Computer Methods in Biomechanics and Biomedical Engineering

Background: Acute pulmonary embolism (PE) is a leading cause of morbidity and mortality with persistent difficulties in choosing interventions and predicting outcomes for patients defined clinically as intermediate risk. Computational fluid dynamics (CFD) tools have been used to understand the hemodynamic environment and plan interventions in the pulmonary arteries across a variety of disease conditions. Several biomechanical metrics have been used to evaluate risk in narrowed vessels, including hemodynamic resistance, power dissipation, and fractional flow reserve (FFR). In this study, we evaluate differences in these CFD-derived biomarkers between healthy controls (HC) and intermediate risk, acute PE patients. Additionally, we examine the response of patient hemodynamics to mechanical thrombectomy and compare values of these biomarkers across post-intervention pressure status. Methods: A CFD framework was developed to simulate patient-specific hemodynamics within the pulmonary vasculature identifiable from clinical imaging. The pipeline involved reconstructing three-dimensional (3D) structures of the pulmonary arteries and modeling blood flow with the finite element method. Patient-specific boundary conditions were derived from matching pre-intervention inlet mPAP to the patient's measured value given their measured CO as steady inflow. Converged simulations allowed for precise quantification of primary hemodynamic characteristics (flow and pressure) as well as secondary flow phenomena, primarily wall shear stress (WSS) and simulated pressure metrics such as fractional flow reserve (FFR). Results: Our simulations revealed significant elevations in resistance, power dissipation, and the number of vessels with low FFR in those patients with acute PE (n=6) compared to HC (n=3). Occlusions of hemodynamic significance were generally found in segmental pulmonary arteries. For patients with normalized pulmonary pressures post-thrombectomy (n=3), we found significantly higher proximal power dissipation and counts of low FFR vessels in comparison to those with elevated pressures after intervention (n=3). Distal resistance, which was derived from the portion of resistance attributed to the outflow boundary conditions, was significantly higher in patients with elevated pressures post-intervention. Across all PE patients, FFR count was significantly correlated with post-thrombectomy pulmonary pressure and cardiac index. Discussion: CFD-derived biomarkers offer a promising tool for understanding disease severity in acute PE. Differences between HCs and acute PE patients reveal expected increases in metrics associated with proximal disease burden. Yet, in examining acute PE patients with varying post-intervention hemodynamics, we found that these metrics of proximal disease burden could also be useful to predict the efficacy of mechanical thrombectomy. Those patients with normalized pressures had higher values for proximal disease metrics and lower values for distal disease metrics than those with continued elevations in pressure. This suggests that accessibility of hemodynamically-significant emboli to thrombectomy may be useful as a predictor for outcomes.

Energy expenditure (EE) during running depends on the interplay between active muscle work and elastic energy storage and return, yet the relative contribution of mechanical power to EE remains debated. Quantifying the relative contributions of segment-level mechanical power can provide a way to address this debate. In this study, we aimed to quantify how segment-level mechanical power contributes to EE during running and to demonstrate that these mechanistic insights support wearable-based EE estimation. Joint dynamics and respiratory gas-based EE were collected from healthy young adults running at multiple speeds. Scale factors were derived to quantitatively link efficiency-weighted segment power to measured EE. The stance leg consistently showed the strongest correlation with EE, and this dominance was preserved across speeds. Including swing-leg hip power further improved accuracy. Scale factors were approximately 0.45, suggesting that active muscle work and elastic energy return contribute comparably to the mechanical power associated with EE. Using a lightweight machine learning model, stance-leg and swing-leg hip joint power were reconstructed from a single sacral IMU, enabling accurate EE prediction. These findings demonstrate that lower-limb mechanical power is a robust predictor of running EE, supporting both the extensibility of biomechanically-informed frameworks and wearable-based EE monitoring.

