Search

Two novel polyketides, called colletotrikalactones A and B (1 and 2), together with eleven known compounds (3–13) were isolated from the culture broth of the mangrove associated fungus Colletotrichum sp. J065. Compounds 1 and 2 featured a rare 5/6/10-fused tricyclic scaffold. The new structures were elucidated through comprehensive spectroscopic analyses and chemical transformation. [ABSTRACT FROM AUTHOR]

Objective: The role of gamma‐aminobutyric acid‐ergic (GABAergic) neuron impairment in Alzheimer's disease (AD), and if and how transplantation of healthy GABAergic neurons can improve AD, remain unknown. Methods: Human‐derived medial ganglionic eminence progenitors (hiMGEs) differentiated from programmed induced neural precursor cells (hiNPCs) were injected into the dentate gyrus region of the hippocampus (HIP). Results: We showed that grafts migrate to the whole brain and form functional synaptic connections in amyloid precursor protein gene/ presenilin‐1 (APP/PS1) chimeric mice. Following transplantation of hiMGEs, behavioral deficits and AD‐related pathology were alleviated and defective neurons were repaired. Notably, exosomes secreted from hiMGEs, which are rich in anti‐inflammatory miRNA, inhibited astrocyte activation invitro and in vivo, and the mechanism was related to regulation of CD4+ Th1 cells mediated tumor necrosis factor (TNF) pathway. Interpretation: Taken together, these findings support the hypothesis that hiMGEs transplantation is an alternative treatment for neuronal loss in AD and demonstrate that exosomes with anti‐inflammatory activity derived from hiMGEs are important factors for graft survival. ANN NEUROL 2024;96:488–507 [ABSTRACT FROM AUTHOR]

This work aims to develop an experimental investigation into the effectiveness of the sliding-mode approach for hybrid vibration-based energy harvesting. A proposed sliding-mode triboelectric-electromagnetic-piezoelectric energy harvesting model involves a cantilever beam with a tip mass exposed to magnetic and frictional forces. The experimental findings indicate that the system can achieve its peak inter-well oscillation output within a low-frequency range of 4Hz–6Hz. Friction has a lesser impact on the open-circuit voltage output at an excitation acceleration of 1.5g compared with 1g. The distribution of tri-stability changes with the presence of friction. This model provides a deeper understanding of the influence of the dry friction coefficient (0.2–0.5) on the interactive behaviors of different generator units. [ABSTRACT FROM AUTHOR]

In recent years, there has been rapid development in electric aircraft, particularly electric vertical takeoff and landing (eVTOL) aircraft, as part of efforts to promote green aviation. During the conceptual design stage, it is crucial to select appropriate values for key parameters and conduct sensitivity analysis on these parameters. This study focuses on an electric tilt-rotor aircraft and proposes a performance analysis method for electric aircraft while developing a general design tool specifically for this type of aircraft. Subsequently, the impact of wing incidence angle, sweep angle, span, propeller solidity, battery-specific energy, and battery mass on range, maximum takeoff weight, and hover power are analyzed. The results show that the battery mass, wingspan, and wingtip chord length have great effects on the maximum takeoff weight; among these, battery mass had the greatest influence. In terms of range, the battery energy density has a great positive effect on range, while the increase in wing angle of incidence, wingtip chord length and battery mass have some negative effects on range. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

A 2-D airfoil shape optimization in transonic low-Reynolds number regime is conducted. A Navier–Stokes flow solver with a transition model (k-ω SST γ-Reθ) is used to evaluate the fitness function. Single-point and multi-point formulations of the optimization results are compared. In addition, the effects of Mach number and angles of attack on aerodynamic characteristics of the optimized airfoils are investigated under low Reynolds number (Re = 17,000) and high-subsonic-flow ( M a , ∞ = 0.6 − 0.9) conditions. The results show that the corresponding drag divergence Mach number curves of the conventional airfoil present almost a parallel shifting at the entire Mach number range. By contrast, the unconventional airfoil starts showing a significant drag reduction when Mach number is greater than 0.75. Besides, the maximum lift-to-drag ratio is highly influenced by the Mach number because of the formation, movement, type, and strength of a shock wave. In addition, the distinguishing difference in the conclusion between two airfoils is that the lift fluctuation of the conventional airfoil amplifies with the increase of the Mach number. However, the unconventional airfoil shows an opposite trend. [ABSTRACT FROM AUTHOR]

