Prediction of directional solidification in freeze casting of biomaterial scaffolds using physics-informed neural networks.

Freeze casting, a manufacturing technique widely applied in biomedical fields for fabricating biomaterial scaffolds, poses challenges for predicting directional solidification due to its highly nonlinear behavior and complex interplay of process parameters. Conventional numerical methods, such as computational fluid dynamics (CFD), require adequate and accurate boundary condition knowledge, limiting their utility in real-world transient solidification applications due to technical limitations. In this study, we address this challenge by developing a physics-informed neural networks (PINNs) model to predict directional solidification in freeze-casting processes. The PINNs model integrates physical constraints with neural network predictions, requiring significantly fewer predetermined boundary conditions compared to CFD. Through a comparison with CFD simulations, the PINNs model demonstrates comparable accuracy in predicting temperature distribution and solidification patterns. This promising model achieves such a performance with only 5000 data points in space and time, equivalent to 250,000 timesteps, showcasing its ability to predict solidification dynamics with high accuracy. The study's major contributions lie in providing insights into solidification patterns during freeze-casting scaffold fabrication, facilitating the design of biomaterial scaffolds with finely tuned microstructures essential for various tissue engineering applications. Furthermore, the reduced computational demands of the PINNs model offer potential cost and time savings in scaffold fabrication, promising advancements in biomedical engineering research and development.

Modeling Hemodynamics in Three-Dimensional, Biomimetic, Branched, Microfluidic, Vascular Networks.

OBJECTIVE: Neovascularization has been extensively studied because of its significant role in both physiological processes and diseases. The significance of vascular microfluidic platforms lies in its essential role in recreating an in vitro environment capable of supporting cellular and tissue systems through the process of neovascularization. Biomechanical properties in a tissue engineered system use fluid flow and transport properties to recapitulate physiological systems. This enables mimicry of organ systems which can further personalized and regenerative medicine. Thus, fluid hemodynamics can be used to study these flow patterns and create a system that mimics real physiological pathways and processes. The establishment of stable flow pathways encourages endothelial cells (ECs) ECs to undergo neovascularization. Specifically, the shear stress applied in capillary beds generates the increased proliferation and differentiation of ECs to build larger microcirculatory beds. MATHEMATICAL FRAMEWORK: Here, we describe a mathematical model that uses branching patterns and vessel morphology to predict hemodynamic parameters in capillary beds. RESULTS: A retinal capillary bed is used as one-use case of our model to show how the mathematical framework can be used to determine hemodynamic parameters for any microfluidic system. CONCLUSION: In doing so, this tool can be altered to be used to supplement emerging research areas in neovascularization.

Predictive models for heat stress assessment in Holstein dairy heifers using infrared thermography and machine learning.

Heat stress is a condition that impairs the animal's productive and reproductive performance, and can be monitored by physiological and environmental variables, including body surface temperature, through infrared thermography. The objective of this work is to develop computational models for classification of heat stress from respiratory rate variable in dairy cattle using infrared thermography. The database used for the construction of the models was obtained from 10 weaned heifers, housed in a climate chamber with temperature control, and submitted to thermal comfort and heat wave treatments. Physiological and environmental data were collected, as well as thermographic images. The machine learning modeling environment used was IBM Watson, IBM's cognitive computing services platform, which has several data processing and mining tools. Classifier models for heat stress were evaluated using the confusion matrix metrics and compared to the traditional method based on Temperature and Humidity Index. The best accuracy obtained for classification of the heat stress level was 86.8%, which is comparable to previous works. The authors conclude that it was possible to develop accurate and practical models for real-time monitoring of dairy cattle heat stress.

Ten Years of the Synthetic Biology Summer Course at Cold Spring Harbor Laboratory.

