Advanced electrochemical membrane technologies for near-complete resource recovery and zero-discharge of urine: Performance optimization and evaluation.

The depletion of nutrient sources in fertilizers demands a paradigm shift in the treatment of nutrient-rich wastewater, such as urine, to enable efficient resource recovery and high-value conversion. This study presented an integrated bipolar membrane electrodialysis (BMED) and hollow fiber membrane (HFM) system for near-complete resource recovery and zero-discharge from urine treatment. Computational simulations and experimental validations demonstrated that a higher voltage (20 V) significantly enhanced energy utilization, while an optimal flow rate of 0.4 L/min effectively mitigated the negative effects of concentration polarization and electro-osmosis on system performance. Within 40 min, the process separated 90.13% of the salts in urine, with an energy consumption of only 8.45 kWh/kgbase. Utilizing a multi-chamber structure for selective separation, the system achieved recovery efficiencies of 89% for nitrogen, 96% for phosphorus, and 95% for potassium from fresh urine, converting them into high-value products such as 85 mM acid, 69.5 mM base, and liquid fertilizer. According to techno-economic analysis, the cost of treating urine using this system at the lab-scale was $6.29/kg of products (including acid, base, and (NH4)2SO4), which was significantly lower than the $20.44/kg cost for the precipitation method to produce struvite. Excluding fixed costs, a net profit of $18.24/m3 was achieved through the recovery of valuable products from urine using this system. The pilot-scale assessment showed that the net benefit amounts to $19.90/m3 of urine, demonstrating significant economic feasibility. This study presents an effective approach for the near-complete resource recovery and zero-discharge treatment of urine, offering a practical solution for sustainable nutrient recycling and wastewater management.

Symmetric spatiotemporal learning network with sparse meter graph for short-term energy-consumption prediction in manufacturing systems.

Short-term energy-consumption prediction is the basis of anomaly detection, real-time scheduling, and energy-saving control in manufacturing systems. Most existing methods focus on single-node energy-consumption prediction and suffer from difficult parameter collection and modelling. Although several methods have been presented for multinode energy-consumption prediction, their prediction performance needs to be improved owing to a lack of appropriate knowledge guidance and learning networks for complex spatiotemporal relationships. This study presents a symmetric spatiotemporal learning network (SSTLN) with a sparse meter graph (SMG) (SSTLN-SMG) that aims to predict multiple nodes based on energy-consumption time series and general process knowledge. The SMG expresses process knowledge by abstracting production nodes, material flows, and energy usage, and provides initial guidance for the SSTLN to extract spatial features. SSTLN, a symmetrical stack of graph convolutional networks (GCN) and gated linear units (GLU), is devised to achieve a trade-off not only between spatial and temporal feature extraction but also between detail capture and noise suppression. Extensive experiments were performed using datasets from an aluminium profile plant. The experimental results demonstrate that the proposed method allows multinode energy-consumption prediction with less prediction error than state-of-the-art methods, methods with deformed meter graphs, and methods with deformed learning networks.

Nanoengineered Nonenzymatic Paper-Based Fluorescent Platform for Pre-Dilution-Free and Visual Real-Time Home Monitoring of Urea for Early Warning of Abnormal Nitrogen-Based Unhealthy Issues.

Distorted urea levels indicate several liver, kidney, or metabolic diseases; however, traditional clinical urea detection relies on urease-based methods enslaved to well-known limitations of high-price, unstable properties, complicated sample pretreatment and analysis procedures, and difficult visual real-time monitoring. Herein, nonenzymatic paper-based fluorescent materials (UFP-BP) are strategically integrated with an on-demand fluorescent-sensor (UFP) self-aggregated nanoparticle on commercial filter paper for pre-dilution-free and visual real-time urea monitoring. The UFP is synthesized and self-aggregated into the fluorescent nanoparticles for selective urea recognition. Then, the nanoparticles are interstitially loaded on filter paper to nanoengineer the UFP-BP, achieving selective quantitative urea detection in the normal concentration range (10-1000 mm). UFP and UFP-BP can successfully monitor urea levels in real rat urine, artificial simulants, and milk. The proposed sensing platform, integrated with smartphones, offers accurate, quantitative, nonenzymatic, noninvasive, pre-dilution-free, on-site, rapid, low-cost, easy-to-operate, real-time visual urea detection in food samples and human body fluids. The designed sensing system can provide early warnings of abnormal nitrogen-based health issues.

