Selective adsorption of high ionization potential value organic pollutants in wastewater.

It is imperative to devise effective removal strategies for high ionization potential (IP) organic pollutants in wastewater as their reduced electron-donating capacity challenges the efficiency of advanced oxidation systems in degradation. Against this backdrop, leveraging the metal-based carbon material structure meticulously, we employed metal-pyridine-N (M-N-C, M=Fe, Co, and Ni) as the electron transfer bridge. This distinctive design facilitated the ordered transfer of electrons from the adsorbent surface to the surface of high IP value pollutants, acting as a "supplement" to compensate for their deficient electron-donating capability, thereby culminating in the selective adsorption of these pollutants. Furthermore, this adsorbent also demonstrated effective removal of trace emerging contaminants (2 mg/L), displayed robust resistance to various salts, exhibited reusability, and maintained stability. These findings carry substantial implications for future carbon-based material design, offering a pathway toward exceptional adsorption performance in treating water pollution.

HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.

While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identification is critical for developing vaccines and cancer immunotherapies. Current prediction methods are limited, primarily due to a lack of high-quality training epitope datasets and algorithmic constraints. To predict the exogenous HLA class II-restricted peptides across most of the human population, we utilized the mass spectrometry data to profile >223 000 eluted ligands over HLA-DR, -DQ, and -DP alleles. Here, by integrating these data with peptide processing and gene expression, we introduce HLAIImaster, an attention-based deep learning framework with adaptive domain knowledge for predicting neoepitope immunogenicity. Leveraging diverse biological characteristics and our enhanced deep learning framework, HLAIImaster is significantly improved against existing tools in terms of positive predictive value across various neoantigen studies. Robust domain knowledge learning accurately identifies neoepitope immunogenicity, bridging the gap between neoantigen biology and the clinical setting and paving the way for future neoantigen-based therapies to provide greater clinical benefit. In summary, we present a comprehensive exploitation of the immunogenic neoepitope repertoire of cancers, facilitating the effective development of "just-in-time" personalized vaccines.

Cone-Opponent Ganglion Cells in the Primate Fovea Tuned to Noncardinal Color Directions.

A long-standing question in vision science is how the three cone photoreceptor types-long (L), medium (M), and short (S) wavelength sensitive-combine to generate our perception of color. Hue perception can be described along two opponent axes: red-green and blue-yellow. Psychophysical measurements of color appearance indicate that the cone inputs to the red-green and blue-yellow opponent axes are M vs. L + S and L vs. M + S, respectively. However, the "cardinal directions of color space" revealed by psychophysical measurements of color detection thresholds following adaptation are L vs. M and S vs. L + M. These cardinal directions match the most common cone-opponent retinal ganglion cells (RGCs) in the primate retina. Accordingly, the cone opponency necessary for color appearance is thought to be established in the cortex. While neurons with the appropriate M vs. L + S and L vs. M + S opponency have been reported in the retina and lateral geniculate nucleus, their existence continues to be debated. Resolving this long-standing debate is necessary because a complete account of the cone opponency in the retinal output is critical for understanding how downstream neural circuits process color. Here, we performed adaptive optics calcium imaging to noninvasively measure foveal RGC light responses in the living Macaca fascicularis eye. We confirm the presence of L vs. M + S and M vs. L + S neurons with noncardinal cone opponency and demonstrate that cone-opponent signals in the retinal output are more diverse than classically thought.

Multi-modality attribute learning-based method for drug-protein interaction prediction based on deep neural network.

Identification of active candidate compounds for target proteins, also called drug-protein interaction (DPI) prediction, is an essential but time-consuming and expensive step, which leads to fostering the development of drug discovery. In recent years, deep network-based learning methods were frequently proposed in DPIs due to their powerful capability of feature representation. However, the performance of existing DPI methods is still limited by insufficiently labeled pharmacological data and neglected intermolecular information. Therefore, overcoming these difficulties to perfect the performance of DPIs is an urgent challenge for researchers. In this article, we designed an innovative 'multi-modality attributes' learning-based framework for DPIs with molecular transformer and graph convolutional networks, termed, multi-modality attributes (MMA)-DPI. Specifically, intermolecular sub-structural information and chemical semantic representations were extracted through an augmented transformer module from biomedical data. A tri-layer graph convolutional neural network module was applied to associate the neighbor topology information and learn the condensed dimensional features by aggregating a heterogeneous network that contains multiple biological representations of drugs, proteins, diseases and side effects. Then, the learned representations were taken as the input of a fully connected neural network module to further integrate them in molecular and topological space. Finally, the attribute representations were fused with adaptive learning weights to calculate the interaction score for the DPIs tasks. MMA-DPI was evaluated in different experimental conditions and the results demonstrate that the proposed method achieved higher performance than existing state-of-the-art frameworks.

DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins.

MOTIVATION: Understanding the intermolecular interactions of ligand-target pairs is key to guiding the optimization of drug research on cancers, which can greatly mitigate overburden workloads for wet labs. Several improved computational methods have been introduced and exhibit promising performance for these identification tasks, but some pitfalls restrict their practical applications: (i) first, existing methods do not sufficiently consider how multigranular molecule representations influence interaction patterns between proteins and compounds; and (ii) second, existing methods seldom explicitly model the binding sites when an interaction occurs to enable better prediction and interpretation, which may lead to unexpected obstacles to biological researchers. RESULTS: To address these issues, we here present DrugMGR, a deep multigranular drug representation model capable of predicting binding affinities and regions for each ligand-target pair. We conduct consistent experiments on three benchmark datasets using existing methods and introduce a new specific dataset to better validate the prediction of binding sites. For practical application, target-specific compound identification tasks are also carried out to validate the capability of real-world compound screen. Moreover, the visualization of some practical interaction scenarios provides interpretable insights from the results of the predictions. The proposed DrugMGR achieves excellent overall performance in these datasets, exhibiting its advantages and merits against state-of-the-art methods. Thus, the downstream task of DrugMGR can be fine-tuned for identifying the potential compounds that target proteins for clinical treatment. AVAILABILITY AND IMPLEMENTATION: https://github.com/lixiaokun2020/DrugMGR.

A Dual-Modal, Label-Free Raman Imaging Method for Rapid Virtual Staining of Large-Area Breast Cancer Tissue Sections.

As one of the most common cancers, accurate, rapid, and simple histopathological diagnosis is very important for breast cancer. Raman imaging is a powerful technique for label-free analysis of tissue composition and histopathology, but it suffers from slow speed when applied to large-area tissue sections. In this study, we propose a dual-modal Raman imaging method that combines Raman mapping data with microscopy bright-field images to achieve virtual staining of breast cancer tissue sections. We validate our method on various breast tissue sections with different morphologies and biomarker expressions and compare it with the golden standard of histopathological methods. The results demonstrate that our method can effectively distinguish various types and components of tissues, and provide staining images comparable to stained tissue sections. Moreover, our method can improve imaging speed by up to 65 times compared to general spontaneous Raman imaging methods. It is simple, fast, and suitable for clinical applications.

Integrated transcriptomic and metabolomic analyses elucidate the mechanism of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress.

