Digital twin models representing human cardiac electrophysiology have evolved from being primarily research-oriented tools to becoming integral components in clinical decision-making and patient-specific simulations. The development of such models fundamentally begins with the construction of an accurate anatomical twin, which is typically derived from high-resolution clinical imaging data. This anatomical modeling phase, while essential, is often computationally intensive and time-consuming, necessitating efficient tools to streamline the workflow. To address this need, PyMeshTool, a Python interface for MeshTool, was developed with the primary objective of simplifying and accelerating the anatomical twinning process. The C/C++ codebase of MeshTool was restructured to avoid unnecessary source code duplication and to ease the development of the Python interface PyMeshTool. Particular emphasis was placed on the design of PyMeshTool as a streamlined interface that exposes core functionalities of MeshTool in a consistent and user-friendly manner. To evaluate the effectiveness of this design, two Python scripts were implemented running the same anatomical twinning pipeline, one utilizing PyMeshTool and the other calling MeshTool as an external tool. For this purpose, a basic bi-ventricular model generation pipeline was implemented that comprises the generation of simplified universal ventricular coordinates and the assignment of rule-based fibers & sheets. Runtimes and data output of the two workflows were compared for a series of numbers of parallel OpenMP threads. In addition to the primary advantage of PyMeshTool- easy interaction with other Python modules via the Python interface and the compatibility with NumPy- the computational benefits of PyMeshTool have been demonstrated in several comparisons: (i) the PyMeshTool workflow produced less output, 4 files with 89.5MB instead of 418 files with 774.9MB (∼88% less storage usage), (ii) the Python source code was ∼63% shorter in terms of code lines, and (iii) an up to four times faster runtime. With the release of the freely available PyMeshTool module, our work aims to streamline image and mesh processing in Python to ease the development of complex pipelines.
Radiomics analyses extract quantitative biomarkers from medical images for precision modeling, yet reproducibility and scalability remain limited by heterogeneous and limited implementations. Existing tools support only partial standards and lack integration with deep learning (DL) radiomics. To address these gaps, we developed PySERA, an open-source, Python-native, standardized radiomics framework designed for automation, reproducibility, and AI integration. PySERA re-implements MATLAB-based SERA (standardized environment for radiomics analysis) in a modular, object-oriented Python architecture. It computes 557 features, including 487 features compliant with the Image Biomarker Standardization Initiative (IBSI) and 10 moment-invariant descriptors, as well as 60 additional diagnostic features, along with DL radiomics embeddings from pre-trained DL: ResNet50 (2048 features) DL radiomics features), DenseNet121 (1024), and VGG16 (512). It includes standardized preprocessing (resampling, discretization, normalization), multi-format I/O (DICOM, NIfTI, NRRD), adaptive memory handling, and a parallel multi-core engine for scalable feature extraction. PySERA integrates directly with libraries: scikit-learn/PyTorch/TensorFlow/MONAI, and others for downstream machine learning applications. PySERA demonstrated >94% IBSI reproducibility, closely matching MITK and substantially outperforming PyRadiomics against the 487 IBSI-compliant feature reference set. Across 8 public datasets, PySERA achieved accuracies of 0.43-0.84, exceeding PyRadiomics for outcome prediction tasks. Benchmarking showed efficient processing (including added higher-order features not implemented in other software): 583 seconds (305 MB) for 166 features, and 2325 seconds (491 MB) for full extraction, with deterministic outputs across platforms. By uniting standardized handcrafted/DL radiomics in a scalable, transparent, and Python-integrable framework, PySERA establishes a reproducible and extensible foundation for next-generation, AI-ready precision imaging research.