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Chemoresistance to cisplatin (DDP) therapy is a major obstacle that needs to be overcome in treating lung cancer patients. Xanthatin has been reported to exhibit an antitumor effect on various cancers, but the function of xanthatin in DDP‐resistance lung cancer remains unclear. The study aimed to explore the effect and mechanisms of xanthatin on proliferation, apoptosis, and migration in DDP‐resistance lung cancer cells. In the present study, xanthatin suppresses the expression of glucose transporter 1 (GLUT1), attenuates the pentose phosphate pathway (PPP), and causes ROS accumulation and apoptosis, thereby mitigating the antioxidative capacity in DDP‐resistance cells. Previous studies have shown that GLUT1 is associated with resistance to platinum drugs. We found that GLUT1 was significantly increased in the DDP‐resistant lung cancer cell line compared to the parental cell line, and xanthatin significantly downregulated GLUT1 expression in DDP‐resistant lung cancer cells. Notably, overexpression of GLUT1 significantly reduced the production of ROS and increased cellular NADPH/NADP+ and GSH/GSSG ratios. Thus, these results suggest that xanthatin induces DDP‐resistance lung cancer cells apoptosis through regulation of GLUT1‐mediated ROS accumulation. These findings might provide a possible strategy for the clinical treatment of DDP‐resistant lung cancer. [ABSTRACT FROM AUTHOR]

Ferrous iron (Fe2+) has more potent hydroxyl radical (⋅OH)‐generating ability than other Fenton‐type metal ions, making Fe‐based nanomaterials attractive for chemodynamic therapy (CDT). However, because Fe2+ can be converted by ferritin heavy chain (FHC) to nontoxic ferric form and then sequestered in ferritin, therapeutic outcomes of Fe‐mediated CDT agents are still far from satisfactory. Here we report the synthesis of siRNA‐embedded Fe0 nanoparticles (Fe0‐siRNA NPs) for self‐reinforcing CDT via FHC downregulation. Upon internalization by cancer cells, pH‐responsive Fe0‐siRNA NPs are degraded to release Fe2+ and FHC siRNA in acidic endo/lysosomes with the aid of oxygen (O2). The accompanied O2 depletion causes an intracellular pH decrease, which further promotes the degradation of Fe0‐siRNA NPs. In addition to initiating chemodynamic process, Fe2+‐catalyzed ⋅OH generation facilitates endo/lysosomal escape of siRNA by disrupting the membranes, enabling FHC downregulation‐enhanced CDT. [ABSTRACT FROM AUTHOR]

Ferrous iron (Fe2+) has more potent hydroxyl radical (⋅OH)‐generating ability than other Fenton‐type metal ions, making Fe‐based nanomaterials attractive for chemodynamic therapy (CDT). However, because Fe2+ can be converted by ferritin heavy chain (FHC) to nontoxic ferric form and then sequestered in ferritin, therapeutic outcomes of Fe‐mediated CDT agents are still far from satisfactory. Here we report the synthesis of siRNA‐embedded Fe0 nanoparticles (Fe0‐siRNA NPs) for self‐reinforcing CDT via FHC downregulation. Upon internalization by cancer cells, pH‐responsive Fe0‐siRNA NPs are degraded to release Fe2+ and FHC siRNA in acidic endo/lysosomes with the aid of oxygen (O2). The accompanied O2 depletion causes an intracellular pH decrease, which further promotes the degradation of Fe0‐siRNA NPs. In addition to initiating chemodynamic process, Fe2+‐catalyzed ⋅OH generation facilitates endo/lysosomal escape of siRNA by disrupting the membranes, enabling FHC downregulation‐enhanced CDT. [ABSTRACT FROM AUTHOR]

Minimum distance to a solid wall is a primary parameter in turbulence models and overset grid assembly for computational fluid dynamics. In present work, a parallel advancing front method is proposed based on partitioned unstructured grids for the sake of further efficient wall distance computation. In order to overcome the inherent problem of conventional advancing front method in parallel environment, a novel "advancing twice and rippling once" strategy is developed to compute wall distance efficiently and further recover the accuracy. Significantly, the established framework is of modular nature to be easily extended to overset grid system for complex multi‐body configurations. The performance of the developed techniques is evidenced by comparison with the existing alternative ways in terms of computing efficiency and accuracy. Subsequently, further case studies are performed to examine its capability to deal with complex engineering applications such as a full transportation, a wing‐store configuration, a helicopter and a F‐16 fighter with onboard payloads. Results show that the proposed advancing front methodology is in practice able to solve extremely complex geometries with both convex and concave shapes with high efficiency and robustness. [ABSTRACT FROM AUTHOR]

