Elucidation of the key role of Pt···Pt interactions in the directional self-assembly of platinum(II) complexes.

Here, we report the use of an amphiphilic Pt(II) complex, K[Pt{(O3SCH2CH2CH2)2bzimpy}Cl] (PtB), as a model to elucidate the key role of Pt···Pt interactions in directing self-assembly by combining temperature-dependent ultraviolet-visible (UV-Vis) spectroscopy, stopped-flow kinetic experiments, quantum mechanics (QM) calculations, and molecular dynamics (MD) simulations. Interestingly, we found that the self-assembly mechanism of PtB in aqueous solution follows a nucleation-free isodesmic model, as revealed by the temperature-dependent UV-Vis experiments. In contrast, a cooperative growth is found for the self-assembly of PtB in acetone­water (7:1, vol/vol) solution, which is further verified by the stopped-flow experiments, which clearly indicates the existence of a nucleation phase in the acetone­water (7:1, vol/vol) solution. To reveal the underlying reasons and driving forces for these self-assembly processes, we performed QM calculations and show that the Pt···Pt interactions arising from the interaction between the pz and dz2 orbitals play a crucial role in determining the formation of ordered self-assembled structures. In subsequent oligomer MD simulations, we demonstrate that this directional Pt···Pt interaction can indeed facilitate the formation of linear structures packed in a helix-like fashion. Our results suggest that the self-assembly of PtB in acetone­water (7:1, vol/vol) solution is predominantly driven by the directional noncovalent Pt···Pt interaction, leading to the cooperative growth and the formation of fibrous nanostructures. On the contrary, the self-assembly in aqueous solution forms spherical nanostructures of PtB, which is primarily due to the predominant contribution from the less directional hydrophobic interactions over the directional Pt···Pt and π−π interactions that result in an isodesmic growth.

Toward Controlled Synthesis of 2D Crystals by CVD: Learning from the Real-Time Crystal Morphology Evolutions.

The rich morphology of 2D materials grown through chemical vapor deposition (CVD), is a distinctive feature. However, understanding the complex growth of 2D crystals under practical CVD conditions remains a challenge due to various intertwined factors. Real-time monitoring is crucial to providing essential data and enabling the use of advanced tools like machine learning for unraveling these complexities. In this study, we present a custom-built miniaturized CVD system capable of observing and recording 2D MoS2 crystal growth in real time. Image processing converts the real-time footage into digital data, and machine learning algorithms (ML) unveil the significant factors influencing growth. The machine learning model successfully predicts CVD growth parameters for synthesizing ultralarge monolayer MoS2 crystals. It also demonstrates the potential to reverse engineer CVD growth parameters by analyzing the as-grown 2D crystal morphology. This interdisciplinary approach can be integrated to enhance our understanding of controlled 2D crystal synthesis through CVD.

Modulation of mechanosensitive genes during embryonic aortic arch development.

BACKGROUND: Early embryonic aortic arches (AA) are a dynamic vascular structures that are in the process of shaping into the great arteries of cardiovascular system. Previously, a time-lapsed mechanosensitive gene expression map was established for AA subject to altered mechanical loads in the avian embryo. To validate this map, we investigated effects on vascular microstructure and material properties following the perturbation of key genes using an in-house microvascular gene knockdown system. RESULTS: All siRNA vectors show a decrease in the expression intensity of desired genes with no significant differences between vectors. In TGFß3 knockdowns, we found a reduction in expression intensities of TGFß3 (≤76%) and its downstream targets such as ELN (≤99.6%), Fbn1 (≤60%), COL1 (≤52%) and COL3 (≤86%) and an increase of diameter in the left AA (23%). MMP2 knockdown also reduced expression levels in MMP2 (≤30%) and a 6-fold increase in its downstream target COL3 with a decrease in stiffness of the AA wall and an increase in the diameter of the AA (55%). These in vivo measurements were confirmed using immunohistochemistry, western blotting and a computational growth model of the vascular extracellular matrix (ECM). CONCLUSIONS: Localized spatial genetic modification of the aortic arch region governs the vascular phenotype and ECM composition of the embryo and can be integrated with mechanically-induced congenital heart disease models.

