Comparison of the monoexponential and biexponential models of diffusion-weighted magnetic imaging in grading clear-cell renal cell carcinoma: a case-control study.

Background: An accurate and noninvasive method to determine the preoperative clear-cell renal cell carcinoma (ccRCC) pathological grade is of great significance for surgical program selection and prognosis assessment. Previous studies have shown that diffusion-weighted imaging (DWI) has moderate value in grading ccRCC. But DWI cannot reflect the diffusion of tissue accurately because it is calculated using a monoexponential model. Intravoxel incoherent motion (IVIM) is the biexponential model of DWI. Only a few studies have examined the value of IVIM in grading ccRCC yet with inconsistent results. This study aimed to compare the value of DWI and IVIM in grading ccRCC. Methods: In this study, 96 patients with pathologically confirmed ccRCC were evaluated by DWI and IVIM on a 3-T scanner. According to the World Health Organization/International Society of Urological Pathology (WHO/ISUP) classification system, these patients were divided into two groups: low-grade (grade I and II) and high-grade (grade III and IV) ccRCC. The apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and perfusion fraction of pseudodiffusion (f) values were calculated. The Mann-Whitney test, receiver-operating characteristic (ROC) analysis, and the Delong test were used for statistical evaluations. Results: (I) According to the WHO/ISUP nuclear grading system, 96 patients were divided into low-grade (grade I and II, 45 patients) and high-grade (grade III and IV, 51 patients) groups. (II) Compared with patients of low-grade ccRCC, the ADC and D values of those with high-grade ccRCC decreased while the D* and f values increased (P<0.05). (III) The cutoff value of the ADC, D, D*, and f in distinguishing low-grade from high-grade ccRCC was 1.50×10-3 mm2/s, 1.12×10-3 mm2/s, and 33.19×10-3 mm2/s, 0.31, respectively; the area under the curve (AUC) for the ADC, D, D*, and f values was 0.871, 0.942, 0.621, and 0.894, respectively, with the AUC of the D value being the highest; the sensitivity for the ADC, D, D*, and f values was 94.12%, 92.16%, 47.06%, and 92.16%, respectively; and the specificity for the ADC, D, D*, and f values was 66.67%, 91.11%, 77.78%, and 73.33%, respectively. (IV) Based on the Delong test, AUCD was significantly higher than AUCADC (P=0.02) and AUCD* (P<0.001), but there was no significant difference between AUCD and AUC f (P=0.18). Conclusions: Compared with the monoexponential model DWI, the biexponential model IVIM was more accurate in grading ccRCC.

Development and validation of a self-attention network-based algorithm to detect mediastinal lesions on computed tomography images.

Background: Diagnosis of mediastinal lesions on computed tomography (CT) images is challenging for radiologists, as numerous conditions can present as mass-like lesions at this site. This study aimed to develop a self-attention network-based algorithm to detect mediastinal lesions on CT images and to evaluate its efficacy in lesion detection. Methods: In this study, two separate large-scale open datasets [National Institutes of Health (NIH) DeepLesion and Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 Mediastinal Lesion Analysis (MELA) Challenge] were collected to develop a self-attention network-based algorithm for mediastinal lesion detection. We enrolled 921 abnormal CT images from the NIH DeepLesion dataset into the pretraining stage and 880 abnormal CT images from the MELA Challenge dataset into the model training and validation stages in a ratio of 8:2 at the patient level. The average precision (AP) and confidence score on lesion detection were evaluated in the validation set. Sensitivity to lesion detection was compared between the faster region-based convolutional neural network (R-CNN) model and the proposed model. Results: The proposed model achieved an 89.3% AP score in mediastinal lesion detection and could identify comparably large lesions with a high confidence score >0.8. Moreover, the proposed model achieved a performance boost of almost 2% in the competition performance metric (CPM) compared to the faster R-CNN model. In addition, the proposed model can ensure an outstanding sensitivity with a relatively low false-positive rate by setting appropriate threshold values. Conclusions: The proposed model showed excellent performance in detecting mediastinal lesions on CT. Thus, it can drastically reduce radiologists' workload, improve their performance, and speed up the reporting time in everyday clinical practice.