Hepatocytes function largely through the secretion of proteins that regulate cell proliferation, metabolism, and intercellular communications. During the progression of hepatocellular carcinoma (HCC), the hepatocyte secretome changes dynamically as both a consequence and a causative factor in tumorigenesis, although the full scope of secreted protein function in this process remains unclear. Here, we show that the secreted pseudo serine protease PRSS35 functions as a tumor suppressor in HCC. Mechanistically, we demonstrate that active PRSS35 is processed via cleavage by proprotein convertases. Active PRSS35 then suppresses protein levels of CXCL2 through targeted cleavage of tandem lysine (KK) recognition motif. Consequently, CXCL2 degradation attenuates neutrophil recruitment to tumors and formation of neutrophil extracellular traps, ultimately suppressing HCC progression. These findings expand our understanding of the hepatocyte secretome's role in cancer development while providing a basis for the clinical translation of PRRS35 as a therapeutic target or diagnostic biomarker. The secretome of hepatocytes and hepatocellular carcinoma (HCC) cells can contribute to cancer progression. Here the authors show that PRSS35 inhibits HCC progression through proteolytic depletion of CXCL2 and subsequently decreased neutrophil recruitment to tumours. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging. [ABSTRACT FROM AUTHOR]

Dysfunction of fronto-striato-thalamic (FST) circuits is thought to contribute to dopaminergic dysfunction and symptom onset in psychosis, but it remains unclear whether this dysfunction is driven by aberrant bottom-up subcortical signalling or impaired top-down cortical regulation. We used spectral dynamic causal modelling of resting-state functional MRI to characterize the effective connectivity of dorsal and ventral FST circuits in a sample of 46 antipsychotic-naïve first-episode psychosis patients and 23 controls and an independent sample of 36 patients with established schizophrenia and 100 controls. We also investigated the association between FST effective connectivity and striatal 18F-DOPA uptake in an independent healthy cohort of 33 individuals who underwent concurrent functional MRI and PET. Using a posterior probability threshold of 0.95, we found that midbrain and thalamic connectivity were implicated as dysfunctional across both patient groups. Dysconnectivity in first-episode psychosis patients was mainly restricted to the subcortex, with positive symptom severity being associated with midbrain connectivity. Dysconnectivity between the cortex and subcortical systems was only apparent in established schizophrenia patients. In the healthy 18F-DOPA cohort, we found that striatal dopamine synthesis capacity was associated with the effective connectivity of nigrostriatal and striatothalamic pathways, implicating similar circuits to those associated with psychotic symptom severity in patients. Overall, our findings indicate that subcortical dysconnectivity is evident in the early stages of psychosis, that cortical dysfunction may emerge later in the illness, and that nigrostriatal and striatothalamic signalling are closely related to striatal dopamine synthesis capacity, which is a robust marker for psychosis. [ABSTRACT FROM AUTHOR]

Transplantation of oligodendrocyte progenitor cell (OPC) at the injury site is being developed as a potential therapeutic strategy for promoting remyelination and locomotor function recovery after spinal cord injury (SCI). To this end, the development of expandable and functional human OPCs is crucial for testing their efficacy in SCI. In mice and rats, the endocannabinoid signaling system is crucial for the survival, differentiation, and maturation of OPCs. Similar studies in humans are lacking currently. Endocannabinoids and exogenous cannabinoids exert their effects mainly via cannabinoid receptors (CB1R and CB2R). We demonstrated that these receptors were differentially expressed in iPSC-derived human NSCs and OPCs, and they could be activated by WIN55212-2 (WIN), a potent CB1R/CB2R agonist to upregulate the endocannabinoid signaling during glial induction. WIN primed NSCs generated more OLIG2 + glial progenitors and migratory PDGFRα + OPC in a CB1/CB2 dependent manner compared to unprimed NSCs. Furthermore, WIN-induced OPCs (WIN-OPCs) robustly differentiated into functional oligodendrocytes and myelinate in vitro and in vivo in a mouse spinal cord injury model. RNA-Seq revealed that WIN upregulated the biological process of positive regulation of oligodendrocyte differentiation. Mechanistically, WIN could act as a partial smoothed (SMO) inhibitor or activate CB1/CB2 to form heteromeric complexes with SMO leading to the inhibition of GLI1 in the Sonic hedgehog pathway. The partial and temporal inhibition of GLI1 during glial induction is shown to promote OPCs that differentiate faster than control's. Thus, CB1R/CB2R activation results in more efficient generation of OPCs that can mature and efficiently myelinate. [ABSTRACT FROM AUTHOR]

