Search

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., (© 2024 American Neurological Association.)

Lipid nanoparticles (LNPs) represent an advanced and highly efficient delivery system for RNA molecules, demonstrating exceptional biocompatibility and remarkable delivery efficiency. This is evidenced by the clinical authorization of three LNP formulations: Patisiran, BNT162b2, and mRNA-1273. To further maximize the efficacy of RNA-based therapy, it is imperative to develop more potent LNP delivery systems that can effectively protect inherently unstable and negatively charged RNA molecules from degradation by nucleases, while facilitating their cellular uptake into target cells. Therefore, this review presents feasible strategies commonly employed for the development of efficient LNP delivery systems. The strategies encompass combinatorial chemistry, a rational design approach, the derivatization strategy of functional molecules, the optimization of LNP formulations, and adjustment of particle size and charge property of LNPs. Prior to introducing these developing strategies, in vivo delivery processes of LNPs, a crucial determinant influencing the clinical translation of LNP formulations, is described to better understand how to develop LNP delivery systems., (© 2024 Wiley‐VCH GmbH.)

This article proposes a practical and generalizable object detector, termed feature extraction-fusion-prediction network (FEFP-Net) for real-world application scenarios. The existing object detection methods have recently achieved excellent performance, however they still face three major challenges for real-world applications, i.e., feature similarity between classes, object size variability, and inconsistent localization and classification predictions. In order to effectively alleviate the current difficulties, the FEFP-Net with three key components is proposed, and the improved detection accuracy is proved in various applications: 1) Extraction Phase: an adaptive fine-grained feature extraction network is proposed to capture features of interest from coarse to fine details, which effectively avoids misclassification due to feature similarity; 2) Fusion Phase: a bidirectional neighbor connection network is designed to identify objects with different sizes by aggregating multilevel features and 3) Prediction Phase: in order to improve the accuracy of object localization and classification, a task specific prediction network is presented, which sufficiently exploits both the spatial and channel information of features. Compared with the State-of-the-Art methods, we achieved competitive results in the MS-COCO dataset. Further, we demonstrated the performance of FEFP-Net in different application fields, such as medical imaging, industry, agriculture, transportation, and remote sensing. These comprehensive experiments indicate that FEFP-Net has satisfactory accuracy and generalizability as a basic object detector.

Tumor cells and surrounding immune cells undergo metabolic reprogramming, leading to an acidic tumor microenvironment. However, it is unclear how tumor cells adapt to this acidic stress during tumor progression. Here we show that carnosine, a mobile buffering metabolite that accumulates under hypoxia in tumor cells, regulates intracellular pH homeostasis and drives lysosome-dependent tumor immune evasion. A previously unrecognized isoform of carnosine synthase, CARNS2, promotes carnosine synthesis under hypoxia. Carnosine maintains intracellular pH (pHi) homeostasis by functioning as a mobile proton carrier to accelerate cytosolic H + mobility and release, which in turn controls lysosomal subcellular distribution, acidification and activity. Furthermore, by maintaining lysosomal activity, carnosine facilitates nuclear transcription factor X-box binding 1 (NFX1) degradation, triggering galectin-9 and T-cell-mediated immune escape and tumorigenesis. These findings indicate an unconventional mechanism for pHi regulation in cancer cells and demonstrate how lysosome contributes to immune evasion, thus providing a basis for development of combined therapeutic strategies against hepatocellular carcinoma that exploit disrupted pHi homeostasis with immune checkpoint blockade., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Caffeic acid (CA) is a naturally occurring phenolic compound with diverse pharmacologic properties. CA plays a crucial role in hemostasis by increasing platelet count. However, the mechanism by which CA regulates platelets to promote hemostasis remains unclear., Objectives: We aim to identify the potential target pathways and genes by which CA regulates platelets to promote hemostasis., Methods: We performed RNA sequencing (RNA-seq) analysis of mouse platelet pools in both the CA-gavaged group and phosphate-buffered saline-gavaged group., Results: The 12,934 expressed transcripts had been annotated after platelet RNA-seq. Compared with the phosphate-buffered saline group, 987 differentially expressed genes (DEGs) were identified, of which 466 were downregulated and 521 were upregulated in CA group. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome gene set enrichment analysis demonstrated that upregulated DEGs were enriched in the pathways of hemostasis, platelet activation, signaling, aggregation, and degranulation. Moreover, Kyoto Encyclopedia of Genes and Genomes and Reactome gene set enrichment analysis revealed that 5 of the 25 cosignificantly upregulated DEGs were essential in CA-mediated platelet regulation to promote hemostasis., Conclusion: Our findings of platelet RNA-seq analysis demonstrate that CA regulates the gene expression of hemostasis and platelet activation-related pathways to increase platelet count and promote hemostasis. It will also provide reference molecular resources for future research on the function and mechanism by which CA regulates platelets to promote hemostasis., (© 2024 The Authors.)

Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Deep learning MRI reconstruction methods are often based on Convolutional neural network (CNN) models; however, they are limited in capturing global correlations among image features due to the intrinsic locality of the convolution operation. Conversely, the recent vision transformer models (ViT) are capable of capturing global correlations by applying self-attention operations on image patches. Nevertheless, the existing transformer models for MRI reconstruction rarely leverage the physics of MRI. In this paper, we propose a novel physics-based transformer model titled, the Multi-branch Cascaded Swin Transformers (McSTRA) for robust MRI reconstruction. McSTRA combines several interconnected MRI physics-related concepts with the Swin transformers: it exploits global MRI features via the shifted window self-attention mechanism; it extracts MRI features belonging to different spectral components via a multi-branch setup; it iterates between intermediate de-aliasing and data consistency via a cascaded network with intermediate loss computations; furthermore, we propose a point spread function-guided positional embedding generation mechanism for the Swin transformers which exploit the spread of the aliasing artifacts for effective reconstruction. With the combination of all these components, McSTRA outperforms the state-of-the-art methods while demonstrating robustness in adversarial conditions such as higher accelerations, noisy data, different undersampling protocols, out-of-distribution data, and abnormalities in anatomy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

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., (© 2023. The Author(s).)

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., (© 2023 Wiley Periodicals LLC.)

Automatic defect detection plays an important role in industrial production. Deep learning-based defect detection methods have achieved promising results. However, there are still two challenges in the current defect detection methods: 1) high-precision detection of weak defects is limited and 2) it is difficult for current defect detection methods to achieve satisfactory results dealing with strong background noise. This article proposes a dynamic weights-based wavelet attention neural network (DWWA-Net) to address these issues, which can enhance the feature representation of defects and simultaneously denoise the image, thereby improving the detection accuracy of weak defects and defects under strong background noise. First, wavelet neural networks and dynamic wavelet convolution networks (DWCNets) are presented, which can effectively filter background noise and improve model convergence. Second, a multiview attention module is designed, which can direct the network attention toward potential targets, thereby guaranteeing the accuracy for detecting weak defects. Finally, a feature feedback module is proposed, which can enhance the feature information of defects to further improve the weak defect detection accuracy. The DWWA-Net can be used for defect detection in multiple industrial fields. Experiment results illustrate that the proposed method outperforms the state-of-the-art methods (mean precision: GC10-DET: 6.0%; NEU: 4.3%). The code is made in https://github.com/781458112/DWWA.

Ferrous iron (Fe 2+ ) 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 Fe 2+ 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 Fe 0 nanoparticles (Fe 0 -siRNA NPs) for self-reinforcing CDT via FHC downregulation. Upon internalization by cancer cells, pH-responsive Fe 0 -siRNA NPs are degraded to release Fe 2+ and FHC siRNA in acidic endo/lysosomes with the aid of oxygen (O 2 ). The accompanied O 2 depletion causes an intracellular pH decrease, which further promotes the degradation of Fe 0 -siRNA NPs. In addition to initiating chemodynamic process, Fe 2+ -catalyzed ⋅OH generation facilitates endo/lysosomal escape of siRNA by disrupting the membranes, enabling FHC downregulation-enhanced CDT., (© 2023 Wiley-VCH GmbH.)

