Lineage reprogramming of fibroblasts into induced cardiac progenitor cells by CRISPR/Cas9-based transcriptional activators

Advancing therapeutic efficacy: nanovesicular delivery systems for medicinal plant-based therapeutics

The utilization of medicinal plant extracts in therapeutics has been hindered by various challenges, including poor bioavailability and stability issues. Nanovesicular delivery systems have emerged as promising tools to overcome these limitations by enhancing the solubility, bioavailability, and targeted delivery of bioactive compounds from medicinal plants. This review explores the applications of nanovesicular delivery systems in antibacterial and anticancer therapeutics using medicinal plant extracts. We provide an overview of the bioactive compounds present in medicinal plants and their therapeutic properties, emphasizing the challenges associated with their utilization. Various types of nanovesicular delivery systems, including liposomes, niosomes, ethosomes, and solid lipid nanoparticles, among others, are discussed in detail, along with their potential applications in combating bacterial infections and cancer. The review highlights specific examples of antibacterial and anticancer activities demonstrated by these delivery systems against a range of pathogens and cancer types. Furthermore, we address the challenges and limitations associated with the scale-up, stability, toxicity, and regulatory considerations of nanovesicular delivery systems. Finally, future perspectives are outlined, focusing on emerging technologies, integration with personalized medicine, and potential collaborations to drive forward research in this field. Overall, this review underscores the potential of nanovesicular delivery systems for enhancing the therapeutic efficacy of medicinal plant extracts in antibacterial and anticancer applications, while identifying avenues for further research and development.

Direct Cardiac Reprogramming in the Age of Computational Biology

Heart disease continues to be one of the most fatal conditions worldwide. This is in part due to the maladaptive remodeling process by which ischemic cardiac tissue is replaced with a fibrotic scar. Direct cardiac reprogramming presents a unique solution for restoring injured cardiac tissue through the direct conversion of fibroblasts into induced cardiomyocytes, bypassing the transition through a pluripotent state. Since its inception in 2010, direct cardiac reprogramming using the transcription factors Gata4, Mef2c, and Tbx5 has revolutionized the field of cardiac regenerative medicine. Just over a decade later, the field has rapidly evolved through the expansion of identified molecular and genetic factors that can be used to optimize reprogramming efficiency. The integration of computational tools into the study of direct cardiac reprogramming has been critical to this progress. Advancements in transcriptomics, epigenetics, proteomics, genome editing, and machine learning have not only enhanced our understanding of the underlying mechanisms driving this cell fate transition, but have also driven innovations that push direct cardiac reprogramming closer to clinical application. This review article explores how these computational advancements have impacted and continue to shape the field of direct cardiac reprogramming.

Advances in microscopy characterization techniques for lipid nanocarriers in drug delivery: a comprehensive review

This review paper provides an in-depth analysis of the significance of lipid nanocarriers in drug delivery and the crucial role of characterization techniques. It explores various types of lipid nanocarriers and their applications, emphasizing the importance of microscopy-based characterization methods such as light microscopy, confocal microscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). The paper also delves into sample preparation, quantitative analysis, challenges, and future directions in the field. The review concludes by underlining the pivotal role of microscopy-based characterization in advancing lipid nanocarrier research and drug delivery technologies.

Exosomes derived from induced cardiopulmonary progenitor cells alleviate acute lung injury in mice

Cardiopulmonary progenitor cells (CPPs) constitute a minor subpopulation of cells that are commonly associated with heart and lung morphogenesis during embryonic development but completely subside after birth. This fact offers the possibility for the treatment of pulmonary heart disease (PHD), in which the lung and heart are both damaged. A reliable source of CPPs is urgently needed. In this study, we reprogrammed human cardiac fibroblasts (HCFs) into CPP-like cells (or induced CPPs, iCPPs) and evaluated the therapeutic potential of iCPP-derived exosomes for acute lung injury (ALI). iCPPs were created in passage 3 primary HCFs by overexpressing GLI1, WNT2, ISL1 and TBX5 (GWIT). Exosomes were isolated from the culture medium of passage 6-8 GWIT-iCPPs. A mouse ALI model was established by intratracheal instillation of LPS. Four hours after LPS instillation, ALI mice were treated with GWIT-iCPP-derived exosomes (5 × 109, 5 × 1010 particles/mL) via intratracheal instillation. We showed that GWIT-iCPPs could differentiate into cell lineages, such as cardiomyocyte-like cells, endothelial cells, smooth muscle cells and alveolar epithelial cells, in vitro. Transcription analysis revealed that GWIT-iCPPs have potential for heart and lung development. Intratracheal instillation of iCPP-derived exosomes dose-dependently alleviated LPS-induced ALI in mice by attenuating lung inflammation, promoting endothelial function and restoring capillary endothelial cells and the epithelial cells barrier. This study provides a potential new method for the prevention and treatment of cardiopulmonary injury, especially lung injury, and provides a new cell model for drug screening.

