Linoleyl acetate and mandenol alleviate HUA-induced ED via NLRP3 inflammasome and JAK2/STAT3 signalling conduction in rats.

Hyperuricemia (HUA) is characterized by elevated blood uric acid levels, which can increase the risk of erectile dysfunction (ED). Clinical studies have demonstrated satisfactory efficacy of a traditional Chinese medicine formula QYHT decoction in improving ED. Furthermore, the main monomeric components of this formula, linoleyl acetate and mandenol, demonstrate promise in the treatment of ED. This study established an ED rat model induced by HUA and the animals were administered with linoleyl acetate and mandenol. HE and TUNEL were performed to detect tissue changes, ELISA to measure the levels of serum testosterone (T), MDA, NO, CRP, and TNF-α and qPCR and WB to assess the expression levels of NLRP3, ASC, Caspase-1, JAK2, and STAT3 in whole blood. The findings showed that linoleyl acetate and mandenol improved kidney tissue morphology, reduced cell apoptosis in penile tissue, significantly increased T and NO levels, while substantially decreasing levels of MDA, CRP, and TNF-α. Meanwhile, the expression of NLRP3, ASC, and Caspase-1 mRNAs and proteins was markedly reduced, and the phosphorylation of JAK2 and STAT3 was inhibited. These findings were further validated through faecal microbiota transplantation results. Taken together, linoleyl acetate and mandenol could inhibit NLRP3 inflammasome activation, reduce inflammatory and oxidative stress responses, suppress the activity of JAK-STAT signalling pathway, ultimately providing a potential treatment for HUA-induced ED.

Cellular senescence: A novel therapeutic target for central nervous system diseases.

The underlying mechanisms of diseases affecting the central nervous system (CNS) remain unclear, limiting the development of effective therapeutic strategies. Remarkably, cellular senescence, a biological phenomenon observed in cultured fibroblasts in vitro, is a crucial intrinsic mechanism that influences homeostasis of the brain microenvironment and contributes to the onset and progression of CNS diseases. Cellular senescence has been observed in disease models established in vitro and in vivo and in bodily fluids or tissue components from patients with CNS diseases. These findings highlight cellular senescence as a promising target for preventing and treating CNS diseases. Consequently, emerging novel therapies targeting senescent cells have exhibited promising therapeutic effects in preclinical and clinical studies on aging-related diseases. These innovative therapies can potentially delay brain cell loss and functional changes, improve the prognosis of CNS diseases, and provide alternative treatments for patients. In this study, we examined the relevant advancements in this field, particularly focusing on the targeting of senescent cells in the brain for the treatment of chronic neurodegenerative diseases (e.g., Alzheimer's disease, Parkinson's disease, and multiple sclerosis) and acute neurotraumatic insults (e.g., ischemic stroke, spinal cord injury, and traumatic brain injury).

Real-Time Wireless Sensing of Cardiomyocyte Contractility by Integrating Magnetic Microbeam and Oriented Nanofibers.

In vitro cardiomyocyte mechano-sensing platform is crucial for evaluating the mechanical performance of cardiac tissues and will be an indispensable tool for application in drug discovery and disease mechanism study. Magnetic sensing offers significant advantages in real-time, in situ wireless monitoring and resistance to ion interference. However, due to the mismatch between the stiffness of traditional rigid magnetic material and myocardial tissue, sensitivity is insufficient and it is difficult to achieve cell structure induction and three-dimensional cultivation. Herein, a magnetic sensing platform that integrates a neodymium-iron-boron/polydimethylsiloxane (NdFeB/PDMS) flexible microbeam with suspended and ordered polycaprolactone (PCL) nanofiber membranes was developed, providing a three-dimensional anisotropic culture environment for cardiomyocyte growth and simultaneously realizing in situ wireless contractility monitoring. The as-prepared sensor presented an ultrahigh sensitivity of 442.2 µV/µm and a deflection resolution of 2 µm. By continuously monitoring the cardiomyocyte growth status and drug stimulation feedback, we verified the capability of the platform to capture dynamic changes in cardiomyocyte contractility. This platform provides a perspective tool for evaluating cardiomyocyte maturity and drug performance.

