A rare morphology of the cardiac fibroma in a child: a case report.

Here we report a rare morphology of a cardiac fibroma in a child. A 2-year and 8-month-old toddler came for "chronic constipation" and was found to have a heart murmur on cardiac auscultation. Further transthoracic echocardiography suggested "a strong echogenic mass in the left ventricular wall, with some part of "a string of beads" in shape extending into left ventricle outflow tract", which was atypical for either a tumor, thrombus or vegetation. The child underwent resection of the mass and mitral valvuloplasty. Pathological examination confirmed the mass as a cardiac fibroma.

How brain structure-function decoupling supports individual cognition and its molecular mechanism.

Functional signals emerge from the structural network, supporting multiple cognitive processes through underlying molecular mechanism. The link between human brain structure and function is region-specific and hierarchical across the neocortex. However, the relationship between hierarchical structure-function decoupling and the manifestation of individual behavior and cognition, along with the significance of the functional systems involved, and the specific molecular mechanism underlying structure-function decoupling remain incompletely characterized. Here, we used the structural-decoupling index (SDI) to quantify the dependency of functional signals on the structural connectome using a significantly larger cohort of healthy subjects. Canonical correlation analysis (CCA) was utilized to assess the general multivariate correlation pattern between region-specific SDIs across the whole brain and multiple cognitive traits. Then, we predicted five composite cognitive scores resulting from multivariate analysis using SDIs in primary networks, association networks, and all networks, respectively. Finally, we explored the molecular mechanism related to SDI by investigating its genetic factors and relationship with neurotransmitter receptors/transporters. We demonstrated that structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. We revealed better performance in cognition prediction is achieved by using high-level hierarchical SDIs, with varying significance of different brain regions in predicting cognitive processes. We found that the SDIs were associated with the gene expression level of several receptor-related terms, and we also found the spatial distributions of four receptors/transporters significantly correlated with SDIs, namely D2, NET, MOR, and mGluR5, which play an important role in the flexibility of neuronal function. Collectively, our findings corroborate the association between hierarchical macroscale structure-function decoupling and individual cognition and provide implications for comprehending the molecular mechanism of structure-function decoupling. PRACTITIONER POINTS: Structure-function decoupling is hierarchical across the neocortex, spanning from primary networks to association networks. High-level hierarchical structure-function decoupling contributes much more than low-level decoupling to individual cognition. Structure-function decoupling could be regulated by genes associated with pivotal receptors that are crucial for neuronal function flexibility.

A high-precision jujube disease spot detection based on SSD during the sorting process.

The development of automated grading equipment requires achieving high throughput and precise detection of disease spots on jujubes. However, the current algorithms are inadequate in accomplishing these objectives due to their high density, varying sizes and shapes, and limited location information regarding disease spots on jujubes. This paper proposes a method called JujubeSSD, to boost the precision of identifying disease spots in jujubes based on a single shot multi-box detector (SSD) network. In this study, a diverse dataset comprising disease spots of varied sizes and shapes, varying densities, and multiple location details on jujubes was created through artificial collection and data augmentation. The parameter information obtained from transfer learning into the backbone feature extraction network of the SSD model, which reduced the time of spot detection to 0.14 s. To enhance the learning of target detail features and improve the recognition of weak information, the traditional convolution layer was replaced with deformable convolutional networks (DCNs). Furthermore, to address the challenge of varying sizes and shapes of disease spot regions on jujubes, the path aggregation feature pyramid network (PAFPN) and balanced feature pyramid (BFP) were integrated into the SSD network. Experimental results demonstrate that the mean average precision at the IoU (intersection over union) threshold of 0.5 (mAP@0.5) of JujubeSSD reached 97.1%, representing an improvement of approximately 6.35% compared to the original algorithm. When compared to existing algorithms, such as YOLOv5 and Faster R-CNN, the improvements in mAP@0.5 were 16.84% and 8.61%, respectively. Therefore, the proposed method for detecting jujube disease spot achieves superior performance in jujube surface disease detection and meets the requirements for practical application in agricultural production.

Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease.

Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.

Syn_SegNet: A Joint Deep Neural Network for Ultrahigh-Field 7T MRI Synthesis and Hippocampal Subfield Segmentation in Routine 3T MRI.

