Analysis of correlative factors of female coronary slow-flow phenomenon: A retrospective study.

The coronary slow-flow phenomenon (CSFP) is a manifestation of coronary artery disease wherein coronary angiography reveals no apparent stenosis; however, there is a delay in blood flow perfusion. Given its increased occurrence in male patients, with the majority of subjects in previous studies being male, this study aimed to explore whether distinct risk factors are present in female patients with CSFP. This single-center retrospective study focused on female patients diagnosed with CSFP by using coronary angiography. Eligible patients meeting the predefined inclusion and exclusion criteria were divided into the study group (presenting with CSFP) and control group (displaying normal epicardial coronary arteries). Comparative analyses of clinical and diagnostic data were performed. Ninety-two patients with CSFP and an equal number of controls were enrolled in this study. Patients with CSFP exhibited a higher prevalence of smokers (P = .017) and a heightened incidence of diabetes mellitus (DM) (P = .007). Significantly elevated levels of total cholesterol (TC) (P = .034) and free fatty acids (FFA) (P = .016) were observed in the CSFP group compared to those in the control group. Additionally, patients with CSFP displayed lower levels of apolipoprotein E (ApoE) (P = .092), free thyroxine (FT4) (P = .001), and total thyroxine (TT4) (P = .025). Logistic regression analysis indicated that smoking (P = .019), FFA (P < .001), ApoE (P = .015), and FT4 (P < .001) were independent risk factors for CSFP, accounting for confounding factors. Additionally, the area under the ROC curve (AUC) of the combined effect of smoking, ApoE, FT4, and FFA on CSFP was 0.793 (95% CI: 0.729-0.857, P < .01). In addition to the established risk factors for smoking, diabetes, and hyperlipidemia, female patients with CSFP exhibited significant differences in apoE, FFA, FT4, and TT4 levels compared to the control group. Smoking, FFA, and FT4 levels emerged as independent risk factors for CSFP.

Who Gets the Flu? Individualized Validation of Influenza-like Illness in Urban Spaces.

Urban dwellers are exposed to communicable diseases, such as influenza, in various urban spaces. Current disease models are able to predict health outcomes at the individual scale but are mostly validated at coarse scales due to the lack of fine-scaled ground truth data. Further, a large number of transmission-driving factors have been considered in these models. Because of the lack of individual-scaled validations, the effectiveness of factors at their intended scale is not substantiated. These gaps significantly undermine the efficacy of the models in assessing the vulnerability of individuals, communities, and urban society. The objectives of this study are twofold. First, we aim to model and, most importantly, validate influenza-like illness (ILI) symptoms at the individual scale based on four sets of transmission-driving factors pertinent to home-work space, service space, ambient environment, and demographics. The effort is supported by an ensemble approach. For the second objective, we investigate the effectiveness of the factor sets through an impact analysis. The validation accuracy reaches 73.2-95.1%. The validation substantiates the effectiveness of factors pertinent to urban spaces and unveils the underlying mechanism that connects urban spaces and population health. With more fine-scaled health data becoming available, the findings of this study may see increasing value in informing policies that improve population health and urban livability.

How regularly do people visit service places?

It is often believed that regularities are embedded in mobile behaviors. Highly regular mobile behaviors, such as daily commutes between home and workplace, have been actively investigated in the context of health risks. Less regular mobile behaviors, such as visits to service places (e.g., supermarkets and healthcare facilities), have not received much attention. This study explores the regularity in service place visits using a deep learning method and the effect of place type on the stability of recurring visits using an entropy assessment. Results reveal both periodic and bursty visit behaviors to service places. The periodic visits are prominent on the weekly and bi-weekly scales, and the bursty visits dominate the multi-day scales. Service place type indeed affects the stability of recurring visits, and certain place types have the strongest effect. The research findings substantially expand the knowledge of mobile behaviors and are valuable in informing both visitor-based and place-based health risks.