Gelatin-based ionic hydrogel for intelligent fire-alarm system with considerable toughness, flame retardancy, and thermoelectric performance.

Temperature-responsive materials with excellent reliability, sensitivity, and flame-retardant properties have always been an urgent need in the field of intelligent fire protection. In this discourse, we introduce a novel thermosensitive ionic hydrogel coating (gelatin/poly(acrylamide-co-acrylic acid)/CaCl2/spindle-shaped aluminum hydroxide nanosheet/glycerol, HCA) synthesized via free radical polymerization. HCA not only demonstrates considerable mechanical properties with a fracture strain of up to 842.5 % and a maximum tensile strength of 0.77 MPa but also exhibits notable flame retardancy and adhesion. It effectively covers combustible surfaces, providing outstanding fire protection. Notably, HCA boasts a Seebeck coefficient of up to 10.1 mV/K, significantly surpassing conventional thermoelectric materials. The well-established linear relationship between the generated voltage and temperature variation enables HCA-based intelligent fire-alarm system to accurately emit continuous alerts during fire incidents and swiftly transmit alarm signals to terminal devices. The development of this intelligent fire-alarm system presents new avenues in intelligent fire-safety technologies.

Architecture design and advanced manufacturing of heart-on-a-chip: scaffolds, stimulation and sensors.

Heart-on-a-chip (HoC) has emerged as a highly efficient, cost-effective device for the development of engineered cardiac tissue, facilitating high-throughput testing in drug development and clinical treatment. HoC is primarily used to create a biomimetic microphysiological environment conducive to fostering the maturation of cardiac tissue and to gather information regarding the real-time condition of cardiac tissue. The development of architectural design and advanced manufacturing for these "3S" components, scaffolds, stimulation, and sensors is essential for improving the maturity of cardiac tissue cultivated on-chip, as well as the precision and accuracy of tissue states. In this review, the typical structures and manufacturing technologies of the "3S" components are summarized. The design and manufacturing suggestions for each component are proposed. Furthermore, key challenges and future perspectives of HoC platforms with integrated "3S" components are discussed. Architecture design concepts of scaffolds, stimulation and sensors in chips.

Genomic and Phenotypic Analysis of bla KPC-2 Associated Carbapenem Resistance in Klebsiella aerogenes: Insights into Clonal Spread and Resistance Mechanisms Across Hospital Departments in Beijing.

Purpose: This study conducted an phenotypic and whole-genome sequencing analysis with Klebsiella aerogenes to elucidate its clinical epidemiological characteristics, antimicrobial resistance (AMR) phenotype, biofilm formation ability and hemolytic activity testing, AMR genes and phylogenetic relationships, so as to provide a further understanding of the intra-hospital strain transmission. Methods: Samples were collected from a hospital in Beijing between 2020 and 2022. All strains underwent bacterial identification, antimicrobial susceptibility testing (AST) using the VITEK-2 compact system. Biofilm formation ability and hemolytic activity were tested. Second-generation sequencing was applied to all strains, with those carrying the bla KPC gene were selected for third-generation sequencing. Whole-genome analysis identified resistance genes, plasmid types, MLST typing, and phylogenetic relationships. Plasmids were assembled to detect plasmid structures and AMR gene location. Results: Among the 42 K. aerogenes isolates, 21 were carbapenem-resistant K. aerogenes (CRKA). All strains exhibited strong biofilm formation and no hemolytic activity. Most were sourced from sputum (83.3%). CRKA demonstrated extensive resistance to antibiotics, particularly ß-lactamase inhibitors and Cefotetan. This resistance pattern was closely associated with the presence of an IncFII(pHN7A8) plasmid, which carried multiple resistance genes, including bla KPC-2, bla CTX-M-65, bla TEM-1, rmtB and a large number of mobile elements. The majority of CRKA strains clustered within the same branch of the phylogenetic tree, exhibiting minimal single nucleotide polymorphism (0-13 SNPs) differences, and they shared the same sequence type (ST292), resistance genes, and plasmids, originating from different departments, suggesting clonal transmission among the hospital. Conclusion: Our research reveals that the clonal transmission of CRKA occurs across various departments within the hospital. The widespread resistance observed in CRKA, attributed to the presence of bla KPC and ESBLs genes, underscores the need for heightened vigilance to prevent the further dissemination of CRKA within the hospital and, potentially, throughout the wider community.