Precise delineation of hippocampus subfields is crucial for the identification and management of various neurological and psychiatric disorders. However, segmenting these subfields automatically in routine 3T MRI is challenging due to their complex morphology and small size, as well as the limited signal contrast and resolution of the 3T images. This research proposes Syn_SegNet, an end-to-end, multitask joint deep neural network that leverages ultrahigh-field 7T MRI synthesis to improve hippocampal subfield segmentation in 3T MRI. Our approach involves two key components. First, we employ a modified Pix2PixGAN as the synthesis model, incorporating self-attention modules, image and feature matching loss, and ROI loss to generate high-quality 7T-like MRI around the hippocampal region. Second, we utilize a variant of 3D-U-Net with multiscale deep supervision as the segmentation subnetwork, incorporating an anatomic weighted cross-entropy loss that capitalizes on prior anatomical knowledge. We evaluate our method on hippocampal subfield segmentation in paired 3T MRI and 7T MRI with seven different anatomical structures. The experimental findings demonstrate that Syn_SegNet's segmentation performance benefits from integrating synthetic 7T data in an online manner and is superior to competing methods. Furthermore, we assess the generalizability of the proposed approach using a publicly accessible 3T MRI dataset. The developed method would be an efficient tool for segmenting hippocampal subfields in routine clinical 3T MRI.

AMNet: Adaptive multi-level network for deformable registration of 3D brain MR images.

Three-dimensional (3D) deformable image registration is a fundamental technique in medical image analysis tasks. Although it has been extensively investigated, current deep-learning-based registration models may face the challenges posed by deformations with various degrees of complexity. This paper proposes an adaptive multi-level registration network (AMNet) to retain the continuity of the deformation field and to achieve high-performance registration for 3D brain MR images. First, we design a lightweight registration network with an adaptive growth strategy to learn deformation field from multi-level wavelet sub-bands, which facilitates both global and local optimization and achieves registration with high performance. Second, our AMNet is designed for image-wise registration, which adapts the local importance of a region in accordance with the complexity degrees of its deformation, and thereafter improves the registration efficiency and maintains the continuity of the deformation field. Experimental results from five publicly-available brain MR datasets and a synthetic brain MR dataset show that our method achieves superior performance against state-of-the-art medical image registration approaches.

Integrated untargeted fecal metabolomics and gut microbiota strategy for screening potential biomarkers associated with schizophrenia.

Schizophrenia (SZ) is a serious neurodevelopmental disorder. As the etiology of SZ is complex and the pathogenesis is not thoroughly understood, the diagnosis of different subtypes still depends on the subjective judgment of doctors. Therefore, there is an urgent need to develop early objective laboratory diagnostic biomarkers to screen different subtypes of patients as early as possible, and to implement targeted prevention and precision medicine to reduce the risk of SZ and improve patients' quality of life. In this study, untargeted metabolomics and 16S rDNA sequencing were used to analyze the differences in metabolites and gut microflora among 28 patients with two types of schizophrenia and 11 healthy subjects. The results showed that the metabolome and sequencing data could effectively discriminate among paranoid schizophrenia patients, undifferentiated schizophrenia patients and healthy controls. We obtained 65 metabolites and 76 microorganisms with significant changes, and fecal metabolite composition was significantly correlated with the differential genera (|r|>0.5), indicating that there was a regulatory relationship between the gut microbiota and the host metabolites. The gut microbiome, as an objective and measurable index, showed good diagnostic value for distinguishing schizophrenia patients from healthy people, especially with a combination of several differential microorganisms, which had the best diagnostic effect (AUC>0.9). Our results are conducive to understanding the complicated metabolic changes in SZ patients and providing valuable information for the clinical diagnosis of SZ.

Bionic Optical Leaf for Photoreduction of CO2 from Noble Metal Atom Mediated Graphene Nanobubble Arrays.

