Normal Flow Research Articles

Kinetic model of multi-component polyatomic gas mixture considering internal energy excitation

In this paper, a kinetic Boltzmann model equation with internal degrees of freedom is established for thermodynamic non-equilibrium multi-component polyatomic gas mixture by using continuous energy levels to deal with rotational energy and vibrational energy. The normalization and conservativeness of the kinetic model are analyzed and proved. Then, numerical algorithm and wall boundary conditions for solving the model equation are given. Then, normal shock-wave structure flow problem and pressure/temperature gradient driven micro-channel flow problem for the two-component gas mixture are used to verify the reliability of the model. The simulation results are in good agreement with those obtained by other methods, which verifies the reliability of the model and algorithm in this paper. Finally, taking 25 N attitude control engine two-dimensional profile as the object, the study for two-dimensional profile nozzle internal and external mixed flow problem is carried out, and the influence of different inlet rarefaction levels on the molecular transports of mixed flow field and the molecular transport phenomena of mixed flow field with different number of components are discussed.

Predictors and Outcomes of Slow Flow/No Reflow in Acute Myocardial Infarction Patients Treated with Pharmacoinvasive Strategies

Objectives: This study aimed to determine the incidence, predictors, and clinical outcomes of slow flow and no reflow (SF/NR) in patients undergoing a pharmacoinvasive strategy following full-dose thrombolytic therapy for acute myocardial infarction (AMI). Methodology: We conducted a prospective observational study at the Department of Cardiology, Hayatabad Medical Complex, Peshawar, from February 2022 to February 2023. The study cohort comprised 130 patients who received percutaneous coronary intervention (PCI) after thrombolytic therapy. Comprehensive data on patient demographics, clinical presentations, procedural details, and outcomes were systematically collected. Multivariate logistic regression was employed to identify independent predictors of SF/NR. Results: The incidence of SF/NR was 13.8%. Significant predictors included hypertension (Odds Ratio [OR]: 2.31, 95% Confidence Interval [CI]: 1.01-5.29, p = 0.047), high thrombus burden (OR: 3.14, 95% CI: 1.38-7.15, p = 0.006), and longer lesion length (OR: 1.29 per mm increase, 95% CI: 1.11-1.50, p < 0.001). Patients with SF/NR experienced significantly higher in-hospital mortality (16.7% vs. 2.7%, p = 0.012), cardiogenic shock (27.8% vs. 5.4%, p < 0.001), heart failure (33.3% vs. 7.1%, p < 0.001), and major adverse cardiac events (MACE) (44.4% vs. 11.6%, p < 0.001). At 30 days post-discharge, the mortality rate was 22.2% in the SF/NR group compared to 4.5% in the normal flow group (p = 0.004). Conclusion: The study highlights a significant incidence of SF/NR in patients undergoing PCI after thrombolytic therapy for AMI. Identified predictors—hypertension, high thrombus burden, and longer lesion length—underscore the need for targeted risk stratification and management strategies to enhance patient outcomes and mitigate adverse effects.

Predictors and clinical outcomes of slow flow phenomenon in diabetic patients with chronic coronary syndrome

