Dual Inhibition of Factor XIIa and Factor XIa Produces a Synergistic Anticoagulant Effect.

ABSTRACT: Clinical practice shows that a critical unmet need in the field of thrombosis prevention is the availability of anticoagulant therapy without bleeding risk. Inhibitors against FXIa or FXIIa have been extensively studied because of their low bleeding risk. However, whether these compounds produce synergistic effects has not yet been explored. In this study, analyses of activated partial thromboplastin time in combination with the FXIa inhibitor PN2KPI and the FXIIa inhibitor Infestin4 at different proportions were performed using the SynergyFinder tool identifying synergistic anticoagulation effects. Both an FeCl 3 -induced carotid artery thrombosis mouse model and a transient occlusion of the middle cerebral artery mouse model showed that the combination of PN2KPI and Infestin4, which are 28.57% and 6.25% of the effective dose, respectively, significantly prevents coagulation, and furthermore, dual inhibition does not cause bleeding risk.

Unraveling the causal relationships between depression and brain structural imaging phenotypes: A bidirectional Mendelian Randomization study.

BACKGROUND: Previous studies have revealed structural brain abnormalities in individuals with depression, but the causal relationship between depression and brain structure remains unclear. METHODS: A genetic correlation analysis was conducted using summary statistics from the largest genome-wide association studies for depression (N = 674,452) and 1,265 brain structural imaging-derived phenotypes (IDPs, N = 33,224). Subsequently, a bidirectional two-sample Mendelian Randomization (MR) approach was employed to explore the causal relationships between depression and the IDPs that showed genetic correlations with depression. The main MR results were obtained using the inverse variance weighted (IVW) method, and other MR methods were further employed to ensure the reliability of the findings. RESULTS: Ninety structural IDPs were identified as being genetically correlated with depression and were included in the MR analyses. The IVW MR results indicated that reductions in the volume of several brain regions, including the bilateral subcallosal cortex, right medial orbitofrontal cortex, and right middle-posterior part of the cingulate cortex, were causally linked to an increased risk of depression. Additionally, decreases in surface area of the right middle temporal visual area, right middle temporal cortex, right inferior temporal cortex, and right middle-posterior part of the cingulate cortex were causally associated with a heightened risk of depression. Validation and sensitivity analyses supported the robustness of these findings. However, no evidence was found for a causal effect of depression on structural IDPs. CONCLUSIONS: Our findings reveal the causal influence of specific brain structures on depression, providing evidence to consider brain structural changes in the etiology and treatment of depression.

Sources, trigger points, and effect size of associations between PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) and fractional exhaled nitric oxide (FeNO): A panel study with 16 follow-up visits over 4 years.

Exposure to fine particulate matter (PM2.5) is a significant concern for respiratory health. However, the sources, trigger points, and effect size of specific associations between PM2.5 components, particularly polycyclic aromatic hydrocarbons (PAHs) and the airway inflammatory marker fractional exhaled nitric oxide (FeNO) have not been fully explored. In this study, 69 healthy college students were enrolled and followed up 16 times from 2014 to 2018. Individual FeNO was measured and ambient air PM2.5 samples were collected for 7 consecutive days before each follow-up. PAHs were quantified using Gas Chromatography-Mass Spectrometry. Linear mixed-effect regression models were employed to evaluate the associations between PM2.5-bound PAHs and FeNO. Additionally, PMF (Positive Matrix Factorization) was utilized to identify sources of PM2.5-bound PAHs and assess their impact on FeNO. Throughout the study, the average (SD) of ΣPAHs concentrations was 78.50 (128.9) ng/m3. PM2.5 and PM2.5-bound PAHs were significantly associated with FeNO at various lag days. Single-day lag analyses revealed maximum effects of PM2.5 on FeNO, with an increase of 7.71% (95% CI: 4.67%, 10.83%) per interquartile range (IQR) (48.10 µg/m3) increase of PM2.5 at lag2, and ΣPAHs showed a maximum elevation in FeNO of 6.40% (95% CI: 2.33%, 10.63%) at lag4 per IQR (57.39 ng/m3) increase. Individual PAHs exhibited diversity peak effects on FeNO at lag3 (6 of 17), lag4 (9 of 17) in the single-day model, and lag0-5 (8 of 17) (from lag0-1 to lag0-6) in the cumulative model. Source apportionment indicated coal combustion as the primary contributor (accounting for 30.7%). However, a maximum effect on FeNO (an increase of 21.57% (95% CI: 13.58%, 30.13%) per IQR increase) was observed with traffic emissions at lag4. The findings imply that strategic regulation of particular sources of PAHs, like traffic emissions, during specific periods could significantly contribute to safeguarding public health.