Digital light processing printed hydrogel scaffolds with adjustable modulus.

Hydrogels are extensively explored as biomaterials for tissue scaffolds, and their controlled fabrication has been the subject of wide investigation. However, the tedious mechanical property adjusting process through formula control hindered their application for diverse tissue scaffolds. To overcome this limitation, we proposed a two-step process to realize simple adjustment of mechanical modulus over a broad range, by combining digital light processing (DLP) and post-processing steps. UV-curable hydrogels (polyacrylamide-alginate) are 3D printed via DLP, with the ability to create complex 3D patterns. Subsequent post-processing with Fe3+ ions bath induces secondary crosslinking of hydrogel scaffolds, tuning the modulus as required through soaking in solutions with different Fe3+ concentrations. This innovative two-step process offers high-precision (10 µm) and broad modulus adjusting capability (15.8-345 kPa), covering a broad range of tissues in the human body. As a practical demonstration, hydrogel scaffolds with tissue-mimicking patterns were printed for cultivating cardiac tissue and vascular scaffolds, which can effectively support tissue growth and induce tissue morphologies.

Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.

To tackle the difficulty of extracting features from one-dimensional spectral signals using traditional spectral analysis, a metabolomics analysis method is proposed to locate two-dimensional correlated spectral feature bands and combine it with deep learning classification for wine origin traceability. Metabolomics analysis was performed on 180 wine samples from 6 different wine regions using UPLC-Q-TOF-MS. Indole, Sulfacetamide, and caffeine were selected as the main differential components. By analyzing the molecular structure of these components and referring to the main functional groups on the infrared spectrum, characteristic band regions with wavelengths in the range of 1000-1400 nm and 1500-1800 nm were selected. Draw two-dimensional correlation spectra (2D-COS) separately, generate synchronous correlation spectra and asynchronous correlation spectra, establish convolutional neural network (CNN) classification models, and achieve the purpose of wine origin traceability. The experimental results demonstrate that combining two segments of two-dimensional characteristic spectra determined by metabolomics screening with convolutional neural networks yields optimal classification results. This validates the effectiveness of using metabolomics screening to determine spectral feature regions in tracing wine origin. This approach effectively removes irrelevant variables while retaining crucial chemical information, enhancing spectral resolution. This integrated approach strengthens the classification model's understanding of samples, significantly increasing accuracy.

Association of Lipoprotein(a) Levels With Myocardial Infarction in Patients With Low-Attenuation Plaque.

BACKGROUND: Lipoprotein(a) (Lp[a]) is associated with an increased risk of myocardial infarction (MI). However, the mechanism underlying this association has yet to be fully elucidated. OBJECTIVES: This multicenter study aimed to investigate whether association between Lp(a) and MI risk is reinforced by the presence of low-attenuation plaque (LAP) identified by coronary computed tomography angiography (CCTA). METHODS: In a derivation cohort, a total of 5,607 patients with stable chest pain suspected of coronary artery disease who underwent CCTA and Lp(a) measurement were prospectively enrolled. In validation cohort, 1,122 patients were retrospectively collected during the same period. High Lp(a) was defined as Lp(a) ≥50 mg/dL. The primary endpoint was a composite of time to fatal or nonfatal MI. Associations were estimated using multivariable Cox proportional hazard models. RESULTS: During a median follow-up of 8.2 years (Q1-Q3: 7.2-9.3 years), the elevated Lp(a) levels were associated with MI risk (adjusted HR [aHR]: 1.91; 95% CI: 1.46-2.49; P < 0.001). There was a significant interaction between Lp(a) and LAP (Pinteraction <0.001) in relation to MI risk. When stratified by the presence or absence of LAP, Lp(a) was associated with MI in patients with LAP (aHR: 3.03; 95% CI: 1.92-4.76; P < 0.001). Mediation analysis revealed that LAP mediated 73.3% (P < 0.001) for the relationship between Lp(a) and MI. The principal findings remained unchanged in the validation cohort. CONCLUSIONS: Elevated Lp(a) augmented the risk of MI during 8 years of follow-up, especially in patients with LAP identified by CCTA. The presence of LAP could reinforce the relationship between Lp(a) and future MI occurrence.