Addressing age measurement errors in fish growth estimation from length-stratified samples.

Fish growth models are crucial for fisheries stock assessments and are commonly estimated using fish length-at-age data. This data is widely collected using length-stratified age sampling (LSAS), a cost-effective two-phase response-selective sampling method. The data may contain age measurement errors (MEs). We propose a methodology that accounts for both LSAS and age MEs to accurately estimate fish growth. The proposed methods use empirical proportion likelihood methodology for LSAS and the structural errors in variables methodology for age MEs. We provide a measure of uncertainty for parameter estimates and standardized residuals for model validation. To model the age distribution, we employ a continuation ratio-logit model that is consistent with the random nature of the true age distribution. We also apply a discretization approach for age and length distributions, which significantly improves computational efficiency and is consistent with the discrete age and length data typically encountered in practice. Our simulation study shows that neglecting age MEs can lead to significant bias in growth estimation, even with small but non-negligible age MEs. However, our new approach performs well regardless of the magnitude of age MEs and accurately estimates SEs of parameter estimators. Real data analysis demonstrates the effectiveness of the proposed model validation device. Computer codes to implement the methodology are provided.

Random effects models of tumour growth for investigating interval breast cancer.

In Nordic countries and across Europe, breast cancer screening participation is high. However, a significant number of breast cancer cases are still diagnosed due to symptoms between screening rounds, termed "interval cancers". Radiologists use the interval cancer proportion as a proxy for the screening false negative rate (ie, 1-sensitivity). Our objective is to enhance our understanding of interval cancers by applying continuous tumour growth models to data from a study involving incident invasive breast cancer cases. Building upon previous findings regarding stationary distributions of tumour size and growth rate distributions in non-screened populations, we develop an analytical expression for the proportion of interval breast cancer cases among regularly screened women. Our approach avoids relying on estimated background cancer rates. We make specific parametric assumptions concerning tumour growth and detection processes (screening or symptoms), but our framework easily accommodates alternative assumptions. We also show how our developed analytical expression for the proportion of interval breast cancers within a screened population can be incorporated into an approach for fitting tumour growth models to incident case data. We fit a model on 3493 cases diagnosed in Sweden between 2001 and 2008. Our methodology allows us to estimate the distribution of tumour sizes at the most recent screening for interval cancers. Importantly, we find that our model-based expected incidence of interval breast cancers aligns closely with observed patterns in our study and in a large Nordic screening cohort. Finally, we evaluate the association between screening interval length and the interval cancer proportion. Our analytical expression represents a useful tool for gaining insights into the performance of population-based breast cancer screening programs.

SpatialWavePredict: a tutorial-based primer and toolbox for forecasting growth trajectories using the ensemble spatial wave sub-epidemic modeling framework.

BACKGROUND: Dynamical mathematical models defined by a system of differential equations are typically not easily accessible to non-experts. However, forecasts based on these types of models can help gain insights into the mechanisms driving the process and may outcompete simpler phenomenological growth models. Here we introduce a friendly toolbox, SpatialWavePredict, to characterize and forecast the spatial wave sub-epidemic model, which captures diverse wave dynamics by aggregating multiple asynchronous growth processes and has outperformed simpler phenomenological growth models in short-term forecasts of various infectious diseases outbreaks including SARS, Ebola, and the early waves of the COVID-19 pandemic in the US. RESULTS: This tutorial-based primer introduces and illustrates a user-friendly MATLAB toolbox for fitting and forecasting time-series trajectories using an ensemble spatial wave sub-epidemic model based on ordinary differential equations. Scientists, policymakers, and students can use the toolbox to conduct real-time short-term forecasts. The five-parameter epidemic wave model in the toolbox aggregates linked overlapping sub-epidemics and captures a rich spectrum of epidemic wave dynamics, including oscillatory wave behavior and plateaus. An ensemble strategy aims to improve forecasting performance by combining the resulting top-ranked models. The toolbox provides a tutorial for forecasting time-series trajectories, including the full uncertainty distribution derived through parametric bootstrapping, which is needed to construct prediction intervals and evaluate their accuracy. Functions are available to assess forecasting performance, estimation methods, error structures in the data, and forecasting horizons. The toolbox also includes functions to quantify forecasting performance using metrics that evaluate point and distributional forecasts, including the weighted interval score. CONCLUSIONS: We have developed the first comprehensive toolbox to characterize and forecast time-series data using an ensemble spatial wave sub-epidemic wave model. As an epidemic situation or contagion occurs, the tools presented in this tutorial can facilitate policymakers to guide the implementation of containment strategies and assess the impact of control interventions. We demonstrate the functionality of the toolbox with examples, including a tutorial video, and is illustrated using daily data on the COVID-19 pandemic in the USA.