Under the dual effect of inflammation and pulmonary fibrosis, CTD-ILD patients possess a greater susceptibility to VTE.

As an autoimmune disease, the persistent systemic inflammatory response associated with connective tissue disease (CTD) is involved in the development of venous thromboembolism (VTE). However, clinical data showed that the risk of VTE in patients differed between subtypes of CTD, suggesting that different subtypes may have independent mechanisms to promote the development of VTE, but the specific mechanism lacks sufficient research at present. The development of pulmonary fibrosis also contributes to the development of VTE, and therefore, patients with CTD-associated interstitial lung disease (CTD-ILD) may be at higher risk of VTE than patients with CTD alone or patients with ILD alone. In addition, the activation of the coagulation cascade response will drive further progression of the patient's pre-existing pulmonary fibrosis, which will continue to increase the patient's risk of VTE and adversely affect prognosis. Currently, the treatment for CTD-ILD is mainly immunosuppressive and antirheumatic therapy, such as the use of glucocorticoids and janus kinase-inhibitors (JAKis), but, paradoxically, these drugs are also involved in the formation of patients' coagulation tendency, making the clinical treatment of CTD-ILD patients with a higher risk of developing VTE challenging. In this article, we review the potential risk factors and related mechanisms for the development of VTE in CTD-ILD patients to provide a reference for clinical treatment and prevention.

Ambient PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) associated with pro-thrombotic biomarkers among young healthy adults: A 16 times repeated measurements panel study.

Studies have shown that the cardio/cerebrovascular toxicity of ambient PM2.5 is related to its bound polycyclic aromatic hydrocarbons (PAHs). Currently, only a few studies have reported the relationship between PM2.5-bound PAHs and promoted blood coagulation and thrombosis, but there isn't a consistent conclusion. Therefore, we conducted a prospective panel study to investigate the association. Thirty-three young healthy adults participated in sixteen repeated visits from 2014 to 2018 in Tianjin, China. During each visit, three pro-thrombotic biomarkers: ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin motif 13), D-dimer and Myeloperoxidase (MPO) were measured. Before each visit, ambient PM2.5 samples were daily collected for one week. Sixteen PAHs were determined using Gas Chromatography-Mass Spectrometer, and the positive matrix factorization (PMF) model was applied to identify the sources. Linear mixed-effects models were fitted to investigate the associations between PM2.5-bound PAHs exposure and the biomarkers. Thirteen time-metrics were defined to identify significant time points of PM2.5-bound PAHs' effects. We observed that the increase of PM2.5-bound PAHs exposure was significantly associated with reduced ADAMTS13, elevated D-dimer and MPO. At lag0, each 5.7 ng/m3 increase in Benzo[j]fluoranthene and per 3.4 ng/m3 increase Dibenz[a,h]anthracene could make a maximum change of -19.08 % in ADAMTS13 and 132.60 % in D-dimer. Additionally, per 16.43 ng/m3 increase in Chrysene could lead to a maximum elevation of 32.14 % in MPO at lag4. The PM2.5-bound PAHs often triggered more significant changes at lag 3,4 and 6. The ambient PM2.5-bound PAHs originated from six sources: coal combustion (43.10 %), biomass combustion (20.77 %), diesel emission (14.78 %), gasoline emission (10.95 %), industrial emission (7.58 %), and cooking emission (2.83 %). The greatest contributors to alterations in ADAMTS13, D-dimer and MPO are industrial emission (-48.43 %), biomass combustion (470.32 %) and diesel emission (13.14 %) at lag4. Our findings indicated that short-term exposure to ambient PM2.5-bound PAHs can induce alterations of pro-thrombotic biomarkers among healthy adults.

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness.

Introduction: Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology. Methods: In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC. Results: Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%). Discussion: Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Purinergic signalling in cancer therapeutic resistance: From mechanisms to targeting strategies.