The Cold Spring Harbor Laboratory (CSHL) Summer Course on Synthetic Biology, established in 2013, has emerged as a premier platform for immersive education and research in this dynamic field. Rooted in CSHL's rich legacy of biological discovery, the course offers a comprehensive exploration of synthetic biology's fundamentals and applications. Led by a consortium of faculty from diverse institutions, the course structure seamlessly integrates practical laboratory sessions, exploratory research rotations, and enriching seminars by leaders in the field. Over the years, the curriculum has evolved to cover essential topics such as cell-free transcription-translation, DNA construction, computational modeling of gene circuits, engineered gene regulation, and CRISPR technologies. In this review, we describe the history, development, and structure of the course, and discuss how elements of the course might inform the development of other short courses in synthetic biology. We also demonstrate the course's impact beyond the lab with a summary of alumni contributions to research, education, and entrepreneurship. Through these efforts, the CSHL Summer Course on Synthetic Biology remains at the forefront of shaping the next generation of synthetic biologists.

Horizontal cortical connections shape intrinsic traveling waves into feature-selective motifs that regulate perceptual sensitivity.

Intrinsic cortical activity forms traveling waves that modulate sensory-evoked responses and perceptual sensitivity. These intrinsic traveling waves (iTWs) may arise from the coordination of synaptic activity through long-range feature-dependent horizontal connectivity within cortical areas. In a spiking network model that incorporates feature-selective patchy connections, we observe iTW motifs that result from shifts in excitatory/inhibitory balance as action potentials traverse these patchy connections. To test whether feature-selective motifs occur in vivo, we examined data recorded in the middle temporal visual area (Area MT) of marmosets performing a visual detection task. We find that some iTWs form motifs that are feature selective, exhibiting direction-selective modulations in spiking activity. Further, motifs modulate the gain of target-evoked responses and perceptual sensitivity if the target matches the preference of the motif. These results suggest that iTWs are shaped by the patchy horizontal fiber projections in the cortex and can regulate neural and perceptual sensitivity in a feature-selective manner.

Computational Medicine: What Electrophysiologists Should Know to Stay Ahead of the Curve.

PURPOSE OF REVIEW: Technology drives the field of cardiac electrophysiology. Recent computational advances will bring exciting changes. To stay ahead of the curve, we recommend electrophysiologists develop a robust appreciation for novel computational techniques, including deterministic, statistical, and hybrid models. RECENT FINDINGS: In clinical applications, deterministic models use biophysically detailed simulations to offer patient-specific insights. Statistical techniques like machine learning and artificial intelligence recognize patterns in data. Emerging clinical tools are exploring avenues to combine all the above methodologies. We review three ways that computational medicine will aid electrophysiologists by: (1) improving personalized risk assessments, (2) weighing treatment options, and (3) guiding ablation procedures. Leveraging clinical data that are often readily available, computational models will offer valuable insights to improve arrhythmia patient care. As emerging tools promote personalized medicine, physicians must continue to critically evaluate technology-driven tools they consider using to ensure their appropriate implementation.

Simulating the impact of tumor mechanical forces on glymphatic networks in the brain parenchyma.

The brain glymphatic system is currently being explored in the context of many neurological disorders and diseases, including traumatic brain injury, Alzheimer's disease, and ischemic stroke. However, little is known about the impact of brain tumors on glymphatic function. Mechanical forces generated during tumor development and growth may be responsible for compromised glymphatic transport pathways, reducing waste clearance and cerebrospinal fluid (CSF) transport in the brain parenchyma. One such force is solid stress, i.e., growth-induced forces from cell hyperproliferation and excess matrix deposition. Because there are no prior studies assessing the impact of tumor-derived solid stress on glymphatic system structure and performance in the brain parenchyma, this study serves to fill an important gap in the field. We adapted a previously developed Electrical Analog Model using MATLAB Simulink for glymphatic transport coupled with Finite Element Analysis for tumor mechanical stresses and strains in COMSOL. This allowed simulation of the impact of tumor mechanical force generation on fluid transport within brain parenchymal glymphatic units-which include perivascular spaces, astrocytic networks, interstitial spaces, and capillary basement membranes. We conducted a parametric analysis to compare the contributions of tumor size, tumor proximity, and ratio of glymphatic subunits to the stress and strain experienced by the glymphatic unit and corresponding reduction in flow rate of CSF. Mechanical stresses intensify with proximity to the tumor and increasing tumor size, highlighting the vulnerability of nearby glymphatic units to tumor-derived forces. Our stress and strain profiles reveal compressive deformation of these surrounding glymphatics and demonstrate that varying the relative contributions of astrocytes vs. interstitial spaces impact the resulting glymphatic structure significantly under tumor mechanical forces. Increased tumor size and proximity caused increased stress and strain across all glymphatic subunits, as does decreased astrocyte composition. Indeed, our model reveals an inverse correlation between extent of astrocyte contribution to the composition of the glymphatic unit and the resulting mechanical stress. This increased mechanical strain across the glymphatic unit decreases the venous efflux rate of CSF, dependent on the degree of strain and the specific glymphatic subunit of interest. For example, a 20% mechanical strain on capillary basement membranes does not significantly decrease venous efflux (2% decrease in flow rates), while the same magnitude of strain on astrocyte networks and interstitial spaces decreases efflux flow rates by 7% and 22%, respectively. Our simulations reveal that solid stress from growing brain tumors directly reduces glymphatic fluid transport, independently from biochemical effects from cancer cells. Understanding these pathophysiological implications is crucial for developing targeted interventions aimed at restoring effective waste clearance mechanisms in the brain. This study opens potential avenues for future experimental research in brain tumor-related glymphatic dysfunction.