Clean air policy reduces the atherogenic lipid profile levels: Results from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) Study.

Evidence of the associations between long-term exposure to PM2.5 and O3 and human blood lipid concentrations is abundant yet inconclusive. Whether clean air policies could improve lipid profiles remains unclear. In total, 2979312 participants from a Chinese nationwide prospective study were included. For cross-sectional analyses, linear mixed-effects models were utilized to assess the associations of pollutants with lipid profiles (TC, LDL-C, TG, HDL-C). For longitudinal analyses, a quasi-experimental design and difference-in-differences models were employed to investigate the impact of China's Clean Air Act. In the cross-sectional analyses, each IQR increase in PM2.5 was associated with 2.49 % (95 % CI: 2.36 %, 2.62 %), 2.51 % (95 % CI: 2.26 %, 2.75 %), 3.94 % (95 % CI: 3.65 %, 4.23 %), and 1.54 % (95 % CI: 1.38 %, 1.70 %) increases in TC, LDL-C, TG, and HDL-C, respectively. For each IQR increase in O3, TC, LDL-C, TG, and HDL-C changed by 1.06 % (95 % CI: 0.95 %, 1.17 %), 1.21 % (95 % CI: 1.01 %, 1.42 %), 1.78 % (95 % CI: 1.54 %, 2.02 %), and -0.63 % (95 % CI: -0.76 %, -0.49 %), respectively. Longitudinal analyses showed that the intervention group experienced greater TC, LDL-C, and HDL-C reductions (1.77 %, 4.26 %, and 7.70 %, respectively). Our findings suggest that clean air policies could improve lipid metabolism and should be implemented in countries with heavy air pollution burdens.

The inoculum dose of Zika virus can affect the viral replication dynamics, cytokine responses and survival rate in immunocompromised AG129 mice.

Zika virus, a mosquito-borne arbovirus, has repeatedly caused large pandemics with symptoms worsening from mild and self-limiting diseases to Guillain-Barré syndrome in adults and fetal microcephaly in newborns. In recent years, Zika virus diseases have posed a serious threat to human health. The shortage of susceptible small animal models makes it difficult to study pathogenic mechanisms and evaluate potential therapies for Zika virus infection. Therefore, we chose immunocompromised mice (AG129 mice) deficient in IFN-α/ß and IFN-γ receptors, which can abolish the innate immune system that prevents Zika virus infection early. AG129 mice were infected with the Zika virus, and this mouse model exhibited replication dynamics, tissue tropism, pathological lesion and immune activation of the Zika virus. Our results suggest that the inoculum dose of Zika virus can affect the viral replication dynamics, cytokine responses and survival rate in AG129 mice. By testing the potential antiviral drug favipiravir, several critical indicators, including replication dynamics and survival rates, were identified in AG129 mice after Zika virus infection. It is suggested that the model is reliable for drug evaluation. In brief, this model provides a potential platform for studies of the infectivity, virulence, and pathogenesis of the Zika virus. Moreover, the development of an accessible mouse model of Zika virus infection will expedite the research and deployment of therapeutics and vaccines.

Therapeutic implication of targeting mitochondrial drugs designed for efferocytosis dysfunction.