The endothelial cells (ECs) that line blood vessels continuously sense and respond to the physical forces exerted by blood flow. In vivo, pulsatile arterial flow interacts with vessel curvature, branching and other anatomical features to generate complex local hemodynamic environments that dictate the magnitude, direction, pulsatility, and oscillatory nature of wall shear stress experienced by ECs. Currently, accessible and reproducible in vitro models of complex pulsatile flow that recapitulate in vivo vascular anatomy remain limited. Here, we combine a novel rotational-flow endothelial culture platform with detailed computational fluid dynamics (CFD) modeling to characterize four well geometries designed to generate distinct hemodynamic environments. CFD analyses demonstrate that these geometries intrinsically generate pulsatile flow and produce reproducible spatially distinct regions of wall shear stress magnitude, pulsatility, and oscillatory shear within a single culture well. Endothelial alignment mapping and functional assays reveal region-specific cellular responses to the predicted local flow conditions that closely corresponded to the predicted local hemodynamic environment, linking complex flow patterns to endothelial adaptation. The technical advancements of our modeling efforts should support a faster, cheaper, simpler, and--importantly--validated framework for future investigation into EC mechanobiology under complex flow conditions. HIGHLIGHTSO_LISimple engineered well geometries generate distinct hemodynamic microenvironments, mimicking in vivo vascular structures, using a conventional orbital shaker. C_LIO_LIComputational fluid dynamics (CFD) reveals spatially distinct patterns of wall shear stress, pulsatility, and oscillatory shear applied to ECs within individual culture wells. C_LIO_LIHigh average wall shear stress and elevated oscillatory shear index induces a unique perpendicular alignment of ECs to the dominant flow vector. C_LI

Diffuse gliomas are deadly because the individual tumor cells invade - they travel far from the imageable mass, so it is impossible to remove the tumor completely. On the cellular level, glioma cells seem to be in either a "go" state (in which they do not divide) or a "grow" state (in which they do not migrate). We investigate what this tiny choice has to say about the large-scale speed of the invasion front and whether the implication is sufficiently strong to rule out the classical description of the Fisher-Kolmogorov-Petrovsky-Piskunov (Fisher-KPP) type, in which a single phenotype migrates and proliferates. We derive a two-phenotype reaction-diffusion model with density-dependent switching, and we prove the cooperative (quasi-monotone) structure and the associated comparison principle and study travelling-wave solutions of the model. A leading-edge linearization gives minimal front speed as minimizer of an explicit dispersion relation, and direct simulation verifies the predicted speed. In the experimentally relevant fast switching limit, we find a closed-form expression for the speed, that is, we obtain an effective Fisher-KPP equation with rescaled diffusivity and growth rate, with the fractions of the phenotypes. The "go-or-grow" (GoG) front can move at a maximum speed of half the Fisher speed for the same single-cell motility $D$ and proliferation rate $r$, which occurs only when the cells divide their time equally between the two phenotypes. This bound is directly testable: measurement of the front speed, plus independent determination of $D$ and $r$, discriminates the two hypotheses, and in the GoG case, yields recovery of the phenotype balance. We then extend the result to anisotropic (DTI-informed) invasion along white-matter tracts and discuss implications for understanding clinical measurements of growth rate.

