However, existing methods for mapping non-coding genomic regions across species are challenged by the multi-mapping, low precision, and low mapping rate issues.Elucidating functionally similar orthologous regulatory regions for human and model organism genomes is critical for exploiting model organism research and advancing our understanding of results from genome-wide association studies (GWAS). Sequence conservation is the We develop Adaptive liftOver (AdaLiftOver), a large-scale computational tool for identifying functionally similar orthologous non-coding regions across species. AdaLiftOver builds on the UCSC liftOver framework to extend the query regions and prioritizes the resulting candidate target regions based on the conservation of the epigenomic and the sequence grammar features. Evaluations of AdaLiftOver with multiple case studies, spanning both genomic intervals from epigenome datasets across a wide range of model organisms and GWAS SNPs, yield AdaLiftOver as a versatile method for deriving hard-to-obtain human epigenome datasets as well as reliably identifying orthologous loci for GWAS SNPs.https://github.com/keleslab/AdaLiftOver.The R package and the data for AdaLiftOver is available from  Genome-wide association studies (GWAS) have revealed many non-coding SNPs for complex human traits . Howeverde facto mapping strategy in cross-species studies. More recently, bnMapper  false positive and 205 (1.2%) false negative cases . Figure loration .Low mapping rates. Unlike the highly conserved orthologous promoters, Recent deep learning applications have yielded advanced investigations of the regulatory code of DNA sequences. Early applications of deep convolutional neural network models used only DNA sequences to predict protein binding, histone modification, and chromatin accessibility . Basset To address the limitations of strictly sequence-based mapping of functionally similar non-coding regions and leverage higher-order regulatory grammar embedded in DNA sequences, we developed Adaptive liftOver (AdaLiftOver). AdaLiftOver is built on the UCSC liftOver framework for identifying and prioritizing orthologous regions by leveraging functional epigenome information. It enables mapping genomic coordinates between any two species with chain files and at least one pair of matched epigenome datasets. AdaLiftOver takes as input query genomic regions, the UCSC chain file, and one or more matched epigenome datasets . We curaR implementation for AdaLiftOver is available at https://github.com/keleslab/AdaLiftOver.We applied AdaLiftOver to a variety of case studies including genomic intervals (peaks) from ATAC-seq and ChIP-seq experiments of seven different species including chicken, cow, horse, mouse, pig, rat, and zebrafish, and SNPs from GWAS datasets as queries. AdaLiftOver yields consistently superior performances than the competing methods for mapping of both the peaks across human and a variety of model organism genomes and the SNPs to the mouse genome. The Q   . We iden (cCREs)  and quan (cCREs) . Moreove (cCREs) . Similar (cCREs) .To tune the parameters of AdaLiftOver, we performed leave-one-out cross-validation (LOOCV) with the 67 matched ENCODE epigenome datasets. Specifically, for each fold of the cross-validation, we ensured that the datasets from the same tissue as the validation dataset were excluded from the training set . We applied AdaLiftOver with a grid of window sizes from 0 to 5 kb and in increments of 400 bp. After mapping mouse query regions, i.e. peaks, we labeled the candidate target regions in the human genome as positives if they overlapped with the corresponding human epigenome peaks and as negatives otherwise . FurtherTo further investigate the robustness of the default parameters of AdaLiftOver set by the LOOCV experiments with the ENCODE repertoire, we applied AdaLiftOver on 46 676 mouse pancreatic islet ATAC-seq peaks  with widTo provide support for the mapped regions in the case studies we presented, we asked whether they resided within genomic regions with relevant epigenomic/genic features in the mapped genome more than expected by chance. The null distributions for quantifying these enrichments were adjusted for background genomic factors such as chromosomes and the PhyloP scores of the mapped regions .We benchmarked AdaLiftOver against other orthologous mapping methods for a large collection of epigenome datasets from the ENCODE project. We utilized 55 human-mouse matched TF ChIP-seq peak sets  from eryMapping with AdaLiftOver in the above settings did not include any epigenome datasets from the tissues/cell types relevant to the query regions. Next, we asked whether including epigenomic feature from the relevant tissue/cell type impacted the performance. Specifically, we leveraged two pairs of matched ENCODE DNase-seq and ATAC-seq datasets from erythroid and lymphoblast cells . We obseNext, to go beyond human and mouse which are relatively closely related species, we mapped between human and six other species, namely, chicken , cow Ke, horse , pig Zh, rat Ri, and zebA key advantage of AdaLiftOver is its ability to score and prioritize the regions that can be mapped to the model organism genome. Application of AdaLiftOver relies on the availability of matching epigenomic data in addition to the chain files of the species. We assessed the influence of the number of matching epigenomic datasets used in the training of AdaLiftOver. Utilizing the zebrafish ATAC-seq brain dataset as the query, we randomly sampled 3–27 samples from the zebrafish dataset collection, excluding those from the same tissue as the query data, and trained AdaLiftOver with these increasing numbers of datasets. We found that AdaLiftOver consistently outperformed bnMapper and UCSC liftOver, even with only three matching epigenome datasets in training. Furthermore, the precision steadily improved as the number of epigenome datasets increased .Group I: query regions that map to target genome uniquely based on sequence conservation (e.g. with the UCSC liftOver).Group II: query regions that map to multiple regions in the target genome (multi-mapper).We further utilized human and zebrafish brain ATAC-seq peak sets to explore to what extent regulatory information can overturn sequence-based support in mapping of orthologous regions. We considered two group of query regions in mapping of human brain ATAC-seq peaks to zebrafish:Then, we investigated the AdaLiftOver mapping scores of these regions. For regions in Group II, we looked at the differences between the maximum and minimum mapping scores after filtering scores <0.4 for all the regions (not to overcrowd the figure). We considered three sets of GWAS SNPs to evaluate AdaLiftOver and the existing methods. The evaluations are largely based on evaluating whether the mapped regions were enriched in biologically relevant genomic regions  in the mapped genome.To evaluate the performances of AdaLiftOver and other methods for mapping GWAS SNPs to model organism mouse, we investigated the 1648 fine-mapped Schizophrenia (SCZ) GWAS SNPs prioritized by To further evaluate AdaLiftOver and other methods in the GWAS SNPs setting, we leveraged human fine-mapped GWAS data for four hematopoietic traits: mean corpuscular volume (MCV), mean platelet volume (MPV), monocyte count (Mono), and lymphocyte count (Lymph) . We mappFoxo3 gene but AdaLiftOver is able to rescue Fox3 with mapping of a BMD GWAS SNP. The SNP rs9486902 resides at the promoter region of human gene FOXO3 while it is located in a gap among human-mouse chain segments leading to a miss by UCSC liftOver. AdaLiftOver is able to identify a mouse orthologue at the promoter region of Foxo3 that has similar epigenomic features (FOXO3 and Foxo3 genes share common transcription factor binding site motifs that are relevant for BMD. Specifically, ARNT co-binds with Ahr which negatively influences osteoblast proliferation (MEN1, which influences bone metabolism (We next showcase how AdaLiftOver can be utilized to map UK Biobank SNPs  to mouseWAS SNPs . In ordeliftOver . Figure features  and sequfeatures . In partferation . FOXN3 itabolism . Nr2e3, tabolism , is relatabolism . Overalls pcHi-C  and its We developed AdaLiftOver to enable mapping and prioritizing of non-coding regions between human and model organism genomes. AdaLiftOver takes as input UCSC chain files and matched epigenome datasets of the species to map query regions. It goes beyond traditional sequence alignment of comparative genomics for lifting over between genomes and simultaneously incorporates comparative epigenomics and sequence grammar similarity. To the best of our knowledge, this is the first systematic benchmark study of different orthologous mapping methods with comprehensive real biological data applications. Compared to other methods, AdaLiftOver is more accurate and robust, and offers a computationally inexpensive way of generating hard-to-obtain functional genomic datasets in other genomes by incorporating epigenomic and sequence grammar features. We found that the majority of orthologues of GWAS SNPs tend to have an enriched but low overlapping percentage with related open chromatin regions in mouse. We expect this result to improve as the epigenomic features leveraged span more cell types and dynamic conditions. In particular, developmental and disease trajectories revealed by single cell ATAC-seq might provide more enrichment for orthologues of GWAS SNPs. With a more comprehensive epigenome space, AdaLiftOver can serve as a versatile approach for pinpointing potential GWAS orthologues in a model organism and can facilitate high-throughput perturbation experiments. Currently, AdaLiftOver is restricted to binary features due to the space requirements and time complexity. We expect a more computationally efficient implementation of AdaLiftOver to incorporate features at other scales.btad149_Supplementary_DataClick here for additional data file.


Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU’s potential as a fast  and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research. Photon-counting detectors (PCDs) offer the potential to improve performance of CT imaging at both clinical and preclinical levels and to extend the use of CT to novel applications . PCDs acHowever, PCCT dose constraints and photon binning result in high noise levels, particularly in high-energy bins, reducing image quality and the accuracy of material decomposition. Multi-energy CT can be denoised using various post-reconstruction algorithms such as non-local means (NLM) filtering ,5. AlterTo address this limitation, DL models can be used to approximate iterative reconstruction, providing robust denoising with reduced computation time. Commonly, DL methods use convolutional neural networks (CNNs) to learn the mapping between noisy and clean images directly from data, eliminating the need for explicit knowledge of the underlying statistical model. Previous studies have demonstrated that DL-based denoising can achieve comparable performance to iterative reconstruction, with significantly reduced computational time . VariousIn this study, we introduce a supervised learning approach to develop a denoising CNN that adapts to various PCD threshold settings. Our goal is to perform fast and reliable denoising of weighted filtered backprojection (wFBP) reconstructions at the level of iterative reconstruction in scans acquired on our in-house developed preclinical PCCT imaging system  with fouFor this study, we acquired all PCD image data using a dual-source micro-CT imaging system consisting of an energy-integrating detector (EID) and a PCD  . We usedThe PCCT scans used in this work are summarized in All PCD micro-CT scan data underwent reconstruction using the weighted filtered backprojection (wFBP) algorithm  with an X) at each energy (e) minimizes the reprojection error (R representing the system projection matrix) relative to log-transformed projection data (Y). To reduce noise in the reconstructed results, this data fidelity term is minimized subject to the bilateral total variation (BTV). BTV measured within and between energies is reduced via RSKR.The reconstructed data , and N K-edge materials as basis functions, The method of material decomposition was applied to the wFBP and iterative RSKR reconstructions, as well as ME NLM, UnetwFBP, and UnetU denoised reconstructions. We performed image-based material decomposition using the method of Alvarez and Macovski  extendedM is a matrix of material sensitivities at each energy. We computed these values by fitting the slope of attenuation measurements taken in vials of known material concentrations. C represents density in g/mL for water and concentration in mg/mL for all other materials. Finally, X is the attenuation coefficient of the voxel under consideration at energy e. Material decomposition was performed by matrix inversion, solving the following linear system at each voxel:In this formulation, Orthogonal subspace projection was used to prevent negative concentrations . Post deIn Our first U-net, named UnetwFBP, represents a conventional U-net denoising approach that serves as a baseline for comparison. UnetwFBP uses wFBP reconstructions as input and iterative RSKR reconstructions as its training labels. Given the non-zero-centered nature of the input data, we included a residual connection between the input and output in UnetwFBP to address intensity bias. We trained UnetwFBP using a mean squared error (MSE) loss function.N is the number of voxels, X and labelX are the prediction and label in the reconstruction domain, U and labelU are the prediction and label in the normalized, high-pass-filtered left singular vector domain of the wFBP, M is the sensitivity matrix used for material decomposition, and blurX and blur,labelX are the prediction and label in the reconstruction domain after blurring axial slices with a 2D Gaussian filter of kernel size 19 × 19 and FWHM 5. Although the first term in the loss function already penalizes error in the reconstruction domain, the fourth term further increases penalization of bias in attenuation values. To ensure that the material decomposition loss term penalizes each material map at a similar intensity scale, the sensitivity matrices (M) used in the loss function were rescaled via multiplication of each row by the corresponding calibration vial concentration. To avoid problems such as excessively long training time per epoch, discrepancies between the magnitudes of cost function terms, and vanishing gradients associated with thresholding, the decompositions in the loss function did not incorporate a non-negativity constraint. We used The second U-net, named UnetU, is a novel approach that improves the ability of a standard U-net CNN to perform generalizable PCCT denoising through our unique preprocessing of the wFBP input. First, we divide each energy channel of the wFBP image by its noise variance measured as the Median Absolute Deviation (MAD) of image intensity gradients  in the c−4, a learning rate scheduler, and early stopping. The learning rate scheduler cuts the learning rate in half every time the validation loss fails to decrease for 10 consecutive epochs, and early stopping terminates training when the validation loss fails to decrease for 20 consecutive epochs. UnetwFBP, UnetU with MSE loss, and UnetU with custom loss were trained on a computer running Linux with four NVIDIA RTX 6000 Ada GPUs. After training was completed, all three models were used to predict estimates of iterative RSKR reconstruction for the test sets described in The UnetwFBP and UnetU models both take 2D axial image slices of size 400 × 400 voxels with 4 channels as the input. Both networks have 4 input channels and 4 output channels to match the number of PCD energy thresholds. We held out one set of matching wFBP and iterative reconstruction volumes as a validation set, with the remaining nine sets used for training. Then, we randomly shuffled the order of the slices in both the training and validation sets. Data augmentation for both UnetwFBP and UnetU consisted of randomly flipping input image slices horizontally and vertically with 50% probability for each flip. For UnetU, we randomly multiplied both the left and right singular vectors by –1 with 50% probability as an additional form of data augmentation. All U-nets were trained to run for a maximum of 2000 epochs with a batch size of 8, Adam optimizer , an initΩ = 113 voxels, patch size Wp = 33 voxels, filtering strength h = 1.2, and noise standard deviation values at each energy derived from homogeneous region of interest (ROI) measurements . Each time we call the ME NLM function, we pass in 31 adjacent axial slices of the wFBP image and take the filtered version of the 16th axial slice as the output.We conducted a thorough evaluation of the performance of the UnetwFBP and UnetU approaches, considering computation time, quantitative metrics, qualitative results, and performance at varying radiation doses. For all analyses, UnetwFBP and UnetU were compared both to each other and to the ME NLM algorithm. As suggested in the original paper , our ME To assess computational burden, we measured the time required for iterative RSKR reconstruction, ME NLM denoising, UnetwFBP prediction, and UnetU prediction on one of the test sets.Our quantitative metrics included average bias in Hounsfield Units (HU) relative to wFBP for each denoising method  and average noise level in HU for wFBP and each denoising method. For each of the 5 test sets, we chose an axial slice with a water vial, defined an ROI within the vial, and measured the mean and standard deviation in this ROI for wFBP and each denoising method at all four energy thresholds. To determine average bias for each denoising method at each energy threshold, we computed the absolute difference between mean value in the water vial ROI from denoised reconstruction and from wFBP in each test set and took the mean of the 5 absolute difference values. The average noise level for wFBP and for each denoising method at each energy threshold was calculated by taking the mean of the 5 standard deviation values from water vial ROIs in the test sets. In addition to computing average bias and noise metrics in the reconstructions, we compared the average root mean square error (RMSE) in HU relative to iterative RSKR reconstruction across the 5 test sets for wFBP reconstruction, ME NLM, UnetwFBP, UnetU with MSE loss and UnetU with custom loss.For each test set, we used the sensitivity matrix derived from calibration vial measurements in the iterative RSKR reconstruction to convert reconstructions from wFBP and each denoising method into material decompositions via sensitivity matrix inversion with the approach described in For qualitative comparison, we show reconstructions and corresponding matrix inversion material decompositions from iterative RSKR, wFBP, ME NLM, UnetwFBP, and UnetU on a test set scan of a phantom containing vials of water and known concentrations of the K-edge contrast materials Gd, Ta, and Bi. This set was chosen because of the very low photon counts and resulting high noise level associated with the high energy thresholds corresponding to the K-edges of its contrast agents . In addition, we show reconstructions and the corresponding matrix inversion decompositions on a test set in vivo scan of a mouse with tumors that was injected both with I and Ba-based contrast agents to demonstrate how our U-net denoising generalizes to threshold settings not included in training. Like our RMSE analysis, these qualitative comparisons treat iterative RSKR reconstruction and its matrix inversion decomposition as the ground truth. Therefore, we also display absolute subtraction maps from the iterative reconstruction for ME NLM, UnetwFBP, and UnetU in order to make their artifacts more visible. The conclusions we draw from these qualitative analyses are based on the assessment of small animal imagers on our team  who have more than a decade of experience.To evaluate each denoising method at different dose levels, we acquired a helical scan with I and Gd K-edge threshold settings of an ex vivo mouse with 2700 projections, which is about 3 times the dose level of a typical preclinical in vivo helical scan (using 900 projections with dose 36 mGy). We performed wFBP reconstruction of the full set of 2700 projections and projection data generated by undersampling factors of 2, 3, 4, 5, and 6 to simulate a wide range of radiation dose levels. For each of the 6 wFBP reconstructions, we used iterative RSKR reconstruction, ME NLM, UnetwFBP, and UnetU to denoise the images. We show wFBP and denoised images from the highest energy threshold (60 keV) for the fully sampled, 3x undersampled, and 6x undersampled cases in an ROI from a coronal slice. We also plot structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) values in this slice for each denoising method at each undersampling factor relative to the iterative RSKR reconstruction of the complete set of 2700 projections.On the same computer used to train the U-nets, iterative RSKR reconstruction took 915 s on a test set with dimensions of 400 × 400 × 400 voxels and 4 energy thresholds, while ME NLM took 854 s, UnetwFBP took 36 s, and UnetU took 61 s to denoise the same test set. Please note that our ME NLM implementation is not optimized for speed, and it is likely that greater reduction in computation time can be achieved with ME NLM.This work presents UnetU as a fast and adaptable denoising method for PCCT. Remarkably, UnetU can denoise a standard four-energy PCCT reconstruction 15 times faster than iterative reconstruction with RSKR. Results from the test set showed that UnetU with custom loss function outputs denoised images with higher spatial resolution than ME NLM and lower bias than UnetwFBP. In addition, UnetU with custom loss consistently has greater accuracy in the material decomposition than both ME NLM and UnetwFBP. Assessment of denoising performance at different dose levels see  showed tThere are a few limitations that merit further discussion. Like all PCDs, the detector used to acquire our training and testing sets is affected by spectral distortions such as charge sharing and pulse pileup . The iteIn addition to these specific limitations, there are more general challenges associated with a DL approach that should be considered. The availability of training data poses a significant challenge, as DL-based denoising methods typically require high-quality data, which in CT often necessitates higher-dose acquisitions. However, our approach leverages iterative RSKR reconstructions as training labels, eliminating the need for additional high-dose images and simplifying the data acquisition process. DL models also demand substantial computational resources for training and inference, which can preclude their use in resource-limited settings. In our study, we were fortunate to have access to high-performance, multi-GPU computers to facilitate our DL training and evaluation. DL models’ inherent complexity can impede interpretability, making it challenging to understand the decision-making process. However, the use of a U-net CNN architecture in our approach mitigates this challenge to some extent, since it has far fewer weights than other more complicated networks. This allows insights into the denoising process and contributes to transparency. Finally, the robustness and adaptability of DL models to different data acquisition protocols needs careful investigation. By training and testing our U-net models on diverse datasets encompassing several PCCT energy threshold settings, we aimed to assess their ability to handle variations encountered in practical scenarios. However, a large test set with systematic variation in multiple acquisition parameters would allow a more complete understanding of the extent to which DL approaches such as UnetU are generalizable.Nevertheless, our work establishes an effective supervised DL approach that enables fast approximation of iterative RSKR reconstruction and accurate material decomposition for a wide variety of PCD scan protocols. A generalizable denoising approach such as UnetU assists in imaging studies involving novel contrast agents, opening new avenues for research in spectral CT.In this work, we presented UnetU, a deep learning approach for fast PCCT denoising that combines a standard U-net CNN architecture with novel input preprocessing and a complex loss function to provide accurate denoising and material decomposition at a variety of PCD energy threshold settings. We determined that UnetU denoises a typical wFBP reconstruction 15 times faster than iterative RSKR reconstruction. UnetU gave lower average test set RMSE (relative to iterative RSKR reconstruction) than UnetwFBP and ME NLM in reconstructions and material maps. This quantitative result was supported by qualitative results, indicating that UnetU provides the best approximation of iterative RSKR reconstruction in both the reconstruction and material decomposition domains.