Background Clinical randomization requires more than approximate 1:1 allocation. It also requires sequence generation that is difficult to subvert, allocation concealment during enrollment, and an audit trail that can withstand retrospective review. Objective The primary objective of this proof-of-concept technical and methodological evaluation was to assess the inspectability and audit-oriented design of a lightweight Python-based two-arm allocation prototype. Secondary objectives were to characterize its short-run demonstration behavior and compare its transparency, traceability, and operational limitations with common clinical randomization workflows. Methods We performed a static review of the supplied Python source file and a retrospective review of the supplied allocation log. Log analysis included descriptive arm counts, exact binomial testing for 1:1 balance, an exploratory runs test, and lag-1 autocorrelation. The implementation was interpreted in light of the clinical-trial randomization and pseudorandom-number-generator literature. Results The current source code implements a lightweight two-arm allocation randomization prototype with features intended to support auditability and tamper-evident traceability. Each allocation advances a xorshift-inspired 64-bit generator, maps the resulting integer to arm 1 or 2, and records the underlying output and assignment arithmetic in a human-readable log. An example test log was run and contained 2,000 allocations, with 502 versus 498 assignments in the first 1,000 events and 1008 versus 992 assignments overall. The current source code also implements session seed capture and a chained SHA-256 digest intended to strengthen sequential traceability and show evidence of tampering. Conclusions The prototype's principal improvement over many ad hoc local workflows is operational transparency rather than proven statistical superiority. Its strongest contribution is a short, inspectable code path and an audit-oriented logging structure. Additional hardening would be required before use in concealment-sensitive or regulated trial settings.
Molecular maps are graphical representations of the molecular mechanisms underlying biological systems. They are a valuable tool for curating, exchanging, and understanding biological knowledge, and may serve as a backbone for data analysis and modelling. Molecular maps are supported by a rich software ecosystem. However, there are currently no tools that support advanced programmatic analysis and processing of maps, in particular the extraction of the biological concepts they represent or their comparison. We introduce momapy, a generic Python library to work with molecular maps programmatically. At its core, momapy allows users to extract and separate the data model of a map from its graphical representation, and perform a variety of base operations on them, including their manipulation and comparison. momapy currently supports the SBGN and CellDesigner formats, two of the main standards to represent molecular maps graphically, and can be easily extended to support additional formats and functionalities. momapy is implemented in Python (RRID:SCR_008394) under a GPLv3 license. The code can be downloaded freely from https://github.com/adrienrougny/momapy and is archived on Zenodo (https://doi.org/10.5281/zenodo.19088611). Complete documentation and a user manual are available at https://adrienrougny.github.io/momapy. Supplementary data are available at Bioinformatics online.
Molecular-dynamics (MD) simulations provide atom-level insight into biomolecular motion and are widely used to complement structure-guided drug discovery, ligand optimization, and mechanistic structural biology. However, reproducible MD remains technically challenging because users must coordinate structure cleanup, force-field-compatible topology generation, ligand parameterization, solvation, ionization, energy minimization, equilibration, production simulation, trajectory processing, and downstream interpretation; errors at any stage can compromise results and discourage non-expert users. Here, we present PyMACS, an open-source Python automation framework for GROMACS-based molecular dynamics using the CHARMM36 or CHARMM36/LJ-PME biomolecular force fields together with CGenFF-compatible small-molecule parameterization. PyMACS accepts common structural input formats, including PDB, CIF, and mmCIF files, and is designed to operate on a broad range of user-supplied biomolecular systems rather than being restricted to a single target class or experimentally resolved protein-ligand complexes. Compatible inputs include experimental structures, docked or model-derived complexes, computationally predicted biomolecular assemblies, apo proteins, protein-ligand systems, cofactor-containing complexes, peptide-protein assemblies, protein-protein complexes, RNA-, DNA-, and mixed nucleic-acid-protein systems for setup and simulation workflows when residue naming and CHARMM/GROMACS topology generation support the input, and PROTAC or other ternary-complex models when force-field compatibility requirements are met. The pipeline automates structure preparation, ligand and component handling, GROMACS topology generation, system assembly, solvation, ionization, equilibration, production MD, checkpoint-aware execution, and post-simulation analysis. PyMACS also converts completed trajectories into interpretable outputs including RMSD, RMSF, radius of gyration, ligand-stability metrics, residue-contact summaries, interaction-network visualizations, CSV exports, and report-ready figures. Designed for both interactive novice use and headless reproducible execution, PyMACS lowers the barrier to rigorous MD simulation while preserving configurable control over scientific parameters, computational resources, and analysis thresholds. By integrating setup, simulation, analysis, and visualization into a transparent workflow, PyMACS enables medicinal chemists and structural biology researchers to evaluate candidate binding hypotheses, compare biomolecular systems, and generate reproducible MD-derived evidence with substantially reduced manual overhead.