Low-intensity vibration (LIV) is a non-invasive mechanical stimulus capable of regulating skeletal adaptation and cellular signaling pathways involved in bone remodeling. Despite growing interest in LIV, substantial methodological heterogeneity persists in the selection of experimental vibration parameters such as frequency, expressed in Hertz (Hz) and intensity, defined as earth's gravitational field (g) (9.81 m/s2). Focusing on micro-computed tomography (CT) derived trabecular bone volume fraction (BV/TV) as the main outcome measure, this study sought to synthesize the effects of different LIV frequency and intensity on BV/TV in small rodents (mice and rats) as they remain as the most studied pre-clinical model. To accomplish this, we performed a systematic review searching for publications in English on PubMed, Web of Science, CINAHL, and Embase databases. Two independent investigators followed inclusion criteria to select only peer-reviewed studies with mature mice, using whole-body vibration experiments without other co-variables. We further restricted to include studies that analyzed non-fractured bones and compared pre- and post-intervention or control values. In addition to these core criteria, a detailed hierarchical screening framework was applied during full-text review. The two independent investigators extracted data independently and considered the characteristics of the study, animals' characteristics, intervention characteristics, and results. For this study we considered load-bearing hindlimbs, femur and tibia, separately but did not include vertebrae in the analysis. A Bayesian network meta-analysis and a revised SYRCLE risk of bias (RoB) tool were used to evaluate the risk of bias across included studies. Seven studies met the inclusion criteria. Results showed that an LIV regime applied at 45Hz at 2g presented higher chances to increase trabecular BV/TV of the mouse tibia (estimated effect 3.22 [CrI 1.98, 4.45]), while LIV regimes applied to the femur at 90Hz and 1.4g (estimated effect 3.08 [CrI -1.99, 7.97]) present better chances to increase trabecular BV/TV results compared to other interventions but with no significant differences. Finally, we applied 45Hz at 0.2g LIV to 5 month old male C57BL/6 for 5 weeks (n=10/group) which showed significantly increased Trabecular Thickness (Tb.Th) for both the tibia (10%, p<0.01) and femur (17%, p<0.001), with the femur showing further increases in trabecular BV/TV (32%, p<0.05) compared to non-LIV controls. We conclude that changes in the microarchitectures of the tibia and femur respond differently to the same application of LIV (45Hz, 0.2g) in mice and rats.

Reaction diffusion (RD) systems play a fundamental role in numerous biochemical and biophysical processes. Here, we present a novel analytical framework for solving RD equations by applying the Wentzel Kramers Brillouin Jeffreys (WKBJ) formalism to Ca nanodomains generated by individual membrane channels, a widely used paradigm for intracellular Ca signaling. Previous models have primarily focused on stationary Ca nanodomains while neglecting diffusion and saturation of intracellular Ca buffers and sensors. In contrast, we derive analytical solutions without these simplifying assumptions. Our analysis demonstrates that sustained Ca influx generates continuously expanding distributions of free Ca, whereas Ca bound buffers and sensors propagate as traveling waves. These predictions are supported experimentally by measurements of one-dimensional fluorescence profiles produced by single-channel activity and two-dimensional profiles generated by whole cell Ca currents. The analytical framework developed here readily extends Michaelis Menten type kinetics to reaction diffusion systems and may therefore be broadly applicable to biochemical and biophysical processes in which diffusion cannot be neglected.

Musculoskeletal models are widely used to relate muscle mechanics to movement patterns in biomechanics. Accurate estimation of muscle parameters is essential for building individualized models, yet most rely on generic parameters derived from cadaveric data that do not reflect subject-specific properties critical to force generation. Here, we introduce a hierarchical Bayesian framework that leverages surface electromyography (EMG) and torque data from isometric elbow tasks to estimate subject-specific muscle parameters, overcoming limitations of generic parameter sets. This approach accounts for both inter-individual variability and uncertainty in measurement and model structure. The model infers six key parameters per subject, including flexor and extensor muscle strength, tendon slack length, moment arm geometry, and nonlinear EMG-to-activation relationships. We estimated model parameters for 14 young, healthy adults performing isometric elbow flexion and extension at multiple joint angles and torque levels. The six-parameter hierarchical-Bayesian musculoskeletal model accurately reproduced measured net elbow torque (R2 = 0.96) and outperformed simpler configurations. Muscle strength parameters varied substantially across individuals, from approximately 1.0 to 3.5. On average, participants exhibited about twice those of the OpenSim 26 generic model. In contrast, tendon slack length estimates varied minimally across subjects. Bilateral testing revealed moderate correlations between left- and right-arm parameters, supporting the models ability to capture subject-specific anatomical features. Cross-validation confirmed robust predictive performance, and convergence diagnostics indicated reliable sampling. Compared to traditional EMG-driven or imaging-based personalization methods, our approach quantifies uncertainty, enables partial pooling across subjects, and avoids reliance on invasive or time-intensive measurements. The framework is extensible to dynamic tasks and adaptable to clinical populations, including individuals post-stroke. These results demonstrate that hierarchical Bayesian inference can robustly personalize musculoskeletal models and advance our understanding of biomechanics.

