Population-based Modeling Research Articles

A Method That Maintains Accuracy in the Prediction of Vitamin A Total Body Stores When Population-Based Modeling of a Limited Number of Theoretical Subjects Is Used With Retinol Isotope Dilution.

Retinol isotope dilution (RID) equations are used to predict vitamin A total body stores (TBSs). Including population-based (supersubject) modeling with RID provides group-specific values for the equation coefficients. The objective was to test an approach that would accommodate a limited supersubject sample size without compromising accuracy in RID predictions of TBS. We used Simulation, Analysis and Modeling software to simulate fraction of dose in plasma (FDp) at 16 times from 3 h to 56 d after tracer ingestion in 20 theoretical adults. Then, we modeled geometric mean FDp (full data set) to determine group mean TBS and the coefficients Fa (FD in stores) and S (specific activity in plasma/stores) in the RID equation TBS (μmol) = FaS/plasma retinol specific activity. Using the same FDp data, we also generated 4 data sets with reduced subject numbers at times other than that designated for RID (day 21). Then, we adjusted individual FDp using the ratio (individual FDp on day 21/mean FDp on day 21; adjusted data sets), modeled each, and determined TBS and FaS for comparison with the full data set values. Mean ratio of model-predicted TBS for adjusted/full data set was 0.962 (range: 0.920-1.06) and for FaS, it was 0.945 (day 14), 0.971 (day 21), and 0.984 (day 28). For these theoretical data, adjusting individual FDp values based on relationship to the group mean FDp at an appropriate time (21 d) maintains the accuracy of model predictions of TBS and the RID composite coefficient FaS. If these results are confirmed using real data, values for FaS determined in a small supersubject study can be applied to confidently predict TBS by RID in that group's individuals. This approach will be especially useful when resources are limited for studies of vitamin A status in community settings.

Lingering impact: Maternal SARS-CoV-2 infection during early pregnancy results in fetal situs inversus

In this issue of Med, Guo etal.1 describe their observed association of maternal SARS-CoV-2 infection in early (4-6weeks) pregnancy with a confirmed fetal diagnosis of situs inversus congenital heart disease. Using sophisticated genomic tools and population-based statistical modeling, the study's authors present a very convincing argument causally linking maternal SARS-CoV-2 infection and resultant fetal situs inversus.

Morphine and hydromorphone pharmacokinetics in human volunteers: population-based modelling of interindividual and opioid-related variability

BackgroundMorphine and hydromorphone have differing onsets, magnitudes, and durations of effects and side-effects. Differences between opioids in their interindividual variabilities in pharmacokinetics and pharmacodynamics might influence rational drug selection. Crossover drug studies can provide more informative interindividual variability data than parallel group studies. Using data from a crossover study of i.v. morphine and hydromorphone in healthy volunteers, we tested the hypothesis that morphine and hydromorphone differ in their interindividual pharmacokinetic variability. MethodsArterial opioid and metabolite concentrations from a randomised crossover study in 51 volunteers receiving a 2-h infusion of hydromorphone (0.05 or 0.1 mg kg−1 i.v.) or morphine (total 0.1 or 0.2 mg kg−1 i.v.) 1–2 weeks apart were evaluated with a three-compartmental model for parent opioid and incorporating glucuronides using population modelling (NONMEM). The primary outcome was interindividual variability in pharmacokinetics, based on the coefficient of variation (%CV) of individual model parameters, calculated as √[exp(ω2)–1]×100 where ω2 is the interindividual variability. ResultsData were analysed per drug and in a combined morphine–hydromorphone model. Both analyses indicate that interindividual variabilities for hydromorphone and morphine were comparable with %CV ranging from 9% to 31% for structural model parameters (combined analysis). Similarly, additive and relative residual errors had comparable variabilities, 20–40% and 72–87%, respectively, for morphine and hydromorphone (combined analysis). ConclusionsMorphine and hydromorphone did not differ in a statistically significant or clinically meaningful manner in their interindividual pharmacokinetic variability. Interindividual pharmacokinetic variability does not appear a meaningful consideration in the choice between these two opioids.