The relationships between cholesterol crystals, NLRP3 inflammasome, and coronary atherosclerotic plaque vulnerability in acute coronary syndrome: An optical coherence tomography study.

Background: Cholesterol crystals (CCs) in lesions are the hallmark of advanced atherosclerotic plaque. Previous studies have demonstrated that CCs could activate NLRP3 inflammasome, which played an important role in atherosclerotic lesion progression. However, the relationship between CCs, NLRP3 inflammasome pathway, and plaque vulnerability in patients with ACS is still not elucidated. Methods: Two hundred sixty-nine consecutive acute coronary syndrome (ACS) patients with 269 culprit lesions were included in this study. CCs and other plaque characteristics within the culprit lesion segment were evaluated by optical coherence tomography (OCT) before percutaneous coronary intervention (PCI). The NLRP3 mRNA expression in peripheral blood mononuclear cells (PBMCs) and the serum levels of interleukin (IL)-1ß, IL-18, and other biological indices were measured. Results: Cholesterol crystals were observed in 105 (39%) patients with 105 culprit lesions. There were no significant differences in baseline clinical characteristics between the patients with CCs (CCs group, n = 105) and the patients without CCs (non-CCs group, n = 164) within the culprit lesion segment except for lipoprotein(a) [Lp(a)]. The CCs group had a higher level of NLRP3 mRNA expression in PBMCs and higher levels of serum cytokine IL-1ß and IL-18. OCT showed that the CCs group had longer lesion length, more severe diameter stenosis, and less minimum luminal area (MLA) than the non-CCs group (all p < 0.05). The frequency of thin-cap fibroatheroma (TCFA), thrombus, accumulation of macrophages, plaque rupture, micro-channel, calcification, spotty calcification, and layered plaque was higher in the CCs group than in the non-CCs groups (all p < 0.05). Multivariate logistic analysis revealed that the level of NLRP3 expression (OR = 10.204), IL-1ß levels (OR = 3.523), IL-18 levels (OR = 1.006), TCFA (OR = 3.593), layered plaque (OR = 5.287), MLA (OR = 1.475), macrophage accumulation (OR = 2.881), and micro-channel (OR = 3.185) were independently associated with CCs. Conclusion: Acute coronary syndrome patients with CCs in culprit lesions had a higher expression of NLRP3, IL-1ß, and IL-18, and had more vulnerable plaque characteristics than patients without CCs. CCs might have interacted with NLRP3 inflammasome activation in patients with ACS, which could contribute to plaque vulnerability in culprit lesions.

Potential urinary biomarkers in young adults with short-term exposure to particulate matter and bioaerosols identified using an unbiased metabolomic approach.

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.

Two Key Amino Acids Variant of α-l-arabinofuranosidase from Bacillus subtilis Str. 168 with Altered Activity for Selective Conversion Ginsenoside Rc to Rd.

α-l-arabinofuranosidase is a subfamily of glycosidases involved in the hydrolysis of l-arabinofuranosidic bonds, especially in those of the terminal non-reducing arabinofuranosyl residues of glycosides, from which efficient glycoside hydrolases can be screened for the transformation of ginsenosides. In this study, the ginsenoside Rc-hydrolyzing α-l-arabinofuranosidase gene, BsAbfA, was cloned from Bacilus subtilis, and its codons were optimized for efficient expression in E. coli BL21 (DE3). The recombinant protein BsAbfA fused with an N-terminal His-tag was overexpressed and purified, and then subjected to enzymatic characterization. Site-directed mutagenesis of BsAbfA was performed to verify the catalytic site, and the molecular mechanism of BsAbfA catalyzing ginsenoside Rc was analyzed by molecular docking, using the homology model of sequence alignment with other ß-glycosidases. The results show that the purified BsAbfA had a specific activity of 32.6 U/mg. Under optimal conditions (pH 5, 40 °C), the kinetic parameters Km of BsAbfA for pNP-α-Araf and ginsenoside Rc were 0.6 mM and 0.4 mM, while the Kcat/Km were 181.5 s-1 mM-1 and 197.8 s-1 mM-1, respectively. More than 90% of ginsenoside Rc could be transformed by 12 U/mL purified BsAbfA at 40 °C and pH 5 in 24 h. The results of molecular docking and site-directed mutagenesis suggested that the E173 and E292 variants for BsAbfA are important in recognizing ginsenoside Rc effectively, and to make it enter the active pocket to hydrolyze the outer arabinofuranosyl moieties at C20 position. These remarkable properties and the catalytic mechanism of BsAbfA provide a good alternative for the effective biotransformation of the major ginsenoside Rc into Rd.

