Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction

BackgroundAcute ischemic stroke (AIS) and acute myocardial infarction (AMI) share several features on multiple levels. These two events may occur in conjunction or in rapid succession, and the occurrence of one event may increase the risk of the other. Owing to their similar pathophysiologies, we aimed to identify immune-related biomarkers common to AIS and AMI as potential therapeutic targets.MethodsWe identified differentially expressed genes (DEGs) between the AIS and control groups, as well as AMI and control groups using microarray data (GSE16561 and GSE123342). A weighted gene co-expression network analysis (WGCNA) approach was used to identify hub genes associated with AIS and/or AMI progression. The intersection of the four gene sets identified key genes, which were subjected to functional enrichment and protein–protein interaction (PPI) network analyses. We confirmed the expression levels of hub genes using two sets of gene expression profiles (GSE58294 and GSE66360), and the ability of the genes to distinguish patients with AIS and/or AMI from control patients was assessed by calculating the receiver operating characteristic values. Finally, the investigation of transcription factor (TF)-, miRNA-, and drug–gene interactions led to the discovery of therapeutic candidates.ResultsWe identified 477 and 440 DEGs between the AIS and control groups and between the AMI and control groups, respectively. Using WGCNA, 2,776 and 2,811 genes in the key modules were identified for AIS and AMI, respectively. Sixty key genes were obtained from the intersection of the four gene sets, which were used to identify the 10 hub genes with the highest connection scores through PPI network analysis. Functional enrichment analysis revealed that the key genes were primarily involved in immunity-related processes. Finally, the upregulation of five hub genes was confirmed using two other datasets, and immune infiltration analysis revealed their correlation with certain immune cells. Regulatory network analyses indicated that GATA2 and hsa-mir-27a-3p might be important regulators of these genes.ConclusionUsing comprehensive bioinformatics analyses, we identified five immune-related biomarkers that significantly contributed to the pathophysiological mechanisms of both AIS and AMI. These biomarkers can be used to monitor and prevent AIS after AMI, or vice versa

A Scaling Scheme for DCT Precoded Optical Intensity-Modulated Direct Detection Systems

A scaling technique is employed to improve the performance of a Discrete Cosine Transform (DCT) precoded optical intensitymodulated direct detection (IM/DD) OFDM system, which fully exploits the dynamic range of a digital-to-analog converter (DAC). The theoretical analysis shows that the proposed scaling scheme can improve the BER performance of DCT precoded and scaled OFDM systems. The experiment results also show that the proposed scheme significantly improves the BER performance without changing the receiver structure. The measured received sensitivity at a BER of 10 −3 for a 4 G samples/s (2.7 Gbits/s) DCT precoded and scaled OFDM signal and after 100 km standard single-mode fiber (SMF) transmission has been improved by 3 and 1.3 dB when compared with the original OFDM system and conventional DCT precoded OFDM system, respectively

Energy Efficient SWIPT Based Mobile Edge Computing Framework for WSN-Assisted IoT

With the increasing deployment of IoT devices and applications, a large number of devices that can sense and monitor the environment in IoT network are needed. This trend also brings great challenges, such as data explosion and energy insufficiency. This paper proposes a system that integrates mobile edge computing (MEC) technology and simultaneous wireless information and power transfer (SWIPT) technology to improve the service supply capability of WSN-assisted IoT applications. A novel optimization problem is formulated to minimize the total system energy consumption under the constraints of data transmission rate and transmitting power requirements by jointly considering power allocation, CPU frequency, offloading weight factor and energy harvest weight factor. Since the problem is non-convex, we propose a novel alternate group iteration optimization (AGIO) algorithm, which decomposes the original problem into three subproblems, and alternately optimizes each subproblem using the group interior point iterative algorithm. Numerical simulations validate that the energy consumption of our proposed design is much lower than the two benchmark algorithms. The relationship between system variables and energy consumption of the system is also discussed

Table_1_Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction.XLSX

BackgroundAcute ischemic stroke (AIS) and acute myocardial infarction (AMI) share several features on multiple levels. These two events may occur in conjunction or in rapid succession, and the occurrence of one event may increase the risk of the other. Owing to their similar pathophysiologies, we aimed to identify immune-related biomarkers common to AIS and AMI as potential therapeutic targets.MethodsWe identified differentially expressed genes (DEGs) between the AIS and control groups, as well as AMI and control groups using microarray data (GSE16561 and GSE123342). A weighted gene co-expression network analysis (WGCNA) approach was used to identify hub genes associated with AIS and/or AMI progression. The intersection of the four gene sets identified key genes, which were subjected to functional enrichment and protein–protein interaction (PPI) network analyses. We confirmed the expression levels of hub genes using two sets of gene expression profiles (GSE58294 and GSE66360), and the ability of the genes to distinguish patients with AIS and/or AMI from control patients was assessed by calculating the receiver operating characteristic values. Finally, the investigation of transcription factor (TF)-, miRNA-, and drug–gene interactions led to the discovery of therapeutic candidates.ResultsWe identified 477 and 440 DEGs between the AIS and control groups and between the AMI and control groups, respectively. Using WGCNA, 2,776 and 2,811 genes in the key modules were identified for AIS and AMI, respectively. Sixty key genes were obtained from the intersection of the four gene sets, which were used to identify the 10 hub genes with the highest connection scores through PPI network analysis. Functional enrichment analysis revealed that the key genes were primarily involved in immunity-related processes. Finally, the upregulation of five hub genes was confirmed using two other datasets, and immune infiltration analysis revealed their correlation with certain immune cells. Regulatory network analyses indicated that GATA2 and hsa-mir-27a-3p might be important regulators of these genes.ConclusionUsing comprehensive bioinformatics analyses, we identified five immune-related biomarkers that significantly contributed to the pathophysiological mechanisms of both AIS and AMI. These biomarkers can be used to monitor and prevent AIS after AMI, or vice versa.</p