Operationalising dietary acceptability: A systematic review on how acceptability is applied in dietary optimisation models

The current environmental, climate and health challenges require critical transitions to diets and food production systems that are more sustainable, in turn providing diets that are adequate nutrition, affordable and environmentally friendly. Recent studies have identified cultural acceptability as an important dimension in sustainable diets(1). Mathematical optimization models have been developed to deal with the complexities of all the dimensions, yet diet acceptability has had limited successful integration into these models(2). This systematic review explored two research questions in the context of dietary optimisation studies; how is acceptability defined and operationalised?; how does including acceptability impact dietary optimisation solutions?A hybrid search strategy was implemented. The first used the PRISMA guidelines to conduct a systematic search on two electronic databases (PubMed, Embase) for articles published between January 2013 and April 2024. Search terms were developed through preliminary readings and included: diet OR diets OR dietary AND modelling OR modeling OR optimisation OR optimization OR optimize OR optimize OR linear programming AND acceptabl*. Articles in English optimising acceptable diets for adults (18+) were included. The second used the snowball method for identifying relevant articles from previous searches(3). Backward snowballing was conducted by identifying references from the initial set and forward snowballing was conducted through Web of Science in June 2023.Fifty-one studies fulfilled the inclusion criteria. European countries had the largest representation (n = 41; 20 from France, 7 from The Netherlands); 43 studies took a population-based modelling approach, 7 on individual-based modelling and 1 study used both approaches. The review identified 12 modelling approaches, where linear programming was most frequently used (n = 26), followed by non-linear programming (n = 7) and quadratic (n = 6). The majority of studies that optimised for acceptability used the minimal deviation from observed diet as the objective function (n = 32). Baseline dietary data for observed diets were primarily from National surveys (n = 36), followed by local surveys (n = 5), however, 25 studies used data that was 8 – 16 years older than the publication date. Majority of studies included at least one acceptability constraint (n = 45), ranging from setting boundaries on certain foods (n = 34), removal of certain foods/food groups (n = 12), limiting deviation (n = 15).The full analysis of the studies is ongoing; however the current results highlight the varying methods used to operationalising ‘acceptable’ diets in diet optimisation literature. A conceptual framework for integrating acceptability into diet optimisation studies is currently under development, aiming to highlight the various ways in which ‘acceptability’ is operationalised through the modelling process from data inputs, models chosen to validation of the solutions. It is anticipated that this work will highlight new opportunities for further modelling for acceptable diets.

Use of Population-Based Compartmental Modeling and Retinol Isotope Dilution to Study Vitamin A Kinetics and Total Body Stores among Ghanaian Women of Reproductive Age

BackgroundLimited data are available on vitamin A kinetics and total body stores (TBS) in women. Such information can be obtained using compartmental modeling and retinol isotope dilution (RID). ObjectivesObjectives were to apply population-based (“super-subject”) modeling to determine retinol kinetics in nonpregnant Ghanaian women of reproductive age and to use RID to predict TBS in the group and its individuals. MethodsWomen (n = 89) ingested a dose of [2H6]retinyl acetate and blood samples (3/woman) were collected from 6 h to 91 d, with all participants sampled at 14 d, about half at either 21 or 28 d, and each at one other time. Composite data (plasma retinol fraction of dose; FDp) were analyzed using Simulation, Analysis and Modeling software to obtain kinetic parameters, TBS, and other state variables as well as model-derived values for the RID composite coefficient FaS. The latter were used in the RID equation TBS (μmol) = FaS × 1/SAp (where SAp is plasma retinol specific activity) to predict TBS at various times. ResultsModel-predicted TBS was 973 μmol (n = 87). Geometric mean RID-predicted TBS was 965, 926, and 1006 μmol at 14, 21, and 28 d, respectively, with wide ranges [for example, 252–3848 μmol on day 14 (n = 86)]; TBS predictions were similar at later times. Participants had a mean 2 y of vitamin A in stores and estimated liver vitamin A concentrations in the normal range. Model-predicted vitamin A disposal rate was 1.3 μmol/d and plasma recycling number was 37. ConclusionsSuper-subject modeling provides an estimate of group mean TBS as well as group-specific values for the RID coefficient FaS; the latter can be used to confidently predict TBS by RID for individual participants in the group under study or in similar individuals at 14 d or more after isotope ingestion. Trial registration numberTrial is registered (NCT04632771) at https://clinicaltrials.gov.

