The Robotic Visual Information Processing System Based on Wavelet Transformation and Photoelectric Hybrid

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Hemodynamic changes in progressive cerebral infarction:An observational study based on blood pressure monitoring

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post‐stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP‐derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non‐progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns were compared between PCI and NPCI groups using t‐test or non‐parametric equivalent for continuous variables, Chi‐squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP

The Origin of the Prompt Emission for Short GRB 170817A: Photosphere Emission or Synchrotron Emission?

The first gravitational-wave event from the merger of a binary neutron star system (GW170817) was detected recently. The associated short gamma-ray burst (GRB 170817A) has a low isotropic luminosity (~1047 erg s−1) and a peak energy E p ~ 145 keV during the initial main emission between −0.3 and 0.4 s. The origin of this short GRB is still under debate, but a plausible interpretation is that it is due to the off-axis emission from a structured jet. We consider two possibilities. First, since the best-fit spectral model for the main pulse of GRB 170817A is a cutoff power law with a hard low-energy photon index ($\alpha =-{0.62}_{-0.54}^{+0.49}$), we consider an off-axis photosphere model. We develop a theory of photosphere emission in a structured jet and find that such a model can reproduce a low-energy photon index that is softer than a blackbody through enhancing high-latitude emission. The model can naturally account for the observed spectrum. The best-fit Lorentz factor along the line of sight is ~20, which demands that there is a significant delay between the merger and jet launching. Alternatively, we consider that the emission is produced via synchrotron radiation in an optically thin region in an expanding jet with decreasing magnetic fields. This model does not require a delay of jet launching but demands a larger bulk Lorentz factor along the line of sight. We perform Markov Chain Monte Carlo fitting to the data within the framework of both models and obtain good fitting results in both cases

Hemodynamic changes in progressive cerebral infarction: An observational study based on blood pressure monitoring.

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post-stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP-derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non-progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns were compared between PCI and NPCI groups using t-test or non-parametric equivalent for continuous variables, Chi-squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis

BackgroundApoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI).MethodsThe tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic biomarkers differentiating ATB from LTBI were identified by combining the data of protein-protein interaction network, differentially expressed gene, Weighted Gene Co-Expression Network Analysis (WGCNA), and receiver operating characteristic (ROC) analyses. Machine learning algorithms were employed to validate the diagnostic ability of the biomarkers. Enrichment analysis for biomarkers was established, and potential drugs were predicted. The association between biomarkers and N6-methyladenosine (m6A) or 5-methylated cytosine (m5C) regulators was evaluated.ResultsSix biomarkers including CASP1, TNFSF10, CASP4, CASP5, IFI16, and ATF3 were obtained for differentiating ATB from LTBI. They showed strong diagnostic performances, with area under ROC (AUC) values > 0.7. Enrichment analysis demonstrated that the biomarkers were involved in immune and inflammation responses. Furthermore, 24 drugs, including progesterone and emricasan, were predicted. The correlation analysis revealed that biomarkers were positively correlated with most m6A or m5C regulators.ConclusionThe six ARGs can serve as effective biomarkers differentiating ATB from LTBI and provide insight into the pathogenesis of Mycobacterium tuberculosis infection