Physics of Fluids

Cyanobacterial colonies often exploit their buoyancy and large size to float upwards rapidly and form dense surface blooms, which can degrade water quality, threaten ecosystems and public health, and impose substantial economic costs. Yet, how the morphology of cyanobacterial colonies controls their vertical velocity remains poorly understood. We conducted detailed three-dimensional morphological characterization of colonies of the cyanobacterium Microcystis in lake samples at the single-colony level and performed controlled flotation experiments in stratified flows. Using particle tracking in a vertical density gradient, we separately quantified the contributions of colony shape and buoyant density at the level of individual colonies. Our results show that the shape factor in Stokes law varies systematically with colony size. Consequently, the vertical velocity of colonies does not scale with the square of colony size but only with a power of 1.13, as larger colonies have a more irregular shape and therefore experience enhanced drag. We therefore correct the commonly used Stokes law to account for the size-dependent change in the shape factor. Interestingly, implementation of this power law relationship in a vertical migration model shows widespread chaotic dynamics in the migration trajectories of Microcystis colonies. These results highlight the importance of morphological plasticity in cyanobacterial colonies and can inform predictive models and hydrodynamic control strategies for toxic blooms. Our methodology to simultaneously determine the density, shape factor and velocity is broadly applicable to suspended aggregates with complex shapes in freshwater and marine systems.

Despite its ubiquity in natural flows, the effects of turbulence on fish locomotion and behavior remain poorly understood. The prevailing hypothesis is that these effects depend on the spatial and temporal scales of the turbulence relative to the fishs size and swimming speed. But in conventional facilities, turbulence usually increases with mean flow, which forces higher swimming speeds and can leave these relative scales unchanged. We therefore present a novel experimental facility that leverages a jet array to decouple the turbulence from the mean flow and systematically control its scales. This approach allows the ratio of turbulent to fish inertial scales to be varied over an order of magnitude, providing a controlled framework for quantifying fish-turbulence interactions. The facility also supports experiments probing strategies fish may use to cope with turbulence, including collective behaviors. Insights from this work have broader implications for ecological studies and engineering applications, including the design of effective fishways and bio-inspired underwater vehicles.

Cerebrospinal fluid (CSF) is a Newtonian fluid that bathes the brain and spinal cord and oscillates in response to the physiological periodic changes in brain volume, of which the cardiac cycle is a major driver. Understanding this motion is essential for clarifying its contribution to solute transport, waste clearance, and drug delivery. In this work, we study oscillatory and steady streaming flow in the cranial subarachnoid space using a lubrication-based theoretical framework. The model represents the cranial CSF compartment as a thin fluid layer bounded internally by the brain surface and externally by the dura, driven by time-dependent brain surface displacements. We first derive simplified governing equations for flow over an arbitrary smooth sphere-like brain surface and obtain analytical solutions for an idealised spherical geometry with uniform displacements. We then incorporate realistic displacement fields reconstructed from MRI measurements in healthy subjects and solve the reduced equations numerically. The results show that oscillatory forcing produces a steady streaming component that may enhance solute transport compared with diffusion alone. This work provides a mechanistic description of the flow generated by physiological brain motion and highlights the potential presence of steady streaming in cranial subarachnoid fluid dynamics.