Point-based risk score for the risk stratification and prediction of hepatocellular carcinoma: a population-based random survival forest modeling study

The precise associations between common clinical biomarkers and hepatocellular carcinoma (HCC) risk remain unclear but hold valuable insights for HCC risk stratification and prediction. We examined the linear and nonlinear associations between the baseline levels of 32 circulating biomarkers and HCC risk in the England cohort of UK Biobank (UKBB) (n=397,702). The participants were enrolled between 2006 and 2010 and followed up to 31st October 2022. The primary outcome is incident HCC cases. We then employed random survival forests (RSF) to select the top ten most informative biomarkers, considering their association with HCC, and developed a point-based risk score to predict HCC. The performance of the risk score was evaluated in three validation sets including UKBB Scotland and Wales cohort (n=52,721), UKBB non-White-British cohort (n=29,315), and the Taizhou Longitudinal Study in China (n=17,269). Twenty-five biomarkers were significantly associated with HCC risk, either linearly or nonlinearly. Based on the RSF model selected biomarkers, our point-based risk score showed a concordance index of 0.866 in the England cohort and varied between 0.814 and 0.849 in the three validation sets. HCC incidence rates ranged from 0.95 to 30.82 per 100,000 from the lowest to the highest quintiles of the risk score in the England cohort. Individuals in the highest risk quintile had a 32-73 times greater risk of HCC compared to those in the lowest quintile. Moreover, over 70% of HCC cases were detected in individuals within the top risk score quintile across all cohorts. Our simple risk score enables the identification of high-risk individuals of HCC in the general population. However, including some biomarkers, such as insulin-like growth factor 1, not routinely measured in clinical practice may increase the model's complexity, highlighting the need for more accessible biomarkers that can maintain or improve the predictive accuracy of the risk score. This work was supported by the National Natural Science Foundation of China (grant numbers: 82204125) and the Science and Technology Support Program of Taizhou (TS202224).

Multiple myeloma incidence, mortality, and prevalence estimates and projections, Australia, 1982-2043: astatistical modelling study.

To examine changes in multiple myeloma incidence and mortality rates during 1982-2018, and to estimate its incidence, mortality, and prevalence for 2019-2043. Population-based statistical modelling study; analysis of and projections based on Australian Institute of Health and Welfare multiple myeloma incidence, mortality, and survival data. Australia, 1982-2018 (historical data) and projections to 2043. Changes in multiple myeloma incidence and mortality rates, 1982-2018, determined by joinpoint regression analysis (age-standardised to 2021 Australian population); projection of rates to 2043 based on age-period-cohort models; estimated 5- and 30-year prevalence of multiple myeloma (modified counting method). The incidence of multiple myeloma increased during 1982-2018 (eg, annual percentage change [APC], 2006-2018, 1.9%; 95% confidence interval [CI], 1.7-2.2%), but the mortality rate declined during 1990-2018 (APC, -0.4%; 95% CI, -0.5% to -0.2%). The age-standardised incidence rate was projected to increase by 14.9% during 2018-2043, from 8.7 in 2018 to 10.0 (95% CI, 9.4-10.7) new cases per 100 000 population in 2043; the mortality rate was projected to decline by 27.5%, from 4.0 to 2.9 (95% CI, 2.6-3.3) deaths per 100 000 population. The annual number of people newly diagnosed with multiple myeloma was estimated to increase by 89.2%, from 2120 in 2018 to 4012 in 2043; the number of deaths from multiple myeloma was projected to increase by 31.7%, from 979 to 1289. The number of people living with multiple myeloma up to 30 years after initial diagnosis was projected to increase by 163%, from 10 288 in 2018 to 27 093 in 2043, including 13 019 people (48.1%) diagnosed during the preceding five years. Although the decline in the mortality rate was projected to continue, the projected increases in the incidence and prevalence of multiple myeloma in Australia over the next 25 years indicate that investment in prevention and early detection research, and planning for prolonged treatment and care, are needed.