Efferocytosis refers to the process by which phagocytes remove apoptotic cells and related apoptotic products. It is essential for the growth and development of the body, the repair of damaged or inflamed tissues, and the balance of the immune system. Damaged efferocytosis will cause a variety of chronic inflammation and immune system diseases. Many studies show that efferocytosis is a process mediated by mitochondria. Mitochondrial metabolism, mitochondrial dynamics, and communication between mitochondria and other organelles can all affect phagocytes' clearance of apoptotic cells. Therefore, targeting mitochondria to modulate phagocyte efferocytosis is an anticipated strategy to prevent and treat chronic inflammatory diseases and autoimmune diseases. In this review, we introduced the mechanism of efferocytosis and the pivoted role of mitochondria in efferocytosis. In addition, we focused on the therapeutic implication of drugs targeting mitochondria in diseases related to efferocytosis dysfunction.

Short-Term Precipitation Radar Echo Extrapolation Method Based on the MS-DD3D-RSTN Network and STLoss Function.

Short-term precipitation forecasting is essential for agriculture, transportation, urban management, and tourism. The radar echo extrapolation method is widely used in precipitation forecasting. To address issues like forecast degradation, insufficient capture of spatiotemporal dependencies, and low accuracy in radar echo extrapolation, we propose a new model: MS-DD3D-RSTN. This model employs spatiotemporal convolutional blocks (STCBs) as spatiotemporal feature extractors and uses the spatial-temporal loss (STLoss) function to learn intra-frame and inter-frame changes for end-to-end training, thereby capturing the spatiotemporal dependencies in radar echo signals. Experiments on the Sichuan dataset and the HKO-7 dataset show that the proposed model outperforms advanced models in terms of CSI and POD evaluation metrics. For 2 h forecasts with 20 dBZ and 30 dBZ reflectivity thresholds, the CSI metrics reached 0.538, 0.386, 0.485, and 0.198, respectively, representing the best levels among existing methods. The experiments demonstrate that the MS-DD3D-RSTN model enhances the ability to capture spatiotemporal dependencies, mitigates forecast degradation, and further improves radar echo prediction performance.

Identifying the toxic mechanisms of emerging electronic contaminations liquid crystal monomers and the construction of a priority control list for graded control.

Liquid crystal monomers (LCMs) are identified as emerging organic contaminations with largely unexplored health impacts. To elucidate their toxic mechanisms, support the establishment of environmental discharge and management standards, and promote effective LCMs control, this study constructs a database covering 20,545 potential targets of 1431 LCMs, highlighting 9 key toxic target proteins that disrupt the nervous system and metabolic functions. GO and KEGG pathway analysis suggests LCMs severely affect nervous system, linked to neurodegenerative diseases and mental health disorders, with toxicity variations driven by electronegativity and structural complexity of LCM terminal groups. To achieve tiered control of LCMs, construct toxicity risk control lists for 9 key toxic target proteins, suitable for the graded control of LCMs, management recommendations are provided based on toxicity levels. These lists were validated for reliability and offer reliable toxicity predictions for LCMs. SHAP analysis points to electronic properties, molecular shape, and structural characteristics of LCMs as primary health impact factors. As the first study integrating machine learning with computational toxicology to outline LCMs health impacts, it aims to enhance public understanding of LCM toxicity risks and support the development of environmental standards, effective management of LCM production and emissions, and reduction of public exposure risks.

Morphology and Alignment Transition of Hexabenzocoronene (HBC) Mesogen Films by Bar Coating: Effect of Coating Speed.

Films of the discotic liquid crystalline hexabenzocoronene (HBC) derivative, HBC-1,3,5-Ph-C12, were prepared on the quartz substrate by the bar-coating method. Depending on the coating speed, regularly spaced stripes or continuous films were observed. In the former case, columns of the HBC derivatives align more along the stripes, which are perpendicular to the coating direction, whereas in the latter case, columns of the HBC derivatives in the film align more along the coating direction. These distinctive structures are confirmed via polarized optical microscopy (POM), polarized UV-vis spectroscopy, and grazing incidence small-angle X-ray scattering measurements.