Validation of Fetal Medicine Foundation charts for fetal growth in twins: nationwide Danish cohort study.

OBJECTIVE: To assess the validity of the Fetal Medicine Foundation (FMF) chorionicity-specific models for fetal growth in twin pregnancy. METHODS: This was an external validation study of the FMF models using a nationwide Danish cohort of twin pregnancies. The cohort included all dichorionic (DC) and monochorionic diamniotic (MCDA) twin pregnancies with an estimated delivery date between 2008 and 2018, which satisfied the following inclusion criteria: two live fetuses at the first-trimester ultrasound scan (11-14 weeks' gestation); biometric measurements available for the calculation of estimated fetal weight (EFW) using the Hadlock-3 formula; and delivery of two liveborn infants. Validation involved assessing the distributional properties of the models and estimating the mean EFW Z-score deviations. Additionally, the models were applied to pregnancies that delivered preterm and attended non-scheduled visits (complicated pregnancies). RESULTS: Overall, 8542 DC and 1675 MCDA twin pregnancies met the inclusion criteria. In DC twins, 17 084 fetuses were evaluated at a total of 95 346 ultrasound scans, of which 44.5% were performed at scheduled visits in pregnancies carried to 37 + 0 weeks or later. The median number of growth scans per DC twin fetus from 20 + 0 weeks onwards was four. The model showed good agreement with the validation cohort for scheduled visits in DC twins delivered at 37 + 0 weeks or later (mean EFW Z-score, -0.14 ± 1.05). In MCDA twins, 3350 fetuses underwent 31 632 eligible ultrasound scans, of which 59.5% were performed at scheduled visits in pregnancies carried to 36 + 0 weeks or later. The median number of growth scans per MCDA twin fetus from 16 + 0 weeks onwards was 10. The model showed favorable agreement with the validation cohort for scheduled visits in MCDA twins delivered at 36 + 0 weeks or later (mean EFW Z-score, -0.09 ± 1.01). Non-scheduled visits and preterm delivery before 37 + 0 weeks for DC twins and before 36 + 0 weeks for MCDA twins corresponded with smaller weight estimates, which was consistent with the study's definition of complicated pregnancy. CONCLUSIONS: The FMF models provide a good fit for EFW measurements in our Danish national cohort of uncomplicated twin pregnancies assessed at routine scans. Therefore, the FMF models establish robust criteria for subsequent investigations and potential clinical applications. Future research should focus on exploring the consequences of clinical implementation, particularly regarding the identification of twins that are small-for-gestational age, as they are especially susceptible to adverse perinatal outcome. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

SHOX and sex difference in height: a hypothesis.

