Automatic liver tumor segmentation is a paramount important application for liver tumor diagnosis and treatment planning. However, it has become a highly challenging task due to the heterogeneity of the tumor shape and intensity variation. Automatic liver tumor segmentation is capable to establish the diagnostic standard to provide relevant radiological information to all levels of expertise. Recently, deep convolutional neural networks have demonstrated superiority in feature extraction and learning in medical image segmentation. However, multi-layer dense feature stacks make the model quite inconsistent in imitating visual attention and awareness of radiological expertise for tumor recognition and segmentation task. To bridge that visual attention capability, attention mechanisms have developed for better feature selection. In this paper, we propose a novel network named Multi Attention Network (MANet) as a fusion of attention mechanisms to learn highlighting important features while suppressing irrelevant features for the tumor segmentation task. The proposed deep learning network has followed U-Net as the basic architecture. Moreover, residual mechanism is implemented in the encoder. Convolutional block attention module has split into channel attention and spatial attention modules to implement in encoder and decoder of the proposed architecture. The attention mechanism in Attention U-Net is integrated to extract low-level features to combine with high-level ones. The developed deep learning architecture is trained and evaluated on the publicly available MICCAI 2017 Liver Tumor Segmentation dataset and 3DIRCADb dataset under various evaluation metrics. MANet demonstrated promising results compared to state-of-the-art methods with comparatively small parameter overhead. For immediate clinical management to be successful in achieving survival, early detection of liver tumors is essential. Tumor burden analysis which consists of major factors of measuring the size and location of the tumor, utmost importance to determine the severity of the disease. Medical imaging is a noninvasive technique to determine the severity and stratification of cancer. Radiologists mostly rely on Computed Tomography (CT) scans for the diagnosis and clinical management prior to the pathological examination. It is because of the contrast enhancement on CT images that can be helpful to distinguish the tumor region from the liver parenchyma. However, recognizing tumor regions is still a challenging task for radiologists due to high inter-class similarity, intra-class variations, and fussy boundaries of the tumors. To address these issues, computer-aided detection system is highly useful to establish diagnostic standards to bridge the cognition gap in all levels of radiological expertise.Liver cancer is one of the major cancer types with the most fatalities recorded around the worldThere are some challenges still remaining to develop computer-aided automatic liver tumor segmentation solution. The high cost of collecting data to conduct experiments. Data labeling is time-consuming and tedious task to prepare proper medical dataset to train and test the model. Another major issue that causes the misclassification of tumor regions is tumor diversity. Tumor can appear in different shapes at different locations with different quantities. The intensity dissimilarity in tumor regions increases the complexity to differentiate tumor regions from the healthy liver.3. Fully connected layers of CNN are replaced with convolutional layers to perform the pixel-level classification. Ronneberger et al.4 proposed U-Net, this model is based on FCN and could achieve massive success in medical image segmentation. Significant advancements were made by researchers who inspired by U-Net. H-DenseUnet proposed by Li et al.5 is a recent development of U-Net as a combination of U-Net with DenseNet6 for efficiently extracting intra-slice and inter-slice features. Zhou et al.7 proposed U-Net++ based on nested and dense skip connections. Semantically high-level features in decoder network fused with low-level semantic features after following deep supervision in skip connection. Deep Residual U-Net (ResUNet) is developed by Zhang et al.8 as a combined architecture by utilizing the strengths of U-Net4 and deep residual learning9. Deep residual learning is proposed to address the problem of degradation in deep convolutional neural networks. Lately, ResUNet++10 is developed as a sophisticated version of ResUNet by further utilizing squeeze and excitation blocks11, Atrous Spatial Pyramidal Pooling (ASPP)12, and attention blocks.To address those challenges, researchers have attempted to develop segmentation methods with various deep learning techniques. Medical image analysis has significantly developed with convolutional neural networks (CNN), which have noticeably improved performance on a wide range of computer vision tasks by automatically learning multi-level feature representations. The rapid development in deep learning, fully convolutional network (FCN) emerged with remarkable accuracy in pixel-level classification, which is proposed by Long et al.13 proposed Attention U-Net architecture based on U-Net and end-to-end-trainable attention module which was proposed for image classification by Jetley et al.14. Attention U-Net has implemented attention mechanism in skip connection to extract salient features to fuse with high-level semantic features. It could enhance learning by highlighting important features while suppressing redundant regions for the specific segmentation task. Furthermore, transformer-based attention mechanism15 which is popular in Natural Language Processing (NLP) applications, is implemented in ResUNet++ architecture. Squeeze-and-excitation11 is developed as a channel attention mechanism using global average pooling to highlight important channels while suppressing channels with minor relevance. This channel attention mechanism is applied in ResUNet++ and other recent developments. Woo et al.16 proposed Convolutional Block Attention Module (CBAM) which is a fusion of channel attention mechanism and spatial attention mechanism. CBAM is a lightweight mechanism that is easy to integrate into neural networks and has demonstrated success in recent developments19. Furthermore, researchers who developed attention mechanisms have emphasized that the accuracy and sensitivity of the prediction greatly improved with the attention mechanisms by utilizing comparatively a smaller number of parameters.The performance of most of the above architectures are demonstrated by dense predictions in multi-stage Cascaded CNNs. However, this method utilizes excessive and redundant computational cost for feature processing during the segmentation task. Along with the developments to reduce the computational cost in segmentation, researchers have been proposed spatial modules with attention mechanisms to suppress irrelevant features while highlighting the most relevant spatial information for the segmentation task. Attention mechanisms could significantly enhance the extraction of salient features to learn focus target with comparably fewer. Oktay et al.4, deep residual learning9, and attention mechanisms16, we designed a novel deep learning architecture named MANet by following U-Net as the base architecture, which has shown state-of-the-art performance in various biomedical applications. Attention mechanisms are utilized for better channel and spatial information extraction to improve the segmentation performance of the model. The proposed model is trained and evaluated with the publicly available LiTS17 dataset20 and 3DIRCADb dataset21 under various evaluation metrics. Our experiment results proved that the proposed model is efficient and effective for tumor segmentation compared to baseline architectures of U-Net, Attention U-Net, and U-Net + Resnet18. In summary, the contribution of the Multi Attention Network (MANet) can be shown as follows. We propose a novel MANet architecture of semantic segmentation neural network by utilizing the strengths of residual blocks, channel attention, and spatial attention mechanisms elaborated in CBAM. The attention mechanism is integrated to extract spatial features from the encoder to combine with corresponding high-level semantic features in the decoder, which is proposed in Attention U-Net architecture. MANet has adapted U-Net architecture as a basis for the development.The attention mechanisms implementation in the encoder path, skip connection, and decoder path greatly improved the focus on the region of interest of the target segmentation, achieving the best sensitivity score in all the experiments.In this paper, we explore the effectiveness of attention mechanisms to improve tumor segmentation performance with less computational cost. Moreover, we investigate the viability of applying these recently developed methods to improve the segmentation of liver tumors with fuzzy boundaries. Inspired by U-Net23. A computer-aided diagnosis system to detect and grading liver tumors based on multi-phase contrast-enhanced magnetic resonance imaging (CE-MRI) proposed by Alksas et al.24. They have explored the effectiveness of imaging markers with machine learning techniques alike support vector machine (SVM), naive bayes classifier (NB), k-nearest neighbors (KNN), and linear discriminant analysis (LDA). Large scale deep learning-based study has conducted with seven types of liver lesions and clinical data23. Seven models have experimented for liver lesion categorization task based on enhanced MRI, unenhanced MRI and clinical data. Two models developed based on MRI imaging and clinical data, proved better diagnostic performance compared to experienced radiologists. A deep learning system which is developed for detection of Hepatocellular Carcinoma based on CE-MRI, has indicated similar capability to less experienced radiologist22. The architecture is designed based on fine-tuned convolutional neural network (CNN), which is approximately six times faster than human. Multi-phasic MRI based convolutional neural network classifier has developed assess the feasibility of liver lesion classification25. Imaging Reporting and Data System (LI-RADS), that radiological experts defined general categorization standards referred to the study. To minimize image interpretation variability, facilitate quality assurance and research development, they suggested to utilize deep learning based automatic categorization standard for systems like LI-RADS.Automatic liver tumor segmentation has been a most trending topic in deep learning based medical research field. To improve the quality and accuracy of the diagnosis, deep learning advancements provide significant contribution by delivering supportive and faster opinion for clinical management. The rapid development in machine learning technology in liver tumor diagnosis, has been reached to closer level of radiologists3. FCN pixel level classification which could give segmentation output by end-to-end training. U-Net architecture gain popularity in medical research field as the extended development of FCN. There are significant number of extensions based on U-Net architecture26, became widely used deep learning architectures in liver and tumor segmentation task. Alirr27 proposed a deep learning based automatic liver and tumor segmentation method which is based on U-Net architecture. HU