Explore the vast range of titles available.

Periodontal ligament stem cells (PDLSCs), which reside in the perivascular space of the periodontium, possess characteristics of mesenchymal stem cells and are a promising tool for periodontal regeneration. Recently, great progress has been made in PDLSC transplantation. Investigators are attempting to maximize the proliferation and differentiation potential of PDLSCs by modifying culture conditions and applying growth factors. Nevertheless, problems remain. First, incomparability among different studies must be minimized by establishing standard guidelines for culture and identification of PDLSCs. Notably, attention should be paid to the biological safety of PDLSC transplantation. The present review updates the latest findings regarding PDLSCs and discusses standard criteria for culture and identification of PDLSCs. Finally, the review calls for careful consideration of PDLSC transplantation safety.

A simple continuum model is proposed for discrete granular avalanches, as observed in slowly rotating drums. Subject to granular transfer across the basal interface, the model evolves the transient surface inclination and mid-slope velocity profile of the avalanches, from failure to arrest. This is done using new boundary conditions that allow entrainment or detrainment contingent on the basal shear rate. For entrainment to occur, the basal shear stress must overcome the erosion resistance of the jammed deposit, while for detrainment to take place the basal shear rate must vanish. The resulting avalanche dynamics is controlled by a single dimensionless number, the ratio of excess slope at failure to excess resistance to erosion, that can be determined from inclination history data. This number provides a measure of slope brittleness, from which the peak flow rate and arrest inclination can be predicted. Analytical techniques are used to derive model solutions, and clarify the resulting behaviour. Finally, the model is tested by comparing solutions with laboratory experiments and discrete particle simulations.

Background Mitochondria are the major source of intracellular adenosine triphosphate (ATP) and play an essential role in a plethora of physiological functions, including the regulation of metabolism and the maintenance of cellular homeostasis. Mutations of mitochondrial DNA, proteins and impaired mitochondrial function have been implicated in the neurodegenerative diseases, stroke and injury of the central nervous system (CNS). The dynamic feature of mitochondrial fusion, fission, trafficking and turnover have also been documented in these diseases. Perspectives A major bottleneck of traditional approach to correct mitochondria-related disorders is the difficulty of drugs or gene targeting agents to arrive at specific sub-compartments of mitochondria. Moreover, the diverse nature of mitochondrial mutations among patients makes it impossible to develop one drug for one disease. To this end, mitochondrial transplantation presents a new paradigm of therapeutic intervention that benefits neuronal survival and regeneration for neurodegenerative diseases, stroke, and CNS injury. Supplement of healthy mitochondria to damaged neurons has been reported to promote neuronal viability, activity and neurite re-growth. In this review, we provide an overview of the recent advance and development on mitochondrial therapy. Conclusion Key parameters for the success of mitochondrial transplantation depend on the source and quality of isolated mitochondria, delivery protocol, and cellular uptake of supplemented mitochondria. To expedite clinical application of the mitochondrial transplantation, current isolation protocol needs optimization to obtain high percentage of functional mitochondria, isolated mitochondria may be packaged by biomaterials for successful delivery to brain allowing for efficient neuronal uptake.

