A review on reactive oxygen species-induced mechanism pathways of pharmaceutical waste degradation: Acetaminophen as a drug waste model.

Innately designed to induce physiological changes, pharmaceuticals are foreknowingly hazardous to the ecosystem. Advanced oxidation processes (AOPs) are recognized as a set of contemporary and highly efficient methods being used as a contrivance for the removal of pharmaceutical residues. Since reactive oxygen species (ROS) are formed in these processes to interact and contribute directly toward the oxidation of target contaminant(s), a profound insight regarding the mechanisms of ROS leading to the degradation of pharmaceuticals is fundamentally significant. The conceptualization of some specific reaction mechanisms allows the design of an effective and safe degradation process that can empirically reduce the environmental impact of the micropollutants. This review mainly deliberates the mechanistic reaction pathways for ROS-mediated degradation of pharmaceuticals often leading to complete mineralization, with a focus on acetaminophen as a drug waste model.

Prophylactic effect of compression stockings for elevated D-dimer levels following endoscopic submucosal dissection.

Objectives: A relationship between endoscopic submucosal dissection (ESD) and deep vein thrombosis has been recognized. We previously reported that a high corrected midazolam dose (total midazolam dose/initial dose of midazolam used to induce sedation) is related to elevated D-dimer levels after ESD. In this study, the effect of compression stockings (CSs) in preventing thrombosis following ESD under sedation was evaluated by measuring D-dimer levels before and after ESD. Methods: The participants were patients who underwent ESD for upper gastrointestinal tumors during the period between April 2018 and October 2022. Patients with pre-ESD D-dimer levels ≥1.6 µg/m and patients with corrected midazolam doses ≤3.0 were excluded. A retrospective investigation of the relationship between CS use and high post-ESD D-dimer levels (difference in D-dimer levels ≥1.0 µg/mL between before and after ESD) was conducted. Results: There were 27 patients in the non-CS group (NCS) and 33 patients in the CS group. The number of patients with high post-ESD D-dimer levels was 13 (48.2%) in the non-CS group and six (18.2%) in the CS group; the number in the CS group was significantly lower (p = 0.024). On logistic regression analysis, a relationship was seen between the wearing of CSs and a lower number of patients with high post-ESD D-dimer levels (odds ratio 0.24, 95% confidence interval 0.08-0.79, p = 0.019). Conclusion: Wearing CSs was related to a lower risk of high post-ESD D-dimer levels. This result suggests that thrombus formation is a cause of elevated D-dimer levels after ESD.

Forecasting the consumptions of coagulation tests using a deep learning model.

Background: Laboratory professionals aim to provide a reliable laboratory service using public resources efficiently while planning a test's procurement. This intuitive approach is ineffective, as seen in the COVID-19 pandemic, where the dramatic changes in admissions (e.g. decreased patient admissions) and the purpose of testing (e.g. D-dimer) were experienced. A model based on objective data was developed that predicts the future test consumption of coagulation tests whose consumptions were highly variable during the pandemic. Methods: Between December 2018 and July 2021, monthly consumptions of coagulation tests (PTT, aPTT, D-dimer, fibrinogen), total-, inpatient-, outpatient-, emergency-, non-emergency -admission numbers were collected. The relationship between input and output is modeled with an external input nonlinear autoregressive artificial neural network (NARX) using the MATLAB program. Monthly test consumption between January and July 2021 was used to test the power of the forecasting model.

Development and performance evaluation of fully automated deep learning-based models for myocardial segmentation on T1 mapping MRI data.

To develop a deep learning-based model capable of segmenting the left ventricular (LV) myocardium on native T1 maps from cardiac MRI in both long-axis and short-axis orientations. Models were trained on native myocardial T1 maps from 50 healthy volunteers and 75 patients using manual segmentation as the reference standard. Based on a U-Net architecture, we systematically optimized the model design using two different training metrics (Sørensen-Dice coefficient = DSC and Intersection-over-Union = IOU), two different activation functions (ReLU and LeakyReLU) and various numbers of training epochs. Training with DSC metric and a ReLU activation function over 35 epochs achieved the highest overall performance (mean error in T1 10.6 ± 17.9 ms, mean DSC 0.88 ± 0.07). Limits of agreement between model results and ground truth were from -35.5 to + 36.1 ms. This was superior to the agreement between two human raters (-34.7 to + 59.1 ms). Segmentation was as accurate for long-axis views (mean error T1: 6.77 ± 8.3 ms, mean DSC: 0.89 ± 0.03) as for short-axis images (mean error ΔT1: 11.6 ± 19.7 ms, mean DSC: 0.88 ± 0.08). Fully automated segmentation and quantitative analysis of native myocardial T1 maps is possible in both long-axis and short-axis orientations with very high accuracy.