Generating a synthetic probabilistic daily activity-location schedule using large-scale, long-term and low-frequency smartphone GPS data with limited activity information.

Household travel survey data is a critical input to travel behavior modeling, and it also can be used to generate trip schedules for activity-based traffic simulation. With emerging information and communication technology (ICT) tools like smartphones, the collection of passive datasets for travelers' real-time information becomes available. Smartphone GPS survey apps have emerged to be a popular tool for conducting household travel surveys. Most existing studies employ high-frequency smartphone GPS data and collect accurate activity information. However, their study periods are still rather short, ranging from a few days to a few weeks. For a long-term GPS survey, the issues of missing activity information and sparse GPS data are inevitable and must be addressed carefully. This paper uses 7-month low-frequency smartphone GPS data collected from over 2000 participants, who report 5 most frequently visited locations weekly. The essential goal is to develop a synthetic model of daily activity-location scheduling to capture data with both known and unknown activities. To handle missing activity data, this research develops a new probabilistic approach, which measures the probability of visiting a place by three scores, global visit score (GVS), temporal visit score (TVS), and periodical visit score (PVS). Three different levels of activity-location schedule are modeled respectively. The first level handles only those data with known activities, while data with unknown activities are disregarded. The second takes unknown activities into account but combines all types of them into a single category. The third one models each location with unknown activities separately. These models are able to generate activity-location schedule in different levels of detail for activity-based traffic simulator. After developing activity-location schedule models, both individual and aggregated validation processes are performed with simulation. The validation result shows that the simulated proportion of activity types and activity duration are close to the survey data, indicating the effectiveness of the proposed approaches. This research sheds a light on building sustainable and long-term travel survey using GPS data with missing activity information. In addition, this study will be valuable to model infectious disease transmission, e.g. COVID-19 and assess health risk in urban areas.

Network effects in influenza spread: The impact of mobility and socio-economic factors.

This paper introduces new methods of modeling and analyzing social networks that emerge in the context of disease spread. Four methods of constructing informative networks are presented, two of which use. static data and two use temporal data, namely individual citizen mobility observations taken over an extensive period of time. We show how the built networks can be analyzed, and how the numerical results can be interpreted, using network permutation-based surprise analysis. In doing so, we explain the relationship of surprise analysis with conventional network hypothesis testing and Quadratic Assignment Procedure regression. Surprise analysis is more comprehensive, and can be without limitation performed with any form(s) of network subgraphs, including those with multiple nodal attributes, weighted links, and temporal features. To illustrate our methodological work in application, we put them to use for interpreting networks constructed from the data collected over one year in an observational study in Buffalo and Erie counties in New York state during the 2016-2017 influenza season. Even with the limitations in the data size, our methods are able to reveal the global (city- and season-wide) patterns in the spread of influenza, taking into account population mobility and socio-economic factors.

What drives disease flows between locations?

Communicable diseases 'flow' between locations. These flows dictate where and when certain communities will be affected. While the prediction of disease flows is essential for the timely intervention of epidemics, few studies have addressed this critical issue. This study predicts disease flows during an epidemic by considering the epidemiological, network, and temporal contextual factors using a deep learning approach. A series of scenario analyses helps identify the effects of these contextual factors on disease flows. Results show that the extended spatial-temporal effect of the epidemiological factors stimulates disease flows. The compound effects of the network factors enhance the transmission efficiency of these flows. Lastly, the temporal effect accelerates the combined effects of epidemiological and network factors on the flows. Findings of this study reveal the intricate nature of disease flows and lay a solid foundation for real-time surveillance of epidemics and pandemics to inform timely interventions for a broad range of communicable diseases.

