MicroRNAs (miRNAs) are a class of small noncoding RNAs, which function in posttranscriptional regulation of gene expression. They are powerful regulators of various cellular activities including cell growth, differentiation, development, and apoptosis. They have been linked to many diseases, and currently miRNA-mediated clinical trial has shown promising results for treatment of cancer and viral infection. This review provides an overview and update on miRNAs biogenesis, regulation of miRNAs expression, their biological functions, and role of miRNAs in epigenetics and cell-cell communication. In addition, alteration of miRNAs following exercise, their association with diseases, and therapeutic potential will be explained. Finally, miRNA bioinformatics tools and conventional methods for miRNA detection and quantification will be discussed.

An overview of microRNAs: Biology, functions, therapeutics, and analysis methods

MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

FOXO3a is a member of the FOXO subfamily of forkhead transcription factors that mediate a variety of cellular processes including apoptosis, proliferation, cell cycle progression, DNA damage and tumorigenesis. It also responds to several cellular stresses such as UV irradiation and oxidative stress. The function of FOXO3a is regulated by a complex network of processes, including post-transcriptional suppression by microRNAs (miRNAs), post-translational modifications (PTMs) and protein–protein interactions. FOXO3a is widely implicated in a variety of diseases, particularly in malignancy of breast, liver, colon, prostate, bladder, and nasopharyngeal cancers. Emerging evidences indicate that FOXO3a acts as a tumor suppressor in cancer. FOXO3a is frequently inactivated in cancer cell lines by mutation of the FOXO3a gene or cytoplasmic sequestration of FOXO3a protein. And its inactivation is associated with the initiation and progression of cancer. In experimental studies, overexpression of FOXO3a inhibits the proliferation, tumorigenic potential, and invasiveness of cancer cells, while silencing of FOXO3a results in marked attenuation in protection against tumorigenesis. The role of FOXO3a in both normal physiology as well as in cancer development have presented a great challenge to formulating an effective therapeutic strategy for cancer. In this review, we summarize the recent findings and overview of the current understanding of the influence of FOXO3a in cancer development and progression.

/pdf/critical-role-of-foxo3a-in-carcinogenesis-5dseir8c7k.pdf

Critical role of FOXO3a in carcinogenesis.

Purinergic signalling, i.e. the role of nucleotides as extracellular signalling molecules, was proposed in 1972. However, this concept was not well accepted until the early 1990’s when receptor subtypes for purines and pyrimidines were cloned and characterised, which includes 4 subtypes of the P1 (adenosine) receptor, 7 subtypes of P2X ion channel receptors and 8 subtypes of the P2Y G protein-coupled receptor. Early studies were largely concerned with the physiology, pharmacology and biochemistry of purinergic signalling. More recently, the focus has been on the pathophysiology and therapeutic potential. There was early recognition of the use of P1 receptor agonists for the treatment of supraventicular tachycardia and A2A receptor antagonists are promising for the treatment of Parkinson’s disease. Clopidogrel, a P2Y¬12 antagonist, is widely used for the treatment of thrombosis and stroke, blocking P2Y¬12 receptor-mediated platelet aggregation. Diquafasol, a long acting P2Y¬2 receptor agonist, is being used for the treatment of dry eye. P2X3 receptor antagonists have been developed that are orally bioavailable and stable in vivo and are currently in clinical trials for the treatment of chronic cough, bladder incontinence, visceral pain and hypertension. Antagonists to P2X7 receptors are being investigated for the treatment of inflammatory disorders, including neurodegenerative diseases. Other investigations are in progress for the use of purinergic agents for the treatment of osteoporosis, myocardial infarction, irritable bowel syndrome, epilepsy, atherosclerosis, depression, autism, diabetes and cancer.

/pdf/purinergic-signalling-therapeutic-developments-29zcy5b0n6.pdf

Purinergic Signalling: Therapeutic Developments.