Cerebral autoregulation: A reliable predictor of prognosis in patients receiving intravenous thrombolysis.

AIMS: To investigate the characteristics of dynamic cerebral autoregulation (dCA) after intravenous thrombolysis (IVT) and assess the relationship between dCA and prognosis. METHODS: Patients with unilateral acute ischemic stroke receiving IVT were prospectively enrolled; those who did not were selected as controls. All patients underwent dCA measurements, by quantifying the phase difference (PD) and gain, at 1-3 and 7-10 days after stroke onset. Simultaneously, two dCA-based nomogram models were established to verify the predictive value of dCA for patients with mild-to-moderate stroke. RESULTS: Finally, 202 patients who received IVT and 238 who did not were included. IVT was positively correlated with higher PD on days 1-3 and 7-10 after stroke onset. PD values in both sides at 1-3 days after stroke onset and in the affected side at 7-10 days after onset were independent predictors of unfavorable outcomes in patients who received IVT. Additionally, in patients with mild-to-moderate stroke who received IVT, the dCA-based nomogram models significantly improved the risk predictive ability for 3-month unfavorable outcomes. CONCLUSION: IVT has a positive effect on dCA in patients with acute stroke; furthermore, dCA may be useful to predict the prognosis of patients with IVT.

A novel framework for three-dimensional electrical impedance tomography reconstruction of maize ear via feature reconfiguration and residual networks.

Electrical impedance tomography (EIT) provides an indirect measure of the physiological state and growth of the maize ear by reconstructing the distribution of electrical impedance. However, the two-dimensional (2D) EIT within the electrode plane finds it challenging to comprehensively represent the spatial distribution of conductivity of the intact maize ear, including the husk, kernels, and cob. Therefore, an effective method for 3D conductivity reconstruction is necessary. In practical applications, fluctuations in the contact impedance of the maize ear occur, particularly with the increase in the number of grids and computational workload during the reconstruction of 3D spatial conductivity. These fluctuations may accentuate the ill-conditioning and nonlinearity of the EIT. To address these challenges, we introduce RFNetEIT, a novel computational framework specifically tailored for the absolute imaging of the three-dimensional electrical impedance of maize ear. This strategy transforms the reconstruction of 3D electrical conductivity into a regression process. Initially, a feature map is extracted from measured boundary voltage via a data reconstruction module, thereby enhancing the correlation among different dimensions. Subsequently, a nonlinear mapping model of the 3D spatial distribution of the boundary voltage and conductivity is established, utilizing the residual network. The performance of the proposed framework is assessed through numerical simulation experiments, acrylic model experiments, and maize ear experiments. Our experimental results indicate that our method yields superior reconstruction performance in terms of root-mean-square error (RMSE), correlation coefficient (CC), structural similarity index (SSIM), and inverse problem-solving time (IPST). Furthermore, the reconstruction experiments on maize ears demonstrate that the method can effectively reconstruct the 3D conductivity distribution.

Baseline Uric Acid Levels and Intravenous Thrombolysis Outcomes in Patients With Acute Ischemic Stroke: A Prospective Cohort Study.

