Biology subjects

Vink, J.

Publications and source records attributed to Vink, J..

10 recordsLinked to original sources

bioRxiv2024

Time-Dependent Material Properties and Composition of the Nonhuman Primate Uterine Layers Through Gestation

The uterus is central to the establishment, maintenance, and delivery of a healthy pregnancy. Biomechanics is an important contributor to pregnancy success, and alterations to normal uterine biomechanical functions can contribute to an array of obstetric pathologies. Few studies have characterized the passive mechanical properties of the gravid human uterus, and ethical limitations have largely prevented the investigation of mid-gestation periods. To address this key knowledge gap, this study seeks to characterize the structural, compositional, and time-dependent micro-mechanical properties of the nonhuman primate (NHP) uterine layers in nonpregnancy and at three time-points in pregnancy: early 2nd, early 3rd, and late 3rd trimesters. Distinct material and compositional properties were noted across the different tissue layers, with the nonpregnant endometrium and pregnant decidua being the least stiff, most viscous, least diffusible, and most hydrated layers of the NHP uterus. Pregnancy induced notable compositional and structural changes in the decidua and myometrium but had no effect on their micro-mechanical properties. Further comparison to published human data revealed marked similarities across species, with minor differences noted for the perimetrium and nonpregnant endometrium. This work provides insights into the material properties of the NHP uterus and demonstrates the validity of NHPs as a model for studying certain aspects of human uterine biomechanics.

bioengineering↗

bioRxiv2024

Uterus and cervix anatomical changes and cervix stiffness evolution throughout pregnancy

The coordinated biomechanical performance, such as uterine stretch and cervical barrier function, within maternal reproductive tissues facilitates healthy human pregnancy and birth. Quantifying normal biomechanical function and detecting potentially detrimental biomechanical dysfunction (e.g., cervical insufficiency, uterine overdistention, premature rupture of membranes) is difficult, largely due to minimal data on the shape and size of maternal anatomy and material properties of tissue across gestation. This study quantitates key structural features of human pregnancy to fill this knowledge gap and facilitate three-dimensional modeling for biomechanical pregnancy simulations to deeply explore pregnancy and childbirth. These measurements include the longitudinal assessment of uterine and cervical dimensions, fetal weight, and cervical stiffness in 47 low-risk pregnancies at four time points during gestation (late first, middle second, late second, and middle third trimesters). The uterine and cervical size were measured via 2-dimensional ultrasound, and cervical stiffness was measured via cervical aspiration. Trends in uterine and cervical measurements were assessed as time-course slopes across pregnancy and between gestational time points, accounting for specific participants. Patient-specific computational solid models of the uterus and cervix, generated from the ultrasonic measurements, were used to estimate deformed uterocervical volume. Results show that for this low-risk cohort, the uterus grows fastest in the inferior-superior direction from the late first to middle second trimester and fastest in the anterior-posterior and left-right direction between the middle and late second trimester. Contemporaneously, the cervix softens and shortens. It softens fastest from the late first to the middle second trimester and shortens fastest between the late second and middle third trimester. Alongside the fetal weight estimated from ultrasonic measurements, this work presents holistic maternal and fetal patient-specific biomechanical measurements across gestation.

bioengineering↗

bioRxiv2024

Equilibrium Tension and Compression Mechanical Properties of the Human Uterus

A successful pregnancy relies on the proper cellular, biochemical, and mechanical functions of the uterus. A comprehensive understanding of uterine mechanical properties during pregnancy is key to understanding different gynecological and obstetric disorders such as preterm birth, placenta accreta, leiomyoma, and endometriosis. This study sought to characterize the macro-scale equilibrium material behaviors of the human uterus in non-pregnancy and late pregnancy under both compressive and tensile loading. Fifty human uterine specimens from 16 patients (8 nonpregnant [NP] and 8 pregnant [PG]) were tested using spherical indentation and uniaxial tension coupled with digital image correlation (DIC). A three-level incremental load-hold protocol was applied to both tests. A microstructurally-inspired material model considering fiber architecture was applied to this dataset. Inverse finite element analysis (IFEA) was then performed to generate a single set of mechanical parameters to describe compressive and tensile behaviors. The freeze-thaw effect on uterine macro mechanical properties was also evaluated. PG tissue exhibits decreased overall stiffness and increased fiber network extensibility compared to NP uterine tissue. Under indentation, ground substance compressibility was similar between NP and PG uterine tissue. In tension, the fiber network of the PG uterus was found to be more extensible and dispersed than in nonpregnancy. Lastly, a single freeze-thaw cycle did not systematically alter the macro-scale material behavior of the human uterus.