Potential impact of nirsevimab and bivalent maternal vaccine against RSV bronchiolitis in infants: A population-based modelling study

BackgroundA new monoclonal antibody (nirsevimab; Beyfortus®) and a bivalent prefusion RSV vaccine (Abrysvo®) for maternal immunization have been approved recently. This is a modelling study to estimate the potential impact of different immunization programs with these products on RSV-bronchiolitis. MethodsPopulation-based real-world data from primary care and hospitalizations were considered. RSV bronchiolitis dynamics in absence of these immunization scenarios were explained by a multivariate age-structured Bayesian model. Then, the potential impact was simulated under different assumptions including the most recent clinical trial data. Differences in endpoints, populations, and timeframes between trials make the two products’ efficacy difficult to compare. ResultsA seasonal with catch-up program, assuming a constant effectiveness of 79.5 % during the first 5 months followed by a linear decay to 0 by month 10 with nirsevimab, would prevent between 5121 and 8846 RSV bronchiolitis per 100,000 infants-years. Assuming 77.3 % effectiveness with the same decay, between 976 and 1686 RSV-hospitalizations per 100,000 infants-years could be prevented depending on the uptake. A year-round maternal immunization program, with 51 % of effectiveness during the first 6 months followed by a linear decay to 0 by month 10 would prevent between 3246 and 5606 RSV bronchiolitis cases per 100,000 infants-years. Assuming 56.9 % effectiveness with the same decay, between 713 and 1231 RSV-hospitalizations per 100,000 infants-years could be prevented. ConclusionsOur results suggest that each strategy would effectively reduce RSV-bronchiolitis.

Smoking patterns by birth cohort in Argentina: an age-period-cohort population-based modeling study

Argentina's smoking rates remain high. We aim to estimate Argentina age-specific histories of smoking initiation, cessation, prevalence, and intensity by birth-cohort to inform policy interventions. Modeling study. Data from three Argentinian nationally representative surveys conducted from 2004 to 2018 (n=268,193) were used to generate smoking histories. The Cancer Intervention and Surveillance Modeling (CISNET) Network Lung Working Group age, period, and cohort modeling approach was used to calculate smoking initiation and cessation probabilities, ever and current smoking prevalence, and intensity (cigarettes per day, CPD) by age, sex, and birth cohort from 1950 to2018. Ever smoking prevalence increases with age up to 25 and decreases with birth cohorts after 1990. Smoking initiation peaks between 15 and 18 years of age. Among females, initiation probabilities increased until the 1955 cohort, reaching a second peak in 1980-85 cohorts and declining thereafter. Males have higher initiation probabilities than females. Among males, initiation has decreased since the 1950 birth cohort, with a slight increase around the 1985 cohort. Current smoking prevalence has been decreasing since the 1960 birth cohort, except for a peak in 1980-85 cohorts. Cessation increases with age. Mean CPD increases with age and peaks around age 40, appearing flat in females since the 1985 cohort. Recent birth cohorts seem to be experiencing lower rates of initiation, stable rates of quitting and lower current and ever smoking prevalence. The stabilization of cessation probabilities and mean CPD indicate the need to strengthen existing tobacco control measures and advance new ones. NIH/NCI U01CA253858 grant.

Effect of recipient age on prioritisation for liver transplantation in the UK: a population-based modelling study