Deep eutectic solvent: Synthesis, classification, properties and application in macromolecular substances.

Deep eutectic solvent (DES) is a kind of solvent prepared by mixing hydrogen bond donors and hydrogen bond acceptors, and have become a hot topic in ecological civilization construction due to its low toxicity and sustainability. Its excellent properties such as low volatility, thermal stability and biodegradability make it stand out among many organic solvents and widely used in fields including medicine, chemical industry and agriculture, with broad development prospects. In recent years, the application of DES in the food field has mostly focused on the extraction of small molecular substances, and there are few summaries on the application of DES in macromolecular substances. In this review, we introduced the synthesis, classification and properties of DES, and summarized the application of DES in the food industry for macromolecular substances, including the extraction of macromolecular substances such as chitosan and pectin, as well as the preparation of related macromolecular substrate films. At the same time, we analyzed the characteristics of DES and its advantages and limitations in application, and provided prospects for future development.

Predicting time-of-flight with Cerenkov light in BGO: a three-stage network approach with multiple timing kernels prior.

Objective.In the quest for enhanced image quality in positron emission tomography (PET) reconstruction, the introduction of time-of-flight (TOF) constraints in TOF-PET reconstruction offers superior signal-to-noise ratio. By employing BGO detectors capable of simultaneously emitting prompt Cerenkov light and scintillation light, this approach combines the high time resolution of prompt photons with the high energy resolution of scintillation light, thereby presenting a promising avenue for acquiring more precise TOF information.Approach.In Stage One, we train a raw method capable of predicting TOF information based on coincidence waveform pairs. In Stage Two, the data is categorized into 25 classes based on signal rise time, and the pre-trained raw method is utilized to obtain TOF kernels for each of the 25 classes, thereby generating prior knowledge. Within Stage Three, our proposed deep learning (DL) module, combined with a bias fine-tuning module, utilizes the kernel prior to provide bias compensation values for the data, thereby refining the first-stage outputs and obtaining more accurate TOF predictions.Main results.The three-stage network built upon the LED method resulted in improvements of 11.7 ps and 41.8 ps for full width at half maximum (FWHM) and full width at tenth maximum (FWTM), respectively. Optimal performance was achieved with FWHM of 128.2 ps and FWTM of 286.6 ps when CNN and Transformer were utilized in Stages One and Three, respectively. Further enhancements of 2.3 ps and 3.5 ps for FWHM and FWTM were attained through data augmentation methods.Significance.This study employs neural networks to compensate for the timing delays in mixed (Cerenkov and scintillation photons) signals, combining multiple timing kernels as prior knowledge with DL models. This integration yields optimal predictive performance, offering a superior solution for TOF-PET research utilizing Cerenkov signals.

Skin Type Diversity in Skin Lesion Datasets: A Review.

Purpose of review: Skin type diversity in image datasets refers to the representation of various skin types. This diversity allows for the verification of comparable performance of a trained model across different skin types. A widespread problem in datasets involving human skin is the lack of verifiable diversity in skin types, making it difficult to evaluate whether the performance of the trained models generalizes across different skin types. For example, the diversity issues in skin lesion datasets, which are used to train deep learning-based models, often result in lower accuracy for darker skin types that are typically under-represented in these datasets. Under-representation in datasets results in lower performance in deep learning models for under-represented skin types. Recent findings: This issue has been discussed in previous works; however, the reporting of skin types, and inherent diversity, have not been fully assessed. Some works report skin types but do not attempt to assess the representation of each skin type in datasets. Others, focusing on skin lesions, identify the issue but do not measure skin type diversity in the datasets examined. Summary: Effort is needed to address these shortcomings and move towards facilitating verifiable diversity. Building on previous works in skin lesion datasets, this review explores the general issue of skin type diversity by investigating and evaluating skin lesion datasets specifically. The main contributions of this work are an evaluation of publicly available skin lesion datasets and their metadata to assess the frequency and completeness of reporting of skin type and an investigation into the diversity and representation of each skin type within these datasets. Supplementary Information: The online version contains material available at 10.1007/s13671-024-00440-0.