The reduction of CO2 to useful chemicals by solar irradiation has been of great interest in recent years to tackle the greenhouse effect. Compared with inorganic metal oxide particles, carbonaceous materials, such as graphene, are excellent in light absorption; however, they lack in activity and selectivity because of the challenge to manipulate the band gap and optimize the electron-hole separation, which drives the photoreduction process. In this work, inspired by the delicate natural plant leaf structure, we fabricated orderly stacked graphene nanobubble arrays with nitrogen dopant for the coordination of noble metal atoms to mimic the natural photoreduction process in plant leaves. This graphene metamaterial not only mimics the optical structure of leaf cells, which scatter and absorb light efficiently, but also drives the CO2 reduction via nitrogen coordinated metal atoms as the chlorophyll does in plants. Our characterizations show that the band gap of nitrogen-doped graphene could be precisely tailored via substitution with different noble metal atoms on the doped site. The noble atoms coordinated on the doped site of graphene metamaterial not only enlarge the light absorption volume but also maximize the utilization of noble metals. The bionic optical leaf metamaterial coordinated with Au atoms exhibits high CO productivity up to 11.14 mmol gcat-1 h-1 and selectivity to 95%, standing as one of the best catalysts among the carbonaceous and metal-based catalysts reported to date. This catalyst also maintained a high performance at low temperatures, manifesting potential applications of this bionic catalyst at polar regions to reduce greenhouse gases.

Multi-view prediction of Alzheimer's disease progression with end-to-end integrated framework.

Alzheimer's disease is a common neurodegenerative brain disease that affects the elderly population worldwide. Its early automatic detection is vital for early intervention and treatment. A common solution is to perform future cognitive score prediction based on the baseline brain structural magnetic resonance image (MRI), which can directly infer the potential severity of disease. Recently, several studies have modelled disease progression by predicting the future brain MRI that can provide visual information of brain changes over time. Nevertheless, no studies explore the intra correlation of these two solutions, and it is unknown whether the predicted MRI can assist the prediction of cognitive score. Here, instead of independent prediction, we aim to predict disease progression in multi-view, i.e., predicting subject-specific changes of cognitive score and MRI volume concurrently. To achieve this, we propose an end-to-end integrated framework, where a regression model and a generative adversarial network are integrated together and then jointly optimized. Three integration strategies are exploited to unify these two models. Moreover, considering that some brain regions, such as hippocampus and middle temporal gyrus, could change significantly during the disease progression, a region-of-interest (ROI) mask and a ROI loss are introduced into the integrated framework to leverage this anatomical prior knowledge. Experimental results on the longitudinal Alzheimer's Disease Neuroimaging Initiative dataset demonstrated that the integrated framework outperformed the independent regression model for cognitive score prediction. And its performance can be further improved with the ROI loss for both cognitive score and MRI prediction.

Feature level-based group lasso method for amnestic mild cognitive impairment diagnosis.

BACKGROUND AND OBJECTIVE: Previous studies have indicated that brain morphological measures change in patients with amnestic mild cognitive impairment (aMCI). However, most existing classification methods cannot take full advantage of these measures. In this study, we improve traditional multitask learning framework by fully considering the relevance among related tasks and supplementary information from other unrelated tasks at the same time. METHODS: We propose a feature level-based group lasso (FL-GL) method in which a feature represents the average value of each ROI for each measure. First, we design a correlation matrix in which each row represents the relationship among different measures for each ROI. And this matrix is used to guide the feature selection based on a group lasso framework. Then, we train specific support vector machine (SVM) classifiers with the selected features for each measure. Finally, a weighted voting strategy is applied to combine these classifiers for a final prediction of aMCI from normal control (NC). RESULTS: We use the leave-one-out cross-validation strategy to verify our method on two datasets, the Xuan Wu Hospital dataset and the ADNI dataset. Compared with the traditional method, the results show that the classification accuracies can be improved by 6.12 and 4.92% with the FL-GL method on the two datasets. CONCLUSIONS: The results of an ablation study indicated that feature level-based group sparsity term was the core of our method. So, considering correlation at the feature level could improve the traditional multitask learning framework and our FL-GL method obtained better classification performance of patients with MCI and NCs.

Atypical neural topographies underpin dysfunctional pattern separation in temporal lobe epilepsy.