NEURON has been widely used as an empirically-based simulation tool, especially for multi-compartment conductance-based neuronal modeling. The network mediating feeding in Aplysia californica has been extensively studied as a model central pattern generator. Understanding the relationship between network parameter values and their effect on animal behavior is of key importance in systems such as the Aplysia feeding apparatus, where detailed biophysical models can be constructed. This study aims to develop a new Python tool called NEURONpyxl that reads parameters from a spreadsheet to construct full neural networks to make it easier to create complex models in the NEURON simulation environment, incorporating short-term forms of plasticity such as depression or facilitation. Test simulations from well-understood networks were created in NEURONpyxl, and compared to simulation results of the same network in another neural simulator, the Simulator for Neural Networks and Action Potentials (SNNAP), which has previously been used to model conductance-based networks that include complex synaptic connections and multiple forms of synaptic plasticity. NEURONpyxl was then used to conduct a parameter grid search to optimize conductances in a previously developed network model of Aplysia feeding behavior. Simulations of the test networks in NEURONpyxl and SNNAP produced numerically equivalent results, with differences remaining within the expected margin of error arising from numerical integration and implementation details. We then located parameter values that generated simulated motor patterns with durations of protraction and retraction that matched biological feeding behavior under different mechanical loads. NEURONpyxl simplifies building and simulating complex neural networks with different forms of synaptic plasticity, and locating physiologically relevant parameter values. With NEURONpyxl, future work may include the creation of ensembles of network models and the integration of biomechanics with complex conductance-based networks.
Lattice-based random sequential adsorption (RSA) simulations can be used to investigate the random packing of shapes on a surface, in particular the adsorption of molecules on a crystalline lattice in 2D. Atomic layer deposition (ALD) and by extension area-selective ALD (AS-ALD) have found applications in the manufacturing of nanoelectronics in the semiconductor industry. During deposition with these techniques, the packing of molecules on a surface─in terms of density and arrangement─largely determines the growth rate and selectivity, respectively. The packaged AdsorPy script performs RSA simulations of molecules represented by their 2D footprints and provides quantitative information about the packing properties. The script provides easy integration of other computational methods used in material science, such as density functional theory calculations. RSA simulations can for example be used to make informed decisions on the choice of inhibitor species in AS-ALD processes. Various case scenarios are demonstrated using the script in order to match the conditions of relevant experiments, such as different dosing schemes.
Understanding the quality of eye-tracking recordings, often characterized using accuracy, precision, and data loss, is crucial for the interpretation of eye tracking data. Eye-tracking data quality can furthermore place fundamental limits on what studies can be conducted with an eye tracker, and one may be required to report eye-tracking data quality when publishing a study. However, how does one determine the quality of eye-tracking data? This article provides an overview of operationalizations of accuracy, precision, and data loss and practical advice for determining eye-tracking data quality. Furthermore, the programming code for calculating various quality metrics for a segment of eye-tracking data is provided in MATLAB, Python, and R. Also provided is ETDQualitizer, a tool designed to enable anyone to easily determine the data quality of their recordings. We provide a version that is browser-based ( https://dcnieho.github.io/ETDQualitizer ) and enables determining eye-tracking data quality without installation or programming, while ensuring data privacy by running entirely locally. ETDQualitizer is further provided as a MATLAB, Python, and R library ( https://github.com/dcnieho/ETDQualitizer ) that can be integrated in one's analysis scripts. We hope that this article enables any researcher to determine, critically evaluate, and report on eye-tracking data quality, and that it spurs researchers to adopt a data quality perspective in all their future eye-tracking studies.
The mcstas_gisans framework is a collection of Python scripts and modules to facilitate the simulation of grazing-incidence small-angle neutron scattering (GISANS) experiments. This approach combines McStas instrument simulation with BornAgain sample modeling capabilities. The Monte Carlo Particle Lists format for particle trajectory allows exchange between simulations that enables seamless transition from instrument modeling to sample scattering analysis. The Python-based processing utilities handle data transformation, scaling to virtual experiment times for absolute intensities, and visualization. The required software environment is managed through Conda, ensuring reproducible deployments across platforms. This integrated approach facilitates accurate simulation and analysis and enables the comparison of the GISANS capability of different neutron scattering instruments.