windowing and median filtering are used in preprocessing steps and tensor-based 3D edge enhancing diffusion (EED) filter is used to enhance training data for the training process. Ayalew et al.28 explored liver and tumor segmentation method based on U-Net with parameter reduction. Class imbalance method and data refinement techniques are utilized to improve the segmentation performance with less computational cost compared to the original U-Net. U-Net++7 is based on nested and dense skip connections and UNet 3+26 is the further developed version with deep supervision in each level of the decoder path. Li et al.29 upgraded UNet++ architecture by applying a channel attention mechanism to long-hop connections. The implementation of channel attention could reduce the eigenvalue loss. Moreover, multi scale feature extractors with dilated convolution kernels utilized to enhance the feature representation for segmentation task in CE-Net30. Lei et al.31 presented further development in DefED-Net to enhance feature extraction and representation ability by using deformable convolution to extract variable liver and tumor shape features in different slices, but both networks required more parameters to enhance the performance.Apart from the deep learning-based classification, the research field turned to another direction with the development of fully convolutional neural network (FCN)13 presented Attention U-Net which could emerge the popularity in attention mechanisms in liver and tumor segmentation research developments. Attention mechanisms investigate the capability to emphasize important features while suppressing irrelevant features for the segmentation task. Attention UNet++32 is utilized the same attention mechanism to improve UNet++ architecture. The developed architecture demonstrated better focus on target regions while suppressing irrelevant areas. Wang et al.33 utilized spatial attention gates to emphasize important features for liver segmentation. Residual learning is implemented to improve UNet based attention networks37. UNet++ network is improved with residual learning and spatial attention to minimize learning errors and improve the semantic gap between features of the encoder and decoder path of the network34. Attention mechanisms could significantly minimize the training parameter count of the networks. RA-UNet38 presented an attention mechanism that is implemented between the encoder and decoder using the max-pooling operation to highlight important features while reducing the noise. And residual connections are applied to retain original features while emphasizing salient features. Apart from the spatial attention mechanism, channel attention plays an important role in better feature enhancement. Global attention and hybrid attention mechanisms are designed to effectively focus on local and global features of the segmentation40. Self-attention based architecture is developed to enhance the feature representation for liver and tumor segmentation41. However, the network has not utilized the strengths of the attention mechanisms for the feature recalibration in the encoder path and skip connections. Furthermore, Hu et al.11 exploited the inter-channel relationship by the Squeeze-and-excitation (SE) module, which has been developed using global average-pooled features to calculate channel-wise attention. Several deep learning architectures have utilized the SE module to increase its sensitivity to relevant features while suppressing redundant features and achieved success in medical image segmentation33. MS-UNet42 is designed with the SE module to improve the channel-wise feature recalibration. HFRU-Net43 is implemented SE module in skip connection to adaptively recalibrate encoder features to fuse with the deep features. Woo et al.16 suggested the improved channel attention using both average-pooling and max-pooling, and proved its effectiveness. Moreover, they proposed Convolutional Block Attention Module (CBAM) which sequentially combined channel attention and spatial attention. Pang et al.44 proposed TA-Net utilizing various deep learning techniques like inception blocks, context blocks, and attention blocks to achieve better feature representation to improve performance in medical image segmentation. They have identified channel attention with both average pooling and max pooling outperforms channel attention with only average pooling, where it is used in shallow feature extraction path and deep feature extraction path separately. Zhao et al.45 used CBAM by splitting it to channel attention for deep feature extraction at the bottom of the architecture and spatial attention for both encoder and decoder at the top level to emphasize salient features in input-level shallow features, and semantically high-level features in output. Small Attention-UNet (SmaAt-UNet)46 is applied CBAM in skip connection and bottleneck of the network. CBAM is utilized to amplify important encoder features to concatenate with deep features in the decoder. The depthwise-separable convolutions have significantly reduced the training parameters of the network. Furthermore, CBAM is utilized to recalibrate channel weights and enhance spatial features at the deeper level of the network in S-Net47, which demonstrated significant improvement in liver tumor segmentation.Oktay et al.4 by utilizing strengths of deep residual learning9 and attention mechanisms16. The architecture is designed with an encoder, bridge, and decoder. The encoder propagates information through residual blocks and channel attentions based on the input image. The decoder generates pixel-wise classification to compute semantic segmentation. Feature propagation at the deepest level by residual unit and feature enhancement in the channel and spatial level computed in the bridge.We propose the MANet architecture that is evaluated for liver tumor segmentation. The block diagram of the proposed architecture is shown in Fig. In general, the deep neural networks suffer from degradation due to error feature learning, we have applied deep residual learning technique to create encoder residual blocks to address the degradation problem in the network. As can be seen in the diagram, the encoder residual block is combined with two convolution layers, batch normalization, and Rectified Linear Unit (ReLU) activation. ReLU activation followed by batch normalization is applied to eliminate the gradient vanishing problem and accelerate the convergence speed of the network. The feature propagation output of each encoder block is directed to the channel attention module to recalibrate channel weights for a better inter-channel relationship to enhance semantic feature extraction. The spatial dimension downsampling operation is performed by applying stride of 2 in the first convolution layer in each residual block. The spatial attention mechanism is applied in skip connection to extract important shallow features with the gate signal, which is extracted by deep features in the lower stage. Lower-stage deep features are up-sampled by transposed convolution and concatenated with corresponding shallow features extracted through the attention mechanism.The input of the decoder block is passed through spatial attention to emphasize semantic information and retain over decoder path. As in the encoder block, batch normalization and ReLU activation are sequentially applied after each convolution layer in decoder blocks too. Two succussive convolution layers with 3 4. The skip connection in U-Net that is applied in each stage of the network could enhance the segmentation performance, and achieve success in the medical image segmentation field. Utilizing skip connections in each stage of the network facilitates information propagation without degradation, further explained in deep residual learning9 which proposed to improve the training errors in deep neural networks. And validated in state-of-the-art approaches10. Inspired by residual connections, we designed an encoder with residual blocks which consist of two 3 In semantic segmentation, the fusion of high-resolution low-level features and high-level semantic features is crucial to obtain better segmentation performance16. Attention mechanisms give the capability to enhance feature representations by utilizing a comparatively small number of parameters. Overall, attention mechanisms can split into two categories, Channel attention, and Spatial attention. Channel attention performs global average pooling to calculate the statistical weight of each channel. Spatial attention performs global pooling operations across the channel dimension to extract contextual information. Moreover, channel attention guides the network to focus on “what” salient features to represent while spatial attention explores “where” important features are located in the feature map. The proposed MANet comprises four attention mechanisms named Skip connection attention gate, Channel attention, Spatial attention, and convolutional block attention module (CBAM).To extract better contextual information, attention mechanisms play a major role in the segmentation task. We proposed a novel MANet inspired by two attention mechanisms proposed inSkip connection attention gate is designed to capture important shallow features from the encoder to concatenate with semantically high-level features in the decoder. The attention is computed by aggregating shallow features b, c is the number of channels and W and b respectively. Input feature maps  are subjected to global pooling operations to compute the global max pooling feature descriptor MLP is designed with two hidden layers and ReLU, formulated in  to calculate spatial feature descriptors Convolutional block attention module (CBAM) is designed by sequentially applying 1D channel attention and 2D spatial attention in the convolution neural network as illustrated in Fig. lated in  and 7)  Convolut20 and 3DIRCADb dataset21. The LiTS dataset consist of 131 CT scans for training and 70 CT scans for testing, which have been collected from several clinical sites from different regions of the world. The dataset contains diverse types of liver tumor diseases and acquired with different CT scanners. LiTS dataset has been provided segmentation masks for liver and tumors only for the training set which consists 131 CT scans. Segmentation masks annotated by trained radiologists at each clinical sites and further verified by three experienced radiologists. Moreover, CT scan volume contains slices in range from 42 to 1026, where the image size is 39. The number of tumors in the scan varies between 0 and 75, size of the tumors varies between The proposed method is evaluated on the public dataset of MICCAI 2017 Liver Tumor Segmentation (LiTS17) challengeThe CT scan slices represent different organs and regions by a wide range of intensity values which varies in between -1000 and 3000. To enhance the liver area from the abdominal scan, image intensities of all the scans are truncated to the range of  Hounsfield Unit (HU) and followed histogram equalization and normalization before feeding to the training process. To evaluate the performance of the proposed liver tumor segmentation method, CT slices with tumor annotations are selected for the experiments. One scan is excluded from the experimental data set due to abnormality, a total of 130 CT scans (7050 slices) have been considered for the experiments. We have