Prenatal heavy metal exposure and pediatric asthma, allergic rhinitis, atopic dermatitis: a systematic review and meta-analysis.

OBJECTIVE: We review the prevalence of allergic diseases in children across prenatal exposures to heavy metals. METHODS: This systematic review and meta-analysis is registered in the PROSPERO database (CRD42023478471). A comprehensive search of PubMed, Web of Science, Medline and Cochrane library was conducted from the database inception until 31 October 2023. The Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of included studies. We used a random-effects model to summarize the effects from the studies. RESULTS: A total of 16 studies were included, 120,065 mother-child pairs enrolled. The NOS scores indicated that the quality of the literature included in the study was of a high standard. CONCLUSION: The final results indicate that prenatal exposure to Pb increased the incidence of wheeze and Eczema in infants, and exposure to Ni and CD increased the incidence of AD in infants.

Molecular composition limits the reaction kinetics of riverine dissolved organic matter decomposition.

The bioavailability and degradation of riverine dissolved organic matter (DOM) play crucial roles in greenhouse gas emissions; however, studies on the kinetic decomposition of fluvial DOM remain scarce. In this study, the decomposition kinetics of dissolved organic carbon (DOC) were characterized using the reactivity continuum model through 28-day bio-incubation experiments with water samples from the Yangtze River. The relationship between DOM composition and decomposition kinetics was analyzed using optical and molecular characterization combined with apparent decay coefficients. Our results revealed that DOM compounds rich in nitrogen and sulfur were predominantly removed, exhibiting a transition from an unsaturated to a saturated state following microbial degradation. These heteroatomic compounds, which constituted 75.61 % of the DOM compounds positively correlated with the decay coefficient k0, underwent preferential degradation in the early stages of bio-incubation due to their higher bioavailability. Additionally, we observed that S-containing fractions with high molecular weight values (MW > 400 Da) may be associated with larger reactivity grades. This study underscored the complex interplay between DOM composition and its kinetic decomposition in river ecosystems, providing further support for the significance of molecular composition in large river DOM as crucial factors affecting decomposition.

Mesenchymal stem cell-derived extracellular vesicles mitigate neuronal damage from intracerebral hemorrhage by modulating ferroptosis.

BACKGROUND: Hemorrhagic stroke is a devastating cerebrovascular event with a high rate of early mortality and long-term disability. The therapeutic potential of mesenchymal stem cell-derived extracellular vesicles (MSC-EVs) for neurological conditions, such as intracerebral hemorrhage (ICH), has garnered considerable interest, has garnered considerable interest, though their mechanisms of action remain poorly understood. METHODS: EVs were isolated from human umbilical cord MSCs, and SPECT/CT was used to track the 99mTc-labeled EVs in a mouse model of ICH. A series of comprehensive evaluations, including magnetic resonance imaging (MRI), histological study, RNA sequencing (RNA-Seq), or miRNA microarray, were performed to investigate the therapeutic action and mechanisms of MSC-EVs in both cellular and animal models of ICH. RESULTS: Our findings show that intravenous injection of MSC-EVs exhibits a marked affinity for the ICH-affected brain regions and cortical neurons. EV infusion alleviates the pathological changes observed in MRI due to ICH and reduces damage to ipsilateral cortical neurons. RNA-Seq analysis reveals that EV treatment modulates key pathways involved in the neuronal system and metal ion transport in mice subjected to ICH. These data were supported by the attenuation of neuronal ferroptosis in neurons treated with Hemin and in ICH mice following EV therapy. Additionally, miRNA microarray analysis depicted the EV-miRNAs targeting genes associated with ferroptosis, and miR-214-3p was identified as a regulator of neuronal ferroptosis in the ICH cellular model. CONCLUSIONS: MSC-EVs offer neuroprotective effects against ICH-induced neuronal damage by modulating ferroptosis highlighting their therapeutic potential for combating neuronal ferroptosis in brain disorders.

