Intensive Ambulance-Delivered Blood-Pressure Reduction in Hyperacute Stroke.

BACKGROUND: Treatment of acute stroke, before a distinction can be made between ischemic and hemorrhagic types, is challenging. Whether very early blood-pressure control in the ambulance improves outcomes among patients with undifferentiated acute stroke is uncertain. METHODS: We randomly assigned patients with suspected acute stroke that caused a motor deficit and with elevated systolic blood pressure (≥150 mm Hg), who were assessed in the ambulance within 2 hours after the onset of symptoms, to receive immediate treatment to lower the systolic blood pressure (target range, 130 to 140 mm Hg) (intervention group) or usual blood-pressure management (usual-care group). The primary efficacy outcome was functional status as assessed by the score on the modified Rankin scale (range, 0 [no symptoms] to 6 [death]) at 90 days after randomization. The primary safety outcome was any serious adverse event. RESULTS: A total of 2404 patients (mean age, 70 years) in China underwent randomization and provided consent for the trial: 1205 in the intervention group and 1199 in the usual-care group. The median time between symptom onset and randomization was 61 minutes (interquartile range, 41 to 93), and the mean blood pressure at randomization was 178/98 mm Hg. Stroke was subsequently confirmed by imaging in 2240 patients, of whom 1041 (46.5%) had a hemorrhagic stroke. At the time of patients' arrival at the hospital, the mean systolic blood pressure in the intervention group was 159 mm Hg, as compared with 170 mm Hg in the usual-care group. Overall, there was no difference in functional outcome between the two groups (common odds ratio, 1.00; 95% confidence interval [CI], 0.87 to 1.15), and the incidence of serious adverse events was similar in the two groups. Prehospital reduction of blood pressure was associated with a decrease in the odds of a poor functional outcome among patients with hemorrhagic stroke (common odds ratio, 0.75; 95% CI, 0.60 to 0.92) but an increase among patients with cerebral ischemia (common odds ratio, 1.30; 95% CI, 1.06 to 1.60). CONCLUSIONS: In this trial, prehospital blood-pressure reduction did not improve functional outcomes in a cohort of patients with undifferentiated acute stroke, of whom 46.5% subsequently received a diagnosis of hemorrhagic stroke. (Funded by the National Health and Medical Research Council of Australia and others; INTERACT4 ClinicalTrials.gov number, NCT03790800; Chinese Trial Registry number, ChiCTR1900020534.).

GADRP: graph convolutional networks and autoencoders for cancer drug response prediction.

Drug response prediction in cancer cell lines is of great significance in personalized medicine. In this study, we propose GADRP, a cancer drug response prediction model based on graph convolutional networks (GCNs) and autoencoders (AEs). We first use a stacked deep AE to extract low-dimensional representations from cell line features, and then construct a sparse drug cell line pair (DCP) network incorporating drug, cell line, and DCP similarity information. Later, initial residual and layer attention-based GCN (ILGCN) that can alleviate over-smoothing problem is utilized to learn DCP features. And finally, fully connected network is employed to make prediction. Benchmarking results demonstrate that GADRP can significantly improve prediction performance on all metrics compared with baselines on five datasets. Particularly, experiments of predictions of unknown DCP responses, drug-cancer tissue associations， and drug-pathway associations illustrate the predictive power of GADRP. All results highlight the effectiveness of GADRP in predicting drug responses, and its potential value in guiding anti-cancer drug selection.

Deubiquitinase USP18 inhibits hepatic stellate cells activation and alleviates liver fibrosis via regulation of TAK1 activity.