Water quality regulation is widely recognized as a highly effective strategy for disease prevention in the field of aquaculture, and it holds significant potential for the development of sustainable aquaculture. Herein, four water quality regulators, including potassium monopersulfate (KMPS), tetrakis hydroxymethyl phosphonium sulfate (THPS), bacillus subtilis (BS), and chitosan (CS), were added to the culture water of Oreochromis niloticus (GIFT tilapia) every seven days. Subsequently, the effects of these four water quality regulators on GIFT tilapia were comprehensively evaluated by measuring the water quality index of daily growth-related performance and immune indexes of GIFT tilapia. The findings indicated that implementing the four water quality regulators resulted in a decrease in the content of ammonia nitrogen, active phosphate, nitrite, total organic carbon (TOC), and chemical oxygen demand (COD) in the water. Additionally, these regulators were found to maintain dissolved oxygen (DO) levels and pH of the water effectively. Furthermore, using these regulators demonstrated positive effects on various physiological parameters of GIFT tilapia, including improvements in final body weight, weight gain rate (WGR), specific growth rate (SGR), condition factor (CF), feed conversion ratio (FCR), spleen index (SI), hepato-somatic index (HSI), immune cell count, the activity of antioxidant-related enzymes (Nitric oxide, NO and Superoxide dismutase, SOD), and mRNA expression levels of immunity-related factors (Tumor Necrosis Factor-alpha, TNF-α and Interleukin-1 beta, IL-1β) in the liver and spleen. Notably, the most significant improvements were observed in the groups treated with the BS and CS water quality regulators. Moreover, BS and CS groups exhibited significantly higher serum levels of albumin (ALB) and total protein (TP) ( P < 0.05), whereas the other indicators showed no significant difference ( P > 0.05) compared to the control group. However, the KMPS and THPS groups of GIFT tilapia exhibited significantly higher serum levels of aspartate aminotransferase (AST), alanine transaminase (ALT), creatinine (CRE) and blood urea nitrogen (BUN) ( P < 0.05), whereas they exhibited significantly decreased HSI ( P < 0.05). In addition, the partially pathological observations revealed the presence of cell vacuolation, nuclear shrinkage, and pyknosis within the liver. In conclusion, these four water quality regulators, mainly BS and CS, could improve the growth performance and immunity of GIFT tilapia to varying degrees by regulating the water quality and then further increasing the expression levels of immune-related factors or the activity of antioxidant-related enzymes of GIFT tilapia. On the contrary, the prolonged use of KMPS and THPS may gradually diminish their growth-enhancing properties and potentially hinder the growth of GIFT tilapia.

The Notch pathway plays critical roles in the development and functional modulation of myeloid cells. Previous studies have demonstrated that Notch activation promotes M1 polarization and phagocytosis of macrophages; however, the downstream molecular mechanisms mediating Notch signal remain elusive. In an attempt to identify Notch downstream targets in bone marrow-derived macrophages (BMDMs) using mass spectrometry, the signal regulatory protein α (SIRPα) appeared to respond to knockout of recombination signal-binding protein Jk (RBP-J), the critical transcription factor of Notch pathway, in macrophages. In this study, we validated that Notch activation could repress SIRPα expression likely the Hes family co-repressors. SIRPα promoted macrophage M2 polarization, which was dependent on the interaction with CD47 and mediated by intracellular signaling through SHP-1. We provided evidence that Notch signal regulated macrophage polarization at least partially through SIRPα. Interestingly, Notch signal regulated macrophage phagocytosis of tumor cells through SIRPα but in a SHP-1-independent way. To access the translational value of our findings, we expressed the extracellular domains of the mouse SIRPα (mSIRPα ) to block the interaction between CD47 and SIRPα. We demonstrated that the soluble mSIRPα polypeptides could promote M1 polarization and increase phagocytosis of tumor cells by macrophages. Taken together, our results provided new insights into the molecular mechanisms of notch-mediated macrophage polarization and further validated SIRPα as a target for tumor therapy through modulating macrophage polarization and phagocytosis.

People with color vision deficiency (CVD) cannot observe the colorful world due to the damage of color reception nerves. In this work, we present an image enhancement approach to assist colorblind people to identify the colors they are not able to distinguish naturally. An image re-coloring algorithm based on eigenvector processing is proposed for robust color separation under color deficiency transformation. It is shown that the eigenvector of color vision deficiency is distorted by an angle in the λ , Y-B, R-G color space. The experimental results show that our approach is useful for the recognition and separation of the CVD confusing colors in natural scene images. Compared to the existing techniques, our results of natural images with CVD simulation work very well in terms of RMS, HDR-VDP-2 and an IRB-approved human test. Both the objective comparison with previous works and the subjective evaluation on human tests validate the effectiveness of the proposed method.