BackgroundCoronary slow flow (CSF) is characterized by late distal coronary perfusion of coronary arteries at the time of angiography despite the vessels appearing normal. The importance of CSF is still debatable. Therefore, this study aimed to investigate CSF’s predictors and clinical outcomes in diabetic patients with chronic coronary syndrome (CCS).Patient and methodsThis retrospective study included 250 diabetic patients diagnosed with chronic stable angina and referred for coronary angiography (CAG), showing normal coronaries with CSF (Group I) and 240 diabetic patients with normal coronaries and normal flow (Group II). The patients in both groups were followed up for one year to evaluate clinical outcomes.ResultsThe incidence of major adverse cardiac events (MACE) was higher in Group I than in Group II, but the difference was not statistically significant except when the composite endpoints of STEMI, NSTEMI, and unstable angina were combined under the term ACS. The independent predictors of CSF, as detected by multivariate regression analysis, were body mass index (BMI) (OR = 0.694, 95% CI = 0.295–0.842, P = 0.010), blood glucose during catheterization (OR = 0.647, 95% CI = 0.298–0.874, P = 0.008), serum triglycerides (OR = 0.574, 95% CI = 0.289–0.746, P = 0.010), and the neutrophil/lymphocyte ratio (NLR) (OR = 0.618, 95% CI = 0.479–0.892, P = 0.001).ConclusionSerum triglyceride levels, BMI, NLR, and high blood glucose levels at the time of catheterization were independent predictors of CSF in diabetic patients. MACE levels were higher in diabetic patients with CSF.

The impact of COVID-19 vaccination rate on traffic recovery

At the beginning of the COVID-19 pandemic in the US, traffic sharply fell due to social distancing policies in many locations. Correspondingly, many regions observed an increase in traffic volume (traffic recovery) as the pandemic eased in 2022. We examine how vaccination rates influence traffic recovery in Los Angeles County (LAC), controlling for differences in case counts, demographics, and socioeconomic factors across areas with different vaccination rates. We use arterial road sensor data as a proxy for the traffic volume within each ZIP code, alongside their respective demographic and socioeconomic characteristics. We find that a higher vaccination rate is statistically significantly associated with a larger traffic recovery, a finding that remains consistent across all explored models. This implies that an increased vaccination rate could reduce the public’s perception of the risks of disease infection, leading to a larger traffic recovery. Moreover, we found that variables including population, income, race, work industry, and commuting preferences were correlated with vaccination rates. This highlights potential inequalities based on race, income, and industry sectors in the COVID-19 vaccination and a return to normal traffic flow.

Flow-based Generative Emulation of Grids of Stellar Evolutionary Models

We present a flow-based generative approach to emulate grids of stellar evolutionary models. By interpreting the input parameters and output properties of these models as multidimensional probability distributions, we train conditional normalizing flows to learn and predict the complex relationships between grid inputs and outputs in the form of conditional joint distributions. Leveraging the expressive power and versatility of these flows, we showcase their ability to emulate a variety of evolutionary tracks and isochrones across a continuous range of input parameters. In addition, we describe a simple Bayesian approach for estimating stellar parameters using these flows and demonstrate its application to asteroseismic data sets of red giants observed by the Kepler mission. By applying this approach to red giants in open clusters NGC 6791 and NGC 6819, we illustrate how large age uncertainties can arise when fitting only to global asteroseismic and spectroscopic parameters without prior information on initial helium abundances and mixing length parameter values. We also conduct inference using the flow at a large scale by determining revised estimates of masses and radii for 15,388 field red giants. These estimates show improved agreement with results from existing grid-based modeling, reveal distinct population-level features in the red clump, and suggest that the masses of Kepler red giants previously determined using the corrected asteroseismic scaling relations have been overestimated by 5%–10%.

Classifier surrogates: sharing AI-based searches with the world

In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed strategy, so-called Classifier Surrogates, to be trained inside the experiments, that only utilise publicly accessible features and truth information. These surrogates approximate the original classifier distribution, and can be shared with the public. Subsequently, such a model can be evaluated by sampling the classification output from high-level information without requiring a sophisticated detector simulation. Technically, we show that continuous normalizing flows are a suitable generative architecture that can be efficiently trained to sample classification results using conditional flow matching. We further demonstrate that these models can be easily extended by Bayesian uncertainties to indicate their degree of validity when confronted with unknown inputs by the user. For a concrete example of tagging jets from hadronically decaying top quarks, we demonstrate the application of flows in combination with uncertainty estimation through either inference of a mean-field Gaussian weight posterior, or Monte Carlo sampling network weights.