There is nervous control of the heart by ATP as a cotransmitter in sympathetic, parasympathetic, and sensory-motor nerves, as well as in intracardiac neurons. Centers in the brain control heart activities and vagal cardiovascular reflexes involve purines. Adenine nucleotides and nucleosides act on purinoceptors on cardiomyocytes, AV and SA nodes, cardiac fibroblasts, and coronary blood vessels. Vascular tone is controlled by a dual mechanism. ATP, released from perivascular sympathetic nerves, causes vasoconstriction largely via P2X1 receptors. Endothelial cells release ATP in response to changes in blood flow (via shear stress) or hypoxia, to act on P2 receptors on endothelial cells to produce nitric oxide, endothelium-derived hyperpolarizing factor, or prostaglandins to cause vasodilation. ATP is also released from sensory-motor nerves during antidromic reflex activity, to produce relaxation of some blood vessels. Purinergic signaling is involved in the physiology of erythrocytes, platelets, and leukocytes. ATP is released from erythrocytes and platelets, and purinoceptors and ectonucleotidases are expressed by these cells. P1, P2Y1, P2Y12, and P2X1 receptors are expressed on platelets, which mediate platelet aggregation and shape change. Long-term (trophic) actions of purine and pyrimidine nucleosides and nucleotides promote migration and proliferation of vascular smooth muscle and endothelial cells via P1 and P2Y receptors during angiogenesis, vessel remodeling during restenosis after angioplasty and atherosclerosis. The involvement of purinergic signaling in cardiovascular pathophysiology and its therapeutic potential are discussed, including heart failure, infarction, arrhythmias, syncope, cardiomyopathy, angina, heart transplantation and coronary bypass grafts, coronary artery disease, diabetic cardiomyopathy, hypertension, ischemia, thrombosis, diabetes mellitus, and migraine.

Purinergic Signaling in the Cardiovascular System.

Objective: to better reconstruct the nasal shape after cleft lip repair with 3D printing assisted autologous costal cartilage augmentation rhinoplasty, especially for patients with radix augmentation needs. Method: 20 patients with nasal deformity secondary to cleft lip repair and radix augmentation needs had received surgical treatment from July 2016 to November 2021. A total of 10 cases were treated with autologous costal cartilage augmentation rhinoplasty for nasal deformity after cleft lip repair, and 10 cases were treated with the help of 3D printing. According to the characteristics of nasal deformity, autologous costal cartilage was carved and implanted into the nose back. Results: 3D printing assisted autologous costal cartilage augmentation in the treatment of nasal deformity after cleft lip repair, the incision healed well, and there were no complications in the thoracic cartilage donor area. The shape of the nose is satisfactory, the height and shape of the nose tip and the size of both nostrils are mostly symmetrical, the nasal columella is elongated, the original nose tip is flat, the collapse of the nose wing is satisfactory, and the nose lip angle is close to normal. Conclusions: 3D printing assisted autologous costal cartilage augmentation is an ideal treatment for nasal deformity after cleft lip repair.

/pdf/3d-printing-technique-assisted-autologous-costal-cartilage-2hoa751e.pdf

3D Printing Technique Assisted Autologous Costal Cartilage Augmentation Rhinoplasty for Patients with Radix Augmentation Needs and Nasal Deformity after Cleft Lip Repair

BACKGROUND
The platysma is involved in the lower face movement, yet limited information on its functional role during perioral expressions can be found.


OBJECTIVES
The authors aimed to use 3-dimensional (3D) photogrammetry to analyze the platysma activity during expressions.


METHODS
Standardized frontal view 3D images were obtained from fifty-four volunteers. Three perioral expressions were analyzed: a full smile, mouth corner depression, and a grimace to show the lower teeth. Using the superimposed image subtraction technique, the positive volume change in the "heat map" of the neck skin surface was used as a sensitive indicator of platysma contraction.


