Nitro-oleic acid (NO2-OA) ameliorates erectile dysfunction in a rat model of diabetes mellitus via modulation of fibrosis, inflammation and autophagy.

Background: Inflammation, fibrosis and autophagy represent closely related factors associated with the pathogenesis of diabetes mellitus erectile dysfunction (DMED). In this study, the therapeutic effect of nitro-oleic acid (NO2-OA) in a streptozotocin-induced rat model of DMED was evaluated. Methods: Sixty rats were randomly divided into four groups: control, DMED, DMED + Vehicle and DMED + NO2-OA. DMED was induced by intraperitoneal injection of streptozotocin in male rats. Blood glucose and body weight were measured every 2 weeks. After 4 weeks of NO2-OA treatment, erectile function was measured by electrical stimulation of cavernous nerve (CN). Western blotting, quantitative real-time polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), immunofluorescence and Masson's trichrome staining were used to verify the related factors and protein expression levels. Results: We found that NO2-OA could significantly increase erectile pressure in the corpus cavernosum of DMED rats. Results of western blot, confocal immunofluorescence and qRT-PCR assays revealed that NO2-OA significantly reduced inflammatory factors and the expression of nuclear factor kappa B (NF-κB). In addition, Masson staining results indicated that NO2-OA significantly reduced the display of fibrotic tissue in the corpus cavernosum. These beneficial effects may be related to reductions in the expression of transforming growth factor-ß1 (TGF-ß1) and connective tissue growth factor (CTGF) and the increase in the expression of α-smooth muscle actin (α-SMA). Finally, NO2-OA treatment increased the expression of the autophagy marker, LC3, while P62 was decreased, effects suggesting that one of the underlying mechanisms of NO2-OA may involve an activation of the PI3K/AKT/mTOR pathway to enhance the capacity for autophagy within this tissue. Conclusions: NO2-OA enhances erectile function within a rat model of DMED by inhibiting inflammation and fibrosis along with activating autophagy.

Nitro-oleic acid ameliorates erectile dysfunction in a streptozotocin-induced rat model of diabetes by inhibiting oxidative stress and apoptosis and activating the NO/cGMP pathway.

The major vascular complications associated with diabetes make the management of diabetic mellitus erectile dysfunction (DMED) a challenging endeavor. Notable factors contributing to DMED include oxidative stress, nitric oxide (NO)/cyclic guanosine monophosphate (cGMP) pathway activation, and apoptosis, while nitro-oleic acid (NO2-OA) has been shown to be beneficial in treating these aspects of this condition. We, herein, investigated the effects and possible mechanisms of NO2-OA on erectile function as assessed in a streptozotocin-induced rat model of diabetes. Our results revealed that the erectile function of DMED rats was significantly impaired compared with that of the control group. However, in response to 4 weeks of NO2-OA treatment, there was an improvement in erectile function. The expression of oxidative stress-related indicators was significantly increased and the NO/cGMP pathway was impaired in the DMED group. The expression of proapoptotic factors was increased, while that of antiapoptotic factors was decreased in the DMED group. Moreover, the cell morphology in the cavernous tissue of the DMED group also changed adversely. NO2-OA treatment significantly reversed all these changes observed in the DMED group. In conclusion, NO2-OA treatment partially improved erectile function in DMED rats through mechanisms that included inhibition of oxidative stress, activation of the NO/cGMP pathway, and a reduction in apoptosis.

Eugenol-derived Organic Liquids as an In-Situ CO2 Capturing and Conversion System for Eugenol-based Polycarbonate Synthesis.

The significant development of catalytic biomass conversion has provided a large library of chemicals ready for subsequent upgrading to polymerisable monomers for the design and preparation of sustainable polymers. In this study, hydroxyethylation of eugenol by using green ethylene carbonate as alkylation reagent and cheap tetrabutylammonium iodide ionic liquids as green solvents and catalysts produced 2-(4-allyl-2-methoxyphenoxy)ethan-1-ol with a 85% yield, which could be used to construct an in situ CO2 capture and conversion system by taking the reversible chemistry of alcoholic compounds with CO2 in the presence of superbases, on which α,ω-diene functionalized carbonate monomers were successfully prepared and were applied in thiol-ene click and acyclic diene metathesis polymerisation (ADMET), producing a series of poly(thioether carbonate)s and unsaturated aromatic aliphatic polycarbonates with moderate molecular weights and satisfactory thermal properties. The structures of the formed CO2 reversible ILs, the polymerisable monomers and the corresponding polymers were fully characterized by various technologies.

N-acetylcysteine improves diabetic associated erectile dysfunction in streptozotocin-induced diabetic mice by inhibiting oxidative stress.