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., (© 2023. The Author(s).)

Introduction: Phosphodiesterase 4B (PDE4B) is a crucial enzyme in the phosphodiesterases (PDEs), acting as a regulator of cyclic adenosine monophosphate (cAMP). It is involved in cancer process through PDE4B/cAMP signaling pathway. Cancer occurs and develops with the regulation of PDE4B in the body, suggesting that PDE4B is a promising therapeutic target., Areas Covered: This review covereed the function and mechanism of PDE4B in cancer. We summarized the possible clinical applications of PDE4B, and highlighted the possible ways to develop clinical applications of PDE4B inhibitors. We also discussed some common PDEs inhibitors, and expected the development of combined targeting PDE4B and other PDEs drugs in the future., Expert Opinion: The existing research and clinical data can strongly prove the role of PDE4B in cancer. PDE4B inhibition can effectively increase cell apoptosis, inhibit cell proliferation, transformation, migration, etc., indicating that PDE4B inhibition can effectively inhibit the development of cancer. Other PDEs may antagonize or coordinate this effect. As for the further study on the relationship between PDE4B and other PDEs in cancer, it is still a challenge to develop multi-targeted PDEs inhibitors.

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., (© 2022. The Author(s).)

Hepatocellular carcinoma (HCC) is often diagnosed at advanced stages with no effective treatment options. Mechanistically, it is a complex biological process. Recently, the main cause of its incidence is changing from viral to non-viral. It has been shown that high cholesterol levels can cause the further transformation of non-alcoholic fatty liver disease (NAFLD) to HCC, but some investigations have found that serum cholesterol levels are negatively correlated with morbidity and mortality. Conflicting experimental results and epidemiological investigations illustrate the complex mechanisms of HCC. Cholesterol is essential for the survival of the body and tumors, although research on the function of cholesterol in tumors is evolving, the use of lowering cholesterol drugs in treating HCC remains limited. In this review, the cholesterol-involved mechanisms that cause the development of HCC or reduce the mortality and the latest progress in the use of cholesterol in the treatment of HCC and prospects for prevention and diagnosis have been summarized., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest that pertain to this work., (Copyright © 2022. Published by Elsevier B.V.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.)

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., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Purpose: The goal of our study is to uncover the pathogenesis of large-artery atherosclerotic ischemic stroke (LAAIS) and small-artery occlusion ischemic stroke (SAOIS) and analyze their difference using RNA sequencing., Methods: RNA sequencing was used to filtrate differentially expressed mRNAs (DEmRNAs) and differentially expressed lncRNAs (DElncRNAs) in LAAIS and SAOIS. Specific DEmRNAs and DElncRNAs in LAAIS and SAOIS were further found. Functional annotation and DElncRNA-DEmRNA co-expression network were built to reveal biological function of DEmRNAs., Results: A total of 832 DEmRNAs and 96 DElncRNAs were identified in LAAIS vs normal controls. 587 DEmRNAs and 105 DElncRNAs were identified in SAOIS vs normal controls. In LAAIS vs SAOIS, 636 DEmRNAs and 112 DElncRNAs were identified. Among which, 571 DEmRNAs and 61 DElncRNAs were LAAIS specific DEmRNAs and DElncRNAs, respectively. 325 DEmRNAs and 66 DElncRNAs were respectively SAOIS specific DEmRNAs and DElncRNAs. We also obtained 3086 LAAIS specific DElncRNA-DEmRNA co-expression pairs and 661 SAOIS specific DElncRNA-DEmRNA co-expression pairs. Oxidative phosphorylation and Alzheimer's disease were significantly enriched pathways in both LAAIS specific DEmRNAs and DEmRNAs in LAAIS specific DElncRNA-DEmRNA co-expression network. ECM-receptor interaction, hypertrophic cardiomyopathy and dilated cardiomyopathy were significantly enriched pathways in both SAOIS specific DEmRNAs and DEmRNAs in SAOIS specific DElncRNA-DEmRNA co-expression network., Conclusion: This finding may help to understand the mechanisms of LAAIS and SAOIS and offer novel clues for finding specific biomarkers for LAAIS and SAOIS., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Aim: To investigate the influence of CYP3A5 and IL-10 polymorphisms on tarcolimus metabolism and renal function for renal transplantation recipients at a stable period. Methods: CYP3A5 and IL-10 polymorphisms, together with other clinical factors, were collected for 149 renal transplantation patients at postoperative stable period. Statistics analysis was performed to explore key factors affecting tarcolimus metabolism. Results: CYP3A5 6986A >G and IL-10 -819C >T significantly impacted tacrolimus metabolism (p < 0.001). CYP3A5 6986A >G G allele and IL-10 -819C >T T allele were associated with poorer tacrolimus metabolic capability. Patients with various tacrolimus metabolism rates presented little difference in renal functions at stable period. Conclusion: Genotyping of CYP3A5 and  IL-10 might benefit the precision dosage of tacrolimus for renal transplantation recipients.