Adaptive integration of history variables in constrained mixture models for organ-scale growth and remodeling.

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Investigating the effect of brain atrophy on transcranial direct current stimulation: A computational study using ADNI dataset.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that uses weak electrical currents to modulate brain activity, thus potentially aiding the treatment of brain diseases. Although tDCS offers convenience, it yields inconsistent electric-field distributions among individuals. This inconsistency may be attributed to certain factors, such as brain atrophy. Brain atrophy is accompanied by increased cerebrospinal fluid (CSF) volume. Owing to the high electrical conductivity of CSF, its increased volume complicates current delivery to the brain, thus resulting in greater inter-subject variability. OBJECTIVE: We aim to investigate the differences in tDCS-induced electric fields between groups with different severities of brain atrophy. METHODS: We classified 180 magnetic resonance images into four groups based on the presence of Alzheimer's disease and sex. We used two montages, i.e., F-3 & Fp-2 and TP-9 & TP-10, to target the left rostral middle frontal gyrus and the hippocampus/amygdala complex, respectively. Differences between the groups in terms of regional volume variation, stimulation effect, and correlation were analyzed. RESULTS: Significant differences were observed in the geometrical variations of the CSF and two target regions. Electric fields induced by tDCS were similar in both sexes. Unique patterns were observed in each group in the correlation analysis. CONCLUSION: Our findings show that factors such as brain atrophy affect the tDCS results and that the factors present complex relationships. Further studies are necessary to better understand the relationships between these factors and optimize tDCS as a therapeutic tool.

3D growth and remodeling theory supports the hypothesis of staphyloma formation from local scleral weakening under normal intraocular pressure.

The purpose of this study was to assess whether growth and remodeling (G&R) theory could explain staphyloma formation from a local scleral weakening-as could occur from age-related elastin degradation, myopia progression, or other factors. A finite element model of a healthy eye was reconstructed, including the lamina cribrosa, the peripapillary sclera, and the peripheral sclera. The homogenized constrained mixture model was employed to simulate the adaptation of the sclera to alterations in its biomechanical environment over a duration of 13.7 years. G&R processes were triggered by reducing the shear stiffness of the ground matrix in the peripapillary sclera and lamina cribrosa by 85%. Three distinct G&R scenarios were investigated: (1) low mass turnover rate in combination with transmural volumetric growth; (2) high mass turnover rate in combination with transmural volumetric growth; and (3) high mass turnover rate in combination with mass density growth. In scenario 1, we observed a significant outpouching of the posterior pole, closely resembling the shape of a Type-III staphyloma. Additionally, we found a notable change in scleral curvature and a thinning of the peripapillary sclera by 84%. In contrast, scenario 2 and 3 exhibited less drastic deformations, with stable posterior staphylomas after approximately 7 years. Our proposed framework suggests that local scleral weakening is sufficient to trigger staphyloma formation under a normal level of intraocular pressure. Our model also reproduced characteristics of Type-III staphylomas. With patient-specific scleral geometries (as could be obtained with wide-field optical coherence tomography), our framework could be clinically translated to help us identify those at risks of developing posterior staphylomas.