There is a vast literature on the voltage gating of ion channels, with a fairly large fraction concerned with potassium channels, especially of the KV1 family, including Shaker. Experimental evidence derived from protein structure has been interpreted to give gating mechanisms that largely disregard water. We propose that the K+ ion, in order to pass through the gating region and enter the cavity pore, must be largely dehydrated. Competitive interactions of each single hydration shell water at the gate, with counterions, protein, or other water molecules, can remove one water at a time. There are several such interactions for the ion hydration shell; for the ion to pass through the gating region, there must be enough such interactions to leave the ion with at most two hydrating water molecules, in which case the gate is open. Protein conformational changes are secondary, small, and mostly unimportant. The hypothesis has a second part: protons, previously shown to be candidate carriers of the gating current (Kariev and Green, JPC B, 2019, Membranes, 2022, 2024) are capable of reaching the gate; adding four protons to the gate prevents dehydration, leaving the ion with at least three hydrating water molecules, enough to block passage. Quantum calculations presented here support the dehydration part of the hypothesis; they also mostly support the second part, concerning the protons, but further work will be required to fully confirm this. The hypothesis explains the experimental finding that the P475D mutant is essentially constitutively open, while the P475S mutant, with a wider gate opening, is closed at all relevant potentials; the computations presented here show the mechanism for this in detail, further confirming the first part of the hypothesis, and largely but not completely confirming the second part, concerning protons, while showing where further work is needed. This mechanism can also qualitatively account for flicker noise and fluctuations, and their consequences.

Older adults face an increased risk of falls that may have severe consequences for their well-being. Routine, accessible clinical screening may help mitigate fall risk through early detection of balance impairments. Portable force plates offer a convenient and practical solution for balance assessment in clinical settings. A new force-plate-based balance measure, the intersection-point-height, has shown particularly promising results in its ability to distinguish between healthy and impaired balance behaviors. However, the intersection-point-height measure requires measurement of shear force during standing, which exhibits magnitudes of less than 0.2% of normal forces (body weight), taxing the dynamic range of most sensor technologies. The ability of existing force plates to measure such low-magnitude shear forces observed during quiet standing is currently unknown. This study presents a force-plate performance assessment method to evaluate shear-force measurement errors and quantify the uncertainty of the intersection-point-height measure. The method was applied to test a commonly used laboratory-grade portable force plate. While the device successfully captured sagittal-plane intersection-point-height at the lowest frequencies, low signal strength prevented precise readings in the frontal plane. Thus, the tested device only marginally met the precision required for quiet-standing analysis, underscoring the critical need for systematic performance validation of portable force plates prior to clinical use. Future efforts should focus on evaluating alternative portable force plates and exploring economical design improvements to enhance shear-force measurement precision.