Following the introduction of an algorithm aiming to maximise life-years gained from liver transplantation in the UK (the transplant benefit score [TBS]), donor livers were redirected from younger to older patients, mortality rate equalised across the age range and short-term waiting list mortality reduced. Understanding age-related prioritisation has been challenging, especially for younger patients and clinicians allocating non-TBS-directed livers. We aimed to assess age-related prioritisation within the TBS algorithm by modelling liver transplantation prioritisation based on data from a UK transplant unit and comparing these data with other regions. In this population-based modelling study, serum parameters and age at liver transplantation assessment of patients attending the Scottish Liver Transplant Unit, Edinburgh, UK, between December, 2002, and November, 2023, were combined with representative synthetic data to model TBS survival predictions, which were compared according to age group (25-49 years vs ≥60 years), chronic liver disease severity, and disease cause. Models for end-stage liver disease (UKELD [UK], MELD [Eurotransplant region], and MELD 3.0 [USA]) were used as validated comparators of liver disease severity. Of 2093 patients with chronic liver disease, 1808 (86%) had complete datasets and liver disease parameters consistent with eligibility for the liver transplant waiting list in the UK (UKELD ≥49). Disease severity as assessed by UKELD, MELD, and MELD 3.0 did not differ by age (median UKELD scores of 56 for patients aged ≥60 years vs 56 for patients aged 25-49 years; MELD scores of 16 vs 16; and MELD 3.0 scores of 18 vs 18). TBS increased with advancing age (R=0·45, p<0·0001). TBS predicted that transplantation in patients aged 60 years or older would provide a two-fold greater net benefit at 5 years than in patients aged 25-49 years (median TBS 1317 [IQR 1116-1436] in older patients vs 706 [411-1095] in younger patients; p<0·0001). Older patients were predicted to have shorter survival without transplantation than younger patients (263 days [IQR 144-473] in older patients vs 861 days [448-1164] in younger patients; p<0·0001) but similar survival after transplantation (1599 days [1563-1628] vs 1573 days [1525-1614]; p<0·0001). Older patients could reach a TBS for which a liver offer was likely below minimum criteria for transplantation (UKELD <49), whereas many younger patients were required to have high-urgent disease (UKELD >60). US and Eurotransplant programmes did not prioritise according to age. The UK liver allocation algorithm prioritises older patients for transplantation by predicting that advancing age increases the benefit from liver transplantation. Restricted follow-up and biases in waiting list data might limit the accuracy of these benefit predictions. Measures beyond overall waiting list mortality are required to fully capture the benefits of liver transplantation. None.

Exploring clozapine pharmacokinetics in Tunisian schizophrenic patients: A population-based modelling approach investigating the impact of genetic and non-genetic variables.

Clozapine is characterized by a large within- and between-patient variability in its pharmacokinetics, attributed to non-genetic and genetic factors. A cross-sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient's habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed- and individual-predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty-three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking.

Getting the dose right using physiologically-based pharmacokinetic modeling: dexamethasone to prevent post-extubation stridor in children as proof of concept.

Critically ill patients show large variability in drug disposition due to e.g., age, size, disease and treatment modalities. Physiologically-based pharmacokinetic (PBPK) models can be used to design individualized dosing regimens taking this into account. Dexamethasone, prescribed for the prevention post-extubation stridor (PES), is metabolized by the drug metabolizing enzyme CYP3A. As CYP3A4 undergoes major changes during childhood, we aimed to develop age-appropriate dosing recommendations for children of dexamethasone for PES, as proof of concept for PBPK modeling to individualize dosing for critically ill patients. All simulations were conducted in Simcyp™ v21 (a population-based PBPK modeling platform), using an available dexamethasone compound model and pediatric population model in which CYP3A4 ontogeny is incorporated. Published pharmacokinetic (PK) data was used for model verification. Evidence for the dose to prevent post-extubation stridor was strongest for 2-6 year old children, hence simulated drug concentrations resulting from this dose from this age group were targeted when simulating age-appropriate doses for the whole pediatric age range. Dexamethasone plasma concentrations upon single and multiple intravenous administration were predicted adequately across the pediatric age range. Exposure-matched predictions of dexamethasone PK indicated that doses (in mg/kg) for the 2-6 years olds can be applied in 3 month-2 year old children, whereas lower doses are needed in children of other age groups (60% lower for 0-2 weeks, 40% lower for 2-4 weeks, 20% lower for 1-3 months, 20% lower for 6-12 year olds, 40% lower for 12-18 years olds). We show that PBPK modeling is a valuable tool that can be used to develop model-informed recommendations using dexamethasone to prevent PES in children. Based on exposure matching, the dose of dexamethasone should be reduced compared to commonly used doses, in infants <3 months and children ≥6 years, reflecting age-related variation in drug disposition. PBPK modeling is an promising tool to optimize dosing of critically ill patients.

Resource allocation during the coronavirus disease 2019 pandemic and the impact on patients with lung cancer: a systematic review.