New mechanistic insights of nanoplastics synergistic cadmium induced overactivation of trypsin: Joint analysis from protein multi-level conformational changes and computational modeling.

Nanoplastics (NPs) are emerging global contaminants that can exacerbate the animal toxicity and cytotoxicity of cadmium (Cd). However, the mechanisms by which NPs influence the toxic effects of Cd on key functional proteins within the body remain unknown. In this study, trypsin, a protein that is prone to coexist with NPs in the digestive tract, was selected as the target protein. The effects and mechanisms of NPs on Cd2+-induced structural damage at multiple levels and alterations in the biological function of trypsin were investigated using multi-spectroscopy techniques, enzyme activity assays, and computational modeling. Results indicated that the Cd2+-induced decrease and red shift of the trypsin backbone peak were exacerbated by the presence of NPs, leading to more serve backbone loosening. Furthermore, compared to Cd2+, NPs@Cd2+ caused a more pronounced reduction in the α-helix content of trypsin. These structural changes led to the opening of the trypsin pocket and the overactivation of the enzyme (NPs@Cd2+: 227.22%; Cd2+: 53.35%). Ultimately, the formation of a "protein corona" around NPs@Cd2+ and the metal contact of Cd2+ to the trypsin surface were identified as the mechanisms by which NPs enhanced the protein toxicity of Cd2+. This study elucidates, for the first time, the effects and underlying mechanisms of NPs on the toxicity of key functional proteins of Cd2+. These findings offer novel mechanistic insights and critical evidence essential for evaluating the risks associated with NPs.

Exploring Early Number Abilities With Multimodal Transformers.

Early number skills represent critical milestones in children's cognitive development and are shaped over years of interacting with quantities and numerals in various contexts. Several connectionist computational models have attempted to emulate how certain number concepts may be learned, represented, and processed in the brain. However, these models mainly used highly simplified inputs and focused on limited tasks. We expand on previous work in two directions: First, we train a model end-to-end on video demonstrations in a synthetic environment with multimodal visual and language inputs. Second, we use a more holistic dataset of 35 tasks, covering enumeration, set comparisons, symbolic digits, and seriation. The order in which the model acquires tasks reflects input length and variability, and the resulting trajectories mostly fit with findings from educational psychology. The trained model also displays symbolic and non-symbolic size and distance effects. Using techniques from interpretability research, we investigate how our attention-based model integrates cross-modal representations and binds them into context-specific associative networks to solve different tasks. We compare models trained with and without symbolic inputs and find that the purely non-symbolic model employs more processing-intensive strategies to determine set size.

Characterization of lignin degrading enzyme PmdC, which catalyzes a key step in the synthesis of polymer precursor 2-pyrone-4,6-dicarboxylic acid (PDC).

Pyrone-2,4-dicarboxylic acid (PDC) is a valuable polymer precursor that can be derived from the microbial degradation of lignin. The key enzyme in the microbial production of PDC is CHMS dehydrogenase, which acts on the substrate 4-carboxy-2-hydroxymuconate-6-semialdehyde (CHMS). We present the crystal structure of CHMS dehydrogenase (PmdC from Comamonas testosteroni) bound to the cofactor NADP, shedding light on its three-dimensional architecture, and revealing residues responsible for binding NADP. Using a combination of structural homology, molecular docking, and quantum chemistry calculations we have predicted the binding site of CHMS. Key histidine residues in a conserved sequence are identified as crucial for binding the hydroxyl group of CHMS and facilitating dehydrogenation with NADP. Mutating these histidine residues results in a loss of enzyme activity, leading to a proposed model for the enzyme's mechanism. These findings are expected to help guide efforts in protein and metabolic engineering to enhance PDC yields in biological routes to polymer feedstock synthesis.

Particle-based MR modeling with diffusion, microstructure, and enzymatic reactions.