Small airways are the primary sites of airflow obstruction in chronic obstructive pulmonary disease. Effective delivery of aerosolized drug particles to these regions is crucial to maximize treatment efficacy while minimizing side effects. However, conventional inhalation therapy approaches (i.e., full-mouth particle release and inhalation (FMD)) typically result in insufficient drug deposition in the small airways and an uneven distribution across the five lung lobes. To address such deficiencies, the goals of this study are triple folds: (1) to develop a fast and accurate framework to secure target drug delivery (TDD) nozzle diameter and location based on the conventional computational fluid particle dynamics (CFPD)-FMD simulations, (2) to develop a CFPD-informed machine learning (ML) inverse-design framework that predicts optimal inhaler nozzle parameters based on patient-specific breathing patterns and drug properties, and (3) to demonstrate the feasibility of embedding this framework into a user-centered smart inhaler prototype to improve uniform TTD to the small airways across all five lung lobes. Specifically, a subject-specific mouth-to-generation-10 human respiratory system was employed, and 108 high-fidelity CFPD-FMD simulations were performed under varied physiological and design parameters, including tidal volume, particle diameter, release location, and release timing. Particle release maps generated from those CFPD-FMD simulations via backtracking identified optimal nozzle diameters and locations that promote uniform multi-lobe drug delivery while limiting off-target deposition. Accordingly, a dataset was compiled with inputs (i.e., flow rate, particle size, release z-coordinate, release time) and targets (i.e., nozzle center x- and y-coordinates, nozzle diameter). These inputs and targets form the CFPD-TDD dataset, on which 16 ML models were trained to learn inverse mapping from patient- and drug-specific inputs to optimal nozzle design parameters. Performance was evaluated using mean squared error (MSE) and mean absolute error (MAE) overall and per target feature. Parametric analysis using CFPD-FMD simulations was conducted to determine how patient-specific and drug-specific factors affect pulmonary air-particle transport dynamics and to explain why achieving CFPD-TDD in small airways with CFPD-FMD strategies remains challenging. Furthermore, the ML evaluation in this feasibility study demonstrated robust learning of the inverse mapping from patient-specific inputs to optimal nozzle parameters. Four top-performing models showed consistently low MSE/MAE across cases, and an ensemble (i.e., mixed model (MixModel)) combining their strengths was formulated. Independent CFPD-TDD simulations beyond the training and testing datasets were used as the ground truth to validate ML-predicted nozzle configurations. Compared with conventional CFPD-FMD strategies, ML-guided nozzle designs significantly improved inter-lobar deposition uniformity and reduced off-target deposition in the upper airways, demonstrating the feasibility of ML-enabled TDD to the small airways. Overall, this study establishes a CFPD-informed ML inverse-design framework as a viable algorithmic foundation for user-centered smart inhalers, enabling adaptive, patient-specific TDD to the small airways with improved deposition uniformity across all five lung lobes. By integrating first-principle-based CFPD with ML, this work provides a methodological pathway toward next-generation smart inhalers for more effective treatment of small airway diseases.

Space-based platforms currently represent the most accurate means to experimentally assess the influence of the space environment on biological systems. However, performing such experiments remains technically challenging and requires highly specialized instrumentation. This study describes the current development and hardware qualification of ExocubeBio, a miniaturized experimental platform for in-situ biological space exposure. This experiment is scheduled for installation on the exterior of the International Space Station in 2027, as part of Exobio, the European Space Agencys new generation exobiology exposure facility. ExocubeBio will expose live microbial samples to the low Earth orbit environment, and combine autonomous in-situ optical density and fluorescence measurements, with the capacity to return preserved samples to Earth. Achieving these experimental goals requires a specialized, robust and reliable hardware system. The ExocubeBio hardware testing described here includes assessment of material biocompatibility and durability, functional validation of the miniaturized fluidic system, and optimization of the integrated optical subsystem for optical density and fluorescence measurements. These results demonstrate that the ExocubeBio experimental hardware components can each execute their core functional and operational requirements; subsystems allow for sample exposure, in-situ measurements of microbial cultures, and the chemical preservation of samples for post-flight analysis. As ExocubeBio transitions from hardware development to mission readiness, the results presented here validate the overall design and engineering approaches utilized. By combining the strengths of in-situ monitoring and sample return, ExocubeBio represents a significant advancement in space-based experimentation, and will provide new insights into microbial responses to the space environment.