The coronavirus disease 2019 (COVID-19) pandemic resulted in unprecedented tolls on both economies and human life. Healthcare resources needed to be reallocated away from the care of patients and towards supporting the pandemic response. In this systematic review, we explore the impact of resource allocation during the COVID-19 pandemic on the screening, diagnosis, management and outcomes of patients with lung cancer during the pandemic. PubMed and Embase were systematically searched for articles investigating the impact of the COVID-19 pandemic on patients with lung cancer. Of the 1605 manuscripts originally screened, 47 studies met the inclusion criteria. Patients with lung cancer during the pandemic experienced reduced rates of screening, diagnostic testing and interventions but did not experience worse outcomes. Population-based modelling studies predict significant increases in mortality for patients with lung cancer in the years to come. Reduced access to resources during the pandemic resulted in reduced rates of screening, diagnosis and treatment for patients with lung cancer. While significant differences in outcomes were not identified in the short term, ultimately the effects of the pandemic and reductions in cancer screening will likely be better delineated in the coming years. Future consideration of the long-term implications of resource allocation away from patients with lung cancer with an attempt to provide equitable access to healthcare and limited interruptions of patient care may help to provide the best care for all patients during times of limited resources.

Knee kinematics are primarily determined by implant alignment but knee kinetics are mainly influenced by muscle coordination strategy

Implant malalignment has been reported to be a primary reason for revision total knee arthroplasty (TKA). In addition, altered muscle coordination patterns are commonly observed in TKA patients, which is thought to alter knee contact loads. A comprehensive understanding of the influence of surgical implantation and muscle recruitment strategies on joint contact mechanics is crucial to improve surgical techniques, increase implant longevity, and inform rehabilitation protocols. In this study, a detailed musculoskeletal model with a 12 degrees of freedom knee was developed to represent a TKA subject from the CAMS-Knee datasets. Using motion capture and ground reaction force data, a level walking cycle was simulated and the joint movement and loading patterns were estimated using a novel technique for concurrent optimization of muscle activations and joint kinematics. In addition, over 12′000 Monte Carlo simulations were performed to predict knee contact mechanics during walking, considering numerous combinations of implant alignment and muscle activation scenarios. Validation of our baseline simulation showed good agreement between the model kinematics and loading patterns against the in vivo data. Our analyses reveal a considerable impact of implant alignment on the joint kinematics, while variation in muscle activation strategies mainly affects knee contact loading. Moreover, our results indicate that high knee compressive forces do not necessarily originate from extreme kinematics and vice versa. This study provides an improved understanding of the complex inter-relationships between loading and movement patterns resulting from different surgical implantation and muscle coordination strategies and presents a validated framework towards population-based modelling in TKA.

Three-dimensional lumbar spine generation using variational autoencoder.

The disease analysis of the lumbar spine often requires a large number of three-dimensional (3D) models. Currently, there is a lack of 3D model of the lumbar spine for research, especially for the diseases such as scoliosis where it is difficult to collect sufficient data in a short period of time. To solve this problem, we develop an end-to-end network based on 3D variational autoencoder for randomly generating 3D lumbar spine model. In this network, the dual path encoder structure is used to fit two individual variables, i.e., mean and variance. Spatial coordinate attention modules are added to the encoder to improve the learning ability of the network to the 3D spatial structure of the lumbar spine. To enhance the power of the network to reconstruct the lumbar spine, a regularization loss is added to constrain the distribution loss. Additionally, Gaussian noise layers are added to the decoder to improve the authenticity and diversity of generated model. The experiments were conducted on the data of the entire lumbar spine and the individual lumbar vertebra, respectively. The results showed that the voxel intersection over union was 0.588 and 0.684, the voxel Dice coefficient was 0.739 and 0.811, the average surface distance was 0.807 and 1.189, and the Hausdorff distance was 2.615 and 3.710, for the entire lumbar spine and individual lumbar vertebra, respectively. The developed approach is comparable to the most commonly used model generation method of statistical shape model (SSM) in both visual and objective indicators, while the developed approach does not require the landmarks that is needed in the SSM method. Therefore, this fully automatic method can be easily used for population-based modeling of the lumbar spine which has the potential to be a powerful clinical tool.