Residential greenness has been associated with reduced heat-related illness, yet the specific role of greenspace morphology at the neighborhood scale remains insufficiently understood. This study quantified the relationship between heat-related illness and multiple dimensions of greenspace morphology using an eight year (2016-2023) unbalanced panel dataset comprising 19,021 block group year observations across 2,427 census block groups in Arizona, USA. One meter high resolution National Agricultural Imagery Program aerial imagery was classified to calculate greenspace percentage, number of greenspaces, average size, shape complexity, connectedness, and distantness, at the block group level. We applied conditional spatial autoregressive models with a negative binomial distribution to estimate associations between each morphology metric and yearly heat-related illness counts, adjusting for sociodemographic and geographic covariates. We found higher greenspace percentage, aggregation, shape complexity, connectedness, and density were consistently associated with lower heat-related illness risk. A one standard deviation increases in shape complexity corresponded to a 12.4% decrease in expected heat-related illness counts (IRR=0.876, 95% CI: 0.834-0.921). Similarly, increases in greenspace percentage (14.6% decrease; IRR=0.855, 95% CI: 0.827-0.885), number of greenspace patches (3.7% decrease; IRR=0.963, 95% CI: 0.937-0.990), average size (4.5% decrease; IRR=0.955, 95% CI: 0.923-0.989), and connectedness (5.5% decrease; IRR=0.945, 95% CI: 0.918-0.972) were all protective. In contrast, larger inter greenspace distances were associated with increased heat-related illness risk (6.1% increase; IRR=1.061, 95% CI: 1.033-1.091). Our findings highlight the critical importance of multiple dimensions of greenspace morphology in mitigating heat-related health risks. These results suggest that heat reduction planning with greening initiatives should consider not only the amount of greenspace but also its spatial configuration to maximize cooling and result in health benefits.

Background: Major soccer tournaments may temporarily change recreational soccer activity, community gatherings, and injury-prevention needs, but evidence for population-level emergency department (ED) injury patterns during these events is limited. Understanding whether ED-treated soccer injury burden changes during Men's FIFA World Cup periods may help inform surveillance readiness and prevention planning for future tournaments. Objective: To evaluate whether Men's FIFA World Cup tournament periods temporally coincided with changes in ED-treated soccer-coded injury burden in the United States and to assess the implications for public health surveillance and injury-prevention preparedness. Methods: We conducted a retrospective, repeated cross-sectional calendar-period analysis of publicly available national ED injury surveillance records from 1999 through 2025. Soccer-coded injuries were identified using product code 1267 in any available product field. The primary exposure was the set of official Men's FIFA World Cup tournament dates from 2002, 2006, 2010, 2014, 2018, and 2022. Tournament dates were compared with matched same-calendar dates in adjacent years, excluding dates that overlapped other FIFA World Cup tournament windows. The primary estimands were the mean daily difference and ratio in weighted national ED-treated soccer-coded injury estimates between tournament and matched-control periods. Results: The analytic cohort included 170,679 soccer-coded ED cases, corresponding to an estimated 5,366,681 ED-treated soccer-coded injuries nationally. Mean daily weighted estimates were 453.1 during Men's World Cup tournament dates and 384.1 during matched control dates. The absolute mean daily difference was 68.9 injuries per day (95% CI, -0.5 to 138.3), and the mean daily ratio was 1.18 (95% CI, 1.00 to 1.39). Tournament-specific estimates were heterogeneous, with a near-null estimate for the 2022 winter tournament and higher estimates for prior summer tournaments. Conclusions: Men's FIFA World Cup periods were associated with a modest, imprecise increase in mean daily ED-treated soccer-coded injury estimates, but the findings were heterogeneous and compatible with no difference to a moderate increase. These results should be interpreted as ecological and hypothesis-generating rather than causal. The primary implication is not that World Cup tournaments directly cause injuries, but that major soccer events provide a practical opportunity for real-time ED injury surveillance, targeted recreational soccer injury-prevention messaging, concussion awareness, and coordinated preparedness for community and fan-event injury patterns during future tournaments.

The suspension culture of pluripotent stem (PS) cells in stirred bioreactors poses a delicate balance between maintaining homogeneous cell dispersion and avoiding excessive shear stress that can compromise cell viability and pluripotency. In this study, we used computational fluid dynamics (CFD) coupled with a discrete particle method (DPM) to simulate iPS cell behavior in a 5 mL delta-impeller stirred tank. Our analysis revealed that upward flow at the tank bottom and downward flow at the top are critical for maintaining a stable suspension. To optimize the stirring protocol, we applied Bayesian optimization to identify a time-dependent stirring schedule that begins with a high-speed phase for resuspension, followed by a low-speed phase for sustained suspension with minimal hydrodynamic stress. The optimized schedule demonstrated improved suspension ratio and reduced slip velocity, indicating lower mechanical stress on cells. These findings provide engineering insights into scalable bioreactor operation, contributing to the design of robust iPS cell manufacturing systems.