BACKGROUND: The study aimed to investigate the relationship between uric acid (UA) levels and functional outcomes at 3 months in patients with acute ischemic stroke (AIS) who underwent intravenous thrombolysis (IVT). METHODS AND RESULTS: This prospective cohort study included 1001 consecutive patients with AIS who underwent IVT. The correlation between UA levels and post-IVT AIS outcomes was examined. Any nonlinear relationship was assessed using a restricted cubic spline function. The nonlinear P value for the association of UA levels with favorable (modified Rankin Scale [mRS] score ≤2) and excellent (mRS score ≤1) outcomes at 3 months post-IVT were <0.001 and 0.001, respectively. However, for patients with and without hyperuricemia, no evident nonlinear relationship was observed between UA levels and favorable 3-month post-IVT outcomes, with nonlinear P values of 0.299 and 0.207, respectively. The corresponding interaction analysis yielded a P value of 0.001, indicating significant heterogeneity. Similar results were obtained for excellent outcomes at 3 months post-IVT. In the hyperuricemia group, increased UA levels by 50 µmol/L reduced the odds of a favorable 3-month post-AIS outcome (odds ratio [OR], 0.75 [95% CI, 0.57-0.97]). Conversely, in the nonhyperuricemia group, a similar UA increase was linked to higher favorable outcome odds (OR, 1.31 [95% CI, 1.15-1.50]). CONCLUSIONS: An inverted U-shaped nonlinear relationship was observed between UA levels and favorable and excellent outcomes at 3 months in patients with AIS who underwent IVT. Higher UA levels predict favorable outcomes in patients without hyperuricemia but unfavorable outcomes in those with hyperuricemia.

GY-SLAM: A Dense Semantic SLAM System for Plant Factory Transport Robots.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network, GY, based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and the SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While mAP@0.5 increased by 0.514% to 95.364, the model simultaneously reduced the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system localization accuracy and robustness. It shows a remarkable 92.59% improvement in RMSE for Absolute Trajectory Error (ATE), along with a 93.11% improvement in RMSE for the translational drift of Relative Pose Error (RPE) and a 92.89% improvement in RMSE for the rotational drift of RPE. Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

Comparison of machine learning-based CT fractional flow reserve with cardiac MR perfusion mapping for ischemia diagnosis in stable coronary artery disease.

OBJECTIVES: To compare the diagnostic performance of machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and cardiac magnetic resonance (MR) perfusion mapping for functional assessment of coronary stenosis. METHODS: Between October 2020 and March 2022, consecutive participants with stable coronary artery disease (CAD) were prospectively enrolled and underwent coronary CTA, cardiac MR, and invasive fractional flow reserve (FFR) within 2 weeks. Cardiac MR perfusion analysis was quantified by stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR). Hemodynamically significant stenosis was defined as FFR ≤ 0.8 or > 90% stenosis on invasive coronary angiography (ICA). The diagnostic performance of CT-FFR, MBF, and MPR was compared, using invasive FFR as a reference. RESULTS: The study protocol was completed in 110 participants (mean age, 62 years ± 8; 73 men), and hemodynamically significant stenosis was detected in 36 (33%). Among the quantitative perfusion indices, MPR had the largest area under receiver operating characteristic curve (AUC) (0.90) for identifying hemodynamically significant stenosis, which is in comparison with ML-based CT-FFR on the vessel level (AUC 0.89, p = 0.71), with comparable sensitivity (89% vs 79%, p = 0.20), specificity (87% vs 84%, p = 0.48), and accuracy (88% vs 83%, p = 0.24). However, MPR outperformed ML-based CT-FFR on the patient level (AUC 0.96 vs 0.86, p = 0.03), with improved specificity (95% vs 82%, p = 0.01) and accuracy (95% vs 81%, p < 0.01). CONCLUSION: ML-based CT-FFR and quantitative cardiac MR showed comparable diagnostic performance in detecting vessel-specific hemodynamically significant stenosis, whereas quantitative perfusion mapping had a favorable performance in per-patient analysis. CLINICAL RELEVANCE STATEMENT: ML-based CT-FFR and MPR derived from cardiac MR performed well in diagnosing vessel-specific hemodynamically significant stenosis, both of which showed no statistical discrepancy with each other. KEY POINTS: • Both machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) and quantitative perfusion cardiac MR performed well in the detection of hemodynamically significant stenosis. • Compared with stress myocardial blood flow (MBF) from quantitative perfusion cardiac MR, myocardial perfusion reserve (MPR) provided higher diagnostic performance for detecting hemodynamically significant coronary artery stenosis. • ML-based CT-FFR and MPR from quantitative cardiac MR perfusion yielded similar diagnostic performance in assessing vessel-specific hemodynamically significant stenosis, whereas MPR had a favorable performance in per-patient analysis.