Episodic memory is the ability to remember events from our past accurately. The process of pattern separation is hypothesized to underpin this ability and is defined as the capacity to orthogonalize memory traces, to maximize the features that make them unique. Contemporary cognitive neuroscience suggests that pattern separation entails complex interactions between the hippocampus and neocortex, where specific hippocampal subregions shape neural reinstatement in the neocortex. To test this hypothesis, the current work studied both healthy controls and patients with temporal lobe epilepsy who presented with hippocampal structural anomalies. We measured neural activity in all participants using functional MRI while they retrieved memorized items or lure items, which shared features with the target. Behaviourally, patients with temporal lobe epilepsy were less able to exclude lures than controls and showed a reduction in pattern separation. To assess the hypothesized relationship between neural patterns in the hippocampus and neocortex, we identified the topographic gradients of intrinsic connectivity along neocortical and hippocampal subfield surfaces and determined the topographic profile of the neural activity accompanying pattern separation. In healthy controls, pattern separation followed a graded topography of neural activity, both along the hippocampal long axis (and peaked in anterior segments that are more heavily engaged in transmodal processing) and along the neocortical hierarchy running from unimodal to transmodal regions (peaking in transmodal default mode regions). In patients with temporal lobe epilepsy, however, this concordance between task-based functional activations and topographic gradients was markedly reduced. Furthermore, person-specific measures of concordance between task-related activity and connectivity gradients in patients and controls were related to inter-individual differences in behavioural measures of pattern separation and episodic memory, highlighting the functional relevance of the observed topographic motifs. Our work is consistent with an emerging understanding that successful discrimination between memories with similar features entails a shift in the locus of neural activity away from sensory systems, a pattern that is mirrored along the hippocampal long axis and with respect to neocortical hierarchies. More broadly, our study establishes topographic profiling using intrinsic connectivity gradients, capturing the functional underpinnings of episodic memory processes in a manner that is sensitive to their reorganization in pathology.

Prediction of Alzheimer's Disease Progression with Multi-Information Generative Adversarial Network.

Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its long-term progression prediction is definitely important. The structural Magnetic Resonance Imaging (sMRI) can be used to characterize the cortical atrophy that is closely coupled with clinical symptoms in AD and its prodromal stages. Many existing methods have focused on predicting the cognitive scores at future time-points using a set of morphological features derived from sMRI. The 3D sMRI can provide more massive information than the cognitive scores. However, very few works consider to predict an individual brain MRI image at future time-points. In this article, we propose a disease progression prediction framework that comprises a 3D multi-information generative adversarial network (mi-GAN) to predict what one's whole brain will look like with an interval, and a 3D DenseNet based multi-class classification network optimized with a focal loss to determine the clinical stage of the estimated brain. The mi-GAN can generate high-quality individual 3D brain MRI image conditioning on the individual 3D brain sMRI and multi-information at the baseline time-point. Experiments are implemented on the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our mi-GAN shows the state-of-the-art performance with the structural similarity index (SSIM) of 0.943 between the real MRI images at the fourth year and the generated ones. With mi-GAN and focal loss, the pMCI vs. sMCI accuracy achieves 6.04% improvement in comparison with conditional GAN and cross entropy loss.

MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation.

BACKGROUND AND OBJECTIVE: Hippocampal subfields (HS) segmentation accuracy on high resolution (HR) MRI images is higher than that on low resolution (LR) MRI images. However, HR MRI data collection is more expensive and time-consuming. Thus, we intend to generate HR MRI images from the corresponding LR MRI images for HS segmentation. METHODS AND RESULTS: To generate high-quality HR MRI hippocampus region images, we use a dual discriminator adversarial learning model with difficulty-aware attention mechanism in hippocampus regions (da-GAN). A local discriminator is applied in da-GAN to evaluate the visual quality of hippocampus region voxels of the synthetic images. And the difficulty-aware attention mechanism based on the local discriminator can better model the generation of hard-to-synthesis voxels in hippocampus regions. Additionally, we design a SemiDenseNet model with 3D Dense CRF postprocessing and an Unet-based model to perform HS segmentation. The experiments are implemented on Kulaga-Yoskovitz dataset. Compared with conditional generative adversarial network (c-GAN), the PSNR of generated HR T2w images acquired by our da-GAN achieves 0.406 and 0.347 improvement in left and right hippocampus regions. When using two segmentation models to segment HS, the DSC values achieved on the generated HR T1w and T2w images are both improved than that on LR T1w images. CONCLUSION: Experimental results show that da-GAN model can generate higher-quality MRI images, especially in hippocampus regions, and the generated MRI images can improve HS segmentation accuracy.

