Experimental study on stromal vascular fraction mediated inhibition of skin pigmentation in guinea pigs.

Background: Pigment disorder dermatoses are common diseases with complex mechanisms. There are various methods for the clinical treatment of pigmentation diseases, but these have a poor curative effect and many adverse reactions. Currently, looking for safe and effective whitening agents is a popular research topic. Stromal vascular fractions (SVFs) are a compound cell component of adipose-derived stem cells (ADSCs) that can promote tissue regeneration, healing, and vascularization. The purpose of this experiment was to investigate the inhibitory effect of SVFs on pigmentation in guinea pigs. Methods: After guinea pig subcutaneous fat was digested and centrifuged, SVFs were isolated and quantified. SVF was injected into the pigmentation area of the prepared guinea pig pigmentation model. The amount of inducible nitric oxide synthase (iNOS) was determined using immunohistochemical analysis, histopathological staining, and the Fontana-Masson (F-M) method for measuring melanin formation. Results: The skin of the guinea pigs obtained stable and homogenous coloration following three treatments with narrow-band ultraviolet B (NB-UVB). Hematoxylin-eosin (HE) staining revealed that compared to the control group, the cuticle, granular layer, and spinous layer were thicker and the number of epidermal melanocytes and melanin granules increased. While the quantity of pigment granules in the treated group dramatically decreased, it did not significantly change in the blank control group. F-M staining revealed that melanin granules greatly expanded following ultraviolet irradiation and were continuously distributed in basal cells and spinous layers. The entire epidermis was evenly covered in melanin granules. The level of melanin dramatically decreased following therapy. According to immunohistochemical labeling, epidermal cells' cytoplasm and membranes are where iNOS is primarily found. In the epidermis of the irradiated group, iNOS expression was much higher than in the control group, and following treatment, it decreased in the experimental group. Conclusions: SVFs have a reliable treatment effect on ultraviolet B (UVB)-induced pigmentation in guinea pig skin. SVFs can significantly inhibit pigmentation, effectively shorten the fading time of pigmentation, and play a role in skin whitening, providing a new breakthrough for the treatment of pigmentation diseases.

Critical Review of Synthesis, Toxicology and Detection of Acyclovir.

Acyclovir (ACV) is an effective and selective antiviral drug, and the study of its toxicology and the use of appropriate detection techniques to control its toxicity at safe levels are extremely important for medicine efforts and human health. This review discusses the mechanism driving ACV's ability to inhibit viral coding, starting from its development and pharmacology. A comprehensive summary of the existing preparation methods and synthetic materials, such as 5-aminoimidazole-4-carboxamide, guanine and its derivatives, and other purine derivatives, is presented to elucidate the preparation of ACV in detail. In addition, it presents valuable analytical procedures for the toxicological studies of ACV, which are essential for human use and dosing. Analytical methods, including spectrophotometry, high performance liquid chromatography (HPLC), liquid chromatography/tandem mass spectrometry (LC-MS/MS), electrochemical sensors, molecularly imprinted polymers (MIPs), and flow injection-chemiluminescence (FI-CL) are also highlighted. A brief description of the characteristics of each of these methods is also presented. Finally, insight is provided for the development of ACV to drive further innovation of ACV in pharmaceutical applications. This review provides a comprehensive summary of the past life and future challenges of ACV.

Molecular Mechanisms of Chondrocyte Proliferation and Differentiation.

Cartilage is a kind of connective tissue that buffers pressure and is essential to protect joint movement. It is difficult to self-recover once cartilage is damaged due to the lack of blood vessels, lymph, and nerve tissues. Repair of cartilage injury is mainly achieved by stimulating chondrocyte proliferation and extracellular matrix (ECM) synthesis. Cartilage homeostasis involves the regulation of multiple growth factors and the transduction of cellular signals. It is a very complicated process that has not been elucidated in detail. In this review, we summarized a variety of signaling molecules related to chondrocytes function. Especially, we described the correlation between chondrocyte-specific regulatory factors and cell signaling molecules. It has potential significance for guiding the treatment of cartilage injury.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.

As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.

Effect of the Stromal Vascular Fraction on Changes in Melanin Formation in B16 Cells Treated by IBMX.

OBJECTIVE: To investigate the effect of the stromal vascular fraction (SVF) on changes in melanin formation and tyrosinase activity in B16 cells treated by 3-isobutyl-1 methylxanthine (IBMX) and to explore the mechanism of SVF-mediated inhibition of pigmentation. METHODS: We co-cultured extracted SVFs and B16 cells treated with IBMX in a certain proportion, and the marker molecule HMB-45 was detected by immunochemistry. Melanin content was determined by NaOH lysis. Activity of tyrosinase was measured by the DOPA oxidation method. RESULTS: HMB-45 was commonly expressed in B16 cells induced by IBMX. After the addition of SVFs, the expression of HMB-45 decreased significantly and positively correlated with increases in SVFs. After the induction of B16 cells by IBMX, melanin content increased significantly. However, melanin decreased after SVF and B16 co-culturing; the effect was more substantial with the increase and decrease in SVFs, and the activity of tyrosinase decreased. CONCLUSION: SVFs inhibit the production of melanin and reduce the activity of tyrosinase, possibly providing a new breakthrough for the treatment of pigment disorders. NO LEVEL ASSIGNED: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Human Microbe-Disease Association Prediction With Graph Regularized Non-Negative Matrix Factorization.