Ischemic stroke pathophysiology: A bibliometric and visualization analysis from 1990 to 2022.

Background: Pathophysiology plays a significant role in the scientific study of ischemic stroke, and has attracted increasing interest from researchers in the field. However, a comprehensive bibliometric analysis is lacking in this field. The purpose of this study is to identify the current research status and hotspots of ischemic stroke pathophysiology from a bibliometric perspective. Methods: The Web of Science Core Collection database was searched for articles published from 1990 to 2022. CiteSpace, VOSviewer, and R package "bibliometrix" software were used to analyze countries/regions, institutions, journals, authors, papers, and keywords to predict the latest trends in ischemic stroke pathophysiology research. Results: This analysis collected 7578 records of ischemic stroke pathophysiology. China and America emerged as the leading countries in this field, with Harvard University being the most active institution. Among journals and authors in this field, journal Stroke and author Gregory YH Lip published the most papers, while Nature Medicine was the journal with the highest citation per article. Keywords and co-citation clusters were closely related to "central nervous system", "mechanisms", "biochemistry & molecular biology" and "radiology, nuclear medicine & medical imaging", while other related fields, such as peripheral organs damage induced by the central nervous system and rehabilitation after ischemic stroke, require further research efforts. Conclusion: This is the first bibliometric study that comprehensively mapped out the knowledge structure and development trends of ischemic stroke pathophysiology in recent 32 years, which may provide a reference for scholars to explore ischemic stroke pathophysiology.

Ischemic Stroke Induces Skeletal Muscle Damage and Alters Transcriptome Profile in Rats.

To establish pathological features of skeletal muscle post-stroke and to provide a background for promising interventions. Adult male SD rats were selected and randomly divided into a control group, a sham group, and a middle cerebral artery occlusion (MCAO) group. The tolerance and capability of exercise were separately collected on days 1, 3, 5, and 7 after the MCAO operation. The neurological deficits, brain infarct volume, soleus histopathology, mRNA-seq analysis, flow cytometry, immunofluorescence, and protein expression analysis were performed on the seventh day. Rats in the MCAO group showed that soleus tissue weight, pulling force, exercise capacity, endurance, and muscle structure were significantly decreased. Moreover, the RNA sequencing array revealed that mitochondrial-mediated autophagy was the critical pathological process, and the result of transcriptomic findings was confirmed at the translational level. The mitochondrial membrane potential and the mfn2 and p62 protein expression were decreased, and the Beclin-1, ATG5, Parkin, PINK1, LC3B, and Drp1 expression were upregulated; these results were consistent with immunohistochemistry. This is the first report on the pathological features of limbic symptoms on day 7 after MCAO surgery in rats. In addition, we further confirmed that autophagy is one of the main causative mechanisms of reduced muscle function after stroke.

Mask configurable readout circuit architecture for an ultra-high-resolution CMOS image sensor: publisher's note.

This publisher's note reports a correction in Appl. Opt.61, 2565 (2022)APOPAI0003-693510.1364/AO.453904.

High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study.

Background: Multicenter medical research is becoming a new trend. However, interagency data privacy security protection has become a major bottleneck of multicenter research. Therefore, to overcome this privacy protection issue, the aim of the present study was to apply a self-developed privacy-preserving machine learning framework for researchers who can build models on medical data from multiple sources, while providing privacy protection for both sensitive data and the learned model. Methods: Based on Arya, a novel privacy computing platform developed by Healink, we constructed a privacy-preserving federated learning (FL) model using the fully connected neural network with datasets from 2-3 individual medical institutions. In the dataset, 80% of records were used for joint modeling on acute myocardial infarction (AMI) diagnosis. Modeling efficacy was evaluated with the remaining 20% of records. As the control, 1,500 medical records from 1 medical institution were used for single-center modeling and efficacy evaluation. During the process, the original data were still kept in individual hospital without moving or transferring out of the hospitial. The diagnostic efficacy (sensitivity, positive predictive value, and accuracy) was evaluated. Results: Our privacy-preserving FL model gives reliable AMI diagnostic efficacy. Three-center modeling (79% sensitivity, 88% positive predictive value, and 82.3% accuracy) and two-center modeling (77.8% or 77.6% sensitivity, 86.7% or 85.30% positive predictive value, and 81% or 79.7% accuracy) achieved relative high diagnostic efficacy; and single-center modeling achieved relative low diagnostic efficacy (76% sensitivity, 84.7% positive predictive value, and 79% accuracy). Conclusions: The Arya privacy computing platform is efficient and practical for the FL model, which could promote multicenter medical research securely without sacrificing diagnostic efficacy.