Identification of the natural product berberine as an antiviral drug.

Drugs targeting the fusion process of viral entry into host cells have been approved for clinical use in the treatment of AIDS. There remains a great need to improve the use of existing drugs for HIV therapy. Berberine is traditionally used to treat diarrhea, bacillary dysentery, and gastroenteritis in clinics, here our research shows that berberine is effective in inhibiting HIV-1 entry. Native polyacrylamide gel electrophoresis studies reveal that berberine can directly bind to both N36 and C34 to form a novel N36-berberine-C34 complex and effectively block the six-helix bundle formation between the N-terminal heptad repeat peptide N36 and the C-terminal heptad repeat peptide C34. Circular dichroism experiments show that binding of berberine produces conformational changes that damages the secondary structures of 6-HB. Computer-aided molecular docking studies suggest a hydrogen bond with T-639 and two polar bonds with Q-563 and T-639 are established, involving the oxygen atom and the C=O group of the indole ring. Berberine completely inhibits six HIV-1 clade B isolates and exhibits antiviral activities in a concentration-dependent manner with IC50 values varying from 5.5 to 10.25 µg/ml. This compound-peptide interaction may represent a mechanism of action of antiviral activities of berberine. As a summary, these studies successfully identify compound berberine as a potential candidate drug for HIV-1 treatment. As a summary, antiviral activity of berberine in combination with its use in clinical practice, this medicine can be used as a potential clinically anti-HIV drug.

Amino Acid Profiling Study of Psidium guajava L. Leaves as an Effective Treatment for Type 2 Diabetic Rats.

Type 2 diabetes mellitus (T2DM) has become a major disease threatening human health worldwide. At present, the treatment of T2DM cannot cure diabetes and is prone to many side effects. Psidium guajava L. leaves have been reported to possess hypoglycemic activity, and they have been widely used in diabetes treatment in the folk. However, the antidiabetic mechanism has not been clearly explained. Also, the change in amino acid profile can reflect a metabolic disorder and provide insights into system-wide changes in response to physiological challenges or disease processes. The study found that P. guajava L. leaves can decrease fasting blood glucose and lipid levels in type 2 diabetic rats induced by streptozotocin. Through the analysis of amino acid profiling following 20 days of gavage administration, the concentration data were modeled by principal component analysis and orthogonal partial least squares discriminant analysis to find the different metabolites and related metabolic pathways (including cysteine and methionine metabolism, valine, leucine, and isoleucine biosynthesis, phenylalanine, tyrosine, and tryptophan biosynthesis) for the explanation of the hypoglycemic mechanism of P. guajava L., which provides an experimental and theoretical basis for diabetes prediction and for the development of new drugs for the treatment of diabetes.

Identification of Interferon Receptor IFNAR2 As a Novel HCV Entry Factor by Using Chemical Probes.

Upon sensing pathogen-associated patterns and secreting interferons (IFNs) into the environment, host cells perceive extracellular type I IFNs by the IFNα/ß receptors IFNAR1 and IFNAR2 to stimulate downstream innate immune signaling cascades. Through the use of chemical probes, we demonstrated that IFNAR2 facilitates hepatitis C virus (HCV) entry. Silencing of IFNAR2 significantly attenuated HCV proliferation. IFNAR2 binds infectious HCV virions through a direct interaction of its D2 domain with the C-terminal end of apolipoprotein E (apoE) on the viral envelope and facilitates virus entry into host cells. The antibody against the IFNAR2 D2 domain attenuates IFNAR2-apoE interaction and impairs HCV infection. The recombinant IFNAR2 protein and the chemical probe potently inhibit major HCV genotypes in various human liver cells in vitro. Moreover, the impact of a chemical probe on HCV genotype 2a is also documented in immune-compromised humanized transgenic mice. Our results not only expand the understanding of the biology of HCV entry and the virus-host relationship but also reveal a new target for the development of anti-HCV entry inhibitors.

The consistency of an optical body surface scanning method compared with computed tomography: a validation study.