Seed propagation is the main method of mulberry expansion in China, an important economic forest species. However, seed germination is the most sensitive stage to various abiotic stresses, especially salinity stress. To reveal the molecular regulatory mechanism of mulberry seed germination under salt stress, flavonoid metabolomics and transcriptomics analyses were performed on mulberry seeds germinated under 50 and 100 mmol/L NaCl stress. Analysis of the flavonoid metabolome revealed that a total of 145 differential flavonoid metabolites (DFMs) were classified into 9 groups, 40 flavonols, 32 flavones, 16 chalcones and 14 flavanones. Among them, 61.4% (89) of the DFMs accumulated continuously with increasing salt concentration, reaching the highest level at a 100 mmol/L salt concentration; these DFMs included quercetin-3-O-glucoside (isoquercitrin), kaempferol (3,5,7,4'-tetrahydroxyflavone), quercetin-7-O-glucoside, taxifolin (dihydroquercetin) and apigenin (4',5,7-trihydroxyflavone), indicating that these flavonoids may be key metabolites involved in the response to salt stress. Transcriptional analysis identified a total of 3055 differentially expressed genes (DEGs), most of which were enriched in flavonoid biosynthesis (ko00941), phenylpropanoid biosynthesis (ko00940) and biosynthesis of secondary metabolites (ko01110). Combined analysis of flavonoid metabolomic and transcriptomic data indicated that phenylalanine ammonia-lyase (PAL), 4-coumarate-CoA ligase (4CL), chalcone synthase (CHS), flavonol synthase (FLS), bifunctional dihydroflavonol 4-reductase/flavanone 4-reductase (DFR) and anthocyanidin reductase (ANR) were the key genes involved in flavonoid accumulation during mulberry seed germination under 50 and 100 mmol/L NaCl stress. In addition, three transcription factors, MYB, bHLH and NAC, were involved in the regulation of flavonoid accumulation under salt stress. The results of quantitative real-time PCR (qRT‒PCR) validation showed that the expression levels of 11 DEGs, including 7 genes involved in flavonoid biosynthesis, under different salt concentrations were consistent with the transcriptomic data, and parallel reaction monitoring (PRM) results showed that the expression levels of 6 key enzymes (proteins) involved in flavonoid synthesis were consistent with the accumulation of flavonoids. This study provides a new perspective for investigating the regulatory role of flavonoid biosynthesis in the regulation of mulberry seed germination under salt stress at different concentrations.

Double-Salts Super Concentrated Carbonate Electrolyte Boosting Electrochemical Performance of Ni-Rich LiNi0.90Co0.05Mn0.05O2 Lithium Metal Batteries.

Current lithium-ion batteries cannot meet the requirement of higher energy density with further large-scale application of electrical vehicles. Lithium metal batteries combined with Ni-rich layered oxides cathode are expected as the one of promising solutions, while the poor electrode and electrolyte interface impedes the commercial development of lithium metal batteries. A new double-salts super concentrated (DSSC) carbonate electrolyte is proposed to improve the electrochemical performance of LiNi0.90Co0.05Mn0.05O2 (NCM9055)||Li metal battery which exhibits stable cycling performance with the capacity retention of 93.04% and reversible capacity of 173.8 mAh g-1 after 100 cycles at 1 C, while cells with conventional 1 m diluted electrolyte remains only 60.55% and capacity of 114.2 mAh g-1. The double salts synergistic effect in super concentrated electrolyte promotes the formation for more balanced stable cathode electrolyte interface (CEI) inorganic compounds of CFx, LiNOx, SOF2, Li2SO4, and less LiF by X-ray photoelectron spectroscopy (XPS) test, and the uniform 2-3 nm rock-salt phase protection layer on the cathode surface by transmission electron microscope (TEM) characterization, improving the cycling performance of the Ni-rich NCM9055 layered oxide cathode. The DSSC electrolyte also can relief the Li dendrite growth on Li metal anode, as well as exhibit better flame retardance, promoting the application of more safety Ni-rich NCM9055||Li metal batteries.

Spherical Shell with CNTs Network Structuring Fe-Based Alluaudite Na2+2 δ Fe2- δ (SO4 )3 Cathode and Novel Phase Transition Mechanism for Sodium-Ion Battery.

Iron-based sulfate cathodes of alluaudite Na2+2 δ Fe2- δ (SO4 )3 (NFS) in sodium-ion batteries with low cost, steady cycling performance, and high voltage are promising for grid-scale energy storage systems. However, the poor electronic conductivity and the limited understanding of the phase-evolution of precursors hinder obtaining high-rate capacity and the pure phase. Distinctive NFS@C@n%CNTs (n = 1, 2, 5, 10) sphere-shell conductive networks composite cathode materials are constructed creatively, which exhibit superior reversible capacity and rate performance. In detail, the designed NFS@C@2%CNTs cathode delivers an initial discharge capacity of 95.9 mAh g-1 at 0.05 C and up to 60 mAh g-1 at a high rate of 10 C. The full NFS@C@2%CNTs//HC cell delivers a practical operating voltage of 3.5 V and mass-energy density of 140 Wh kg-1 at 0.1 C, and it can also retain 67.37 mAh g-1 with a capacity retention rate of 96.4% after 200 cycles at 2 C. On the other hand, a novel combination reaction mechanism is first revealed for forming NFS from the mixtures of Na2 Fe(SO4 )2 ·nH2 O (n = 2, 4) and FeSO4 ·H2 O during the sintering process. The inspiring results would provide a novel perspective to synthesize high-performance alluaudite sulfate and analogs by aqueous methods.

