Development and validation of a radiomics-based prediction pipeline for the response to stereotactic radiosurgery therapy in brain metastases.

OBJECTIVES: The first treatment strategy for brain metastases (BM) plays a pivotal role in the prognosis of patients. Among all strategies, stereotactic radiosurgery (SRS) is considered a promising therapy method. Therefore, we developed and validated a radiomics-based prediction pipeline to prospectively identify BM patients who are insensitive to SRS therapy, especially those who are at potential risk of progressive disease. METHODS: A total of 337 BM patients (277, 30, and 30 in the training set, internal validation set, and external validation set, respectively) were enrolled in the study. 19,377 radiomics features (3 masks × 3 MRI sequences × 2153 features) extracted from 9 ROIs were filtered through LASSO and Max-Relevance and Min-Redundancy (mRMR) algorithms. The selected radiomics features were combined with 4 clinical features to construct a two-stage cascaded model for the prediction of BM patients' response to SRS therapy using SVM and an ensemble learning classifier. The performance of the model was evaluated by its accuracy, specificity, sensitivity, and AUC curve. RESULTS: Radiomics features were integrated with the clinical features of patients in our optimal model, which showed excellent discriminative performance in the training set (AUC: 0.95, 95% CI: 0.88-0.98). The model was also verified in the internal validation set and external validation set (AUC 0.93, 95% CI: 0.76-0.95 and AUC 0.90, 95% CI: 0.73-0.93, respectively). CONCLUSIONS: The proposed prediction pipeline could non-invasively predict the response to SRS therapy in patients with brain metastases thus assisting doctors to precisely designate individualized first treatment decisions. CLINICAL RELEVANCE STATEMENT: The proposed prediction pipeline combines the radiomics features of multi-modal MRI with clinical features to construct machine learning models that noninvasively predict the response of patients with brain metastases to stereotactic radiosurgery therapy, assisting neuro-oncologists to develop personalized first treatment plans. KEY POINTS: • The proposed prediction pipeline can non-invasively predict the response to SRS therapy. • The combination of multi-modality and multi-mask contributes significantly to the prediction. • The edema index also shows a certain predictive value.

Membrane Design Criteria and Practical Viability of Pressure-Driven Distillation.

Pressure-driven distillation (PD) is a novel desalination technology based on hydraulic pressure driving force and vapor transport across a hydrophobic porous membrane. In theory, PD offers near-perfect rejection for nonvolatile solutes, chlorine resistance, and the ability to decouple water and solute transport. Despite its advantages, pore wetting and the development of a reverse transmembrane temperature difference are potential critical concerns in PD, with the former compromising the salt rejection and the latter reducing or even eliminating the driving force for vapor transport. We herein present an analysis to evaluate the practical viability and membrane design principles of PD with a focus on the dependence of flux and salt rejection (SR) on membrane properties. By modeling the mass transfer in a PD process under different conditions, we arrive at two important conclusions. First, a practically detrimental reverse transmembrane temperature difference does not develop in PD under all relevant circumstances and is thus not a practical concern. Second, for a PD process to achieve an acceptable SR, the membrane pores should be at the nanometer scale with a highly uniform pore size distribution. This analysis demonstrates the practical viability of PD and provides the principles for designing robust and high-performance PD membranes.

Nanocomposite Hydrogel Engineered Janus Membrane for Membrane Distillation with Robust Fouling, Wetting, and Scaling Resistance.