The impact of variations in subject geometry, respiration and coil repositioning on the specific absorption rate in parallel transmit abdominal imaging at 7 T.

Parallel transmit MRI at 7 T has increasingly been adopted in research projects and provides increased signal-to-noise ratios and novel contrasts. However, the interactions of fields in the body need to be carefully considered to ensure safe scanning. Recent advances in physically flexible body coils have allowed for high-field abdominal imaging, but the effects of increased variability on energy deposition need further exploration. The aim of this study was to assess the impact of subject geometry, respiration phase and coil positioning on the specific absorption rate (SAR). Ten healthy subjects (body mass index[BMI] = 25 ± 5 kg m-2 ) were scanned (at 3 T) during exhale breath-hold and images used to generate body models. Seven of these subjects were also scanned during inhale. Simplifications of the coil and body models were first explored, and then finite-difference time-domain simulations were run with a typical eight-channel parallel transmit coil positioned over the abdomen. Simulations were used to generate 10 g averaged SAR (SAR10g ) maps across 100,000 phase settings, and the worst-case scenario 10 g averaged SAR (wocSAR10g ) was identified using trigonometric maximisation. The average maximum SAR10g across the 10 subjects with 1 W input power per channel was 1.77 W kg-1 . Hotspots were always close to the body surface near the muscle wall boundary. The wocSAR10g across the 10 subjects ranged from 2.3 to 3.2 W kg-1 and was inversely correlated to fat volume percentage (R = 8) and BMI (R = 0.6). The coefficient of variation values in SAR10g due to variations in subject geometry, respiration phase and realistic coil repositioning were 12%, 4% and 12%, respectively. This study found that the variability due to realistic coil repositioning was similar to the variability due to differing healthy subject geometries for abdominal imaging. This is important as it suggests that population-based modelling is likely to be more useful than individual modelling in setting safe thresholds for abdominal imaging.

Bioanalytical Method Validation of Metformin Hydrochloride in Human Plasma by HPLC-UV for Preliminary Population-Based Pharmacokinetic Modeling Study

This study aims to validate the method for measuring metformin hydrochloride plasma concentrations using High-Performance Liquid Chromatography (HPLC). This research performed chromatography on a 250 mm 4.6 mm 5 µm purosphere® Star RP-18 column at ambient temperature with a UV detector system at 233 nm. The mobile phase components were 70% phosphate buffer (KH2PO4) (10 mM), sodium dodecyl sulfate (0.3 mM), and 30% acetonitrile. It was pumped at an isocratic flow rate of 1.2 mL/min. Metformin HCl and ranitidine HCl (internal standard) were extracted using acetonitrile. The calibration curve was linear (R2 = 0.9998) in the 0.18–6 µg/mL concentration range. The lower limit of quantification (LLOQ) was 0.18 µg/mL. For intraday accuracy and precision, the percent difference and the coefficient of variation were less than 4 and 7%, and for inter-day were lower than 8 and 6%. The recovery average was 100.96%. The short-term plasma stability test was stable at 24 h at ambient temperature, and the long-term stability test was steady for 30 d at −20 °C. It was also stable after three freeze-thaw cycles. The method meets selectivity, sensitivity, linearity, accuracy, precision, recovery, carryover, and stability requirements and can be applied to population-based pharmacokinetic modeling.

Evaluating the role of the nuclear microenvironment in gene function by population-based modeling.

The nuclear folding of chromosomes relative to nuclear bodies is an integral part of gene function. Here, we demonstrate that population-based modeling-from ensemble Hi-C data-provides a detailed description of the nuclear microenvironment of genes and its role in gene function. We define the microenvironment by the subnuclear positions of genomic regions with respect to nuclear bodies, local chromatin compaction, and preferences in chromatin compartmentalization. These structural descriptors are determined in single-cell models, thereby revealing the structural variability between cells. We demonstrate that the microenvironment of a genomic region is linked to its functional potential in gene transcription, replication, and chromatin compartmentalization. Some chromatin regions feature a strong preference for a single microenvironment, due to association with specific nuclear bodies in most cells. Other chromatin shows high structural variability, which is a strong indicator of functional heterogeneity. Moreover, we identify specialized nuclear microenvironments, which distinguish chromatin in different functional states and reveal a key role of nuclear speckles in chromosome organization. We demonstrate that our method produces highly predictive three-dimensional genome structures, which accurately reproduce data from a variety of orthogonal experiments, thus considerably expanding the range of Hi-C data analysis.