This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS. However, conventional mechanical wireless vibration sensors (WVSs) are unsuitable for specific environments, and the limitations in power supply have restricted the advancement of wireless sensor networks (WSNs). In the face of energy-intensive big data environments, exploring energy consumption models (ECMs) for relational database workloads, referred to as the ECM, has emerged as a pivotal pursuit for crafting energy-efficient DBMSs2. This research direction encounters multiple hurdles and challenges. Firstly, traditional WVSs fall short in meeting the demands of specialized environments and fail to capture real-time and precise vibration energy data. Secondly, the constrained power supply remains a hindrance for WSNs, limiting their application scope and performance. Furthermore, due to the intricacy and high energy consumption traits of big data environments, the precise analysis and modeling of energy consumption within relational database workloads, along with the ability to make predictions and optimizations, present formidable challenges. In response to the aforementioned issues, this paper seeks to offer a solution by employing the least square support vector machine (LSSVM) algorithm and self-powered WVSs to construct a model for energy consumption in relational database workloads. The introduction of self-powered WVSs tackles traditional sensors’ limitations in specific environments, while the LSSVM algorithm equips the model with effective energy consumption prediction capabilities. This model can achieve a more precise analysis and assessment of energy consumption within relational database workloads, thereby providing a theoretical foundation and practical guidance for creating energy-efficient DBMS. Hence, the significance of this paper lies in its ability to overcome the limitations of applying WVSs in specialized settings and combining the LSSVM algorithm to establish a model for energy consumption in relational database workloads tailored to big data environments, thereby offering a novel solution for constructing energy-efficient DBMS.Data volume and generation speed have surged with the rapid growth and widespread adoption of mobile internet, the Internet of Things, and cloud computing technologies. Analyzing and modeling energy consumption under varying workloads is essential for creating energy-efficient and eco-friendly database management systems (DBMSs)3 introduced an innovative hybrid optimization algorithm to forecast monthly precipitation and empirically verified its effectiveness and superiority. This hybrid model leverages the strengths of swarm intelligence optimization algorithms and ensemble learning algorithms, improving the identification of features and model parameters for enhanced prediction accuracy and reliability3. In recent studies, Hu et al.4 presented an energy-saving approach that combined hardware and software to create and validate the ECM for each component of a system, with the relational database’s energy consumption as the key evaluation metric. Dembele et al.5 estimated energy consumption through Greenplum database queries, investigated strategies to enhance connection energy efficiency and evaluated the variance in connection calculation time under different scales and connection modes. Zhou et al.6 focused on the calculation and energy-efficient collaborative scheduling for Green DBMS centers to optimize their overall energy efficiency. In a study by Asha and Santhosh7, an energy-balanced routing protocol tailored for WSNs was developed to mitigate monitoring blind spots arising from uneven energy consumption distribution among nodes. Existing research predominantly revolves around predicting database energy consumption, with less emphasis on gathering sensor energy data and predicting load energy consumption within relational databases8. The WVS network comprises various nodes, often located in hard-to-reach positions and immobile. Power sources limit the expansion of the WVS network, mandating nodes to harvest energy from their surroundings, achieve self-sufficiency, and manage equipment status monitoring9. Self-powered electrical WVSs harness mechanical vibration signals and energy attributes to monitor mechanical equipment operations10. These self-powered WVS nodes capture energy generated during mechanical equipment operation for subsequent utilization11. On a different note, the LSSVM is an effective tool for data analysis. It shares the attributes of support vector machine (SVM), which include suitability for small sample sizes, handling nonlinearity, high dimensionality, and superior prediction accuracy. SVM is widely adopted for tasks like power load forecasting and constructing ECMs12. In summary, current research on analyzing and modeling energy consumption in relational databases has some shortcomings and deficiencies. Firstly, traditional mechanical WVSs are unsuitable for specialized environments, preventing the collection of real-time and accurate vibration energy data. Secondly, the limitation of convenient power supply hinders the progress of WSNs, restricting their scope and performance. Moreover, in the complex and high-energy big data environments, accurately analyzing, modeling, predicting, and optimizing the energy consumption of relational database workloads poses a challenging problem. The solution proposed in this paper addresses these limitations by introducing self-powered WVSs and utilizing the LSSVM algorithm, providing effective energy consumption prediction capabilities. Furthermore, the model presented in this paper offers significant advantages in analyzing and modeling energy consumption in relational database workloads. In comparison to models such as a LSSVM with Improved Multi-Verse Optimization, Long Short-Term Memory with Information Gain, Relevance Vector Machine with Improved Multiple Regression Fitness Optimization, Extreme Learning Machine with Joint Feature Optimization, and Support Vector Machine with Fast and Flexible Adaptive Particle Swarm Optimization, the model proposed here overcomes the limitations of traditional sensors and provides more accurate energy consumption prediction capabilities. Furthermore, this model specifically focuses on energy consumption in relational database workloads, offering a crucial theoretical foundation and practical guidance for the development of energy-efficient green DBMSs. In conclusion, the model presented here demonstrates outstanding advantages in energy analysis and modeling.Priestly et al.Building upon the theoretical foundations discussed earlier, this study applies the principles of energy harvesting from self-powered WVSs and the ECM based on an LSSVM to predict the energy consumption of relational database workloads. The accuracy of the proposed ECM is validated through comparisons with other methods. The most innovative aspect is the integration of the energy harvesting principles of self-powered WVSs with LSSVM, effectively managing the energy consumption of relational databases. This research strongly emphasizes energy consumption issues in the DBMS design process, aiming to explore sustainable, low-energy, green DBMSs. This study is expected to provide valuable theoretical references to enhance the stability and accuracy of dynamic ECM in various system environments. This integrated approach and research direction bring significant advantages and innovation to the field of database energy consumption, offering fresh insights for the development of energy-efficient and optimized DBMSs.13. They operate autonomously, and the comprehensive network architecture is depicted in Fig. The self-powered WVS network comprises numerous nodes, typically positioned in concealed locations. These self-powered WVS nodes harness mechanical vibration signals and energy properties to oversee the status of mechanical equipment14. The PET operates based on the piezoelectric effect, generating alternating energy. Subsequently, the vibration energy collection and management system efficiently gathers and handles the PET’s low-power energy. This system transforms the weak energy into low-power energy for collecting high-power energy for supply and manages energy distribution through a power filter. This collected energy is then utilized to power the self-powered WVS network nodes15.Figure 16. Figure The vibration energy collection and management system serves as a vital energy source for self-powered WVS network nodes. It harnesses low-power energy generated by PET and transforms it into an abundant energy supply to support the high-power operations of these nodes17.The vibration energy collection and management system comprises several key elements: a voltage energy converter, full bridge rectifier, filter protection equipment, energy storage capacitor, step-down and voltage stabilization equipment, low-power energy collection equipment, high-power energy supply equipment, supercapacitor, and energy status monitoring. This system is responsible for converting the mechanical vibration-induced low-power energy into high-power energy18.In contrast to neural networks, SVMs are well-suited for handling small datasets while also accommodating nonlinearity, high-dimensionality, and ensuring accurate predictions, particularly when dealing with limited training samples19. The algorithm’s framework is visually outlined in Fig. The LSSVM, a variant of the least squares regression model, proves highly effective for data analysis20.Figure m dimensions, n denotes the number of training samples. The transformation takes the original sample space of the prediction data from a nonlinear estimation problem to a linear function estimation problem in a high-dimensional space. This transformation can be mathematically expressed as Eq. ..