Background Accurate pain assessment is fundamental to effective cancer pain management. However, subjective scales such as the Numerical Rating Scale (NRS) have limitations in pediatric and elderly patients with impaired verbal communication. This study aimed to develop a real-time facial expression-based pipeline for NRS estimation and to evaluate its technical feasibility as a proof of concept (PoC). Methods A real-time analysis pipeline was implemented in Python (version 3.11; Python Software Foundation, Fredericksburg, VA, USA), integrating MediaPipe for facial detection and DeepFace for emotion estimation. Seven emotion probability scores extracted from 30 fps video streams were used as predictor variables to estimate NRS values using several regression models, including Random Forest (RF). Synthetic datasets generated for technical validation were evaluated using leave-one-out cross-validation (LOOCV). Performance was assessed using Spearman's rank correlation coefficient (ρ) and mean absolute error (MAE). Results In the pediatric dataset, the RF model achieved ρ = 0.7383 (p < 0.001) and MAE = 1.5195, demonstrating improved performance compared with the baseline model (ρ = 0.2765). In the elderly dataset, the RF model showed ρ = 0.7566 (p < 0.001) and MAE = 1.5760. Feature importance analysis indicated that "Fear" contributed prominently in both datasets, whereas "Neutral" also showed relatively higher importance in the elderly dataset. Conclusions This study demonstrated the technical feasibility of a real-time NRS estimation pipeline using artificial intelligence (AI)-based facial expression analysis. The findings suggest potential applicability as a complementary pain assessment approach for patients with limited verbal communication, pending future clinical validation.
Anxiety is influenced by a combination of lifestyle, psychological, and demographic factors. This study aimed to evaluate these associations and explore the potential of machine learning in predicting anxiety severity. Anxiety levels were evaluated using a large survey-based dataset of 11, 000 adults alongside demographic, physiological, and psychological measures. Descriptive statistics and inferential analyses were conducted in IBM SPSS to identify associations between key variables. Several machine learning regression algorithms, including linear, regularized, and ensemble models, were implemented in Python to predict anxiety levels. Model performance was evaluated using standard error metrics. Our findings revealed significant associations of anxiety with stress and sleep duration, while demographic attributes such as family history of anxiety and occupation also influenced outcomes. Ensemble machine learning algorithms achieved superior performance compared to single and linear-model approaches. Feature importance analysis identified stress, sleep, and caffeine intake as top predictors of anxiety. The integration of statistical approaches with machine learning applications highlights the multifactorial nature of anxiety and demonstrates the potential of predictive modeling in mental health care. Future research should emphasize longitudinal designs and the incorporation of biological and digital markers to enhance clinical applicability and prediction.
Exploring the dynamical and structural properties of molecular complexes involving DNA is a fundamentally important aspect of understanding many biological processes. Although tools exist for modeling linear DNA and simple complexes, significant challenges remain in generating intricate biomolecular assemblies and incorporating biologically relevant modifications. These limitations restrict the ability to create accurate starting configurations for advanced molecular simulation studies. Here, we introduce MDNA, a molecular modeling toolkit that bridges these gaps by enabling the construction and analysis of complex DNA structures. MDNA provides a versatile solution to generate DNA shapes using a spline-based mapping technique that enables the construction of DNA configurations with arbitrary shapes. Key features include support for (non-)canonical base modifications, such as Watson-Crick-Franklin to Hoogsteen transitions, DNA methylation, and the ability to refine structures using Monte Carlo minimization. The toolkit also provides geometric analysis tools based on rigid body formalism to evaluate DNA structures and trajectories. Together, these features enable users to model and analyze DNA configurations in high detail with a modular Python interface. By integrating structure generation and analysis into a single workflow, MDNA facilitates the study of DNA-protein interactions, supporting new insights into DNA dynamics and molecular simulations.
Antimicrobial peptides (AMPs) are promising alternatives to conventional antibiotics, but progress in computational AMP discovery has been difficult to quantify due to inconsistent datasets and evaluation protocols. We introduce QMAP, a domain-specific benchmark for predicting AMP antimicrobial potency (MIC) and hemolytic toxicity (HC50) with homology-aware, predefined test sets. QMAP enforces strict sequence homology constraints between training and test data, ensuring that model performance reflects true generalization rather than overfitting. Applying QMAP, we reassess existing MIC models and establish baselines for MIC and HC50 regression. Results suggest limited progress over six years, poor performance for high-potency MIC regression, and low predictability for hemolytic activity, emphasizing the need for standardized evaluation and improved modeling approaches for highly potent peptides. We release a Python package facilitating practical adoption, and with a Rust-accelerated engine enabling efficient data manipulation, installable with pip install qmap-benchmark.