Ultrasound Induces Similar Temporal Endothelial Expression Patterns of eNOS and KLF2 as Normal Flow

ObjectiveTo determine the sensitivity of vascular endothelial cells to long durations of low-intensity pulsed ultrasound (LIPUS) compared to normal flow and identify the duration that maximizes expression of two mechanosensitive genes related to healthy endothelial function, endothelial nitric oxide synthase (eNOS) and Krüppel-like factor 2 (KLF2). MethodsCustom ultrasound exposure tanks were developed and the acoustic field was characterized. Human umbilical vein endothelial cells were seeded into culture plates and exposed to LIPUS at a frequency of 1 MHz and acoustic pressure of 217 kPa for 20 min, 1 h, 6 h, 9 h, or 24 h. As a comparator, other cells were exposed to normal flow. RT-qPCR was used to assess mRNA expression of eNOS and KLF2. ResultsMaximum eNOS and KLF2 expression occurred at 6 h and was localized to the beam path. Both genes exhibited qualitatively similar patterns of expression under LIPUS compared to normal flow. LIPUS induced a more rapid beneficial response compared to normal flow, but flow induced higher expression of both genes. eNOS expression after 6 h of LIPUS was dependent on RNA yield and culture duration prior to experiments. ConclusionEndothelial cells exposed to longer durations of LIPUS than typically employed exhibited greater expression of beneficial genes. The temporal gene expression patterns resulting from LIPUS and normal flow suggest activation of similar signaling pathways. However, LIPUS also caused increased RNA yield that may be linked to proliferation, which would suggest more of a wound healing than atheroprotective phenotype.

Normalizing flow regularization for photoacoustic tomography

Abstract Proper regularization is crucial in inverse problems to achieve high-quality reconstruction, even with an ill-conditioned measurement system. This is particularly true for three-dimensional photoacoustic tomography, which is computationally demanding and requires rapid scanning, often leading to incomplete measurements. Deep neural networks, known for their efficiency in handling big data, are anticipated to be adept at extracting underlying information from images sharing certain characteristics, such as specific types of natural or medical images. We introduce a \ac{NFR} method designed to reconstruct images from incomplete and noisy measurements. The method involves training a normalizing flow network to understand the statistical distribution of sample images by mapping them to Gaussian distributions. This well-trained network then acts as a regularization tool within a Bayesian inversion framework. Additionally, we explore the concept of adaptive regularization selection, providing theoretical proof of its admissibility. A significant challenge in three-dimensional image training is the extensive memory and computation requirements. We address this by training the normalizing flow model using only small-size images and applying a patch-based model for reconstructing larger images. Our approach is model-independent, allowing the reuse of a well-trained network as regularization for various imaging systems. Moreover, as a data-driven prior, \ac{NFR} effectively leverages the available dataset information, outperforming artificial priors. This advantage is demonstrated through numerical simulations of three-dimensional photoacoustic tomography under various conditions of sparsity, noise levels, and limited-view scenarios.

Management of vascular access inflow-outflow imbalance: A bimodal approach.

A more accurate descriptive and clinically useful diagnosis based upon pathophysiology for what is commonly referred to as venous outflow stenosis is inflow-outflow imbalance. In these cases, the total outflow capacity of the AV access is inadequate to handle the inflow volume (Qa) without an increase in pressure. The relative inadequacy of the access outflow capacity in comparison to Qa results in increased outflow resistance and a proportional increase in intraluminal pressure. The clinical indicators associated with venous stenosis are the resulting manifestations of this imbalance. The point at which this occurs is dependent upon variations in these two parameters-Qa and outflow resistance. The variations in these two parameters are considerable and reciprocal. Excessive Qa results in or can lead to an entire list of serious problems that adversely affect patient morbidity and mortality. Most studies dealing with AV access Qa reduction have been for the treatment of an existing condition rather than its prevention; however, prevention of disease rather than waiting for its development is an important tenet of medical practice. The resulting clinical picture of inflow-outflow imbalance is taken as an indication for corrective treatment. In the past, in most cases this has meant angioplasty to open the outflow if it is reduced; however, this clinical picture may be associated with an excessive Qa and angioplasty in these cases creates the risk for a further increase in Qa. It is the authors' opinion that access flow measurements should be a part of the evaluation of these cases prior to planning treatment. Using this information, a bimodal approach to primary treatment should be adopted involving either angioplasty for cases with a low or normal Qa or flow reduction in cases with an elevated Qa.