A compact, in-house developed ultraviolet germicidal irradiation (UVGI) system adaptable to static, mobile, or robotic platforms was developed for the effective sterilization of bacteria and fungi using a wireless mode of operation. Under controlled laboratory conditions, its efficacy was evaluated against three representative biofilm-forming pathogens: Bacillus subtilis (Gram-positive, spore-forming, motile bacterium), Escherichia coli K12 (Gram-negative, non-spore-forming, non-motile bacterium), and Candida albicans M-207 (multi-drug-resistant, clinical yeast isolate). Microbial viability following UVGI exposure was assessed using colony-forming unit (CFU) and MTT assays, and morphological alterations were characterized by scanning electron microscopy (SEM). Cultures were exposed to UV-C radiation at distances of 1-5 m for 15-90 min. CFU assay demonstrated 100% kill of all tested organisms at 1 m and 15 min, corresponding to doses of 600.3, 576 & 697.5 mJ/cm{superscript 2}. Although MTT assays indicated residual metabolic activity under the same conditions, CFU results confirmed that surviving cells were unable to proliferate, highlighting the robustness of UV treatment for long-term inactivation. SEM confirmed distinct morphological alterations such as complete destruction of extracellular matrix & reduction in number of cells indicating cell death with increase in UV dose as compared to controls. A dose & time-dependent inactivation of biofilm-forming bacteria & fungi was observed on exposure to UVGI. Therefore, this pilot study validates the effectiveness of the newly developed UVGI surface sterilizer against biofilm-forming bacterial and fungal pathogens. Overall, the system demonstrates proof-of-concept efficacy under laboratory conditions and holds strong potential for future development and validation in hospitals and other contaminated public spaces. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/715580v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@150cefcorg.highwire.dtl.DTLVardef@450831org.highwire.dtl.DTLVardef@1cfd6borg.highwire.dtl.DTLVardef@1419ba8_HPS_FORMAT_FIGEXP M_FIG C_FIG IMPORTANCEMicroorganisms that form biofilms on surfaces are difficult to eliminate and contribute to the spread of infections in healthcare and indoor environments. There is a need for practical, easy-to-use disinfection technologies that can effectively reduce such contamination. In this study, we developed a compact, in-house, wireless UV-C disinfection system designed for flexible operation across different surface types. The system was evaluated under controlled laboratory conditions using representative biofilm-forming bacterial and fungal pathogens. Our findings show that the system can effectively reduce microbial contamination, demonstrating proof-of-concept efficacy. This work highlights the potential of accessible, non-chemical UV-based technologies and supports their further validation for applications in real-world disinfection settings.

Understanding how cells migrate through confined environments is crucial for elucidating fundamental biological processes, including cancer invasion, immune surveillance, and tissue morphogenesis. The nucleus, as the largest and stiffest cellular organelle, often limits cellular deformability, making it a key factor in migration through narrow pores or highly constrained spaces. In this work, we introduce a geometric surface partial differential equation (GS-PDE) model in which the cell plasma membrane and nuclear envelope are described as evolving energetic closed surfaces governed by force-balance equations. We replicate the results of a biophysical experiment, where a microfluidic device is used to impose compressive stresses on cells by driving them through narrow microchannels under a controlled pressure gradient. The model is validated by reproducing cell entry into the microchannels. A parametric sensitivity analysis highlights the dominant influence of specific parameters, whose accurate estimation is essential for faithfully capturing the experimental setup. We found that surface tension and confinement geometry emerge as key determinants of translocation efficiency. Although tailored to this specific setup for validation purposes, the framework is sufficiently general to be applied to a broad range of cell mechanics scenarios, providing a robust and flexible tool for investigating the interplay between cell mechanics and confinement. It also offers a solid foundation for future extensions integrating more complex biochemical processes such as active confined migration.

PurposeLarge quantities of human pluripotent stem cells (hPSCs) are required for clinical applications. 3D suspension cultures are suitable for large scale manufacturing of hPSCs but yield, viability and quality are affected by the hydrodynamic environment. This paper characterizes the hydrodynamic environment inside vertical wheel bioreactors (VWBRs) as a function of size and agitation rates, measures its effect on cell aggregation and proliferation, and proposes the use of Lagrangian-based shear stress and energy dissipation rate (EDR) exposures to support scale-up. MethodsIn silico: Transient, 3D, turbulent flow simulations are conducted for two VWBR sizes (100, 500 mL) at five agitation rates between 20 and 80 rpm. Trajectories of cell aggregates of sizes from 200 to 1,000 microns are calculated, and shear stress and EDR exposures are collected along these trajectories. In vitro: ESI-017 hPSCs were cultured in VWBRs for 6 days. Aggregation efficiency and daily fold ratios were calculated based on cell counts and initial inoculation density. ResultsAggregate size, agitation rate and bioreactor size modulate cell aggregate exposures to EDR and shear stress, which significantly depart from maximum or volume average metrics used for scale-up. Combined in vitro/in silico results show EDR affects aggregation efficiency, cell counts and aggregate size, and has a small effect on daily fold ratios but a significant effect on total fold ratio. ConclusionHistory of trajectory-based cell aggregate exposures to EDRs provide a better scale-up basis for VWBRs than volume-averaged EDR. Shear stress does not significantly affect hPSC aggregation, proliferation and expansion in VWBRs under the tested conditions.