Construction of an IFNAR1 knockout MDBK cell line using CRISPR/Cas9 and its effect on bovine virus replication.

The type I interferon (IFN) pathway is important for eukaryotic cells to resist viral infection, as well as an impediment to efficient virus replication. Therefore, this study aims to create an IFNAR1 knockout (KO) Madin-Darby bovine kidney (MDBK) cell line using CRISPR/Cas9 and investigate its application and potential mechanism in increasing viral replication of bovines. The IFNAR1 KO cells showed increased titers of bovine viral diarrhea virus (BVDV) (1.5 log10), with bovine enterovirus and bovine parainfluenza virus type 3 (0.5-0.8 log10). RNA-seq revealed reduced expression of the genes related IFN-I pathways including IFNAR1, STAT3, IRF9, and SOCS3 in IFNAR1 KO cells compared with WT cells. In WT cells, 306 differentially expressed genes (DEGs) were identified between BVDV-infected and -uninfected cells. Of these, 128 up- and 178 down-regulated genes were mainly associated with growth cycle and biosynthesis, respectively. In IFNAR1 KO cells, 286 DEGs were identified, with 82 up-regulated genes were associated with signaling pathways, and 204 down-regulated genes. Further, 92 DEGs were overlapped between WT and IFNAR1 KO cells including ESM1, IL13RA2, and SLC25A34. Unique DEGs in WT cells were related to inflammation and immune regulation, whereas those unique in IFNAR1 KO cells involved in cell cycle regulation through pathways such as MAPK. Knocking down SLC25A34 and IL13RA2 in IFNAR1 KO cells increased BVDV replication by 0.3 log10 and 0.4 log10, respectively. Additionally, we constructed an IFNAR1/IFNAR2 double-knockout MDBK cell line, which further increased BVDV viral titers compared with IFNAR1 KO cells (0.6 log10). Overall, the IFNAR1 KO MDBK cell line can support better replication of bovine viruses and therefore provides a valuable tool for bovine virus research on viral pathogenesis and host innate immune response.

Branched Oligomer-Based Reversible Adhesives Enabled by Controllable Self-Aggregation.

Supramolecular adhesion material systems based on small molecules have shown great potential to unite the great contradiction between strong adhesion and reversibility. However, these material systems suffer from low adhesion strength/narrow adhesion span, limited designability, and single interaction due to fewer covalent bond content and action sites in small molecules. Herein, an ultrahigh-strength and large-span reversible adhesive enabled by a branched oligomer controllable self-aggregation strategy is developed. The dense covalent bonds present in the branched oligomers greatly enhance adhesion strength without compromising reversibility. The resulting adhesive exhibits a large-span reversible adhesion of ≈140 times, switching between ultra-strong and tough adhesion strength (5.58 MPa and 5093.92 N m-1) and ultralow adhesion (0.04 MPa and 87.656 N m-1) with alternating temperature. Moreover, reversible dynamic double cross-linking endows the adhesive with stable reversible adhesion transitions even after 100 cycles. This reversible adhesion property can also be remotely controlled via a voltage of 8 V, with a loading voltage duration of 45 s. This work paves the way for the design of reversible adhesives with long-span outstanding properties using covalent polymers and offers a pathway for the rational design of high-performance adhesives featuring both robust toughness and exceptional reversibility.

DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras.