BACKGROUND & AIMS: Activation of hepatic stellate cells (HSCs) is the key process underlying liver fibrosis. Unveiling its molecular mechanism may provide an effective target for inhibiting liver fibrosis. Protein ubiquitination is a dynamic and reversible process. Deubiquitinases (DUBs) catalyze the removal of ubiquitin chains from substrate proteins, thereby inhibiting the biological processes regulated by ubiquitination signals. However, there are few studies revealing the role of deubiquitination in the activation of HSCs. METHODS & RESULTS: Single-cell RNA sequencing (scRNA-seq) revealed significantly decreased USP18 expression in activated HSCs when compared to quiescent HSCs. In mouse primary HSCs, continuous activation of HSCs led to a gradual decrease in USP18 expression whilst restoration of USP18 expression significantly inhibited HSC activation. Injection of USP18 lentivirus into the portal vein of a CCl4-induced liver fibrosis mouse model confirmed that overexpression of USP18 can significantly reduce the degree of liver fibrosis. In terms of mechanism, we screened some targets of USP18 in mouse primary HSCs and found that USP18 could directly bind to TAK1. Furthermore, we demonstrated that USP18 can inhibit TAK1 activity by interfering with the K63 ubiquitination of TAK1. CONCLUSIONS: Our study demonstrated that USP18 inhibited HSC activation and alleviated liver fibrosis via modulation of TAK1 activity; this may prove to be an effective target for inhibiting liver fibrosis.

TOXRIC: a comprehensive database of toxicological data and benchmarks.

The toxic effects of compounds on environment, humans, and other organisms have been a major focus of many research areas, including drug discovery and ecological research. Identifying the potential toxicity in the early stage of compound/drug discovery is critical. The rapid development of computational methods for evaluating various toxicity categories has increased the need for comprehensive and system-level collection of toxicological data, associated attributes, and benchmarks. To contribute toward this goal, we proposed TOXRIC (https://toxric.bioinforai.tech/), a database with comprehensive toxicological data, standardized attribute data, practical benchmarks, informative visualization of molecular representations, and an intuitive function interface. The data stored in TOXRIC contains 113 372 compounds, 13 toxicity categories, 1474 toxicity endpoints covering in vivo/in vitro endpoints and 39 feature types, covering structural, target, transcriptome, metabolic data, and other descriptors. All the curated datasets of endpoints and features can be retrieved, downloaded and directly used as output or input to Machine Learning (ML)-based prediction models. In addition to serving as a data repository, TOXRIC also provides visualization of benchmarks and molecular representations for all endpoint datasets. Based on these results, researchers can better understand and select optimal feature types, molecular representations, and baseline algorithms for each endpoint prediction task. We believe that the rich information on compound toxicology, ML-ready datasets, benchmarks and molecular representation distribution can greatly facilitate toxicological investigations, interpretation of toxicological mechanisms, compound/drug discovery and the development of computational methods.

Identification and characterization of extrachromosomal circular DNA in large-artery atherosclerotic stroke.

Extrachromosomal circular DNA (eccDNA) is a new biomarker and regulator of diseases. However, the role of eccDNAs in large-artery atherosclerotic (LAA) stroke remains unclear. Through high-throughput circle-sequencing technique, the length distribution, genomic characteristic and motifs feature of plasma eccDNA from healthy controls (CON) and patients with LAA stroke were analysed. Then, the potential functions of the annotated eccDNAs were investigated using GO and KEGG pathway analyses. EccDNAs mapped to the reference genome showed SHN3 and BCL6 were LAA stroke unique transcription factors. The genes of differentially expressed eccDNAs between LAA stroke patients and CON were mainly involved in axon/dendrite/neuron projection development and maintenance of cellular structure via Wnt, Rap1 and MAPK pathways. Moreover, LAA stroke unique eccDNA genes played a role in regulation of coagulation and fibrinolysis, and there were five LAA stroke unique eccDNAs (Chr2:12724406-12724784, Chr4:1867120-186272046, Chr4:186271494-186271696, Chr7:116560296-116560685 and Chr11:57611780-5761192). Additionally, POLR2C and AURKA carried by ecDNAs (eccDNA size >100 kb) of LAA stroke patients were significantly associated with development of LAA stroke. Our data firstly revealed the characteristics of eccDNA in LAA stroke and the functions of LAA stroke unique eccDNAs and eccDNA genes, suggesting eccDNA is a novel biomarker and mechanism of LAA stroke.