PURPOSE: To develop a novel particle-based in silico MR model and demonstrate applications of this model to signal mechanisms which are affected by the spatial organization of particles, including metabolic reaction kinetics, microstructural effects on diffusion, and radiofrequency (RF) refocusing effects in gradient-echo sequences. METHODS: The model was developed by integrating a forward solution of the Bloch equations with a Brownian dynamics simulator. Simulation configurations were then designed to model MR signal dynamics of interest, with a primary focus on hyperpolarized 13C MRI methods. Phantom scans and spectrophotometric assays were conducted to validate model results in vitro. RESULTS: The model accurately reproduced the reaction kinetics of enzyme-mediated conversion of pyruvate to lactate. When varying proportions of restrictive structure were added to the reaction volume, nonlinear changes in the reaction rate measured in vitro were replicated in silico. Modeling of RF refocusing effects characterized the degree of diffusion-weighted contribution from preserved residual magnetization in nonspoiled gradient-echo sequences. CONCLUSIONS: These results show accurate reproduction of a range of MR signal mechanisms, establishing the model's capability to investigate the multifactorial signal dynamics such as those underlying hyperpolarized 13C MRI data.

In silico and in vitro models reveal the molecular mechanisms of hypocontractility caused by TPM1 M8R.

Dilated cardiomyopathy (DCM) is an inherited disorder often leading to severe heart failure. Linkage studies in affected families have revealed hundreds of different mutations that can cause DCM, with most occurring in genes associated with the cardiac sarcomere. We have developed an investigational pipeline for discovering mechanistic genotype-phenotype relationships in DCM and here apply it to the DCM-linked tropomyosin mutation TPM1 M8R. Atomistic simulations predict that M8R increases flexibility of the tropomyosin chain and enhances affinity for the blocked or inactive state of tropomyosin on actin. Applying these molecular effects to a Markov model of the cardiac thin filament reproduced the shifts in Ca2+sensitivity, maximum force, and a qualitative drop in cooperativity that were observed in an in vitro system containing TPM1 M8R. The model was then used to simulate the impact of M8R expression on twitch contractions of intact cardiac muscle, predicting that M8R would reduce peak force and duration of contraction in a dose-dependent manner. To evaluate this prediction, TPM1 M8R was expressed via adenovirus in human engineered heart tissues and isometric twitch force was observed. The mutant tissues manifested depressed contractility and twitch duration that agreed in detail with model predictions. Additional exploratory simulations suggest that M8R-mediated alterations in tropomyosin-actin interactions contribute more potently than tropomyosin chain stiffness to cardiac twitch dysfunction, and presumably to the ultimate manifestation of DCM. This study is an example of the growing potential for successful in silico prediction of mutation pathogenicity for inherited cardiac muscle disorders.

Monte Carlo simulation of spatial frequency domain imaging for breast tumors during compression.

Significance: Near-infrared optical imaging methods have shown promise for monitoring response to neoadjuvant chemotherapy (NAC) for breast cancer, with endogenous contrast coming from oxy- and deoxyhemoglobin. Spatial frequency domain imaging (SFDI) could be used to detect this contrast in a low-cost and portable format, but it has limited imaging depth. It is possible that local tissue compression could be used to reduce the effective tumor depth. Aim: To evaluate the potential of SFDI for therapy response prediction, we aim to predict how changes to tumor size, stiffness, and hemoglobin concentration would be reflected in contrast measured by SFDI under tissue compression. Approach: Finite element analysis of compression on an inclusion-containing soft material is combined with Monte Carlo simulation to predict the measured optical contrast. Results: When the effect of compression on blood volume is not considered, contrast gain from compression increases with the size and stiffness of the inclusion and decreases with the inclusion depth. With a model of reduction of blood volume from compression, compression reduces imaging contrast, an effect that is greater for larger inclusions and stiffer inclusions at shallower depths. Conclusions: This computational modeling study represents a first step toward tracking tumor changes induced by NAC using SFDI and local compression.

Computational Modeling Differentiates Learning Rate From Reward Sensitivity Deficits Produced by Early-Life Adversity in a Rodent Touchscreen Probabilistic Reward Task.