The mean height is taller in males than in females, except for early teens. In this regard, previous studies have revealed that (1) distribution of the mean adult heights in subjects with disorders accompanied by discordance between sex chromosome complement and bioactive sex steroids and in control subjects (the British height standards) indicates that, of the ~12.5 cm of sex difference in the mean adult height, ~9 cm is accounted for by the difference in the sex chromosome complement and the remaining ~3.5 cm is explained by the dimorphism in sex steroids (primarily due to the growth-promoting effect of gonadal androgens); (2) according to the infancy-childhood-puberty growth model, the sex difference in the childhood growth function produces height differences of ~1 cm in childhood and 8-10 cm at 18-20 years of age, whereas the sex difference in the pubertal growth function yields height difference of ~4.5 cm at 18-20 years of age; and (3) SHOX expression and methylation analyses using knee cartilage tissues and cultured chondrocytes have shown lower SHOX expression levels in female samples than in male samples and methylation patterns consistent with partial spreading of X-inactivation affecting SHOX in female samples. These findings suggest that small but persistent sex difference in SHOX expression dosage leads to the variation in the sex steroid independent childhood growth function, thereby yielding the sex difference in height which remains small in childhood but becomes obvious in adulthood.

Public health events and economic growth in a neoclassical framework.

Public health events (PHEs) have emerged as significant threats to human life, health, and economic growth. PHEs, such as COVID-19, have prompted a reevaluation for enhanced regular prevention and control (RPC). In this study, we focus on the core concept of prevention and control intensity (PCI), and establish a neoclassical economic growth model from the long-term and macro perspective to balance life protection and economic growth. The model construct the mechanism of PCI on economic growth through population dynamics and capital accumulation under the backdrop of RPC for PHEs. We find the conditions for PCI when the economy achieves steady state, and provides an algorithm establishing the optimal strategy that maximises per capita disposable income based on the optimal PCI and consumption. Simulation result quantifies an inverted U-shaped relationship between PCI and capital per capita, output per capita and consumption per capita in the steady state. The model suggests that, given the PHEs of inducing potential unemployment shock, it is worthwhile to combine the implementation of moderate PCI with coordinated policies of income distribution.

Negative life events predict depressive trends: the moderating effect of overgeneral autobiographical memory and early parenting behaviour.

The Emerging Adulthood is a complex and chaotic period and depression is one of the main psychological health problems during this period. Overgeneral autobiographical memory (OGM) is prevalent among patients with clinical depression. However, the prediction of OGM in groups with non-clinical depression and its influencing mechanisms remain inconclusive. Studies have shown that OGM and early parenting behaviour are vulnerable factors of depression in emerging adulthood, which may be triggered by negative life events. Our longitudinal study included 241 participants (Mage = 21.88 years). At baseline, participants completed measures of current negative life events, depression, early parenting behaviour and an autobiographical memory test. Thereafter, they were tracked for depression every 35 days. We used the latent class growth model to differentiate levels and trends of depression among non-clinical participants. The analysis showed that the effect of negative life events on depression was moderated by OGM and early parenting behaviour. However, this moderating effect was found only in the low-risk depression group. Our findings indicate that early parenting behaviour might account for the different mechanisms of OGM production in non-clinical groups. Moreover, it underlines the importance of OGM and early parenting behaviour as potential predictors of future depression in non-clinical groups.

Developmental Trajectories of Adolescents' Math Motivation: The Role of Mindset and Perceptions of Informal STEM Learning Site Inclusivity.

Motivation is a key factor in engagement, achievement, and career choices in science, technology, engineering, and mathematics (STEM). While existing research has focused on student motivation toward math in formal school programs, new work is needed that focuses on motivation for those involved in informal STEM programs. Specifically, the role of math mindset and perceived inclusivity of informal STEM sites (to those of varying gender and ethnic backgrounds) on longitudinal trajectories of adolescents' math motivation has not been explored. This study investigates longitudinal changes in math expectancy, interest, and utility values and the effects of math fixed mindset, math growth mindset, and perceptions of the inclusivity of informal STEM learning sites on these changes for adolescents participating in STEM programs at these informal sites in the United Kingdom and the United States (n = 249, MT1age = 15.2, SD = 1.59). Three latent growth curve models were tested. The data suggest that math expectancy, interest, and utility values declined over three years. Growth mindset positively predicted changes in utility, while fixed mindset negatively predicted changes in utility. Inclusivity positively influenced the initial levels of utility. Girls reported lower initial expectancy than boys. Age influenced both the initial levels and rate of change for expectancy. Older adolescents had lower levels of expectancy compared to their younger counterparts; however, they had a less steep decline in expectancy over three years. These findings suggest that designing inclusive learning environments and promoting growth mindset may encourage math motivation.