\documenHere, The estimation of LSSVM regression can be achieved by solving the linear equations, as depicted in Eq. .10\docum22. The RBF can be computed using Eq. , a common choice in machine learning. The RBF maps the input sample points into a new eigenvector and calculates the point multiplication. Essentially, it transforms each sample point into an infinite-dimensional feature spacesing Eq. .11\documIn Eq. , \docume23.A relational database is designed to work with relational models and stands as one of the most vital and prevalent DBMSs in use today. The market is dominated by relational databases, including well-known names like Sybase, Oracle, Informix, and Structured Query Language (SQL)Figure 24. However, one drawback is their limited capacity to handle high levels of concurrent read and write operations. When faced with tens of thousands of concurrent requests or hundreds of millions of SQL queries, relational databases can become inefficient. Furthermore, these databases face limitations in expanding performance and load capacity through hardware and service node modifications25.As depicted in Fig. 26. The execution of SQL statements involves the consumption of the system’s Central Processing Unit (CPU), Random Access Memory (RAM), and hard disk resources. The cumulative energy consumption of these components constitutes the overall energy expenditure associated with SQL statements27.Within a relational database, users interact with the database using SQL statements. The actions performed by upper-level application programs on the database are translated into these SQL statements, and their execution significantly impacts the energy consumption of the database. The query optimizer generates a series of query plans to execute a submitted SQL statement. Consequently, an ECM is established for each SQL statement to ensure efficient performance while conserving energy28. In the initial stage, the energy consumption of the CPU is determined. The baseline power consumption of the database is established by configuring the power consumption for every 10,000 CPU instructions as The energy collection principle of self-powered WVSs and the LSSVM energy prediction algorithm are employed to address the ECM of the relational databasementclasspt{minimaIn Eq. , \documeNext, the energy consumption of the hard disk is evaluated. Assuming the power consumed by a single block read operation is d by Eq.  when theIn Eq. , \documeAssuming the power consumed by a multi-block read operation is sing Eq. .14\documIn Eq. , \documeNow, when the SQL statement’s IO type is single block read, the system’s dynamic power during the execution of the SQL statement can be calculated as Eq. .15\documWhen the SQL statement’s IO type is multi-block read, the dynamic power consumed during the execution of the SQL statement is given by:Furthermore, the dynamic energy consumption incurred during the execution of SQL statements in a mixed IO type environment can be determined by Eq. .17\documIn Eq. , \documeThis study introduces a model for predicting the energy consumption of SQL statements in relational databases. It combines self-powered WVSs with an LSSVM energy forecasting algorithm. Compared to other models, this approach offers several integrated advantages. Firstly, it considers critical components during the execution of SQL statements, such as CPU, memory, and hard drive usage. This comprehensive assessment helps researchers and practitioners better understand the impact of SQL statements on database energy consumption, providing more precise guidance for optimizing database performance and energy management. Secondly, by incorporating self-powered WVSs and the LSSVM algorithm, this model can collect and analyze real-time energy consumption data from the database, enabling accurate predictions of future energy consumption. This combination leverages the sensors’ self-powered capability and the LSSVM algorithm’s modeling ability, providing a reliable foundation for energy consumption prediction. Additionally, the model accounts for the impact of different types of I/O operations on energy consumption. It distinguishes between single block reads and multiple block reads, improving the accuracy of energy consumption prediction. Furthermore, the model also includes CPU instruction counts and execution time overhead in energy consumption prediction, providing a more realistic reflection of the impact of SQL statements on CPU energy consumption. By considering these factors comprehensively, this model can accurately predict and assess the energy consumption of SQL statements in relational databases. It offers tailored energy management strategies for database administrators, facilitating performance optimization and energy conservation. However, it is important to note that this model requires further validation and optimization in practical applications. Depending on different database environments and workloads, adjustments to model parameters and algorithms may be necessary to achieve the best energy consumption prediction results. Additionally, addressing energy consumption analysis in large-scale and complex database systems involving more factors remains a challenge that requires further research and improvement. Details of the model’s parameter design are illustrated in Table This study develops a predictive model for estimating the energy consumption of relational database workloads. Optimization steps are incorporated to enhance the model’s performance and address data noise issues during its application. Figure As illustrated in Fig. 29.The experimental environment for the model is established based on pertinent literature and research findings, with the relevant parameters outlined in Table 230. The architecture of the energy consumption prediction platform is depicted in Fig. SQL statements serve as the standard interface for interfacing with and operating existing relational databases. Notably, a substantial portion of database resources, ranging from 70 to 90%, is dedicated to executing SQL statements. The execution time of SQL statements and their resource utilization significantly influence both the performance and energy consumption of the database load. Dual-computer communication is employed to ensure precise data collection of energy consumption. The Transaction Processing Performance Council-H (TPC-H) standard and a set of 22 complex SQL statements are adopted for experimental testing. The training set is based on a 10 GB database, while the test set employs a 1 GB databaseAs illustrated in Fig. This study focuses on the evaluation of system performance under varying conditions, using energy consumption as a crucial metric. Energy consumption is a practical and relevant measure, directly impacting energy management and optimizing database systems. The model’s reliability and real-world applicability are assessed by examining energy consumption patterns and prediction accuracy.Additionally, the research verified the self-sustaining capabilities of self-powered WVSs. Through experiments measuring voltage fluctuations during energy storage and consumption and analyzing energy variations under different operating modes, the study demonstrated that WVS nodes can efficiently accumulate energy and perform real-time tasks like network connectivity, command processing, and data transmission. This underscores the suitability of WVS nodes for applications in mechanical vibration testing, meeting their energy requirements. Furthermore, the study employs the tailored ECM designed for relational databases to predict and analyze the energy consumption associated with SQL statements. Through rigorous statistical analysis and training data, the ECM accurately predicts the energy consumption of SQL statements, a critical contribution to managing and optimizing energy in database queries. This achievement provides a foundational framework for the development of energy-efficient DBMSs. Lastly, the study delves into the examination of prediction errors under diverse environmental and resource conditions. The results underscore that in both isolated and competitive settings, the ECM exhibits lower prediction errors when compared to alternative models. Furthermore, the accuracy of ECM predictions varies under distinct memory resource conditions, highlighting the influence of environmental and resource factors on model performance. This recognition emphasizes the need for specific research and optimization targeting these unique conditions. In summary, this study conducts a comprehensive assessment of the model, utilizing multiple metrics to analyze the results and understand the contributing factors. These findings offer practical insights into energy management and database system optimization, providing guidance and a rationale for further research and refinements.Tests are conducted to examine the voltage fluctuations during the energy storage and energy consumption processes to validate the self-sustaining capabilities of self-powered WVSs. During the experiment, the speed regulator is adjusted to control the vibration test-bed’s speed, and the PET is fine-tuned to achieve a resonant state, resulting in a significant energy output. Figure In Fig. This section aims to validate the accuracy of the proposed relational database-oriented ECM. The ECM undergoes training using the exclusive DBMS resources as provided by the training set. Subsequently, relevant statistical data are gathered to employ the proposed ECM to predict the energy consumption of 22 SQL statements. The specific results are presented in Fig. As depicted in Fig. Eight of the 22 SQL statements used in the experiment are selected based on significant experimental results. These SQL statements are executed concurrently, involving both single-block and multi-block read operations. The analysis of operation proportions and model prediction error rates is presented in Fig. As shown in Fig. This section provides further verification of the relationship between multi-block and single-block read operations, and their respective influence on the accuracy of the proposed ECM is provided. The experimental results are presented in Fig. As illustrated in Fig. The error rate of the proposed ECM is analyzed under exclusive and competitive environments. An exclusive environment refers to a system where only the DBMS runs without other concurrent execution programs. In contrast, a competitive environment involves other programs in the system competing with the DBMS for resources. The proposed relational database-oriented ECM is compared with other models. It is important to note that these other models do not incorporate SVMs and are typical methods used for predicting database query energy consumption. The results for prediction error rates are presented in Fig. According to Fig. This section analyzes the error rates in exclusive and competitive environments by comparing the proposed ECM with other models under random reading operations. The results are presented in Fig. As depicted in Fig. In this section, various memory sizes  are configured to scan and sort four different data tables. The model’s prediction errors regarding energy consumption under different memory resources are computed. The specific results are presented in Fig. As depicted in Fig. In the following section, an analysis is conducted to compare the predicted energy consumption provided by the proposed relational database-oriented ECM against the actual energy consumption for ten distinct SQL query statements. The database size is set to 1 GB, and the results are presented in Fig. Evidently, the predicted energy consumption for query operations, as generated by the proposed relational database-oriented ECM, closely aligns with the actual values for various SQL statements, showcasing an error rate ranging from 1 to 4%. Except for specific SQL statements, the overall predicted values consistently fall below the actual values. This observation underscores the high level of accuracy exhibited by the proposed ECM in forecasting the load and energy consumption associated with relational databases.This study empirically confirms the self-sustaining capabilities of self-powered WVS. The experiments clearly show that WVS nodes efficiently accumulated energy and executed real-time tasks like network connectivity, command processing, and data transmission. Even under conditions with minimal energy storage, a single cycle of energy accumulation can sufficiently power multiple active nodes, meeting the energy