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.

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., (© 2022. The Author(s).)

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2022. The Author(s).)

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., (© 2021 International Society for Magnetic Resonance in Medicine.)

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., (© The Author(s) 2022. Published by Oxford University Press GigaScience.)

N6-methyladenosine (m 6 A) RNA methylation has been considered the most prevalent, abundant, and conserved internal transcriptional modification throughout the eukaryotic mRNAs. Typically, m 6 A RNA methylation is catalyzed by the RNA methyltransferases (writers), is removed by its demethylases (erasers), and interacts with m 6 A-binding proteins (readers). Accumulating evidence shows that abnormal changes in the m 6 A levels of these regulators are increasingly associated with human tumorigenesis and drug resistance. However, the molecular mechanisms underlying m 6 A RNA methylation in tumor occurrence and development have not been comprehensively clarified. We reviewed the recent findings on biological regulation of m 6 A RNA methylation and summarized its potential therapeutic strategies in various human cancers., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Chen, Hu, Jin, Yang, Ding, Zhang, Li and Pan.)

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., (© 2019 John Wiley& Sons, Ltd.)

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., (© 2021 Wiley Periodicals LLC.)

α-Enolase 1 (ENO1) is a critical glycolytic enzyme whose aberrant expression drives the pathogenesis of various cancers. ENO1 has been indicated as having additional roles beyond its conventional metabolic activity, but the underlying mechanisms and biological consequences remain elusive. Here, we show that ENO1 suppresses iron regulatory protein 1 (IRP1) expression to regulate iron homeostasis and survival of hepatocellular carcinoma (HCC) cells. Mechanistically, we demonstrate that ENO1, as an RNA-binding protein, recruits CNOT6 to accelerate the messenger RNA decay of IRP1 in cancer cells, leading to inhibition of mitoferrin-1 (Mfrn1) expression and subsequent repression of mitochondrial iron-induced ferroptosis. Moreover, through in vitro and in vivo experiments and clinical sample analysis, we identified IRP1 and Mfrn1 as tumor suppressors by inducing ferroptosis in HCC cells. Taken together, this study establishes an important role for the ENO1-IRP1-Mfrn1 pathway in the pathogenesis of HCC and reveals a previously unknown connection between this pathway and ferroptosis, suggesting a potential innovative cancer therapy., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Background: Xanthatin is a plant-derived bioactive sesquiterpene lactone from the Xanthium strumarium L., and it has been used as a traditional Chinese medicine. Recently, many studies have reported that xanthatin has anticancer activity. However, a comprehensive understanding of the mechanism underlying the antitumor effects of xanthatin is still lacking., Objective: To systematically and comprehensively identify the underlying mechanisms of xanthatin on cancer cells, quantitative proteomic techniques were performed., Methods: Xanthatin induced HT-29 colon cancer cells death was detected by lactate dehydrogenase (LDH) release cell death assay. Differentially abundant proteins in two groups (xanthatin treatment groups and control groups) of human HT-29 colon cancer cells were identified using tandem mass tag (TMT) quantitative proteomic techniques. All the significant differentially abundant proteins were generally characterized by performing hierarchical clustering, Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We chose Western blot analysis to validate the candidate proteins in the proteomics results., Results: A total of 5637 proteins were identified, of which 397 significantly differentially abundant proteins in the groups were quantified. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, we found that p53-related signaling played an important role in xanthatin-treated HT-29 colon cancer cells. p53- upregulated modulator of apoptosis (Puma), Sestrin-2 and p14ARF, which were selected from among p53-related signaling proteins, were further validated, and the results were consistent with the tandem mass tag quantitative proteomic results., Conclusion: We first investigated the molecular mechanism underlying the effects of xanthatin treatment on HT-29 colon cancer cells using tandem mass tag quantitative proteomic methods and provided a global comprehensive understanding of the antitumor effects of xanthatin. However, it is necessary to further confirm the function of the differentially abundant proteins and the potentially associated signaling pathways., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Peptide hydrogels are widely used for biomedical applications owing to their good biocompatibility and unique advantages in terms of amino acid-based structures and functions. However, the exploration of the peptide/saccharide composite hydrogels as potential biomaterials for chronic diabetic wound healing is still limited. Herein, hyaluronic acid (HA) was incorporated into diphenylalanine (FF) conjugated with different aromatic moieties by a one-pot reaction. Our results showed that the dipeptide derivatives modified by benzene (B), naphthalene (N), and pyrene (P) self-assembled into composite hydrogels with uniform distribution and good mechanical properties in the presence of HA. The obtained N-FF/HA composite hydrogel exhibited greatly improved self-healing properties via injection syringe needle operation and good biocompatibility on human skin fibroblast (HSF) cells. Besides, the structure of thinner nanofibers and honeycomb networks inside the composite hydrogel allowed for a longer sustained release of curcumin, a hydrophobic drug for anti-inflammation and wound healing. The curcumin-loaded N-FF/HA composite hydrogels could promote chronic wound healing in the streptozotocin-induced type I diabetic mouse model. The results suggested that our developed saccharide-peptide hydrogels could serve as very promising synthetic biomaterials for applications in both drug delivery and wound healing in the future.

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., (© 2021. The Author(s).)

Radiation exposure in positron emission tomography (PET) imaging limits its usage in the studies of radiation-sensitive populations, e.g., pregnant women, children, and adults that require longitudinal imaging. Reducing the PET radiotracer dose or acquisition time reduces photon counts, which can deteriorate image quality. Recent deep-neural-network (DNN) based methods for image-to-image translation enable the mapping of low-quality PET images (acquired using substantially reduced dose), coupled with the associated magnetic resonance imaging (MRI) images, to high-quality PET images. However, such DNN methods focus on applications involving test data that match the statistical characteristics of the training data very closely and give little attention to evaluating the performance of these DNNs on new out-of-distribution (OOD) acquisitions. We propose a novel DNN formulation that models the (i) underlying sinogram-based physics of the PET imaging system and (ii) the uncertainty in the DNN output through the per-voxel heteroscedasticity of the residuals between the predicted and the high-quality reference images. Our sinogram-based uncertainty-aware DNN framework, namely, suDNN, estimates a standard-dose PET image using multimodal input in the form of (i) a low-dose/low-count PET image and (ii) the corresponding multi-contrast MRI images, leading to improved robustness of suDNN to OOD acquisitions. Results on in vivo simultaneous PET-MRI, and various forms of OOD data in PET-MRI, show the benefits of suDNN over the current state of the art, quantitatively and qualitatively., Competing Interests: Declaration of Competing Interest None of the authors have any conflicts of interest to report, (Copyright © 2021 Elsevier B.V. All rights reserved.)