Purinergic signalling, consisting of extracellular purines and purinergic receptors, modulates cell proliferation, invasion and immunological reaction during cancer progression. Here, we focus on current evidence that suggests the crucial role of purinergic signalling in mediating cancer therapeutic resistance, the major obstacle in cancer treatment. Mechanistically, purinergic signalling can modulate the tumor microenvironment (TME), epithelial-mesenchymal transition (EMT) and anti-tumor immunity, thus affecting drug sensitivity of tumor cells. Currently, some agents attempting to target purinergic signalling either in tumor cells or in tumor-associated immune cells are under preclinical or clinical investigation. Moreover, nano-based delivery technologies significantly improve the efficacy of agents targeting purinergic signalling. In this review article, we summarize the mechanisms of purinergic signalling in promoting cancer therapeutic resistance and discuss the potentials and challenges of targeting purinergic signalling in future cancer treatment.

3D model retrieval based on interactive attention CNN and multiple features.

3D (three-dimensional) models are widely applied in our daily life, such as mechanical manufacture, games, biochemistry, art, virtual reality, and etc. With the exponential growth of 3D models on web and in model library, there is an increasing need to retrieve the desired model accurately according to freehand sketch. Researchers are focusing on applying machine learning technology to 3D model retrieval. In this article, we combine semantic feature, shape distribution features and gist feature to retrieve 3D model based on interactive attention convolutional neural networks (CNN). The purpose is to improve the accuracy of 3D model retrieval. Firstly, 2D (two-dimensional) views are extracted from 3D model at six different angles and converted into line drawings. Secondly, interactive attention module is embedded into CNN to extract semantic features, which adds data interaction between two CNN layers. Interactive attention CNN extracts effective features from 2D views. Gist algorithm and 2D shape distribution (SD) algorithm are used to extract global features. Thirdly, Euclidean distance is adopted to calculate the similarity of semantic feature, the similarity of gist feature and the similarity of shape distribution feature between sketch and 2D view. Then, the weighted sum of three similarities is used to compute the similarity between sketch and 2D view for retrieving 3D model. It solves the problem that low accuracy of 3D model retrieval is caused by the poor extraction of semantic features. Nearest neighbor (NN), first tier (FT), second tier (ST), F-measure (E(F)), and discounted cumulated gain (DCG) are used to evaluate the performance of 3D model retrieval. Experiments are conducted on ModelNet40 and results show that the proposed method is better than others. The proposed method is feasible in 3D model retrieval.

Oxidative Stress, Inflammation, Gut Dysbiosis: What Can Polyphenols Do in Inflammatory Bowel Disease?

Inflammatory bowel disease (IBD) is a long-term, progressive, and recurrent intestinal inflammatory disorder. The pathogenic mechanisms of IBD are multifaceted and associated with oxidative stress, unbalanced gut microbiota, and aberrant immune response. Indeed, oxidative stress can affect the progression and development of IBD by regulating the homeostasis of the gut microbiota and immune response. Therefore, redox-targeted therapy is a promising treatment option for IBD. Recent evidence has verified that Chinese herbal medicine (CHM)-derived polyphenols, natural antioxidants, are able to maintain redox equilibrium in the intestinal tract to prevent abnormal gut microbiota and radical inflammatory responses. Here, we provide a comprehensive perspective for implementing natural antioxidants as potential IBD candidate medications. In addition, we demonstrate novel technologies and stratagems for promoting the antioxidative properties of CHM-derived polyphenols, including novel delivery systems, chemical modifications, and combination strategies.

Nano-Based Drug Delivery of Polyphenolic Compounds for Cancer Treatment: Progress, Opportunities, and Challenges.

Polyphenols and their derivates, a kind of natural product distributed in herb plants, vegetables, and fruits, are the most abundant antioxidants in the human diet and have been found to display cancer-preventative effects in several epidemiological studies. The scientific community has also validated the anti-cancer bioactivities and low toxicities of polyphenolic compounds, including flavones, tannins, phenolic acids, and anthocyanins, through in vitro and in vivo studies. However, the low stability, weak targeting ability, poor solubility, and low bioavailability of pure polyphenolic agents have significantly impaired their treatment efficacy. Nowadays, nano-based technology has been applied to surmount these restrictions and maximize the treatment efficacy of polyphenols. In this review, we summarize the advantages and related mechanisms of polyphenols in cancer treatment. Moreover, aiming at the poor solubility and low bioavailability of pure polyphenols in vivo, the advantages of nano-based delivery systems and recent research developments are highlighted. Herein, particular emphasis is mainly placed on the most widely used nanomaterials in the delivery of natural products, including liposomes, micelles, and nanogels. Finally, we present an overview and the challenges of future implementations of nano-based delivery systems of polyphenolic compounds in the cancer therapeutic field.