In vivo H2O2 visualization is crucial for disease diagnosis. Catalytic reaction‐based probes show potential in H2O2 detection, yet their in vivo application remains challenging because catalysts always require a specific pH to function and cellular glutathione (GSH) may suppress signaling by depletion of hydroxyl radicals and oxidized substrates. Here, a microenvironment‐tailored catalytic nanoprobe (MTCN) comprising Fe2+, citric acid (CA), 2,2′‐azino‐bis(3‐ethylbenzothiazoline‐6‐sulfonic acid) diammonium salt (ABTS), and downconversion nanoparticles in the liposomal cavity as well as a reference dye in the lipid membrane is reported, which utilizes the selective permeability of the liposomal membrane to offer a favorable pH for Fe2+ catalyst with the aid of CA and to avoid GSH‐triggered signal loss by preventing entry of GSH into the cavity. The MTCN displays a large NIR‐II fluorescence (FL) ratio between 1550 and 1080 nm (FL1550Em,808Ex/FL1080Em,980Ex), but a small photoacoustic (PA) ratio between 808 and 1048 nm (PA808/PA1048). Upon exposure of MTCN to H2O2, catalytic conversion of ABTS into its oxidized form ABTS·+ with 808 nm absorption causes a noticeable increment in PA808/PA1048 accompanied by an apparent decrement in FL1550Em,808Ex/FL1080Em,980Ex, enabling bimodal ratiometric imaging of H2O2 in the tumor and lymphatic metastasis. [ABSTRACT FROM AUTHOR]

Breast carcinoma is one of the common causes of cancer-related mortality in women. Maternally expressed gene 3 (MEG3), a lncRNA located at 14q32, can be involved in carcinogenesis. In this study, we discovered that MEG3 was downregulated by CpG hypermethylation within its gene promoter. Functionally, treatment of breast cancer cells with the DNA methylation inhibitor 5-AzadC as well as silencing of DNA methyltransferase-1 (DNMT1) could decrease the abnormal hypermethylation of the MEG3 promoter, reverse MEG3 expression, inhibit cell proliferation and promote cell apoptosis. In addition, we found that MEG3 expression was negatively correlated with DNMT1. Mechanistically, MEG3 knockdown combined with 5-AzadC or sh-DNMT1 treatment restored the expression of Notch1 receptor, leading to the Notch1 pathway activation, and promoted the progression of epithelial mesenchymal transformation (EMT). Finally, the mice tumor model experiments showed that DNMT1 knockdown can increase MEG3 expression and inhibit tumor growth. Collectively, our findings uncovered that DNMT1-mediated MEG3 demethylation leads to MEG3 upregulation, which in turn inhibits the Notch1 pathway and EMT process in breast cancer. [ABSTRACT FROM AUTHOR]

Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques. [ABSTRACT FROM AUTHOR]

Objective: Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods: We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results: The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation: Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS. [ABSTRACT FROM AUTHOR]

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented. [ABSTRACT FROM AUTHOR]

Level of Evidence: 5 Technical Efficacy Stage: 3 [ABSTRACT FROM AUTHOR]

Copyright of Acta Mechanica Sinica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Amyotrophic lateral sclerosis (ALS) results in progressive impairment of upper and lower motor neurons. Increasing evidence from both in vivo and ex vivo studies suggest that iron accumulation in the motor cortex is a neuropathological hallmark in ALS. An in vivo neuroimaging marker of iron dysregulation in ALS would be useful in disease diagnosis and prognosis. Magnetic resonance imaging (MRI), with its unique capability to generate a variety of soft tissue contrasts, provides opportunities to image iron distribution in the human brain with millimeter to sub‐millimeter anatomical resolution. Conventionally, MRI T1‐weighted, T2‐weighted, and T2*‐weighted images have been used to investigate iron dysregulation in the brain in vivo. Susceptibility weighted imaging has enhanced contrast for para‐magnetic materials that provides superior sensitivity to iron in vivo. Recently, the development of quantitative susceptibility mapping (QSM) has realized the possibility of using quantitative assessments of magnetic susceptibility measures in brain tissues as a surrogate measurement of in vivo brain iron. In this review, we provide an overview of MRI techniques that have been used to investigate iron dysregulation in ALS in vivo. The potential uses, strengths, and limitations of these techniques in clinical trials, disease diagnosis, and prognosis are presented and discussed. We recommend further longitudinal studies with appropriate cohort characterization to validate the efficacy of these techniques. We conclude that quantitative iron assessment using recent advances in MRI including QSM holds great potential to be a sensitive diagnostic and prognostic marker in ALS. The use of multimodal neuroimaging markers in combination with iron imaging may also offer improved sensitivity in ALS diagnosis and prognosis that could make a major contribution to clinical care and treatment trials. Level of Evidence: 2 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