A microbe is a microscopic organism which may exists in its single-celled form or in a colony of cells. In recent years, accumulating researchers have been engaged in the field of uncovering microbe-disease associations since microbes are found to be closely related to the prevention, diagnosis, and treatment of many complex human diseases. As an effective supplement to the traditional experiment, more and more computational models based on various algorithms have been proposed for microbe-disease association prediction to improve efficiency and cost savings. In this work, we developed a novel predictive model of Graph Regularized Non-negative Matrix Factorization for Human Microbe-Disease Association prediction (GRNMFHMDA). Initially, microbe similarity and disease similarity were constructed on the basis of the symptom-based disease similarity and Gaussian interaction profile kernel similarity for microbes and diseases. Subsequently, it is worth noting that we utilized a preprocessing step in which unknown microbe-disease pairs were assigned associated likelihood scores to avoid the possible negative impact on the prediction performance. Finally, we implemented a graph regularized non-negative matrix factorization framework to identify potential associations for all diseases simultaneously. To assess the performance of our model, cross validations including global leave-one-out cross validation (LOOCV) and local LOOCV were implemented. The AUCs of 0.8715 (global LOOCV) and 0.7898 (local LOOCV) proved the reliable performance of our computational model. In addition, we carried out two types of case studies on three different human diseases to further analyze the prediction performance of GRNMFHMDA, in which most of the top 10 predicted disease-related microbes were verified by database HMDAD or experimental literatures.

Human Microbe-Disease Association Prediction Based on Adaptive Boosting.

There are countless microbes in the human body, and they play various roles in the physiological process. There is growing evidence that microbes are closely associated with human diseases. Researching disease-related microbes helps us understand the mechanisms of diseases and provides new strategies for diseases diagnosis and treatment. Many computational models have been proposed to predict disease-related microbes, in this paper, we developed a model of Adaptive Boosting for Human Microbe-Disease Association prediction (ABHMDA) to reveal the associations between diseases and microbes by calculating the relation probability of disease-microbe pair using a strong classifier. Our model could be applied to new diseases without any known related microbes. In order to assess the prediction power of the model, global and local leave-one-out cross validation (LOOCV) were implemented. As shown in the results, the global and local LOOCV values reached 0.8869 and 0.7910, respectively. What's more, 10, 10, and 8 out of the top 10 microbes predicted to be most likely to be associated with Asthma, Colorectal carcinoma and Type 1 diabetes were all verified by relevant literatures or database HMDAD, respectively. The above results verify the superior predictive performance of ABHMDA.

HNMDA: heterogeneous network-based miRNA-disease association prediction.

Recently, accumulating evidences have shown that microRNAs (miRNAs) could play key roles in the development and progression of multiple important human diseases. Nonetheless, due to the shortcoming of being expensive and time-consuming existing in experimental approaches, computational methods are needed for the prediction of potential miRNA-disease associations. In our study, we proposed a computational model named Heterogeneous Network-based MiRNA-Disease Association prediction (HNMDA) for the latent miRNA-disease association prediction by integrating known miRNA-disease associations, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile kernel similarity. The Gaussian interaction profile kernel similarity can make up for the shortages of the traditional similarity calculation methods. Furthermore, we applied a heterogeneous network-based method, in which we first implemented a network diffusion algorithm of random walk with restart, and then we applied a method to find the optimal projection from miRNA space to disease space, which enabled the prediction of new miRNA-disease associations that are not experimentally confirmed so far. In the cross-validation, HNMDA obtained the AUC of 0.8394, which achieved improvement compared with previous methods. In the case studies of breast neoplasms, esophageal neoplasms and kidney neoplasms based on known miRNA-disease associations in the HMDD V2.0 database, there were 82, 76 and 84% of top 50 predicted related miRNAs that were confirmed to have associations with these three diseases, respectively. In the further case studies for new diseases without any known related miRNAs and the case using HMDD V1.0 database as known associations, there were also high ratio of the predicted miRNAs confirmed by experimental reports.

Obstructive sleep apnoea and the incidence and mortality of cancer: a meta-analysis.

As there are conflicting reports regarding the association between obstructive sleep apnoea (OSA) and cancer incidence and mortality, a meta-analysis was performed to evaluate whether OSA is independently associated with cancer incidence and mortality. Pubmed, EMBASE and Web of Science were searched up until November 2014. Studies that assessed OSA and the future risk of cancer incidence or mortality were included. Pooled hazard ratios (HR) and corresponding 95% confidence intervals (CI) were calculated. Subgroup analysis was conducted based on the polysomnographic variable, apnoea-hypopnoea index. Six studies, which involved 114 105 participants, were pooled in this meta-analysis. Fixed-effects analysis showed the pooled adjusted HR of cancer incidence as 0.91 (95% CI, 0.74-1.13; P = 0.408) for mild OSA, 1.07 (95% CI, 0.86-1.33; P = 0.552) for moderate OSA and 1.03 (95% CI, 0.85-1.26; P = 0.743) for severe OSA. Random-effects analysis demonstrated neither mild OSA (adjusted HR, 0.79; 95% CI, 0.46-1.34; P = 0.381), moderate OSA (adjusted HR, 1.92; 95% CI, 0.63-5.88; P = 0.251) nor severe OSA (adjusted HR, 2.09; 95% CI, 0.45-9.81; P = 0.349) correlated with cancer mortality. This meta-analysis indicates that OSA is not independently associated with cancer incidence and mortality according to currently available data. Additional experimental and human research is required to determine the exact association between OSA and cancer.