conducted experiments based on slices and volumes to evaluate the models. All the data randomly split into 4:1 ratio for the training set and test set. In the slice-based experiment, all the slices randomly split into 5640 slices for the training set and 1410 slices for test set. And all the scans randomly split into 104 volumes (5408 slices) for the training set and 26 volumes (1642 slices) for the test set to conduct the volume-based experiment. The training set and test set for the volume-based experiment include 8 and 7 volumes, respectively, of the 3DIRCADb dataset. We have not split the dataset into a validation set due to the limited amount of biomedical data.48, which randomly transform the batch of the data without increasing number of slices. The random transformations provide no duplication data among training process in different epochs. Vertical flip, shift, scale, rotate operations are applied during the random transformations.To minimize the risk of overfitting, we have used real-time data augmentation with AlbumentationsWe run all the experiments on a workstation with Windows 11 operating system, RTX2070 GPU with 8 GB memory, 32 GB of RAM, Intel(R) Core (TM) i7-9750H CPU @ 2.60GHz 2.59 GHz (6 cores), and PyTorch 1.9 deep learning framework for implementation. In the training phase, the initial value of the learning rate is set to 0.0001 and is attenuated by 0.1 in every 30 epochs. All the experiment networks are trained for 80 epochs to ensure the model convergence and best performance for the test set  is one of the most frequently used evaluation metrics, Jaccard index is known as intersection over union (IoU), volume overlap error (VOE) is the corresponding error metric for the Jaccard index (1 - Jaccard index), accuracy, sensitivity , specificity are denoted as overlap-based methods. The average symmetric surface distance (ASSD) is calculated average distance from points in the predicted binary mask and the ground truth binary mask, is denoted as a boundary-distance-based methodentclass1pt{minimaThis section provides the quantitative and qualitative analysis by comparing the proposed model and the baseline methods. Further evaluation is conducted with state-of-the-art methods to prove the effectiveness and robustness of the proposed network. The ablation study is conducted to evaluate the effectiveness of the architectural design of the proposed network. Moreover, we further conducted a detailed comparison of the number of parameters and the computational cost among all the compared models and the future direction of the research. The proposed architecture can be considered as 7 blocks architecture that is strengthened with attention mechanisms and residual blocks. The depth of the architecture is limited to four to minimize the parameter count and the complexity of the model. To make a fair comparison, we have utilized baseline architectures of Attention UNet, UNet+Resnet18, and UNet+CBAM, which are having the same depth as same as the proposed architecture. And the traditional UNet architecture depth is 5, which is the basis for the proposed architecture and other baseline architectures in the experiments. We have referred to original papers and the codes of the baseline architectures to conduct the experiments. Furthermore, UNet+Resnet18 is UNet architecture with Resnet18 backbone, since the encoder of the proposed architecture is designed with residual blocks. UNet+CBAM is UNet architecture integrated with CBAM where the stages of proposed architecture are designed with CBAM and the submodules  with different medical imaging modalities like MRI, PET, and US. Our goal is to develop this architecture utilizing the latest deep learning state-of-the-art techniques to minimize computational complexity while improving the segmentation performance with a higher level of stability.In this study we have presented a multi-attention network (MANet) for automatic liver tumor segmentation based on UNet, which can be beneficial to radiologist to perform tumor diagnosis and tumor burden analysis for patient treatment planning. The proposed architecture is strengthened with attention mechanisms and residual learning. In particular, we use channel attention to recalibrate features in channels while spatial attentions to capture localization features of the tumors. Moreover, skip connection attention gates implemented to extract highly relevant low level semantic features to propagate high level semantic information. In addition, residual learning employed in encoder to minimize the degradation to improve the gradient flow. We have evaluated the effectiveness of the proposed design on the LiTS17 dataset and 3DIRCADb dataset by conducting slice-based segmentation and volume-based segmentation experiments, that further demonstrated superiority of our network compared to baseline methods. Moreover, we have conducted empirical study to compare the quantitative and qualitative study to evaluate the performance, which could be further validated the effectiveness and robustness of the multilevel attention strategies implemented in the proposed design. Moreover, the effectiveness of the proposed architecture is further evaluated in the ablation study. However, we should note that there was a significant performance gap in between slice-based segmentation and volume-based segmentation. This should be the challenges due to high variability in data sources in terms of the shape of liver tumors and intensity variations. Therefore, there are important issues to be addressed to generalize the model to perform volume-based segmentation in real clinical environment.


Scientific Data 10.1038/s41597-022-01925-y, published online 05 January 2023Correction to: In the Usage Notes section of the Data Descriptor, the last paragraph contained inaccurate information regarding the project accession numbers in the GEO database for the raw data, with the accession number “GSE198074” repeated twice. The correct details are as follows:Raw data is stored in the database in FASTQ format (uploaded in Gene Expression Omnibus). Subject accession number: GSE207078 for bTBl; GSE198074 for Sham.This has been updated in the pdf and HTML versions of the article.


Surgical management of deep-seated brain tumors requires precise functional navigation and minimally invasive surgery. Preoperative mapping using navigated transcranial magnetic stimulation (nTMS), intraoperative neurophysiological monitoring (IONM), and minimally invasive parafascicular surgery (MIPS) act together in a functional-sparing approach. nTMS also provides a rehabilitation tool to maximize functional recovery. This is a single-center retrospective proof-of-concept cohort study between January 2022 and June 2023 of patients admitted for surgery with motor eloquent deep-seated brain tumors. The study enrolled seven adult patients, five females and two males, with a mean age of 56.28 years old. The lesions were located in the cingulate gyrus (three patients), the central core (two patients), and the basal ganglia (two patients). All patients had preoperative motor deficits. The most common histological diagnosis was metastasis (five patients). The MIPS approach to the mid-cingulate lesions involved a trajectory through the fronto-aslant tract (FAT) and the fronto-striatal tract (FST). No positive nTMS motor responses were resected as part of the outer corridor for MIPS. Direct cortical stimulation produced stable motor-evoked potentials during the surgeries with no warning signs. Gross total resection (GTR) was achieved in three patients and near-total resection (NTR) in four patients. Post-operatively, all patients had a deterioration of motor function with no ischemia in the postoperative imaging (cavity-to-CST distance 0–4 mm). After nTMS with low-frequency stimulation in the contralateral motor cortex, six patients recovered to their preoperative functional status and one patient improved to a better functional condition. A combined Tractography-MIPS-IONM-TMS approach provides a successful functional-sparing approach to deep-seated motor eloquent tumors and a rehabilitation framework for functional recovery after surgery. Surgical treatment of deep-seated brain tumors is particularly challenging for neurosurgeons and patients because of the dilemma between the oncological advantage of maximal safe resection ,4,5,6,7 Resection of highly motor eloquent brain tumors without cortical expression requires precise functional navigation and minimally disruptive surgical approaches. Preoperative mapping using navigated transcranial magnetic stimulation (nTMS), tractography, intraoperative neurophysiological monitoring (IONM), and minimally invasive parafascicular surgery (MIPS) are combined to provide a functional-sparing approach.Although advances in technology have made brain surgery safer and more effective, patients often experience surgery-related side effects and complications such as cognitive deficits and impaired motor function . These dIt is well understood and proven that subcortical injury is the primary cause of neurological deficits in awake craniotomies. Trihn et al.  have demNeurorehabilitation aims to restore these lost functions through targeted exercises and techniques. One promising technique that has emerged in recent years is transcranial magnetic stimulation (TMS). It provides a rehabilitation tool that can potentially maximize functional recovery after functional-eloquent surgery .The aim of this article is to present the results of a proof-of-concept study that employed an integrative approach for cortical–subcortical functional preservation and rehabilitation in neuro-oncology.This is a single-center prospective proof-of-concept cohort study between January 2022 and June 2023 of patients admitted for surgery with motor eloquent deep-seated brain tumors. Our inclusion criteria were the following: adult patients admitted for highly motor eloquent subcortical tumor surgery—invasion/contact with corticospinal tract (CST) documented with preoperative lesion-to-CST distance < 1 mm, nTMS preoperative mapping, and tractography; minimal invasive parafascicular surgery (MIPS); intraoperative neurophysiological monitoring; and nTMS after surgery for motor rehabilitation. Patients of any age meeting all criteria were included. The exclusion criteria were incomplete data. Data collected for each patient were age at diagnosis, clinical and radiological features, surgical approach, extent of resection, histological diagnosis, rehabilitation, and outcomes. Preoperative nTMS was performed for the identification and characterization of the primary motor cortex. This information was integrated into the surgical planning to establish a safe outer corridor for the MIPS approach .The MIPS procedure consisted of a transulcal approach using either the superior frontal or the intraparietal sulci. NICO BrainPath© System and NICO Myriad© microdebrider were used for tumor resection. Electrification of the microdebrider was performed as per a previously published technique . pars opercularis); and cingulum—frontal cingulate and parietal cingulate subcortical area, was performed. Pre-processing using the Cranial application of STEALTH Medtronic was performed using eddy current correction. For the purpose of this study, lesion segmentation was performed. The T1-weighted Gadolinium MRI volumetric scan was used for the segmentation, as all lesions were