Use of Compartmental Modeling and Retinol Isotope Dilution to Determine Vitamin A Stores in Young People with Sickle Cell Disease Before and After Vitamin A Supplementation

BackgroundSuboptimal plasma retinol concentrations have been documented in US children with sickle cell disease (SCD) hemoglobin SS type (SCD-HbSS), but little is known about vitamin A kinetics and stores in SCD. ObjectivesThe objectives were to quantify vitamin A total body stores (TBS) and whole-body retinol kinetics in young people with SCD-HbSS and use retinol isotope dilution (RID) to predict TBS in SCD-HbSS and healthy peers as well as after vitamin A supplementation in SCD-HbSS subjects. MethodsComposite plasma [13C10]retinol response data collected from 22 subjects with SCD-HbSS for 28 d after isotope ingestion were analyzed using population-based compartmental modeling (“super-subject” approach); TBS and retinol kinetics were quantified for the group. TBS was also calculated for the same individuals using RID, as well as for healthy peers (n = 20) and for the subjects with SCD-HbSS after 8 wk of daily vitamin A supplements (3.15 or 6.29 μmol retinol/d [900 or 1800 μg retinol activity equivalents/d]). ResultsModel-predicted group mean TBS for subjects with SCD-HbSS was 428 μmol, equivalent to ∼11 mo of stored vitamin A; vitamin A disposal rate was 1.3 μmol/d. Model-predicted TBS was similar to that predicted by RID at 3 d postdosing (mean, 389 μmol; ∼0.3 μmol/g liver); TBS predictions at 3 compared with 28 d were not significantly different. Mean TBS in healthy peers was similar (406 μmol). RID-predicted TBS for subjects with SCD-HbSS was not significantly affected by vitamin A supplementation at either dose. ConclusionsDespite differences in plasma retinol concentrations, TBS was the same in subjects with SCD-HbSS compared with healthy peers. Because 56 d of vitamin A supplementation at levels 1.2 to 2.6 times the Recommended Dietary Allowance did not increase TBS in these subjects with SCD-HbSS, further work will be needed to understand the effects of SCD on retinol metabolism. This trial was registered as NCT03632876 at clinicaltrials.gov.

Explaining declining hip fracture rates in Norway: a population-based modelling study.

Although age-standardised hip fracture incidence has declined in many countries during recent decades, the number of fractures is forecast to increase as the population ages. Understanding the drivers behind this decline is essential to inform policy for targeted preventive measures. We aimed to quantify how much of this decline could be explained by temporal trends in major risk factors and osteoporosis treatment. We developed a new modelling approach, Hip-IMPACT, based on the validated IMPACT coronary heart disease models. The model applied sex- and age stratified hip fracture numbers and prevalence of pharmacologic treatments and risk/preventive factors in 1999 and 2019, and best available evidence for independent relative risks of hip fracture associated with each treatment and risk/preventive factor. Hip-IMPACT explained 91% (2500/2756) of the declining hip fracture rates during 1999-2019. Two-thirds of the total decline was attributed to changes in risk/preventive factors and one-fifth to osteoporosis medication. Increased prevalence of total hip replacements explained 474/2756 (17%), increased body mass index 698/2756 (25%), and increased physical activity 434/2756 (16%). Reduced smoking explained 293/2756 (11%), and reduced benzodiazepine use explained (366/2756) 13%. Increased uptake of alendronate, zoledronic acid, and denosumab explained 307/2756 (11%), 104/2756 (4%) and 161/2756 (6%), respectively. The explained decline was partially offset by increased prevalence of type 2 diabetes and users of glucocorticoids, z-drugs, and opioids. Two-thirds of the decline in hip fractures from 1999 to 2019 was attributed to reductions in major risk factors and approximately one-fifth to osteoporosis medication. The Research Council of Norway.