Oxidative stress appears to play a role in the pathogenesis of diabetes mellitus erectile dysfunction (DMED). This study aimed to investigate the effect of N-acetylcysteine (NAC) on DMED in streptozotocin-induced diabetic mice and to explore potential mechanisms. In the present study, we show that an erectile dysfunction is present in the streptozotocin-induced mouse model of diabetes as indicated by decreases in intracavernous pressure responses to electro-stimulation as well as from results of the apomorphine test of erectile function. After treatment of NAC, the intracavernous pressure was increased. In these DMED mice, oxidative stress and inflammatory responses were significantly reduced within the cavernous microenvironment, while activity of antioxidant enzymes in this cavernous tissue was enhanced after NAC treatment. These changes protected mitochondrial stress damage and a significant decreased in apoptosis within the cavernous tissue of DMED mice. This appears to involve activation of the nuclear factor erythroid 2-like-2 (Nrf2) signalling pathway, as well as suppression of the mitogen-activated protein kinase (MAPK) p38/ NF-κB pathway within cavernous tissue. In conclusion, NAC can improve erectile function through inhibiting oxidative stress via activating Nrf2 pathways and reducing apoptosis in streptozotocin-induced diabetic mice. NAC might provide a promising therapeutic strategy for individuals with DMED.

Efficient Antibacterial Agent Delivery by Mesoporous Silica Aerogel.

Bacterial infections still cause many health problems for human beings. Silica aerogels with a three-dimensional (3D) porous structure and a large surficial area are promising candidates for drug delivery, but they have rarely been investigated for antibacterial agent delivery. Herein, we study mesoporous silica aerogels as carriers for delivery of three slightly soluble antibacterial agents including cinnamaldehyde (CA, liquid), salicylic acid (SAA, solid), and sorbic acid (SOA, solid) under supercritical fluid carbon dioxide. Notably, all three antibacterial agents form uniform nanocrystals in the mesopores of silica aerogels and the loading efficiency reaches 56 wt %, which assists in overcoming the obstacles of low bioavailability of slightly soluble antibacterial agents. Benefiting from nanocrystallized antibacterial agents, the agent-loaded aerogels exhibit an inhibition rate of 99.99% against Escherichia coli during the initial release; notably, they still have a 95% inhibition rate even after ∼90% of CA is released. Importantly, the agent-loaded silica aerogels demonstrate good biocompatibility in vitro. This work indicates that mesoporous silica aerogels are a promising platform for antibacterial agent delivery.

AIE-active polyelectrolyte based photosensitizers: the effects of structure on antibiotic-resistant bacterial sensing and killing and pollutant decomposition.

A facile and effective multifunctional platform with high bacterial detection sensitivity, good antibacterial activity, and excellent dye decomposition efficiency holds great promise for wastewater treatment. To explore the design rationality and mechanism of material platforms with various integrated components into a single molecule for wastewater treatment applications, herein, four kinds of polyelectrolyte photosensitizers with aggregation-induced emission (AIE) fluorescent units are synthesized and systematically studied to investigate the structure-property relationship that influences the level of conjugation and the hydrophobicity-hydrophilicity balance. By improving the strength of the conjugation, the new AIE photosensitizers DBPVEs (including DBPVE-4 and DBPVE-6) generate a reactive oxygen species (ROS), and a decomposition efficiency of around 55% is obtained for dyes when they are exposed to DBPVEs under white light irradiation, which is higher than those of DBPEs (including DBPE-4 and DBPE-6). More importantly, owing to the longer and more flexible aliphatic chains of DBPVE-6 that facilitate efficient intercalation into cell membranes, the staining ability of DBPVE-6 for methicillin-resistant S. epidermidis (MRSE) is greatly enhanced as compared to that of DBPVE-4. It should be noted that the antibacterial experiment indicates that DBPVE-6 displays potent toxicity to MRSE with 99.9% killing efficiency under white light irradiation. This work provides essential theoretical and experimental guidance on the designing of new photosensitizers for wastewater treatment.

Sustainable Aromatic Aliphatic Polyesters and Polyurethanes Prepared from Vanillin-Derived Diols via Green Catalysis.

The design and preparation of polymers by using biobased chemicals is regarded as an important strategy towards a sustainable polymer chemistry. Herein, two aromatic diols, 4-(hydroxymethyl)-2-methoxyphenol and 2-(4-(hydroxymethyl)-2-methoxyphenoxy)ethanol, have been prepared in good yields through the direct reduction of vanillin and hydroxyethylated vanillin (4-(2-hydroxyethoxy)-3-methoxybenzaldehyde) using NaBH4, respectively. The diols were submitted to traditional polycondensation and polyaddition with acyl chlorides and diisocyanatos, and serials of new polyesters and polyurethanes were prepared in high yields with moderate molecular weight ranging from 17,000 to 40,000 g mol-1. Their structures were characterized by 1H NMR, 13C NMR and FTIR, and their thermal properties were studied by TGA and differential scanning calorimetry (DSC), indicating that the as-prepared polyesters and polyurethanes have Tg in the range of 16.2 to 81.2 °C and 11.6 to 80.4 °C, respectively.