Life Satisfaction Trajectories During the Transition from Adolescence to Emerging Adulthood and the Role of Gender and Achievement Attribution: A Longitudinal Study of Chinese Youth.

Life satisfaction is vital for a smooth transition to emerging adulthood. However, research on age and gender-related fluctuations in life satisfaction has yielded inconclusive and culture-specific results, which necessitates further investigation in more diverse contexts, including the Chinese culture. Despite the attribution theory and the value-as-a-moderator model highlighting the significance of achievement attribution in this period characterized by the pursuit of success, little is known about the specific impact of internal and external achievement attribution on the development of life satisfaction. This study examined life satisfaction trajectories from adolescence to emerging adulthood and explored the moderating effects of gender and achievement attribution, using five waves of data from the China Family Panel Studies (CFPS). The sample consists of 504 youth (52.2% female), whose life satisfaction was tracked from 2012 (Mage = 16.55, SD = 0.50) to 2020 (Mage = 24.56, SD = 0.50). The multilevel growth models revealed that life satisfaction increased from ages 16 to 18, peaked at age 18, fell until age 20, and then remained stable from ages 20 to 24. No significant association was found between gender or external achievement attribution and life satisfaction development. Youth with higher internal achievement attribution had a greater increase in life satisfaction from ages 16 to 18, but had a larger decrease from ages 18 to 20. Adjusting internal achievement attribution may help alleviate the vulnerability of youth experiencing declining life satisfaction when transitioning to adulthood.

Longitudinal Relationships Among Child School Engagement, Parental Monitoring, and Child Prosocial Behavior: A Child-Parent Synergistic Mechanism.

Numerous contextual factors have been identified that impact the development of children's prosocial behavior, yet the influence of child-initiated factors on prosocial behavior and its underlying mechanism remains unclear. This study employed three longitudinal models to examine in depth how children's school engagement may promote the development of their own prosocial behavior. Three-wave longitudinal data from 4691 children (M age = 9.480, SD = 0.507; 48.2% female) with 2-year intervals were used. Sequentially, a cross-lagged panel model, a random intercept cross-lagged panel model, and a parallel process latent growth model were constructed. The findings indicated that children's school engagement consistently predicted the future level, dynamic changes at within-person level, and long-term trends in their prosocial behavior, and these longitudinal relationships were partially mediated by parental monitoring. These results reveal a child-parent synergistic mechanism for the development of prosocial behavior, wherein children's school engagement both directly promotes their own prosocial behavior and simultaneously enhances prosocial behavior through eliciting increased parental monitoring.

Chinese mothers use idioms in shared book reading: A predictor for children's Chinese vocabulary growth?

Idioms play an important role in language; however, little research has examined idioms in children's natural language settings. This study explored idioms usage in maternal talk during mother-child shared book reading and its relation to children's vocabulary development. Thirty-three Chinese children in Norway (aged 3;0-5;5) and their mothers participated. We observed shared reading at the onset of the study and assessed children's receptive and expressive vocabulary in Chinese three times across one year. Results demonstrated that mothers used an average of 1.8 idioms and explained one-third of the idioms. Maternal idiom usage was correlated with their talk amount and lexical diversity. Individual growth modeling revealed that the number of idioms mothers used predicted the growth of children's receptive vocabulary in Chinese. We speculate that idiom usage could be an effective and understudied marker of parental linguistic sophistication. This study underscores the importance of idiom exposure in children's language environment.

Longitudinal joint modeling for assessing parallel interactive development of latent ability and processing speed using responses and response times.