Data_Sheet_1_Identification of immune-related biomarkers co-occurring in acute ischemic stroke and acute myocardial infarction.PDF

BackgroundAcute ischemic stroke (AIS) and acute myocardial infarction (AMI) share several features on multiple levels. These two events may occur in conjunction or in rapid succession, and the occurrence of one event may increase the risk of the other. Owing to their similar pathophysiologies, we aimed to identify immune-related biomarkers common to AIS and AMI as potential therapeutic targets.MethodsWe identified differentially expressed genes (DEGs) between the AIS and control groups, as well as AMI and control groups using microarray data (GSE16561 and GSE123342). A weighted gene co-expression network analysis (WGCNA) approach was used to identify hub genes associated with AIS and/or AMI progression. The intersection of the four gene sets identified key genes, which were subjected to functional enrichment and protein–protein interaction (PPI) network analyses. We confirmed the expression levels of hub genes using two sets of gene expression profiles (GSE58294 and GSE66360), and the ability of the genes to distinguish patients with AIS and/or AMI from control patients was assessed by calculating the receiver operating characteristic values. Finally, the investigation of transcription factor (TF)-, miRNA-, and drug–gene interactions led to the discovery of therapeutic candidates.ResultsWe identified 477 and 440 DEGs between the AIS and control groups and between the AMI and control groups, respectively. Using WGCNA, 2,776 and 2,811 genes in the key modules were identified for AIS and AMI, respectively. Sixty key genes were obtained from the intersection of the four gene sets, which were used to identify the 10 hub genes with the highest connection scores through PPI network analysis. Functional enrichment analysis revealed that the key genes were primarily involved in immunity-related processes. Finally, the upregulation of five hub genes was confirmed using two other datasets, and immune infiltration analysis revealed their correlation with certain immune cells. Regulatory network analyses indicated that GATA2 and hsa-mir-27a-3p might be important regulators of these genes.ConclusionUsing comprehensive bioinformatics analyses, we identified five immune-related biomarkers that significantly contributed to the pathophysiological mechanisms of both AIS and AMI. These biomarkers can be used to monitor and prevent AIS after AMI, or vice versa.</p

An Intelligent Epileptic Prediction System Based on Synchrosqueezed Wavelet Transform and Multi-Level Feature CNN for Smart Healthcare IoT

Epilepsy is a common neurological disease worldwide, characterized by recurrent seizures. There is currently no cure for epilepsy. However, seizures can be controlled by drugs and surgeries in about 70% of epileptic patients. A timely and accurate prediction of seizures can prevent injuries during seizures and improve the patients&rsquo; quality of life. In this paper, we proposed an intelligent epileptic prediction system based on Synchrosqueezed Wavelet Transform (SWT) and Multi-Level Feature Convolutional Neural Network (MLF-CNN) for smart healthcare IoT network. In this system, we used SWT to map EEG signals to the frequency domain, which was able to measure the energy changes in EEG signals caused by seizures within a well-defined Time-Frequency (TF) plane. MLF-CNN was then applied to extract multi-level features from the processed EEG signals and classify the different seizure segments. The performance of our proposed system was evaluated with the publicly available CHB-MIT dataset and our private ZJU4H dataset. The system achieved an accuracy of 96.99% and 94.25%, a sensitivity of 96.48% and 97.76%, a specificity of 97.46% and 94.07% and a false prediction rate (FPR/h) of 0.031 and 0.049 FPR/h on the CHB-MIT dataset and the ZJU4H dataset, respectively

Refining and psychometric evaluation of the falling risk assessment tool in ophthalmology inpatients

