Association between obstructive sleep apnea and arrhythmia and heart rate variability among hypertensive patients.

BACKGROUND: The relationship between obstructive sleep apnea (OSA) and the occurrence of arrhythmias and heart rate variability (HRV) in hypertensive patients is not elucidated. Our study investigates the association between OSA, arrhythmias, and HRV in hypertensive patients. METHODS: We conducted a cross-sectional analysis involving hypertensive patients divided based on their apnea-hypopnea index (AHI) into two groups: the AHI ≤ 15 and the AHI > 15. All participants underwent polysomnography (PSG), 24-hour dynamic electrocardiography (DCG), cardiac Doppler ultrasound, and other relevant evaluations. RESULTS: The AHI > 15 group showed a significantly higher prevalence of frequent atrial premature beats and atrial tachycardia (P = 0.030 and P = 0.035, respectively) than the AHI ≤ 15 group. Time-domain analysis indicated that the standard deviation of normal-to-normal R-R intervals (SDNN) and the standard deviation of every 5-minute normal-to-normal R-R intervals (SDANN) were significantly higher in the AHI > 15 group (P = 0.020 and P = 0.033, respectively). Frequency domain analysis revealed that the low-frequency (LF), high-frequency (HF) components, and the LF/HF ratio were also significantly elevated in the AHI > 15 group (P < 0.001, P = 0.031, and P = 0.028, respectively). Furthermore, left atrial diameter (LAD) was significantly larger in the AHI > 15 group (P < 0.001). Both univariate and multivariable linear regression analyses confirmed a significant association between PSG-derived independent variables and the dependent HRV parameters SDNN, LF, and LF/HF ratio (F = 8.929, P < 0.001; F = 14.832, P < 0.001; F = 5.917, P = 0.016, respectively). CONCLUSIONS: Hypertensive patients with AHI > 15 are at an increased risk for atrial arrhythmias and left atrial dilation, with HRV significantly correlating with OSA severity.

Incorporating prior information in gene expression network-based cancer heterogeneity analysis.

Cancer is molecularly heterogeneous, with seemingly similar patients having different molecular landscapes and accordingly different clinical behaviors. In recent studies, gene expression networks have been shown as more effective/informative for cancer heterogeneity analysis than some simpler measures. Gene interconnections can be classified as "direct" and "indirect," where the latter can be caused by shared genomic regulators (such as transcription factors, microRNAs, and other regulatory molecules) and other mechanisms. It has been suggested that incorporating the regulators of gene expressions in network analysis and focusing on the direct interconnections can lead to a deeper understanding of the more essential gene interconnections. Such analysis can be seriously challenged by the large number of parameters (jointly caused by network analysis, incorporation of regulators, and heterogeneity) and often weak signals. To effectively tackle this problem, we propose incorporating prior information contained in the published literature. A key challenge is that such prior information can be partial or even wrong. We develop a two-step procedure that can flexibly accommodate different levels of prior information quality. Simulation demonstrates the effectiveness of the proposed approach and its superiority over relevant competitors. In the analysis of a breast cancer dataset, findings different from the alternatives are made, and the identified sample subgroups have important clinical differences.

The effects of obstructive sleep apnea on blood pressure variability and load in patients with hypertension.

BACKGROUND: Hypertension frequently coexists with obstructive sleep apnea (OSA), and their interplay substantially impacts the prognosis of affected individuals. Investigating the influence of OSA on blood pressure variability (BPV) and blood pressure load (BPL) in hypertensive patients has become a focal point of clinical research. METHODS: This cross-sectional study recruited hypertensive patients (n = 265) without discrimination and classified them into four groups based on their apnea-hypopnea index (AHI): control group (n = 40), AHI < 5; mild group (n = 74), 5 ≤ AHI ≤ 15; moderate group (n = 68), 15 < AHI ≤ 30; severe group (n = 83), AHI > 30. All participants underwent comprehensive assessments, including polysomnography (PSG) monitoring, 24-h ambulatory blood pressure (ABP) monitoring, cardiac Doppler ultrasound, and additional examinations when indicated. RESULTS: BPV and BPL exhibited significant elevations in the moderate and severe OSA groups compared to the control and mild OSA groups (P < 0.05). Moreover, interventricular septum thickness and left ventricular end-diastolic volume (LVEDV) demonstrated higher values in the moderate and severe OSA groups (P < 0.05). Multiple stepwise regression analysis identified noteworthy risk factors for elevated BPV in hypertensive patients with OSA, including AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea. CONCLUSION: Hypertensive patients with moderate to severe OSA exhibited substantially increased BPV and BPL. Moreover, BPV was correlated with AHI, maximum apnea time, total times of oxygen reduction, and mean time of apnea in hypertensive patients with OSA.