Exosomes derived from mesenchymal stem cells repair a Parkinson's disease model by inducing autophagy.

Parkinson's disease (PD) is a progressively debilitating neurodegenerative condition that leads to motor and cognitive dysfunction. At present, clinical treatment can only improve symptoms, but cannot effectively protect dopaminergic neurons. Several reports have demonstrated that human umbilical cord mesenchymal stem cells (hucMSCs) afford neuroprotection, while their application is limited because of their uncontrollable differentiation and other reasons. Stem cells communicate with cells through secreted exosomes (Exos), the present study aimed to explore whether Exos secreted by hucMSCs could function instead of hucMSCs. hucMSCs were successfully isolated and characterized, and shown to contribute to 6-hydroxydopamine (6-OHDA)-stimulated SH-SY5Y cell proliferation; hucMSC-derived Exos were also involved in this process. The Exos were purified and identified, and then labeled with PKH 26, it was found that the Exos could be efficiently taken up by SH-SY5Y cells after 12 h of incubation. Pretreatment with Exos promoted 6-OHDA-stimulated SH-SY5Y cells to proliferate and inhibited apoptosis by inducing autophagy. Furthermore, Exos reached the substantia nigra through the blood-brain barrier (BBB) in vivo, relieved apomorphine-induced asymmetric rotation, reduced substantia nigra dopaminergic neuron loss and apoptosis, and upregulated the level of dopamine in the striatum. These results demonstrate that hucMSCs-Exos have a treatment capability for PD and can traverse the BBB, indicating their potential for the effective treatment of PD.

Effects of Smoking Cessation with Nicotine Replacement Therapy on Vascular Endothelial Function, Arterial Stiffness, and Inflammation Response in Healthy Smokers.

The effects of nicotine replacement therapy (NRT)-aided smoking cessation on vascular function are not fully clarified. We investigated 100 healthy smokers who were motivated to quit and received NRT for a 3-month period. Vascular endothelial function (measured by reactive hyperemia-peripheral arterial tonometry [RH-PAT]), arterial stiffness (measured by augmentation index [AI] and brachial-ankle pulse wave velocity [baPWV]), and systemic inflammation markers (including serum soluble intercellular adhesion molecule-1 [sICAM-1] and interleukin-1ß [IL-1ß]) were assessed at baseline and 3 and 12 months of follow-up. After 3 months of intervention, endothelial function, arterial stiffness, and inflammatory markers significantly improved (RH-PAT increased, AI and baPWV decreased, sICAM-1 and IL-1ß decreased, all P < .05) for the participants who abstained from smoking completely, but for those who did not abstained completely, RH-PAT, AI, baPWV, and IL-1ß remained unchanged. At 12 months follow-up, endothelial function (RH-PAT), arterial stiffness (AI and baPWV), and inflammatory markers (sICAM-1 and IL-1ß) were further improved in participants who abstained from smoking (P < .001), while the above parameters deteriorated in continued smokers (P < .05). In conclusion, vascular dysfunction can be reversible after NRT-aided smoking cessation in healthy smokers and vascular function could be further damaged if they continue smoking.

A rare intrahepatic subcapsular hematoma (ISH) after laparoscopic cholecystectomy: a case report and literature review.