A numerical investigation on propeller-induced flow effects in tractor configurations on a Zimmerman wing-fuselage using the cambered thin airfoil is presented in this paper. The Reynolds number based on the mean aerodynamic chord was 1.3 × 105. Significant aerodynamic performance benefits could be found for a propeller in the tractor configuration. The numerical results showed that the propeller slipstream effect on the wings was highly dependent on the size of the propeller, and the major slipstream effect was working at 60% inboard wingspan, whereas less effects were observed towards the wingtip. The propeller slipstream increased the local angle of attack on the up-going blade side. This effect simultaneously augmented the section lift. The unsteady Reynolds-averaged Navier–Stokes (URANS) simulations helped to improve understanding of the interaction of the propeller wake and the wing-fuselage, which is an important aspect to guide the design of future efficient and controllable micro air vehicles. The results indicated that, in MAV designs, the slipstream from the propeller had a significant effect on the wing aerodynamics, regarding both performance and stability of the vehicle. [ABSTRACT FROM AUTHOR]

N6-methyladenosine (m6A) RNA methylation has been considered the most prevalent, abundant, and conserved internal transcriptional modification throughout the eukaryotic mRNAs. Typically, m6A RNA methylation is catalyzed by the RNA methyltransferases (writers), is removed by its demethylases (erasers), and interacts with m6A-binding proteins (readers). Accumulating evidence shows that abnormal changes in the m6A levels of these regulators are increasingly associated with human tumorigenesis and drug resistance. However, the molecular mechanisms underlying m6A RNA methylation in tumor occurrence and development have not been comprehensively clarified. We reviewed the recent findings on biological regulation of m6A RNA methylation and summarized its potential therapeutic strategies in various human cancers. [ABSTRACT FROM AUTHOR]

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data‐driven deep learning approach. A simulation framework was developed to generate motion‐corrupted images from motion‐free images using randomly generated motion profiles. An Inception‐ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder‐decoder network. The network was trained on simulated motion‐corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real‐world experimental motion‐corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real‐world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5–10% better for the proposed method. In conclusion, a novel, data‐driven motion correction technique has been developed that can suppress motion artefacts from motion‐corrupted MR images. The proposed technique is a standalone, post‐processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice. [ABSTRACT FROM AUTHOR]

A wing is an important part of the aircraft to improve aerodynamic performance. The current study is focused on an adaptive surrogate algorithm for airfoil aerodynamic optimization, which is based on a multi-output Gaussian process model. The conventional design method seriously relies on wind tunnel experiments and expensive computational simulations. The metamodels can significantly improve design efficiency and hence reduce the overall design costs. An active learning algorithm is proposed to improve the effectiveness of the multi-output Gaussian process model. The NSGA-II algorithm is adopted to obtain the optimal Pareto set with the optimization objectives of lift and drag coefficients for adaptive airfoil shapes. Besides, the Bezier curve and radial basis function are utilized in this study for airfoil mesh deformation. The results show that the airfoil shape can be obtained effectively by integrating the metamodel, active learning algorithm, and multi-objective optimization algorithm. The optimized results are of great engineering applications. [ABSTRACT FROM AUTHOR]

In this paper, the permitted temperature value of the battery cell and DC‐DC converter is proposed. The flow and temperature field of the lithium‐ion batteries is obtained by the computational fluid dynamic method. Thus, the package structure of the battery pack is optimized based on four influencing factors. The results indicate that (1) setting a new inlet on the wall, I can improve ventilation and the inlet is better located below the waist of the battery pack. (2) Air inlet location close to the fan is easy to generate short air circuit, which leads to DC‐DC converter in poor condition of heat dissipation. (3) Adjusting the size of the air inlet mainly affects the temperature distribution of the cells but has little effect on the temperature of the DC‐DC converter. (4) Regulating the gap size can enhance the cell temperature uniformity. (5) The optimized battery pack structure is obtained, where the maximum cell surface temperature is 297.51 K, and the maximum surface temperature of the DC‐DC converter is 339.93 K. The above results provide an approach to exploring the optimal design method of lithium‐ion batteries for the container storage system with better thermal performance. [ABSTRACT FROM AUTHOR]