Motor unit (MU) decomposition using high-density surface electromyography (HD-sEMG) has been widely used to characterize MU behavior in neurophysiology and to build neural-machine interfaces for wearable robots. Recently, many open-source software tools for MU decomposition have been made available on GitHub, which could reduce the effort of researchers in the field. However, the consistency among these open-source tools has never been studied, making researchers hesitate to use them. In this study, we collected 7 open-source software tools on GitHub and applied them to decompose MUs from an open-source HD-sEMG dataset (including 11 isometric contraction trials) to investigate the consistency among these tools. To create a comprehensive MU pool for reference, we combined all unique MUs identified by seven tools, visually inspected and removed bad MUs, and manually edited all remaining MU spike trains. Across 7 tools for 11 trials, the number of identified MUs ranges from 167 to 736. The number of valid MUs after expert inspection ranges from 29 to 210, which is 10% to 72% of the reference pool. The rate of agreement between the raw MUSTs and the manually edited MUSTs ranges from 0.86 to 0.94, and the averaged number of edits per MU to correct misalignments ranges from 14 to 39. The results show inconsistency in the implementation and procedures of each tool, which results in an inconsistent number of identified MUs and valid MUs (29 vs 210). In general, a substantial amount of effort is required to process the raw MUSTs from each tool to conduct further research analysis. This study provided a guideline for using open-source software tools for MU decomposition and indicated that it would be beneficial to develop tools to automatically edit the MUSTs.

Background Brucellosis is a severe zoonotic disease with pronounced seasonality and regional heterogeneity in high-incidence areas of China. Reliable forecasting tools are needed to inform prevention strategies, but the optimal modeling approach across different regions remains unclear. Principal Findings We collected monthly brucellosis incidence and 17 environmental variables from 2014 to 2024 across five high-incidence provinces: Inner Mongolia, Xinjiang, Shanxi, Heilongjiang, and Hebei. A three-step procedure--cross-correlation analysis, multicollinearity diagnostics, and stepwise regression--was used to select exogenous predictors. We then compared four time-series models: seasonal autoregressive integrated moving average (SARIMA), SARIMA with exogenous variables (SARIMAX), long short-term memory (LSTM), and LSTM with exogenous variables (LSTMX). All five provinces showed a unimodal seasonal pattern with peaks between April and July, though environmental drivers and optimal lag periods varied substantially by region, ranging from 1 to 6 months. In forecasting performance, LSTM achieved the highest accuracy in Shanxi (R2=0.925), Hebei (R2=0.876), and Xinjiang (R2=0.829), outperforming SARIMA and SARIMAX. LSTMX performed best in Inner Mongolia (R2=0.759) and Heilongjiang (R2=0.772) but showed weaker performance than LSTM in Shanxi and Hebei. Overall, adding exogenous variables did not consistently improve predictions across provinces. Conclusions Our findings demonstrate that LSTM-based models offer clear advantages for brucellosis forecasting in most high-incidence provinces, but the value of incorporating environmental predictors is region-dependent. These results support the development of tailored early warning systems and precision prevention strategies for brucellosis in high-risk areas of China.