As climate change intensifies, health risks from extreme heat are rising. Accurate assessment of heat vulnerability at high spatial resolution is crucial for developing effective adaptation strategies, particularly in socioeconomically heterogeneous urban settings. However, the identification of key indicators underlying heat vulnerability remains challenging. Using Chicago, Illinois (USA) as a case study, we systematically compare different variable selection strategies in community-level heat vulnerability assessments. We take the conventional unsupervised principal component analysis (PCA)-based Heat Vulnerability Index (HVI) as a baseline, and compare it with supervised approaches that incorporate variable selection, including machine learning algorithms (Lasso regression, Random Forest, and XGBoost) as well as traditional statistical methods (simple linear regression and polynomial regression). Using the vulnerability indicator subsets identified by each variable selection method, we construct multiple HVIs and evaluate their performance against heat-related excess mortality. Our work indicates that supervised variable selection improves the performance of HVIs in capturing heat-related health risks. Among all methods, the Random Forest-based variable selection algorithm achieves the best overall results, highlighting the potential of machine learning to enhance heat vulnerability assessment tools. Our results demonstrate that poverty rate, lack of air conditioning, and proportion of residents aged 65 and above are robust determinants of heat vulnerability in Chicago.

BackgroundTo control leishmaniasis, chemotherapy drugs are currently under development. However, these drugs often exhibit poor efficacy and are associated with toxicity, adverse effects, and drug resistance. At present, no specific drug is available for the treatment of leishmaniasis. Meanwhile, vaccine research is ongoing. Recent studies have analysed some experimental vaccines using mathematical models. AimIn previous work, drug targeting was focused on the entire human body rather than specifically addressing infected macrophages and parasites. In our current approach, we aim to eliminate infected macrophages and parasites through nano-drug design. Specifically, we utilise two types of nanoparticles: iron oxide and citric acid-coated iron oxide. Moving forward, we plan to advance this strategy using mathematical modelling of macrophage-parasite interactions. MethodsWe design PDE-based models of macrophages and parasites, incorporating cytokine dynamics, to support nano-drug development. Drug efficacy is estimated using posterior distributions to analyse phenotypic fluctuations of macrophages and parasites during the design phase. We investigate implicit and semi-implicit treatment schemes, focusing on energy decay properties. To model drug flow during treatment, we introduce a three-phase moving boundary problem. Comparative analyses are conducted to evaluate macrophage and parasite behaviour with and without treatment. Finally, the entire framework is implemented within a virtual lab environment. ResultsThe results show that the nano-drug exhibits better efficacy compared to combined drug doses. We analysed and compared two types of nano-drug particles: iron oxide and citric acid-coated iron oxide. We discuss how the drug effectively targets and eliminates infected macrophages and parasites. ConclusionOur models results and simulations will support researchers conducting further studies in nano-drug design for leishmaniasis. These simulations are performed within a virtual lab environment.

Silver (Ag+) ions are known to be toxic to bacteria, cells, organisms and living systems; yet its impacts on the locomotion of surface-crawling organisms remain poorly quantified. Here we investigated the short-term (0-6 hours) effects of Ag+ ions on the locomotion of Drosophila melanogaster larvae on flat agarose surfaces containing Ag+ ions at different concentrations (0, 1, 10, and 100 mM). By quantifying their locomotion, we found that Drosophila larvae showed shorter accumulated distances and reduced crawling speed. Additionally, we quantified the go/stop dynamics and peristalsis of the larvae and observed that Ag+ ions disrupted the normal, rhythmic, peristaltic contraction of the larvae and "trapped" them in the stop phase. Such toxic effects were dependent on Ag+ concentration and exposure duration.