PCSK9 inhibitor added to high-intensity statin therapy to prevent cardiovascular events in patients with acute coronary syndrome after percutaneous coronary intervention: a randomized, double- blind, placebo-controlled, multicenter SHAWN study.

BACKGROUND: It is currently uncertain whether the combination of a proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor and high-intensity statin treatment can effectively reduce cardiovascular events in patients with acute coronary syndrome (ACS) who have undergone percutaneous coronary intervention (PCI) for culprit lesions. METHODS: This study protocol describes a double-blind, randomized, placebo-controlled, multicenter study aiming to investigate the efficacy and safety of combining a PCSK9 inhibitor with high-intensity statin therapy in patients with ACS following PCI. A total of 1,212 patients with ACS and multiple lesions will be enrolled and randomly assigned to receive either PCSK9 inhibitor plus high-intensity statin therapy or high-intensity statin monotherapy. The randomization process will be stratified by sites, diabetes, initial presentation and use of stable (≥4 weeks) statin treatment at presentation. PCSK 9 inhibitor or its placebo is injected within 4 hours after PCI for the culprit lesion. The primary endpoint is the composite of cardiovascular death, myocardial infarction, stroke, re-hospitalization due to ACS or heart failure, or any ischemia-driven coronary revascularization at 1-year follow-up between 2 groups. Safety endpoints mean PCSK 9 inhibitor and statin intolerance. CONCLUSION: The SHAWN study has been specifically designed to evaluate the effectiveness and safety of adding a PCSK9 inhibitor to high-intensity statin therapy in patients who have experienced ACS following PCI. The primary objective of this study is to generate new evidence regarding the potential benefits of combining a PCSK9 inhibitor with high-intensity statin treatment in reducing cardiovascular events among these patients.

DTI-HETA: prediction of drug-target interactions based on GCN and GAT on heterogeneous graph.

Drug-target interaction (DTI) prediction plays an important role in drug repositioning, drug discovery and drug design. However, due to the large size of the chemical and genomic spaces and the complex interactions between drugs and targets, experimental identification of DTIs is costly and time-consuming. In recent years, the emerging graph neural network (GNN) has been applied to DTI prediction because DTIs can be represented effectively using graphs. However, some of these methods are only based on homogeneous graphs, and some consist of two decoupled steps that cannot be trained jointly. To further explore GNN-based DTI prediction by integrating heterogeneous graph information, this study regards DTI prediction as a link prediction problem and proposes an end-to-end model based on HETerogeneous graph with Attention mechanism (DTI-HETA). In this model, a heterogeneous graph is first constructed based on the drug-drug and target-target similarity matrices and the DTI matrix. Then, the graph convolutional neural network is utilized to obtain the embedded representation of the drugs and targets. To highlight the contribution of different neighborhood nodes to the central node in aggregating the graph convolution information, a graph attention mechanism is introduced into the node embedding process. Afterward, an inner product decoder is applied to predict DTIs. To evaluate the performance of DTI-HETA, experiments are conducted on two datasets. The experimental results show that our model is superior to the state-of-the-art methods. Also, the identification of novel DTIs indicates that DTI-HETA can serve as a powerful tool for integrating heterogeneous graph information to predict DTIs.

An enhanced cascade-based deep forest model for drug combination prediction.