Invariant natural killer T cells drive hepatic homeostasis in nonalcoholic fatty liver disease via sustained IL-10 expression in CD170+ Kupffer cells.

Kupffer cells (KCs) are liver-resident macrophages involved in hepatic inflammatory responses, including nonalcoholic fatty liver disease (NAFLD) development. However, the contribution of KC subsets to liver inflammation remains unclear. Here, using high-dimensional single-cell RNA sequencing, we characterized murine embryo-derived KCs and identified two KC populations with different gene expression profiles: KC-1 and KC-2. KC-1 expressed CD170, exhibiting immunoreactivity and immune-regulatory abilities, while KC-2 highly expressed lipid metabolism-associated genes. In a high-fat diet-induced NAFLD model, KC-1 cells differentiated into pro-inflammatory phenotypes and initiated more frequent communications with invariant natural killer T (iNKT) cells. In KC-1, interleukin (IL)-10 expression was unaffected by the high-fat diet but impaired by iNKT cell ablation and upregulated by iNKT cell adoptive transfer in vivo. Moreover, in a cellular co-culture system, primary hepatic iNKT cells promoted IL-10 expression in RAW264.7 and primary KC-1 cells. CD206 signal blocking in KC-1 or CD206 knockdown in RAW264.7 cells significantly reduced IL-10 expression. In conclusion, we identified two embryo-derived KC subpopulations with distinct transcriptional profiles. The CD206-mediated crosstalk between iNKT and KC-1 cells maintains IL-10 expression in KC-1 cells, affecting hepatic immune balance. Therefore, KC-based therapeutic strategies must consider cellular heterogeneity and the local immune microenvironment for enhanced specificity and efficiency.

Gut microbiota in hypertensive patients with versus without obstructive sleep apnea.

We investigated the alteration of gut microbiota and the associated metabolic risks in hypertensive patients with obstructive sleep apnea (OSA) comorbidity. Fecal and blood samples were collected from 52 hypertensive patients, who were divided into three groups: A (controls, apnea-hypopnea index[AHI] < 5, n = 15), B (mild OSA, 5 < AHI < 20, n = 17), and C (moderate-to-severe OSA, AHI > 20, n = 20). The composition of the gut microbiota was studied through 16s RNA sequencing of variable regions 3-4. Analysis of the results revealed that group C had a significant higher concentration of total cholesterol, low-density lipoprotein, and IL-1ß compared with group A. The Shannon index showed that bacterial biodiversity was lower in OSA patients. At the phylum level, the ratio of Firmicutes to Bacteroidetes (F/B) was significantly higher in group C than in groups A and B. At the genus level, the relative abundance of short-chain fatty acids (SCFA)-producing bacteria (e.g., Bacteroides and Prevotella) was lower while the number of inflammation-related bacteria (e.g., Lactobacillus) was increased in patients with OSA. We found that the IL-1ß level was negatively correlated with Bacteroidetes. The area under the receiver operating characteristic curve was .672 for F/B ratio in determining hypertensive patients with OSA. In patients with hypertension, OSA was associated with worse gut dysbiosis, as evidenced by decreased levels of short-chain fatty acids-producing bacteria and increased number of inflammation-related bacteria. The differences in gut microbiota discriminate hypertensive patients with OSA from those without and may result in an enhanced inflammatory response and increase the risk of metabolic diseases.

The Level of HbA1c Evaluates the Extent of Coronary Atherosclerosis Lesions and the Prognosis in Diabetes with Acute Coronary Syndrome.