RESULTS
During the three expressions, the proportion of platysma contraction was 42.6%, 75.9%, and 92.6%, respectively. We found platysma worked as a more critical dynamic force than expected. In most smiles involving the platysma, its upper portion contracted. With the laterally downward traction on the lower lip increasing, the upper and lower portions of the platysma contracted together and formed muscular bands. The platysma contracted extensively in the other two expressions. In some cases, platysma bands were observed by the vigorous contraction of muscle fibers. Besides, the platysma was found to be highly functionally active in young individuals.


CONCLUSIONS
This study introduced a simple method that could sensitively identify the functional status of the platysma. The results showed its highly active function in perioral expressions in young adults. It may assist surgeons in personalized decision-making toward platysma muscle-weakening strategies and work as an objective tool for the outcome evaluation.

The Underappreciated Role of Platysma Muscle in the Perioral Expressions in Young Adults.

Microdrop Botox Technique for Dynamic Wrinkles in the Upper Face

Background: The literature presents the tear trough and lid-cheek junction’s controversial anatomical explanations. In this study, the authors explored the anatomical characterization of the tear trough deformity and lid-cheek junction. Methods: Twelve human cadaver specimens were obtained from the authors’ anatomy department. Iodine staining and high-resolution micro–computed tomography was used to observe the suborbital area’s anatomy. Anatomical studies and histologic evaluation were performed. Results: The orbicularis retaining ligament is composed of a main septum right on the rim and multiple fibrous septa in the retro-orbicularis plain. The septum arises from the deep fascia of the palpebral part of the orbicularis oculi at approximately the level of the medial corneoscleral limbus, anchors the muscle to the orbital rim, and continues laterally as the lateral orbital thickening at the lateral canthus. Along the lid-cheek junction, multiple fibrous septa emanating from the deep fascia of the orbital part of the orbicularis oculi converge to the above septum. Using three-dimensional simulation, the skin-insert junction between the pre–palpebral part of the orbicularis oculi and pre–orbicularis oculi fibers precisely corresponded with the tear trough and the lid-cheek groove. Anatomical studies and histologic evaluation confirmed these observations. Conclusions: The orbicularis retaining ligament’s subtle anatomical features are clearly seen using the approach described. The tear trough and the lid-cheek junction’s anatomical characterization is defined by a suborbital ligament-fascia system whose description has implications in designing lower eyelid cosmetic surgery.

Anatomical Characterization of the Tear Trough and the Lid-Cheek Junction: An Iodine Staining Technique Based on High-Resolution Micro–Computed Tomography

Background Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age-estimation models are trained on public datasets with the Caucasian race as the main reference, thus they are impractical for clinical application in Chinese patients. Methods To develop and select an age-estimation model appropriate for Chinese patients receiving rejuvenation treatment, we obtained a face database of 10 529 images from 1821 patients from the author's hospital and selected two representative age-estimation algorithms for the model training. The prediction accuracies and the interpretability of calculation logic of these two facial age predictors were compared and analyzed. Results The mean absolute error (MAE) of a traditional support vector machine-learning model was 10.185 years; the proportion of absolute error ≤6 years was 35.90% and 68.50% ≤12 years. The MAE of a deep-learning model based on the VGG-16 framework was 3.011 years; the proportion of absolute error ≤6 years was 90.20% and 100% ≤12 years. Compared with deep learning, traditional machine-learning models have clearer computational logic, which allows them to give clinicians more specific treatment recommendations. Conclusion Experimental results show that deep-learning exceeds traditional machine learning in the prediction of Chinese cosmetic patients' age. Although traditional machine learning model has better interpretability than deep-learning model, deep-learning is more accurate for clinical quantitative evaluation. Knowing the decision-making logic behind the accurate prediction of deep-learning is crucial for deeper clinical application, and requires further exploration. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/srt.13402

Exploring artificial intelligence from a clinical perspective: A comparison and application analysis of two facial age predictors trained on a large‐scale Chinese cosmetic patient database

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Tao Song

Author Tools

Chat about Author