Efficacy of acupuncture as an adjunctive treatment to patients with stable COPD: a multicenter, randomized, sham-controlled trial protocol.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a common respiratory disease and the third leading cause of death worldwide. Previous evidence has shown that acupuncture may be an effective complementary alternative therapy for stable COPD. However, large-sample, rigorously designed long-term follow-up studies still need to be completed. Notably, the relationship between the frequency of acupuncture and clinical efficacy in studies on acupuncture for stable COPD still needs further validation. This study aims to evaluate the efficacy and safety of acupuncture for stable COPD and further investigate the dose-effect relationship of acupuncture. METHODS/DESIGN: This is a multicenter, randomized, controlled trial that uses central randomization to randomly allocate 550 participants in a 1:1:1:1:1 ratio to once a week acupuncture group, twice a week acupuncture group, three times a week acupuncture group, sham acupuncture group and waiting-list control group. The sham acupuncture group will receive placebo acupuncture treatments three times per week, and the waiting-list control group will not receive any form of acupuncture intervention. The study consists of a 2-week baseline, 12-week of treatment, and 52-week of follow-up. Patients with COPD between 40 to 80 years old who have received stable Western medication within the previous 3 months and have had at least 1 moderate or severe acute exacerbation within the past 1 year will be included in the study. Basic treatment will remain the same for all participants. The primary outcome is the proportion of responders at week 12. Secondary outcomes include the proportion of responders at week 64, change in the St. George's Respiratory Questionnaire (SGRQ) Scale, change in the Modified-Medical Research Council (mMRC) Scale, change in the COPD Assessment Test (CAT) Scale, change in the Lung Function Screening Indicators (LFSI), change in the 6-min walk distance (6-MWD), change in Short-Form 36 Health Survey (SF-36) Scale, the number of moderate and severe acute exacerbations and adverse event rate during the follow-up period. DISCUSSION: This study will provide robust evidence on whether acupuncture is safe and effective for treating stable COPD. Meanwhile, comparing the differences in efficacy between different acupuncture frequencies will further promote the optimization of acupuncture for stable COPD. TRIAL REGISTRATION: This study was registered in the Chinese Clinical Trial Registry (ChiCTR2200058757), on April 16, 2022.

Acupuncture as adjunctive therapy for patients with AECOPD: study protocol for a multicenter, randomized controlled trial.

Background: The acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a common respiratory disease among older adults, which imposes a significant burden on individuals and society and poses a major challenge to the global public health system due to its high morbidity and mortality. Acupuncture is effective for AECOPD, but its efficacy has been questioned due to the limited methodological quality. Thus, we aim to investigate the efficacy of acupuncture as adjunctive therapy for AECOPD and determine whether the efficacy of acupuncture differs with the type of acupoint combinations. Methods and analysis: This study proposes a prospective, multicenter randomized controlled trial that will comprise four groups, including two acupuncture treatment groups, one sham acupuncture group, and one basic treatment group. The acupuncture treatment groups will be distinguished by their focus on different patterns of acupoint combination, namely the Xi-cleft and He-sea acupoint combination and the Eight Confluence points acupoint combination, which may vary in clinical efficacy based on traditional acupuncture theories. The study aims to randomize 556 patients in a 1:1:1:1 ratio across the four groups. Each patient in acupuncture group or sham acupuncture group will receive routine drug therapy and 7 sessions of acupuncture treatment over 1 week. Participants in the basic treatment group will only receive routine drug therapy. The trial will be conducted in seven hospitals located in China. The primary outcomes in this trial will include differences in the Breathlessness, Cough, and Sputum Scale (BCSS) before randomization, 7 days after randomization, 5 and 9 weeks after randomization. Ethics and dissemination: Ethical approval was obtained from the Sichuan Regional Ethics Review of Committee on Traditional Chinese Medicine (Approval ID: 2022KL-068). The results of this study will be distributed through peer-reviewed journals.Clinical Trial Registration: ClinicalTrials.gov, identifier ChiCTR2200064484.

Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012-2021) Multicenter Retrospective Case-control study.

