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S pontaneous intracerebral hemorrhage (ICH) accounts for 10% to 15% of all strokes and is one of the leading causes of stroke-related mortality and morbidity worldwide. Patients with ICH are generally at risk of developing stroke-associated pneumonia (SAP) during acute hospitalization. Evidence has shown that SAP not only increases the length of hospital stay (LOS) and medical cost 1,2 but also is an important risk factor of mortality and morbidity after acute stroke. 3,4 Several risk factors for SAP have been identified, such as older age, 4-12 male sex, 5,6,10,11,13 current smoking, 12 diabetes mellitus, 6 hypertension, 14 atrial fibrillation, 7,10,12 congestive heart failure, 7,12,13,15 chronic obstructive pulmonary disease, 8,[12][13][14] preexisting dependency, 8,12,13,16 stroke severity, 5,6,8,12,17,18 dysphagia, [8][9][10][11][12]14,[18][19][20] and blood glucose. 12 Meanwhile, based on these risk factors, a few risk models have been developed for SAP after acute ischemic stroke. [8][9][10][11][12] Currently, no valid scoring system is available for predicting SAP after ICH in routine clinical practice or clinical trial. We hypothesized that there might be some common grounds for the development of pneumonia after acute ischemic stroke and ICH, and those predictors for SAP after acute ischemic stroke might also be useful for predicting SAP after ICH. For clinical practice, an effective risk-stratification and prognostic model for SAP after ICH would be helpful to identify vulnerable patients, allocate relevant medical resources, and implement tailored preventive strategies. In addition, for clinical trial, it could be used in nonrandomized studies to control for case-mix variation and in controlled studies as a selection criterion.Background and Purpose-We aimed to develop a risk score (intracerebral hemorrhage-associated pneumonia score, ICH-APS) for predicting hospital-acquired stroke-associated pneumonia (SAP) after ICH. Methods-The ICH-APS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Variables routinely collected at presentation were used for predicting SAP after ICH. For testing the added value of hematoma volume measure, we separately developed 2 models with (ICH-APS-B) and without (ICH-APS-A) hematoma volume included. Multivariable logistic regression was performed to identify independent predictors. The area under the receiver operating characteristic curve (AUROC), Hosmer-Lemeshow goodness-of-fit test, and integrated discrimination index were used to assess model discrimination, calibration, and reclassification, respectively. Results-The SAP was 16.4% and 17.7% in the overall derivation (n=2998) and validation (n=2000) cohorts, respectively.A 23-point ICH-APS-A was developed based on a set of predictors and showed good discrimination in the overall derivation (AUROC, 0.75; 95% confidence interval, 0. Ji et al Risk Score to Predict SAP After ICH 2621In the study, we aimed to ...

The threat of antibiotic resistant bacteria has called for alternative antimicrobial strategies that would mitigate the increase of classical resistance mechanism. Many bacteria employ quorum sensing (QS) to govern the production of virulence factors and formation of drug-resistant biofilms. Targeting the mechanism of QS has proven to be a functional alternative to conventional antibiotic control of infections. However, the presence of multiple QS systems in individual bacterial species poses a challenge to this approach. Quorum sensing inhibitors (QSI) and quorum quenching enzymes (QQE) have been both investigated for their QS interfering capabilities. Here, we first simulated the combination effect of QQE and QSI in blocking bacterial QS. The effect was next validated by experiments using AiiA as QQE and G1 as QSI on Pseudomonas aeruginosa LasR/I and RhlR/I QS circuits. Combination of QQE and QSI almost completely blocked the P. aeruginosa las and rhl QS systems. Our findings provide a potential chemical biology application strategy for bacterial QS disruption.The emerging threat of antibiotic resistant bacterial pathogens has called for alternative strategies that could replace the usage of current antibiotics and minimize the development of resistance mechanism. One such strategy is to interfere with the bacterial signaling pathways governing the social behaviors involved in pathogenesis and drug-resistant biofilm formation 1 . Microbial organisms exhibit social behaviors and communicate with each other through quorum sensing (QS) [2][3][4] . By synthesizing small signal molecules, they respond collectively to regulate expression of virulence factors, biofilm development, secondary metabolite production, interactions with host and other microbes in a population-density dependent manner 5 . Targeting QS mechanisms has been put forward as an attractive approach to conventional infection control 1 .Acylhomoserine lactone (AHL)-based QS signals are found in more than 70 bacterial species, in which many of them are pathogens 3,6 . In most cases, the structures of the AHLs are conserved with a homoserine lactone (HSL) ring connected to an acyl group with different chain length (n = 4-16) 5,7 . Multiple AHL-based QS systems often co-exist in individual bacterial species. There are two AHL-mediated QS systems in the opportunistic