MicroRNA-588 is upregulated in human prostate cancer with prognostic and functional implications.

BACKGROUND: Epigenetic factors of microRNAs (miRNAs) are important biomarkers and modulators in human prostate cancer (PCa). In this work, we characterized the expression, biomarker-potential and functional regulation of miR-588 in PCa. METHODS: Endogenous miR-588 levels were quantified by qRT-PCR in both prostate-specific antigen (PSA)-negative and PSA-positive PCa cell lines, as well as human PCa tumors. The associations between endogenous miR-588 and PCa patients' clinical outcomes and postoperative overall survival were statistically examined. Moreover, in both PSA-negative DU145 and PSA-positive LNCaP, downregulation of miR-588 was achived by transduction of miR-588 inhibitor lentivirus. The subsequent effects of miR-588 downregulation on PCa cell developments were investigated both in vitro and in vivo. RESULTS: MiR-588 was profoundly upregulated in both PSA-negative and PSA-positive PCa cells, as well as in PCa tumors. Significant miR-588 upregulation was found to be closely associated with PCa patients' poor clinical outcomes and shorter postoperative overall survivals. In DU145 and LNCaP cell lines, lentiviral transduction markedly downregulated endogenous miR-588 levels. MiR-588 downregulation was shown to profoundly inhibit PCa proliferation in vitro and xenograft in vivo. CONCLUSION: Aberrant upregulation of endogenous miR-588 in PCa patients may be a prognostic biomarker, indicative of their poor clinical outcomes. Inhibiting endogenous miR-588 may also serve as a therapeutic target for PCa treatment. This article is protected by copyright. All rights reserved.

Facial Color Management for Mobile Health in the Wild.

Nowadays, mobile technologies have changed the patient routine health care and management. With a large amount of mobile health applications developed, massive and valuable health data are possibly collected with a smart mobile phone in hand. Facial color images are recently proved to be available and effective for health condition diagnosis both in modern medicine and ancient medicine perspectives. One significant issue of facial color health condition diagnosis system is color management, in which its primary procedure is to obtain reliable and device-independent facial color images in the wild. The solution is known as utilizing color correction technology to recover the intrinsic color properties of facial skin. However, current color correction approaches are hard to meet the need of mobile health management in the wild, due to some limitations of precision-challenged algorithm, inconvenient color imaging device, strong scenario assumption and so forth. Therefore, in this paper, we consider several facial skin color characteristics and show that it is valuable to build facial color related correction model for facial color images in the wild. Then we propose two kinds of facial color correction strategies to realize the facial color management of mobile health in the wild. The first one is reference-based approach, and the other one is skin-based approach without requirement of colorchecker. Experimental results with qualitative and quantitative assessments on the indoor and outdoor scenarios demonstrate that the proposed reference-based approach is more outstanding than our previous method and other color constancy methods. In addition, given a facial color image only, the skin-based method can still achieve effective results compared with other color constancy methods.

A hybrid imputation approach for microarray missing value estimation.

BACKGROUND: Missing data is an inevitable phenomenon in gene expression microarray experiments due to instrument failure or human error. It has a negative impact on performance of downstream analysis. Technically, most existing approaches suffer from this prevalent problem. Imputation is one of the frequently used methods for processing missing data. Actually many developments have been achieved in the research on estimating missing values. The challenging task is how to improve imputation accuracy for data with a large missing rate. METHODS: In this paper, induced by the thought of collaborative training, we propose a novel hybrid imputation method, called Recursive Mutual Imputation (RMI). Specifically, RMI exploits global correlation information and local structure in the data, captured by two popular methods, Bayesian Principal Component Analysis (BPCA) and Local Least Squares (LLS), respectively. Mutual strategy is implemented by sharing the estimated data sequences at each recursive process. Meanwhile, we consider the imputation sequence based on the number of missing entries in the target gene. Furthermore, a weight based integrated method is utilized in the final assembling step. RESULTS: We evaluate RMI with three state-of-art algorithms (BPCA, LLS, Iterated Local Least Squares imputation (ItrLLS)) on four publicly available microarray datasets. Experimental results clearly demonstrate that RMI significantly outperforms comparative methods in terms of Normalized Root Mean Square Error (NRMSE), especially for datasets with large missing rates and less complete genes. CONCLUSIONS: It is noted that our proposed hybrid imputation approach incorporates both global and local information of microarray genes, which achieves lower NRMSE values against to any single approach only. Besides, this study highlights the need for considering the imputing sequence of missing entries for imputation methods.