contrast-enhancing. Co-registration of preoperative and postoperative structural imaging (MRI T1 Gad) was performed for the calculation of the shortest distance between the tumor or the cavity and the tract (lesion-to-tract and cavity-to-tract distances). Tractography was performed with a constrained spherical deconvolution algorithm using Cranial Medtronic©. Region-of-interest-based dissection of the corticospinal tract (CST)—precentral gyrus and ipsilateral mesenchephalon; fronto-striatal tract (FST)—supplementary motor area (SMA) and caudate nucleus; fronto-aslant tract (FAT)—pre-SMA and inferior fronto-operculum  was inserted in the split sulcus and secured on the BUDDE halo. The correct position was confirmed with STEALTH .The MIPS approach in the 4 patients with the mid-cingulate lesions involved a trajectory through the fronto-aslant tract (FAT) and fronto-striatal tract (FST). Continuous neuro-monitoring and direct cortical stimulation were utilized to evaluate the motor-evoked potentials. Progressive debulking of the tumor with bipolar, suction, and NICO Myriad© microdebrider was performed. Hemostasis of the surgical cavity was achieved prior to the retraction of the tube. ICG was performed after the removal of the tubular retractor to identify the arterial and venous perfusion. We performed a multimodal intraoperative neuromonitoring (IONM) using direct cortical stimulation for motor-evoked potentials (MEPs) and dynamic subcortical mapping with a suction probe (spesmedica reg) or a modified microdebrider that was electrified with a sterile crocodile clip for subcortical identification of the corticospinal tract. The same strip electrode that was used to perform direct cortical stimulation (DCS) was used to record cortical somatosensory evoked potentials (SSEPs) and electrocorticography (ECoG). Transcranial MEPs and SSEPs were also utilized to record opening baselines and to assess for ischemia at the end of the resection. Free-running EMG was recorded to confirm the electro-clinical correlation of possible seizures in case there were changes in the electrocorticography (EEG).2 (FOV 256 × 256 mm2).Postoperative imaging was acquired within 72 h of the surgical procedure. Diffusion-weighted images were acquired on a 3-T MRI scanner using a cardiac-gated single-shot spin-echo echo-planar imaging multiband sequence  along 90 diffusion directions with a b-value of 2500 sec/mmNavigated repetitive TMS (nrTMS) was performed as per a previously published protocol by Ille et al. . Low-freWritten informed consent was obtained from all the parents of the patients for preoperative and intraoperative mapping procedures as well as postoperative nTMS for motor rehabilitation. The manuscript was conducted ethically in accordance with the World Medical Association Declaration of Helsinki.The raw data were entered into Microsoft Excel (Version 10.14 for Mac). Statistical analyses were performed via R  and RStudio (version 1.2.1335). Standard descriptive statistics were used to describe the characteristics of cases . The study enrolled seven adult patients, five females, and two males. The mean age was 56.3 years old (range: 26–79 years old). Four patients presented with motor weakness, one of them with expressive dysphasia, one with simple focal seizures, and one only with headaches. On clinical examination before surgery, all patients experienced motor deficits (ranging from 0 to 4/5 MRC hemiplegia/hemiparesis).3 (range: 3.3–32.6 cm3).In three patients the lesion was located at the cingulate gyrus. In two of them, the tumor was centered in the precentral gyrus, and in the final two, at the basal ganglia involving the thalamus and the internal capsule. Four patients had left-sided lesions and three had right-sided lesions. The mean distance between the lesions and the cortex as measured in the preoperative MRIs was 30.9 mm (range: 7 mm–46 mm) . The meaThe most common histological diagnosis was metastasis in five patients; one patient had a pilocytic astrocytoma (Grade 1 WHO) and one GBM ). Intra-operatively, the mean distance from CST after subcortical stimulation with the monopolar probe was 3.6 mA (range: 2–5 mA). In all cases, there was no deterioration of the MEPs during the operation. In two cases, there was an improvement in the amplitude of the potentials after the resection of the lesion. Gross total resection (GTR) was achieved in three patients, and near-total resection (NTR) in four patients . No ischPost-operatively, in all patients the motor power in the affected side deteriorated. More specifically, two patients had a drop of three grades (from 4/5 to 1/5 and from 3/5 to 0) and one patient had a drop of one grade (from 4/5 to 3/5) in the MRC scale in both upper and lower limbs. One patient had a drop of three grades only in the upper limb (from 3 to 0) and one deteriorated by two grades only in the lower limb (from 2/5 to 0/5). One patient had a postoperative deficit affecting only the fascial muscles and one patient remained unchanged. The immediate postoperative deterioration of motor function was attributed to initiation (trans-FAT or FST approach for mid-cingulate lesions) and manipulation close to CST . In the cohort of the patients with metastatic brain disease, after navigated repetitive TMS with low-frequency stimulation in the contralateral motor cortex, four patients recovered to their preoperative functional status—performance status (PS) 0 or 1 and one patient improved to a better functional condition—PS 0 from PS 1 . The patThe mean length of hospital stay for the other six patients was 18.5 days (range: 3–56 days). Two of them were discharged to a rehabilitation unit and the other four were able to return home. It is important to highlight that the reason for the delayed discharge of the patient with the longer hospital stay (56 days) was a lack of available beds in the rehabilitation unit. Furthermore, in two patients the hospital stay was deliberately prolonged in order to continue the postoperative TMS-assisted rehabilitation.All the clinical and peri-operative details of the seven patients can be found in The ultimate goal in neuro-oncology resection surgery is to achieve the maximal safe tumor resection in order to accomplish a prolonged survival with simultaneous preservation of the neurological function. The above becomes increasingly challenging in deep-seated highly eloquent tumor cases in close proximity to the white tract motor pathways. Our integrated approach combines treatment planning at three consecutive levels: preoperative—nTMS and tractography; intraoperative—navigation and intraoperative neuromonitoring and mapping; and postoperative—nrTMS for rehabilitation. Together, these technological advances support an efficacious functional-sparing technique to facilitate maximal surgical resection. Our protocol used the framework published by Jennings et al.  and goesThe impact of the above-mentioned techniques on motor outcomes has been previously established. Krieg et al.  showed tIntraoperative neurophysiological monitoring and mapping is currently an irreplaceable tool in oncology surgery. The utilization of intraoperative monitoring can facilitate both the maximization of the resection margins and the reduction of postoperative neurologic deficits . This prThe anatomical preservation of the structural networks that sub-serve the motor function is crucial for potential motor recovery and rehabilitation after surgery. Multiple authors ,19,20 haMultiple studies have shown the potential of nTMS in monitoring and predicting functional recovery after functional-eloquent brain surgery ,23. IvreMoreover, TMS is proven to facilitate neuroplasticity, the brain’s ability to reorganize and form new neural connections in response to changes in its environment, therefore being an attractive tool in neurorehabilitation after stroke for the treatment of both acute and chronic motor and language deficits with positive results ,28,29,30In recent years, several studies have investigated the use of TMS in postoperative neurorehabilitation following brain surgery ,32,33,34Our study used nTMS for motor rehabilitation after successful preservation of the motor pathway using a preoperative and intraoperative functional-informed MIPS technique. This was confirmed by positive nTMS responses in the postoperative mapping for every patient included and by the motor recovery experienced by these patients with the nrTMS for rehabilitation. As rightly pointed out by Schmidt et al. , we beliOur study has not been able to fully incorporate other preoperative imaging modalities such as fMRI and magnetoencephalography (MEG). fMRI is a validated tool for preoperative planning in neuro-oncology patients. Many studies in the literature up to now have demonstrated that it can be very sensitive in detecting functional motor cortex areas adjacent to a focal brain lesion ,39. On tAnother limitation of our study is the heterogeneity of the histological diagnoses in our patients. However, the main focus of this study is the functional outcome and not the overall survival. It is true that in a small cohort, survival outcomes cannot be measured due to the heterogeneity of the patients included. Nevertheless, surrogate information can be suggested based on the impact of the extent of resection in the included pathologies as these were lesions that potentially could not be resected or would not have been resected.Additionally, it is important to highlight that there is uncertainty regarding the impact of nTMS in rehabilitation as our patients were not randomized to this intervention. However, the previous randomized trial by Ille et al.  supporteFinally, our study is limited by the small number of patients; thus we are not able to conclude any statistically significant results. It is important to notice that the aim of this work is to provide a proof-of-concept research idea and evaluate the feasibility of this peri-operative framework, without comparing different peri-operative surgical strategies. Furthermore, we should notice that highly motor eloquent tumors patients that proceed with surgery are few and this is the main reason why a proof-of-concept study was performed. Further work is required to assess differences in the impact of this strategy in terms of the different histological diagnoses of the tumors as well as the specific tumor grading. This could be achieved by adopting a multicenter setting given the low number of patients assessed by a single center of patients with such highly eloquent lesions.Nevertheless, and as far as we are aware, this is the first study in the literature incorporating an extensive and detailed surgical protocol based on pre-, intra-, and postoperative stages with enhanced TMS-based rehabilitation. This study demonstrated that an integrated approach involving a combined Tractography-MIPS-IONM-TMS is a valuable tool for preserving cortical–subcortical functional anatomy during surgery, minimizing operative morbidity, and maximizing postoperative functional outcomes in patients with deep-seated motor eloquent tumors. The protocol is a safe and feasible technique that can be integrated into everyday practice, allowing patients to benefit from a successful functional-sparing approach and a rehabilitation framework for functional recovery after surgery. The results of this proof-of-concept study are promising and warrant further investigation in future research.