PROteolysis TArgeting Chimeras (PROTACs) has recently emerged as a promising technology. However, the design of rational PROTACs, especially the linker component, remains challenging due to the absence of structure-activity relationships and experimental data. Leveraging the structural characteristics of PROTACs, fragment-based drug design (FBDD) provides a feasible approach for PROTAC research. Concurrently, artificial intelligence-generated content has attracted considerable attention, with diffusion models and Transformers emerging as indispensable tools in this field. In response, we present a new diffusion model, DiffPROTACs, harnessing the power of Transformers to learn and generate new PROTAC linkers based on given ligands. To introduce the essential inductive biases required for molecular generation, we propose the O(3) equivariant graph Transformer module, which augments Transformers with graph neural networks (GNNs), using Transformers to update nodes and GNNs to update the coordinates of PROTAC atoms. DiffPROTACs effectively competes with existing models and achieves comparable performance on two traditional FBDD datasets, ZINC and GEOM. To differentiate the molecular characteristics between PROTACs and traditional small molecules, we fine-tuned the model on our self-built PROTACs dataset, achieving a 93.86% validity rate for generated PROTACs. Additionally, we provide a generated PROTAC database for further research, which can be accessed at https://bailab.siais.shanghaitech.edu.cn/service/DiffPROTACs-generated.tgz. The corresponding code is available at https://github.com/Fenglei104/DiffPROTACs and the server is at https://bailab.siais.shanghaitech.edu.cn/services/diffprotacs.

[Changes and clinical significance of NLRP3 inflammasomes and related factors in patients with spinal fracture complicated with acute spinal cord injury].

OBJECTIVE: To investigate the changes and clinical significance of NOD like receptor protein 3 (NLRP3) inflammasomes and related factors in patients with spinal fractures complicated with acute spinal cord injury (SCI). METHODS: Eighty-six spinal fracture patients complicated with acute SCI admitted to hospital from June 2019 to March 2022 were selected as SCI group, There were 48 males and 38 females, with an average age of (43.48±6.58) years old. And 100 healthy volunteers who underwent physical examination during the same time were selected as control group, including 56 males patients and 44 females patients, with an average age of (45.13±6.43) years old. Peripheral blood mononuclear cell (PBMC) were collected, and the mRNA expressions of NLRP3 and Caspase-1 were detected. Serum was collected and the levels of interleukin (IL)- 1ß, IL-18 were detected. According to Frankel's grade, the SCI group was divided into complete injury patients and incomplete injury patients, and according to the Japanese Orthopedic Society (JOA) grade, the SCI group was divided into good prognosis group and poor prognosis group. The difference of NLRP3, Caspase-1, IL-1ß, IL-18 among groups were compared, the influencing factors for poor prognosis in SCI patients was analyzed by Logistic regression. RESULTS: The mRNA expression levels of NLRP3 (1.41±0.33) and Caspase-1 (1.44±0.35) in PBMC and the levels of IL-1ß(45.34±13.22) pg·ml-1, IL-18(40.95±8.77) pg·ml-1 in serum of SCI group were higher than those of the control group[(1.00±0.19), (1.00±0.16), (16.58±4.24) pg·ml-1, (12.57±3.68) pg·ml-1] (P<0.05). The mRNA expression levels of NLRP3(1.63±0.34) and Caspase-1 (1.67±0.27) in PBMC and the levels of IL-1ß(51.09±11.10) pg·ml-1, IL-18 (47.65±7.93) pg·ml-1 in serum of patients with complete injury in the SCI group were higher than those of patients with incomplete injury [(1.31±0.27), (1.34±0.33), (42.85±13.36) pg·ml-1, (38.05±7.48) pg·ml-1](P<0.05). The mRNA expression levels of NLRP3 (1.66±0.31) and Caspase-1 (1.72±0.31)in PBMC and the levels of IL-1ß(51.21±11.31) pg·ml-1, IL-18 (45.70±7.25) pg·ml-1 in serum, the proportion of complete injury(21 patients), and the proportion of spinal cord edema or bleeding of patients(15 patients) with poor prognosis in the SCI group were higher than those of patients with good prognosis[(1.28±0.26), (1.37±0.36), (42.79±13.25) pg·ml-1、(38.90±8.63) pg·ml-1, 5、20 cases](P<0.05). Complete injury and the mRNA expression of NLRP3 in PBMC were the influencing factors for poor prognosis in the SCI group (P<0.05). CONCLUSION: The activation of NLRP3 inflammasomes in patients with spinal fractures complicated with acute SCI is associated with worsening injury and poor prognosis, and NLRP3 expression can serve as a marker for evaluating prognosis.