BACKGROUND: Intrahepatic subcapsular hematoma (ISH) is an extremely rare, life-threatening complication after laparoscopic cholecystectomy (LC). Only few cases have been reported. Herein, we reported a rare giant ISH after LC and summarized all of the reported cases. CASE PRESENTATION: A 32-year old woman with recurrent acute cholecystitis for one year, underwent elective LC without intra-operative complications and was discharged 2 days after operation. On the next day after discharge, she developed severe right upper abdominal pain and was sent to our emergency department. The computed tomography scan showed a 10.9 × 12.5 × 6.6 cm ISH in the right liver without free fluid and the hemoglobin dropped to 86 g/l from 127 g/l. Postoperative hemorrhagic shock and a giant ISH after LC were diagnosed. After fluid resuscitation, the hemodynamic was still unstable and the hemoglobin kept dropping. An emergency laparoscopic exploration was performed and the ISH was confirmed, however no active bleeding point was found. A drainage tube was placed under liver for early warning of rupture. Patient was discharged home 10 days after readmission. CONCLUSIONS: Giant ISH is an extremely rare, life-threatening complication after LC. This case showed that the need to consider this rare complication in patients suffering abdominal pain after LC and timely and correct diagnosis and treatment were crucial to saving the lives of the patients.

Highly Selective Production of Compound K from Ginsenoside Rd by Hydrolyzing Glucose at C-3 Glycoside Using ß-Glucosidase of Bifidobacterium breve ATCC 15700.

To investigate a novel ß-glucosidase from Bifidobacterium breve ATCC 15700 (BbBgl) to produce compound K (CK) via ginsenoside F2 by highly selective and efficient hydrolysis of the C-3 glycoside from ginsenoside Rd, the BbBgl gene was cloned and expressed in E. coli BL21. The recombinant BbBgl was purified by Ni-NTA magnetic beads to obtain an enzyme with specific activity of 37 U/mg protein using pNP-Glc as substrate. The enzyme activity was optimized at pH 5.0, 35°C, 2 or 6 U/ml, and its activity was enhanced by Mn2+ significantly. Under the optimal conditions, the half-life of the BbBgl is 180 h, much longer than the characterized ß-glycosidases, and the Km and Vmax values are 2.7 mM and 39.8 µmol/mg/min for ginsenoside Rd. Moreover, the enzyme exhibits strong tolerance against high substrate concentration (up to 40 g/l ginsenoside Rd) with a molar biotransformation rate of 96% within 12 h. The good enzymatic properties and gram-scale conversion capacity of BbBgl provide an attractive method for large-scale production of rare ginsenoside CK using a single enzyme or a combination of enzymes.

Low fT3 is associated with diminished health-related quality of life in patients with acute coronary syndrome treated with drug-eluting stent: a longitudinal observational study.

Acute coronary syndrome (ACS) patients with low triiodothyronine (T3) syndrome characterized by low free T3 (fT3) levels with normal thyroxine (T4) and thyroid-stimulating hormone (TSH) have a higher rate of death. The impact of fT3 on Health related quality of life (HRQOL) in patients with ACS is still unknown. 528 ACS patients treated with drug-eluting stent (DES) were included in this prospective, observational study. Patients were classified into low fT3 group (n=126) and normal fT3 group (n=402) according to serum fT3 level. Every patient was prospectively interviewed at baseline and 1 year following percutaneous coronary intervention (PCI). HRQOL was assessed with the use of the Medical Outcomes Study 36-Item Short-Form Health Survey (SF-36). Low fT3 patients had poorer HRQOL than normal fT3 patients both at baseline and 1-year follow-up (all p<0.05). During 1-year follow-up, HRQOL scores for all patients were significantly higher than baseline. Low fT3 patients had lesser gains in physical functioning, bodily pain, general health, vitality, social functioning and role emotional (all p<0.05). Generally, low fT3 patients demonstrated less improvement in Physical Component Score (PCS) (p=0.008) and Mental Component Score (MCS) (p=0.001). The percentage of patients reaching MCID for PCS and MCS was lower in low fT3 group than that in normal fT3 group (p<0.001). Multivariate linear regression analyses showed that low level of fT3 was an independent risk factor for PCS and MCS improvements. In conclusion, a low fT3 level is a predictor of worse HRQOL improvement in ACS patients treated with DES.

Analysis of Pollution Hazard Intensity: A Spatial Epidemiology Case Study of Soil Pb Contamination.