An intelligent prediagnosis system for disease prediction and examination recommendation based on electronic medical record and a medical-semantic-aware convolution neural network (MSCNN) for pediatric chronic cough.

Background: Due to the phenotypic similarities among different pediatric respiratory diseases with chronic cough, primary doctors often misdiagnose and the misuse of examinations is prevalent. In the pre-diagnosis stage, the patients' chief complaints and other information in the electronic medical record (EMR) provide a powerful reference for respiratory experts to make preliminary disease judgment and examination plan. In this paper, we proposed an intelligent prediagnosis system to predict disease diagnosis and recommend examinations based on EMR text. Methods: We examined the clinical notes of 178,293 children with chronic cough symptoms from retrospective EMR data. The dataset is split into 7:3 for training and testing. From the testing set, we also extract 5% of samples for validation. We proposed a medical-semantic-aware convolution neural network (MSCNN) framework that can accomplish two downstream tasks from the same medical language model through transfer learning. First, a medical language model based on the word2vec algorithm was built to generate embeddings for the text data. Then, text convolutional neural network (TextCNN) was used to build models for disease prediction and examination recommendation. Results: We implemented 5 algorithms for disease prediction. In the disease prediction task, our algorithm outperformed the baseline methods on all metrics, with a top-1 accuracy (AC) of 0.68 and a top-3 AC of 0.923 on the testing set. By adding data enhancement, the top-3 AC reached 0.926. In the examination recommendation task, the overall AC on the testing set was 0.93 and the macro average (MA) F1-score was 0.88. The average area under the curve (AUC) on the training set was 0.97 while on the testing set it was 0.86. Conclusions: We constructed an intelligent prediagnosis system with an MSCNN framework that can predict diseases and make examination recommendations based on EMR data. Our approach achieved good results on a retrospective clinical dataset and thus has great potential for the application of automated diagnosis assist in clinical practice during pre-diagnosis stage, which will provide help for primary level doctors or doctors in basic-level hospitals. Due to the generality of the proposed framework, it can be straight forwardly extended to prediagnosis for other diseases.

Design and Analysis of Hospital Throughput Maximization Algorithm under COVID-19 Pandemic.

Under the global pandemic of COVID-19, public health facilities, such as hospitals, are required to readjust, design, and plan a safe movement flow of people to meet the social distance rules and quarantine COVID-19 and the non-COVID-19 patients to prevent cross-infection. However, readjustments to separate patients have significantly reduced the maximum throughput of public health facilities, worsening already scarce public health resources. Therefore, this paper proposes throughput maximization algorithms based on the one-way street problem which meets the requirements of social distance rules. First, the floor plan of a hospital is transformed into a graph, each node is traversed by breadth-first search. Then, this paper considers patients' node pair sets as different set unions, the direction of edges, and the color of links based on DFS-XOR algorithm are designed to distinguish the paths of COVID-19 and non-COVID-19 patients. Finally, this paper utilizes minimum shared link algorithms to determine the minimized sharing links between paths linking different set unions and components. The throughput is maximized by reducing the number of shared links and alternating links. The results indicate that compared with the brute force algorithms, the algorithms proposed in this paper significantly improve the maximum throughput.

Alliance chain-based simulation on a new clinical research data pricing model.