Bioactive Naphthoquinone and Phenazine Analogs from the Endophytic Streptomyces sp. PH9030 as α-Glucosidase Inhibitors.

A talented endophytic Streptomyces sp. PH9030 is derived from the medicinal plant Kadsura coccinea (Lem.) A.C. Smith. The undescribed naphthoquinone naphthgeranine G (5) and seven previously identified compounds, 6-12, were obtained from Streptomyces sp. PH9030. The structure of 5 was identified by comprehensive examination of its HRESIMS, 1D NMR, 2D NMR and ECD data. The inhibitory activities of all the compounds toward α-glucosidase and their antibacterial properties were investigated. The α-glucosidase inhibitory activities of 5, 6, 7 and 9 were reported for the first time, with IC50 values ranging from 66.4 ± 6.7 to 185.9 ± 0.2 µM, as compared with acarbose (IC50 = 671.5 ± 0.2 µM). The molecular docking and molecular dynamics analysis of 5 with α-glucosidase further indicated that it may have a good binding ability with α-glucosidase. Both 9 and 12 exhibited moderate antibacterial activity against methicillin-resistant Staphylococcus aureus, with minimum inhibitory concentration (MIC) values of 16 µg/mL. These results indicate that 5, together with the naphthoquinone scaffold, has the potential to be further developed as a possible inhibitor of α-glucosidase.

Novel 4-chlorophenoxyacetate dioxygenase-mediated phenoxyalkanoic acid herbicides initial catabolism in Cupriavidus sp. DL-D2.

Microbial metabolism is an important driving force for the elimination of 4-chlorophenoxyacetic acid residues in the environment. The α-Ketoglutarate-dependent dioxygenase (TfdA) or 2,4-D oxygenase (CadAB) catalyzes the cleavage of the aryl ether bond of 4-chlorophenoxyacetic acid to 4-chlorophenol, which is one of the important pathways for the initial metabolism of 4-chlorophenoxyacetic acid by microorganisms. However, strain Cupriavidus sp. DL-D2 could utilize 4-chlorophenoxyacetic acid but not 4-chlorophenol for growth. This scarcely studied degradation pathway may involve novel enzymes that has not yet been characterized. Here, a gene cluster (designated cpd) responsible for the catabolism of 4-chlorophenoxyacetic acid in strain DL-D2 was cloned and identified, and the dioxygenase CpdA/CpdB responsible for the initial degradation of 4-chlorophenoxyacetic acid was successfully expressed, which could catalyze the conversion of 4-chlorphenoxyacetic acid to 4-chlorocatechol. Then, an aromatic cleavage enzyme CpdC further converts 4-chlorocatechol into 3-chloromuconate. The results of substrate degradation experiments showed that CpdA/CpdB could also degrade 3-chlorophenoxyacetic acid and phenoxyacetic acid, and homologous cpd gene clusters were widely discovered in microbial genomes. Our findings revealed a novel degradation mechanism of 4-chlorophenoxyacetic acid at the molecular level.

Association of the triglyceride-glucose index with all-cause and cause-specific mortality: a population-based cohort study of 3.5 million adults in China.

