Droplet microarray platforms for high-throughput drug screening.

High-throughput screening platforms are fundamental for the rapid and efficient processing of large amounts of experimental data. Parallelization and miniaturization of experiments are important for improving their cost-effectiveness. The development of miniaturized high-throughput screening platforms is essential in the fields of biotechnology, medicine, and pharmacology. Currently, most laboratories use 96- or 384-well microtiter plates for screening; however, they have disadvantages, such as high reagent and cell consumption, low throughput, and inability to avoid cross-contamination, which need to be further optimized. Droplet microarrays, as novel screening platforms, can effectively avoid these shortcomings. Here, the preparation method of the droplet microarray, method of adding compounds in parallel, and means to read the results are briefly described. Next, the latest research on droplet microarray platforms in biomedicine is presented, including their application in high-throughput culture, cell screening, high-throughput nucleic acid screening, drug development, and individualized medicine. Finally, the challenges and future trends in droplet microarray technology are summarized.

Advancements in microfluidics for skin cosmetic screening.

With the global penetration of skin care awareness and upgrading of personal care awareness, the use rate of cosmetics and personal skin care products has been increasing worldwide. It is particularly important to monitor the quality and safety of skin cosmetics. In accordance with the requirements of the 7th Amendment of the European Cosmetics Directive 1223/2009, in vitro test methods have been developed to replace animal experiments, such as the 2D test, 3D test, microfluidic skin chip test, etc. The microfluidic skin chip overcomes the shortcomings of the 2D test and the 3D test that lack the complexity of human skin through fine control of the human skin microenvironment and induction of relevant mechanical stimulation. High similarity to real human skin through simulation of the vascular system and immune response. Therefore, the microfluidic skin chip is considered as a valuable and effective tool for the in vitro screening of cosmetics. In this paper, we reviewed the detection methods and technologies of common chemical substances, toxic elements, active substances and adverse reactions in vitro in quality monitoring of cosmetics. The most advantageous microfluidic skin chip technology is also introduced. The material and technology progress of skin chips used in cosmetic screening is reviewed and discussed. Then the application of microfluidic design in cosmetic screening in vitro is summarized.

lncRNA-disease association prediction based on the weight matrix and projection score.

With the development of medical science, long noncoding RNA (lncRNA), originally considered as a noise gene, has been found to participate in a variety of biological activities. Several recent studies have shown the involvement of lncRNA in various human diseases, such as gastric cancer, prostate cancer, lung cancer, and so forth. However, obtaining lncRNA-disease relationship only through biological experiments not only costs manpower and material resources but also gains little. Therefore, developing effective computational models for predicting lncRNA-disease association relationship is extremely important. This study aimed to propose an lncRNA-disease association prediction model based on the weight matrix and projection score (LDAP-WMPS). The model used the relatively perfect lncRNA-miRNA relationship data and miRNA-disease relationship data to predict the lncRNA-disease relationship. The integrated lncRNA similarity matrix and the integrated disease similarity matrix were established by fusing various methods to calculate the similarity between lncRNA and disease. This study improved the existing weight algorithm, applied it to the lncRNA-miRNA-disease triple network, and thus proposed a new lncRNA-disease weight matrix calculation method. Combined with the improved projection algorithm, the lncRNA-miRNA relationship and miRNA-disease relationship were used to predict the lncRNA-disease relationship. The simulation results showed that under the Leave-One-Out-Cross-Validation framework, the area under the receiver operating characteristic curve of LDAP-WMPS could reach 0.8822, which was better than the latest result. Taking adenocarcinoma and colorectal cancer as examples, the LDAP-WMPS model was found to effectively infer the lncRNA-disease relationship. The simulation results showed good prediction performance of the LDAP-WMPS model, which was an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease relationship data.

lncRNA-disease association prediction method based on the nearest neighbor matrix completion model.

State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA.

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

Application of Microfluidics in Drug Development from Traditional Medicine.

While there are many clinical drugs for prophylaxis and treatment, the search for those with low or no risk of side effects for the control of infectious and non-infectious diseases is a dilemma that cannot be solved by today's traditional drug development strategies. The need for new drug development strategies is becoming increasingly important, and the development of new drugs from traditional medicines is the most promising strategy. Many valuable clinical drugs have been developed based on traditional medicine, including drugs with single active ingredients similar to modern drugs and those developed from improved formulations of traditional drugs. However, the problems of traditional isolation and purification and drug screening methods should be addressed for successful drug development from traditional medicine. Advances in microfluidics have not only contributed significantly to classical drug development but have also solved many of the thorny problems of new strategies for developing new drugs from traditional drugs. In this review, we provide an overview of advanced microfluidics and its applications in drug development (drug compound synthesis, drug screening, drug delivery, and drug carrier fabrication) with a focus on its applications in conventional medicine, including the separation and purification of target components in complex samples and screening of active ingredients of conventional drugs. We hope that our review gives better insight into the potential of traditional medicine and the critical role of microfluidics in the drug development process. In addition, the emergence of new ideas and applications will bring about further advances in the field of drug development.