demands of mechanical vibration testing applications. As a result, WVS nodes are well-suited to fulfill the energy requirements of such testing applications.Furthermore, the study introduces an ECM specifically designed for relational databases, enabling the prediction of energy consumption related to SQL statements. The experimental findings support the model’s practical utility by demonstrating its ability to reasonably predict energy consumption. For most SQL statements, prediction errors remain below 10%, with an average error rate of less than 6%. This model serves as a valuable tool and point of reference for the development of energy-efficient and eco-friendly DBMSs. In addition, the study assesses the model’s prediction accuracy under varying read operation scenarios. The results emphasize that the model excels in predicting single-block read operations but experienced a reduction in accuracy when dealing with a large volume of multi-block reads. This observation highlights the importance of considering different read operation scenarios during the design and optimization of the ECM. Furthermore, the study examines the model’s accuracy under different environmental conditions and memory resource availability. The experimental results reveal that when the DBMS monopolized system resources exclusively, the ECM exhibited lower prediction error rates. In competitive settings, the ECM still maintained relatively lower error rates. Moreover, ample memory resources positively impact ECM’s prediction accuracy and stability, particularly in read operations.This study offers valuable guidance and insights for achieving energy conservation and environmentally friendly database management. While the model performs well, there is room for further optimization, particularly in scenarios involving a high number of multi-block reads, resource competition, and limited memory. Future research endeavors can concentrate on enhancing the model’s algorithms and techniques to improve its accuracy and stability, broadening its utility to a wider range of scenarios and applications.Self-powered WVS nodes exhibit impressive energy accumulation capabilities. Within a mere 10 min, they can elevate stored energy from 0 to 3 V. In scenarios with lightweight energy storage, a single energy storage cycle proves sufficient to meet the energy requirements of multiple activations of self-powered nodes. Each energy storage cycle empowers nodes to sustain network connectivity and command transmission for up to 5 min. These findings underscore the ability of self-powered WVS network nodes to swiftly gather energy and perform real-time tasks, meeting strict time-related demands.The energy consumption prediction model demonstrates effectiveness in forecasting workload energy consumption, maintaining prediction errors largely below 10%. The average error rate for energy consumption remains under 6%, signifying a low level of inaccuracies.Prediction error rates within the model decrease proportionally with the increase in the ratio of multi-block reads to single-block reads. Accuracy witnesses a decline as the proportion of multi-block reads expands. When specifically considering single-block read operations, the model exhibits significantly higher accuracy compared to predictions for multi-block read operations.In the context of sequential and random read modes, a comparison between prediction error rates in scenarios where the DBMS exclusively monopolizes resources and in competitive environments demonstrated relatively low prediction error rates for SQL statements in DBMS-exclusive conditions. Moreover, the prediction error rate for sequential read mode is even lower, signifying higher accuracy relative to other models.As the data table size for SQL statement operations increases, the model’s prediction errors correspondingly escalate. This variation highlights a clear association between model inaccuracies and the volume of data involved in SQL statement operations.A comprehensive analysis of the energy consumption predictions in comparison to actual values for ten distinct SQL query statements underscores the design model’s remarkable accuracy in forecasting the energy consumption of relational database workloads.The study examines the architecture of self-powered WVS network nodes and the structure of vibration energy harvesting and management systems, which are critical components of energy-efficient and green database systems. Given the growing concerns regarding energy consumption and environmental impacts stemming from big data, researching the energy usage of relational database workloads assumes a pivotal role in advancing the development of energy-efficient green database systems. Drawing from the foundational principles of LSSVM, this study extends its scope to construct an energy consumption prediction model tailored for relational database workloads using LSSVM modeling techniques. The study establishes an energy consumption prediction platform and conducts experiments to evaluate the capabilities of self-powered WVSs. Moreover, the study assesses the model’s performance in predicting energy consumption related to SQL statements within relational databases. The study also examines the accuracy of the ECM under various conditions, including different numbers of read operations, environmental settings, and varying memory resource constraints. Finally, the study scrutinizes the model’s predictions related to ten distinct SQL query statements. The results are as follows:Nonetheless, it’s essential to acknowledge the limitations of this study. Firstly, the experimental setup did not completely replicate real-world situations, potentially influencing the reliability of our findings. Secondly, the sample size was relatively modest, which could affect the generalizability of the results. In future research, a more extensive investigation could encompass diverse types of databases and database workloads in various business contexts, thereby fine-tuning the model for improved prediction accuracy and reliability. Moreover, there is room for exploration into more advanced machine learning algorithms or deep learning frameworks, which could provide enhanced capabilities for analyzing and predicting database workload energy consumption.Supplementary Information.


Inducing recovery in myocardial ischemia is limited to a timely reopening of infarct vessels and clearing the cardiac microcirculation, but additional molecular factors may impact recovery.In this scoping review, we identify the paradigm shifts decoding the branching points of experimental and clinical evidence of pressure-controlled intermittent coronary sinus occlusion (PICSO), focusing on myocardial salvage and molecular implications on infarct healing and repair.The reporting of evidence was structured chronologically, describing the evolution of the concept from mainstream research to core findings dictating a paradigm change. All data reported in this scoping review are based on published data, but new evaluations are also included.Previous findings relate hemodynamic PICSO effects clearing reperfused microcirculation to myocardial salvage. The activation of venous endothelium opened a new avenue for understanding PICSO. A flow-sensitive signaling molecule, miR-145-5p, showed a five-fold increase in porcine myocardium subjected to PICSO.r2 = 0.90, p < 0.05; r2 = 0.98, p < 0.03), suggesting a flow- and pressure-dependent secretion of signaling molecules into the coronary circulation. Furthermore, cardiomyocyte proliferation by miR-19b and the protective role of miR-101 against remodeling show another potential interaction of PICSO in myocardial healing.Verifying our theory of “embryonic recall,” an upregulation of miR-19b and miR-101 significantly correlates to the time of pressure increase in cardiac veins during PICSO (Molecular signaling during PICSO may contribute to retroperfusion toward deprived myocardium and clearing the reperfused cardiac microcirculation. A burst of specific miRNA reiterating embryonic molecular pathways may play a role in targeting myocardial jeopardy and will be an essential therapeutic contribution in limiting infarcts in recovering patients.  Nothing conserves ischemic myocardium better than early reperfusion. However, an uncontrolled inflow of oxygenated blood into the damaged microcirculation has many side effects, a phenomenon known to cardiac surgeons since the pre-cardioplegia era in open-heart surgery –3.One option for myocardial protection evolved from an old idea when Pratt used cardiac veins to access the myocardium in isolated feline hearts . This co”  . After a” .clinicaltrials.gov; five are completed, one is terminated, one is not yet recruiting, and recently two, among one the randomized trial, are recruiting. The objective of the randomized trials is “to assess the efficacy and safety of Pressure-controlled intermittent Coronary Sinus Occlusion (PICSO) therapy started post flow restoration but before stenting during the percutaneous coronary intervention (PCI) compared to standard PCI in the setting of acute ST-segment elevation anterior myocardial infarction (STEMI)” .It took exactly 30 years after this publication until another randomized trial started, recruiting patients with acute coronary syndromes investigating PICSO in a prospective, multicenter, randomized, controlled study during reperfusion aiming to reduce infarct size. Currently, 10 PICSO trials are listed on Current clinical trials of PICSO in early reperfusion mimic the study objective of our first trial in open-heart surgery and represent the mainstream of PICSO research., as verified by Kenner in 1985, using blood density measurements during controlled sequences of coronary sinus occlusions, was the first indication of a branching point. The underappreciated complexity of Kenner's microhematocrit measurements in cardiac veins is now, in retrospect, the critical, still valid crossroad of PICSO research. Although corroborated by several authors using different methodologies of coronary venous retroperfusion and measurement modalities, like contrast disappearance or xenon washout, the impact and consequence of this scientific achievement, not well understood, remained unscrutinized , formulated “the dose makes the poison,” which is also relevant in myocardial drainage limitation. Gradual and timed outflow resistance may be beneficial in ameliorating ischemia. However, sudden and complete occlusion of outflow of cardiac veins is detrimental and must be avoided. The experimental and later clinical evidence shows that PICSO induces salvage by redistributing flow into the deprived myocardium . Recently, Egred et al. reported in patients with ACS treated with PICSO a significant absolute of 6.9%, a 33% relative decrease of infarct size as a percentage of left ventricular mass in a propensity-matched analysis compared to historical controls (INFUSE MI) measured by CMR 5 days after the infarct  region as well as in the right ventricle (RV) with independent myocardial drainage.p < 0.05). Although miR-21 has repeatedly been associated with cardiac events such as response to oxidative stress, NF-kB activation, or