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Qualitative and quantitative analysis for facial complexion in traditional Chinese medicine.

Facial diagnosis is an important and very intuitive diagnostic method in Traditional Chinese Medicine (TCM). However, due to its qualitative and experience-based subjective property, traditional facial diagnosis has a certain limitation in clinical medicine. The computerized inspection method provides classification models to recognize facial complexion (including color and gloss). However, the previous works only study the classification problems of facial complexion, which is considered as qualitative analysis in our perspective. For quantitative analysis expectation, the severity or degree of facial complexion has not been reported yet. This paper aims to make both qualitative and quantitative analysis for facial complexion. We propose a novel feature representation of facial complexion from the whole face of patients. The features are established with four chromaticity bases splitting up by luminance distribution on CIELAB color space. Chromaticity bases are constructed from facial dominant color using two-level clustering; the optimal luminance distribution is simply implemented with experimental comparisons. The features are proved to be more distinctive than the previous facial complexion feature representation. Complexion recognition proceeds by training an SVM classifier with the optimal model parameters. In addition, further improved features are more developed by the weighted fusion of five local regions. Extensive experimental results show that the proposed features achieve highest facial color recognition performance with a total accuracy of 86.89%. And, furthermore, the proposed recognition framework could analyze both color and gloss degrees of facial complexion by learning a ranking function.

Computer-assisted lip diagnosis on Traditional Chinese Medicine using multi-class support vector machines.

BACKGROUND: In Traditional Chinese Medicine (TCM), the lip diagnosis is an important diagnostic method which has a long history and is applied widely. The lip color of a person is considered as a symptom to reflect the physical conditions of organs in the body. However, the traditional diagnostic approach is mainly based on observation by doctor's nude eyes, which is non-quantitative and subjective. The non-quantitative approach largely depends on the doctor's experience and influences accurate the diagnosis and treatment in TCM. Developing new quantification methods to identify the exact syndrome based on the lip diagnosis of TCM becomes urgent and important. In this paper, we design a computer-assisted classification model to provide an automatic and quantitative approach for the diagnosis of TCM based on the lip images. METHODS: A computer-assisted classification method is designed and applied for syndrome diagnosis based on the lip images. Our purpose is to classify the lip images into four groups: deep-red, red, purple and pale. The proposed scheme consists of four steps including the lip image preprocessing, image feature extraction, feature selection and classification. The extracted 84 features contain the lip color space component, texture and moment features. Feature subset selection is performed by using SVM-RFE (Support Vector Machine with recursive feature elimination), mRMR (minimum Redundancy Maximum Relevance) and IG (information gain). Classification model is constructed based on the collected lip image features using multi-class SVM and Weighted multi-class SVM (WSVM). In addition, we compare SVM with k-nearest neighbor (kNN) algorithm, Multiple Asymmetric Partial Least Squares Classifier (MAPLSC) and Naïve Bayes for the diagnosis performance comparison. All displayed faces image have obtained consent from the participants. RESULTS: A total of 257 lip images are collected for the modeling of lip diagnosis in TCM. The feature selection method SVM-RFE selects 9 important features which are composed of 5 color component features, 3 texture features and 1 moment feature. SVM, MAPLSC, Naïve Bayes, kNN showed better classification results based on the 9 selected features than the results obtained from all the 84 features. The total classification accuracy of the five methods is 84%, 81%, 79% and 81%, 77%, respectively. So SVM achieves the best classification accuracy. The classification accuracy of SVM is 81%, 71%, 89% and 86% on Deep-red, Pale Purple, Red and lip image models, respectively. While with the feature selection algorithm mRMR and IG, the total classification accuracy of WSVM achieves the best classification accuracy. Therefore, the results show that the system can achieve best classification accuracy combined with SVM classifiers and SVM-REF feature selection algorithm. CONCLUSIONS: A diagnostic system is proposed, which firstly segments the lip from the original facial image based on the Chan-Vese level set model and Otsu method, then extracts three kinds of features (color space features, Haralick co-occurrence features and Zernike moment features) on the lip image. Meanwhile, SVM-REF is adopted to select the optimal features. Finally, SVM is applied to classify the four classes. Besides, we also compare different feature selection algorithms and classifiers to verify our system. So the developed automatic and quantitative diagnosis system of TCM is effective to distinguish four lip image classes: Deep-red, Purple, Red and Pale. This study puts forward a new method and idea for the quantitative examination on lip diagnosis of TCM, as well as provides a template for objective diagnosis in TCM.