OBJECTIVES: To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study. METHODS: Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity. RESULTS: Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001). CONCLUSIONS: The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

A novel model for predicting prolonged stay of patients with type-2 diabetes mellitus: a 13-year (2010-2022) multicenter retrospective case-control study.

BACKGROUND: Length of stay (LOS) is an important metric for evaluating the management of inpatients. This study aimed to explore the factors impacting the LOS of inpatients with type-2 diabetes mellitus (T2DM) and develop a predictive model for the early identification of inpatients with prolonged LOS. METHODS: A 13-year multicenter retrospective study was conducted on 83,776 patients with T2DM to develop and validate a clinical predictive tool for prolonged LOS. Least absolute shrinkage and selection operator regression model and multivariable logistic regression analysis were adopted to build the risk model for prolonged LOS, and a nomogram was taken to visualize the model. Furthermore, receiver operating characteristic curves, calibration curves, and decision curve analysis and clinical impact curves were used to respectively validate the discrimination, calibration, and clinical applicability of the model. RESULTS: The result showed that age, cerebral infarction, antihypertensive drug use, antiplatelet and anticoagulant use, past surgical history, past medical history, smoking, drinking, and neutrophil percentage-to-albumin ratio were closely related to the prolonged LOS. Area under the curve values of the nomogram in the training, internal validation, external validation set 1, and external validation set 2 were 0.803 (95% CI [confidence interval] 0.799-0.808), 0.794 (95% CI 0.788-0.800), 0.754 (95% CI 0.739-0.770), and 0.743 (95% CI 0.722-0.763), respectively. The calibration curves indicated that the nomogram had a strong calibration. Besides, decision curve analysis, and clinical impact curves exhibited that the nomogram had favorable clinical practical value. Besides, an online interface ( https://cytjt007.shinyapps.io/prolonged_los/ ) was developed to provide convenient access for users. CONCLUSION: In sum, the proposed model could predict the possible prolonged LOS of inpatients with T2DM and help the clinicians to improve efficiency in bed management.

Coordinate-based (ALE) meta-analysis of acupuncture for musculoskeletal pain.

Background: Neuroimaging studies have been widely used to investigate brain regions' alterations in musculoskeletal pain patients. However, inconsistent results have hindered our understanding of the central modulatory effects of acupuncture for musculoskeletal pain. The main objective of our investigation has been to obtain comprehensive evidence of acupuncture for musculoskeletal pain diseases. Methods: The PubMed, Web of Science, Google Scholar, Embase, China National Knowledge Infrastructure (CNKI), VIP Database, China Biology Medicine disc Database, Clinical Trial Registration Platform, and Wanfang Database were searched for neuroimaging studies on musculoskeletal pain diseases published from inception up to November 2021. Then, the relevant literature was screened to extract the coordinates that meet the criteria. Finally, the coordinate-based meta-analysis was performed using the activation likelihood estimation algorithm. Results: A total of 15 neuroimaging studies with 183 foci of activation were included in this study. The ALE meta-analysis revealed activated clusters in multiple cortical and sub-cortical brain structures in response to acupuncture across studies, including the thalamus, insula, caudate, claustrum, and lentiform nucleus. Conclusions: The studies showed that acupuncture could modulate different brain regions, including the thalamus, insula, caudate, claustrum, and lentiform nucleus. The findings offer several insights into the potential mechanisms of acupuncture for musculoskeletal pain and provide a possible explanation for the observed clinical benefit of this therapy. Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=227850, identifier: CRD42021227850.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study.

Background: Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters. Methods: Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC). Results: The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram. Conclusions: The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

Human Motion Segmentation via Velocity-Sensitive Dual-Side Auto-Encoder.