Background: Multicenter clinical research faces many challenges, including how to quantitatively evaluate the data contribution of each research center. However, few data pricing model meets the requirements to the scenario. Thus, a suitable mechanism to measure the data value for clinical research is required. Methods: Extensive documents were acquired and analyzed, including a rare disease list from the National Health Commission, data structures of the electronic medical records (EMR) system, diagnosis-related groups (DRGs) regulations from the Health Commission of Zhejiang Province, and the Clinical Service Price List of Zhejiang Province. Nine senior experts were invited as consultants from hospital and enterprises with professional field of clinical research, data governance, and health economics. After brainstorming and expert evaluation, seven data attributes were identified as the main factors affecting the value of medical data. Different weights were assigned for each attribute based on its influence on data value. Each attribute was quantized to an index based on proposed algorithms. The data value models for chronic diseases and other diseases were distinguished given the different sensitivity of data timeliness. A simulation system using blockchain and federated learning techniques was constructed to verify the data pricing model in the scenario of clinical research. Results: A comprehensive clinical data pricing model is proposed and the simulation of three research centers with 50 million real clinical data entries was conducted to verify its effectiveness. It demonstrates that the proposed model can compute medical data value quantitatively. Conclusions: Quantitative evaluation of the value of medical data for multicenter clinical research based on the proposed data pricing model works well in simulation. This model will be improved by real-world applications in the near future.

The diagnostic value of diffusion kurtosis imaging in Parkinson's disease: a systematic review and meta-analysis.

Background: Under the background that diffusion kurtosis imaging (DKI) has become a research hotspot of central nervous system diseases, there are no studies with large sample size evaluating the value of DKI in diagnosing Parkinson's disease (PD). Moreover, the diagnostic efficacy of DKI in PD is not consistent. Therefore, the main purpose of this study is to use the method of meta-analysis, to summarize and evaluate the diagnostic efficacy of DKI in the identification of PD, and to explore the value of its clinical application. Methods: We use PICOS principles for project design. The included patients were PD patients, and the control group were healthy volunteers. We hope to use DKI to make a differential diagnosis between the two, and this study is a diagnostic test. We performed a literature search of English (PubMed, Embase, Cochrane Library, etc.) and Chinese (China knowledge Network, Wanfang Data Knowledge Service platform, China Science and Technology Journal Database, China Biomedical Literature Service system) databases for related literatures on the efficacy of DKI in the differential diagnosis of PD published before March 29, 2022. We used Revman 5.3 software to assess the quality of the literature, Meta-Disc 1.4 software for summarizing sensitivity (Sen), specificity (Spe), diagnostic odds ratios, and heterogeneity tests, and for subgrouping, and Stata 16.0 software for publication bias analysis. Results: Fourteen articles were included through the literature search. The 14 studies included 535 patients with PD and 486 patients without PD. Most of the included literature had good clinical applicability and relatively low risk. By merging statistics, the results obtained were as follows: Sen =0.78 [95% confidence interval (CI): 0.74-0.81], Spe =0.83 (95% CI: 0.79-0.86), and the area under the summary receiver operating characteristic (SROC) curve was 0.8870. Discussion: The results of the meta-analysis showed that magnetic resonance DKI has comparable diagnostic accuracy in the diagnosis of PD. However, this study also has limitations, and the use of different diagnostic gold standards in the included studies may have some impact on the case selection in the study.

Mask configurable readout circuit architecture for an ultra-high-resolution CMOS image sensor.

As the level of detail in today's images increases, so does the demand for resolution. Due to the necessity of mask stitching technology for full exposure of large array chips, we propose a mask configurable readout circuit architecture, which is suitable for large array structures. However, the stitchable readout circuit architecture has some non-ideal effects: row driver function failure and the column non-consistency problem. In our design, we solve the problem of column non-consistency after stitching. At the same time, we changed the signal transmission structure in order to avoid the row driver function failure caused by the mask stitching. In this paper, a prototype 2130×2130 CMOS image sensor is fabricated in 0.11 µm CMOS technology. The chip can capture images at 20 fps and reduce fixed pattern noise (FPN) from 3.5% to 1.5% through correction techniques. The architecture proposed in this paper is suitable for large array image sensors.

Network pharmacology and in vitro study of Fengreqing oral liquid in the intervention of wind-heat pattern.