Combination therapy has shown an obvious curative effect on complex diseases, whereas the search space of drug combinations is too large to be validated experimentally even with high-throughput screens. With the increase of the number of drugs, artificial intelligence techniques, especially machine learning methods, have become applicable for the discovery of synergistic drug combinations to significantly reduce the experimental workload. In this study, in order to predict novel synergistic drug combinations in various cancer cell lines, the cell line-specific drug-induced gene expression profile (GP) is added as a new feature type to capture the cellular response of drugs and reveal the biological mechanism of synergistic effect. Then, an enhanced cascade-based deep forest regressor (EC-DFR) is innovatively presented to apply the new small-scale drug combination dataset involving chemical, physical and biological (GP) properties of drugs and cells. Verified by the dataset, EC-DFR outperforms two state-of-the-art deep neural network-based methods and several advanced classical machine learning algorithms. Biological experimental validation performed subsequently on a set of previously untested drug combinations further confirms the performance of EC-DFR. What is more prominent is that EC-DFR can distinguish the most important features, making it more interpretable. By evaluating the contribution of each feature type, GP feature contributes 82.40%, showing the cellular responses of drugs may play crucial roles in synergism prediction. The analysis based on the top contributing genes in GP further demonstrates some potential relationships between the transcriptomic levels of key genes under drug regulation and the synergism of drug combinations.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs.

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

Computational methods, databases and tools for synthetic lethality prediction.

Synthetic lethality (SL) occurs between two genes when the inactivation of either gene alone has no effect on cell survival but the inactivation of both genes results in cell death. SL-based therapy has become one of the most promising targeted cancer therapies in the last decade as PARP inhibitors achieve great success in the clinic. The key point to exploiting SL-based cancer therapy is the identification of robust SL pairs. Although many wet-lab-based methods have been developed to screen SL pairs, known SL pairs are less than 0.1% of all potential pairs due to large number of human gene combinations. Computational prediction methods complement wet-lab-based methods to effectively reduce the search space of SL pairs. In this paper, we review the recent applications of computational methods and commonly used databases for SL prediction. First, we introduce the concept of SL and its screening methods. Second, various SL-related data resources are summarized. Then, computational methods including statistical-based methods, network-based methods, classical machine learning methods and deep learning methods for SL prediction are summarized. In particular, we elaborate on the negative sampling methods applied in these models. Next, representative tools for SL prediction are introduced. Finally, the challenges and future work for SL prediction are discussed.

COMMO: a web server for the identification and analysis of consensus gene modules across multiple methods.

SUMMARY: A variety of computational methods have been developed to identify functionally related gene modules from genome-wide gene expression profiles. Integrating the results of these methods to identify consensus modules is a promising approach to produce more accurate and robust results. In this application note, we introduce COMMO, the first web server to identify and analyze consensus gene functionally related gene modules from different module detection methods. First, COMMO implements eight state-of-the-art module detection methods and two consensus clustering algorithms. Second, COMMO provides users with mRNA and protein expression data for 33 cancer types from three public databases. Users can also upload their own data for module detection. Third, users can perform functional enrichment and two types of survival analyses on the observed gene modules. Finally, COMMO provides interactive, customizable visualizations and exportable results. With its extensive analysis and interactive capabilities, COMMO offers a user-friendly solution for conducting module-based precision medicine research. AVAILABILITY AND IMPLEMENTATION: COMMO web is available at https://commo.ncpsb.org.cn/, with the source code available on GitHub: https://github.com/Song-xinyu/COMMO/tree/master.