Membrane distillation (MD) is considered to be rather promising for high-salinity wastewater reclamation. However, its practical viability is seriously challenged by membrane wetting, fouling, and scaling issues arising from the complex components of hypersaline wastewater. It remains extremely difficult to overcome all three challenges at the same time. Herein, a nanocomposite hydrogel engineered Janus membrane has been facilely constructed for desired wetting/fouling/scaling-free properties, where a cellulose nanocrystal (CNC) composite hydrogel layer is formed in situ atop a microporous hydrophobic polytetrafluoroethylene (PTFE) substrate intermediated by an adhesive layer. By the synergies of the elevated membrane liquid entry pressure, inhibited surfactant diffusion, and highly hydratable surface imparted by the hydrogel/CNC (HC) layer, the resultant HC-PTFE membrane exhibits robust resistance to surfactant-induced wetting and oil fouling during 120 h of MD operation. Meanwhile, owing to the dense and hydroxyl-abundant surface, it is capable of mitigating gypsum scaling and scaling-induced wetting, resulting in a high normalized flux and low distillate conductivity at a concentration factor of 5.2. Importantly, the HC-PTFE membrane enables direct desalination of real hypersaline wastewater containing broad-spectrum foulants with stable vapor flux and robust salt rejection (99.90%) during long-term operation, demonstrating its great potential for wastewater management in industrial scenarios.

Influence of Pyrophosphate on the Generation of Soluble Mn(III) from Reactions Involving Mn Oxides and Mn(VII).

The detection of soluble Mn(III) is typically accomplished using strong complexing agents to trap Mn(III), but the generation of soluble Mn(III) induced by strong complexing agents has seldom been considered. In this study, pyrophosphate (PP), a nonredox active ligand, was chosen as a typical Mn(III) chelating reagent to study the influence of ligands on soluble Mn(III) formation in reactions involving Mn oxides and Mn(VII). The presence of excess PP induced the generation of soluble Mn(III)-PP from α- and δ-MnO2 and led to the conproportionation reaction of α-, ß-, δ-, or colloidal MnO2 with Mn(II) at pH 7.0. Compared to MnO2 minerals, colloidal MnO2 showed much higher reactivity toward Mn(II) in the presence of PP and the conproportionation rate of colloidal MnO2 with Mn(II) elevated with increasing PP dosage and decreasing pH. The generation of Mn(III) was not observed in MnO4-/S2O32- or MnO4-/NH3OH+ system without PP while the introduction of excess PP induced the generation of Mn(III)-PP. Thermodynamic calculation results were consistent with the experimental observations. These findings not only provide evidence for the unsuitability of using strong ligands in quantification of soluble Mn(III) in manganese-involved redox reactions, but also advance the understanding of soluble Mn(III) generation in aquatic environment.

Hydrogen Gas Attenuates Myocardial Ischemia Reperfusion Injury Independent of Postconditioning in Rats by Attenuating Endoplasmic Reticulum Stress-Induced Autophagy.

BACKGROUND/AIMS: To study the effect of inhaling hydrogen gas on myocardial ischemic/reperfusion(I/R) injury in rats. METHODS: Seventy male Wistar albino rats were divided into five groups at random as the sham group (Sham). The I/R group (I/R), The ischemic postconditioning group (IPo), The I/R plus hydrogen group (IH2) and the ischemic postconditioning plus hydrogen group (IPoH2). The Sham group was without coronary occlusion. In I/R group, Ischemic/reperfusion injury was induced by coronary occlusion for 1 hour. Followed by 2 hours of reperfusion. In the IPo and IPoH2 group, four cycles of 1 min reperfusion/1 min ischemia was given at the end of 1 hour coronary occlusion. While 2% hydrogen was administered by inhalation 5 min before reperfusion till 2 hours after reperfusion in both the IPoH2 and IH2 group. The heart and blood samples were harvested at the end of the surgical protocol. Then the myocardium cell endoplasmic reticulum(ER) stress and autophagy was observed by electron microscope. In addition, the cardiac ER stress and autophagy related proteins expression were detected by Western blotting analysis. RESULTS: Both inhaling 2% hydrogen and ischemic postconditioning treatment reduced the ischemic size and serum troponin I level in rats with I/R injury, and inhaling hydrogen showed a more curative effect compared with ischemic postconditioning treatment. Meanwhile inhaling hydrogen showed a better protective effect in attenuating tissue reactive oxygen species. Malondialdehyde levels and immunoreactivities against 8-hydroxy-2'-deoxyguanosine and inhibiting cardiac endoplasmic reticulum stress and down-regulating autophagy as compared with ischemic postconditioning treatment. CONCLUSION: These results revealed a better protective effect of hydrogen on myocardial ischemic/reperfusion injury in rats by attenuating endoplasmic reticulum stress and down-regulating autophagy compared with ischemic postconditioning treatment.