Tumor cells exhibit higher glycolysis and rely on abnormal energy metabolism to produce ATP, which is essential for cell proliferation and migration. Abnormal energy metabolism inhibition is considered a promising tumor treatment strategy. Xanthatin is an active sesquiterpene lactone isolated from Xanthium strumarium L. This study evaluated the effect of xanthatin on the energy metabolism of human colon cancer cells. The results showed that xanthatin significantly inhibited the migration and invasion of human HT‐29 and HCT‐116 colon cancer cells. We found that xanthatin effectively reduced the production of ATP and promoted the accumulation of lactate. Xanthatin inhibited glycolysis which may be related to the reduction of glucose transporter 1 (Glut1) and monocarboxylate transporter 4 (MCT4) mRNA and protein levels. Concomitantly, xanthatin promoted complex II activity and oxidative phosphorylation (OXPHOS), resulting in mitochondrial damage and cell death in HT‐29 cells. Furthermore, xanthatin inhibited the phosphorylation of mTOR, the phosphorylation of 4E‐binding protein 1 (4E‐BP1) and c‐myc in HT‐29 cells. Moreover, rapamycin, a mTOR inhibitor, could enhance the cytotoxicity effect in xanthatin treated HT‐29 cells. Additionally, HT‐29 cells transfected with si‐mTOR aggravated xanthatin induced cell viability inhibition. Based on these results, we observed that the effect of xanthatin on energy metabolism may be related to its inhibition of the mTOR signaling pathway. Collectively, this study provides important insights into xanthatin's anticancer effect, which occurs by regulation of the energy metabolism of human colon cancer cells, and suggest that xanthatin has potential as a botanical drug against abnormal tumor energy metabolism. [ABSTRACT FROM AUTHOR]

Background "Functional" [18F]-fluorodeoxyglucose positron emission tomography (FDG-fPET) is a new approach for measuring glucose uptake in the human brain. The goal of FDG-fPET is to maintain a constant plasma supply of radioactive FDG in order to track, with high temporal resolution, the dynamic uptake of glucose during neuronal activity that occurs in response to a task or at rest. FDG-fPET has most often been applied in simultaneous BOLD-fMRI/FDG-fPET (blood oxygenation level–dependent functional MRI fluorodeoxyglucose functional positron emission tomography) imaging. BOLD-fMRI/FDG-fPET provides the capability to image the 2 primary sources of energetic dynamics in the brain, the cerebrovascular haemodynamic response and cerebral glucose uptake. Findings In this Data Note, we describe an open access dataset, Monash DaCRA fPET-fMRI, which contrasts 3 radiotracer administration protocols for FDG-fPET: bolus, constant infusion, and hybrid bolus/infusion. Participants (n = 5 in each group) were randomly assigned to each radiotracer administration protocol and underwent simultaneous BOLD-fMRI/FDG-fPET scanning while viewing a flickering checkerboard. The bolus group received the full FDG dose in a standard bolus administration, the infusion group received the full FDG dose as a slow infusion over the duration of the scan, and the bolus-infusion group received 50% of the FDG dose as bolus and 50% as constant infusion. We validate the dataset by contrasting plasma radioactivity, grey matter mean uptake, and task-related activity in the visual cortex. Conclusions The Monash DaCRA fPET-fMRI dataset provides significant reuse value for researchers interested in the comparison of signal dynamics in fPET, and its relationship with fMRI task-evoked activity. [ABSTRACT FROM AUTHOR]

This article reports an investigation into dynamic characteristics of the laminar separation bubbles (LSBs) associated with aerodynamic loads unsteadiness of a cambered thin airfoil in pitching-up motions at low Reynolds number flows. Unsteady Reynolds-averaged Navier–Stokes (URANS) simulations were conducted for a 4%c cambered thin airfoil at Reynolds number of 30,000 and 60,000. The airfoil pitches up from 0° to 25°angles of attack at dimensionless pitch rate α ˙ of 0.0398 and 0.0199. The k − ω SST γ − R ˜ e θt turbulence transition model was used to account for the effect of transition on LSBs' development. The LSBs are shown to evolve in their shape and size during the pitching motion. The influence of the LSBs on the airfoil upper surface during pitching motion continues to a higher incidence in comparison with that under static conditions before developing into a fully detached flow. Vortex merging is observed in the rear part of the LSBs in the turbulent portion for a Reynolds number of 30,000. At Reynolds number 60,000, the changing of the LSB length during pitching-up motion is similar to that of steady cases, except a delayed transition is observed as incidence increases. The results show further insight into the dynamic characteristics of the LSBs and their relation to the aerodynamic performance of the airfoil. [ABSTRACT FROM AUTHOR]

A multi-bifurcated cantilever piezoelectric energy harvester (BCPEH) is designed and verified to achieve a wide and adjustable response frequency band. The theoretical model is derived based on the Euler-Bernoulli beam theory and continuity boundary conditions to investigate the dynamic response of the BCPEH. The displacement frequency response function and the voltage frequency response function of the BCPEH are deduced based on the Galerkin method, and the theoretical results of a typical multi-bifurcated cantilever piezoelectric energy harvester, the Y-shaped BCPEH, are verified by the finite element method (FEM) and experiments. In addition, by comparing experimental output power of the Y-shaped BCPEH with that of the traditional cantilever-based piezoelectric energy harvester with the same mass of the bifurcated part at the beam-tip, it demonstrates that the Y-shaped BCPEH has a wider operational frequency band. Moreover, it is found that the Y-shaped BCPEH can be designed with an asymmetric configuration to adjust its response frequency distribution. The number of resonant frequencies and the output power of the asymmetric Y-shaped BCPEH are higher than that of the symmetric Y-shaped BCPEH. And the Y-shaped BCPEH has even better performance than L-shaped BCPEH. This study provides a new design concept for enhanced energy harvester. [ABSTRACT FROM AUTHOR]