Robust Superhydrophobic Composite Fabric with Self-Healing and Chemical Durability.

Superhydrophobic fabrics with multiple functions have become a research hotspot. However, it is challenging to make self-healing mechanically robust and eco-friendly superhydrophobic fabrics, which are limited by complex fabrication processes and excessive use of environmentally unfriendly solvents during fabrication. Herein, inspired by the secretion of a waxy substance from the surface of lotus leaves to restore water repellency, self-healing superhydrophobic composite fabrics (as-synthesized PA66/6-PET@Tico) are obtained by constructing a papillary TiO2 and tentacle-like fluorinated acrylate polymer (FCB015) coating on polyester-nylon composite fabrics using two-step hydrothermal method. The result indicates that PA66/6-PET@Tico with hierarchical micro/nanostructure exhibits excellent superhydrophobic and self-healing properties. Compared with FCB015 coated fabric, the contact angles (CA) of water and soybean oil rise to 172.2° and 166.8° from 137.4° and 98.8°, respectively. After mechanical abrasion, PA66/6-PET@Tico recovers a water contact angle (WCA) of 165.6° at room temperature. The WCA remains higher than 155° after 18 h of chemical corrosion. Furthermore, the bacterial inhibition rates of PA66/6-PET@Tico for Staphylococcus Aureus and Escherichia Coli are 99.90 and 98.38%, respectively. In this work, a new idea is proposed for designing a simple and effective self-healing superhydrophobic coating, expecting to promote the large-scale industrial production and application of functional surfaces.

Uncovering the Nonequilibrium Evolution Mechanism between Na2+2 δFe2- δ(SO4)3 Cathode and Impurities in the Na2SO4-FeSO4·7H2O Binary System for High-Voltage Sodium-Ion Batteries.

Sodium-ion batteries are increasingly recognized as ideal for large-scale energy storage applications. Alluaudite Na2+2 δFe2- δ(SO4)3 has become one of the focused cathode materials in this field. However, previous studies employing aqueous-solution synthesis often overlooked the formation mechanism of the impurity phase. In this study, the nonequilibrium evolution mechanism between Na2+2 δFe2- δ(SO4)3 and impurities by adjusting ratios of the Na2SO4/FeSO4·7H2O in the binary system is investigated. Then an optimal ratio of 0.765 with reduced impurity content is confirmed. Compared to the poor electrochemical performance of the Na2.6Fe1.7(SO4)3 (0.765) cathode, the optimized Na2.6Fe1.7(SO4)3@CNTs (0.765@CNTs) cathode, with improved electronic and ionic conductivity, demonstrates an impressive discharge specific capacity of 93.8 mAh g-1 at 0.1 C and a high-rate capacity of 67.84 mAh g-1 at 20 C, maintaining capacity retention of 71.1% after 3000 cycles at 10 C. The Na2.6Fe1.7(SO4)3@CNTs//HC full cell reaches an unprecedented working potential of 3.71 V at 0.1 C, and a remarkable mass-energy density exceeding 320 Wh kg-1. This work not only provides comprehensive guidance for synthesizing high-voltage Na2+2 δFe2- δ(SO4)3 cathode materials with controllable impurity content but also lays the groundwork of sodium-ion batteries for large-scale energy storage applications.

Multiple strategies, including 6mA methylation, affecting plant alternative splicing in allopolyploid peanut.