OBJECTIVE: To investigate the underlying mechanism of the effect of Fengreqing oral liquid (, FOL) on wind-heat pattern (WHP). METHODS: In this study, we predicted the potential targets of FOL via the approach of network pharmacology and verified it by in vitro inflammation model. In the network pharmacology part, two strategies, namely the direct target search and the indirect one, were used to collect the target sets of FOL in WHP treatment. The enrichment analysis was carried out by David database and ClueGo plug-in in Cytoscape. Furthermore, the potential targets were mapped in the candidate pathways. In the verification experiment section, in vitro model of lipopolysaccharide (LPS) induced RAW 264.7 was used to confirm the predictive results in the network pharmacology part. RESULTS: Through the two screening strategies, a total of 141 non-repetitive intervention targets of FOL on WHP were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the intervention effect was mainly focused on the anti-inflammatory effect, and the Toll-like receptor signaling pathway was one of the most critical regulatory pathways. Further mapping analysis showed that phosphatidylinositol 3-kinase (PI3K)-protein kinase B (AKT) signaling transfer might be the key part of regulating the concentration of inflammation mediators of FOL in the Toll-like receptor signaling pathway. In vitro experiment showed that FOL significantly reduced the levels of NO, IL-1, IL-6, and TNF-α produced by RAW264.7 induced by LPS. Further immunofluorescence found that this effect is related to the regulation of PI3K-AKT pathway activity by FOL. CONCLUSION: FOL can intervene in WHP by regulating the content of inflammatory mediators via the PI3K-AKT pathway.

Data governance system of the National Clinical Research Center for Child Health in China.

BACKGROUND: Since the national big data strategy was unveiled at the fifth plenary session of the 18th CPC (Communist Party of China) Central Committee, the big data industry has been flourishing in China. Various successful industrial data governance systems have emerged with the rapid development of big data technologies and data management theories. City Brain and Enterprise Data Middle Platform are considered the best data governance systems in urban and corporate governance, respectively. However, in the health and medical sectors, issues of data operation occur frequently due to a lack of systematic data governance. These problems need to be urgently addressed, as health and medical data have been defined as national fundamental strategic resources. Clinical researchers have an increasing demand for data analysis. METHODS: Therefore, the Medical Data Governance System (MDGS) has been designed to improve data quality and provide simple and convenient data analysis tools for the National Clinical Research Center for Child Health. The MDGS consists of the Medical Data Platform (MDP) and Operation Management System (OMS). The MDP comprises acquisition layer, middle platform, and application layer that persistently elevates data quality and significantly shortens data analysis duration. Organization construction, management regulations, and technical standards are included in the OMS, which guarantees the sustainable operation of the MDGS. The MDGS was established to advance state-of-the-art and state-of-practice data governance for the health and medical sectors in China. RESULTS: With the first phase of the MDGS, the quantity and quality of research projects increase, research transformation speeds up, and the researchers' job satisfaction increased. CONCLUSIONS: Based on our preliminary achievements, it was necessary and feasible to establish the MDGS. It is important to have comprehensive requirement study, top-level design, refined planning, phase-by-phase implementation, and continual optimization.

Structures of human mGlu2 and mGlu7 homo- and heterodimers.

The metabotropic glutamate receptors (mGlus) are involved in the modulation of synaptic transmission and neuronal excitability in the central nervous system1. These receptors probably exist as both homo- and heterodimers that have unique pharmacological and functional properties2-4. Here we report four cryo-electron microscopy structures of the human mGlu subtypes mGlu2 and mGlu7, including inactive mGlu2 and mGlu7 homodimers; mGlu2 homodimer bound to an agonist and a positive allosteric modulator; and inactive mGlu2-mGlu7 heterodimer. We observed a subtype-dependent dimerization mode for these mGlus, as a unique dimer interface that is mediated by helix IV (and that is important for limiting receptor activity) exists only in the inactive mGlu2 structure. The structures provide molecular details of the inter- and intra-subunit conformational changes that are required for receptor activation, which distinguish class C G-protein-coupled receptors from those in classes A and B. Furthermore, our structure and functional studies of the mGlu2-mGlu7 heterodimer suggest that the mGlu7 subunit has a dominant role in controlling dimeric association and G-protein activation in the heterodimer. These insights into mGlu homo- and heterodimers highlight the complex landscape of mGlu dimerization and activation.

Structures of Gi-bound metabotropic glutamate receptors mGlu2 and mGlu4.