Background: The strong inter-individual pharmacokinetic variability and the narrow therapeutic window of tacrolimus (TAC) have hampered the clinical application. Gene polymorphisms play an important role in TAC pharmacokinetics. Here, we investigate the influence of genotypes of IL-10, CYP3A5, CYP2C8, and ABCB1 on dose-adjusted trough blood concentrations (the C 0 /D ratio) of TAC to reveal unclear genetic factors that may affect TAC dose requirements for renal transplant recipients., Methods: Genetic polymorphisms of IL-10, CYP3A5, CYP2C8, and ABCB1 in 188 renal transplant recipients were determined using Kompetitive Allele Specific PCR (KASP). Statistical analysis was applied to examine the effect of genetic variation on the TAC C 0 /D at 5, 10, 15, and 30 days after transplantation., Results: Recipients carrying the IL-10 -819C > T TT genotype showed a significantly higher TAC C 0 /D than those with the TC/CC genotype (p < 0.05). Additionally, the TAC C 0 /D values of recipients with the capacity for low IL-10 activity (-819 TT) engrafted with CYP3A5 non-expressers were higher compared to the intermediate/high activity of IL-10 -819C > T TC or CC carrying CYP3A5 expressers, and the difference was statistically significant at different time points (p < 0.05)., Conclusions: Genetic polymorphisms of IL-10 -819C > T and CYP3A5 6986A > G influence the TAC C 0 /D, which may contribute to variation in TAC dose requirements during the early post-transplantation period. Detecting IL-10 -819C > T and CYP3A5 6986A > G polymorphisms may allow determination of individualized tacrolimus dosage regimens for renal transplant recipients during the early post-transplantation period., (© 2021. Maj Institute of Pharmacology Polish Academy of Sciences.)

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&#95;circ&#95;0000745, hsa&#95;circ&#95;0001459, hsa&#95;circ&#95;0003694 and hsa&#95;circ&#95;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., (© 2021. The Author(s).)

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., (© 2019 Wiley Periodicals, Inc.)

A deep learning (DL) method for accelerated magnetic resonance (MR) imaging is presented that incorporates domain knowledge of parallel MR imaging to augment the DL networks for accurate and stable image reconstruction. The proposed DL method employs a novel loss function consisting of a combination of mean absolute error, structural similarity, and sobel edge loss. The DL model takes both original measurements and images reconstructed by the parallel imaging method as inputs to the network. The accuracy of the proposed method was evaluated using two anatomical regions and six MRI contrasts and was compared with state-of-the-art parallel imaging and deep learning methods. The proposed method significantly outperformed the other methods for all the six different contrasts in terms of structural similarity, peak signal to noise ratio, and normalized mean squared error. The out-of-sample performance of the proposed method was assessed for a truly "unseen" case in a volunteer scan. The method produced images without any artificial features, often occurring in the DL image reconstruction methods. A stability analysis was performed by adding perturbations to the input, which demonstrated that the proposed method is robust and stable with respect to small structural changes, and different undersampling ratios. Comprehensive validation on large datasets demonstrated that incorporation of domain knowledge sufficiently regularizes the DL based image reconstruction and produces accurate and stable image enhancement., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Increasing evidence has suggested that noncoding RNAs (ncRNAs) have vital roles in cardiovascular tissue homeostasis and diseases. As a main subgroup of ncRNAs, long ncRNAs (lncRNAs) have been reported to play important roles in lipid metabolism, inflammation, vascular injury, and angiogenesis. They have also been implicated in many human diseases including atherosclerosis, arterial remodeling, hypertension, myocardial injury, cardiac remodeling, and heart failure. Importantly, it was reported that lncRNAs were dysregulated in the development and progression of cardiovascular diseases (CVDs). A variety of studies have demonstrated that lncRNAs could influence gene expression at transcription, post-transcription, translation, and post-translation level. Particularly, emerging evidence has confirmed that the crosstalk among lncRNAs, mRNA, and miRNAs is an important underlying regulatory mechanism of lncRNAs. Nevertheless, the biological functions and molecular mechanisms of lncRNAs in CVDs have not been fully explored yet. In this review, we will comprehensively summarize the main findings about lncRNAs and CVDs, highlighting the most recent discoveries in the field of lncRNAs and their pathophysiological functions in CVDs, with the aim of dissecting the intrinsic association between lncRNAs and common risk factors of CVDs including hypertension, high glucose, and high fat. Finally, the potential of lncRNAs functioning as the biomarkers, therapeutic targets, as well as specific diagnostic and prognostic indicators of CVDs will be discussed in this review.