Abstract Objectives: To estimate surveillance-adjusted county-level residual syphilis risk, quantify posterior support for elevated risk, and identify the geographic distribution of stably high-risk areas across the contiguous United States and the District of Columbia. Methods: County-year primary and secondary syphilis counts from 3,109 counties during 2010-2022 were analyzed using a Bayesian negative-binomial spatial model with county-level covariates capturing social vulnerability and healthcare and surveillance related structure. Residual spatial risk, posterior exceedance probabilities, and stably high-risk counties were estimated. External validation examined whether county-level residual syphilis risk was associated with HIV and gonorrhea burden. Results: A total of 850 stably high-risk counties were identified. These counties were concentrated in the southeastern United States and along the Gulf Coast, with additional clusters in the north-central region and along the Atlantic and Pacific coasts. The social vulnerability index showed the strongest positive association with reported syphilis rates, followed by primary care physician density. External validation and sensitivity analyses showed that higher county-level residual syphilis risk estimates were positively associated with higher HIV diagnosis rates and gonorrhea rates, indicating that these estimates were not merely model-derived numerical outputs but were meaningfully related to the county-level distribution of sexually transmitted infection risk. These findings indicate that surveillance-adjusted residual spatial risk estimates and posterior exceedance probabilities may provide useful county-level evidence for syphilis control prioritization and resource allocation.

Statistical prediction models for binary outcomes are becoming increasingly popular. One significant challenge is calibrating these models to suit the characteristics of a target population that is structurally different from the original population. Calibration is especially challenging when there is no training data available from the target population. To address this problem, we propose a novel calibration method, SimCal, which uses synthetic data generated from the model development data in conjunction with marginal statistics from the calibration cohort. We show that expert judgment modeling (EJM) may be used for calibration if cross-sectional data from the target population are available comprising expert judgments about the potential outcome and the covariates. We describe three alternative calibration approaches when calibration data are lacking: similarity-binning averaging (SBA), adaptive calibration of predictions (ACP), and Elkan calibration. In a simulation study, we compare SBA, ACP, Elkan calibration, and SimCal. R code for applying these methods is provided from the re-analysis of data on coronary artery disease. We illustrate all 5 calibration approaches with a real data set for predicting functional outcome after stroke and all approaches but EJM in the re-analysis of the Cleveland Clinic data. None of the approaches performed convincingly well in all situations. SimCal performed well when model parameters were correctly specified. EJM failed on the stroke data. Further research is urgently required for calibration in the absence of calibration data.

Extrahepatic delivery of lipid nanoparticles (LNPs) to non-phagocytic cells is a major challenge, with the leading strategy involving surface functionalization with target-specific monoclonal antibody (mAb) ligands. We investigate the stability of mAb-conjugated LNPs using two anchoring systems: the commonly used DSPE-PEG2kDa-maleimide and a block copolymer, PCL5kDa-b-PEG2kDa -maleimide, with the hypothesis that conjugation to a 150,000 Da antibody could overwhelm the relatively small ~600 Da aliphatic anchor on the PEG-lipid in vivo. Shedding of the mAB would compromise targeting. Conjugation integrity following IV injection was assessed by tagging LNPs and mAbs with metal ion tracers that could be quantified by ICP-MS. Results show that DSPE-PEG-mAb rapidly (within 1h) dissociates from LNPs in blood, leading to accelerated LNP clearance. In contrast, mAbs conjugated using PCL-b-PEG remained stably associated with the LNP over the 24h circulation and clearance of the construct. Results are connected to a thermodynamic model that reproduces experimental findings for PEG-anchor(-mAb) shedding in vitro and in vivo. This study identifies anchoring strength as a critical, unconsidered parameter for in vivo performance when conjugating mAbs to LNPs for extrahepatic delivery.