Heavy industrialization has resulted in the contamination of soil by metals from anthropogenic sources in Anniston, Alabama. This situation calls for increased public awareness of the soil contamination issue and better knowledge of the main factors contributing to the potential sources contaminating residential soil. The purpose of this spatial epidemiology research is to describe the effects of physical factors on the concentration of lead (Pb) in soil in Anniston AL, and to determine the socioeconomic and demographic characteristics of those residing in areas with higher soil contamination. Spatial regression models are used to account for spatial dependencies using these explanatory variables. After accounting for covariates and multicollinearity, results of the analysis indicate that lead concentration in soils varies markedly in the vicinity of a specific foundry (Foundry A), and that proximity to railroads explained a significant amount of spatial variation in soil lead concentration. Moreover, elevated soil lead levels were identified as a concern in industrial sites, neighborhoods with a high density of old housing, a high percentage of African American population, and a low percent of occupied housing units. The use of spatial modelling allows for better identification of significant factors that are correlated with soil lead concentrations.

Scale Effects on Spatially Embedded Contact Networks.

Spatial phenomena are subject to scale effects, but there are rarely studies addressing such effects on spatially embedded contact networks. There are two types of structure in these networks, network structure and spatial structure. The network structure has been actively studied. The spatial structure of these networks has received attention only in recent years. Certainly little is known whether the two structures respond to each other. This study examines the scale effects, in terms of spatial extent, on the network structure and the spatial structure of spatially embedded contact networks. Two issues are explored, how the two types of structures change in response to scale changes, and the range of the scale effects. Two sets of areal units, regular grids with 24 different levels of spatial extent and census units of three levels of spatial extent, are used to divide one observed and two reference random networks into multiple scales. Six metrics are used to represent the two structures. Results show different scale effects. In terms of the network structure, the properties of the observed network are sensitive to scale changes at fine scales. In comparison, the clustered spatial structure of the network is scale independent. The behaviors of the network structure are affected by the spatial structure. This information helps identify vulnerable households and communities to health risks and helps deploy intervention strategies to spatially targeted areas.

A location-centric network approach to analyzing epidemic dynamics.

Recent health threats, such as the SARS, H1N1, and Ebola pandemics, have stimulated great interest in network models to study the transmission of communicable diseases through human interaction and mobility. Most current network models have focused on an individual-centric perspective where individuals are represented as nodes, and the interactions among them as edges. Few of these models are concerned with the discovery of the spatial patterns and dynamics of epidemics. We propose a location-centric, transmission network approach, in which nodes denote locations and edges denote possible disease transmissions between locations. We then identify the dynamics of transmission flows, the dynamics of critical locations, and the spatial-temporal extent of transmission pathways to assess the impact of these spatial dynamics on the evolution of an epidemic. Results show that transmission flows shift from elementary schools to middle schools and finally universities and professional schools at different phases of an epidemic. Critical locations, identified using network analysis, are responsible for the upsurge in transmission flows during the peaks of the epidemic. The length of transmission pathways shows a power law distribution and their spatial extent is rather small. Insights gained from this study will help devise spatially sensitive strategies to control communicable diseases.

Association between long-term prescription of aldosterone antagonist and the progression of heart failure with preserved ejection fraction in hypertensive patients.