Functional positron emission tomography (fPET) imaging using continuous infusion of [18F]-fluorodeoxyglucose (FDG) is a novel neuroimaging technique to track dynamic glucose utilization in the brain. In comparison to conventional static or dynamic bolus PET, fPET maintains a sustained supply of glucose in the blood plasma which improves sensitivity to measure dynamic glucose changes in the brain, and enables mapping of dynamic brain activity in task-based and resting-state fPET studies. However, there is a trade-off between temporal resolution and spatial noise due to the low concentration of FDG and the limited sensitivity of multi-ring PET scanners. Images from fPET studies suffer from partial volume errors and residual scatter noise that may cause the cerebral metabolic functional maps to be biased. Gaussian smoothing filters used to denoise the fPET images are suboptimal, as they introduce additional partial volume errors. In this work, a post-processing framework based on a magnetic resonance (MR) Bowsher-like prior was used to improve the spatial and temporal signal to noise characteristics of the fPET images. The performance of the MR guided method was compared with conventional denosing methods using both simulated and in vivo task fPET datasets. The results demonstrate that the MR-guided fPET framework denoises the fPET images and improves the partial volume correction, consequently enhancing the sensitivity to identify brain activation, and improving the anatomical accuracy for mapping changes of brain metabolism in response to a visual stimulation task. The framework extends the use of functional PET to investigate the dynamics of brain metabolic responses for faster presentation of brain activation tasks, and for applications in low dose PET imaging., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist.

Simultaneous [18F]-fluorodeoxyglucose positron emission tomography functional magnetic resonance imaging (FDG-PET/fMRI) provides the capacity to image 2 sources of energetic dynamics in the brain-glucose metabolism and the hemodynamic response. fMRI connectivity has been enormously useful for characterizing interactions between distributed brain networks in humans. Metabolic connectivity based on static FDG-PET has been proposed as a biomarker for neurological disease, but FDG-sPET cannot be used to estimate subject-level measures of "connectivity," only across-subject "covariance." Here, we applied high-temporal resolution constant infusion functional positron emission tomography (fPET) to measure subject-level metabolic connectivity simultaneously with fMRI connectivity. fPET metabolic connectivity was characterized by frontoparietal connectivity within and between hemispheres. fPET metabolic connectivity showed moderate similarity with fMRI primarily in superior cortex and frontoparietal regions. Significantly, fPET metabolic connectivity showed little similarity with FDG-sPET metabolic covariance, indicating that metabolic brain connectivity is a nonergodic process whereby individual brain connectivity cannot be inferred from group-level metabolic covariance. Our results highlight the complementary strengths of fPET and fMRI in measuring the intrinsic connectivity of the brain and open up the opportunity for novel fundamental studies of human brain connectivity as well as multimodality biomarkers of neurological diseases., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Simultaneous magnetic resonance and positron emission tomography provides an opportunity to measure brain haemodynamics and metabolism in a single scan session, and to identify brain activations from multimodal measurements in response to external stimulation. However, there are few analysis methods available for jointly analysing the simultaneously acquired blood-oxygen-level dependant functional MRI (fMRI) and 18-F-fluorodeoxyglucose functional PET (fPET) datasets. In this work, we propose a new multimodality concatenated ICA (mcICA) method to identify joint fMRI-fPET brain activations in response to a visual stimulation task. The mcICA method produces a fused map from the multimodal datasets with equal contributions of information from both modalities, measured by entropy. We validated the method in silico, and applied it to an in vivo visual stimulation experiment. The mcICA method estimated the activated brain regions in the visual cortex modulated by both BOLD and FDG signals. The mcICA provides a fully data-driven analysis approach to analyse cerebral haemodynamic response and glucose uptake signals arising from exogenously induced neuronal activity., (Copyright © 2020. Published by Elsevier Inc.)