Mito-LND and (E)-Akt inhibitor-IV: novel compounds inducing endoplasmic reticulum stress and ROS accumulation against hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related mortality. Although multi-kinase inhibitors can prolong the overall survival of late-stage HCC patients, the emergence of drug resistance diminishes these benefits, ultimately resulting in treatment failure. Therefore, there is an urgent need for novel and effective drugs to impede the progression of liver cancer. METHODS: This study employed a concentration gradient increment method to establish acquired sorafenib or regorafenib-resistant SNU-449 cells. Cell viability was assessed using the cell counting kit-8 assay. A library of 793 bioactive small molecules related to metabolism screened compounds targeting both parental and drug-resistant cells. The screened compounds will be added to both the HCC parental cells and the drug-resistant cells, followed by a comprehensive assessment. Intracellular adenosine triphosphate (ATP) levels were quantified using kits. Flow cytometry was applied to assess cell apoptosis and reactive oxygen species (ROS). Real-time quantitative PCR studied relative gene expression, and western blot analysis assessed protein expression changes in HCC parental and drug-resistant cells. A xenograft model in vivo evaluated Mito-LND and (E)-Akt inhibitor-IV effects on liver tumors, with hematoxylin and eosin staining for tissue structure and immunohistochemistry staining for endoplasmic reticulum stress protein expression. RESULTS: From the compound library, we screened out two novel compounds, Mito-LND and (E)-Akt inhibitor-IV, which could potently kill both parental cells and drug-resistant cells. Mito-LND could significantly suppress proliferation and induce apoptosis in HCC parental and drug-resistant cells by upregulating glycolytic intermediates and downregulating those of the tricarboxylic acid (TCA) cycle, thereby decreasing ATP production and increasing ROS. (E)-Akt inhibitor-IV achieved comparable results by reducing glycolytic intermediates, increasing TCA cycle intermediates, and decreasing ATP synthesis and ROS levels. Both compounds trigger apoptosis in HCC cells through the interplay of the AMPK/MAPK pathway and the endoplasmic reticulum stress response. In vivo assays also showed that these two compounds could significantly inhibit the growth of HCC cells and induce endoplasmic reticulum stress. CONCLUSION: Through high throughput screening, we identified that Mito-LND and (E)-Akt inhibitor-IV are two novel compounds against both parental and drug-resistant HCC cells, which could offer new strategies for HCC patients.

Constructibility of AdS Supergluon Amplitudes.

We prove that all tree-level n-point supergluon (scalar) amplitudes in AdS_{5} can be recursively constructed, using factorization and flat-space limit. Our method is greatly facilitated by a natural R-symmetry basis for planar color-ordered amplitudes, which reduces the latter to "partial amplitudes" with simpler pole structures and factorization properties. Given the n-point scalar amplitude, we first extract spinning amplitudes with n-2 scalars and one gluon by imposing "gauge invariance," and then use a special "no-gluon kinematics" to determine the (n+1)-point scalar amplitude completely (which in turn contains the n-point single-gluon amplitude). Explicit results of up to 8-point scalar amplitudes and up to 6-point single-gluon amplitudes are included as Supplemental Material.

CircRNA and Stroke: New Insight of Potential Biomarkers and Therapeutic Targets.

Stroke, the second-largest cause of death and the leading cause of disability globally, presents significant challenges in terms of prognosis and treatment. Identifying reliable prognosis biomarkers and treatment targets is crucial to address these challenges. Circular RNA (circRNA) has emerged as a promising research biomarkers and therapeutic targets because of its tissue specificity and conservation. However, the potential role of circRNA in stroke prognosis and treatment remains largely unexplored. This review briefly elucidate the mechanism underlying circRNA's involvement in stroke pathophysiology. Additionally, this review summarizes the impact of circRNA on different forms of strokes, including ischemic stroke and hemorrhagic stroke. And, this article discusses the positive effects of circRNA on promoting cerebrovascular repair and regeneration, maintaining the integrity of the blood-brain barrier (BBB), and reducing neuronal injury and immune inflammatory response. In conclusion, the significance of circRNA as a potential prognostic biomarker and a viable therapeutic target was underscored.

Thromboelastography-Guided Antiplatelet Therapy for Patients with Ischemic Cerebrocardiovascular Diseases: A Systematic Review and Meta-Analysis.