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18–49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics. Measurement(s) brain activity Technology Type(s) functional magnetic resonance imaging • FDG-Positron Emission Tomography Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14977851 [ABSTRACT FROM AUTHOR]

Background: Ischemic stroke is a disease with high rate of death and disability worldwide. CircRNAs, as a novel type of non-coding RNAs, lacking 5' caps and 3' poly-A tails, has been associated with ischemic stroke. This study aimed to investigate key circRNAs related to ischemic stroke.Methods: RNA sequencing was performed obtain the circRNA expression profiles from peripheral whole blood of three ischemic stroke patients and three healthy individuals. Through bioinformatic analysis, differentially expressed circRNAs (DEcircRNAs) were identified, and GO and pathway analyses for the host genes of DEcircRNAs were conducted. The expression levels of selected circRNAs were analyzed with qRT-PCR. To further explore the functions of key circRNAs, a DEcircRNA-miRNA interaction network was constructed.Results: A total of 736 DEcircRNAs were detected in ischemic stroke. Functional annotation of host genes of DEcircRNAs revealed several significantly enriched pathways, including Fc epsilon RI signaling pathway, B cell receptor signaling pathway, and T cell receptor signaling pathway. The qRT-PCR results were largely in keeping with our RNA-seq data. The ROC curve analyses indicated that hsa_circ_0000745, hsa_circ_0001459, hsa_circ_0003694 and hsa_circ_0007706 with relatively high diagnostic value. A circRNA-miRNA network, including 1544 circRNA-miRNA pairs, 456 circRNAs and 4 miRNAs, was obtained.Conclusions: The results of our study may help to elucidate the specific mechanism underlying ischemic stroke. [ABSTRACT FROM AUTHOR]

Head motion is a major source of image artefacts in neuroimaging studies and can lead to degradation of the quantitative accuracy of reconstructed PET images. Simultaneous magnetic resonance‐positron emission tomography (MR‐PET) makes it possible to estimate head motion information from high‐resolution MR images and then correct motion artefacts in PET images. In this article, we introduce a fully automated PET motion correction method, MR‐guided MAF, based on the co‐registration of multicontrast MR images. The performance of the MR‐guided MAF method was evaluated using MR‐PET data acquired from a cohort of ten healthy participants who received a slow infusion of fluorodeoxyglucose ([18‐F]FDG). Compared with conventional methods, MR‐guided PET image reconstruction can reduce head motion introduced artefacts and improve the image sharpness and quantitative accuracy of PET images acquired using simultaneous MR‐PET scanners. The fully automated motion estimation method has been implemented as a publicly available web‐service. [ABSTRACT FROM AUTHOR]

Moderate cases account for the majority in hospitalized patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and can also progress to severe/critical condition. Here, we investigated the clinical course and management of hospitalized moderate SARS-CoV-2 patients. The medical records and follow-up data were analyzed from the SARS-CoV-2 patients outside Wuhan. A total of 73 moderate patients (38 men, 35 women) were included, with median age of 47.0 (38.5–57.5) years. Among them, only one patient (1.4%) died using active treatment to improve symptoms. The median duration of the four main symptoms cough, fever, chest tightness, and fatigue were 11.0, 8.0, 11.0, and 7.0 days, respectively; the median duration of the positive nucleic acid test (NAT) results for SARS-CoV-2 was 16.5 days; the median hospitalization time was 25.0 days in 72 moderate survivors. The duration of cough and fever was positively correlated with the duration of the positive NAT results. On admission, 50% had lymphopenia; less than 30% had abnormal blood biochemistry findings involving hyperglycemia, liver function and myocardial enzymes. At discharge, the laboratory indexes were substantially improved. Two weeks after discharge, 5.6% survivors experienced a recurrence of the positive NAT results. Moderate SARS-CoV-2 patients have a good prognosis by the active treatment. A small proportion of the recovered moderate patients still may be virus carriers and require an additional round of viral detection. [ABSTRACT FROM AUTHOR]

Purpose: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images. Methods: We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancer patients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC μ-maps and the reconstructed PET images. Results: For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC μ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC μ-map in the pelvic region was 1.9% for μ-mapGAN + aug compared with 6.4% for μ-mapdixon, 5.9% for μ-mapdixon + bone, 2.1% for μ-mapU-Net and 2.0% for μ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug.. Conclusion: The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated μ-map and consequently the PET quantification compared to the state of the art. [ABSTRACT FROM AUTHOR]

