Soluble ST2 for predicting heart failure, atrial fibrillation and death in patients with coronary heart disease with or without renal insufficiency.

Background: This study aimed to investigate the relationship between baseline soluble suppression of tumorigenesis-2 (sST2) concentration and the outcomes of heart failure (HF), atrial fibrillation (AF) or death in patients with coronary heart disease (CHD) with or without renal insufficiency (RI). Methods: Between March 2011 and December 2015, 3454 patients with CHD from the Chinese PLA General Hospital were enrolled in this cohort study. The patients were followed up until October 2021. AF, HF, and death events were recorded. Associations between baseline sST2 concentrations and clinical outcomes were assessed using Kaplan-Meier (K-M) curves, and Cox regression and generalised additive models. Subgroup analysis were carried out between RI and non-RI groups. Results: Among the patients with CHD (61.5 ± 11.8 years; 78.6 % men), 415 (12.02 %) had RI. During a median follow-up of 8.37 years, HF and AF were reported in 216 (6.25 %) and 174 (5.04 %) patients, respectively, and 297 (8.60 %) died. The K-M curves indicated that patients in the higher quartiles of sST2 concentrations were correlated with a poor survival rate of HF, AF, or death (all Ps < 0.001). Generalised additive model (GAM) demonstrated a nonlinear positive association between sST2 concentration and the risk of HF, AF, and death in CHD patients. The cut-off value of sST2 for predicting HF, AF and death were 32.1, 25.4 and 28.6 ng/mL, respectively. CHD patients with sST2 higher than the cut-off value had higher risks of HF (HR: 3.02, 95%CI: 2.24-4.05), AF (HR: 2.86; 95%CI: 2.10-3.90), and death (HR:2.11, 95%CI: 1.67-2.67). Furthermore, in patients with RI (12.02 %, n = 415), the prognostic value of sST2 over the cut-off value for HF and death remained unchanged (HR: 3.21 and 2.35; P < 0.05). In patients with CHD with or without RI, sST2 improved the area under the curve (AUC) of traditional risk models for predicting clinical endpoint events. Conclusions: The biomarker sST2 may be useful for predicting HF, AF, and death in patients with CHD. The predicted value was not affected by renal function.

Comparative Efficacy of Curative Effects of Sacubitril/Valsartan in HFpEF versus HFrEF Treatment.

Objective: This study aims to assess the differential efficacy of Sacubitril/Valsartan in treating heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF), thereby enhancing medication guidance for heart failure management. Methods: A retrospective cohort study was conducted, and a cohort of 89 patients, comprising 48 with HFrEF and 41 with HFpEF, treated at our hospital from February 2020 to January 2022, received Sacubitril/Valsartan. Adverse reactions were documented, and pre- and post-treatment cardiac and renal functions were evaluated. Mobility and quality of life were assessed using the 6-minute walk test (6MWT) and the Minnesota Living with Heart Failure Questionnaire (MLHFQ) . Patient outcomes were tracked for one year, measuring readmission and survival rates. Results: The study found no significant difference in the overall incidence of adverse reactions or cardiac function before and after treatment between the two groups (P > .05). However, patients with heart failure with preserved ejection fraction (HFpEF) exhibited notably greater disparities in serum creatinine and blood urea nitrogen levels, as well as in 6MWT and MLHFQ scores before and after treatment compared to those with heart failure with reduced ejection fraction (HFrEF) (P < .05). Additionally, the HFpEF group showed a lower prognostic readmission rate compared to the HFrEF group, although the difference in prognostic survival was not statistically significant (P > .05). Conclusions: Sacubitril/Valsartan demonstrates superior efficacy in improving renal function and enhancing quality of life in HFpEF patients while providing comparable prognostic benefits for HFrEF patients. This finding underscores its potential as a therapeutic foundation in the management of heart failure across ejection fraction categories.

The METTL3-m6A-YTHDC1-AMIGO2 axis contributes to cell proliferation and migration in esophageal squamous cell carcinoma.