INTRODUCTION: The effectiveness of thromboelastography (TEG)-guided antiplatelet therapy in patients with ischemic cerebrocardiovascular diseases is not well-established. This systematic review evaluates the efficacy and safety of TEG-guided antiplatelet therapy compared to standard treatment in patients with ischemic cerebrocardiovascular diseases. METHODS: Randomized controlled trials (RCTs) and observational studies comparing TEG-guided antiplatelet therapy with standard therapy in patients suffering from ischemic stroke (IS) or coronary artery disease (CAD) were identified. The primary efficacy measure was a composite of ischemic and hemorrhagic events. Secondary efficacy measures included any ischemic events, while safety was assessed by the occurrence of bleeding events. RESULTS: Ten studies involving 4 RCTs and 6 observational studies with a total of 1,678 patients were included. When considering a composite of ischemic and hemorrhagic events in RCTs, a significant reduction was observed in IS or CAD patients under TEG-guided therapy compared to standard therapy (OR: 0.45, 95% CI: 0.27-0.75, p = 0.002). After pooling RCTs and observational studies together, compared to standard antiplatelet therapy, TEG-guided therapy significantly reduced the risk of a composite of ischemic and hemorrhagic events (OR: 0.26, 95% CI: 0.19-0.37; p < 0.00001), ischemic events (OR: 0.28, 95% CI: 0.19-0.41; p < 0.00001), and bleeding events (OR: 0.31, 95% CI: 0.16-0.62; p = 0.0009) in patients with IS or CAD. CONCLUSION: TEG-guided antiplatelet therapy appears to be both effective and safe for patients with IS or CAD. These findings support the use of TEG testing to tailor antiplatelet therapy in individuals with ischemic cerebrocardiovascular diseases.

Curative Effect Analysis of Endoscopic Submucosal Dissection in Giant Laterally Spreading Rectal Tumors.

GOAL: The objective of this study was to investigate the clinical efficacy of endoscopic submucosal dissection (ESD) in the treatment of giant lateral developing rectal-type tumors (laterally spreading tumors, LSTs). BACKGROUND: There are no specialized studies on the efficacy of ESD in the treatment of LSTs measuring >5 cm in diameter, surgery was often used in the past, but it has the disadvantages of large trauma, many complications, and high cost. METHODS: The data of 185 patients with rectal LSTs who had undergone ESD in the digestive endoscopy center of our hospital from January 2012 to June 2020 were retrospectively analyzed. Based on the size of the lesions, the patients were divided into 2 groups: diameter ≤5 cm (110 cases) and diameter >5 cm (75 cases), and we summarized and analyzed the en bloc resection rate, curative resection rate, procedure time, muscle injury, bleeding, perforation, postoperative stricture, and recurrence. RESULTS: There was no difference in the en bloc resection rate and R0 resection rate between the 2 groups ( P =0.531). Moreover, there was no difference in the incidence of delayed perforation, postoperative stenosis, and recurrence, but the incidence of delayed bleeding was significantly higher in the giant LST group than the small LST group ( P =0.001). Moreover, for giant rectal LSTs, the growth pattern of the lesion, JNET classification, and the extent of postoperative mucosal defect do not significantly affect the efficacy of ESD. It is worth mentioning that the operation time was longer in the group with a diameter >5 cm, in which perforation was more frequent and the muscle layer was more likely to be injured during ESD ( P <0.001). The muscle injury during ESD was mainly related to the diameter of the lesion, the crossing the rectal pouch, and the operation time. CONCLUSIONS: The use of ESD to treat giant rectal LSTs (>5 cm) is relatively difficult and can easily lead to intraoperative muscle injury, perforation, and late postoperative bleeding. However, if active intervention is performed, patients can still achieve good efficacy and prognosis, which can be applied in hospitals with certain conditions.

Selectively lighting up glyoxal in living cells using an o-phenylenediamine fused hemicyanine.