Anti-Oxidant and Pro-Oxidant Effects of Peroxiredoxin 6: A Potential Target in Respiratory Diseases.

Peroxiredoxin 6 (PRDX6) is widely distributed in several organs, especially the lungs. The role of PRDX6 in oxidative stress is controversial and even contradictory, as indicated by research conducted over the past 20 years. PRDX6 has anti-oxidant or pro-oxidant effects on oxidative stress in different diseases. It can even exhibit both anti-oxidant and pro-oxidant effects in the same disease. These findings are attributed to the fact that PRDX6 is a multifunctional enzyme. The peroxidase and phospholipase A2 activity of PRDX6 is closely related to its anti-oxidant and pro-oxidant effects, which leads to the conflicting regulatory effects of PRDX6 on oxidative stress in respiratory diseases. Moreover, PRDX6 interacts with multiple redox signaling pathways to interfere with cell proliferation and apoptosis. PRDX6 has become a new target in respiratory disease research due to its important regulatory role in oxidative stress. In this paper, the role of PRDX6 in oxidative stress in respiratory diseases and the research progress in targeting PRDX6 are reviewed.

Brain-Targeted HFn-Cu-REGO Nanoplatform for Site-Specific Delivery and Manipulation of Autophagy and Cuproptosis in Glioblastoma.

Durable glioblastoma multiforme (GBM) management requires long-term chemotherapy after surgery to eliminate remaining cancerous tissues. Among chemotherapeutics, temozolomide is considered as the first-line drug for GBM therapy, but the treatment outcome is not satisfactory. Notably, regorafenib, an oral multi-kinase inhibitor, has been reported to exert a markedly superior effect on GBM suppression compared with temozolomide. However, poor site-specific delivery and bioavailability significantly restrict the efficient permeability of regorafenib to brain lesions and compromise its treatment efficacy. Therefore, human H-ferritin (HFn), regorafenib, and Cu2+ are rationally designed as a brain-targeted nanoplatform (HFn-Cu-REGO NPs), fulfilling the task of site-specific delivery and manipulating autophagy and cuproptosis against GBM. Herein, HFn affords a preferential accumulation capacity to GBM due to transferrin receptor 1 (TfR1)-mediated active targeting and pH-responsive delivery behavior. Moreover, regorafenib can inhibit autophagosome-lysosome fusion, resulting in lethal autophagy arrest in GBM cells. Furthermore, Cu2+ not only facilitates the encapsulation of regorafenib to HFn through coordination interaction but also disturbs copper homeostasis for triggering cuproptosis, resulting in a synergistical effect with regorafenib-mediated lethal autophagy arrest against GBM. Therefore, this work may broaden the clinical application scope of Cu2+ and regorafenib in GBM treatment via modulating autophagy and cuproptosis.

Diagnostic Predictive Value of Tryptase, Serum Amyloid A and Lipoprotein-Associated Phospholipase A2 Biomarker Groups for Large Atherosclerotic Cerebral Infarction.

Background: There has been a gradual trend towards younger ageing of acute cerebral infarction in recent years. Atherosclerotic plaque rupture followed by dislodgement of emboli and resulting arterial embolism is an important mechanism for the development of acute cerebral infarction. Traditional independent risk factors for cerebral infarction have received attention from clinicians, but the risk factors for large artery atherosclerotic cerebral infarction are still unclear. Various blood biomarkers have an important role in the early diagnosis of large artery atherosclerotic cerebral infarction. Objective: To assess the diagnostic predictive value of a group of biomarkers for large artery atherosclerotic cerebral infarction. Methods: Lipoprotein-associated phospholipase A2 (LP-PLA2), trypsin-like protein (TPS), serum amyloid A (SAA), and supersensitive C-reactive protein (hs-CRP) levels were measured in the case group (30 cases) and control group (54 cases), respectively. Results: The differences in the general data between the two groups were not statistically significant (P > 0.05). Logistic regression and ROC curve analysis showed that Lp-PLA2, TPS, and SAA were positively associated with the diagnosis of large atherosclerotic cerebral infarction (P < 0.05). The area under the ROC curve of the multivariate model for the biomarker group reached 0.995. Conclusion: Biomarkers are closely associated with the occurrence of large atherosclerotic cerebral infarction and can be used as clinical adjuncts for diagnosis and assessment of prognosis.