The incidence of early-onset colorectal cancer (CRC) has risen sharply in recent decades1, yet the biological basis underlying the distinct behavior of tumors arising in young versus aged tissues remains poorly understood. Here we show that aging reprograms the epigenetic landscape of the colon, restricting colon tumor growth through stable silencing of developmental and fetal gene programs. We find that colon tumors arising in aged mice are intrinsically less proliferative than those arising in young animals. Multi-omic profiling of normal colon and colon tumors reveals that aging drives DNA hypermethylation, loss of Polycomb-associated chromatin states, and reduced chromatin accessibility at a defined set of developmental genes that are bivalent (marked by both H3K27me3 and H3K4 methylation), transcriptionally active in colon tumors from young animals and repressed in both tumors and normal tissue from old animals. Among the genes most strongly repressed in old animals is Tacstd2 (Trop2), a regulator of fetal intestinal programs and epithelial stemness. Pharmacologic inhibition of DNA methylation reactivates the aging-silenced gene network in organoids from old animals, whereas genetic disruption of Tacstd2 suppresses growth and developmental transcriptional programs in young tumor organoids. TACSTD2, fetal gene signatures, and the aging-associated bivalent gene program are likewise repressed in late-onset vs. early-onset human colorectal cancers. Collectively, these findings identify age-associated epigenetic silencing of developmental gene programs as a causal mechanism that constrains colorectal tumor growth and provide a mechanistic framework for understanding the distinct biology of early-onset colorectal cancer.

Blockade by sedimentary particles, such as mineral crystals, is a continuous risk the kidney tubule faces. To prevent that, kidney resident macrophages form transepithelial protrusions and remove intratubular sedimentary particles, a behavior particularly prevailing in the medulla over the cortex. However, the molecular mechanisms underlying this characteristic behavior of medulla macrophages are incompletely understood. In this study, we identified that the medulla had higher mechanical stiffness than the cortex in steady state, which was further elevated when kidney stone formed. Increased tissue rigidity was sensed by medulla macrophages via mechanoreceptor Piezo1, which promoted macrophage protrusion formation and their ability to clean the tubules. Loss of Piezo1 expression in kidney macrophages predisposed mice to intratubular accumulation of mineral crystal in steady state and accelerated kidney stone formation during oxalate intake challenge. Signaling via Piezo1 mobilized molecules involved in cell adhesion and protrusion assembly, including Talin2 and focal adhesion kinase (FAK). Finally, we developed a first-of-its-kind cell-based therapy for the treatment of experimental nephrolithiasis by exploiting macrophage Piezo1 activity, and this strategy shows great promise for future translational research.

Spectral bleed-through remains a persistent practical problem in multichannel fluorescence microscopy. Signal from one fluorophore can be recorded in the detection channel of another, thereby biasing intensity measurements, inflating apparent colocalization, and complicating the interpretation of dynamic microscopy data. Although many correction strategies exist, routine workflows often remain fragmented across ad hoc scripts, manually tuned graphical procedures, or method-specific blind-unmixing implementations with limited provenance. Here we present spectral-unmixing, an open-source Python package for reproducible linear spectral unmixing in multidimensional microscopy stacks. The package unifies directed two-channel correction with multiple alpha-estimation strategies, optional bidirectional two-channel correction through explicit inversion of a 2 x 2 mixing model, and PICASSO-family blind unmixing for multichannel data. Microscopy inputs are normalized at the API boundary to canonical TZCY X stacks, allowing the same unmixing code to be applied across file formats without manual axis handling. Machine-readable sidecar reports preserve the effective processing configuration and estimated coefficients for every output, so that workflows can be audited and reproduced. Synthetic and real-data-derived benchmarks show that the implemented workflows accurately estimate and correct bleed-through when their model assumptions are satisfied. In fixed-alpha two-channel simulations, the mean-ratio and linear-fit estimators recovered {approx} 0.283 for a ground-truth value of 0.28 and reduced target-channel normalized root mean squared error from approximately 0.029 to 0.003. In time-varying simulations, per-time-point estimation tracked coefficient drift substantially better than reference-time-point estimation. Bidirectional inversion recovered reciprocally mixed channels accurately when coefficients were known or well estimated. PICASSO-family benchmarks further showed a practical trade-off between reducing residual inter-channel dependence and preserving fluorophore identity, with MATLAB-style workflows behaving more conservatively and source-sink formulations providing stronger dependence suppression when meaningful directional priors are available. Together, these elements make spectral-unmixing a practical, transparent, and extensible platform for reproducible spectral unmixing of fluorescence microscopy data in neuroscience and other quantitative bioimage-analysis settings.