Human motion segmentation (HMS) aims to segment a long human action video into a bunch of short and meaningful action clips. Existing supervised learning approaches need a large amount of training data which may be costly in real-world scenario, while most unsupervised clustering methods cannot fully explore the temporal correlations among human motions and hard to achieve promising performances. In our paper, we design a novel unsupervised framework, called Velocity-Sensitive Dual-Side Auto-Encoder (VSDA), for HMS task. Specifically, a multi-neighbor auto-encoder (MNA) is proposed to extract informative temporal features, which fully explores the local temporal patterns of human motions. In addition, a long-short distance encoding (LSE) strategy is designed. It constrains the encoded representations of close (short-distance) frames becoming similar while the representations of far-away (long-distance) frames becoming distinctive. Similarly, this strategy is also deployed on the decoded outputs as the long-short distance decoding (LSD) module. The LSE/LSD guides the learning process explicitly and implicitly to achieve the dual-side structure. Moreover, we consider the energy variations during the human motion to propose the velocity-sensitive (VS) guidance mechanism for further model improvement. VSDA leverages the temporal characteristics of human motion and derives promising HMS performance. Comprehensive experiments on six real-world human motion datasets illustrate the effectiveness of our proposed model.

Simple and Green Synthesis of Carbonized Polymer dots from Nylon 66 Waste Fibers and its Potential Application.

Carbonized polymer dots (CPDs) have attracted widespread attention owing to their unique properties and are usually prepared from monomers of polymers or polymers. To reduce the waste of high-value petropolymers and environmental pollution, a simple and green method for the preparation of CPDs using a hydrothermal technique based on the cross-linking enhanced emission effect was proposed, in which nylon 66 waste fibers were used as a precursor and glutaraldehyde as a cross-linking agent. The as-prepared CPDs possessed polymer/carbon hybrid structures with a 3.5 nm average diameter, and hydroxyl (-OH), carboxyl (-COOH), and amino (-NH2) groups were present on the surface of CPDs. It can be found that the as-prepared CPDs display excitation-dependent photoluminescence emission, which is mainly attributed to the molecular state luminescence center. Because the molecular state fluorescence of CPDs could be affected by the presence of Fe3+ and the change of pH values, the as-prepared CPDs can be used as a probe for the detection of the concentration of Fe3+ and the pH variations of solution. The fluorescence intensity of CPDs was selectively quenched by Fe3+ in the range from 1 to 145 µM. In virtue of the static quenching of CPDs by Fe3+, a sensing system was fabricated for the quantitative detection of Fe3+, and its limit of detection was 0.1 µM. Based on the electrostatic doping/charging of CPDs, a pH sensor was fabricated. It showed that the fluorescence intensity of CPDs decreased along with the increase of pH from 2.60 to 12.6. What is more, the CPDs were found to be an alternative to traditional fluorescent inks for encryption and information storage.

Semi-Supervised Dual Relation Learning for Multi-Label Classification.

In a real-world scenario, an object could contain multiple tags instead of a single categorical label. To this end, multi-label learning (MLL) emerged. In MLL, the feature distributions are long-tailed and the complex semantic label relation and the long-tailed training samples are the main challenges. Semi-supervised learning is a potential solution. While, existing methods are mainly designed for single class scenario while ignoring the latent label relations. In addition, they cannot well handle the distribution shift commonly existing across source and target domains. To this end, a Semi-supervised Dual Relation Learning (SDRL) framework for multi-label classification is proposed. SDRL utilizes a few labeled samples as well as large scale unlabeled samples in the training stage. It jointly explores the inter-instance feature-level relation and the intra-instance label-level relation even from the unlabeled samples. In our model, a dual-classifier structure is deployed to obtain domain invariant representations. The prediction results from the classifiers are further compared and the most confident predictions are extracted as pseudo labels. A trainable label relation tensor is designed to explicitly explore the pairwise latent label relations and refine the predicted labels. SDRL is able to effectively and efficiently explore the feature-label relation as well as the label-label relation knowledge without any extra semantic knowledge. We evaluated SDRL in general and zero-shot multi-label classification tasks and we concluded that SDRL is superior to other SOTA baselines. Furthermore, extensive ablation studies have been done which reveal the effectiveness of each component in our framework.

A conserved ubiquitin- and ESCRT-dependent pathway internalizes human lysosomal membrane proteins for degradation.