Purpose: To evaluate the clinical utility of the application of a deep learning motion correction technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice., Methods: An encoder-decoder deep learning network inspired by InceptionResnet was trained on public datasets. The clinical utility of the trained network was evaluated retrospectively on 27 3D MPRAGE T1 weighted motion degraded MR images identified by radiologists during reporting. The assessment of image quality was performed by one board-certified radiologist and one senior radiology trainee for nine neuroanatomical regions of the brain using a five-point visual grading scale., Results: The deep learning motion correction technique resulted in reduced ghosting, ringing and blurring for all the brain regions investigated. The larger regions of interest such as ventricles improved the least (1.81 to 1.16, p-value: < 0.0001) while the smaller but complex regions such as the hippocampus improved most (3.0 to 1.67, p-value: < 0.0001). The Wilcox rank tests of image quality differences for the nine neuroanatomical regions were all statistically significant (p < 0.001). Overall, 60 % of the neuroanatomical regions were improved, 39 % were unchanged and 1 % were degraded. Out of the unchanged cases, 28 % were already scored at the highest image quality before motion correction. It was found that approximately 13 % of repeated scans could be avoided using the DL motion correction approach., Conclusion: The deep learning motion correction technique improved the overall visual perception of the 3D T1 weighted MPRAGE brain images. This would improve the clinical utility of otherwise motion degraded images and allow visualisation of normal anatomy and even subtle pathology., (Copyright © 2020 Elsevier B.V. All rights reserved.)

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.

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain - cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18-20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures.

Background: Dysregulation of iron in the cerebral motor areas has been hypothesized to occur in individuals with amyotrophic lateral sclerosis (ALS). There is still limited knowledge regarding iron dysregulation in the progression of ALS pathology. Our objectives were to use magnetic resonance based quantitative susceptibility mapping (QSM) to investigate the association between iron dysregulation in the motor cortex and clinical manifestations in patients with limb-onset ALS, and to examine changes in the iron concentration in the motor cortex in these patients over a 6-month period., Methods: Iron concentration was investigated using magnetic resonance based QSM in the primary motor cortex and the pre-motor area in 13 limb-onset ALS patients (including five lumbar onset, six cervical onset and two flail arm patients), and 11 age- and sex-matched control subjects. Nine ALS patients underwent follow-up scans at 6 months., Results: Significantly increased QSM values were observed in the left posterior primary motor area (P=0.02, Cohen's d =0.9) and right anterior primary motor area (P=0.02, Cohen's d =0.92) in the group of limb-onset ALS patients compared to that of control subjects. Increased QSM was observed in the primary motor and pre-motor area at baseline in patients with lumbar onset ALS patients, but not cervical limb-onset ALS patients, compared to control subjects. No significant change in QSM was observed at the 6-month follow-up scans in the ALS patients., Conclusions: The findings suggest that iron dysregulation can be detected in the motor cortex in limb-onset ALS, which does not appreciably change over a further 6 months. Individuals with lumbar onset ALS appear to be more susceptible to motor cortex iron dysregulation compared to the individuals with cervical onset ALS. Importantly, this study highlights the potential use of QSM as a quantitative radiological indicator in early disease diagnosis in limb-onset ALS and its subtypes. Our serial scans results suggest a longer period than 6 months is needed to detect significant quantitative changes in the motor cortex., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/qims-20-187). The authors have no conflicts of interest to declare., (2020 Quantitative Imaging in Medicine and Surgery. All rights reserved.)