Preparation of boronic acid and carboxyl-modified molecularly imprinted polymer and application in a novel chromatography mediated hollow fiber membrane to selectively extract glucose from cellulose hydrolysis.

A novel boronic acid and carboxyl-modified glucose molecularly imprinted polymer were prepared through suspension polymerization, which is based on 1.0 mmol glucose as a template, 1.2 mmol methacrylamidophenylboronic acid, and 6.8 mmol methacrylic acids as monomers, 19 mmol ethyleneglycol dimethacrylate, and 1 mmol methylene-bis-acrylamide as crosslinkers. The prepared glucose-molecularly imprinted polymer had a particle size of 25-70 µm, and was thermally stable below 215°C, with a specific surface area of 174.82 m2/ g and average pore size of 9.48 nm. The best selectivity between glucose and fructose was 2.71 and the maximum adsorption capacity of glucose- molecularly imprinted polymer was up to 236.32 mg/ g which was consistent with the Langmuir adsorption model. The similar adsorption abilities in six successive runs and the good desorption rate (99.4%) verified glucose-molecularly imprinted polymer could be reused. It was successfully used for extracting glucose from cellulose hydrolysis. The adsorption amount of glucose was 2.61 mg/mL and selectivity between glucose and xylose reached 4.12. A newly established chromatography (glucose-molecularly imprinted polymer) mediated hollow fiber membrane method in time separated pure glucose from cellulose hydrolysates on a large scale, and purified glucose solution with a concentration of 3.84 mg/mL was obtained, which offered a feasible way for the industrial production of glucose from cellulose hydrolysates.

The relationship between NLR/PLR/LMR levels and survival prognosis in patients with non-small cell lung carcinoma treated with immune checkpoint inhibitors.

BACKGROUND: The relationship between neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR) and the dire prognosis of non-small cell lung carcinoma patients who received immune checkpoint inhibitors (ICIs) are not known yet. METHODS: We screened the articles that meet the criteria from the database. The relationship between NLR/PLR/LMR levels and the survival and prognosis of non-small cell lung cancer patients treated with ICIs was analyzed. Summarize hazard ratio (HR) with 95% confidence interval (CI) to study progression-free survival (PFS) and overall survival (OS). RESULTS: Thirty-four studies involving 3124 patients were enrolled in the final analysis. In short, high pre-treatment NLR was related to poor OS (HR = 2.13, 95% CI:1.74-2.61, P < .001, I2 = 83.3%, P < .001) and PFS (HR = 1.77, 95% CI:1.44-2.17, P < .001, I2 = 79.5%, P < .001). Simultaneously, high pre-treatment PLR was related to poor OS (HR = 1.49, 95% CI:1.17-1.91, P < .001, I2 = 57.6%, P = .003) and PFS (HR = 1.62, 95% CI:1.38-1.89, P < .001, I2 = 47.1%, P = .036). In all subgroup analysis, most subgroups showed that low LMR was related to poor OS (HR = 0.45, 95% CI: 0.34-0.59, P < .001) and PFS (HR = 0.60, 95% CI: 0.47-0.77, P < 0.001, I2 = 0.0%, P < .001). CONCLUSION: High pre-treatment NLR and pre-treatment PLR in non-small cell lung carcinoma patients treated with ICIs are associated with low survival rates. Low pre-treatment and post-treatment LMR are also related to unsatisfactory survival outcomes. However, the significance of post-treatment NLR and post-treatment PLR deserve further prospective research to prove.

Preparation of a magnetic and recyclable superparamagnetic silica support with a boronic acid group for immobilizing Pd catalysts and its applications in Suzuki reactions.