A dual-functional Pt(IV) prodrug, Pt–furoxan, can release cytotoxic cisplatin and signaling molecule NO upon cellular internalization. NO modulates the cellular response towards cisplatin, leading to a synergistic anti-proliferation effect and a promising anti-metastasis effect both in vitro and in vivo. [ABSTRACT FROM AUTHOR]

An implementation of Non‐Fourier chirp‐encoding in 3D Gradient Recalled Echo (GRE), susceptibility‐weighted imaging (SWI) and Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequences is presented with compressive sensing reconstruction. 3D GRE and MPRAGE sequences were designed, in which the phase encoding (PE) direction was encoded with spatially selective chirp encoding Radio Frequency (RF) pulses, while the slice and the readout directions were Fourier encoded using gradients. During each excitation along the PE direction, a different spatially‐selective RF excitation pulse was used to encode the PE direction with a complete set of unitary chirp encoding basis. Multichannel compressive sensing reconstruction on the undersampled in vivo data demonstrated that images reconstructed from chirp encoded data were able to preserve the spatial resolution better than the Fourier encoding. The mean Structural Similarity (SSIM) across five subjects at the acceleration factor of 6, for chirp encoded MPRAGE was 0.934 compared to 0.912 for Fourier encoded MPRAGE. The implementation of prospective undersampling demonstrated the feasibility of using chirp encoding in clinical practice for accelerated imaging. The minimum intensity projection of the compressive sensing (CS) reconstructed susceptibility weighted images revealed that chirp encoding is able to delineate small vessels better than the Fourier encoding with the SSIM of 0.960 for chirp encoding compared to the SSIM of 0.949 for the Fourier encoding. Improved performance of chirp encoding for CS reconstruction and SWI, along with the feasibility of implementation makes them a practical candidate for clinical MRI scans. [ABSTRACT FROM AUTHOR]

Mastering the "arcana of neuroimaging analysis", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, "end-to-end" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites. [ABSTRACT FROM AUTHOR]

Simultaneous Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) scanning is a recent major development in biomedical imaging. The full integration of the PET detector ring and electronics within the MR system has been a technologically challenging design to develop but provides capacity for simultaneous imaging and the potential for new diagnostic and research capability. This article reviews state‐of‐the‐art MR‐PET hardware and software, and discusses future developments focusing on neuroimaging methodologies for MR‐PET scanning. We particularly focus on the methodologies that lead to an improved synergy between MRI and PET, including optimal data acquisition, PET attenuation and motion correction, and joint image reconstruction and processing methods based on the underlying complementary and mutual information. We further review the current and potential future applications of simultaneous MR‐PET in both systems neuroscience and clinical neuroimaging research. We demonstrate a simultaneous data acquisition protocol to highlight new applications of MR‐PET neuroimaging research studies. [ABSTRACT FROM AUTHOR]

Abstract: Background and Aim: The aim of our study was to investigate the immunomodulatory effect and short‐term efficacy and long‐term prognosis of decompensated liver cirrhosis patients caused by hepatitis B after a double transplantation with human umbilical cord mesenchymal stem cells (hUCMSCs). Methods: Fifty inpatients were recruited and given the same medical treatments, receiving hUCMSCs injection intravenously. Fifty‐three patients (Group B) matched for age, sex, and baseline alanine aminotransferase, aspartate aminotransferase, albumin, total bilirubin, prothrombin time, and model for end‐stage liver disease score and Child–Pugh classification, acted as the control group. Results: Interleukin‐6 and tumor necrosis factor alpha levels markedly decreased, and interleukin‐10 level apparently increased in Group A at 2 and 4 weeks after treatment. Transforming growth factor beta in Group A increased more remarkably at 2 weeks after treatment. T4 cells and Treg cells in Group A were apparently higher than those in Group B at 2 and 4 weeks after treatment, and T8 cells and B cells were significantly lower than those in Group B. Aspartate aminotransferase levels in Group A were dramatically declining at 8 and 12 weeks after treatment. Levels of albumin, total bilirubin, and prothrombin time in Group A were apparently improved from 4 to 12 weeks after treatment. The improvements in model for end‐stage liver disease and Child–Pugh scores in Group A were notably superior to those in Group B from 4 to 36 weeks after treatment. There were no remarkable differences in the incidence of developing liver failure throughout the follow‐up period, but the mortality rate of Group A was lower than that of Group B. Conclusion: This therapeutic method may be an appropriate choice for patients with decompensated liver cirrhosis. [ABSTRACT FROM AUTHOR]