OBJECTIVE: The ideal noninvasive method for evaluation of pectus excavatum remains to be defined. We sought to verify the accuracy of an optical body surface scanning method compared with conventional CT scan. MATERIALS AND METHODS: A PrimeSense 3D sensor was used to obtain data from patients undergoing surgical or noninvasive treatment for pectus excavatum. The Haller index, external Haller index, and depth ratio were then calculated from both body scan and computed tomography scan data for the same patients. Statistical analyses were carried out to find if there is consistency between data from body scanning and computed tomography. RESULTS: Data acquisition was complete. In total, 40 patients (median age: 5.03 years, 11 female) with pectus excavatum undergoing nonoperative (n = 13) or surgical Nuss treatment (n = 27) were included. The Haller index was lower in vacuum bell patients, which also had a higher female proportion. Pearson correlation coefficient between external Haller indices from body scanning and from computed tomography and between the depth ratios from body scanning and from computed tomography were 0.63 and 0.84, respectively. By intraclass correlation coefficient method, the correlation coefficient was 0.56 between external Haller indices from body scanning and from computed tomography and 0.80 between depth ratios from body scanning and from computed tomography. CONCLUSION: The optical body surface scanning is a reliable approach to the measurement of PE severity and could be routinely used in the monitoring of PE development of treatment, especially in the pediatric population. STUDY TYPE: Diagnostic test. LEVEL OF EVIDENCE: Level II.

A facile phase transformation strategy for fabrication of novel Z-scheme ternary heterojunctions with efficient photocatalytic properties.

With increasing pollution of water resources and demand for hydrogen energy, photocatalysis, as a "green chemistry" technology, has attracted great attention. To meet the practical application requirements, photocatalysts should possess enhanced efficiency and be of low cost. Here, a novel Z-scheme ternary ZnTiO3/Zn2Ti3O8/ZnO heterojunction has been prepared by a solvothermal-calcination process. The phase transformation process of the sample can be defined as two processes, dehydration and thermal decomposition (ZnTiO3 → Zn2Ti3O8 + ZnO). The ZnTiO3/Zn2Ti3O8/ZnO heterojunction produced in this facile phase transformation strategy displayed highly efficient photocatalytic performance in water splitting for hydrogen production and pollutant removal, e.g. phenol, dye, and heavy metal Cr(vi). On the basis of the PL spectra, photocurrent response, radical trapping experiments and ESR tests, we found that a nontraditional transport of photoinduced carriers created by a single Z-scheme mechanism played a significant role in the efficient removing of target pollutants and hydrogen generation. This work provides a facile phase transformation approach to construct a Z-scheme semiconductor heterostructure system with high efficiency for hydrogen production and water pollution treatment.

Discovery of 2'-α-C-Methyl-2'-ß-C-fluorouridine Phosphoramidate Prodrugs as Inhibitors of Hepatitis C Virus.

2'-α-C-Methyl-2'-ß-C-fluorouridine and its phosphoramidate prodrugs were synthesized and evaluated for their inhibitory activity against HCV. The structure-activity relationship analysis of the phosphoramidate moiety found that 17m, 17q, and 17r exhibit potent activities against HCV, with EC50 values of 1.82 ± 0.19, 0.88 ± 0.12, and 2.24 ± 0.22 µM, respectively. The docking study revealed that the recognition of the 2'-ß-F by Arg158, 3'-OH by N291, and the Watson-Crick pairing with the template allowed 23 to form the in-line conformation necessary for its incorporation into the viral RNA chain.

Synthesis and structure-activity relationship of α-keto amides as enterovirus 71 3C protease inhibitors.

α-Keto amide derivatives as enterovirus 71 (EV71) 3C protease (3C(pro)) inhibitors have been synthesized and assayed for their biochemical and antiviral activities. structure-activity relationship (SAR) study indicated that small moieties were primarily tolerated at P1' and the introduction of para-fluoro benzyl at P2 notably improved the potency of inhibitor. Inhibitors 8v, 8w and 8x exhibited satisfactory activity (IC50=1.32±0.26µM, 1.88±0.35µM and 1.52±0.31µM, respectively) and favorable CC50 values (CC50>100µM). α-Keto amide may represent a good choice as a warhead for EV71 3C(pro) inhibitor.