Background: Exposure to adversity, including unpredictable environments, during early life is associated with neuropsychiatric illness in adulthood. One common factor in this sequela is anhedonia, the loss of responsivity to previously reinforcing stimuli. To accelerate the development of new treatment strategies for anhedonic disorders induced by early-life adversity, animal models have been developed to capture critical features of early-life stress and the behavioral deficits that such stressors induce. We have previously shown that rats exposed to the limited bedding and nesting protocol exhibited blunted reward responsivity in the probabilistic reward task, a touchscreen-based task reverse translated from human studies. Methods: To test the quantitative limits of this translational platform, we examined the ability of Bayesian computational modeling and probability analyses identical to those optimized in previous human studies to quantify the putative mechanisms that underlie these deficits with precision. Specifically, 2 parameters that have been shown to independently contribute to probabilistic reward task outcomes in patient populations, reward sensitivity and learning rate, were extracted, as were trial-by-trial probability analyses of choices as a function of the preceding trial. Results: Significant deficits in reward sensitivity, but not learning rate, contributed to the anhedonic phenotypes in rats exposed to early-life adversity. Conclusions: The current findings confirm and extend the translational value of these rodent models by verifying the effectiveness of computational modeling in distinguishing independent features of reward sensitivity and learning rate that complement the probabilistic reward task's signal detection end points. Together, these metrics serve to objectively quantify reinforcement learning deficits associated with anhedonic phenotypes.

Exposure to early-life adversity can lead to psychiatric illness, including anhedonia, the loss of pleasure from previously rewarding activities. This article describes findings from rats exposed to a model of simulated poverty on a touchscreen-based assay reverse translated from a task used to characterize anhedonia in humans. We documented the ability of Bayesian computational modeling and probability analyses, identical to those used with humans, to objectively quantify reinforcement learning deficits associated with anhedonia in rats.

Computational analysis of heart valve growth and remodeling after the Ross procedure.

During the Ross procedure, an aortic heart valve is replaced by a patient's own pulmonary valve. The pulmonary autograft subsequently undergoes substantial growth and remodeling (G&R) due to its exposure to increased hemodynamic loads. In this study, we developed a homogenized constrained mixture model to understand the observed adaptation of the autograft leaflets in response to the changed hemodynamic environment. This model was based on the hypothesis that tissue G&R aims to preserve mechanical homeostasis for each tissue constituent. To model the Ross procedure, we simulated the exposure of a pulmonary valve to aortic pressure conditions and the subsequent G&R of the valve. Specifically, we investigated the effects of assuming either stress- or stretch-based mechanical homeostasis, the use of blood pressure control, and the effect of root dilation. With this model, we could explain different observations from published clinical studies, such as the increase in thickness, change in collagen organization, and change in tissue composition. In addition, we found that G&R based on stress-based homeostasis could better capture the observed changes in tissue composition than G&R based on stretch-based homeostasis, and that root dilation or blood pressure control can result in more leaflet elongation. Finally, our model demonstrated that successful adaptation can only occur when the mechanically induced tissue deposition is sufficiently larger than tissue degradation, such that leaflet thickening overrules leaflet dilation. In conclusion, our findings demonstrated that G&R based on mechanical homeostasis can capture the observed heart valve adaptation after the Ross procedure. Finally, this study presents a novel homogenized mixture model that can be used to investigate other cases of heart valve G&R as well.

Intra-abdominal temperature variation during hyperthermic intraperitoneal chemotherapy evaluated via computational fluid dynamics modeling.