3D Aligned Nanofiber Scaffold Fabrication with Trench-Guided Electrospinning for Cardiac Tissue Engineering.

Constructing three-dimensional (3D) aligned nanofiber scaffolds is significant for the development of cardiac tissue engineering, which is promising in the field of drug discovery and disease mechanism study. However, the current nanofiber scaffold preparation strategy, which mainly includes manual assembly and hybrid 3D printing, faces the challenge of integrated fabrication of morphology-controllable nanofibers due to its cross-scale structural feature. In this research, a trench-guided electrospinning (ES) strategy was proposed to directly fabricate 3D aligned nanofiber scaffolds with alternative ES and a direct ink writing (DIW) process. The electric field effect of DIW poly(dimethylsiloxane) (PDMS) side walls on guiding whipping ES nanofibers was investigated to construct trench design rules. It was found that the width/height ratio of trenches greatly affected the nanofiber alignment, and the trench width/height ratio of 1.5 provided the nanofiber alignment degree over 60%. As a proof of principle, 3D nanofiber scaffolds with controllable porosity (60-80%) and alignment (30-60%) were fabricated. The effect of the scaffolds was verified by culturing human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), which resulted in the uniform 3D distribution of aligned hiPSC-CMs with ∼1000 µm thickness. Therefore, this printing strategy shows great potential for the efficient engineered tissue construction.

Genomic insights and antimicrobial resistance profiles of CRKP and non-CRKP isolates in a Beijing geriatric medical center: emphasizing the blaKPC-2 carrying high-risk clones and their spread.

Background: The escalating resistance of Klebsiella pneumoniae, a prevalent pathogen in healthcare settings, especially its carbapenem-resistant K. pneumoniae (CRKP), to a wide array of antibiotics, notably ß-lactams, constitutes a formidable challenge for healthcare and global public health management. Methods: This research compared the resistance phenotypes and genomic profiles of CRKP and Non-CRKP isolates in a Beijing hospital, focusing on high-risk blaKPC-2 gene-bearing CRKP clones and the structure of mobile genetic elements facilitating their spread across hospital departments. Forty K. pneumoniae isolates were collected from various departments of the hospital and subjected to antimicrobial susceptibility testing and whole-genome sequencing to analyze their resistance phenotypes and genomic features. Results: The study revealed that among the 31 CRKP isolates, ST11 is the most common sequence type, with K47 and OL101 being the dominant capsule types, primarily observed in the respiratory department. In terms of antimicrobial susceptibility: 87.5% of the isolates exhibited multidrug resistance (MDR), with a high resistance rate of 30% against tigecycline. All CRKP isolates demonstrated resistance to multiple drug classes (≥5 CLSI classes). Non-CRKP isolates also showed high resistance rates to minocycline and doxycycline (77.8%). the ST11-KL47-OL101 type emerged as the predominant clone among the CRKP isolates carrying the blaKPC-2 gene. This dominance appears to be mediated by the pKpnR03_2 plasmid, which harbors not only blaKPC-2 and rmtb but also gene clusters pertinent to iron transport and arsenic resistance. These isolates, clustering in the C3 clade of the phylogenetic tree, exhibited minor genetic variations and close evolutionary relationships, suggesting a plasmid-driven spread across various hospital departments. Conclusion: In summary, our study highlights the extensive spread of antibiotic-resistant K. pneumoniae across various departments in our hospital, with a particular emphasis on the dominant clonal proliferation of the ST11-KL47-OL101 CRKP strain. This finding underscores the significant role of plasmid-mediated gene transfer in the evolution and dissemination of resistant strains within hospital environments. The study emphasizes the necessity for ongoing surveillance of antibiotic resistance and genomic analysis in hospital settings to effectively monitor and manage these challenges.