Compartment calcium model of frog skeletal muscle during activation.

Skeletal muscle contraction is triggered by a rise in calcium (Ca(2+)) concentration in the myofibrillar space. The objective of this study was to develop a voltage dependent compartment model of Ca(2+) dynamics in frog skeletal muscle fibers. The compartment model corresponds to the myofibrillar space (MS) and a calcium store, the sarcoplasmic reticulum (SR). Ca(2+) is released from the SR to the MS based on the voltage and is able to bind to several proteins in the MS. We use a detailed model to account for voltage dependent Ca(2+) release and inactivation. With this model, we are able to match previous experimental data for Ca(2+) release and binding to proteins for an applied (fixed) voltage. We explore the sensitivity of parameters in the model and illustrate the importance of inactivation of the SR; during a long depolarization, the SR must be inactivated in order to achieve realistic Ca(2+) concentrations in the MS. A Hodgkin Huxley type model was also developed to describe voltage at the surface membrane using electrophysiological data from previous experiments. This voltage model was then used as the time dependent voltage to determine Ca(2+) release from the SR. With this fully coupled model, we were able to match previous experimental results for Ca(2+) concentrations for a given applied current. Additionally, we examined simulated Ca(2+) concentrations in the case of twitch and tetanus, corresponding to different applied currents. The developed model is robust and reproduces many aspects of voltage dependent calcium signaling in frog skeletal muscle fibers. This modeling framework provides a platform for future studies of excitation contraction coupling in skeletal muscle fibers.

SRTRP-Net: A multi-task learning network for segmentation and prediction of stereotactic radiosurgery treatment response in brain metastases.

Before the Stereotactic Radiosurgery (SRS) treatment, it is of great clinical significance to avoid secondary genetic damage and guide the personalized treatment plans for patients with brain metastases (BM) by predicting the response to SRS treatment of brain metastatic lesions. Thus, we developed a multi-task learning model termed SRTRP-Net to provide prior knowledge of BM ROI and predict the SRS treatment response of the lesion. In dual-encoder tumor segmentation Network (DTS-Net), two parallel encoders encode the original and mirrored multi-modal MRI images. The differences in the dual-encoder features between foreground and background are enhanced by the symmetrical visual difference block (SVDB). In the bottom layer of the encoder, a transformer is used to extract local contextual features in the spatial and depth dimensions of low-resolution images. Then, the decoder of DTS-Net provides the prior knowledge for predicting the response to SRS treatment by performing BM segmentation. SRS response prediction network (SRP-Net) directly utilizes shared multi-modal MRI features weighted by the signed distance map (SDM) of the masks. The bidirectional multi-dimensional feature fusion module (BMDF) fuses the shared features and the clinical text information features to obtain comprehensive tumor information for characterizing tumors and predicting SRS treatment response. Experiments based on internal and external clinical datasets have shown that SRTRP-Net achieves comparable or better results. We believe that SRTRP-Net can help clinicians accurately develop personalized first-time treatment regimens for BM patients and improve their survival.

MHD-Net: Memory-Aware Hetero-Modal Distillation Network for Thymic Epithelial Tumor Typing With Missing Pathology Modality.