The baseline and six-month comparisons of sRANKL and OPG revealed no statistically significant differences between the two groups. The combined Nd: YAG—Er: YAG laser surgical therapy of peri-implantitis seemed to lead to more favorable improvements in regard to bleeding on probing six months after treatment compared to the conventional mechanical decontamination of the implant surface. None of the methods was found superior in the modification of bone loss biomarkers  six months after treatment.The current study aimed to investigate the effect of the combined Nd-Er: YAG laser on the surgical treatment of peri-implantitis by evaluating clinical markers and biomarkers of bone loss (RANKL/OPG). Twenty (20) patients having at least 1 implant diagnosed with peri-implantitis were randomly assigned to two groups for surgical treatment. In the test group (n = 10), Er: YAG laser was used for granulation tissue removal and implant surface decontamination, while Nd: YAG laser was employed for deep tissue decontamination and biomodulation. In the control group (n = 10), an access flap was applied, and mechanical instrumentation of the implant surface was performed by using titanium curettes. The following clinical parameters were evaluated at baseline and six months after treatment: Full-mouth Plaque Score (FMPS), Probing Pocket Depth (PPD), Probing Attachment Levels (PAL), recession (REC), and Bleeding on probing (BoP). Peri-implant crevicular fluid (PICF) was collected at baseline and six months for the evaluation of soluble RANKL and OPG utilizing enzyme-linked immunosorbent assay (ELISA). Baseline clinical values were similar for both groups, with no statistical differences between them. The study results indicated statistically significant improvements in the clinical parameters during the 6-month observation period in both groups. More specifically, PPD, PAL, and REC were improved in the test and control groups with no differences in the between-groups comparisons. However, a greater reduction in the BoP-positive sites was noted for the laser group (Mean change 22.05 ± 33.92 vs. 55.00 ± 30.48,  The most common biological complications regarding dental implants are Peri-implant mucositis and Peri-implantitis with peri-implantitis being described as an inflammatory lesion affecting the peri-implant mucosa characterized by gradual bone loss . SeveralTreatment of peri-implant lesions typically includes mechanical debridement of biofilm and calculus . This is2 laser in combination with bone augmentation indicated substantial bone gain compared to the control group , while a slight reduction was found in the laser group [6.63 ± 5.42–3.35 ± 3.22) pg/30 s], though the changes could not be assessed statistically. Those results should be interpreted with caution as they do not reflect the treatment effects. A more accurate way to determine the treatment effects would be the RANKL/OPG ratio, which could not be analyzed in the current study due to the reduced number of samples and missing values. Reports on the effects of treatment on RANKL/OPG are scarce in the literature. In the case of peri-implantitis, only one study utilizing surgical peri-implantitis therapy by means of titanium curettes and chlorhexidine has tested the effects on the RANKL/OPG system . The stuPrior to conclusions some limitations of the study were the relatively small number of partcipants. Though the sample size calculation indicated sufficient power of the sample, it is our belief that a higher number of participants would elucidate more the exact role of the biomarkers. In addition, the number of samples that were below the detection limit did not allow for a RANKL/OPG ratio calculation to assess the immunomodulatory effects of the treatment as described earlier.The combined Nd: YAG—Er: YAG laser surgical therapy of peri-implantitis seems to lead to more significant improvements in bleeding on probing six months after treatment compared to conventional mechanical decontamination of the implant surface.None of the methods was found superior in modifying the behavior of bone loss biomarkers  six months after treatment.Within the limits of the current study, it can be concluded that:The current study indicated a potential for the combined laser application to reduce inflammation of peri-implant tissues. Since this is the first study using a combined laser irradiation therapy in the treatment of peri-implantitis, future prospective studies including a higher number of participants should be planned to evaluate the full potential of the treatment. In addition, long-term follow-up studies should investigate the effects of the combined treatment in a more objective way.The exact role of RANKL and OPG in peri-implantitis needs to be further investigated by cohort studies with a large number of participants, and the effect of peri-implantitis treatment on the later biomarkers should be addressed in order to evaluate the diagnostic and prognostic accuracy of both proteins.