The upregulation of methyltransferase-like 3 (METTL3) has been associated with the progression of esophageal cancer. However, METTL3-induced N6-methyladenosine (m6A) alterations on the downstream target mRNAs in esophageal squamous cell carcinoma (ESCC) are not yet fully understood. Our study revealed that silencing METTL3 resulted in a significant decrease in ESCC cell proliferation and metastasis in vitro and in vivo. Additionally, the adhesion molecule with Ig like domain 2 (AMIGO2) was identified as a potential downstream target of both METTL3 and YTH Domain-Containing Protein 1 (YTHDC1) in ESCC cells. Functionally, AMIGO2 augmented the malignant behaviors of ESCC cells in vitro and in vivo, and its overexpression can rescue the inhibition of the proliferation and migration in ESCC cells induced by METTL3 or YTHDC1 knockdown. Furthermore, our findings revealed that knockdown of METTL3 decreased m6A modification in the 5'-untranslated regions (5'UTR) of AMIGO2 precursor mRNA (pre-mRNA), and YTHDC1 interacted with AMIGO2 pre-mRNA to regulate AMIGO2 expression by modulating the splicing process of AMIGO2 pre-mRNA in ESCC cells. These findings highlighted a novel role of the METTL3-m6A-YTHDC1-AMIGO2 axis in regulating ESCC cell proliferation and motility, suggesting its potential as a therapeutic target for ESCC.

Association between allostatic load and adverse outcomes among older patients with heart failure with preserved ejection fraction.

BACKGROUND: The allostatic load (AL) refers to the cumulative weakening of multiple physiological systems caused by repeated adaptation of the body to stressors There are still no studies have focused on the association between AL and the prognosis of patients with heart failure with preserved ejection fraction (HFpEF). The present study aimed to investigate the association between AL and adverse outcomes, including mortality and HF admission, among elderly male patients with HFpEF. METHODS: We conducted a prospective cohort study of 1111 elderly male patients with HFpEF, diagnosed between 2015 and 2019 and followed up through 2021. We constructed an AL measure using a combination of 12 biomarkers. The diagnosis of HFpEF was made according to the 2021 European Society of Cardiology guidelines. A Cox proportional hazards model was used to determine the associations between AL and adverse outcomes. RESULTS: In multivariate analysis, AL was significantly associated with increased risk of all-cause mortality (medium AL: adjusted hazard ratio [HR] = 2.53; 95% confidence interval [CI] 1.37-4.68; high AL: HR = 4.21; 95% CI 2.27-7.83; per-score increase: HR = 1.31; 95% CI 1.18-1.46), cardiovascular mortality (medium AL: HR = 2.67; 95% CI 1.07-6.68; high AL: HR = 3.13; 95% CI 1.23-7.97; per-score increase: HR = 1.20; 95% CI 1.03-1.40), non-cardiovascular mortality (medium AL: HR = 2.45; 95% CI 1.06-5.63; high AL: HR = 5.81; 95% CI 2.55-10.28; per-score increase: HR = 1.46; 95% CI 1.26-1.69), and HF admission (medium AL: HR = 2.68; 95% CI 1.43-5.01; high AL: HR = 3.24; 95% CI 1.69-6.23; per-score increase: HR = 1.24; 95% CI 1.11-1.39). Consistent results were found in multiple subgroup analyses. CONCLUSIONS: A higher AL was associated with poor prognosis in elderly men with HFpEF. AL relies on information that is easily obtained in physical examinations and laboratory parameters and can be assessed in various care and clinical settings to help risk stratification of HFpEF patients.

Integrated network pharmacology and fecal metabolomic analysis of the combinational mechanisms of Shexiang Baoxin Pill against atherosclerosis.

Shexiang Baoxin Pill (SBP) has an excellent therapeutic effect on atherosclerosis (AS), but the combinational mechanisms of SBP against AS remain unclear. This study aimed to investigate the combinational mechanisms of SBP against AS by comprehensive network pharmacology and fecal metabolomic analysis. Bufonis venenum, one of the adjuvant medicines in SBP, is an animal medicine with a narrow therapeutic window. Considering animal protection, we evaluated the anti-AS effect of SBP without BV (SBP-BV) using ApoE-/- mouse models, culture cells, and metabolomic methods. Our data suggested that SBP showed remarkable anti-atherosclerotic effects through multiple targets and multiple pathways, while each component in SBP played different roles in their synergistic effect. Notably, SBP-BV showed comparable effects with SBP in the treatment of AS. Both SBP and SBP-BV could reduce cholesterol uptake in RAW264.7 cells and prevent the occurrence and development of AS in WD-induced ApoE-/- mice by attenuating the atherosclerotic plaque area, and reducing inflammatory cytokines and cholesterol levels in vivo. Our finding might provide new insights into the research and development of new anti-atherosclerosis drugs.