Ultrasound-guided drainage of a popliteal ganglion cyst extending to the adventitia of the popliteal artery: a case report of cystic adventitial disease of synovial origin.

We report a case of a 67-year-old woman suffering from intermittent claudication secondary to severe popliteal stenosis due to compression by a popliteal ganglion cyst extending into the adventitia of the popliteal artery. After declining vein bypass grafting, this patient was successfully treated using ultrasound-guided aspiration of the cyst, which restored normal flow to the popliteal artery. Whilst aspiration of Baker's cysts causing claudication has been attempted before, this represents the first reported case of successful recanalization of the popliteal artery by ganglion cyst aspiration and further supports an important possible aetiology and treatment for cystic adventitial disease.

Foam Cells: The Origin of Formation and Target for Plant-derived Bioactive Compounds.

Foam cells play a crucial role in the initiation and progression of atherosclerosis, a condition marked by the development and growth of plaques that narrow blood vessel lumens. This narrowing can prevent normal blood flow and, in severe cases, lead to plaque rupture and blood clot formation, which can cause stroke or myocardial infarction. The origin of foam cells is diverse, arising from monocytes, vascular smooth muscle cells, stem/progenitor cells, and dendritic and endothelial cells. In their attempt to eliminate excess lipoproteins and cholesterol, foam cells inadvertently contribute to plaque development and rupture. Cholesterol uptake, efflux, and esterification are the major processes regulating foam cell formation. Advances in technology, such as the identification of cell-surface markers for lineage tracing and single-cell RNA sequencing, have unveiled diverse molecular mechanisms involved in the formation of foam cells from different origins, offering new insights into plaque formation and potential targets for anti-foam cell therapies. In this review, we focus on recent studies exploringthe inhibitory effects of medicinal plants and their bioactive components on foam cell formation. Various mechanisms are explored, including the inhibition of cholesterol uptake and the up-regulation of cholesterol efflux, as well as the suppression of inflammatory and adhesion activities. Emphasizing a cellular target-based therapeutic approach, this review envisions the development of innovative plant-based medications for atherosclerosis treatment.

Characterizing Normal Upper Extremity Lymphatic Flow with 99mTc In-House Dextran: A Retrospective Study.

Lymphoscintigraphy evaluates the lymphatic system using radiocolloid compounds like 99mTc-sulfur colloid and 99mTc-nanocolloid, which vary in particle size and distribution timing. A local in-house Dextran kit (15-40 nm) was developed in 2005 and began clinical use in 2008 to localize sentinel lymph nodes; diagnose lymphedema; and detect lymphatic leakage. The normal drainage pattern remains unexplored. We retrospectively analyzed 84 upper extremity lymphoscintigraphies from 2008 to 2021. 99mTc in-house Dextran was intradermally injected into both hands, followed by whole-body imaging at specified intervals (≤15 min; 16-30 min; 31-45 min; 46-60 min), with some receiving delayed imaging. Visual and quantitative analyses recorded axillary and forearm lymph nodes and liver, kidney, and urinary bladder activity. Results showed 92% (77/84) upper extremity lymphatic tract visualization within 45 min. Axillary node detection rates increased from 46% (≤15 min) to 86% (46-60 min). Delayed imaging further revealed nodes. Epitrochlear or brachial node visualization was rare (4%, 3/84). Hepatic, renal, and urinary bladder activity was noted in 54%, 71%, and 93% at 1 h, respectively. The axillary node uptake ratio was minimal (<2.5% of injection site activity; median 0.33%). This study characterizes normal upper extremity lymphatic drainage using 99mTc in-house Dextran, offering insights into its clinical application.