This paper presents a feasibility study on monitoring earthquake-caused furniture vibrations using radiofrequency identification (RFID) sensor tags. Finding unstable objects by exploiting the vibrations caused by weaker earthquakes is effective as one of the potential countermeasures for large-scale earthquakes in earthquake-prone areas. For this purpose, a previously proposed ultrahigh-frequency (UHF)-band RFID-based batteryless vibration/physical shock sensing system enabled long-term monitoring. This RFID sensor system introduced standby and active modes for long-term monitoring. This system enabled lower-cost wireless vibration measurements without affecting the vibration of furniture because the RFID-based sensor tags provide lightweight, low-cost, and battery-free operations. This RFID sensor system observed earthquake-cased furniture vibrations in a room on the fourth floor of a building eight stories high at Ibaraki University, Hitachi, Ibaraki, Japan. The observation results revealed that the RFID sensor tags identified the vibrations of furniture caused by earthquakes. The RFID sensor system also observed the vibration duration times of the objects in a room and specified the most unstable reference object. Hence, the proposed vibration sensing system helped achieve safe living in indoor environments. Natural disasters are increasing in many parts of the world and are major obstacles to sustainable development. Reducing disasters and mitigating damages are important issues for international communities. Among the top ten natural disasters worldwide according to the death toll in 2021, earthquakes are listed as number one ,4,5,6. IThe Internet of things (IoT) has attracted much attention because it can connect things to the network using wireless IoT devices . IoT devOrdinary RFID sensor systems continuously transmit electromagnetic waves from an RFID reader to obtain time series of sensing data. Because earthquakes are probabilistic events that are difficult to predict, RFID sensor systems work for a long time to monitor vibrations caused by earthquakes. However, these long-term measurements lead to energy waste and temperature increases in the RFID reader. Moreover, RFID sensor systems cause considerable useless data in long-term measurements. Uploading all the resulting data to an IoT cloud is likely to be a heavy load on the network. As a result, Song et al.  proposedThis paper presents a feasibility study on monitoring earthquake-caused furniture vibrations using RFID-based vibration/physical-shock sensor tags . FindingThis section provides application examples for the long-term monitoring of the earthquake-caused vibration of furniture in a home. Moreover, this section explains the operational principle of an RFID sensor tag with tilt switches and the entire system structure .This subsection introduces an RFID-based vibration/physical-shock sensor tag. Because earthquakes are unpredictable and burst events, long-term monitoring is necessary. An event-driven sensing capability, referred to as edge processing, is demanded . Figure The standby mode has a longer reading interval by employing a low-duty cycle setting , therebyThe RFID reader triggered the active mode when it sampled no readings. An earthquake, therefore, altered the operating modes from standby to active. The active mode had a radio irradiation time of 200 ms by employing a high-duty cycle. A high-duty cycle increased the number of RFID sensor tag readings, enabling accurate vibration and physical-shock monitoring. By considering the characteristics of the earthquake, this paper employed an active mode duration time of 60 s.This section presents observation results for four earthquakes observed at the laboratory of Ibaraki University, Hitachi, Japan. Ibaraki Prefecture is one of the most earthquake-prone cities on the main island of Japan and has an average of approximately 200 earthquakes each year.The measurement principle for long-term monitoring explained in the previous section was implemented in RFID reader control software. This control software program was based on a software development kit (SDK) implemented in C#. An RFID reader, DOTR-3200 , identifRFID sensor tags were installed on furniture in a room described in this paper. An artificial object consisting of empty cardboard boxes and a rectangle made from foam polystyrene was used as an unstable reference object. The experimental room was located in the building shown in In this experiment, an acceleration sensor measured the vibrations and physical shocks caused by earthquakes to compare the observed data of the RFID sensor tags with those of the acceleration sensor. This acceleration sensor measured the vibration and physical shocks of the computer desk together with RFID sensor tag 1. The acceleration sensor system observed a vibration at 5:40:12 JST, as shown in The RFID sensor tags had higher detection accuracies because they triggered the active mode three seconds before being detected by the acceleration sensor. RFID sensor tags 1 and 3 returned to ordinary levels at a similar time. RFID sensor tag 2 also returned to the ordinary level but 2 s after RFID sensor tags 1 and 3 because RFID sensor tag 2 monitored the unstable reference object. In addition, all the RFID sensor tags also detected vibrations at the time indicated by a black elliptical circle during the active mode. At this time, RFID sensor tags 1 and 3 had comparable metric values, and RFID sensor tag 2 detected a much lower value. These natural results confirmed that the reference object was the most unstable, and the computer desk and table with the acrylic showcase had comparable vibrations.The acceleration sensor system did not observe any vibrations, as shown in The acceleration sensor system and RFID sensor tags 1 and 3 did not detect vibrations because the seismic intensity was weak. Only RFID sensor tag 2 on the reference object detected vibrations. Therefore, the unstable reference object enabled us to confirm the sensitivity of the RFID sensor tag because a seismic intensity of one was the minimum indicator. Comparing experimental results in This paper presented a feasibility study on monitoring earthquake-caused furniture vibrations using RFID sensor tags. A batteryless earthquake vibration sensing system based on UHF-band RFID observed the vibration of furniture during earthquakes. Observations in this paper exhibited the employment of the standby and active modes for monitoring vibrations for a long time. These modes achieved accurate vibration and physical-shock sensing while reducing the temperature rise, energy consumption, and quantity of data of an RFID reader . FindingThe observed results also validated that the RFID sensor system monitored the vibration of furniture for a seismic intensity of two or more. The observed results verified that higher seismic intensities led to lower metrics and longer vibration duration times. This finding is crucial for building monitoring systems and achieving safe living in indoor environments. RFID sensor tags are easy to install on furniture because they have no internal battery. Therefore, monitoring the vibrations of furniture makes it possible to achieve safe living in a home.


Coronary flow obstruction following transcatheter aortic valve-in-valve implantation (VIV-TAVI) is associated with a high mortality risk. The aim of this work was to quantify the coronary perfusion after VIV-TAVI in a high-risk aortic root anatomy. 3D printed models of small aortic root were used to simulate the implantation of a TAVI prosthesis (Portico 23) into surgical prostheses (Trifecta 19 and 21). The aortic root models were tested in a pulsatile in vitro bench setup with a coronary perfusion simulator. The tests were performed at baseline and post-VIV-TAVI procedure in aligned and misaligned commissural configurations under simulated hemodynamic rest and exercise conditions. The experimental design provided highly controllable and repeatable flow and pressure conditions. The left and right coronary mean flow did not differ significantly at pre- and post-VIV-TAVI procedure in any tested configurations. The commissural misalignment did not induce any significant alterations to the coronary flow.High-risk aortic root anatomy did not trigger coronary ostia obstruction or coronary flow alteration after transcatheter aortic valve implantation in a surgical bioprosthesis as shown from in-vitro flow loop tests.The online version contains supplementary material available at 10.1007/s12265-023-10364-y. Transcatheter aortic valve-in-valve implantation (VIV-TAVI) is, currently, a IIa class recommendation for surgical aortic valve bioprosthesis (SAVB) failure . SupportCoronary ostia obstruction rate has been reported to be 4- to sixfold higher in VIV-TAVI than what has been reported for TAVI in native aortic valve, particularly for SAVB with externally mounted leaflets . In VIVISome morphometrical features of the AR, namely the sinotubular junction (STJ) diameter, the sinus of Valsalva diameter (SOVD), the coronary ostia height, and the valve-to-coronary distance (VTCD), have been identified clinically as factors associated to the impairment of coronary flow after VIV-TAVI . Narrow In order to preserve coronary ostia access and consequently an adequate coronary flow, it has been postulated that keeping an alignment of the transcatheter valve commissures with respect to the native aortic valve commissures is of paramount importance .Coronary occlusion is associated with very high mortality rates, but coronary flow after VIV-TAVI has been rarely investigated and the underlying mechanisms are poorly understood. Clinically, the phenomenon is complex to be studied due to multiple confounding factors. There is a need for a highly controllable experimental approach that enables in-depth studies of coronary occlusion or alteration phenomena in VIV-TAVI.The aim of this work is the development of a high reproducible bench model and an experimental protocol for the evaluation of VIV-TAVI performance able to quantify the coronary perfusion after VIV-TAVI in a high-risk AR configuration. The tests were performed under rest and exercise conditions to investigate any possible coronary flow difference induced by stress conditions.A self-expandable TAVI prosthesis  with label size 23 (2 samples) was implanted in a SAVB with externally mounted leaflets ) of label size 19 (3 samples) or 21 (3 samples), inside a rigid, 3D-printed AR model, as shown schematically in Fig. The Trifecta GT 19 was housed inside an AR model with aortic annulus diameter (AD) of 19 mm, while the Trifecta GT 21 was housed inside an AR model with AD of 21 mm; such AR models will be referred to as AR model 19 and AR model 21, respectively. Both AR models were made of an upper and lower part to ease SAVB mounting. The SAVB suturing ring was placed in a housing  equipped with a clamp-on 1″ probe and the coronary flowrates were measured with TS410  equipped with two in-line 4PXN probes. Aortic pressure was measured by a piezoresistive transducer . All signals were acquired with an A/D converter  at a sampling frequency of 200 Hz. A fiberscope  was used to directly inspect the kinematics of the prosthesis. The hemodynamic data was averaged