BACKGROUND: Hypertension complicated with left ventricular hypertrophy (LVH) and diastolic dysfunction is independently related to increasing risk of subsequent incident heart failure with preserved ejection fraction (HFpEF). This study was designed to evaluate the influences of long-term aldosterone antagonist prescription in these patients. METHODS: Using a propensity score matching of 1:2 ratio, this retrospective claims database study compared spironolactone prescription (n=65) and non-spironolactone therapy (n=130) in hypertensive patients with LVH [left ventricular mass index (LVMI)>125g/m(2) for men and >110g/m(2) for women] and suspected diastolic dysfunction (E/E' ratio between 8 and 15) and without clinical signs or symptoms of heart failure. RESULTS: With a median follow-up of 7.4years, the new-onset symptomatic HFpEF occurred in 3 of 65 patients in the spironolactone group and 21 of 130 patients in the non-spironolactone group (P=0.021). Spironolactone also generated more prominent improvement in diastolic function and LVH. And multivariate logistic regression model revealed that spironolactone prescription (OR 0.177, 95% CI: 0.045-0.687, P=0.012) was associated with a reduced risk of new onset of symptomatic HFpEF, and the elevation of LVMI (OR 1.053, 95% CI: 1.011-1.097, P=0.012) or E/E' (OR 1.280, 95% CI: 1.015-1.615, P=0.037) was associated with a high risk of new onset of symptomatic HFpEF. CONCLUSIONS: Long-term aldosterone antagonist exposure was associated with protective effects in terms of the incidence of new-onset symptomatic HFpEF, LV diastolic dysfunction and LVH in hypertensive patients, which might be beneficial for the delay of HFpEF progression.

Increase of rutin antioxidant activity by generating Maillard reaction products with lysine.

Rutin exists in medicinal herbs, fruits, vegetables, and a number of plant-derived sources. Dietary sources containing rutin are considered beneficial because of their potential protective roles in multiple diseases related to oxidative stresses. In the present study, the change and antioxidation activity of rutin in Maillard reaction with lysine through a heating process were investigated. There is release of glucose and rhamnose that interact with lysine to give Maillard reaction products (MRPs), while rutin is converted to less-polar quercetin and a small quantity of isoquercitrin. Because of their high cell-membrane permeability, the rutin-lysine MRPs increase the free radical-scavenging activity in HepG2 cells, showing cellular antioxidant activity against Cu(2+)-induced oxidative stress higher than that of rutin. Furthermore, the MRPs significantly increased the Cu/Zn SOD (superoxide dismutase) activity and Cu/Zn SOD gene expression of HepG2 cells, consequently enhancing antioxidation activity.

Long-term prescription of beta-blocker delays the progression of heart failure with preserved ejection fraction in patients with hypertension: A retrospective observational cohort study.

BACKGROUND: Hypertension complicated with left ventricular hypertrophy (LVH) and diastolic dysfunction is one of the most common risks for heart failure with preserved ejection fraction (HFpEF). This study was designed to evaluate the influences of long-term beta-blocker prescription in these patients. METHODS: This retrospective analysis included eligible patients diagnosed with hypertension, LVH (left ventricular (LV) mass index >125 g/m(2) for men and >110 g/m(2) for women) and suspected diastolic dysfunction (E/E' ratio between 8 and 15) and without clinical signs or symptoms of heart failure in our hospital medical record database (January 2005-December 2009). A total of eligible 1498 patients were enrolled, of whom 803 received beta-blocker prescription and 695 accepted non-beta-blocker therapy. RESULTS: With a median follow-up of 7.2 years, the new-onset symptomatic HFpEF occurred in 48 of 803 patients in the beta-blocker group (6.0%) and 92 of 695 patients in the non-beta-blocker group (13.2%, p < 0.001). Beta-blockers also generated more prominent improvement in diastolic function and LVH. And Cox proportional hazards model revealed that beta-blocker (hazard ratio (HR) 0.327, 95% confidence interval (CI): 0.121-0.540, p = 0.009) or angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker (ACEI/ARB) exposure (HR 0.422, 95% CI: 0.210-0.699, p = 0.015) was associated with a reduced risk of new onset of symptomatic HFpEF, and the elevation of LVMI (HR 1.210, 95% CI: 1.069-1.362, p = 0.040) or E/E' (HR 1.398, 95% CI: 1.306-1.541, p = 0.032) was associated with a high risk of new onset of symptomatic HFpEF. CONCLUSIONS: Long-term beta-blocker exposure was associated with protective effects in terms of the incidence of new-onset symptomatic HFpEF, LV diastolic dysfunction and LVH, which might be beneficial for the delay of HFpEF progression.