The lysosome is an essential organelle to recycle cellular materials and maintain nutrient homeostasis, but the mechanism to down-regulate its membrane proteins is poorly understood. In this study, we performed a cycloheximide (CHX) chase assay to measure the half-lives of approximately 30 human lysosomal membrane proteins (LMPs) and identified RNF152 and LAPTM4A as short-lived membrane proteins. The degradation of both proteins is ubiquitin dependent. RNF152 is a transmembrane E3 ligase that ubiquitinates itself, whereas LAPTM4A uses its carboxyl-terminal PY motifs to recruit NEDD4-1 for ubiquitination. After ubiquitination, they are internalized into the lysosome lumen by the endosomal sorting complexes required for transport (ESCRT) machinery for degradation. Strikingly, when ectopically expressed in budding yeast, human RNF152 is still degraded by the vacuole (yeast lysosome) in an ESCRT-dependent manner. Thus, our study uncovered a conserved mechanism to down-regulate lysosome membrane proteins.

Public awareness and anxiety during COVID-19 epidemic in China: A cross-sectional study.

OBJECTIVE: The study aims to investigate public awareness of coronavirus disease 2019 (COVID-19) and measure levels of anxiety during the outbreak. METHOD: A total of 2115 subjects from 34 provinces in China were evaluated. A questionnaire was designed, which covers demographic characteristics, knowledge of COVID-19, and factors that influenced anxiety during the outbreak to test public awareness and determine the impact of the outbreak on people's lives. In addition, a generalized anxiety disorder (GAD) scale was utilized to assess anxiety levels during the outbreak. Lastly, the chi-square test and multiple logistic regression analysis were used to identify factors associated with levels of public anxiety. RESULTS: A majority of respondents reported high levels of awareness of COVID-19. A total of 1107 (52.3%), 707 (33.4%), 154 (7.3%), and 147 (7%) respondents exhibited no, mild, moderate, and severe levels of anxiety, respectively. Results of the chi-square test and multiple logistic regression analysis demonstrated that respondents (a) with no college education, (b) are unaware of neighbors who may have been infected, (c) who spent considerable time collecting information and browsing negative information related to the virus, (d) are unhealthy, and (e) displayed low levels of awareness of the transmission routes were highly likely to be anxious. CONCLUSION: During the outbreak, the majority of people exhibited high levels of awareness and knowledge regarding preventive measures from COVID-19. The absence of psychological anxiety was observed in more than half of the respondents. Adaptive responses to anxiety and high levels of awareness about COVID-19 may have protected the public during the outbreak.

Application of Machine Learning for the Prediction of Etiological Types of Classic Fever of Unknown Origin.

Background: The etiology of fever of unknown origin (FUO) is complex and remains a major challenge for clinicians. This study aims to investigate the distribution of the etiology of classic FUO and the differences in clinical indicators in patients with different etiologies of classic FUO and to establish a machine learning (ML) model based on clinical data. Methods: The clinical data and final diagnosis results of 527 patients with classic FUO admitted to 7 medical institutions in Chongqing from January 2012 to August 2021 and who met the classic FUO diagnostic criteria were collected. Three hundred seventy-three patients with final diagnosis were divided into 4 groups according to 4 different etiological types of classical FUO, and statistical analysis was carried out to screen out the indicators with statistical differences under different etiological types. On the basis of these indicators, five kinds of ML models, i.e., random forest (RF), support vector machine (SVM), Light Gradient Boosting Machine (LightGBM), artificial neural network (ANN), and naive Bayes (NB) models, were used to evaluate all datasets using 5-fold cross-validation, and the performance of the models were evaluated using micro-F1 scores. Results: The 373 patients were divided into the infectious disease group (n = 277), non-infectious inflammatory disease group (n = 51), neoplastic disease group (n = 31), and other diseases group (n = 14) according to 4 different etiological types. Another 154 patients were classified as undetermined group because the cause of fever was still unclear at discharge. There were significant differences in gender, age, and 18 other indicators among the four groups of patients with classic FUO with different etiological types (P < 0.05). The micro-F1 score for LightGBM was 75.8%, which was higher than that for the other four ML models, and the LightGBM prediction model had the best performance. Conclusions: Infectious diseases are still the main etiological type of classic FUO. Based on 18 statistically significant clinical indicators such as gender and age, we constructed and evaluated five ML models. LightGBM model has a good effect on predicting the etiological type of classic FUO, which will play a good auxiliary decision-making function.

A selective transmembrane recognition mechanism by a membrane-anchored ubiquitin ligase adaptor.