Effectiveness and Factors Influencing Success of Transcanalicular Laser-Assisted Endoscopic Dacryocystorhinostomy: Cohort Study.

Laser dacryocystorhinostomy (LDCR) is a surgical procedure designed to treat obstructions in the lacrimal duct system, which can cause excessive tearing, infections, and discomfort. This technique involves creating a new passage for tear drainage, thereby restoring normal tear flow and alleviating symptoms associated with duct obstruction. A retrospective study was conducted on 48 patients who underwent the LDCR procedure, amounting to 56 eyes. The primary outcome measured was the anatomical success rate, defined as the restoration of duct patency. Patients were examined postoperatively from 6 months to 3.5 years. The LDCR method demonstrated a 95% success rate, encompassing both anatomical and functional outcomes. The procedure's effectiveness was determined by achieving a patent osteotomy and resolving symptoms. Anatomical success was measured by the creation of a viable drainage pathway, while functional success pertained to the resolution of symptoms such as epiphora. The efficacy of the procedure was found to be independent of both age and gender. Among patients with successful anatomical outcomes, there was a statistically significant improvement in their Munk scores. The LDCR method is highly effective in treating lacrimal duct obstruction. These findings highlight the importance of the Munk score as a predictive indicator of procedural success in LDCR.

Local–global normality learning and discrepancy normalizing flow for unsupervised image anomaly detection

The unsupervised detection and localization of image anomalies hold significant importance across various domains, particularly in industrial quality inspection. Despite its widespread utilization, this task remains inherently challenging due to its reliance solely on defect-free normal knowledge. This paper presents the local–global normality learning and discrepancy normalizing flow, a new state-of-the-art model for unsupervised image anomaly detection and localization. In contrast to existing methods, It adopts a two-stream approach that considers both local and global semantics, ensuring stable detection of abnormalities. The framework comprises two key components: the dual-branch Transformer and the discrepancy normalizing flow, facilitating reconstruction and discrimination. The proposed framework leverages pre-trained convolutional neural networks to extract multi-scale feature embeddings, followed by a novel dual-branch transformer that achieves feature reconstruction from local and global perspectives. The local reconstruction employs self-attention, while the global reconstruction incorporates global prototype tokens and semantic query tokens by the aggregation-cross attention mechanism. Moreover, discrepancy normalizing flow is developed to estimate the likelihood of anomalies based on the discrepancy between pre-trained features and local/global reconstruction results. Extensive validation on established public benchmarks confirms that our method achieves state-of-the-art performance with the proposed local–global reconstruction and discrimination dual-stream framework.

Flooding promotes the coalescence of microbial community in estuarine habitats

Microbial community coalescence describes the mixing of microbial communities and their integration with the surrounding environment, which is common in natural ecosystems and has potential impacts on ecological processes. However, few studies have focused on microbial community coalescence between different habitats in estuarine regions. In this study, we comprehensively investigated the environmental characteristics and bacterial community changes of different habitats (water body (Water), subtidal sediments (SS) and intertidal salt marsh sediments (SM)) in Luanhe estuary during flood and normal flow periods. The results showed that flood event significantly reduced the salinity of the estuarine habitats, changed the nutrient structure and intensified the eutrophication of estuarine water. By calculating the proportion of overlapping groups and applying the ‘FEAST’ algorithm, we revealed that flood event facilitated the migration of bacterial communities along alternative pathways across habitats, markedly enhanced the cross-habitat mobility of bacterial communities, which underscores the pivotal role of flood event in driving bacterial community coalescence. Flood-induced community coalescence not only increased the α-diversity of bacterial communities within habitats, but also increased the proportion of overlapped species between habitats, ultimately leading to homogenization between habitats. Canonical correlation analysis combined co-occurrence network analysis revealed that flood event attenuated the role of environmental filtration in microbial assembly, while increased the impact of dispersal processes and intensified interspecific competition among microorganisms, led to the change of keystone species and reduced the complexity and stability of bacterial communities. In conclusion, this study demonstrates the complex effects of flood events on estuarine microbial communities from the perspective of multi-habitat interactions in the estuary, and emphasizes the key role of river hydrodynamic conditions in facilitating the coalescence of estuarine microbial communities. We look forward to further attention and research on estuarine microbial coalescence, which will provide new insights into assessing the stability and resilience of estuarine ecosystems under flood challenges and the sustainable management of estuarine wetlands.