Understanding how airborne particulates disrupt the human alveolar barrier requires in vitro systems that accurately replicate its composition and function. We present a biodegradable lung alveoli-on-a-chip that reproduces the architecture and physiology of the human air-blood interface using a porous poly(lactic-co-glycolic acid) (PLGA) membrane positioned between epithelium and endothelium under air-liquid interface (ALI) culture. The membrane, fabricated by porogen-assisted nonsolvent-induced phase separation, exhibited >50 % porosity, [~]2 {micro}m thickness, and mechanical compliance over 100-fold higher than conventional Transwell inserts, closely resembling the native interstitium. During co-culture, gradual PLGA degradation was compensated by cell-secreted extracellular-matrix (ECM) proteins such as collagen IV and laminin, forming a self-remodeling barrier that maintained integrity for at least 11 days. The platform supported stable epithelial-endothelial co-culture, high transepithelial electrical resistance, and physiologically relevant permeability. To demonstrate its utility, the chip was used to assess pulmonary toxicity of four types of waste-combustion-derived particulates, including rubber, plastic bags, plastic bottles, and textile fibers, delivered apically under ALI conditions. All combustion products reduced cell viability, increased hydrogen-peroxide release, and elevated {gamma}-H2AX expression, indicating oxidative and genotoxic stress, while disrupting barrier permeability. Rubber combustion particles elicited the most severe toxicity, causing the greatest loss of viability, accumulation of reactive oxygen species, and formation of DNA double-strand breaks. Together, these results establish a biodegradable, ECM-remodeling lung alveoli-on-a-chip as a physiologically relevant platform for investigating source-specific particulate toxicity and alveolar-barrier pathophysiology. By bridging environmental exposure models with human-relevant lung biology, this system provides a quantitative and translatable tool for evaluating respiratory risks and therapeutic interventions.

Particle bombardment systems are widely used for plant transformation, but commercial devices are expensive and rely on high-pressure helium gas. This study aimed to develop a cost-effective and helium gas-free alternative using an air duster gun connected to a commercial compressor. A nozzle (for DNA with transgenes), gold particles (as DNA carriers), nozzle-to-sample distance, and a method for coating gold particles with DNA were optimized to yield better transformation efficiency in targeting onion epidermal cells and rice calli. From the rice calli transformed with the newly developed system (a tool to shoot genes with massive air from a compressor: TSGMAC), stable transgenic plants could be obtained. TSGMAC offers a low-cost and helium gas-free solution for plant transformation and genome editing and can enhance accessibility to particle bombardment-based techniques.

BackgroundActivated platelets release polyphosphate (polyP), a linear polymer of inorganic phosphate residues, from dense granules. Experiments performed under no-flow conditions show that polyP alters the kinetics of tissue factor (TF) pathway reactions, accelerating FXI activation by thrombin and FV activation by FXa and thrombin, and may impact inhibition by tissue factor pathway inhibitor (TFPI). How polyP influences this pathway in conjunction with platelet deposition under flow remains understudied. ObjectivesTo investigate how polyP-mediated acceleration of FV and FXI activation modulates thrombin generation under flow in TF-initiated coagulation. MethodsWe extended a previously validated mathematical model of platelet deposition and coagulation under flow to examine polyP-mediated effects following a small vascular injury during intravascular clotting. Simulations varied the surface density of TF exposed, wall shear rate, and plasma TFPI concentration. ResultsPolyP shifts the threshold TF density for a thrombin burst to lower TF densities. For TF densities above this threshold, polyP shortens the lag time to thrombin generation in a TF- and shear-rate-dependent manner. Although no explicit effect of polyP on TFPI function was included in the model, thrombin generation was much less sensitive to TFPI concentration with polyP, in a TF-dependent manner. Relative contributions of accelerations of FV and FXI activations depend on incompletely known enhancements by polyP. ConclusionsThe experimentally observed influence of polyP on TFPI function depends on TF density and may arise indirectly from accelerated FV and FXI activation, with the dominant effect arising through accelerated thrombin-mediated conversion of FV to FVa.