Palladium is one of the best metal catalysts for Suzuki cross-coupling reaction to synthesize unsymmetrical biaryl compounds. However, homogeneous palladium (Pd) is limited in an industrial scale due to the high cost, separation, removal, and recovery issues. In this paper, a novel, high activity magnetic nanoparticles (Fe3O4@SiO2-APBA-Pd) catalyst was prepared by a simple, cost-effective procedure. The as-prepared functional nanoparticles (Fe3O4@SiO2-APBA) with boric acid group immobilized Pd through adding Pd(OAc)2 to Fe3O4@SiO2-APBA in absolute ethanol and maintaining for a certain time under a nitrogen atmosphere. The as-prepared catalyst was characterized by FT-IR, SEM, EDX, TEM, ICP-MS, XPS, and XRD. The results showed that the Pd (0.2-0.6 nm) was successfully anchored on the magnetic silica material with boric acid group. The amount of Pd was 0.800 mmol g-1. This magnetic nanostructure (8-15 nm) is especially beneficial as a nanocatalyst because each nanoparticle can catalyze a reaction in a certain time without steric restriction, which could effectively improve the reaction efficiency. The current nanoparticles with the Pd catalyst could be used as a novel, green, and efficient heterogeneous catalyst for Suzuki reactions. This catalyst showed promising catalytic activity and excellent yields toward 14 kinds of Suzuki coupling reactions under mild reaction conditions, which was similar to homogeneous Pd and many reported heterogeneous Pd catalysts. In addition, the turnover number (TON) and turnover frequency (TOF) for the Suzuki reaction were high. TOF and TON were 9048 h-1 and 20 250 for the Suzuki reaction of bromobenzene and phenylboronic acid. Furthermore, the nanoparticles could be easily separated by a magnet, and could be used repeatedly seven times without any significant loss in activity.

12-Plex UHPLC-MS/MS analysis of sarcosine in human urine using integrated principle of multiplex tags chemical isotope labeling and selective imprint enriching.

Urinary sarcosine was considered to be a potential biomarker for prostate cancer (Pca). In this work, an integrated strategy of multiplex tags chemical isotope labeling (MTCIL) combined with magnetic dispersive solid phase extraction (MDSPE), was proposed for specific extraction and high-throughput determination of sarcosine by ultra high performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). In the past three months, we have developed 8-plex MTCIL reagents with excellent qualitative and quantitative performance. In this work, the multiplexing capacity of MTCIL reagents (MTCIL360/361/362/363/364/365/366/375/376/378/379/381) was increased 1.5-fold from 8-plex to 12-plex. MTCIL359 was prepared and used to label sarcosine standard as internal standard (IS). The structural analogue derivative (MTCIL373-sarcosine) of all targeted MTCIL-sarcosine derivatives was synthesized and used as a novel dummy template to prepare dummy magnetic molecularly imprinted polymers (DMMIPs). The integration of MTCIL and DMMIPs procedures were extremely favorable to excellent chromatographic separation and efficient mass spectrometric detection. The labeling efficiency, chromatographic retention and mass spectrometry responses of MTCIL reagents were consistent for sarcosine. In a single UHPLC-MS/MS run (2.0 min), this method can simultaneously quantify sarcosine in 12-plex urine samples and achieve unbiased concentrations comparison between different urine samples. Analytical parameters including linearity (R2 0.989-0.997), detection limits (0.02 nM), precision (2.6-11.5%), accuracy (96.1-107.4%), matrix effect, labeling and extraction efficiency were carefully validated. The proposed method was successfully applied for urinary sarcosine determination of healthy male individuals and Pca patients. It was found that the sarcosine concentrations in these two groups were statistically extremely significantly different (P < 0.001). The developed method was a powerful analytical tool to substantially promote the analysis throughput and large-scale experiments about the potential biomarker research.

13-Plex UHPLC-MS/MS Analysis of Hexanal and Heptanal Using Multiplex Tags Chemical Isotope Labeling Technology.

In this work, a new series of chemical isotope labeling reagents, levofloxacin-hydrazide-based mass tags (LHMTs) named as LHMT359/360/361/362/363/364/365/366/373/375/376/378/379/381 were first designed and synthesized for the high-throughput analysis of potential biomarkers containing hexanal and heptanal of lung cancer. We exploited a new core structure of levofloxacin-d3, which significantly enhanced the multiplexing capability. Among them, LHMT359 was used for labeling standard compounds as internal standards for quantification. Using LHMT373-heptanal as dummy template, dummy magnetic molecularly imprinted polymers (DMMIPs) were prepared for magnetic dispersive solid-phase extraction after derivatization procedure. Other 12 LHMTs were established for high-throughput labeling hexanal and heptanal in human serum samples. The presynthesized DMMIPs can selectively extract LHMTs-derivatives of hexanal and heptanal from equally mixed derivatization solutions. The enriched derivatives of hexanal and heptanal were quantified by ultrahigh-performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS). A single UHPLC-MS/MS run enabled simultaneously quantifying hexanal and heptanal from 12 serum samples only within 2 min. The limits of detection were all 0.5 pM for hexanal and heptanal. The accuracies from human serum samples ranged from -10.2% to +11.0% with the intra- and interday precisions less than 11.3%. Meanwhile, this method was successfully applied for the analysis of hexanal and heptanal in serum samples from healthy people and lung cancer patients. The results show that this method has the significant advantages of high sensitivity, accuracy, selectivity, and analysis-throughput. The method application indicates that the developed method is promising in the screening of suspected lung cancer patients.