PI-YOLO: dynamic sparse attention and lightweight convolutional based YOLO for vessel detection in pathological images.

Vessel density within tumor tissues strongly correlates with tumor proliferation and serves as a critical marker for tumor grading. Recognition of vessel density by pathologists is subject to a strong inter-rater bias, thus limiting its prognostic value. There are many challenges in the task of object detection in pathological images, including complex image backgrounds, dense distribution of small targets, and insignificant differences between the features of the target to be detected and the image background. To address these problems and thus help physicians quantify blood vessels in pathology images, we propose Pathological Images-YOLO (PI-YOLO), an enhanced detection network based on YOLOv7. PI-YOLO incorporates the BiFormer attention mechanism, enhancing global feature extraction and accelerating processing for regions with subtle differences. Additionally, it introduces the CARAFE upsampling module, which optimizes feature utilization and information retention for small targets. Furthermore, the GSConv module improves the ELAN module, reducing model parameters and enhancing inference speed while preserving detection accuracy. Experimental results show that our proposed PI-YOLO network has higher detection accuracy compared to Faster-RCNN, SSD, RetinaNet, YOLOv5 network, and the latest YOLOv7 network, with a mAP value of 87.48%, which is 2.83% higher than the original model. We also validated the performance of this network on the ICPR 2012 mitotic dataset with an F1 value of 0.8678, outperforming other methods, demonstrating the advantages of our network in the task of target detection in complex pathology images.

Deep learning-assisted diagnosis of benign and malignant parotid gland tumors based on automatic segmentation of ultrasound images: a multicenter retrospective study.

Objectives: To construct deep learning-assisted diagnosis models based on automatic segmentation of ultrasound images to facilitate radiologists in differentiating benign and malignant parotid tumors. Methods: A total of 582 patients histopathologically diagnosed with PGTs were retrospectively recruited from 4 centers, and their data were collected for analysis. The radiomics features of six deep learning models (ResNet18, Inception_v3 etc) were analyzed based on the ultrasound images that were obtained under the best automatic segmentation model (Deeplabv3, UNet++, and UNet). The performance of three physicians was compared when the optimal model was used and not. The Net Reclassification Index (NRI) and Integrated Discrimination Improvement (IDI) were utilized to evaluate the clinical benefit of the optimal model. Results: The Deeplabv3 model performed optimally in terms of automatic segmentation. The ResNet18 deep learning model had the best prediction performance, with an area under the receiver-operating characteristic curve of 0.808 (0.694-0.923), 0.809 (0.712-0.906), and 0.812 (0.680-0.944) in the internal test set and external test sets 1 and 2, respectively. Meanwhile, the optimal model-assisted clinical and overall benefits were markedly enhanced for two out of three radiologists (in internal validation set, NRI: 0.259 and 0.213 [p = 0.002 and 0.017], IDI: 0.284 and 0.201 [p = 0.005 and 0.043], respectively; in external test set 1, NRI: 0.183 and 0.161 [p = 0.019 and 0.008], IDI: 0.205 and 0.184 [p = 0.031 and 0.045], respectively; in external test set 2, NRI: 0.297 and 0.297 [p = 0.038 and 0.047], IDI: 0.332 and 0.294 [p = 0.031 and 0.041], respectively). Conclusions: The deep learning model constructed for automatic segmentation of ultrasound images can improve the diagnostic performance of radiologists for PGTs.

A hybrid deep learning approach to solve optimal power flow problem in hybrid renewable energy systems.