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Microglia are the representative myeloid cells in the brain, and their over-activation plays an important role in the pathogenesis of Parkinson's disease (PD). Microglia activation is believed to be regulated by the CD200-CD200R signaling. As the peripheral counterpart of microglia, monocyte-derived macrophages (MDMs) share the same progenitor and antigen markers, and they have similar biological behaviors and mirror microglial function in the brain. Here, we studied CD200R expression and its regulation in MDMs from 32 PD cases, 27 age-matched old controls, and 28 young controls. We found that the basal CD200R expression is similar in MDMs from young control, old control and PD patients. However, the induction of CD200R expression in MDMs under various conditions is impaired in the old groups, especially in PD patients. There was a selective decrease in CD200R expression induced by co-culture with dying PC12 cells in MDMs from PD cases, as compared with MDMs from the age-matched controls. We also found that the inducible CD200R expression correlated inversely with the onset age of PD and to tumor necrosis factor-alpha (TNF-alpha) released from MDMs. These results suggest an intrinsic abnormality in the CD200-CD200R signaling in MDMs during aging and, especially, in PD. We speculate that in the PD brain,microglia might undergo abnormalities similar to MDMs.

Missing data, especially a block of missing data, inevitably occur in structural health monitoring systems. Because of their severe negative effects, many methods that use measured data to infer missing data have been proposed in previous research to solve the problem. However, capturing complex correlations from raw measured signal data remains a challenge. In this study, empirical mode decomposition is combined with a long short-term memory deep learning network for the recovery of the measured signal data. The proposed hybrid method converts the missing data imputation task as a time series prediction task, which is then solved by a “divide and conquer” strategy. The core concept of this strategy is the prediction of the subsequences of the raw measured signal data, which are decomposed by empirical mode decomposition rather than directly predicted, as the decomposition can assist in the modeling of the irregular periodic changes of the measured signal data. In addition, the long short-term memory network in the hybrid model can remember more long-range correlations of subsequences than can the traditional artificial neural network. Three widely used prediction models, namely, the autoregressive integrated moving average, support vector regression, and artificial neural network models, are also implemented as benchmark models. Raw acceleration data collected from a cable-stayed bridge are used to evaluate the performance of the proposed method for missing measured signal data imputation. The recovery results of the measured signal data demonstrate that the proposed hybrid method exhibits excellent performance from two perspectives. First, the decomposition by empirical mode decomposition can improve the accuracy of the core long short-term memory prediction model. Second, the long short-term memory model outperforms other benchmark models because it can fit more microscopic changes of measured values. The experiments conducted in this study also suggest that the change patterns of raw measured signal data are complex, and it is therefore important to extract the features before modeling.

Rasagiline mesylate is a highly potent, selective and irreversible monoamine oxidase type B (MAOB) inhibitor and is effective as monotherapy or adjunct to levodopa for patients with Parkinson's disease (PD). However, few studies have evaluated the efficacy and safety of rasagiline in the Chinese population. This study was designed to investigate the safety and efficacy of rasagiline as adjunctive therapy to levodopa treatment in Chinese PD patients. This was a randomized, double-blind, placebo-controlled, parallel-group, multi-centre trial conducted over a 12-wk period that enrolled 244 PD patients with motor fluctuations. Participants were randomly assigned to oral rasagiline mesylate (1 mg) or placebo, once daily. Altogether, 219 patients completed the trial. Rasagiline showed significantly greater efficacy compared with placebo. During the treatment period, the primary efficacy variable--mean adjusted total daily off time--decreased from baseline by 1.7 h in patients treated with 1.0 mg/d rasagiline compared to placebo (p < 0.05). Scores using the Unified Parkinson's Disease Rating Scale also improved during rasagiline treatment. Rasagiline was well tolerated. This study demonstrated that rasagiline mesylate is effective and well tolerated as an adjunct to levodopa treatment in Chinese PD patients with fluctuations.

Glioblastoma are among the most common forms of cancer affecting the central nervous system, and yet there is currently no effective means of treating them. In the current study, we reported that tousled-like kinase 2 (TLK2) is a key factor in glioblastoma that modulates SRC signaling, thereby driving tumor malignancy. TLK2 is commonly upregulated in glioblastoma, and such upregulation was associated with poor patient outcomes. TLK2 overexpression induced cell growth, migration, invasion, and epithelial-mesenchymal transition, and cell cycle arrest, while TLK2 knockdown had the opposite effect. SRC pathway inhibition by Saracatinib resulted in reduced TLK2-mediated glioblastoma migration, invasion, confirming a key role for SRC signaling in regulating the functions of TLK2. Together, our findings demonstrate that glioblastoma TLK2 overexpression acts as a key driver of tumor malignancy via SRC signaling pathway.