Derivatization-based magnetic dummy molecularly imprinted polymers integrated with 4-plex stable isotope labeling derivatization strategy for specific and rapid determination of L-hydroxyproline in human serum.

The specific determination of L-hydroxyproline (Hyp) can serve as a potential indicator for early clinical diagnosis of liver fibrosis. In this work, an integrated strategy based on 4-plex stable isotope labeling derivatization combined with dummy magnetic molecularly imprinted polymers (QSILD-DMMIPs) was developed for specific extraction and rapid determination of Hyp in human serum by ultra high performance liquid chromatography tandem mass spectrometry. A new series of QSILD reagents d0/d1/d2/d3-6-N-methyl-rhodamine 6G-N-hydroxysuccinimidyl formate (d0/d1/d2/d3-MRSF) were designed, synthesized and applied for the high-throughput labeling of Hyp in serum samples. The structural analogue derivative of Hyp with 6-N-ethyl-rhodamine 6G-N-hydroxysuccinimidyl formate (ERSF-Hyp) was synthesized and used as a novel dummy template to prepare DMMIPs. The DMMIPs were well characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), fourier transform infrared spectroscopy (FTIR), Brunner Emmet Teller (BET) measurements, thermogravimetric analysis (TGA), X-ray diffraction (XRD), zeta potential and adsorption experiments. All d0/d1/d2/d3-MRSF-Hyp derivatives were conveniently and specifically adsorbed by DMMIPs in magnetic dispersive solid phase extraction procedure before injection. Method validation results including linearity (0.2-100 ng mL-1), limits of detection and quantitation (0.05 and 0.2 ng mL-1), accuracy, precision, stability, matrix effect and derivatization efficiency were satisfactory. The analytical performances benefited from efficient integration of QSILD and specific DMMIPs extraction. The proposed strategy was successfully applied for Hyp determination in human serum of liver fibrosis patients and healthy controls, which was of great significance to early diagnosis.

Multi-Feature Fusion Method Based on EEG Signal and its Application in Stroke Classification.

Electroencephalogram (EEG) analysis has been widely used in the diagnosis of stroke diseases for its low cost and noninvasive characteristics. In order to classify the EEG signals of stroke patients with cerebral infarction and cerebral hemorrhage, this paper proposes a novel EEG stroke signal classification method. This method has two highlights. The first is that a multi-feature fusion method is given by combining wavelet packet energy, fuzzy entropy and hierarchical theory. The second highlight is that a suitable classification model based on ensemble classifier is constructed for perfectly classification stroke signals. Entropy is an accessible way to measure information and uncertainty of time series. Many entropy-based methods have been developed these years. By comparing with the performances of permutation entropy, sample entropy, approximate entropy in measuring the characteristic of stroke patient's EEG signals, it can be found that fuzzy entropy has best performance in characterization stroke EEG signal. By combining hierarchical theory, wavelet packet energy and fuzzy entropy, a multi-feature fusion method is proposed. The method first calculates wavelet packet energy of EEG stroke signal, then extracts hierarchical fuzzy entropy feature by combining hierarchical theory and fuzzy entropy. The experimental results show that, compared with the fuzzy entropy feature, the classification accuracy based on the fusion feature of wavelet packet energy and hierarchical fuzzy entropy is much higher than benchmark methods. It means that the proposed multi-feature fusion method based on stroke EEG signal is an efficient measure in classifying ischemic and hemorrhagic stroke. Support vector machine (SVM), decision tree and random forest are further used as the stroke signal classification models for classifying ischemic stroke and hemorrhagic stroke. Experimental results show that, based on the proposed multi-feature fusion method, the ensemble method of random forest can get the best classification performance in accuracy among three models.