Fusing multi-modal radiology and pathology data with complementary information can improve the accuracy of tumor typing. However, collecting pathology data is difficult since it is high-cost and sometimes only obtainable after the surgery, which limits the application of multi-modal methods in diagnosis. To address this problem, we propose comprehensively learning multi-modal radiology-pathology data in training, and only using uni-modal radiology data in testing. Concretely, a Memory-aware Hetero-modal Distillation Network (MHD-Net) is proposed, which can distill well-learned multi-modal knowledge with the assistance of memory from the teacher to the student. In the teacher, to tackle the challenge in hetero-modal feature fusion, we propose a novel spatial-differentiated hetero-modal fusion module (SHFM) that models spatial-specific tumor information correlations across modalities. As only radiology data is accessible to the student, we store pathology features in the proposed contrast-boosted typing memory module (CTMM) that achieves type-wise memory updating and stage-wise contrastive memory boosting to ensure the effectiveness and generalization of memory items. In the student, to improve the cross-modal distillation, we propose a multi-stage memory-aware distillation (MMD) scheme that reads memory-aware pathology features from CTMM to remedy missing modal-specific information. Furthermore, we construct a Radiology-Pathology Thymic Epithelial Tumor (RPTET) dataset containing paired CT and WSI images with annotations. Experiments on the RPTET and CPTAC-LUAD datasets demonstrate that MHD-Net significantly improves tumor typing and outperforms existing multi-modal methods on missing modality situations.

A Multi-center Dental Panoramic Radiography Image Dataset for Impacted Teeth, Periodontitis, and Dental Caries: Benchmarking Segmentation and Classification Tasks.

Panoramic radiography imaging plays a crucial role in the diagnostic process of dental diseases. However, current artificial intelligence research datasets for panoramic radiography dental image processing are often limited to single-center and single-task scenarios, making it difficult to generalize their results. To address this, we present a multi-center, multi-task labeled dataset. In this study, our dataset comprises three datasets obtained from different hospitals. The first set has 4940 panoramic radiography images and corresponding labels from the Stemmatological Hospital of the General Hospital of Ningxia Medical University. The second set includes 716 panoramic radiography images and labels from the People's Hospital of Yinchuan City, Ningxia. The third dataset contains 880 panoramic radiography images and labels from a hospital in Shenzhen, Guangdong Province. This comprehensive dataset encompasses three types of dental diseases: impacted teeth, periodontitis, and dental caries. Specifically, it comprises 2555 images related to impacted teeth, 2735 images related to periodontitis, and 1246 images related to dental caries. In order to evaluate the performance of the dataset, we conducted benchmark tests for segmentation and classification tasks on our dataset. The results show that the presented dataset could be effectively used for benchmarking segmentation and classification tasks critical to the diagnosis of dental diseases. To request our multi-center dataset, please visit the address: https://github.com/qinxin99/qinxini .

A spherically-shaped PZT thin film ultrasonic transducer with an acoustic impedance gradient matching layer based on a micromachined periodically structured flexible substrate.

This paper presents the microfabrication of an acoustic impedance gradient matching layer on a spherically-shaped piezoelectric ultrasonic transducer. The acoustic matching layer can be designed to achieve higher acoustic energy transmission and operating bandwidth. Also included in this paper are a theoretical analysis of the device design and a micromachining technique to produce the novel transducer. Based on a design of a lead titanium zirconium (PZT) micropillar array, the constructed gradient acoustic matching layer has much better acoustic transmission efficiency within a 20-50 MHz operation range compared to a matching layer with a conventional quarter-wavelength thickness Parylene deposition. To construct the transducer, periodic microcavities are built on a flexible copper sheet, and then the sheet forms a designed curvature with a ball shaping. After PZT slurry deposition, the constructed PZT micropillar array is released onto a curved thin PZT layer. Following Parylene conformal coating on the processed PZT micropillars, the PZT micropillars and the surrounding Parylene comprise a matching layer with gradient acoustic impedance. By using the proposed technique, the fabricated transducer achieves a center frequency of 26 MHz and a -6 dB bandwidth of approximately 65%.

TUCNet: A channel and spatial attention-based graph convolutional network for teeth upsampling and completion.