Antibacterial and Alkali-responsive Cationic Waterborne Polyurethane Based on Modification of Aloe Emodin.

Cationic water-based polyurethane(CWPU) was synthesized to explore aloe-emodin modifies to obtain CWPU materials with better comprehensive performance. It provides a simple way to synthesize antibacterial waterborne polyurethane, which is to introduce the end-blocking group of herbal extracts into the structure. It contains synergistic antibacterial effect of herbal antibacterial and quaternary ammonium ion on Escherichia coli. It makes the material resist the erosion of bacterial, and increase the service life of materials. When the pH value of the environment changes, the UV absorbance of the aloe-emodin modified cationic water-based polyurethane(AE-CWPU) also changes. Therefore, within a certain detection range, AE-CWPU has great applications in the field of smart response materials. The modified thermodynamic properties have been improved, and the mechanical properties basically maintained the maximum stress, and the elongation at break was reduced.

Blind Additive Gaussian White Noise Level Estimation from a Single Image by Employing Chi-Square Distribution.

The additive Gaussian white noise (AGWN) level in real-life images is usually unknown, for which the empirical setting will make the denoising methods over-smooth fine structures or remove noise incompletely. The previous noise level estimation methods are easily lost in accurately estimating them from images with complicated structures. To cope with this issue, we propose a novel noise level estimation scheme based on Chi-square distribution, including the following key points: First, a degraded image is divided into many image patches through a sliding window. Then, flat patches are selected by using a patch selection strategy on the gradient maps of those image patches. Next, the initial noise level is calculated by employing Chi-square distribution on the selected flat patches. Finally, the stable noise level is optimized by an iterative strategy. Quantitative, with association, to qualitative results of experiments on synthetic real-life images validate that the proposed noise level estimation method is effective and even superior to the state-of-the-art methods. Extensive experiments on noise removal using BM3D further illustrate that the proposed noise level estimation method is more beneficial for achieving favorable denoising performance with detail preservation.

Coherent excitation energy transfer in model photosynthetic reaction center: Effects of non-Markovian quantum environment.

Excitation energy transfer (EET) and electron transfer (ET) are crucially involved in photosynthetic processes. In reality, the photosynthetic reaction center constitutes an open quantum system of EET and ET, which manifests interplay of pigments, solar light, and phonon baths. So far, theoretical studies have been mainly based on master equation approaches in the Markovian condition. The non-Markovian environmental effect, which may play a crucial role, has not been sufficiently considered. In this work, we propose a mixed dynamic approach to investigate this open system. The influence of phonon bath is treated via the exact dissipaton equation of motion (DEOM), while that of photon bath is via the Lindblad master equation. Specifically, we explore the effect of non-Markovian quantum phonon bath on the coherent transfer dynamics and its manipulation on the current-voltage behavior. Distinguished from the results of the completely Markovian-Lindblad equation and those adopting the classical environment description, the mixed DEOM-Lindblad simulations exhibit transfer coherence up to a few hundred femtoseconds and the related environmental manipulation effect on the current. These non-Markovian quantum coherent effects may be extended to more complex and realistic systems and be helpful in the design of organic photovoltaic devices.

Polyimidazolyl acetate ionic liquid grafted on cellulose filter paper as thin-film extraction phase for extraction of non-steroidal anti-inflammatory drugs from water.