While it is well-known that E3 ubiquitin ligases can selectively ubiquitinate membrane proteins in response to specific environmental cues, the underlying mechanisms for the selectivity are poorly understood. In particular, the role of transmembrane regions, if any, in target recognition remains an open question. Here, we describe how Ssh4, a yeast E3 ligase adaptor, recognizes the PQ-loop lysine transporter Ypq1 only after lysine starvation. We show evidence of an interaction between two transmembrane helices of Ypq1 (TM5 and TM7) and the single transmembrane helix of Ssh4. This interaction is regulated by the conserved PQ motif. Strikingly, recent structural studies of the PQ-loop family have suggested that TM5 and TM7 undergo major conformational changes during substrate transport, implying that transport-associated conformational changes may determine the selectivity. These findings thus provide critical information concerning the regulatory mechanism through which transmembrane domains can be specifically recognized in response to changing environmental conditions.

Highly sensitive qualitative and quantitative detection of saturated fatty aldehydes in edible vegetable oils using a "turn-on" fluorescent probe by high performance liquid chromatography.

A new turn-on fluorescent probe, based on a hydrazine group placed in the meso-position of the BODIPY molecule, was synthesized. It was then used for detecting long-chain fatty aldehydes, which can be harmful to human health, in edible vegetable oils. In acetonitrile, the probe produced strong "turn on" and 100-fold fluorescence enhancement with high sensitivity and rapid response to saturated fatty aldehydes. A highly sensitive detection method for long-chain fatty aldehydes was established using pre-column derivation fluorescence procedure by high-performance liquid chromatography. The chromatographic method established provided satisfactory precision (1.91%-5.93%), good linearity (R2 ≥ 0.999), an acceptable accuracy (83.7%-108%) and a low limit of detection (6.4-12.4 ng/mL). The experimental results indicated that the probe could qualitatively and quantitatively detect six fatty aldehydes in vegetable oils, thus providing the potential for use in routine analysis for identifying the type of vegetable oil and for controlling its quality and safety.

TORC1 regulates vacuole membrane composition through ubiquitin- and ESCRT-dependent microautophagy.

Cellular adaptation in response to nutrient limitation requires the induction of autophagy and lysosome biogenesis for the efficient recycling of macromolecules. Here, we discovered that starvation and TORC1 inactivation not only lead to the up-regulation of autophagy and vacuole proteins involved in recycling but also result in the down-regulation of many vacuole membrane proteins to supply amino acids as part of a vacuole remodeling process. Down-regulation of vacuole membrane proteins is initiated by ubiquitination, which is accomplished by the coordination of multiple E3 ubiquitin ligases, including Rsp5, the Dsc complex, and a newly characterized E3 ligase, Pib1. The Dsc complex is negatively regulated by TORC1 through the Rim15-Ume6 signaling cascade. After ubiquitination, vacuole membrane proteins are sorted into the lumen for degradation by ESCRT-dependent microautophagy. Thus, our study uncovered a complex relationship between TORC1 inactivation and vacuole biogenesis.

Contributions of Ag Nanowires to the Photoelectric Conversion Efficiency Enhancement of TiO2 Dye-Sensitized Solar Cells.

Ag nanowires (AgNWs) were employed in mesoporous TiO2 dye-sensitized solar cells (DSSCs) to enhance the photoelectric conversion efficiency (PCE). The possible reasons for PCE improvement, i.e., improvement in electron transport and light harvesting due to light scattering and plasmonic resonance effect of AgNWs are investigated. Electrochemical impedance spectra (EIS) study proved that addition of AgNWs can enhance the conductivity of TiO2 thin film photoanode, which is an important reason for the increase of photocurrent. Furthermore, through the comparison experiments as well as the UV-Vis absorption and IPCE characterization, contributions of the light scattering and plasmonic resonance effect to the enhancement of light harvest, and thus PCE of the DSSCs were demonstrated. It was found that fast electron transport of AgNWs played more important role for the PCE improvement than the light harvest enhancement due to light scattering and plasmonic effect. Based on these investigations, the AgNWs modified TiO2 thin film DSSCs were optimized. After integrating AgNWs into the photoanode, the photocurrent increased significantly and PCE increased -50% comparing with the pure TiO2-based DSSCs.