With the increasing popularity of the use of 3D scanning equipment in capturing oral cavity in dental health applications, the quality of 3D dental models has become vital in oral prosthodontics and orthodontics. However, the point cloud data obtained can often be sparse and thus missing information. To address this issue, we construct a high-resolution teeth point cloud completion method named TUCNet to fill up the sparse and incomplete oral point cloud collected and output a dense and complete teeth point cloud. First, we propose a Channel and Spatial Attentive EdgeConv (CSAE) module to fuse local and global contexts in the point feature extraction. Second, we propose a CSAE-based point cloud upsample (CPCU) module to gradually increase the number of points in the point clouds. TUCNet employs a tree-based approach to generate complete point clouds, where child points are derived through a splitting process from parent points following each CPCU. The CPCU learns the up-sampling pattern of each parent point by combining the attention mechanism and the point deconvolution operation. Skip connections are introduced between CPCUs to summarize the split mode of the previous layer of CPCUs, which is used to generate the split mode of the current CPCUs. We conduct numerous experiments on the teeth point cloud completion dataset and the PCN dataset. The experimental results show that our TUCNet not only achieves the state-of-the-art performance on the teeth dataset, but also achieves excellent performance on the PCN dataset.

BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors.

Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.

BTSC-TNAS: A neural architecture search-based transformer for brain tumor segmentation and classification.

Glioblastoma (GBM), isolated brain metastasis (SBM), and primary central nervous system lymphoma (PCNSL) possess a high level of similarity in histomorphology and clinical manifestations on multimodal MRI. Such similarities have led to challenges in the clinical diagnosis of these three malignant tumors. However, many existing models solely focus on either the task of segmentation or classification, which limits the application of computer-aided diagnosis in clinical diagnosis and treatment. To solve this problem, we propose a multi-task learning transformer with neural architecture search (NAS) for brain tumor segmentation and classification (BTSC-TNAS). In the segmentation stage, we use a nested transformer U-shape network (NTU-NAS) with NAS to directly predict brain tumor masks from multi-modal MRI images. In the tumor classification stage, we use the multiscale features obtained from the encoder of NTU-NAS as the input features of the classification network (MSC-NET), which are integrated and corrected by the classification feature correction enhancement (CFCE) block to improve the accuracy of classification. The proposed BTSC-TNAS achieves an average Dice coefficient of 80.86% and 87.12% for the segmentation of tumor region and the maximum abnormal region in clinical data respectively. The model achieves a classification accuracy of 0.941. The experiments performed on the BraTS 2019 dataset show that the proposed BTSC-TNAS has excellent generalizability and can provide support for some challenging tasks in the diagnosis and treatment of brain tumors.

An integrative non-invasive malignant brain tumors classification and Ki-67 labeling index prediction pipeline with radiomics approach.

BACKGROUND: The histological sub-classes of brain tumors and the Ki-67 labeling index (LI) of tumor cells are major factors in the diagnosis, prognosis, and treatment management of patients. Many existing studies primarily focused on the classification of two classes of brain tumors and the Ki-67LI of gliomas. This study aimed to develop a preoperative non-invasive radiomics pipeline based on multiparametric-MRI to classify-three types of brain tumors, glioblastoma (GBM), metastasis (MET) and primary central nervous system lymphoma (PCNSL), and to predict their corresponding Ki-67LI. METHODS: In this retrospective study, 153 patients with malignant brain tumors were involved. The radiomics features were extracted from three types of MRI (T1-weighted imaging (T1WI), fluid-attenuated inversion recovery (FLAIR), and contrast-enhanced T1-weighted imaging (CE-T1WI)) with three masks (tumor core, edema, and whole tumor masks) and selected by a combination of Pearson correlation coefficient (CORR), LASSO, and Max-Relevance and Min-Redundancy (mRMR) filters. The performance of six classifiers was compared and the top three performing classifiers were used to construct the ensemble learning model (ELM). The proposed ELM was evaluated in the training dataset (108 patients) by 5-fold cross-validation and in the test dataset (45 patients) by hold-out. The accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-Score, and the area under the receiver operating characteristic curve (AUC) indicators evaluated the performance of the models. RESULTS: The best feature sets and ELM with the optimal performance were selected to construct the tri-categorized brain tumor aided diagnosis model (training dataset AUC: 0.96 (95% CI: 0.93, 0.99); test dataset AUC: 0.93) and Ki-67LI prediction model (training dataset AUC: 0.96 (95% CI: 0.94, 0.98); test dataset AUC: 0.91). The CE-T1WI was the best single modality for all classifiers. Meanwhile, the whole tumor was the most vital mask for the tumor classification and the tumor core was the most vital mask for the Ki-67LI prediction. CONCLUSION: The developed radiomics models led to the precise preoperative classification of GBM, MET, and PCNSL and the prediction of Ki-67LI, which could be utilized in clinical practice for the treatment planning for brain tumors.