Recently, pharmaceuticals and personal care products in the water environment exhibited potential risks to both human and aquatic organisms. In order to improve the sensitivity and accuracy of pharmaceutical detection, the polyimidazolyl acetate ionic liquid was synthesized by Radziszewski reaction and coated on cellulose filter papers as a thin-film extraction phase for extraction of non-steroidal anti-inflammatory drugs from water. The attenuated total reflection-infrared spectrometry, thermogravimetric analysis, and scanning electron microscope analyses demonstrated that the polyimidazolyl acetate ionic liquid was successfully prepared and attached to the surface of the cellulose filter paper through chemical bonding. The adsorption capacity of the homemade thin-film extraction material for the four non-steroidal anti-inflammatory drugs was greater than 8898 ng/cm2 under the optimum conditions, and the desorption rate was over 90%. Then, a paper-based thin-film extraction phase-high-performance liquid chromatography-tandem mass spectrometry method was established for the extraction of non-steroidal anti-inflammatory drugs in water. This method provided limits of detection and limits of quantification were in the range of 0.02-0.15 and 0.17-0.50 µg/L, respectively. Hence, the obtained thin-film extraction phase showed excellent recovery and reproducibility for the target non-steroidal anti-inflammatory drugs with carboxyl groups from water.

Impact of the CYP2D6 Genotype on Metoprolol Tolerance and Adverse Events in Elderly Chinese Patients With Cardiovascular Diseases.

The latest consensus has changed CYP2D6 genotyping among Chinese population, while its impact on metoprolol tolerance and adverse events in elderly Chinese patients with cardiovascular diseases remains unclear. In this study, we prospectively included elderly patients who started metoprolol treatment for cardiovascular indications. According to the latest consensus on CYP2D6 genotype-to-phenotype translation, the patients were categorized as normal, intermediate, or poor metabolizers (NMs, IMs, or PMs, respectively) by detecting the presence of the CYP2D6*1, *2, *5, *10, and *14. Logistic regression model was used to analyze the correlation between the CYP2D6 phenotype and incidence of adverse events, which were assessed over a 12-week period. In this study, there were 651 (62.7%) NMs, 385 (37.1%) IMs, and 3 (0.3%) PMs. After 12 weeks of follow-up, compared with NMs, IMs had the lower maintenance dose [50.0 (25.0-50.0) mg/day vs. 25.0 (25.0-50.0) mg/day, p < 0.001] and lower weight-adjusted maintenance doses (0.52 ± 0.25 mg/day/kg vs. 0.42 ± 0.22 mg/day/kg, p < 0.001), and had higher incidence of postural hypotension (6.0% vs. 10.9%, p = 0.006), bradycardia (21.5% vs. 28.6%, p = 0.011), asystole (0.8% vs. 3.1%, p = 0.009) and syncope (2.0% vs. 6.2%, p = 0.001). In logistic regression model, the overall incidence of adverse events was 1.37-fold larger in IMs than in NMs (odds ratio = 1.37, 95% confidence interval = 1.05-1.79, p = 0.021). We conclude that IMs have lower tolerance and higher incidence of metoprolol-related adverse events than NMs in elderly Chinese patients with cardiovascular diseases. CYP2D6 genotyping is justifiable in elderly patients to minimize the risk of adverse events and ensure the benefits of metoprolol.

Human disease prediction from microbiome data by multiple feature fusion and deep learning.

Human disease prediction from microbiome data has broad implications in metagenomics. It is rare for the existing methods to consider abundance profiles from both known and unknown microbial organisms, or capture the taxonomic relationships among microbial taxa, leading to significant information loss. On the other hand, deep learning has shown unprecedented advantages in classification tasks for its feature-learning ability. However, it encounters the opposite situation in metagenome-based disease prediction since high-dimensional low-sample-size metagenomic datasets can lead to severe overfitting; and black-box model fails in providing biological explanations. To circumvent the related problems, we developed MetaDR, a comprehensive machine learning-based framework that integrates various information and deep learning to predict human diseases. Experimental results indicate that MetaDR achieves competitive prediction performance with a reduction in running time, and effectively discovers the informative features with biological insights.

Increasing prediction performance of colorectal cancer disease status using random forests classification based on metagenomic shotgun sequencing data.

Dysfunction of microbial communities in various human body sites has been shown to be associated with a variety of diseases raising the possibility of predicting diseases based on metagenomic samples. Although many studies have investigated this problem, there are no consensus on the optimal approaches for predicting disease status based on metagenomic samples. Using six human gut metagenomic datasets consisting of large numbers of colorectal cancer patients and healthy controls from different countries, we investigated different software packages for extracting relative abundances of known microbial genomes and for integrating mapping and assembly approaches to obtain the relative abundance profiles of both known and novel genomes. The random forests (RF) classification algorithm was then used to predict colorectal cancer status based on the microbial relative abundance profiles. Based on within data cross-validation and cross-dataset prediction, we show that the RF prediction performance using the microbial relative abundance profiles estimated by Centrifuge is generally higher than that using the microbial relative abundance profiles estimated by MetaPhlAn2 and Bracken. We also develop a novel method to integrate the relative abundance profiles of both known and novel microbial organisms to further increase the prediction performance for colorectal cancer from metagenomes.