fibrosis formation, its expression was chosen as an averagely expressed miRNA, which was subject to minor variation in our analyzed cardiac tissue samples, consistent with our theory, that PICSO alone does not change miR-21 expression  .Since the posterior interventricular vein flow is typically unhindered by PICSO, we consider that activating collaterals and molecular events or soluble factors released and washed into the independent drainage zones are causing positive effects on vasodilation, as first described by Weigel et al. in 2007 .In 2018, we reported acute molecular changes in secreted molecules by PICSO. Eight out of 32 patients undergoing resynchronization device implantation were treated with PICSO for 20 min. Coronary sinus blood samples were collected before and after PICSO or 20 min in untreated patients, and pre-interventional sera were compared to post-interventional sera in both groups as well as among groups. The miRNA patterns between samples collected before and after the treatment and compared to controls were differentially expressed. In addition, these samples were used as a medium for cultures with cardiomyocytes from cardiac tissue harvested during heart transplantation, also displaying differences in proliferation . Comparep < 0.048) and miR-145 (p < 0.047) increased, both targeting a network of transcription factors, including KLF-4 (Krüppel-like factor 4), an evolutionarily conserved member of the KLF family of zinc finger transcription factors promoting differentiation and repress proliferation of vascular smooth muscle cells. In addition, other transcription factors showed favorable trends by PICSO, as MeF2a (myocyte enhancer factor 2a), a prominent member of the transcription family, acting as a myocardial enhancer in cardiac development and adaption to hypertension-induced cardiac hypertrophy, although not statistically significant, due to the small number of patient tested , alleviating endothelial cell apoptosis. The miR-17-92 cluster is a highly conserved gene cluster that encodes various conserved miRNAs, including miR-19b (p < 0.001) and adult cardiomyocytes (p < 0.004) harvested from a transplant recipient compared with controls. In addition, adult cardiomyocytes showed a seven-fold increase of the transcription factor KLF-4 protein when cultured with treated sera compared with controls. Increased miR-19b and the observed proliferation induced by PICSO suggest a causal relationship , suggesting a flow- and pressure-dependent secretion of signaling molecules into the coronary circulation. Our observed relationship in patients with heart failure corroborates Weigel's correlation between heme oxygenase with the developed pressure in coronary veins during PICSO, indicating cytoprotection during ischemia/reperfusion injury. For the first time, this causal relationship connects the hemodynamic consequences of PICSO via signaling events to published evidence on the proliferation of cardiac cells -induced injury by targeting DNA damage-inducible factor 4 (DDIT4) to regulate autophagy, indicating that miR-101 or DDIT4 may be potential therapeutic targets for heart injury and may play a role in apoptosis .Interestingly, miR-101 plays a protective role against cardiac remodeling following MI via inactivation of the RUNX1 (runt-related transcription factor1), proposing miR-101 and RUNX1 as potential therapeutic targets for MI , 65.Recently, Wang et al. showed that miR-101a-loaded mesenchymal stem cells in extracellular nanovesicles significantly improved heart function, decreased infarct size, and decreased fibrosis after MI even when injected into the myocardium 13 days after the infarct, suggesting the equivalent upregulation of miR-101 in PICSO patients as a favorable event .p < 0.023). Both miR-320b miR-25 (p < 0.023) decreased, and proposed targets of anti-miR application to improve contractility in the failing heart. Wang 2014 provided a novel mechanism for the exosomal miR-320 functionally downregulated its target genes IGF-1, Hsp20, and Ets2  (Other disadvantageous non-coding RNA found in our study on acute changes in patients with heart failure influenced by PICSO are miR-320b and miR-25 (yocytes) . In the yocytes) .Therefore, since signaling molecules sequestered into the coronary venous blood are differentially expressed by PICSO, and relate to pressure increase during PICSO, their role in ACS outcome can be predicted. Relating PICSO parameters to PICSO effects on non-coding RNA coincide with the report by Syeda et al., convincingly showing that the sequence of occlusion release phases and the increase in systolic pressure over time is related to the amount of myocardium salvaged . AlthougAlso, the contribution by Weigel et al. was found in a positive and statistically significant correlation between mean and developed peak coronary sinus pressure and the HO-1 and VEGF gene transcription. These findings are now completed with a relationship between signaling molecules and PICSO parameters, strongly suggesting that mechanical strain and stretch produced by the pulsatile action of the reflowing blood in the deprived venous vasculature triggers the activation of a genomic program within the myocardium .Clearing ischemic/reperfused microcirculation and its relationship to myocardial salvage is the mainstream PICSO claim to be tested in ongoing clinical trials. PICSO can influence the pathophysiology of structural changes in the post-reperfusion myocardium. The plasma skimming theorem of PICSO was tested using microhematocrit changes in cardiac veins substantiating measurements of blood density as the crucial parameter of PICSO effectiveness. Separating cellular components in more prominent veins and pressing plasma into the microcirculation, thus varying hematocrit levels measured in the coronary sinus, is the typical denominator improving conductance in the reperfused microcirculation. This effect seems to be the principal cause of impact on molecular secretion see . A more p < 0.044)  .p < 0.0149). Adjusting for restenosis, the risk for MACE was nine times higher for the control group than for the PICSO group , as an index for injured microcirculation, was also associated with a higher incidence of MACE in patients with acute coronary syndromes . FurtherThe second important mechanism, interpreting our results on restenosis and MACE, is the molecular signaling event, influencing the relationship between intrinsic apoptosis, necroptosis, and pyroptosis, influencing infarct healing and correcting the inflammatory process in ACS. In our study, the recurrence of reinfarction was reduced. The relative risk for re-MI was 17 times higher for the control group than for the PICSO group, even after including other risk factors .In a recent article, Crea summarized the importance of inflammation in post-ischemic events, implying the inflammatory risk and its relationship to the recurrence of ischemic events .As said, PICSO seems to activate alternate pathways limiting inflammation and reopening canonical pathways described in our “Embryonic Recall” hypothesis. In addition, the plasma skimming machinery of PICSO next to producing molecular signals reopens the venous microcirculation, liberating edema and toxic metabolites.Molecular events during reperfusion of a deprived myocardial territory are a complex problem with multiple signaling and sometimes counteracting events. Therefore, it can be expected that a burst of several non-coding RNA, many hitherto unknown, in context with other regenerative molecules, are involved in the signaling event, both induced by the disease process and by PICSO .Reversal of adverse remodeling to prevent heart failure is one of the targets of recent research, and measurements of ventricular volumes are a diagnostic option , 75. In We are expecting results of ongoing clinical trials of PICSO research since they will eventually answer the questions asked 30 years ago and whether it is safely applicable in the routine logistics of cath labs in the 2020s, correcting reperfusion injury. It remains to be seen whether infarct size reduction is the correct signal, relating patient outcome with apparent advantages for patient survival and quality of life.These hypothesis-generating observations of a noticeable paradigm change will guide the exploration of the novel approach to treating myocardial microcirculation during jeopardy. In addition to salvage, an overall cardioprotective effect can be anticipated, leveraging problematic infarct size measurements as expected in our hypothesis. Hemodynamic PICSO forces reopen the ischemic/reperfused microcirculation, mainly by redistribution of flow and washout, but initiating cascades of molecular signaling seems to be an essential factor as well. In the next step, corroborating molecular signs and signals of PICSO effects in pivotal trials is mandatory since we must verify heart failure studies of non-coding RNA in patients with acute myocardial infarction. More extensive, primarily clinical studies will eventually confirm molecular signaling as one of the significant effects and establish PICSO as valuable therapy for ischemic, reperfused microcirculation treatment in patients with STEMI and beyond.The future of clinical PICSO is based on an analytical appraisal of yesterday's conceptualization, an open eye for identifying cross roads, even unexpected counterintuitive findings, and an available vision of the future's scientific perspective in parallel to clinical evidence.This notion and the uncertainty of whether an additional infarct size reduction can be achieved as an add-on therapy during reperfusion dictates another paradigm change in PICSO research, concentrating on repair pathways and starting even before reperfusion. This newly defined PICSO target needs sophisticated technology and new clinical research perspectives to mitigate patient outcomes.The information presented here is a roadmap of findings with counterintuitive results forcing research into unexpected territories. What started as an anti-ischemic procedure developed into a complex signaling and network of molecular pathways. Abstracting scientific evidence warranted the formulation of our hypothesis, “embryonic recall.” It is apparent, however, that tested miRNAs are not the only signaling pathways, and many more molecular signals must be detected.The development of the PICSO concept bridged several decades and was started before revascularization therapies became routine. The current understanding of “time is muscle” and early revascularization nowadays limiting PICSO applications into the reperfusion period falls short, considering ambivalent results in determining reperfusion injury.Another significant limitation is that the administration of heparin interacts with miRNA activity as routine treatment in acute coronary syndromes and may be a serious disadvantage of a PICSO application.Therefore, future research must concentrate on the molecular role of PICSO in routine revascularization procedures. It may very well be that an application during ischemia on the way to the cath lab or at any other time during infarct healing supports recovery and may better interact with myocardial decay.