To measure the parallel interactive development of latent ability and processing speed using longitudinal item response accuracy (RA) and longitudinal response time (RT) data, we proposed three longitudinal joint modeling approaches from the structural equation modeling perspective, namely unstructured-covariance-matrix-based longitudinal joint modeling, latent growth curve-based longitudinal joint modeling, and autoregressive cross-lagged longitudinal joint modeling. The proposed modeling approaches can not only provide the developmental trajectories of latent ability and processing speed individually, but also exploit the relationship between the change in latent ability and processing speed through the across-time relationships of these two constructs. The results of two empirical studies indicate that (1) all three models are practically applicable and have highly consistent conclusions in terms of the changes in ability and speed in the analysis of the same data set, and (2) additional analysis of the RT data and acquisition of individual processing speed measurements can reveal the parallel interactive development phenomena that are difficult to detect using RA data alone. Furthermore, the results of our simulation study demonstrate that the proposed Bayesian Markov chain Monte Carlo estimation algorithm can ensure accurate model parameter recovery for all three proposed longitudinal joint models. Finally, the implications of our findings are discussed from the research and practice perspectives.

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The prevention and control requirements for HIV/AIDS vary significantly among different populations, posing substantial challenges to the formulation and implementation of intervention strategies. Dynamically assessing the heterogeneity and disease progression trajectories of various groups is crucial. Latent class growth model (LCGM) serves as a statistical approach that fits a longitudinal data into N subgroups of individual development trajectories, identifying and analyzing the progression paths of different subgroups, thereby offering a novel perspective for disease control strategies. LCGM has shown significant advantages in the application of HIV/AIDS prevention and control, especially in gaining a deeper understanding and analysis of epidemiological characteristics, risk behaviors, psychological research, heterogeneity in testing, and dynamic changes. Summarizing the advantages and limitations of applying LCGM can provide a reliable basis for precise prevention and control of HIV/AIDS.

Evolution under Spatially Heterogeneous Selection in Solid Tumors.

Spatial genetic and phenotypic diversity within solid tumors has been well documented. Nevertheless, how this heterogeneity affects temporal dynamics of tumorigenesis has not been rigorously examined because solid tumors do not evolve as the standard population genetic model due to the spatial constraint. We therefore, propose a neutral spatial (NS) model whereby the mutation accumulation increases toward the periphery; the genealogical relationship is spatially determined and the selection efficacy is blunted (due to kin competition). In this model, neutral mutations are accrued and spatially distributed in manners different from those of advantageous mutations. Importantly, the distinctions could be blurred in the conventional model. To test the NS model, we performed a three-dimensional multiple microsampling of two hepatocellular carcinomas. Whole-genome sequencing (WGS) revealed a 2-fold increase in mutations going from the center to the periphery. The operation of natural selection can then be tested by examining the spatially determined clonal relationships and the clonal sizes. Due to limited migration, only the expansion of highly advantageous clones can sweep through a large part of the tumor to reveal the selective advantages. Hence, even multiregional sampling can only reveal a fraction of fitness differences in solid tumors. Our results suggest that the NS patterns are crucial for testing the influence of natural selection during tumorigenesis, especially for small solid tumors.

Influence of management practices on water-related grain yield determinants.

Adequate management of N supply, plant density, row spacing, and soil cover has proved useful for increasing grain yields and/or grain yield stability of rainfed crops over the years. We review the impact of these management practices on grain yield water-related determinants: seasonal crop evapotranspiration (ET) and water use efficiency for grain production per unit of evapotranspired water during the growing season (WUEG,ET,s). We highlight a large number of conflicting results for the impact of management on ET and expose the complexity of the ET response to environmental factors. We analyse the influence of management practices on WUEG,ET,s in terms of the three main processes controlling it: (i) the proportion of transpiration in ET (T/ET), (ii) transpiration efficiency for shoot biomass production (TEB), and (iii) the harvest index. We directly relate the impact of management practices on T/ET to their effect on crop light interception and provide evidence that management practices significantly influence TEB. To optimize WUEG,ET,s, management practices should favor soil water availability during critical periods for seed set, thereby improving the harvest index. The need to improve the performance of existing crop growth models for the prediction of water-related grain yield determinants under different management practices is also discussed.