Background: The triglyceride-glucose (TyG) index has been recognized as a crucial risk factor for cardiovascular diseases. However, the association between the TyG index and mortality in the general population remains elusive. Methods: Participants were enrolled from the China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART), a nationwide prospective cohort study. The outcomes of interest were all-cause, cardiovascular, and cancer mortality. Restricted cubic splines and Cox regression models were used to assess the associations between the TyG index and outcomes. Findings: In total, 3,524,459 participants with a median follow-up of 4.6 (IQR, 3.1-5.8) years were included. The associations of the TyG index with all-cause and cardiovascular mortality were reverse L-shaped, with cut-off values of 9.75 for all-cause mortality and 9.85 for cardiovascular mortality. For each 1-unit increase in the TyG index, when below the cut-off values, the TyG index was not significantly associated with all-cause mortality (HR = 1.02, 95% CI: 1.00-1.03) and was only modestly associated with cardiovascular mortality (HR = 1.09, 95% CI: 1.06-1.11). Conversely, when the cut-off values were exceeded, the HRs (95% CI) were 2.10 (1.94-2.29) for all-cause mortality and 1.99 (1.72-2.30) for cardiovascular mortality. However, the association between the TyG index and cancer mortality was linearly negative (HR = 0.97, 95% CI: 0.94-0.99). Interpretation: The associations of the TyG index with all-cause and cardiovascular mortality displayed reverse L-shaped patterns, while an elevated TyG index showed a slight negative association with cancer mortality. We suggest that <9.75 could be the optimal TyG index cut-off value among the Chinese general population. Individuals at high risk of mortality might benefit from proper management of a high TyG index. Funding: The National High Level Hospital Clinical Research Funding (2023-GSP-ZD-2, 2023-GSP-RC-01), the Ministry of Finance of China and National Health Commission of China.

Association and its population heterogeneities between low-density lipoprotein cholesterol and all-cause and cardiovascular mortality: A population-based cohort study.

BACKGROUND: The association and population heterogeneities between low-density lipoprotein cholesterol (LDL-C) and all-cause and cardiovascular mortality remain unknown. We aimed to examine the dose-dependent associations of LDL-C levels with specific types of cardiovascular disease (CVD) mortality and heterogeneities in the associations among different population subgroups. METHODS: A total of 2,968,462 participants aged 35-75 years from China Health Evaluation And risk Reduction through nationwide Teamwork (ChinaHEART) (2014-2019) were included. Cox proportional hazard models and Fine-Gray subdistribution hazard models were used to estimate associations between LDL-C categories (<70.0, 70.0-99.9, 100.0-129.9 [reference group], 130.0-159.9, 160.0-189.9, and ≥190.0 mg/dL) and all-cause and cause-specific mortality. RESULTS: During a median follow-up of 3.7 years, 57,391 and 23,241 deaths from all-cause and overall CVD were documented. We observed J-shaped associations between LDL-C and death from all-cause, overall CVD, coronary heart disease (CHD), and ischemic stroke, and an L-shaped association between LDL-C and hemorrhagic stroke (HS) mortality (P for non-linearity <0.001). Compared with the reference group (100.0-129.9 mg/dL), very low LDL-C levels (<70.0 mg/dL) were significantly associated with increased risk of overall CVD (hazard ratio [HR]: 1.10, 95% confidence interval [CI]: 1.06-1.14) and HS mortality (HR: 1.37, 95% CI: 1.29-1.45). Very high LDL-C levels (≥190.0 mg/dL) were associated with increased risk of overall CVD (HR: 1.51, 95% CI: 1.40-1.62) and CHD mortality (HR: 2.08, 95% CI: 1.92-2.24). The stronger associations of very low LDL-C with risk of CVD mortality were observed in individuals with older age, low or normal body mass index, low or moderate 10-year atherosclerotic CVD risk, and those without diagnosed CVD or taking statins. Stronger associations between very high LDL-C levels and all-cause and CVD mortality were observed in younger people. CONCLUSIONS: People with very low LDL-C had a higher risk of all-cause, CVD, and HS mortality; those with very high LDL-C had a higher risk of all-cause, CVD, and CHD mortality. On the basis of our findings, comprehensive health assessment is needed to evaluate cardiovascular risk and implement appropriate lipid-lowering therapy for people with very low LDL-C.