Multidimensional, heteronuclear NMR relaxation methodsare used extensively to characterize the dynamics of biologicalmacromolecules. Acquisition of relaxation datasets on proteins typicallyrequires significant measurement time, often several days. Accordionspectroscopy offers a powerful means to shorten relaxation rate measurementsby encoding the “relaxation dimension” into the indirect evolution period inmultidimensional experiments. Time savings can also be achieved bynon-uniform sampling (NUS) of multidimensional NMR data, which is usedincreasingly to improve spectral resolution or increase sensitivity per unittime. However, NUS is not commonly implemented in relaxation experiments,because most reconstruction algorithms are inherently nonlinear, leading toproblems when estimating signal intensities, relaxation rate constants andtheir error bounds. We have previously shown how to avoid these shortcomingsby combining accordion spectroscopy with NUS, followed by datareconstruction using sparse exponential mode analysis, thereby achieving adramatic decrease in the total length of longitudinal relaxationexperiments. Here, we present the corresponding transverse relaxationexperiment, taking into account the special considerations required for itssuccessful implementation in the framework of the accordion-NUS approach.We attain the highest possible precision in the relaxation rate constants byoptimizing the NUS scheme with respect to the Cramér–Rao lower bound ofthe variance of the estimated parameter, given the total number of samplingpoints and the spectrum-specific signal characteristics. The resultingaccordion-NUS  Most commonly, relaxationexperiments on proteins are acquired as a series of two-dimensional (2D)spectra, in order to resolve as many resonances as possible, whereinrelaxation rates are measured via their effect on the resonance intensitiesin a “third dimension” obtained by parametrically varying the length of arelaxation time period or the refocusing frequency of an appliedradio-frequency field, or both. Thus, relaxation experiments often involvesignificant time requirements and may take up to several days. An ingeniousalternative to these lengthy experiments is offered by the accordionapproach originally developed by Bodenhausen and Ernst to study chemical exchange. In accordion spectroscopy, thethird dimension is incremented synchronously with the second (indirect)dimension, with the result that the relaxation decay is encoded into theinterferogram of the indirect evolution period. Consequently, the totalexperiment time is reduced significantly. More recent implementationsinclude the constant-time accordion experiment, from which relaxation rate constants can be extracted usingeither time-domain analysis of the interferogram  or line-shape analysis of the Fourier transformed data.Non-uniform sampling (NUS) of the indirect dimensions of multidimensional NMRdata can greatly shorten the total experiment time and has become commonplace in the last decade.However, most spectral reconstruction algorithms suffer from nonlinearity ofsignal intensities, which limits “plug-and-play” use of NUS in quantitativeexperiments and requires careful consideration of both sampling schemes anddata modeling to produce consistent results and reliable error estimates. We recently introduced an approach thatavoids these problems by combining accordion spectroscopy with NUS and analyzing theresulting data using DSURE (damped super-resolution estimator), a sparsereconstruction technique enabling maximum-likelihood estimation of thetime-domain signal parameters from NUS data. We stress the point that accordion spectroscopy encodes thedesired relaxation rate constants in the interferogram of themultidimensional dataset; hence, the analysis does not rely on measuringintensities in multiple NUS datasets. Moreover, maximum likelihoodestimation of model parameters makes it straightforward to derive reliableerror bounds. Our approach leads to accumulated time savings through boththe accordion and NUS methods. Compared to a conventional relaxationexperiment, accordion reduces the experiment time by a factor of A number of considerations are of general importance when choosing thedetailed sampling scheme for NUS, including the need to keep the totalnumber of increments small in order to speed up data acquisition and to sampleshort Here, we introduce accordion-NUS 22.1For completeness, here we briefly outline the salient features of theconstant-time accordion method . Figure 1shows the accordion 2.2Uniformly 2.3All pulse sequences were based on the Conventional 2.4NUS schemes were generated by selecting data points from the uniformlysampled accordion 2.5Non-accordion  datasets were processed using NMRPipe, with forward linear prediction todouble the number of data points, cosine-squared window apodization, andzero-filling to twice the size rounded to the nearest power of two.The accordion datasets were processed and analyzed using the DSURE algorithm implemented in MATLAB . DSURE reconstruction was performed on individual 2.6To compare the performance of the different approaches for measuringtransverse relaxation rates, we used four different metrics. The relativedifference and absolute deviation between datasets 33.1The accordion-NUS 3.2The The two alignment variants yield highly similar results in the context ofthe accordion experiment, and the same is true for the two combinations ofaccordion modes . The RMSD between the two alignment variants is0.19 s3.3Next, we tested the performance of the accordion Figure 3 illustrates the performance of the accordion-NUS 3.4\opttitle{Spectral characteristics affecting accuracy and precision ofaccordion-NUS $R_{{1\rho}}$ relaxation rate constants}Next, we investigated how various spectral characteristics affect theaccuracy and precision of the estimated relaxation rate constant. Wecalculated the absolute deviation (4We have described a non-uniformly sampled accordion


Apical hypertrophic cardiomyopathy (ApHCM) is a rare form of hypertrophic cardiomyopathy which predominantly affects the apex of the left ventricle. The diagnosis can be challenging due to several factors, ranging from no typical clinical and electrocardiogram (EKG) findings to potential difficulties in executing and interpreting the echocardiographic examination.We report the case of an 84-year-old woman who came to our echo-lab to undergo a routine echocardiogram. She had a history of permanent atrial fibrillation, paced rhythm and previous episodes of heart failure (HF), allegedly explained by a diagnosis of hypertensive heart disease that had been confirmed many times over the previous 20 years. The clinical examination and the EKG were unremarkable. The echocardiographic images were poor quality. But a senior cardiologist, expert in imaging and echocardiography, noted the lack of delineation of the endocardial border of the left ventricular (LV) apex region. Contrast echocardiography was performed and severe apical hypertrophy discovered.ApHCM can be a challenging diagnosis. Contrast echocardiography must always be applied in cases of poor delineation of the LV apical endocardial border at baseline echocardiography. Timely detection and appropriate lifestyle intervention might slow the development of LV hypertrophy, and possibly minimize and delay heart failure (HF) related symptoms and arrhythmias. The prognosis remains relatively benign during long term follow-up. Apical hypertrophic cardiomyopathy (ApHCM) is a form of hypertrophic cardiomyopathy, more frequently found in Asian population  , 2. The The diagnosis is based on clinical examinations, electrocardiogram (EKG) and echocardiography but each of these approaches can present limitations to such an extent that diagnosis can be missed as in our case for a long time. However critical interpretation of both the clinical symptoms and the echocardiogram can suggest the use of contrast echocardiography that can appropriately reveal the left ventricular apical abnormality. This is illustrated in the reported clinical case.An 84-year-old woman with a history of obesity, hypertension, dyslipidaemia, carotid atherosclerosis and chronic obstructive pulmonary disease, presented to our echo-lab to undergo a routine echocardiogram. At the time, she had no complaints of chest pain, palpitations and dyspnoea at rest. There was no family history of sudden death, congestive heart failure or cardiomyopathy.Twenty years before (in 2003) at the age of 64, after a syncopal episode, she had been implanted with a bicameral pacemaker for alleged sinoatrial node dysfunction; 9 years later the depleted battery was replaced.About 15 years ago,  she was diagnosed with permanent atrial fibrillation and had since been on direct-acting oral anticoagulants.In the last three years, she complained of dyspnoea following normal and less than normal physical activity (New York Heart Association [NYHA] class II-III). She also had limited mobility, due to back pain caused by multiple vertebral collapses, that further limited her physical capacity during effort. Therefore, the NYHA class was probably imprecise.In 2020, a Holter EKG examination to verify pacemaker function identified an asymptomatic horizontal-down sloping ST-segment depression, suspicious for silent ischemia; for this reason she underwent coronary computed tomography angiography, that showed diffuse plaque involving the left anterior descending coronary artery (LAD), with >50% maximal segmental lumen narrowing. In the same year, she underwent coronary angiography; however, this did not confirm the critical LAD stenosis by Coronary Computed Angiography, so ruling out any significant coronary narrowing. Since intravascular ultrasound and coronary flow reserve were not assessed, diffuse coronary atherosclerosis could not be surely ruled out .In 2022, the patient was admitted to an Internal Medicine ward, complaining of persistent fever lasting for several days and a confusional status. During hospitalization, a chest x-ray was performed which detected a consolidation in the right lung associated with an increase in inflammatory markers (C reactive protein = 17.8 mg/L), for which effective antibiotic therapy was initiated. Moreover, mild hyponatremia (129 mEq/L) was also observed and corrective treatment was administered. At discharge, a diagnosis of worsening heart failure, complicated by an acute bronchopneumonia episode, was made, although no clear explanation for this “chronic” heart failure was given. Increased dosage (50 mg/die) of a loop diuretic (furosemide) was prescribed.Over the last twenty years, the patient had undergone several follow-up echocardiograms at qualified Hospitals, all in agreement as to the diagnosis of hypertensive heart disease.Blood tests showed constant, modestly abnormal high pro-BNP (B-type natriuretic peptide) in the last year , which wA timeline of the events occurring over the 20 years before the diagnosis of ApHCM is reported on the left side of the 2), with difficulty in walking. Physicals showed mildly increased systolic blood pressure (145/80 mmHg) (The patient was overweight (BMI 35.11 kg/m80 mmHg) , a