bioengineering↗

bioRxiv2023

Validating models of TMS effects with concurrent TMS/fMRI

The exact dose and spatial pattern of brain activation induced by transcranial magnetic stimulation (TMS) is of importance to all applications of TMS, both clinical and for investigational use. How TMS coils induce electrical fields has been investigated and validated by several groups, but models of how evoked currents interact with neuronal tissue and where activation is induced have hardly been validated empirically. Here we propose a detailed model of TMS induced currents in the brain building on our previous work on different TMS coil models, taking into account various electromagnetic head tissue properties with finite element modeling and several competing macroscopic models of how brain activation is achieved by the TMS induced electrical field. Importantly, we validate these models using TMS coil discharges administered to the brain of 6 healthy volunteers inside a 3T MRI scanner, while acquiring functional MRI scans at the same time. We conclude that the mere magnitude of the electric field in the cortical gray matter tissue near the stimulation site provides the best metric to predict neuronal activation as measured by BOLD fMRI, whereas models of brain activation taking into account orientation of E-fields with respect to the cortical columns and layers do not (yet) yield better predictions of neuronal activation induced by TMS. This report is relevant for neuronavigated TMS approaches attempting to take into account evoked E-fields and neuronal activation to improve precision of TMS administration for clinical and investigational purposes.

neuroscience↗

bioRxiv2023

A validation approach for computational models of TMSinduced brain currents using motor evoked potentials

The adoption of transcranial magnetic stimulation (TMS) has steadily increased in research as a tool capable to safely and non-invasively stimulate both the central and peripheral nervous systems. Initial clinical applications were limited to diagnostic use of TMS and readout signals such as electromyograms (EMG). Subsequently, repetitive TMS (rTMS) was appreciated for its therapeutics benefits as well. However, even after a decade of use of rTMS as an alternative treatment of major depression disorder in psychiatry, the mechanism of action is still not well understood. Computer models predicting the induced electric field distribution in the brain have been suggested before in the hope to resolve at least some of the uncertainty and resulting variable treatment response associated with the clinical use of TMS. We constructed a finite element model (FEM) of the head using individual volumetric tissue meshes obtained from an MRI scan and a detailed model of a TMS coil that together can predict the current induced in the head of a patient at any given location with any given coil position and orientation. We further designed several potential metrics of how a TMS induced current induced neuronal activation in the motor cortex, and added this to the model. We validated this model with motor evoked potentials (MEPs), EMG responses of the hand muscles after TMS on the motor cortex, in an experiment on 9 healthy subjects. We adopted a tailored MEP mapping protocol for model validation, which unlike traditional grid mappings, varies the TMS machine output intensity between stimulation locations. We further varied coil orientation on each point stimulated to allow exploration of the angular dependency of the model MEPs. Taken together, this approach covers a wide domain and scope of the modeled and measured responses, which are optimally suited for model validation. For each subject the motor hotspot was carefully identified using individual cortical anatomy and BOLD fMRI measurements. Modeled activation in the motor cortex did not show a good correlation to the observed magnitude of the observed MEPs, for none of the neuronal activation metrics adopted. For an activation metric that was asymmetric, taking into account induced current direction with respect to the motor cortex sulcal wall, was marginally better than other metrics. Generally all activation metrics based on induced currents performed better than a control metric agnostic of induced electric field magnitude. Our results suggest that one should take into account components of the injected currents and their relationship to the morphology of the underlying motor cortex, but the coarse metrics we used to model the relationship between induced current and neuronal activation probably did not do justice to the complex neuronal circuitry of the cortical sheet. Furthermore, it seemed MEP magnitudes in our experiment are too variable over subsequent stimulations, which could be mitigated by more repetitions per stimulation location and orientation. Further efforts to construct validated models predicting TMS effects in individual patients brains should incorporate microcircuits interactions in the cortical sheet, in addition to induced electrical field models, and take into account inherent trial to trial variability of MEPs.

neuroscience↗

bioRxiv2020

Longitudinal ultrasonic dimensions and parametric solid models of the gravid uterus and cervix

Tissue mechanics is central to pregnancy, during which maternal anatomic structures undergo continuous remodeling to serve a dual function to first protect the fetus in utero while it develops and then facilitate its passage out. In this study of normal pregnancy using biomechanical solid modeling, we used standard clinical ultrasound images to obtain measurements of structural dimensions of the gravid uterus and cervix throughout gestation. 2-dimensional ultrasound images were acquired from the uterus and cervix in 30 pregnant subjects in supine and standing positions at four time points during pregnancy (8-14, 14-16, 22-24, and 32-34 weeks). Offline, three observers independently measured from the images of multiple anatomic regions. Statistical analysis was performed to evaluate inter-observer variance, as well as effect of gestational age, gravity, and parity on maternal geometry. A parametric solid model developed in the Solidworks computer aided design (CAD) software was used to convert ultrasonic measurements to a 3-dimensional solid computer model, from which estimates of uterine and cervical volumes were made. This parametric model was compared against previous 3-dimensional solid models derived from magnetic resonance frequency images in pregnancy. In brief, we found several anatomic measurements easily derived from standard clinical imaging are reproducible and reliable, and provide sufficient information to allow biomechanical solid modeling. This structural dataset is the first, to our knowledge, to provide key variables to enable future computational calculations of tissue stress and stretch in pregnancy, making it possible to characterize the biomechanical milieu of normal pregnancy. This vital dataset will be the foundation to understand how the uterus and cervix malfunction in pregnancy leading to adverse perinatal outcomes.

bioengineering↗

bioRxiv2020

Extracting transition rates in single-particle tracking using analytical diffusion distribution analysis