Background: Hyperthermic intraperitoneal chemotherapy (HIPEC) targets intraperitoneal tumors with heated drug solutions via catheters inserted into the peritoneal space. Although studies have focused on clinical outcomes, the flow dynamics at specific intra-abdominal locations at-risk of harboring malignant cells remain poorly understood but are likely to impact the drug pharmacokinetics. Consequently, optimal protocols remain uncertain, with efficacy critically dependent on drug temperature and flow rate. This study tested the hypothesis that fluid flow dynamics at specific at-risk locations could be evaluated via a computational fluid dynamics (CFD) model of closed HIPEC in a simulated human abdominal cavity, with the goal to enable protocol optimization. Methods: A computer-aided-design (CAD) model of a human intraperitoneal cavity (30 L) was coupled with computational fluid dynamics analysis. The tested HIPEC cycle parameters included catheter position and flow rates. The cavity was subjected to forward (superior to inferior flow) or reverse flow directions at 800 or 1,120 cc/min through four catheters, two as inlets and two as outlets, placed in upper and lower abdominal positions (net fluid volume: 18.5 L). Probes to measure temperature and flow were simulated between small and large bowels, inferior to small bowel mesentery, next to duodenum, superior to liver, superior to fundus, posterior to stomach, and posterior to liver. Results: The simulations highlight heterogeneity in temperatures and flow that may occur during HIPEC at particular at-risk locations as a function of chemotherapy flow rate and direction. Temperature and fluid flow over the course of 90 min respectively varied from 0.93 K and <0.001 m/s inferior to small bowel mesentery (800 cc/min forward flow) to 3.6 K and 0.01 m/s next to the duodenum (either 800 or 1,120 cc/min forward flow). The results further suggest that monitoring outflow temperature may be inadequate for assessing HIPEC performance at at-risk locations. Conclusions: Without intra-abdominal temperature monitoring at at-risk locations, it may be unfeasible to determine whether target temperatures and temperature homogeneity are being achieved during HIPEC. This work demonstrates that computational analysis offers the capability to monitor intra-abdominal locations at-risk of suboptimal heating and fluid flow given specific HIPEC parameters, and represents a first step towards designing efficacious tumor targeting during HIPEC.

Formononetin inhibits IgE by huPlasma/PBMCs and mast cells/basophil activation via JAK/STAT/PI3-Akt pathways.

Rationale: Food allergy is a prevalent disease in the U.S., affecting nearly 30 million people. The primary management strategy for this condition is food avoidance, as limited treatment options are available. The elevation of pathologic IgE and over-reactive mast cells/basophils is a central factor in food allergy anaphylaxis. This study aims to comprehensively evaluate the potential therapeutic mechanisms of a small molecule compound called formononetin in regulating IgE and mast cell activation. Methods: In this study, we determined the inhibitory effect of formononetin on the production of human IgE from peripheral blood mononuclear cells of food-allergic patients using ELISA. We also measured formononetin's effect on preventing mast cell degranulation in RBL-2H3 and KU812 cells using beta-hexosaminidase assay. To identify potential targets of formononetin in IgE-mediated diseases, mast cell disorders, and food allergies, we utilized computational modeling to analyze mechanistic targets of formononetin from various databases, including SEA, Swiss Target Prediction, PubChem, Gene Cards, and Mala Cards. We generated a KEGG pathway, Gene Ontology, and Compound Target Pathway Disease Network using these targets. Finally, we used qRT-PCR to measure the gene expression of selected targets in KU812 and U266 cell lines. Results: Formononetin significantly decreased IgE production in IgE-producing human myeloma cells and PBMCs from food-allergic patients in a dose-dependent manner without cytotoxicity. Formononetin decreased beta-hexosaminidase release in RBL-2H3 cells and KU812 cells. Formononetin regulates 25 targets in food allergy, 51 in IgE diseases, and 19 in mast cell diseases. KEGG pathway and gene ontology analysis of targets showed that formononetin regulated disease pathways, primary immunodeficiency, Epstein-Barr Virus, and pathways in cancer. The biological processes regulated by formononetin include B cell proliferation, differentiation, immune response, and activation processes. Compound target pathway disease network identified NFKB1, NFKBIA, STAT1, STAT3, CCND1, TP53, TYK2, and CASP8 as the top targets regulated at a high degree by formononetin. TP53, STAT3, PTPRC, IL2, and CD19 were identified as the proteins mostly targeted by formononetin. qPCR validated genes of Formononetin molecular targets of IgE regulation in U266 cells and KU812 cells. In U266 cells, formononetin was found to significantly increase the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. In basophils KU812 cells, formononetin significantly increased the gene expression of NFKBIA, TP53, and BCL-2 while decreasing the gene expression of BTK, TYK, CASP8, STAT3, CCND1, STAT1, NFKB1, IL7R. Conclusion: These findings comprehensively present formononetin's mechanisms in regulating IgE production in plasma cells and degranulation in mast cells.