The reliable operation of power systems while integrating renewable energy systems depends on Optimal Power Flow (OPF). Power systems meet the operational demands by efficiently managing the OPF. Identifying the optimal solution for the OPF problem is essential to ensure voltage stability, and minimize power loss and fuel cost when the power system is integrated with renewable energy resources. The traditional procedure to find the optimal solution utilizes nature-inspired metaheuristic optimization algorithms which exhibit performance drop in terms of high convergence rate and local optimal solution while handling uncertainties and nonlinearities in Hybrid Renewable Energy Systems (HRES). Thus, a novel hybrid model is presented in this research work using Deep Reinforcement Learning (DRL) with Quantum Inspired Genetic Algorithm (DRL-QIGA). The DRL in the proposed model effectively combines the proximal policy network to optimize power generation in real-time. The ability to learn and adapt to the changes in a real-time environment makes DRL to be suitable for the proposed model. Meanwhile, the QIGA enhances the global search process through the quantum computing principle, and this improves the exploitation and exploration features while searching for optimal solutions for the OPF problem. The proposed model experimental evaluation utilizes a modified IEEE 30-bus system to validate the performance. Comparative analysis demonstrates the proposed model's better performance in terms of reduced fuel cost of $620.45, minimized power loss of 1.85 MW, and voltage deviation of 0.065 compared with traditional optimization algorithms.

Brain-GCN-Net: Graph-Convolutional Neural Network for brain tumor identification.

BACKGROUND: The intersection of artificial intelligence and medical image analysis has ushered in a new era of innovation and changed the landscape of brain tumor detection and diagnosis. Correct detection and classification of brain tumors based on medical images is crucial for early diagnosis and effective treatment. Convolutional Neural Network (CNN) models are widely used for disease detection. However, they are sometimes unable to sufficiently recognize the complex features of medical images. METHODS: This paper proposes a fused Deep Learning (DL) model that combines Graph Neural Networks (GNN), which recognize relational dependencies of image regions, and CNN, which captures spatial features, is proposed to improve brain tumor detection. By integrating these two architectures, our model achieves a more comprehensive representation of brain tumor images and improves classification performance. The proposed model is evaluated on a public dataset of 10847 MRI images. The results show that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. RESULTS: The fused DL model achieves 93.68% accuracy in brain tumor classification. The results indicate that the proposed model outperforms the existing pre-trained models and traditional CNN architectures. CONCLUSION: The numerical results suggest that the model should be further investigated for potential use in clinical trials to improve clinical decision-making.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.

BACKGROUND: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome. RESULTS: We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017). CONCLUSIONS: We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.

Bilateral antegrade cerebral perfusion during hypothermic circulatory arrest before sternal reentry for aortic pseudoaneurysm repair.

Sternal reentry for repair of aortic pseudoaneurysms poses a unique technical challenge to prevent exsanguination. Initiation of peripheral cardiopulmonary bypass and deep hypothermic circulatory arrest prior to reentry are the cornerstones of a successful surgical approach. Adjunctive bilateral antegrade cerebral perfusion increases safe arrest time and reduces neurologic morbidity. Herein, we describe our safe reentry technique for aortic pseudoaneurysm repair in two patients.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area.

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Unified control of diverse actions in a wastewater treatment activated sludge system using reinforcement learning for multi-objective optimization.

The operation of modern wastewater treatment facilities is a balancing act in which a multitude of variables are controlled to achieve a wide range of objectives, many of which are conflicting. This is especially true within secondary activated sludge systems, where significant research and industry effort has been devoted to advance control optimization strategies, both domain-driven and data-driven. Among data-driven control strategies, reinforcement learning (RL) stands out for its ability to achieve better than human performance in complex environments. While RL has been applied to activated sludge process optimization in existing literature, these applications are typically limited in scope, and never for the control of more than three actions. Expanding the scope of RL control has the potential to increase the optimization potential while concurrently reducing the number of control systems that must be tuned and maintained by operations staff. This study examined several facets of the implementation of multi-action, multi-objective RL agents, namely how many actions a single agent could successfully control and what extent of environment data was necessary to train such agents. This study observed improved control optimization with increasing action scope, though control of waste activated sludge remains a challenge. Furthermore, agents were able to maintain a high level of performance under decreased observation scope, up to a point. When compared to baseline control of the Benchmark Simulation Model No. 1 (BSM1), an RL agent controlling seven individual actions improved the average BSM1 performance metric by 8.3 %, equivalent to an annual cost savings of $40,200 after accounting for the cost of additional sensors.

Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks.

The accurate prediction of chlorophyll-a (chl-a) concentration in coastal waters is essential to coastal economies and ecosystems as it serves as the key indicator of harmful algal blooms. Although powerful machine learning methods have made strides in forecasting chl-a concentrations, there remains a gap in effectively modeling the dynamic temporal patterns and dealing with data noise and unreliability. To wiggle out of quagmires, we introduce an innovative deep learning prediction model (termed ChloroFormer) by integrating Transformer networks with Fourier analysis within a decomposition architecture, utilizing coastal in-situ data from two distinct study areas. Our proposed model exhibits superior capabilities in capturing both short-term and middle-term dependency patterns in chl-a concentrations, surpassing the performance of six other deep learning models in multistep-ahead predictive accuracy. Particularly in scenarios involving extreme and frequent blooms, our proposed model shows exceptional predictive performance, especially in accurately forecasting peak chl-a concentrations. Further validation through Kolmogorov-Smirnov tests attests that our model not only replicates the actual dynamics of chl-a concentrations but also preserves the distribution of observation data, showcasing its robustness and reliability. The presented deep learning model addresses the critical need for accurate prediction on chl-a concentrations, facilitating the exploration of marine observations with complex dynamic temporal patterns, thereby supporting marine conservation and policy-making in coastal areas.

Deep brain stimulation targeted at lateral hypothalamus-medial forebrain bundle reverses depressive-like symptoms and related cognitive deficits in rat: Role of serotoninergic system.

The aim of the study is to understand the rationale behind the application of deep brain stimulation (DBS) in the treatment of depression. Male Wistar rats, rendered depressive with chronic unpredictable mild stress (CUMS) were implanted with electrode in the lateral hypothalamus-medial forebrain bundle (LH-MFB) and subjected to deep brain stimulation (DBS) for 4 h each day for 14 days. DBS rats, as well as controls, were screened for a range of parameters indicative of depressive state. Symptomatic features noticed in CUMS rats like the memory deficit, anhedonia, reduction in body weight and 5-hydroxytryptamine (5-HT) and 5-hydroxyindoleacetic acid (5-HIAA) levels in mPFC and elevated plasma corticosterone were reversed in rats subjected to DBS. DBS arrested CUMS induced degeneration of 5-HT cells in interfascicular region of dorsal raphe nucleus (DRif) and fibers in LH-MFB and induced dendritic proliferation in mPFC neurons. MFB is known to serve as a major conduit for the DRif-mPFC serotoninergic pathway. While the density of serotonin fibers in the LH-MFB circuit was reduced in CUMS, it was upregulated in DBS-treated rats. Furthermore, microinjection of 5-HT1A receptor antagonist, WAY100635 into mPFC countered the positive effects of DBS like the antidepressant and memory-enhancing action. In this background, we suggest that DBS at LH-MFB may exercise positive effect in depressive rats via upregulation of the serotoninergic system. While these data drawn from the experiments on rat provide meaningful clues, we suggest that further studies aimed at understanding the usefulness of DBS at LH-MFB in humans may be rewarding.

Infected subcutaneous hematoma in percutaneous deep venous arterialization with an off-the shelf device and venous arterialization simplified technique.

A 65-year-old man with no-option chronic limb-threatening ischemia underwent percutaneous deep venous arterialization (pDVA). An arteriovenous fistula (AVF) was created using a modified venous arterialization simplified technique. During the balloon dilation of the AVF site, the venous puncture site was accidentally also dilated, resulting in massive bleeding. The angiographic bleeding was controlled by stent graft deployment, and the final angiography revealed good DVA flow. Two weeks post-pDVA, the patient developed right shin pain. Suspecting a subcutaneous hematoma and infection, extensive debridement was performed. The patient's wounds completely healed approximately 7 months after the pDVA. Learning Objective: Modified venous arterialization simplified technique (m-VAST) is a feasible technique for percutaneous deep venous arterialization; however, it may lead to unexpected complications. When performing m-VAST, the possibility of puncture site complications should be carefully considered.