Impaired kidney function and the risk of all-cause mortality and cardiovascular disease among Chinese hypertensive adults: Using three different equations to estimate the glomerular filtration rate.

OBJECTIVES: We aimed to seek accurate assessments of the glomerular filtration rate (GFR) in a Chinese hypertensive population to identify individuals at high risk for chronic kidney disease (CKD) progression. Then, the risk of cardiovascular disease (CVD) and all-cause death due to kidney injury were further investigated under appropriate GFR-estimation equations. METHODS: In this prospective follow-up cohort study of 10,171 hypertensive patients, we compared the discrimination power of a trio of GFR-estimation equations using Harrell's C-index, measuring the model fit by calculating the Akaike information criterion. Univariate and multivariable logistic regression analyses were respectively used to calculate the hazard ratio (HR) and 95% confidence interval [CI] values for CKD progression. In addition, we also assessed the risk of CVD and all-cause death with impaired renal function using multivariable-adjusted Cox regression models. RESULTS: The Modification of Diet in Renal Disease (MDRD) equation showed the highest C-index range for the predicted probability of CKD progression in the fully adjusted model. During MDRD analysis, a low eGFR (60-89 mL/min/1.73m2 or < 60 mL/min/1.73m2) was an independent risk factor for CVD, especially stroke (1.28 [95% CI, 1.05-1.55] and 1.89 [95% CI, 1.08-3.31]), as well as all-cause mortality (1.28 [95% CI, 1.09-1.50] and 1.68 [95% CI, 1.01-2.78]). CONCLUSIONS: The MDRD equation seems to be more suitable for screening CKD progression in Chinese hypertensive populations, targeting potential risk factors for effective prevention to reduce renal impairment so as to further limit CVD morbidity and mortality.

Analysis of cardiovascular function in diabetic patients using EEMD-ICA fusion multi-scale percussion entropy.

BACKGROUND: Diabetes is a chronic disease that can lead to a variety of complications and even cause death. The signal characteristics of the photoplethysmography signals (PPG) and electrocardiogram signals (ECG) can reflect the autonomic and vascular aspects of the effects of diabetes on the body. OBJECTIVE: Based on the complex mechanism of interaction between PPG and ECG, a set of ensemble empirical mode decomposition-independent component analysis (EEMD-ICA) fusion multi-scale percussion entropy index (MSPEI) method was proposed to analyze cardiovascular function in diabetic patients. METHODS: Firstly, the original signal was decomposed into multiple Intrinsic Mode Function (IMFs) by ensemble empirical mode decomposition EEMD, principal components of IMF were extracted by independent component analysis (ICA), then the extracted principal components were reconstructed to eliminate the complex high and low frequency noise of physiological signals. In addition, the MSPEI was calculated for the ECG R-R interval and PPG amplitude sequence.(RRI and Amp) The results showed that, compared with EEMD method, the SNR of EEMD-ICA method increases from 2.1551 to 11.3642, and the root mean square error (RMSE) decreases from 0.0556 to 0.0067. This algorithm can improve the performance of denoising and retain more feature information. The large and small scale entropy of MSPEI (RRI,Amp) was significantly different between healthy and diabetic patients (p< 0.01). RESULTS: Compared with arteriosclerosis index (AI) and multi-scale cross-approximate entropy (MCAE): MSPEISS (RRI,Amp) indicated that diabetes can affect the activity of human autonomic nervous system, while MSPEILS (RRI,Amp) indicated that diabetes can cause or worsen arteriosclerosis. CONCLUSION: Multi-scale Percussion Entropy algorithm has more advantages in analyzing the influence of diabetes on human cardiovascular and autonomic nervous function.