High-density multielectrode arrays (HD-MEAs) generate large, complex datasets that are challenging to efficiently manage and analyze with existing tools, especially in open-source environments. To address this, we developed the BYU Seizure and Analytics Tool (YSA), an open-source graphical user interface built in Python and C++ for efficient analysis and visualization of HD-MEA recordings. The YSA features raster plots, automated discharge detection and tracking, downsampling, playback, and export functions, enabling streamlined workflows for large-scale neural data. We demonstrate the utility of the tool in the context of seizure and status epilepticus-like activity, highlighting how the YSA facilitates rapid exploration of the spatiotemporal dynamics in brain networks. This platform provides an accessible and practical solution for HD-MEA data analysis, supporting a range of neuroscience applications.
Accurate center detection in electron diffraction patterns is critical for all subsequent processing of experimental diffractograms. This study presents and compares several automated approaches - maximum intensity detection, phase cross-correlation, autocorrelation-based detection, pseudo-Voigt profile fitting and Hough-transform-based detection - applied to both polycrystalline diffractograms with characteristic diffraction rings and single-crystal diffractograms showing discrete diffraction spots. The methods were evaluated in terms of accuracy, robustness, speed, preprocessing requirements and applicability across diverse materials that produce a variety of diffraction patterns. Our findings provide practical guidance for selecting center detection techniques in automated diffractogram processing workflows, thus facilitating improved data quality and reliability in crystallographic analyses. Phase cross-correlation has been proven to deliver high performance consistently on polycrystalline diffractograms with diffraction rings, while pseudo-Voigt profile fitting is best suited to monocrystal-like diffractograms with discrete diffraction spots. All the above-mentioned algorithms have been implemented in the recent version of our open-source Python package EDIFF, which now offers a user-friendly, flexible and fully automated solution for center detection in diffractograms. These algorithms determine the center of the individual two-dimensional diffraction patterns, while the processing of complete three-dimensional electron diffraction or four-dimensional scanning transmission electron microscopy datasets often includes accurate center determination as part of structure refinement workflows.
To develop and illustrate the potential of a new, flexible, open-source software engine for task-based optimisation of exposure parameter settings in x-ray projection imaging.

Approach: The engine was built with several modules scripted in Python, to automate the different processes of optimisation of exposure parameters. Input is taken from a set of pre-calculated data containing image quality and dose metrics, and system parameters defined by the user. Modular code is employed, with classes responsible for image quality and dose calculations. For this study, the image quality (IQ) module
incorporated the standard signal-to-noise ratio (SNR) and a version of SNR (SNRw) that is weighted for the influence of the x-ray focus size and finite x-ray pulse width on the task. Optimal x-ray factors for a specified task are established by the Optimizer class that maximizes an FOM defined as SNR2 or SNRw2 divided by dose. A set of six experiments with different degrees of complexity was performed to illustrate the
engine and the influence of x-ray factor selection for a cardiac imaging task.

Main Results: A full parameter search covering 2400 different combinations of tube potential, additional copper filtration and focal spot size for 11 distinct patient thicknesses took approximately 30 minutes. The six experiments demonstrated that it is essential to consider x-ray tube power limitations and, when applicable, object motion and dose limits when determining the optimal exposure parameters.

Significance: The proposed engine automates optimal exposure parameter selection for user-defined image quality metrics and dose estimates. The influence of x-ray system parameters on system performance can be explored systematically. The engine is provided as an open-source resource with a modular structure that can be extended to include different figures of merit, image quality and dose metrics. The repository containing the engine is available at https://gitlab.kuleuven.be/medphysqa/deploy/flexpose/flexpose.
Strain-resolved metagenomics characterizes microbial communities at nucleotide-level resolution, enabling researchers to differentiate identical from closely related organisms and characterize population structure and gene content variation. Here we introduce ZipStrain, a program that performs highly accurate strain-resolved metagenomics over 500× faster than available methods while offering superior RAM management. Applied to a dataset of 2,754 samples spanning human populations, we identify a strain-sharing gradient across social relationships, reveal striking variation in clonal structure across bacteria and bacteriophage, and pinpoint genes whose nucleotide identity deviates from genome-wide expectations. ZipStrain is distributed as an open-source Python package and accompanying Nextflow pipeline at https://github.com/OlmLab/ZipStrain .