Background Doctors can detect symptoms of diabetic retinopathy (DR) early by using retinal ophthalmoscopy, and they can improve diagnostic efficiency with the assistance of deep learning to select treatments and support personnel workflow. Conventionally, most deep learning methods for DR diagnosis categorize retinal ophthalmoscopy images into training and validation data sets according to the 80/20 rule, and they use the synthetic minority oversampling technique (SMOTE) in data processing (e.g., rotating, scaling, and translating training images) to increase the number of training samples. Oversampling training may lead to overfitting of the training model. Therefore, untrained or unverified images can yield erroneous predictions. Although the accuracy of prediction results is 90%–99%, this overfitting of training data may distort training module variables. Results This study uses a 2-stage training method to solve the overfitting problem. In the training phase, to build the model, the Learning module 1 used to identify the DR and no-DR. The Learning module 2 on SMOTE synthetic datasets to identify the mild-NPDR, moderate NPDR, severe NPDR and proliferative DR classification. These two modules also used early stopping and data dividing methods to reduce overfitting by oversampling. In the test phase, we use the DIARETDB0, DIARETDB1, eOphtha, MESSIDOR, and DRIVE datasets to evaluate the performance of the training network. The prediction accuracy achieved to 85.38%, 84.27%, 85.75%, 86.73%, and 92.5%. Conclusions Based on the experiment, a general deep learning model for detecting DR was developed, and it could be used with all DR databases. We provided a simple method of addressing the imbalance of DR databases, and this method can be used with other medical images.

Involved in many diseases such as cancer, diabetes, neurodegenerative, inflammatory and respiratory disorders, G-protein-coupled receptors (GPCRs) are among the most frequent targets of therapeutic drugs. It is time-consuming and expensive to determine whether a drug and a GPCR are to interact with each other in a cellular network purely by means of experimental techniques. Although some computational methods were developed in this regard based on the knowledge of the 3D (dimensional) structure of protein, unfortunately their usage is quite limited because the 3D structures for most GPCRs are still unknown. To overcome the situation, a sequence-based classifier, called \"iGPCR-drug\", was developed to predict the interactions between GPCRs and drugs in cellular networking. In the predictor, the drug compound is formulated by a 2D (dimensional) fingerprint via a 256D vector, GPCR by the PseAAC (pseudo amino acid composition) generated with the grey model theory, and the prediction engine is operated by the fuzzy K-nearest neighbour algorithm. Moreover, a user-friendly web-server for iGPCR-drug was established at http://www.jci-bioinfo.cn/iGPCR-Drug/. For the convenience of most experimental scientists, a step-by-step guide is provided on how to use the web-server to get the desired results without the need to follow the complicated math equations presented in this paper just for its integrity. The overall success rate achieved by iGPCR-drug via the jackknife test was 85.5%, which is remarkably higher than the rate by the existing peer method developed in 2010 although no web server was ever established for it. It is anticipated that iGPCR-Drug may become a useful high throughput tool for both basic research and drug development, and that the approach presented here can also be extended to study other drug - target interaction networks.

Background: Diabetic retinopathy (DR) is indicated as a major cause of blindness in the world. Emerging evidence supports the interaction of iron metabolism with diabetes. However, little research is available concerning the relationship between iron metabolism and DR. The intent of this paper is to describe the correlation between serum iron and the occurrence of DR. Methods: A total of 5321 participants who underwent related examinations as part of the National Health and Nutrition Examination Survey (2005–2008) were included. DR was defined by the criteria of the Early Treatment for Diabetic Retinopathy Study based on nonmydriatic fundus photography. The cutoff point of serum iron for DR was explored by the receiver operating characteristics curve. The relationship of serum iron with the occurrence of DR was explored by multivariate logistic regression models. Results: Participants with DR had significantly lower serum iron than the control group. Serum iron was negatively correlated with the occurrence of DR after the adjustment of pertinent variables (an odds ratio (OR) of 0.995 (95% CI: 0.992–0.999)). After dividing serum iron into quartiles, the third quartile was associated with DR with an OR of 0.601 (95% CI: 0.418–0.863). Furthermore, the cutoff point of serum iron had an inverse relationship for the occurrence of DR with an OR of 0.766 (95% CI: 0.597–0.984). Conclusion: Serum iron has an inverse association with the occurrence of DR in diabetic adults. The assessment of serum iron levels might be a part of follow-up visits with diabetic patients.