Alternative splicing (AS), an important post-transcriptional regulation mechanism in eukaryotes, can significantly increase transcript diversity and contribute to gene expression regulation and many other complicated developmental processes. While plant gene AS events are well described, few studies have investigated the comprehensive regulation machinery of plant AS. Here, we use multi-omics to analyse peanut AS events. Using long-read isoform sequencing, 146 464 full-length non-chimeric transcripts were obtained, resulting in annotation corrections for 1782 genes and the identification of 4653 new loci. Using Iso-Seq RNA sequences, 271 776 unique splice junctions were identified, 82.49% of which were supported by transcriptome data. We characterized 50 977 polyadenylation sites for 23 262 genes, 12 369 of which had alternative polyadenylation sites. AS allows differential regulation of the same gene by miRNAs at the isoform level coupled with polyadenylation. In addition, we identified many long non-coding RNAs and fusion transcripts. There is a suppressed effect of 6mA on AS and gene expression. By analysis of chromatin structures, the genes located in the boundaries of topologically associated domains, proximal chromosomal telomere regions, inter- or intra-chromosomal loops were found to have more unique splice isoforms, higher expression, lower 6mA and more transposable elements (TEs) in their gene bodies than the other genes, indicating that chromatin interaction, 6mA and TEs play important roles in AS and gene expression. These results greatly refine the peanut genome annotation and contribute to the study of gene expression and regulation in peanuts. This work also showed AS is associated with multiple strategies for gene regulation.

The diagnostic value of MRI-based vertebral bone quality score for osteoporosis or osteopenia in patients undergoing lumbar surgery: a meta-analysis.

The importance of osteoporosis assessment before lumbar surgery is well recognized. The MRI-based Vertebral Bone Quality (VBQ) score is introduced to evaluate bone quality; however, its diagnostic value has not been well documented. The purpose of this meta-analysis was to summarize the diagnostic value of the VBQ score for osteoporosis or osteopenia in patients undergoing lumbar surgery. We comprehensively searched electronic databases for studies exploring the diagnostic accuracy of the VBQ score for osteoporosis/osteopenia in patients with lumbar disease following the PRISMA guidelines. The quality of the included studies was assessed. The VBQ scores were compared between the groups, and the pooled sensitivity, specificity, and summary receiver operating characteristic (ROC) were calculated. Publication bias was assessed, and meta-regression was conducted. We included 17 studies with a total of 2815 patients, with a mean age of 66.4 years and a percentage of females of 72.5%. According to the QUADAS-2 tool, the quality of the included studies was relatively high. The results showed a significantly higher VBQ score in the osteoporosis/osteopenia group compared with the control group. According to the mean VBQ cutoff value of 3.02 ± 0.38 for the diagnosis of osteoporosis, the pooled sensitivity and specificity were 0.76 and 0.74, respectively, and the AUC was 0.81. According to the mean VBQ cutoff value of 2.31 ± 0.18 for the diagnosis of osteopenia, the pooled sensitivity and specificity were 0.78 and 0.58, respectively, and the AUC was 0.76. The MRI-based VBQ score could provide useful information for identifying patients with low bone mass who need further evaluation. Future prospective studies are still needed to evaluate the complementary role of the VBQ score.

Metasurface-Assisted Quantum Nonlocal Weak-Measurement Microscopy.

In standard quantum weak measurements, preselection and postselection of quantum states are implemented in the same photon. Here we go beyond this restrictive setting and demonstrate that the preselection and postselection can be performed in two different photons, if the two photons are polarization entangled. The Pancharatnam-Berry phase metasurface is incorporated in the weak measurement system to perform weak coupling between probe wave function and spin observable. By introducing nonlocal weak measurement into the microscopy imaging system, it allows us to remotely switch different microscopy imaging modes of pure-phase objects, including bright-field, differential, and phase reconstruction. Furthermore, we demonstrate that the nonlocal weak-measurement scheme can prevent almost all environmental noise photons from detection and thus achieves a higher image contrast than the standard scheme at a low photon level. Our results provide the possibility to develop a quantum nonlocal weak-measurement microscope for label-free imaging of transparent biological samples.

Designing future peanut: the power of genomics-assisted breeding.