over 10 cardiac cycles and the following parameters were derived: cardiac output , as mean aortic flowrate; stroke volume  as the integral of the aortic flow curve during the systole; backflow volume  as the integral of the aortic flow curve during the diastole; mean (cycle-averaged) aortic pressure . The coronary flowrate (in mL/min) was evaluated as the mean flowrate during the whole cardiac cycle, or during the systole and during the diastole for the left and right branches. The coronary flowrate parameters were expressed both as absolute values, and in percentage normalized to the baseline  mean value. The normalized coronary flowrate conceptually corresponds to the fractional flow reserve (FFR), a clinical parameter used in case of coronary stenosis, defined as the ratio between the maximum achievable myocardial blood flow and the maximum theoretical myocardial blood flow. In case of no obstructions , FFR is close to 100%, while FFR below 80% was associated with significant coronary stenosis . Based oFor each SAVB size, the tests were performed at baseline condition (only SAVB) and post-VIV-TAVI procedure with aligned and misaligned commissures. Misalignment was simulated with a 60° rotation angle of the TAVI commissures with respect to the SAVB commissures. All tests were run under typical rest loading conditions  and under simulated exercise conditions . The coronary simulator resistances of each branch were adjusted at the baseline rest conditions to obtain physiological left/right diastolic/systolic mean flowrate values . Each tep value of < 0.05 was assumed as statistically significant. To assess for potential relevant coronary flow impairment due to VIV-TAVI procedure, an equivalence test was carried out. The 95% confidence intervals (CI) of post-VIV-TAVI normalized mean left and right coronary flowrates were compared with the cut-off value of 80% inspired to the use of FFR when analyzing clinical data [Following normal distribution assessment using Shapiro–Wilk test, the data were presented as mean ± standard deviation. The variables were compared using repeated measures three-way ANOVA considering the treatment , the SAVB label size , and the loading conditions  as independent factors. The Bonferroni correction was used in post hoc analysis. A cal data .In total, 72 experimental tests were carried out. Coronary occlusion did not occur in any of the tested experimental conditions post-VIV-TAVI, regardless of the very limited space available for fluid flow between the internal wall of the AR models and the implanted prostheses. Exemplary endoscopic diastolic and systolic snapshots of the AR model 19 pre- and post-VIV-TAVI are shown in Fig. The tests were performed under controllable pressure and flow conditions Table . The aorThe maximum mean difference of AoP throughout all experimental tests was of 2.1 mmHg and 3.3 mmHg for rest and exercise conditions, respectively. Similarly, stroke volume was well controllable with maximum mean difference throughout test conditions of 2.7 mL/cycle. In case of AR model 19 at rest conditions, the stroke volume decreased significantly post-VIV-TAVI with respect to baseline conditions, which could be related to minor leakages from the circuit between the AR model and the flow probe. Nonetheless, the mean absolute differences (< 2.2 mL/cycle) can be considered negligible.p < 0.001) which was related to the increase of BV post-VIV-TAVI. The increase of BV (max mean difference < 3 mL/cycle) could be linked to cumulative effect of transvalvular (among TAVI leaflets) and paravalvular (between TAVI skirt and SAVR leaflets) leakages in the VIV-TAVI configuration with respect to the baseline configuration.Three-way ANOVA Table  revealedp < 0.001), as expected.Passing from rest to exercise conditions, a statistically significant increase of CO and AoP and decrease of BF were obtained  of the coronary flowrate was 4%.p < 0.001). The AR model size did not yield statistically significant difference in left coronary flow parameters. Significant difference of the mean systolic (p = 0.04) and diastolic (p = 0.006) right coronary flowrate related to the AR model size was detected. This was associated with the differences in the absolute values in the baseline conditions due to manual setting of the coronary simulator. In fact, when the coronary flowrate parameters were normalized to the baseline mean values, the AR model size no more resulted to induce statistically significant changes.In any of the performed tests, the implantation of TAVI prosthesis in SAVB did not cause coronary ostia occlusion or relevant coronary flow impairment. Three-way ANOVA Table  did not In all the tested conditions Fig.  the 95% Coronary obstruction in VIV-TAVI is a dreadful condition associated with a very high mortality risk. Its mechanism is related to the displacement and fixing of the SAVB leaflets in the proximity of the coronary ostia by the radial forces of the TAVI stent. A narrow AR reduces the area available for the blood flow entering the sinus of Valsalva with potential direct or indirect  conThese mechanisms are mainly dependent on the AR geometry and the SAVB characteristics  . FurtherClinically, the complex interplay between these factors limits the possibilities to isolate them and study their separate influence on coronary perfusion alteration in VIV-TAVI. The high mortality risk due to coronary occlusion, however, urges for in-depth understanding for better risk stratification and VIV-TAVI safety improvement. In the literature, 3D printed ARs have been already exploited for procedural pre-planning , 25. StoOur study is the first experimental in vitro work in which the AR geometry was considered a factor potentially affecting the coronary perfusion in VIV-TAVI scenario. The AR computer-aided design allowed us to obtain a versatile paradigmatic model, which potentially can replicate different morphometries by changing the model’s geometric parameters. The 3D printing technology allowed for rapid and high-accuracy manufacturing of the AR models. The pulse duplicator and the coronary flow simulator provided high-controllable physiological-like hemodynamic conditions in terms of pressures and aortic and coronary flow waveforms. All these aspects support the reliability of the obtained results.Particularly, in this study, the AR model design inputs were defined based on the realistic clinical insights and recreated a high-risk anatomical scenario based on VTCD, SOVD, and SOVH dimensions. Moreover, the smallest SAVB label sizes (19 and 21), with pericardium mounted outside the pivot, were selected to enhance coronary flow impairment risk. The TAVI deployment was controllable in terms of implantation depth and commissural alignment. This in vitro model allowed analyzing coronary flow after VIV-TAVI under rest and exercise simulated conditions.The results of this study provided no evidence of coronary obstruction or coronary flow disturbance in any of the 72 experimental tests. The chosen AR root anatomies did not trigger any clinically relevant coronary flow alterations post-VIV-TAVI, neither in case of the TAVI commissural misalignment nor under simulated exercise conditions in the analyzed SAVB and TAVI types.Clinically, the influence of commissural alignment between TAVI and SAVB on the coronary flow is debated. In this in vitro study, the misalignment of the TAVI commissures did not result as a significant factor altering the coronary perfusion. This finding is in line with the experimental results of Azadani et al. , 27 and The used setup allowed us to isolate the effect of hemodynamic conditions on the coronary perfusion (fixed resistances) from the physiological factors that would influence the coronary resistance in the in vivo situation. In this scenario, the change from rest to exercise loading conditions (in terms of heart rate and AoP increase) induced significant increase of coronary flowrate at baseline  and at VIV-TAVI conditions . In such controllable experimental conditions, no significant differences between baseline and VIV-TAVI configurations in terms of coronary flowrate parameters were found throughout the same loading condition. Moreover, no relevant changes in coronary perfusion were detected post-VIV-TAVI when compared to the 80% FFR reference cut-off value. The reasonable interpretation is that the local hemodynamics induced by VIV-TAVI complex in the simulated exercise conditions did not change in a way to influence the coronary flow. Of note, the simulated exercise conditions represented a physical activity tolerable by elderly patients, the target population of TAVI. Increasing the heart rate to 160 bpm in the future studies could take into account the expansion of TAVI treatment towards low- and intermediate-risk younger patients’ population .This is a proof-of-concept study, which demonstrated the reliability of the applied in vitro methodology to study the coronary perfusion post-VIV-TAVI, with future application potential. The results should be however carefully translated to clinical practice as the simulated conditions represent a narrow cohort of patients where VTCD was set to 4 mm, i.e., clinical cut-off value for high-risk coronary obstruction, and lower VTCD were not tested. Moreover, in the present study, the AR models had paradigmatic anatomy with symmetric sinuses of Valsalva and centered coronary ostia. The applied methodologies however permit AR geometry variations, including, e.g., SAVB angular inclination with respect to the AR axis, or replicating patient-specific AR anatomies, or housing other types of SAVB and TAVI prostheses. The AR models used in this study were rigid, lacking the compliance of the natural AR. This allowed accurate AR models’ geometry reproduction which was also independent from the hemodynamic conditions. Normally, in VIV-TAVI, the SAVB is degenerated, and calcifications present on the leaflets could play a role on coronary perfusion after VIV-TAVI. The SABV used in this study were not degenerated, as calcification simulation would be uncontrollable, and would introduce additional confounding factors . In the future, experimental SAVB degeneration strategies could be employed . In thisOur in vitro study does not provide any evidence for coronary obstruction or coronary flow impairment during VIV-TAVI in a small aortic root configuration with TAVI commissural aligned or misaligned configuration, neither in rest nor in exercise conditions. The data highlight that test setup represents a high-fidelity reproducible tool for coronary flow analysis. The versatility of our setup allows to replicate multiple clinical scenarios. The setup will be further employed for in-depth studies of the coronary flow alteration phenomenon in VIV-TAVI, potentially improving our knowledge and providing possible insights for better risk stratification, towards a better patients’ safety.Supplementary file1 Endoscopic en face videos of tested prostheses housed in an aortic root model of geometry considered risky for coronary occlusion at the baseline (Trifecta GT 19) and post valve-in-valve TAVI treatment (Trifecta GT 19 + Portico 23) with commissural alignment and misalignment configuration. (MP4 82384 KB)Below is the link to the electronic supplementary material.