Abstract Aims The aim of this study was to refine the Falling Risk Assessment Tool in Ophthalmology Inpatients (FRAT) and assess its psychometric properties. Design A cross‐sectional design was used. Methods A convenience sample of 730 patients in the ophthalmology department was recruited in a level A tertiary hospital in Guangdong Province from July 2021 to January 2022. Data were analysed using item analysis, interrater reliability, content validation, internal consistency reliability and exploratory factor analysis. Results Five factors were extracted, accounting for 63.039% of the variance. The interrater reliability of the tool was 0.97. Cronbach's α was 0.658. The I‐CVI was 0.75–1.00, the S‐CVI/UA was 0.95 and the adjusted mean values of Kappa for indicators ranged from 0.72 to 1.00, as evaluated by the expert group. The FRAT showed satisfactory reliability and validity, and can be used to measure the fall risk assessment in ophthalmology inpatients. Patient or Public Contribution After explaining the purpose, the patients received our fall risk assessment and answered the corresponding questionnaire questions

Genomic Evolution of ST11 Carbapenem-Resistant <i>Klebsiella pneumoniae</i> from 2011 to 2020 Based on Data from the Pathosystems Resource Integration Center

(1) Objective: ST11 carbapenem-resistant Klebsiella pneumoniae (CRKP) is widespread throughout the world, and the mechanisms for the transmission and evolution of major serotypes, ST11-KL47 and ST11-KL64, were analyzed to investigate the global distribution and evolutionary characteristics of ST11 CRKP; (2) Methods: The Pathosystems Resource Integration Center (PATRIC) database was downloaded and all K. pneumoniae from 2011 to 2020 were screened to obtain ST11 CRKP genome assemblies with basic information. The relationship of serotype evolution between KL47 and KL64 was then investigated using statistical and bioinformatic analysis; (3) Results: In total, 386 ST11 CRKP isolates were included for analysis. Blood (31.09%, 120/386), respiratory tract (23.06%, 89/386), and feces (20.21%, 78/386) were the major sources of samples. China was the leading country where ST11 CRKP was isolated. KL47 and KL64 were found to be the most prevalent serotypes. ST11-KL64 CRKP [median 78(P25~P75: 72~79.25)] had remarkably more virulence genes than the KL47 [median 63(P25~P75: 63~69)], and the distinction was statistically significant (p rmpA/rmpA2, iucABCD, iutA, etc. The comparison of the recombination of serotype-determining regions between the two serotypes revealed that KL64 CRKP carried more nucleotide sequences in the CD1-VR2-CD2 region than KL47 CRKP. More nucleotide sequences added approximately 303 base pairs (bp) with higher GC content (58.14%), which might facilitate the evolution of the serotype toward KL64; (4) Conclusions: KL47 and KL64 have become the predominant serotypes of ST11 CRKP. KL64 CRKP carries more virulence genes than KL47 and has increased by approximately 303 bp through recombinant mutations, thus facilitating the evolution of KL47 to KL64. Stricter infection prevention and control measures should be developed to deal with the epidemic transmission of ST11-KL64 CRKP

Efficient Biocatalytic System for Biosensing by Combining Metal–Organic Framework (MOF)-Based Nanozymes and G-Quadruplex (G4)-DNAzymes

International audienceA high catalytic efficiency associated with a robust chemical structure are among the ultimate goals when developing new biocatalytic systems for biosensing applications. To get ever closer to these goals, we report here on a combination of metal-organic framework (MOF)-based nanozymes and a G-quadruplex (G4)-based catalytic system known as G4-DNAzyme. This approach aims at combining the advantages of both partners (chiefly, the robustness of the former and the modularity of the latter). To this end, we used MIL-53(Fe) MOF and linked it covalently to a G4-forming sequence (F3TC), itself covalently linked to its cofactor hemin. The resulting complex (referred to as MIL-53(Fe)/G4-hemin) exhibited exquisite peroxidase-mimicking oxidation activity and an excellent robustness (being stored in water for weeks). These properties were exploited to devise a new biosensing system based on a cascade of reactions catalyzed by the nanozyme (ABTS oxidation) and an enzyme, the alkaline phosphatase (or ALP, ascorbic acid 2-phosphate dephosphorylation). The product of the latter poisoning the former, we thus designed a biosensor for ALP (a marker of bone diseases and cancers), with a very low limit of detection (LOD, 0.02 U L-1), which is operative in human plasma samples

The beginning and the end: flanking nucleotides induce a parallel G-quadruplex topology

International audienceAbstract Genomic sequences susceptible to form G-quadruplexes (G4s) are always flanked by other nucleotides, but G4 formation in vitro is generally studied with short synthetic DNA or RNA oligonucleotides, for which bases adjacent to the G4 core are often omitted. Herein, we systematically studied the effects of flanking nucleotides on structural polymorphism of 371 different oligodeoxynucleotides that adopt intramolecular G4 structures. We found out that the addition of nucleotides favors the formation of a parallel fold, defined as the ‘flanking effect’ in this work. This ‘flanking effect’ was more pronounced when nucleotides were added at the 5′-end, and depended on loop arrangement. NMR experiments and molecular dynamics simulations revealed that flanking sequences at the 5′-end abolish a strong syn-specific hydrogen bond commonly found in non-parallel conformations, thus favoring a parallel topology. These analyses pave a new way for more accurate prediction of DNA G4 folding in a physiological context