The metabotropic glutamate receptors (mGlus) have key roles in modulating cell excitability and synaptic transmission in response to glutamate (the main excitatory neurotransmitter in the central nervous system)1. It has previously been suggested that only one receptor subunit within an mGlu homodimer is responsible for coupling to G protein during receptor activation2. However, the molecular mechanism that underlies the asymmetric signalling of mGlus remains unknown. Here we report two cryo-electron microscopy structures of human mGlu2 and mGlu4 bound to heterotrimeric Gi protein. The structures reveal a G-protein-binding site formed by three intracellular loops and helices III and IV that is distinct from the corresponding binding site in all of the other G-protein-coupled receptor (GPCR) structures. Furthermore, we observed an asymmetric dimer interface of the transmembrane domain of the receptor in the two mGlu-Gi structures. We confirmed that the asymmetric dimerization is crucial for receptor activation, which was supported by functional data; this dimerization may provide a molecular basis for the asymmetric signal transduction of mGlus. These findings offer insights into receptor signalling of class C GPCRs.

Long-term straw return with N addition alters reactive nitrogen runoff loss and the bacterial community during rice growth stages.

Crop residue return is an effective, eco-friendly tillage method for decreasing reactive nitrogen (Nr) losses via surface runoff. However, the associated variation in Nr characteristics and its prospective mechanisms are not well understood. We systematically evaluated the response of Nr runoff loss and N variation in standing water to the abiotic and biotic parameters of soil in a paddy field after 6 years of straw return. Five experimental treatments of different fertilization strategies in combination with straw return were tested during the rice growth season. The results indicated that under equivalent fertilizer input, long-term straw return significantly reduced Nr runoff loss by 11.5% (P < 0.05), even though the loss increased with N fertilizer addition. We report that variations in abiotic soil properties (P < 0.05) and bacterial communities (P < 0.01) were both responsible for Nr loss differences between the rice growth stages and among the tested fertilizing patterns. Soil inorganic nitrogen (r = 0.18) had a significant positive influence on Nr runoff loss, but this effect was surpassed by the overall negative influence of soil organic carbon (r = -0.43), soil pH, (r = -0.40), and bacterial community composition (r = -0.14), which was especially apparent during the tillering stage. Our results emphasize the importance of jointly considering biotic and abiotic factors in soil and standing water when characterizing the effects of long-term straw return and N addition on Nr runoff loss, which will aid in mitigating N-based agricultural non-point pollution.

[Investigation of Lentiviral Vectors Based Integrin ß8 RNAi System in Neonatal Rats' Brain].

OBJECTIVE: To construct lentiviral vectors expressing pSicoR-ß8 shRNA and evaluate its efficiency of RNA interference in neonatal rats' brain. METHODS: Plasmid vectors pSicoR-ß8 shRNA and pSicoR-control,as well as lentiviral packaging system pDM2G,g/p RRE and pRSV Rev were amplified respectively and plasmid DNA was identified by restriction enzyme digestion. Lentiviral packaging system and expressing vector pSicoR-ß8 shRNA/pSicoR-control were co-transfected into packaging cell line 293T. Lentiviral particles expressing ß8-shRNA or control sequence packaged and secreted by 293T were collected,concentrated by PEG-it,and viral titers were assayed by 50% tissue culture infective dose (TCID50). RNAi for integrin ß8 in neonatal rats' brain was performed by intraventricular injection of lentivirus expressing ß8-shRNA and rats received lentivirus expressing ß8-shRNA were served as control. Green fluorescent protein (GFP) expression after intraventricular injection of GFP-Lentivirus was observed under fluorescence microscope,ß8 mRNA and ß8 protein expression were detected by RT-PCR and Western blot respectively,all of which were performed to evaluate the RNAi efficiency and to choose the optimal time for intervention. RESULTS: Restrictive endonuclease digestion and agarose gel electrophoresis showed plasmids as same as the expected size. Lentiviral titers for LV-control after concentration was 1.0×108 PFU/mL,and for LV-ß8 shRNA 5.0×108 PFU/mL.One day after intraventricular injection of lentiviral vectors containing GFP sequence,lenticivirus genome was integrated into host cells and emitted green fluorescence. A relatively strong green fluorescence could be observed in brain slides 2 d,3 d and 5 d after intraventricular injection. Western blot and RT-PCR demonstrated a maximum inhibition happened 3 d after intraventricular injection of LV-ß8 shRNA,the inhibitory rate for ß8 mRNA and ß8 protein were 56% and 51%,respectively. CONCLUSION: Lentiviral vectors expressing ß8-shRNA are successfully constructed and lentiviral mediated ß8-RNAi is successfully applied for in vivo use.