Cross-Boosted Multi-Target Domain Adaptation for Multi-Modality Histopathology Image Translation and Segmentation.

Recent digital pathology workflows mainly focus on mono-modality histopathology image analysis. However, they ignore the complementarity between Haematoxylin & Eosin (H&E) and Immunohistochemically (IHC) stained images, which can provide comprehensive gold standard for cancer diagnosis. To resolve this issue, we propose a cross-boosted multi-target domain adaptation pipeline for multi-modality histopathology images, which contains Cross-frequency Style-auxiliary Translation Network (CSTN) and Dual Cross-boosted Segmentation Network (DCSN). Firstly, CSTN achieves the one-to-many translation from fluorescence microscopy images to H&E and IHC images for providing source domain training data. To generate images with realistic color and texture, Cross-frequency Feature Transfer Module (CFTM) is developed to pertinently restructure and normalize high-frequency content and low-frequency style features from different domains. Then, DCSN fulfills multi-target domain adaptive segmentation, where a dual-branch encoder is introduced, and Bidirectional Cross-domain Boosting Module (BCBM) is designed to implement cross-modality information complementation through bidirectional inter-domain collaboration. Finally, we establish Multi-modality Thymus Histopathology (MThH) dataset, which is the largest publicly available H&E and IHC image benchmark. Experiments on MThH dataset and several public datasets show that the proposed pipeline outperforms state-of-the-art methods on both histopathology image translation and segmentation.

Role of pyrophosphate on the degradation of sulfamethoxazole by permanganate combined with different reductants: Positive or negative.

Activating permanganate with reductants has gained increasing attention recently for efficient organic contaminants abatement via reactive intermediate Mn species. However, few studies have been conducted to explore the role of pyrophosphate (PP), a typical complexing agent for intermediate Mn species, in activated permanganate systems. In this study, taking sulfamethoxazole (SMX) as a probe compound, the influences of PP on SMX degradation by permanganate/thiosulfate and permanganate/hydroxylamine were extensively studied. It was found that both thiosulfate and hydroxylamine were able to activate permanganate for oxidation of SMX in the absence of PP. However, upon the introduction of PP, opposite effects were observed in the two systems where PP further improved the activation of permanganate by thiosulfate but dampened the performance of permanganate/hydroxylamine markedly. For permanganate/hydroxylamine system, MnO2 was determined to be the only reactive oxidative species accounting for SMX degradation in the absence of PP, and its generation could be completely inhibited by PP. While in permanganate/thiosulfate system, both Mn(V) and MnO2 were responsible for SMX oxidation, and the introduction of PP could strengthen the oxidative ability of Mn(V). These results could shed some insights on the suitability of applying PP to explore the kinetics and mechanisms of manganese involved redox reactions. PRACTITIONER POINTS: Both Na2 S2 O3 and NH2 OH·HCl can activate KMnO4 for SMX removal without PP. MnO2 is the reactive oxidative species involved in KMnO4 /NH2 OH·HCl system. Mn(V) and MnO2 account for the SMX oxidation by KMnO4 /Na2 S2 O3 system. PP could inhibit the formation of MnO2 but enhance the oxidative ability of Mn(V).