The interactions of droplets and filaments can lead to mutual deformations and complex combined behavior. Such interactions also occur within the cell, where biomolecular condensates, distinct liquid phases often composed of proteins, have been observed to structure and affect the organization of the cytoskeleton. In particular, biomolecular condensates have been shown to undergo characteristic deformations when cytoskeletal filaments are fully embedded within them. However, a full understanding of the underlying physical mechanisms is still missing. Here, we combine experiments with coarse-grained molecular dynamics simulations and analytical models to uncover the physical mechanisms that define emerging shapes of droplets containing filaments. We find that the surface tension of the liquid phase and the bending energy of the filament(s) suffice to accurately capture emerging shapes if the length of the filament is small compared to the liquid volume. As the volume fraction of filament(s) increases, wetting effects become increasingly important, setting physical constraints within which surface and bending energies compete to define the droplet shapes. We find that mutual deformations of condensate and filament extend accessible shapes beyond classical stability considerations, leading to structuring and entrapment of contained filaments. Shape deformations may further affect ripening dynamics that favor certain geometries. Our findings provide a physical framework for a better understanding of the possible roles of biomolecular condensates in cytoskeletal organization.

Microplastic (MP) pollution, which is present in the ecosystem in vast quantities, adversely affects human health and the environment, making it imperative to develop methods for its mitigation. The challenge of detecting or capturing MPs could potentially be addressed using plastic-binding peptides (PBPs). The ideal PBP for MP remediation would not only bind strongly to plastic, but also have other properties such as high solubility in water or great binding specificity to a certain plastic. However, the scarcity or absence of known PBPs for common plastics along with the lack of methods that can discover PBPs with all of the desired properties precludes the development of peptide-based MP remediation strategies. In this study, we discovered short linear PBPs with high predicted water solubility and binding specificity by employing an in-silico discovery pipeline that combines deep learning and biophysical modeling. First, a long short-term memory (LSTM) network was trained on biophysical modeling data to predict peptide affinity to plastic. High affinity peptides were generated by pairing the trained LSTM with a Monte Carlo tree search (MCTS) algorithm. Molecular dynamics (MD) simulations showed that the PBPs discovered for polyethylene, the most common plastic, had 15% lower binding free energy than PBPs obtained using biophysical modeling alone. PBPs with both high affinity and high predicted solubility in water were found by including the CamSol solubility score in the MCTS peptide scoring function, increasing the average solubility score from 0.2 to 0.9, while only minimally decreasing affinity for polyethylene. The framework also discovered peptides with high binding specificity between polystyrene and polyethylene, two major constituents of MP pollution, using a competitive MCTS approach that optimized the difference in affinity between the two plastics. MD simulations showed that competitive MCTS increased the binding specificity of PBPs for polystyrene and identified peptides with relatively great preference for either of the two plastics. The framework can readily be applied to design PBPs for other types of plastic. Overall, the high-affinity PBPs with desirable properties discovered by marrying artificial intelligence and biophysics can be valuable for remediating MP pollution and protecting the health of humans and the environment.

Head rotation is the leading cause of diffuse brain injuries from cycling accidents, with severe, long-term or even fatal consequences. Here, we present a novel helmet safety technology, the Release Layer System (RLS), designed to enhance conventional helmets and reduce the likelihood of such injuries. RLS is located on the outer side of the helmet and thus gets impacted first. The force of the impact activates a rolling mechanism triggering the release of an outer polycarbonate panel, thereby dispersing and transforming a substantial portion of the incident rotational energy. To evaluate the effectiveness of the technology, we conducted oblique impact tests on three popular helmet types, in conventional and RLS-equipped configurations, at three impact locations. RLS-equipped helmets reduced Peak Angular Velocity (PAV) by 57-66%, averaged across impact locations, compared to their conventional counterparts. This corresponds to a 68-86% reduction in the probability of an AIS2+ brain injury, as estimated by the Brain Injury Criterion. The most notable improvement was observed at the pYrot location (front impacts, mid-sagittal plane), with up to 85% PAV reduction. Testing across headforms further demonstrated the effectiveness of the technology in mitigating head rotation irrespective of variations in evaluation setups. This work introduces a novel mechanism for rotational impact mitigation and provides evidence of its potential benefits compared with conventional helmets. As an outer-layer approach, RLS may offer an alternative pathway for managing rotational kinematics in future helmet designs.