Association between plasma leptin level and systemic inflammatory markers in patients with aggressive periodontitis.

BACKGROUND: Increasing evidence supports an association between periodontitis and systemic diseases. Leptin is involved both in the energy metabolism and inflammatory processes and is suggested to be a link between periodontal infection and systemic health. The present study aimed to evaluate the peripheral leptin concentration in patients with aggressive periodontitis (AgP) and to explore the relationship between leptin and systemic inflammation. METHODS: Ninety patients with AgP visiting the Clinic of the Periodontology Department, Peking University School and Hospital of Stomatology between July 2001 and May 2006, and 44 healthy controls (staff and student volunteers in the same institute) were recruited. Plasma levels of leptin and inflammatory cytokines including interleukin (IL)-1ß, IL-6, tumor necrosis factor-α (TNF-α) and C-reactive protein (CRP) were measured by enzyme-linked immunosorbent assay. Correlation and multiple linear regression analysis were performed to analyze the association between plasma leptin level and other variables. RESULTS: Plasma leptin level of AgP group was significantly higher than that of the control group (19.7 ± 4.4 ng/ml vs. 7.5 ± 1.3 ng/ml, P < 0.01). After controlling for age, gender, and body mass index, positive correlation was observed between plasma leptin concentration and log-transformed levels of pro-inflammatory cytokines (IL-1ß, IL-6, TNF-α and CRP), and the partial correlation coefficients ranged from 0.199 to 0.376 (P < 0.05). Log-transformed IL-1ß and IL-6 levels entered the final regression model (standardized ß were 0.422 and 0.461 respectively, P < 0.01). CONCLUSIONS: Elevated plasma leptin concentration may be associated with increased systemic levels of inflammatory markers in AgP patients.

Ultra-deep desulfurization via reactive adsorption on peroxophosphomolybdate/agarose hybrids.

A catalyst system composed of peroxophosphomolybdates as catalytic center and agarose as matrix material had been designed. The [C16H33N(CH3)3]3[PO4{MoO(O2)2}4]/agarose (C16PMo(O2)2/agarose) hybrid was found to be active for oxidation desulfurization (ODS) of dibenzothiophene (DBT) or real fuel into corresponding sulfone by H2O2 as an oxidant, while the sulfur content could be reduced to 5ppm. The higher activity comes from its components including [PO4{MoO(O2)2}4] catalytic sites, the hydrophobic quaternary ammonium cation affinity to low polarity substrates, and agarose matrix affinity to H2O2 and sulfone. During the oxidative reaction, the mass transfer resistance between H2O2 and organic sulfurs could be decreased and the reaction rate could increase by the assistance of agarose and hydrophobic tails of [C16H33N(CH3)3]3[PO4{MoO(O2)2}4]. Meanwhile, the oxidative products could be adsorbed by agarose matrix to give clean fuel avoiding the post-treatment. In addition, the hybrid was easily regenerated to be reused.

[Association between gingival crevicular fluid leptin level and periodontal parameters in patients with aggressive periodontitis].

OBJECTIVE: To determine the level of leptin in gingival crevicular fluid (GCF) of patients with aggressive periodontitis (AgP) and to analyze the relationship between leptin and periodontal cilinical parameters. METHODS: Fifty-four patients with AgP and 30 healthy controls were recruited. Detailed clinical examinations were conducted, and clinical parameters such as bleeding index (BI), probing depth (PD), attachment loss (AL) were recorded. Two teeth were selected as test teeth in each subject, one from posterior area and the other from anterior region. The level of GCF leptin was measured by enzyme linked immunosorbent assay. RESULTS: The level of GCF leptin in AgP patients was significantly lower than that of control subjects [(16.5±4.6) ng vs.(26.0±6.0) ng, P < 0.05]. The level of GCF leptin was negatively related to BI (-0.306, P < 0.01), PD (-0.346, P < 0.01) and AL (-0.250, P < 0.01). CONCLUSIONS: AgP patients have significantly decreased level of GCF leptin and the level of leptin was negatively related to BI, PD and AL.