KEY MESSAGE: Integrating GAB methods with high-throughput phenotyping, genome editing, and speed breeding hold great potential in designing future smart peanut cultivars to meet market and food supply demands. Cultivated peanut (Arachis hypogaea L.), a legume crop greatly valued for its nourishing food, cooking oil, and fodder, is extensively grown worldwide. Despite decades of classical breeding efforts, the actual on-farm yield of peanut remains below its potential productivity due to the complicated interplay of genotype, environment, and management factors, as well as their intricate interactions. Integrating modern genomics tools into crop breeding is necessary to fast-track breeding efficiency and rapid progress. When combined with speed breeding methods, this integration can substantially accelerate the breeding process, leading to faster access of improved varieties to farmers. Availability of high-quality reference genomes for wild diploid progenitors and cultivated peanuts has accelerated the process of gene/quantitative locus discovery, developing markers and genotyping assays as well as a few molecular breeding products with improved resistance and oil quality. The use of new breeding tools, e.g., genomic selection, haplotype-based breeding, speed breeding, high-throughput phenotyping, and genome editing, is probable to boost genetic gains in peanut. Moreover, renewed attention to efficient selection and exploitation of targeted genetic resources is also needed to design high-quality and high-yielding peanut cultivars with main adaptation attributes. In this context, the combination of genomics-assisted breeding (GAB), genome editing, and speed breeding hold great potential in designing future improved peanut cultivars to meet market and food supply demands.

Cultivated alien plants with high invasion potential are more likely to be traded online in China.

Biological invasions have become a worldwide problem, and measures to efficiently prevent and control invasions are still in development. Like many other parts of the world, China is undergoing a dramatic increase in plant invasions. Most of the currently 933 established (i.e., naturalized) plant species, of which 214 are categorized as invasive, have been introduced into China for cultivation. It is likely that many of those species are still being traded, particularly online, by plant nurseries. However, studies assessing whether naturalized and invasive species are currently being traded more or less than nonnaturalized aliens are rare. We extracted online-trade information for 13,718 cultivated alien plant taxa on 1688.com, the largest website for domestic B2B in China. We analyzed how the presence in online-nursery catalogs, the number of online nurseries that offerred the species for sale, and the product type (i.e., seeds, live plants and vegetative organs) differed among nonnaturalized, naturalized noninvasive, and invasive species. Compared to nonnaturalized taxa, naturalized noninvasive and invasive taxa were 3.7-5.2 times more likely to be available for purchase. Naturalized noninvasive and invasive taxa were more frequently offered as seeds by online nurseries, whereas nonnaturalized taxa were more frequently offered as live plants. Based on these findings, we propose that, to reduce the further spread of invasive and potentially invasive plants, implementation of plant-trade regulations and a monitoring system of the online horticultural supply chain will be essential.

Membraneless Electrochemical Synthesis Strategy toward Nitrate-to-Ammonia Conversion.

Electroreduction of nitrate (NO3RR) to ammonia in membraneless electrolyzers is of great significance for reducing the cost and saving energy consumption. However, severe chemical crossover with side reactions makes it challenging to achieve ideal electrolysis. Herein, we propose a general strategy for efficient membraneless ammonia synthesis by screening NO3RR catalysts with inferior oxygen reduction activity and matching the counter electrode (CE) with good oxygen evolution activity while blocking anodic ammonia oxidation. Consequently, screening the available Co-Co system, the membraneless NO3--to-NH3 conversion performance was significantly higher than H-type cells using costly proton-exchange membranes. At 200 mA cm-2, the full-cell voltage of the membraneless system (∼2.5 V) is 4 V lower than that of the membrane system (∼6.5 V), and the savings are 61.4 kW h (or 56.9%) per 1 kg NH3 produced. A well-designed pulse process, inducing reversible surface reconstruction that in situ generates and restores the active Co(III) species at the working electrode and forms favorable Co3O4/CoOOH at the CE, further significantly improves NO3--to-NH3 conversion and blocks side reactions. A maximum NH3 yield rate of 1500.9 µmol cm-2 h-1 was achieved at -0.9 V (Faraday efficiency 92.6%). This pulse-coupled membraneless strategy provides new insights into design complex electrochemical synthesis.

Predictive value of the serum uric acid to high-density lipoprotein cholesterol ratio for culprit plaques in patients with acute coronary syndrome.