Glyoxal (GL) is a reactive α-dicarbonyl compound generated from glycated proteins in the Maillard reaction. It has attracted particular attention over the past few years because of its possible clinical significance in chronic and age-related diseases. In this work, a reaction-based red emission fluorescent probe GL1 has been synthesized successfully by grafting an alkyl group onto an amino group to regulate its selectivity for GL. Under physiological conditions, the fluorescence intensity of GL1 at 640 nm obviously increased with the increase of GL concentration, and it exhibited high selectivity for GL over other reactive carbonyl compounds, as well as a lower detection limit (0.021 µM) and a larger Stokes shift (112 nm). At the same time, GL1 can selectively accumulate in mitochondria and can be used to detect exogenous and endogenous GL in living cells with low cytotoxicity.

Characterization of genital chlamydia trachomatis infection among women attending infertility and gynecology clinics in Hunan, China.

BACKGROUND: Genital infection with Chlamydia trachomatis (C. trachomatis) is a major public health issue worldwide. It can lead to cervicitis, urethritis, and infertility. This study was conducted to determine the characteristics of genital C. trachomatis infection among women attending to the infertility and gynecology clinics. METHODS: Endocervical swabs were collected from 8,221 women for C. trachomatis nucleotide screening and genotyping, while serum samples were collected for C. trachomatis pgp3 antibody determination using luciferase immunosorbent assays. RESULTS: High C. trachomatis DNA prevalence (3.76%) and seroprevalence (47.46%) rates were found, with genotype E (27.5%) being the most prevalent. C. trachomatis omp1 sense mutation was associated with cervical intraepithelial neoplasia (CIN) (odds ratio [OR] = 6.033, 95% confidence interval [CI] = 1.219-39.185, p = 0.045). No significant differences in C. trachomatis seroprevalence rates were observed between women with detectable C. trachomatis DNA in the infertility and routine physical examination groups (86.67% vs. 95%, p > 0.05); however, among women with negative C. trachomatis DNA, the former group had a markedly higher seroprevalence than the latter group (56.74% vs. 20.17%, p < 0.001). C. trachomatis DNA, but not pgp3 antibody, was significantly associated with CIN (OR = 4.087, 95% CI = 2.284-7.315, p < 0.001). CONCLUSION: Our results revealed a high prevalence, particularly seroprevalence, of C. trachomatis among women with infertility. Furthermore, we found an association between C. trachomatis omp1 sense mutations and CIN. Therefore, C. trachomatis serves as a risk factor for CIN.

Nitrate sources and transformation in surface water and groundwater in Huazhou District, Shaanxi, China: integrated research using hydrochemistry, isotopes and MixSIAR model.

Global water resources affected by excessive nitrate (NO3-) have caused a series of human and ecological problems. Therefore, identification of NO3- sources and transformations is of pivotal significance in the strategic governance of widespread NO3- contaminant. In this investigation, a combination of statistical analysis, chemical indicators, isotopes, and MixSIAR model approaches was adopted to reveal the hydrochemical factors affecting NO3- concentrations and quantify the contribution of each source to NO3- concentrations in surface water and groundwater. The findings revealed that high groundwater NO3- concentration is concentrated in the southwestern region, peaking at 271 mg/L. NO3- concentration in the Wei River and Yuxian River exhibited an increase from upstream to downstream, but in the Shidi River and Luowen River, its concentration was highest in the upstream. Groundwater NO3- has noticeable correlation with Na+, Ca2+, Mg2+, Cl-, HCO3-, TDS, EC, and ORP. In surface water, NO3- level is significantly correlated with NH4+ and ORP. Major sources of NO3- in surface and groundwater comprise manure & sewage and soil nitrogen. Surface water was calculated by MixSIAR model to obtain soil nitrogen (57.7%), manure & sewage (23.8%), chemical fertilizer (12%), and atmospheric deposition (6.4%). In groundwater, soil nitrogen and manure & sewage accounted for 19% and 63.8%, respectively. Both surface water and groundwater exhibited strong oxidation, with nitrification the primary process. It is expected that this study will provide insights into the dynamics of NO3- and contribute to the development of effective strategies for mitigating NO3- contaminant, leading to sustainable management of water resources.