Scaling up microvessel culture systems is essential for producing vascularized clinically relevant tissues, yet current platforms offer little guidance on how to preserve flow conditions during scale-up. Here, we present a computational-experimental framework using computational fluid dynamics (CFD) to guide the design and scaling of microvessel bioreactors. Interstitial flow distributions were pre-dicted in two perfusion-based platforms-a permeable insert and a rhomboidal microfluidic chamber-across multiple scaling factors and hydrostatic pressures. CFD identified IF ranges conducive to vascu-logenesis and quantified how geometry and pressure modulate flow uniformity. Scaled-up bioreactors generated microvessel networks with consistent morphology and connectivity over a 30-fold increase in culture volume, confirming that maintaining equivalent IF ensures reproducible outcomes. The permeable insert platform maintained uniform IF across scales, while the rhomboidal chamber produced spatially varying IF resulting in heterogeneous but physiologically relevant networks. These findings establish CFD as a predictive tool for rationally scaling perfusion bioreactors, enabling microvessel production at clinically relevant scales with controllable morphology. Significance StatementScaling up microvessel bioreactors is critical for engineering large pre-vascularized tissues. However, larger scales may disrupt flow conditions that drive vessel formation. This study demonstrates that computational fluid dynamics (CFD) can predict interstitial flow and guide the rational scale-up while preserving the vasculogenic microenvironment. Experiments across 30+-fold size increase confirmed that matching inter-stitial flow results in morphologically identical microvessel networks. By linking simulation-based design with experimental validation, this work establishes CFD as design tool for scalable perfusion bioreactors for production of microvessel networks at clinically relevant scales.

As human space exploration expands to the Moon, Mars, and beyond, there is a growing need to study the effects of altered gravity on the microbial systems that we will bring with us for life support. Because spaceflight experiment opportunities are rare and resource-intensive, most space biology experiments are conducted using ground-based simulators. The most common microgravity simulator for microbial experiments, the rotating wall vessel, can approximate the low-shear and low-turbulence conditions that characterize microgravity. However, current designs do not allow for real-time measurement of growth or metabolic activity during rotation: experiments require destructive sampling or disruption of the microgravity simulation conditions. Here, we describe the development of an in situ spectroscopy system compatible with the Cell Spinpod rotating wall vessel, which enables measurement of both optical absorbance and fluorescence with high temporal resolution, producing growth curves similar to those from an off-the-shelf plate reader. These results are validated using two common microbial hosts: Escherichia coli and Saccharomyces cerevisiae. The Spinpod Optical System has the potential to diversify the types of microbiology experiments possible in simulated microgravity, allowing the measurement of not only growth curve parameters but also metabolic activity, gene expression, or community dynamics. It thus has the potential to improve the quality of experiments seeking to characterize microbial responses to spaceflight conditions.

Endocavity ultrasound transducers, particularly transvaginal ultrasound (TVUS) probes, contain intricate structures such as notches, grooves, lens surfaces, and handle edges that are highly susceptible to microbial contamination yet difficult to disinfect using conventional high-level disinfection (HLD) methods. This study evaluated the efficacy of a novel ultraviolet-C light-emitting diode (UV-C LED) HLD system in eliminating microbial contamination from these complex probe surfaces. Two TVUS probes were sampled from predefined high-risk regions before and after disinfection following clinical use. Probe A was sampled at the top and bottom notches and both sides of the handle, while Probe B was assessed at the lens, edges, and bent groove regions. Microbial contamination was quantified using swab sampling, culture on agar plates, and identification via MALDI-TOF. Environmental sampling of examination and disinfection rooms was also performed. To assess this system robustness, probe sites were repeatedly inoculated with Bacillus subtilis spores and evaluated following UV-C treatment. Before UV-C treatment, contamination rates ranged from 25% to 57% across sampled regions, with microbial loads reaching up to 3.9 log CFU. Identified organisms included Staphylococcus epidermidis, Pseudomonas koreensis, Bacillus cereus, and Propionibacterium spp. Probe sheaths were also predominantly contaminated with Staphylococcus epidermidis., with counts reaching up to 4.3 log CFU, Environmental sampling revealed diverse microbiota, with higher contamination levels in examination rooms compared to disinfection areas. Following 90 seconds of UV-C exposure, no microbial growth was detected on any sampled site, indicating 100% decontamination. Additionally, UV-C treatment achieved >6.7 log reduction of B. subtilis spores across all tested regions. These findings demonstrate that UV-C LED technology provides rapid, effective, and consistent high-level disinfection of complex TVUS probe surfaces, supporting its potential as a rapid and reliable disinfection modality in clinical setting.