th percentile interface strains: Collared = 733 µϵ, collarless = 767 µϵ, and similar remodeling stimuli were predicted). The slight differences observed were small in comparison with the inter‐patient variability. Statement of clinical significance: Our results suggest that the presence/absence of a collar is unlikely to substantially alter the bone‐implant biomechanics nor the initial mechanical environment. Hence, a collar is likely to have minimal clinical impact. Analysis using different femoral stem designs is recommended before generalising these findings. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1185–1195, 2018. [ABSTRACT FROM AUTHOR]

ABSTRACT: The primary stability achieved during total hip arthroplasty determines the long‐term success of cementless acetabular cups. Pre‐clinical finite element testing of cups typically use a model of a single patient and assume the results can be extrapolated to the general population. This study explored the variability in predicted primary stability of a Pinnacle® cementless acetabular cup in 103 patient‐specific finite element models of the hemipelvis and examined the association between patient‐related factors and the observed variability. Cups were inserted by displacement‐control into the FE models and then a loading configuration simulating a complete level gait cycle was applied. The cohort showed a range of polar gap of 284–1112 μm and 95th percentile composite peak micromotion (CPM) of 18–624 μm. Regression analysis was not conclusive on the relationship between patient‐related factors and primary stability. No relationship was found between polar gap and micromotion. However, when the patient‐related factors were categorised into quartile groups, trends suggested higher polar gaps occurred in subjects with small and shallow acetabular geometries and cup motion during gait was affected most by low elastic modulus and high bodyweight. The variation in primary stability in the cohort for an acetabular cup with a proven clinical track record may provide benchmark data when evaluating new cup designs. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:1012–1023, 2018. [ABSTRACT FROM AUTHOR]

The aim of this research is to characterize the practices of asynchronous cooperation implemented by radiographers in radiotherapy in order to guarantee the quality of this treatment. This study is a part of an industrial project to design a computerized tool supporting cooperative work (workflow tool) in radiotherapy. The methodology involves observation of the entire radiotherapy process and interviews with radiographers who work in the downstream and upstream phases of the treatment. The results describe the type and function of asynchronous cooperation practices used by radiographers to manage patient variability and to organise treatment sessions in order to guarantee quality of care. They open up perspectives concerning the consideration of asynchronous cooperation practices embedded in the design of collaborative tools in healthcare field.

Goal: Automated glucose control (AGC) has not yet reached the point where it can be applied clinically [3]. Challenges are accuracy of subcutaneous (SC) glucose sensors, physiological lag times, and both inter- and intraindividual variability. To address above issues, we developed a novel scheme for MPC that can be applied to AGC. Results: An individualizable generic whole-body physiology-based pharmacokinetic and dynamics (PBPK/PD) model of the glucose, insulin, and glucagon metabolism has been used as the predictive kernel. The high level of mechanistic detail represented by the model takes full advantage of the potential of MPC and may make long-term prediction possible as it captures at least some relevant sources of variability [4]. Robustness against uncertainties was increased by a control cascade relying on proportional-integrative derivative-based offset control. The performance of this AGC scheme was evaluated in silico and retrospectively using data from clinical trials. This analysis revealed that our approach handles sensor noise with a MARD of 10%–14%, and model uncertainties and disturbances. Conclusion: The results suggest that PBPK/PD models are well suited for MPC in a glucose control setting, and that their predictive power in combination with the integrated database-driven (a priori individualizable) model framework will help overcome current challenges in the development of AGC systems. Significance: This study provides a new, generic, and robust mechanistic approach to AGC using a PBPK platform with extensive a priori (database) knowledge for individualization. [ABSTRACT FROM AUTHOR]

This paper addresses inter- and intra-patient variability in the context of automated drug delivery during anesthesia. A combined strategy of model predictive control (MPC) and least squares online parameter estimation for the control of the hypnotic depth, measured by the Bispectral Index (BIS), under uncertainty is presented, where the uncertainty originates from patient variability. The parameter with the highest sensitivity, C 50 the effect site concentration at 50% drug effect, is estimated online. The performance of the closed loop control design is shown for induction and maintenance of volatile anesthesia. In the maintenance phase, the control strategy is evaluated for predefined disturbances that are commonly occurring during surgery. The presented approach shows an improved performance compared to the nominal MPC controller under uncertainty. [ABSTRACT FROM AUTHOR]

For clinically predictive testing and design-phase evaluation of prospective total knee replacement (TKR) implants, devices should ideally be evaluated under physiological loading conditions which incorporate population-level variability. A challenge exists for experimental and computational researchers in determining appropriate loading conditions for wear and kinematic knee simulators which reflect in vivo joint loading conditions. There is a great deal of kinematic data available from fluoroscopy studies. The purpose of this work was to develop computational methods to derive anterior-posterior (A-P) and internal-external (I-E) tibiofemoral (TF) joint loading conditions from in vivo kinematic data. Two computational models were developed, a simple TF model, and a more complex lower limb model. These models were driven through external loads applied to the tibia and femur in the TF model, and applied to the hip, ankle and muscles in the lower limb model. A custom feedback controller was integrated with the finite element environment and used to determine the external loads required to reproduce target kinematics at the TF joint. The computational platform was evaluated using in vivo kinematic data from four fluoroscopy patients, and reproduced in vivo A-P and I-E motions and compressive force with a root-mean-square (RMS) accuracy of less than 1 mm, 0.1°, and 40 N in the TF model and in vivo A-P and I-E motions, TF flexion, and compressive loads with a RMS accuracy of less than 1 mm, 0.1°, 1.4°, and 48 N in the lower limb model. The external loading conditions derived from these models can ultimately be used to establish population variability in loading conditions, for eventual use in computational as well as experimental activity simulations. [ABSTRACT FROM AUTHOR]

Total hip replacements must be robust to patient variability for long‐term success in the population. The challenge during the design process is evaluating an implant in a diverse population but the computational cost of simulating a population of subject‐specific finite element (FE) models is not practical. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models to approximate the variability in output metrics. Comparing with the median and distribution of the 95th percentile composite peak micromotion (CPM) and polar gap in the full cohort (CPM median: 136 μm, interquartile range [IQR]: 74‐230 μm) (Polar Gap median: 467 μm, IQR: 434‐548 μm), the Anatomic Sampling strategy (12 subjects) achieved the best balance of computational cost and approximation of the output metrics (CPM median: 169 μm, IQR: 78‐236 μm) (Polar Gap median: 469 μm, IQR: 448‐537 μm). Convex hull sampling (41 subjects) more closely captured the output metrics (CPM median: 99 μm, IQR: 70‐191 μm) (Polar Gap median: 456 μm, IQR: 418‐533 μm) but required over three times the number of subjects. Volume reduction of the convex hull captured the extremes of variability with subsets of 5 to 20 subjects, while the largest minimum‐distance strategy captured the variability toward the middle of the cohort. These strategies can estimate the level of variability in FE model output metrics with a low computational cost when evaluating implants during the design process. The challenge during designing new implants in total hip replacement is evaluating their performance in a diverse population before a clinical trial. We examined five strategies to generate representative subsets of subjects from a cohort of 103 implanted hip joint FE models in order to approximate the patient variability in output metrics of primary stability. The strategies presented could grossly indicate the level of patient variability in FE model output metrics of the full cohort with a fraction of the subjects. [ABSTRACT FROM AUTHOR]