BACKGROUND: Hyperuricemia and low level of high-density lipoprotein cholesterol (HDL-C) are both risk factors for coronary artery disease (CAD). The uric acid to HDL-C ratio (UHR) has recently been identified as a new inflammatory and metabolic biomarker. However, the relationship between the UHR and coronary culprit plaques has not been fully investigated in patients with acute coronary syndrome (ACS). METHODS: A total of 346 patients with ACS were enrolled in this study. Culprit lesion characteristics were assessed by optical coherence tomography (OCT). Logistic regression and linear correlation analyses were performed to assess the association between the UHR and culprit plaques. The predictive value of the UHR was investigated by receiver operating characteristic (ROC) curve analysis. RESULTS: The percentages of typical culprit plaques, including ruptures, erosions and thrombi, were greater in the high-UHR subgroup than those in the low-UHR subgroup. A positive relationship was also found between the UHR and diameter stenosis (r = 0.160, P = 0.003) and between the UHR and area stenosis (r = 0.145, P = 0.007). The UHR was found to be independently associated with plaque rupture, erosion and thrombus. Furthermore, ROC analysis suggested that the UHR had a better predictive value than low-density lipoprotein cholesterol. CONCLUSIONS: An elevated UHR level was independently related to the occurrence rate of culprit plaques. The UHR is a simple and easily acquired parameter for detecting culprit plaques in patients with ACS.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review.

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Growth differentiation factor-15 predicts all-cause death and major adverse cardiovascular events in patients with coronary heart disease: a prospective cohort study.

The prognostic value of growth differentiation factor-15 (GDF-15) in predicting long-term adverse outcomes in coronary heart disease (CHD) patients remains limited. Our study examines the association between GDF-15 and adverse outcomes over an extended period in CHD patients and firstly assesses the incremental prognostic effect of incorporating GDF-15 into the Framingham risk score (FRS)-based model. This single-center prospective cohort study included 3,321 patients with CHD categorized into 2,479 acute coronary syndrome (ACS) (74.6%) and 842 non-ACS (25.4%) groups. The median age was 61.0 years (range: 53.0-70.0), and 917 (27.6%) were females. Mortality and major adverse cardiovascular events (MACEs) included cardiovascular mortality, myocardial infarction (MI), stroke, and heart failure (HF) (inclusive of HF episodes requiring outpatient treatment and/or hospital admission). Cox regression models assessed the associations between GDF-15 and the incidence of all-cause mortality and MACEs. Patients were stratified into three groups based on GDF-15 levels: the first tertile group (< 1,370 ng/L), the second tertile group (1,370-2,556 ng/L), and the third tertile group (> 2,556 ng/L). The C-index, integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA) were used to assess incremental value. Over a median 9.4-year follow-up, 759 patients (22.9%) died, and 1,291 (38.9%) experienced MACEs. The multivariate Cox model indicated that GDF-15 was significantly associated with all-cause mortality (per ln unit increase, HR = 1.49, 95% CI: 1.36-1.64) and MACEs (per ln unit increase, HR = 1.29, 95% CI: 1.20-1.38). These associations persisted when GDF-15 was analyzed as an ordinal variable (p for trend < 0.05). Subgroup analysis of ACS and non-ACS for the components of MACEs separately showed a significant association between GDF-15 and both cardiovascular mortality and HF, but no association was observed between GDF-15 and MI /stroke in both ACS and non-ACS patients. The addition of GDF-15 to the FRS-based model enhanced the discrimination for both all-cause mortality (∆ C-index = 0.009, 95% CI: 0.005-0.014; IDI = 0.030, 95% CI: 0.015-0.047; continuous NRI = 0.631, 95% CI: 0.569-0.652) and MACEs (∆ C-index = 0.009, 95% CI: 0.006-0.012; IDI = 0.026, 95% CI: 0.009-0.042; continuous NRI = 0.593, 95% CI: 0.478-0.682). DCA suggested that incorporating GDF-15 into the FRS-based model demonstrated higher net benefits compared to FRS-based models alone (All-cause mortality: FRS-based model: area under the curve of DCA (AUDC) = 0.0903, FRS-based model + GDF-15: AUDC = 0.0908; MACEs: FRS-based model: AUDC = 0.1806, FRS-based model + GDF-15: AUDC = 0.1833). GDF-15 significantly associates with the long-term prognosis of all-cause mortality and MACEs in CHD patients and significantly improves the prognostic accuracy of the FRS-based model for both outcomes.