We present a 40 kHz single-axis acoustic levitator for Small-Angle X-ray Scattering (SAXS) experiments. The levitator consists of 48 ultrasonic transducers of 9.8 mm in diameter and a small opposing plane reflector. The levitation stability is evaluated by using a high-speed camera for recording the position over time of a suspended drop. To demonstrate the capability of the levitator, it was assembled in a laboratory SAXS instrument (Xenocs Xeuss 2.0) for investigating the evaporation kinetics of liquid drops containing metallic silver nanoparticles. The spontaneous evaporation of the levitated water molecules decreases the droplet volume, leading to a systematic increase in the concentration of the particles in the system. As a result, the average distance between the particles decreases, giving rise to enhanced particle-particle interactions. For the evaluation of the droplet size and selection of correct scattering data for sample and blank scattering, a Python™ package was developed, allowing automatic video analysis and accurate subtraction of the background associated with the solvent in which the particles are suspended. Although the proposed levitator was used in SAXS experiments, it can also be used in many other research fields, such as pharmacy, biology, chemistry, and material sciences.
MRI in patients with auditory implants is limited by implant-related signal void, geometric distortion, and intravoxel dephasing. Artifact extent is often described qualitatively. We aimed to quantify MRI artifact burden from a novel transcutaneous bone-conduction implant (SentioTM) and to compare it with cochlear implant controls under routine, label-compliant 1.5 T clinical conditions. We retrospectively analyzed MRI examinations of patients with a SentioTM implant (n = 7) and cochlear implant controls (n = 5). All scans were performed at 1.5 T, the manufacturer-approved field strength for the SentioTM Ti implant, on the same scanner using routine transverse turbo spin-echo sequences (T2-weighted and T1-weighted; 5‑mm slice thickness). Artifacts were quantified slice-by-slice with a custom Python-based DICOM pipeline. The artifact-to-brain ratio (ABR, %) and maximum void area (cm2) were computed; ABRmax (peak slice) served as patient-level summary. Groups were compared using the Mann-Whitney U test and Cliff's delta. SentioTM implants showed smaller and more confined artifacts. Median ABRmax was 1.1% [IQR 0.2] with a median maximum void area of 1.7 cm2 [IQR 0.4]. Cochlear implant controls showed larger artifacts (ABRmax 2.6% [IQR 1.0]; median maximum void area 3.8 cm2 [IQR 2.0]; p = 0.007-0.01; δ = 0.81-0.89). Supratentorial diagnostic image quality outside the immediate implant region was preserved in all T2w/T1w SentioTM examinations and partially preserved in 3/5 cochlear implant examinations. Quantitative artifact assessment using ABRmax is feasible in routine 1.5 T MRI. Under identical protocol conditions, the SentioTM implant produced a smaller and more localized MRI artifact burden than the cochlear implant controls examined here. Routine turbo spin-echo imaging remained diagnostically useful outside the implant region.
Preeclampsia is a leading cause of maternal death during pregnancy, and the role of circadian rhythms in predicting preeclampsia is not well understood. We aimed to determine whether glucose circadian rhythm disruption is associated with preeclampsia and can be used to predict this disorder. We analyzed a dataset of 92 pregnant individuals recruited with Continuous Glucose Monitoring (CGM). To study rhythmicity, we performed a cosinor analysis using the packages cosinor and cosinor2, and we calculated the non-parametric circadian rhythm variables using the nparACT package in R. Furthermore, we performed multiple-component cosinor analysis to detect internal oscillations and identify glucose postprandial peaks using the package CosinorPy in Python. Seventy-one participants (20 women with preeclampsia) had sufficient data for studying glucose circadian rhythmicity and performing all the chronobiological analyses. All the participants exhibited a significant circadian rhythm in their glucose oscillation. We developed a model including the time difference between the first postprandial peak and the last one, L5 start-time, and age that was predictive for preeclampsia. Patients diagnosed with preeclampsia from this model had a reduced amplitude and less robust glucose rhythmicity. We conclude that identifying abnormal glucose circadian rhythm during pregnancy may help to anticipate pregnancy-related disorders like preeclampsia.