Coarse-Grained Molecular Dynamics with Normalizing Flows.

We propose a sampling algorithm relying on a collective variable (CV) of midsize dimension modeled by a normalizing flow and using nonequilibrium dynamics to propose full configurational moves from the proposition of a refreshed value of the CV made by the flow. The algorithm takes the form of a Markov chain with nonlocal updates, allowing jumps through energy barriers across metastable states. The flow is trained throughout the algorithm to reproduce the free energy landscape of the CV. The output of the algorithm is a sample of thermalized configurations and the trained network that can be used to efficiently produce more configurations. We show the functioning of the algorithm first in a test case with a mixture of Gaussians. Then, we successfully tested it on a higher-dimensional system consisting of a polymer in solution with a compact state and an extended stable state separated by a high free energy barrier.

High-Output Heart Failure Secondary to Post-Traumatic Arteriovenous Fistula

Introduction: Arteriovenous fistulas are abnormal connections between the arterial and venous systems that bypass normal anatomical capillary beds. Any device, implement, or projectile that passes through an artery and vein has the potential to lead to one, thereby preventing normal blood flow through the capillary bed. Late complications of untreated arteriovenous fistulas include proximal arterial dilation, venous congestion, congestive heart failure, and distal ischemia. Objective: Describe the clinical manifestations, diagnostic, therapeutic, prognostic complexities and complications present in post-traumatic arteriovenous fistula and its association with heart failure. Material and methods: Retrospective study presentation of a clinical case. Results: The case of a 55-year-old female patient is presented, with a 2-year history of ischemic heart disease and a gunshot wound 20 years ago. Who was admitted for bypass and coronary angiography + cardiac angiography in whom no etiology of his dilated ischemic heart disease was found until Doppler ultrasound and tomography angiography was performed due to recurrence of pain in the lower limbs and changes in skin color, which revealed presence of arteriovenous fistula. The same one that was resolved with distal ligation of the popliteal vein + exclusion of aneurysm. Conclusions: Risk factors for the development of this pathology are difficult to establish. Therefore, the shotgun trauma to the left leg 20 years ago, in addition to her obesity and female sex, led to the formation of an arteriovenous fistula that would gradually produce high-grade heart failure. Endovascular AVF repair is a treatment alternative for those patients considered high risk from anesthesia, those who have a hostile groin due to excessive scar tissue, or who cannot tolerate the consequences of bleeding that can occur with surgical control. of the FAV.

Enabling uncertainty quantification in a standard full-waveform inversion method using normalizing flows