Oscillatory cortical forces promote three dimensional cell intercalations that shape the murine mandibular arch.

Multiple vertebrate embryonic structures such as organ primordia are composed of confluent cells. Although mechanisms that shape tissue sheets are increasingly understood, those which shape a volume of cells remain obscure. Here we show that 3D mesenchymal cell intercalations are essential to shape the mandibular arch of the mouse embryo. Using a genetically encoded vinculin tension sensor that we knock-in to the mouse genome, we show that cortical force oscillations promote these intercalations. Genetic loss- and gain-of-function approaches show that Wnt5a functions as a spatial cue to coordinate cell polarity and cytoskeletal oscillation. These processes diminish tissue rigidity and help cells to overcome the energy barrier to intercalation. YAP/TAZ and PIEZO1 serve as downstream effectors of Wnt5a-mediated actomyosin polarity and cytosolic calcium transients that orient and drive mesenchymal cell intercalations. These findings advance our understanding of how developmental pathways regulate biophysical properties and forces to shape a solid organ primordium.

Dual Regulatory Functions of SUFU and Targetome of GLI2 in SHH Subgroup Medulloblastoma.

SUFU alterations are common in human Sonic Hedgehog (SHH) subgroup medulloblastoma (MB). However, its tumorigenic mechanisms have remained elusive. Here, we report that loss of Sufu alone is unable to induce MB formation in mice, due to insufficient Gli2 activation. Simultaneous loss of Spop, an E3 ubiquitin ligase targeting Gli2, restores robust Gli2 activation and induces rapid MB formation in Sufu knockout background. We also demonstrated a tumor-promoting role of Sufu in Smo-activated MB (∼60% of human SHH MB) by maintaining robust Gli activity. Having established Gli2 activation as a key driver of SHH MB, we report a comprehensive analysis of its targetome. Furthermore, we identified Atoh1 as a target and molecular accomplice of Gli2 that activates core SHH MB signature genes in a synergistic manner. Overall, our work establishes the dual role of SUFU in SHH MB and provides mechanistic insights into transcriptional regulation underlying Gli2-mediated SHH MB tumorigenesis.

Inhalation of Hydrogen Attenuates Progression of Chronic Heart Failure via Suppression of Oxidative Stress and P53 Related to Apoptosis Pathway in Rats.

Background: Continuous damage from oxidative stress and apoptosis are the important mechanisms that facilitate chronic heart failure (CHF). Molecular hydrogen (H2) has potentiality in the aspects of anti-oxidation. The objectives of this study were to investigate the possible mechanism of H2 inhalation in delaying the progress of CHF. Methods and Results: A total of 60 Sprague-Dawley (SD) rats were randomly divided into four groups: Sham, Sham treated with H2, CHF and CHF treated with H2. Rats from CHF and CHF treated with H2 groups were injected isoprenaline subcutaneously to establish the rat CHF model. One month later, the rat with CHF was identified by the echocardiography. After inhalation of H2, cardiac function was improved vs. CHF (p < 0.05), whereas oxidative stress damage and apoptosis were significantly attenuated (p < 0.05). In this study, the mild oxidative stress was induced in primary cardiomyocytes of rats, and H2 treatments significantly reduced oxidative stress damage and apoptosis in cardiomyocytes (p < 0.05 or p < 0.01). Finally, as a pivotal transcription factor in reactive oxygen species (ROS)-apoptosis signaling pathway, the expression and phosphorylation of p53 were significantly reduced by H2 treatment in this rat model and H9c2 cells (p < 0.05 or p < 0.01). Conclusion: As a safe antioxidant, molecular hydrogen mitigates the progression of CHF via inhibiting apoptosis modulated by p53. Therefore, from the translational point of view and speculation, H2 is equipped with potential therapeutic application as a novel antioxidant in protecting CHF in the future.