Breast Cancer Growth on Serial MRI: Volume Doubling Time Based on 3-Dimensional Tumor Volume Assessment.

BACKGROUND: The volume doubling time (VDT) of breast cancer was most frequently calculated using the two-dimensional (2D) diameter, which is not reliable for irregular tumors. It was rarely investigated using three-dimensional (3D) imaging with tumor volume on serial magnetic resonance imaging (MRI). PURPOSE: To investigate the VDT of breast cancer using 3D tumor volume assessment on serial breast MRIs. STUDY TYPE: Retrospective. SUBJECTS: Sixty women (age at diagnosis: 57 ± 10 years) with breast cancer, assessed by two or more breast MRI examinations. The median interval time was 791 days (range: 70-3654 days). FIELD STRENGTH/SEQUENCE: 3-T, fast spin-echo T2-weighted imaging (T2WI), single-shot echo-planar diffusion-weighted imaging (DWI), and gradient echo dynamic contrast-enhanced imaging. ASSESSMENT: Three radiologists independently reviewed the morphological, DWI, and T2WI features of lesions. The whole tumor was segmented to measure the volume on contrast-enhanced images. The exponential growth model was fitted in the 11 patients with at least three MRI examinations. The VDT of breast cancer was calculated using the modified Schwartz equation. STATISTICAL TESTS: Mann-Whitney U test, Kruskal-Wallis test, Chi-squared test, intraclass correlation coefficients, and Fleiss kappa coefficients. A P-value <0.05 was considered statistically significant. The exponential growth model was evaluated using the adjusted R2 and root mean square error (RMSE). RESULTS: The median tumor diameter was 9.7 mm and 15.2 mm on the initial and final MRI, respectively. The median adjusted R2 and RMSE of the 11 exponential models were 0.97 and 15.8, respectively. The median VDT was 540 days (range: 68-2424 days). For invasive ductal carcinoma (N = 33), the median VDT of the non-luminal type was shorter than that of the luminal type (178 days vs. 478 days). On initial MRI, breast cancer manifesting as a focus or mass lesion showed a shorter VDT than that of a non-mass enhancement (NME) lesion (median VDT: 426 days vs. 665 days). DATA CONCLUSION: A shorter VDT was observed in breast cancer manifesting as focus or mass as compared to an NME lesion. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

An in silico model of the capturing of magnetic nanoparticles in tumour spheroids in the presence of flow.

One of the main challenges in improving the efficacy of conventional chemotherapeutic drugs is that they do not reach the cancer cells at sufficiently high doses while at the same time affecting healthy tissue and causing significant side effects and suffering in cancer patients. To overcome this deficiency, magnetic nanoparticles as transporter systems have emerged as a promising approach to achieve more specific tumour targeting. Drug-loaded magnetic nanoparticles can be directed to the target tissue by applying an external magnetic field. However, the magnetic forces exerted on the nanoparticles fall off rapidly with distance, making the tumour targeting challenging, even more so in the presence of flowing blood or interstitial fluid. We therefore present a computational model of the capturing of magnetic nanoparticles in a test setup: our model includes the flow around the tumour, the magnetic forces that guide the nanoparticles, and the transport within the tumour. We show how a model for the transport of magnetic nanoparticles in an external magnetic field can be integrated with a multiphase tumour model based on the theory of porous media. Our approach based on the underlying physical mechanisms can provide crucial insights into mechanisms that cannot be studied conclusively in experimental research alone. Such a computational model enables an efficient and systematic exploration of the nanoparticle design space, first in a controlled test setup and then in more complex in vivo scenarios. As an effective tool for minimising costly trial-and-error design methods, it expedites translation into clinical practice to improve therapeutic outcomes and limit adverse effects for cancer patients.