Inhibition of NLRP3 inflammasome: a new protective mechanism of cinnamaldehyde in endotoxin poisoning of mice.

CONTEXT: Cinnamaldehyde (CA) has a protective effect in endotoxin poisoning of mice, but there is no direct evidence for the protective effect of CA through inhibition of NLRP3 inflammasome activation in endotoxin poisoning of mice. OBJECTIVE: We aimed to investigate the protective mechanism of CA in endotoxin poisoned mice through NLRP3 inflammasome. MATERIALS AND METHODS: First, we evaluated the anti-inflammatory effect of CA in phorbol-12-myristate acetate-differentiated THP-1 cells through the NLRP3 inflammasome. Second, in a mouse model of lipopolysaccharide (LPS)-induced endotoxin poisoning, CA was administrated for 5 d (once a day) before the 15 mg/kg LPS challenge. Then, the levels of IL-1ß in serum were measured, and the effect of CA on the NLRP3 inflammasome activation and the expression of cathepsin B and P2X7R proteins in lung were explored. RESULTS: In vitro, CA decreased the levels of p20, pro-IL-1ß and IL-1ß in cell culture supernatants, as well as the expression of NLRP3 and IL-1ß mRNA in cells. In vivo, CA decreased IL-1ß production in serum. Furthermore, CA suppressed LPS-induced NLRP3, p20, Pro-IL-1ß, P2X7 receptor (P2X7R) and cathepsin B protein expression in lung, as well as the expression of NLRP3 and IL-1ß mRNA. CONCLUSIONS: CA has a protective effect in the endotoxin poisoned mice through the inhibition of NLRP3 inflammasome activation. Furthermore, CA suppresses the NLRP3 inflammasome activation by inhibiting the expression of cathepsin B and P2X7R protein expression. CA can be considered as a potential therapeutic candidate for diseases involved in endotoxin poisoning such as sepsis.

Ligand binding to a G protein-coupled receptor captured in a mass spectrometer.

G protein (heterotrimeric guanine nucleotide-binding protein)-coupled receptors belong to the largest family of membrane-embedded cell surface proteins and are involved in a diverse array of physiological processes. Despite progress in the mass spectrometry of membrane protein complexes, G protein-coupled receptors have remained intractable because of their low yield and instability after extraction from cell membranes. We established conditions in the mass spectrometer that preserve noncovalent ligand binding to the human purinergic receptor P2Y1. Results established differing affinities for nucleotides and the drug MRS2500 and link antagonist binding with the absence of receptor phosphorylation. Overall, therefore, our results are consistent with drug binding, preventing the conformational changes that facilitate downstream signaling. More generally, we highlight opportunities for mass spectrometry to probe effects of ligand binding on G protein-coupled receptors.

Biomass-involved synthesis of N-substituted benzofuro[2,3-d]pyrimidine-4-amines and biological evaluation as novel EGFR tyrosine kinase inhibitors.

Shikimic acid (1) is a renewable biomass which could be obtained sustainably through natural product isolation or metabolic engineering. Owing to its great potential in chemical conversion, the value-added utilization of this non-grain biomass has received much attention in recent years. Based on the established transformation route from shikimic acid (1) to methyl 3-dehydroshikimate (3-MDHS, 2) and to the multi-functionalized methyl 2-amino-3-cyanobenzofuran-5-carboxylate (3), we disclose a facile and transition metal-free method to access a series of N-substituted benzofuro[2,3-d]pyrimidine-4-amines in 63%-90% yields. The identification of these compounds as EGFR tyrosine kinase inhibitors has also been described. Among them, compound 5h exhibited the most potent inhibitory effect against EGFR tyrosine kinase with an IC50 of 1.7 nM and excellent antiproliferative activity against A431 and A549 cell lines with a GI50 of 5.1 and 12.3 µM, respectively.