Cohorts of healthy younger adults (18–50yrs) and healthy older adults (60–75yrs) were immunized intramuscularly or intranasally with an adenovirus-vectored RSV vaccine (PanAd3-RSV) as a prime dose and boosted with PanAd3-RSV or a poxvirus-vectored vaccine (MVA-RSV) encoding the same insert. Whole blood gene expression was measured at baseline, 3- and 7-days post vaccination. Intramuscular prime vaccination with PanAd3-RSV induced differential expression of 643 genes . Intranasal prime vaccination with PanAd3-RSV did not induce any differentially expressed genes (DEGs) in blood samples at 3 days post vaccination. Intranasally primed participants showed greater numbers of DEGS on boosting than intramuscularly primed participants. The most highly enriched biological processes related to DEGs after both prime and boost vaccination were type-1 interferon related pathways, lymphocytic and humoral immune responses. PanAd3 vaccination of healthy adults induces type-1 interferon and humoral immune responses.  Viral vectors that deliver gene products from unrelated organisms are one of the leading platforms for the development of novel vaccines  and are Analysis is reported here of the early host transcriptomic responses to two novel respiratory syncytial virus (RSV) viral-vectored vaccines in humans (PanAd3-RSV and MVA-RSV) encoding the same three antigens . In this study, participants were given PanAd3-RSV either by intramuscular or intra-nasal prime followed by homologous or heterologous boost, allowing insight into the host response to the same vaccine given by a different route, and to the same antigen delivered by a different viral vector. The aims of this study were to identify a transcriptomic signature in response to vaccination, and demonstrate which elements of this signature are linked to generation of desirable neutralizing antibody responses. The safety and immunogenicity of this vaccine have been previously assessed , the preThe RSV001 clinical trial was an open-label, dose-escalation, phase one evaluation of safety and immunogenicity of four combinations of prime/boost PanAd3-RSV and MVA-RSV vaccine, using combinations of vaccine in 42 healthy adults aged 18–50 years, with the primary findings from the study previously published . VolunteGene expression was determined using Illumina Human HT-12 v4 bead chips (Illumina Inc). Samples were randomised across beadchips to minimize clustering of volunteers or time-points after vaccination by batch effect. The fluorescent intensity values were scanned with an Illumina iScan System by the Wellcome Centre for Human Genetics core facility , and raw data were extracted and formatted using GenomeStudio software.lmfit’ and ‘eBayes’ functions of the limma package [P-value of <0.05 were used as criteria for the identification of differentially expressed probes.Linear regression with an empirical Bayes model was applied to the expression value of each probe after prime and boost with adjustment for volunteer age, sex, vaccine dose, day of sample collection and sample scan date using the ‘ package . The dup package , 21. A lRanked gene set enrichment analysis (GSEA) using the GSEA algorithm developed by the broad institute  was run In this study, whole blood samples were analysed from participants before, 3 days (early) and 7 days (late) after vaccination with IM or IN PanAd-RSV and following heterologous or homologous IM boost.Compared with baseline, a total of 212 genes  were difCompared with baseline, a total of 9 genes were found to be differentially expressed between baseline and 7 days following IM PanAd3-RSV prime  biological process terms , GO moleA total of 75 genes  were difThirty-one genes were found to be differentially expressed 7 days post boost vaccination in patients primed with IM-PanAd3-RSV and boosted with IM-MVA-RSV , all of p < 0.05), in particular the expansion of F-specific IFN-γ T-cells , the appearance of F-specific IgG and IgA antibody secreting cells 7 days after vaccination, and with changes in vector-specific and total-RSV neutralizing antibody titres 28 days after vaccination (Changes in gene expression at 3 days post IM prime vaccination and 3 days post boost vaccination (for both priming routes) were found to have statistical correlations with the change in key measures of RSV immunity in the blood of vaccine recipients  induced immune responses in the lungs and spleen without any notable changes in immunity measured in blood that were protective to experimental challenges . Notablyt-statistics for differential gene expression) of the entire probeset showed enrichment for a number of terms related to the development of memory CD4+ and CD8+ T-cell responses and humoral immunity at these time points. The development of these memory and humoral immune responses seven days post vaccination with a viral vector vaccine has been previously observed, and is consistent with what is accepted and desirable for these vaccinations [While our analyses showed a modest number of differentially expressed genes seven days post prime vaccination, with PanAd3-RSV and boost vaccination with MVA-RSV, ranked gene set enrichment analysis , when 6/10 group 2 individuals were actually sampled 2 days after vaccination and all other samples were obtained 3 days after vaccination . Adding the day of sampling as well as the baseline serum PanAd3 (vector) neutralizing antibody titre, which was also significantly different between study groups 1 and 2 volunteers, as covariates into the lmfit model did not significantly alter the number of differentially expressed probes following IM PanAd3-RSV prime (data not shown). Using DNA microarray data from whole blood is representative of the gene expression from a diversity of cell populations in circulation. What remains unclear is the extent to which the fold-change in gene expression from whole blood is influenced by relative changes in different cell populations after vaccination. For example, neutrophils transiently declined 3 days after vaccination (data not shown) which was not included in the lmfit model.This was a post hoc analysis and subject to limitations common to first-in-man clinical trials in that there were only a small number of volunteers in each prime/boost study group and the potential for confounding chance observations. This included insufficient statistical power for direct between-group comparisons. The within group analyses used here were applied with all effort to correct for baseline heterogeneity using the The findings of this study should be confirmed in further studies. The capacity to predict neutralizing antibody responses should ideally be modelled in a controlled study designed specifically to measure the relationship between expression of these genes and neutralization. This could be done in further mouse and human experiments where participants are vaccinated with RSV001, and expression of specific genes are assessed in a controlled study designed to determine the relationship between expression of these genes and a neutralization response. It would also be interesting to assess if the protein expression of the genes identified in this study are also associated with neutralization via an assay that could quantify protein expression, such as western blot. This would allow us to assess if the effects on neutralization of these genes is due to some downstream genes regulated by the genes identified in this study, or by expression of the identified genes themselves.It is difficult with this study design to determine the efficacy of the vaccine in the populations studied, as rates of infection after vaccination were not tested. While it is possible in this study to compare gene expression to markers of vaccine efficacy, such as neutralization and expression of markers such as interferons, there is not yet a single widely accepted correlate for protection against RSV. Testing for relationship of this biomarker with protective efficacy of the vaccine would require a study where infection rates of each participant are tracked over an RSV season and rate at which RSV infections are developed, as well as the severity of those infections is tracked and compared with gene expression markers.As PanAd3 and MVA constructs without the RSV insert were not included in this study, it was impossible to make the comparison between the response to the vector and the more specific responses to the insert.A small, shared core of transcriptional responses 3 days after vaccination, mostly related to type I interferon responses typically followed each dose of IM vaccine, with MVA responsible for a greater magnitude of transcriptional changes than the adenovirus vector. Analysis of the gene expression profiles has revealed the potential for expression of discrete and inter-dependent molecular pathways in response to different route of immunisation and different viral vectors.uxad003_suppl_Supplementary_MaterialClick here for additional data file.


The objectives were 2-fold: (1) to describe the utilization patterns of new users of triptan therapy and (2) to measure the direct  costs of migraine-related health care services in moderate-to-severe migraine patients treated with drug prophylaxis compared with migraine patients who are not treated with drug prophylaxis.A retrospective administrative database study was conducted from the perspective of a managed care health plan. Patients initiating triptan therapy were identified, and utilization in the 12 months following initiation of drug therapy was determined. In addition, moderate-to-severe migraine patients were identified based on the quantity of triptan medication dispensed. Patients were classified as utilizing or not utilizing migraine prophylaxis. Migraine-specific health services costs in the 12 months following identification were determined. A multivariate ordinary least squares regression model was constructed to determine the impact of the use of drug prophylaxis on total cost. Utilizing the model, the difference in health services costs was predicted for each subject and the average treatment effect was computed.Thirty-nine percent of new triptan users received only 1 triptan claim during the 12-month follow-up period, accounting for 11.5% of the total triptan cost incurred by the health plan for this cohort. For new triptan users, triptan use in the first or second quarter was correlated with triptan use in the entire 12-month follow-up period . The mean migraine related pharmacy cost per patient during the follow-up was $871; however, continuous users had mean costs  nearly 3 times the mean costs for new users . The average treatment effect of drug prophylaxis in moderate-to-severe migraine patients was a decrease of $560 ($514-$607) per patient per year in 1998-2001 dollars.High utilizers of migraine therapy can be identified early in treatment. Drug prophylaxis for migraine is cost saving, and an intervention program that increases the use of migraine prophylaxis in potential candidates could be cost beneficial.