Background: The level of HbA1c can reflect the average level of blood glucose over 3 months, which is the gold standard indicator for monitoring blood glucose. The relationship between the level of HbA1c and the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) remains poorly understood. Aims: To explore whether the level of HbA1c can evaluate the extent of coronary atherosclerosis lesions or the prognosis in diabetes with acute coronary syndrome (ACS) using the SYNTAX score, the Global Registry of Acute Coronary Events (GRACE) score, left ventricular function (LVEF), left ventricular end-diastolic volume (LVEDV), and major adverse cardiac events (MACEs) in the hospital and 12 months after discharge. Methods: This study was a prospective, randomized, open-label, and parallel group study. Patients with diabetes with ACS were recruited into this study indiscriminately, and all the participants were divided into two groups according to the level of HbA1c: HbA1c level ≤ 7%group and HbA1c level > 7%group. The followings were used as the evaluation indicators: SYNTAX score, GRACE score, LVEF, LVEDV, and MACEs in hospital and 12 months after discharge. Results: A total of 233 patients with diabetes and ACS were enrolled and assigned to two groups according to their level of HbA1c: the HbA1c ≤ 7%group (n = 92) and the HbA1c > 7%group (n = 141). The results showed that the proportion of STEMI was higher in the HbA1c ≤7% group (p < 0.05), while the proportion of NSTEMI has not significantly higher in the HbA1c >7% group (p > 0.05). Regression analysis indicated that HbA1c level was significantly positively correlated with GRACE score (r = 0.156, F = 5.784, p = 0.017, n = 233) and SYNTAX score (r = 0.237, F = 13.788, p < 0.001, n = 233), and there were no statistically significant differences in LVEDV and LVEF between the two groups (p > 0.05). The total MACEs rate showed no significant difference between the two groups during hospitalization (p > 0.05) but showed significant differences at 12 months after discharge (p < 0.05). Conclusions: This study shows that HbA1c level was positively correlated with the extent of coronary atherosclerosis lesions and the prognosis in diabetes with ACS. The higher the HbA1c level is, the more severe the coronary atherosclerotic lesion and the worse the prognosis in diabetes with ACS are.

Study of Texture Indicators Applied to Pavement Wear Analysis Based on 3D Image Technology.

Pavement texture characteristics can reflect early performance decay, skid resistance, and other information. However, most statistical texture indicators cannot express this difference. This study adopts 3D image camera equipment to collect texture data from laboratory asphalt mixture specimens and actual pavement. A pre-processing method was carried out, including data standardisation, slope correction, missing value and outlier processing, and envelope processing. Then the texture data were calculated based on texture separation, texture power spectrum, grey level co-occurrence matrix, and fractal theory to acquire six leading texture indicators and eight extended indicators. The Pearson correlation coefficient was used to analyse the correlation of different texture indicators. The distinction vector based on the information entropy is calculated to analyse the distinction of the indicators. High correlations between ENE (energy) and ENT (entropy), ENT and D (Minkowski dimension) were found. The CON (contrast) has low correlations with HT (macro-texture power spectrum area), ENT and D. However, the differentiation of ENE and HT is more prominent, and the differentiation of the CON is smaller. ENE, ENT, CON and D indicators based on macro-texture and the corresponding original texture have strong linear correlations. However, the microtexture indicators are not linearly correlated with the corresponding original texture indicators. D, WT (micro-texture power spectrum area) and ENT exhibit high degrees of numerical concentration for the same road sections and may be more statistically helpful in distinguishing the characteristics of the pavement performance decay of the road sections.

Network-based cancer heterogeneity analysis incorporating multi-view of prior information.

MOTIVATION: Cancer genetic heterogeneity analysis has critical implications for tumour classification, response to therapy and choice of biomarkers to guide personalized cancer medicine. However, existing heterogeneity analysis based solely on molecular profiling data usually suffers from a lack of information and has limited effectiveness. Many biomedical and life sciences databases have accumulated a substantial volume of meaningful biological information. They can provide additional information beyond molecular profiling data, yet pose challenges arising from potential noise and uncertainty. RESULTS: In this study, we aim to develop a more effective heterogeneity analysis method with the help of prior information. A network-based penalization technique is proposed to innovatively incorporate a multi-view of prior information from multiple databases, which accommodates heterogeneity attributed to both differential genes and gene relationships. To account for the fact that the prior information might not be fully credible, we propose a weighted strategy, where the weight is determined dependent on the data and can ensure that the present model is not excessively disturbed by incorrect information. Simulation and analysis of The Cancer Genome Atlas glioblastoma multiforme data demonstrate the practical applicability of the proposed method. AVAILABILITY AND IMPLEMENTATION: R code implementing the proposed method is available at https://github.com/mengyunwu2020/PECM. The data that support the findings in this paper are openly available in TCGA (The Cancer Genome Atlas) at https://portal.gdc.cancer.gov/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.