Paper-Based Fluidic Sensing Platforms for ß-Adrenergic Agonist Residue Point-of-Care Testing.

The illegal use of ß-adrenergic agonists during livestock growth poses a threat to public health; the long-term intake of this medication can cause serious physiological side effects and even death. Therefore, rapid detection methods for ß-adrenergic agonist residues on-site are required. Traditional detection methods such as liquid chromatography have limitations in terms of expensive instruments and complex operations. In contrast, paper methods are low cost, ubiquitous, and portable, which has led to them becoming the preferred detection method in recent years. Various paper-based fluidic devices have been developed to detect ß-adrenergic agonist residues, including lateral flow immunoassays (LFAs) and microfluidic paper-based analytical devices (µPADs). In this review, the application of LFAs for the detection of ß-agonists is summarized comprehensively, focusing on the latest advances in novel labeling and detection strategies. The use of µPADs as an analytical platform has attracted interest over the past decade due to their unique advantages and application for detecting ß-adrenergic agonists, which are introduced here. Vertical flow immunoassays are also discussed for their shorter assay time and stronger multiplexing capabilities compared with LFAs. Furthermore, the development direction and prospects for the commercialization of paper-based devices are considered, shedding light on the development of point-of-care testing devices for ß-adrenergic agonist residue detection.

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering.

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

lncRNA-disease association prediction based on latent factor model and projection.

Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.

The mediating effect of perceived social support on the relationship between mindfulness and burnout in special education teachers.

This study aimed to explore the mediating effect of perceived social support on the relationship between mindfulness and burnout in Chinese special education teachers. Three hundred and seven teachers completed the Five-Facet Mindfulness Questionnaire, Multi-dimensional Scale of Perceived Social Support Scale, and Teacher Burnout Inventory. The results showed that burnout was negatively correlated with mindfulness and perceived social support, while perceived social support was positively correlated with mindfulness. Moreover, perceived social support partially mediated the effect of mindfulness on special education teachers' burnout. These results suggest that the use of mindfulness combined with perceived social support may be beneficial for preventing and mitigating burnout among special education teachers.

Lactobacillus rhamnosus GG supplementation for preventing respiratory infections in children: a meta-analysis of randomized, placebo-controlled trials.

OBJECTIVE: To systematically review the effectiveness of administering Lactobacillus rhamnosus GG (LGG) for preventing respiratory infections in children. DESIGN: Systematic Review and Meta-analysis. DATA SOURCES: Electronic databases and trial registries. RESULTS: Four RCTs involving 1805 participants met the inclusion criteria. Compared with placebo, LGG administration was associated with a reduced incidence of acute otitis media (four RCTs, n=1805, RR 0.76, 95% CI 0.64-0.91, fixed effects model, NNT 17, 95% CI 11-46), a reduced risk of upper respiratory infections (one RCT, n=281, RR 0.62, 95% CI 0.50-0.78, NNT 4, 95% CI 3-8) and antibiotic treatments (four RCTs, n=1805, RR 0.80, 95% CI 0.71-0.91, fixed effects model). There was no significant difference between the LGG and the control groups in the risk of overall respiratory infections and the incidence of lower respiratory infections. However, subgroup analysis of two studies on children older than 1 year showed significant reduction in the risk of overall respiratory infections (two RCTs, n=794, RR 0.73, 95% CI 0.57-0.92, random effects model, NNT 8, 95% CI 5-14). Adverse effects were similar in both groups. No serious adverse events were reported. CONCLUSION: The administration of Lactobacillus rhamnosus GG compared with placebo has the potential to reduce the incidence of acute otitis media, the upper respiratory infections and antibiotic use in children.

Do the eyes have it? Extraction of identity and positive expression from another's eyes in autism, probed using "Bubbles".

BACKGROUND: It has been debated whether attending to a particular facial region, such as the eyes, is impaired in children with autism. The purpose of this study was to verify the poor eye gaze hypothesis postulating that children with High-Functioning Autism (HFA)/AS are impaired in their ability to attend to another's eyes. METHODS: Our study used the "Bubbles" method. A group with ASD (n=15) and a paired non-ASD group (n=18) completed an identity judgment task requiring a binary judgment of the identity of a person in an image, and an emotion judgment task requiring perception of expressed happiness in a facial image. RESULTS: Results indicated that similar to non-ASD individuals, ASD individuals used information from other people's eyes to judge identity as well as emotion, and performed as successfully as the non-ASD group both in identity and emotion judgment tasks. The results challenge the conventional hypothesis that individuals with ASD cannot attend to or derive information from another's eyes. CONCLUSION: Our findings combined with the results of poor eye gaze to expressions of fear in previous studies suggest that ASD individuals can derive information pertaining to positive emotion, but cannot sufficiently extract information pertaining to negative emotion from another's eyes.