To maximize the utility of seismic imaging and inversion results, we need to compute not only a final image but also quantify the uncertainty in the image. Although the most thorough approach to quantify the uncertainty is to use a method such as Markov chain Monte Carlo, which systematically samples the entire posterior distribution, this is often inefficient, and not all applications require a full representation of the posterior. We use normalizing flows (NFs), a machine learning technique to perform uncertainty quantification (UQ) in full-waveform inversion (FWI), specifically for time-lapse data. As with any machine learning algorithm, the NF learns only the mapping from the part of the prior spanned by the training data to the distribution of final models spanned by the training data. Here, we make use of this property to perform UQ efficiently by learning a mapping from the prior to the distribution that characterizes the model perturbations within a specific range. Our approach involves using a range of starting models paired with final models from a standard FWI as training data. Although this does not capture the full posterior of the FWI problem, it enables us to quantify the uncertainties associated with updating from an initial to a final model. Because our target is to perform UQ for time-lapse imaging, we use a local wave-equation solver that allows us to solve the wave equation in a small subset of our entire model, thereby keeping computational costs low. Numerical examples demonstrate that incorporating the training step for NF provides a distribution of model perturbations, which is dependent on a designated prior, to quantify the uncertainty of FWI results.

Neural simulation-based inference of the neutron star equation of state directly from telescope spectra

Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such as mass and radius, which affect the star's thermal X-ray emissions. However, the telescope spectra of these emissions are also affected by the stellar distance, hydrogen column, and effective surface temperature, which are not always well-constrained. Uncertainties on these nuisance parameters must be accounted for when making a robust estimation of the equation of state. In this study, we develop a novel methodology that, for the first time, can infer the full posterior distribution of both the equation of state and nuisance parameters directly from telescope observations. This method relies on the use of neural likelihood estimation, in which normalizing flows use samples of simulated telescope data to learn the likelihood of the neutron star spectra as a function of these parameters, coupled with Hamiltonian Monte Carlo methods to efficiently sample from the corresponding posterior distribution. Our approach surpasses the accuracy of previous methods, improves the interpretability of the results by providing access to the full posterior distribution, and naturally scales to a growing number of neutron star observations expected in the coming years.

Taohong Siwu decoction enhances the chemotherapeutic efficacy of doxorubicin by promoting tumor vascular normalization

BackgroundInstead of completely suppressing blood vessels inside tumors, vascular normalization therapy is proposed to normalize and prune the abnormal vasculature in tumor microenvironment (TME) to acquire a normal and stable blood flow and perfusion. The theoretical basis for the use of “blood-activating and stasis-resolving” formulas in Traditional Chinese Medicine to treat cancer is highly consistent with the principle of vascular normalization therapy, suggesting the potential application of these traditional formulas in vascular normalization therapy. PurposeTo study the underlying mechanisms of a classical “blood-activating and stasis-resolving” formula, Taohong Siwu decoction (TSD), in enhancing the efficacy of chemotherapy for breast cancer treatment. Study designHUVECs and transgenic zebrafish embryos were used as the major model in vitro. A 4T1 mouse breast cancer model was applied to study tumor vasculature normalization of TSD and the combination effects with DOX. ResultsOur data showed that TSD exhibited anti-angiogenic potential in HUVECs and transgenic zebrafish embryos. After 20 days treatment, TSD significantly normalized the tumor vasculature by remodeling vessel structure, reducing intratumoral hypoxia and vessel leakage, and promoting vessel maturation and blood perfusion in 4T1 breast tumor-bearing mice. Moreover, the anti-tumor efficacy of doxorubicin liposome in 4T1 breast tumors was significantly improved by TSD, including the suppression of tumor cell proliferation, angiogenesis, hypoxia, and the increase of cell apoptosis, which is likely through the vascular normalization induced by TSD. TSD also shifted the macrophage polarization from M2 to M1 phenotype in TME during the combination therapy, as evidenced by the reduced number of CD206+ macrophages and increased number of CD86+ macrophages. Additionally, TSD treatment protected against doxorubicin-induced cardiotoxicity in animals, as evidenced by the reduced cardiomyocytes apoptosis and improved heart function. ConclusionThis study demonstrated for the first time that TSD as a classical Chinese formula can enhance the drug efficacy and reduce the side effects of doxorubicin. These findings can support that TSD could be used as an adjuvant therapy in combination with conventional chemotherapy for the future breast cancer treatment.