Single-particle tracking is an important technique in the life sciences to understand the kinetics of biomolecules. Observed diffusion coefficients in vivo, for example, enable researchers to determine whether biomolecules are moving alone, as part of a larger complex or are bound to large cellular components such as the membrane or chromosomal DNA. A remaining challenge has been to retrieve quantitative kinetic models especially for molecules that rapidly interchange between different diffusional states. Here, we present analytic diffusion distribution analysis (anaDDA), a framework that allows extracting transition rates from distributions of observed diffusion coefficients. We show that theoretically predicted distributions accurately match simulated distributions and that anaDDA outperforms existing methods to retrieve kinetics especially in the fast regime of 0.1-10 transitions per imaging frame. AnaDDA does account for the effects of confinement and tracking window boundaries. Furthermore, we added the option to perform global fitting of data acquired at different frame times, to allow complex models with multiple states to be fitted confidently. Previously, we have started to develop anaDDA to investigate the target search of CRISPR-Cas complexes. In this work, we have optimized the algorithms and reanalysed experimental data of DNA polymerase I diffusing in live E. coli. We found that long-lived DNA interaction by DNA polymerase are more abundant upon DNA damage, suggesting roles in DNA repair. We further revealed and quantified fast DNA probing interactions that last shorter than 10 ms. AnaDDA pushes the boundaries of the timescale of interactions that can be probed with single-particle tracking and is a mathematically rigorous framework that can be further expanded to extract detailed information about the behaviour of biomolecules in living cells.

biophysics↗

bioRxiv2020

Expanding the Genetic Architecture of Nicotine Dependence and its Shared Genetics with Multiple Traits: Findings from the Nicotine Dependence GenOmics (iNDiGO) Consortium

Cigarette smoking is the leading cause of preventable morbidity and mortality. Knowledge is evolving on genetics underlying initiation, regular smoking, nicotine dependence (ND), and cessation. We performed a genome-wide association study using the Fagerstrom Test for ND (FTND) in 58,000 smokers of European or African ancestry. Five genome-wide significant loci, including two novel loci MAGI2/GNAI1 (rs2714700) and TENM2 (rs1862416) were identified, and loci reported for other smoking traits were extended to ND. Using the heaviness of smoking index (HSI) in the UK Biobank (N=33,791), rs2714700 was consistently associated, but rs1862416 was not associated, likely reflecting ND features not captured by the HSI. Both variants were cis-eQTLs (rs2714700 for MAGI2-AS3 in hippocampus, rs1862416 for TENM2 in lung), and expression of genes spanning ND-associated variants was enriched in cerebellum. SNP-based heritability of ND was 8.6%, and ND was genetically correlated with 17 other smoking traits (rg=0.40-0.95) and co-morbidities. Our results emphasize the FTND as a composite phenotype that expands genetic knowledge of smoking, including loci specific to ND.

genomics↗

bioRxiv2019

Direct visualization of native CRISPR target search in live bacteria reveals Cascade DNA surveillance mechanism

CRISPR-Cas systems encode RNA-guided surveillance complexes to find and cleave invading DNA elements. While it is thought that invaders are neutralized minutes after cell entry, the mechanism and kinetics of target search and its impact on CRISPR protection levels have remained unknown. Here we visualized individual Cascade complexes in a native type I CRISPR-Cas system. We uncovered an exponential relationship between Cascade copy number and CRISPR interference levels, pointing to a time-driven arms race between invader replication and target search, in which 20 Cascade complexes provide 50% protection. Driven by PAM-interacting subunit Cas8e, Cascade spends half its search time rapidly probing DNA ([~]30 ms) in the nucleoid. We further demonstrate that target DNA transcription and CRISPR arrays affect the integrity of Cascade and impact CRISPR interference. Our work establishes the mechanism of cellular DNA surveillance by Cascade that allows the timely detection of invading DNA in a crowded, DNA-packed environment.\n\nOne sentence summaryThe results from in vivo tracking of single CRISPR RNA-surveillance complexes in the native host cell explain their ability to rapidly recognize invader sequences.

microbiology↗

bioRxiv2017

Causal Associations Between Body Mass Index and Mental Health: A Mendelian Randomization Study

BackgroundBody Mass Index (BMI) is negatively correlated with subjective well-being and positively correlated with depressive symptoms. Whether these associations reflect causal effects or confounding is unclear.\n\nMethodsWe examined causal effects between BMI and subjective well-being/depressive symptoms with bi-directional, two-sample Mendelian randomization using summary-level data from large genome-wide association studies. Genetic variants robustly related to the exposure variable acted as instrumental variable (two thresholds were used; p<5e-08 and p<1e-05). These instruments were then associated with the outcome variable. Pleiotropy was corrected for by two sensitivity analyses.\n\nResultsSubstantial evidence was found for a causal effect of BMI on mental health, such that a higher BMI decreased subjective well-being and increased depressive symptoms. No consistent evidence was found for causality in the other direction.\n\nConclusionsThis study provides support for a higher BMI causing poorer mental health. Further research should corroborate these findings and consider non-linear effects and sex differences.

genetics↗