well The resting electrocardiogram showed a paced rhythm alternating with rare native QRS complexes; the native QRS complex showed no typical for ApHCM giant T waves but only minimal atypical repolarization anomalies of the QRS in V4 .Chest x-ray showed a slightly enlarged heart with partial calcification of the aortic arch and a thickened thoracic aorta.The echocardiogram performed in our echo Lab was of poor quality, especially the apical window projections. It showed a mild thickening of the inter-ventricular septum , with a dilated left atrium (antero-posterior LA diameter = 41 mm) and hyper-normal (74%) LV ejection fraction. Both ventricles were apparently of normal size . There was no evidence of wall motion abnormalities or dynamic left ventricular outflow tract obstruction. At first glance, the LV apical cavity did not show any abnormality, but endocardial delineation at the apex level was absent . In addiRegarding valve function, we found moderate-severe tricuspid regurgitation  and mildThe right ventricle showed mild ventricle contractile dysfunction: mildly dilated secondary to significant tricuspid regurgitation but with normal  systolic function. The inferior vena cava and the hepatic veins were dilated, with reduced vena cava inspiratory collapse indicating pressure elevation, and in the supra-hepatic veins, systolic retrograde flow (only during inspiration) as the effect of significant tricuspid regurgitation. The significant tricuspid valve incompetence appeared to be the combination of primary tricuspid dysfunction (leaflets damage) induced by the pace-maker ventricular wire that could damage the valve at the valve crossing points, along with annular dilation induced by the mild-moderate pulmonary hypertension.μl of sulphur hexafluorane in the microbubbles, equivalent to 45 micrograms , at increased dosage to 50 mg/day after the last episode of heart failure. Neither angiotensin converting enzyme (ACE) inhibitors nor angiotensin II receptor blockers nor aldosterone inhibitors were prescribed. However, by reducing the circulating blood volume the use of loop diuretics can stimulate the renin angiotensin system and both directly and indirectly (by renin and angiotensin), the sympathetic outflow, especially without any ACE inhibition . The ensIt is ironic that even though ACE inhibition drugs are widely used in hypertension, they were not used when specific ACE inhibition was potentially needed and worth trying . On the Moreover, the LV diastolic dysfunction could have accelerated progression of the apical LV hypertrophy through stimulating the immune system, creating a vicious circle and worsening the disease. Left ventricular diastolic dysfunction, in fact, has been associated to the release of pro-inflammatory cytokines, prolonged hypoxemia, and excessive activation of neuroendocrine and autonomic nerve function, further aggravating the hormonal storms that foster myocardial hypertrophy and fibrosis . ExperimFinally, the patient was moderately overweight and this, with the ensuing hyperinsulinemia, could have further fueled the progression of the LV hypertrophy .implantable cardioverter-defibrillator application (when indicated) that act on life threatening arrhythmias. Moreover, since ApHCM less frequently exhibits clear genetic familiarity, both European Society of Cardiology and American Heart Association HCM guidelines provide no ApHCM specific genotyping or family screening recommendations. Nevertheless, our opinion is to recommend serial cardiological evaluations, including EKG and echocardiography integrated with contrast enhancement in case of poor LV endocardial delineation, for the first-degree relatives of the patient, and to intensify follow-up in case of symptoms or apparent abnormalities thanks to the harmless effect of ultrasound energy.In conclusion, accurate and timing diagnosis with contrast echocardiography can prompt more precocious interventions in order to possibly prevent or delay the onset of the ApHCM complications; strain-independent factors (fibrosis and hypertrophy) are the more important target of the therapeutic interventions: weight reduction in obese patients with appropriate diet and normalization of hyperglycemia along with blood pressure normalization may reduce LV fibrosis/hypertrophy by several mechanisms  includinApHCM can be a challenging diagnosis. Contrast echocardiography must always be applied in cases of poor delineation of the LV apical endocardial border at baseline echocardiography. Timely detection and appropriate lifestyle intervention might reduce the severity of LV hypertrophy and minimize and delay HF related symptoms and arrhythmias. The prognosis is seen to remain relatively benign after long term follow-up (20 years).


COL3A1 gene mutation encoding type III collagen. Given the possible fatal prognosis if not treated timely, it is important to suspect and diagnose as soon as possible. Despite advances in endovascular technique, access point complications remain a serious challenge in patients with vEDS. Here, we describe a 30-year-old male patient who was diagnosed with vEDS after consecutive events of bilateral iliac vessels at an interval of 3 months: (1) spontaneous dissecting aneurysm of right iliac artery and (2) arteriovenous fistula between left internal iliac artery (IIA) and left common iliac vein. This patient was treated with iliac stent-grafts and overlapping femoral interposition graft (Dacron) in the 1st operation and access artery repair with surgical dissection after coil embolization of IIA and stent-graft insertion into left common to external iliac arteries in the 2nd operation. The patient has been treated with beta-blockers and anticoagulants for the management of vEDS and postoperative deep vein thrombosis, respectively. The stent-grafts in both iliac arteries and the access sites have been well-tolerated without any adverse effects for 14 months following the 2nd operation. In conclusion, given the vascular fragility and the potential for future events, additional vascular manipulation should be avoided unless it is in a life-threatening condition. In particular, meticulous hybrid interventions can be effective treatments.Vascular Ehlers-Danlos Syndrome (vEDS) is a rare connective tissue disorder associated with  COL3A1 gene encoding the pro-alpha-1 chains of type III collagen, which is a major protein of blood vessel walls and hollow organs (Vascular Ehlers-Danlos Syndrome (vEDS) is usually inherited in an autosomal dominant manner. It is caused by mutations in the w organs . Therefow organs –6. Here,COL3A1 gene: c.674G>A, heterozygous, p.(Gly225Asp). At four months after the second operation, the patient visited ER with right lower quadrant pain in the abdomen. Based on a diagnosis of acute appendicitis without perforation, laparoscopic appendectomy was performed without any complications. Fourteen months after the second operation, the patient is currently on a beta-blocker to control blood pressure and has been monitored in an outpatient clinic  but tachycardiac (115/min). Physical examination revealed abdominal tenderness in the right lower quadrant. The patient had a history of bruising easily since childhood and his skin was so lucent that the superficial vein was markedly visible . An abdot clinic , S3.More than 60% patients with vEDS tend to present life-threatening vascular complications before the age of 40 . VariousThe patient is about 181 cm tall and 85 kg (BMI 25.9) with a medium build. In retrospect, easy bruising and lucent skin with vascular visibility and tooth decay have been noted since childhood. The patient also had prominent eyes and a long thin nose. Even during postoperative period, periorbital ecchymosis without any trauma was observed. However, no other physical characteristics, such as skin hyper-extensibility or delayed wound healing, typical of classic EDS were found.de novo mutations cannot be excluded considering that half of the probands also reported no family history of vEDS  detection or sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) for the identification of protein deletions or duplications.Since the publication of Ehlers-Danlos syndromes: 1997 Villefranche classification by Ehlers-Danlos National Foundation (USA) and Ehlers-Danlos Support Group (UK), the clinical diagnosis of specific EDS subtypes was based on biochemical and molecular features. The 2017 revised international EDS consortium guidelines recommended molecular screening for ith vEDS . To exclDespite the rarity of the disease, Eagleton et al. suggested a few general guidelines for open repair, including the use of padded vascular clamps, felt reinforcement or tissue sealants along the suture line, and interrupted sutures to prevent unnecessary traumatic injury to friable vessels . While tIn the second operation for ilio-iliac AVF, a self-expanding stent rather than a covered stent was used to cover AVF opening and insert coils precisely by increasing the friction between IIA and coils, especially in the proximal IIA with a short length (5 mm) before fistula. However, we failed to cover the AVF completely and a piece of coil entered the blood flow through the AVF opening, which increased the risk of postoperative DVT below the left CIV. It was imperative to achieve complete orifice coverage through the addition of a supplementary stent, coupled with a heightened focus on precise execution during coil embolization considering the high flow rate.The insufficient tensile strength of the vessel wall in patients with vEDS can hamper surgical procedures, resulting in potentially fatal outcomes. Therefore, it is more important to suspect the diagnosis and consider every possible precaution in extreme scenarios preoperatively. Not surprisingly, the incidence of perioperative complications such as frequency of open repair and intraoperative mortality was higher during emergency surgical procedures without diagnostic information . HoweverOnce vEDS is diagnosed, the patients should be under strict multi-disciplinary care. Extra attention is needed to prevent the risk of bleeding due to vascular fragility. Life-style modification such as avoiding collision sports and isometric activities is necessary to reduce the chance of traumatic injury. Meanwhile, medical management of vEDS has been reported to prevent catastrophic results , 19. TheFortunately, this patient had no vascular events or bleeding complications after the second operation despite anticoagulant therapy for about a year. However, as in this case, a dilemma arises concerning the appropriate utilization of antithrombotics for patients with vEDS who develop DVT or had been treated with endovascular prosthesis.Once vEDS is suspected in young patients with unexpected non-traumatic arterial events, careful planning before operations and vigilance during procedures are crucial for managing unforeseen events. Given the vascular fragility and the potential for future events, it is recommended to refrain from employing additional vascular manipulation unless it is in a life-threatening condition. In particular, meticulous hybrid interventions can be effective treatments.