DeepLINK: Deep learning inference using knockoffs with applications to genomics.

We propose a deep learning-based knockoffs inference framework, DeepLINK, that guarantees the false discovery rate (FDR) control in high-dimensional settings. DeepLINK is applicable to a broad class of covariate distributions described by the possibly nonlinear latent factor models. It consists of two major parts: an autoencoder network for the knockoff variable construction and a multilayer perceptron network for feature selection with the FDR control. The empirical performance of DeepLINK is investigated through extensive simulation studies, where it is shown to achieve FDR control in feature selection with both high selection power and high prediction accuracy. We also apply DeepLINK to three real data applications to demonstrate its practical utility.

A novel full symmetric diarylethene-based ratiometric fluorescent sensor for lysine and the application for a logic circuit.

A novel diarylethene-based ratiometric fluorescent sensor with full symmetric structure, 1o, was designed and synthesized successfully. 1o could identify lysine (Lys) with high selectivity and sensitivity and the fluorescence emission peak was red shifted 85 nm upon addition of Lys, which could realize ratio recognition. It exhibited excellent anti-interference performance in the presence of various amino acids in CH3 CN/H2 O (7/3, v/v) solution. Moreover, the limit of detection of 1o to Lys could reach 0.019 µM based on a good linear range of 0-40 µM. In addition, the fluorescence emission intensity of 1o could be turned off/on by ultraviolet/visible light due to the special structure of diarylethene. A logic circuit was designed with three inputs. The ratiometric fluorescent sensor 1o could be as a new tool and provide a new method for detection of Lys.

Correction to: MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations.

Following publication of the original paper [1], Dr. Nayfach kindly pointed out an error and the authors would like to report the following correction.

MicroPro: using metagenomic unmapped reads to provide insights into human microbiota and disease associations.

We develop a metagenomic data analysis pipeline, MicroPro, that takes into account all reads from known and unknown microbial organisms and associates viruses with complex diseases. We utilize MicroPro to analyze four metagenomic datasets relating to colorectal cancer, type 2 diabetes, and liver cirrhosis and show that including reads from unknown organisms significantly increases the prediction accuracy of the disease status for three of the four datasets. We identify new microbial organisms associated with these diseases and show viruses play important prediction roles in colorectal cancer and liver cirrhosis, but not in type 2 diabetes. MicroPro is freely available at https://github.com/zifanzhu/MicroPro .

Gut microbial and metabolomic profiles after fecal microbiota transplantation in pediatric ulcerative colitis patients.

Ulcerative colitis is a chronic inflammatory disease of the colon that carries a significant disease burden in children. Therefore, new therapeutic approaches are being explored to help children living with this disease. Fecal microbiota transplantation (FMT) has been successful in some children with ulcerative colitis. However, the mechanism of its therapeutic effect in this patient population is not well understood. To characterize changes in gut microbial and metabolomic profiles after FMT, we performed 16S rRNA gene sequencing, shotgun metagenomic sequencing, virome analysis and untargeted metabolomics by gas chromatography-time of flight-mass spectrometry on stool samples collected before and after FMT from four children with ulcerative colitis who responded to this treatment. Alpha diversity of the gut microbiota increased after intervention, with species richness rising from 251 (S.D. 125) to 358 (S.D. 27). In responders, the mean relative abundance of bacteria in the class Clostridia shifted toward donor levels, increasing from 33% (S.D. 11%) to 54% (S.D. 16%). Patient metabolomic and viromic profiles exhibited a similar but less pronounced shift toward donor profiles after FMT. The fecal concentrations of several metabolites were altered after FMT, correlating with clinical improvement. Larger studies using a similar multi-omics approach may suggest novel strategies for the treatment of pediatric ulcerative colitis.