Ultrasonic synthesis of green lipid nanocarriers loaded with Scutellaria barbata extract: a sustainable approach for enhanced anticancer and antibacterial therapy

Ultrasonic manufacturing has emerged as a promising eco-friendly approach to synthesize lipid-based nanocarriers for targeted drug delivery. This study presents the novel ultrasonic preparation of lipid nanocarriers loaded with Scutellaria barbata extract, repurposed for anticancer and antibacterial use. High-frequency ultrasonic waves enabled the precise self-assembly of DSPE-PEG, Span 40, and cholesterol to form nanocarriers encapsulating the therapeutic extract without the use of toxic solvents, exemplifying green nanotechnology. Leveraging the inherent anticancer and antibacterial properties of Scutellaria barbata, the study demonstrates that lipid encapsulation enhances the bioavailability and controlled release of the extract, which is vital for its therapeutic efficacy. Dynamic light scattering and transmission electron microscopy analyses confirmed the increase in size and successful encapsulation post-loading, along with an augmented negative zeta potential indicating enhanced stability. A high encapsulation efficiency of 91.93% was achieved, and in vitro assays revealed the loaded nanocarriers' optimized release kinetics and improved antimicrobial potency against Pseudomonas aeruginosa, compared to the free extract. The combination of ultrasonic synthesis and Scutellaria barbata in an eco-friendly manufacturing process not only advances green nanotechnology but also contributes to sustainable practices in pharmaceutical manufacturing. The data suggest that this innovative nanocarrier system could provide a robust platform for the development of nanotechnology-based therapeutics, enhancing drug delivery efficacy while aligning with environmental sustainability.

Highly efficient electrocatalytic oxidation of levodopa as a Parkinson therapeutic drug based on modified screen-printed electrode

The current study presents the creation of a straightforward and sensitive sensor based on ZnO/Co3O4 nanocomposite modified screen-printed electrode (ZnO/Co3O4NC/SPE) for levodopa determination. At ZnO/Co3O4NC/SPE, an oxidative peak for levodopa solution in pH 6.0 phosphate buffer solution (PBS) were seen that were both more resolved and more enhanced. Levodopa was measured using differential pulse voltammetry (DPV), which showed an excellent linear range (0.001-800.0 μM) and detection limit (0.81 nM). The presence of interference did not affect the electrochemical response of levodopa at ZnO/Co3O4NC/SPE, demonstrating high selectivity. Levodopa in a real samples have been successfully detected using the manufactured sensor.

Stealth Nanocarriers in Cancer Therapy: a Comprehensive Review of Design, Functionality, and Clinical Applications

Nanotechnology has significantly transformed cancer treatment by introducing innovative methods for delivering drugs effectively. This literature review provided an in-depth analysis of the role of nanocarriers in cancer therapy, with a particular focus on the critical concept of the 'stealth effect.' The stealth effect refers to the ability of nanocarriers to evade the immune system and overcome physiological barriers. The review investigated the design and composition of various nanocarriers, such as liposomes, micelles, and inorganic nanoparticles, highlighting the importance of surface modifications and functionalization. The complex interaction between the immune system, opsonization, phagocytosis, and the protein corona was examined to understand the stealth effect. The review carefully evaluated strategies to enhance the stealth effect, including surface coating with polymers, biomimetic camouflage, and targeting ligands. The in vivo behavior of stealth nanocarriers and their impact on pharmacokinetics, biodistribution, and toxicity were also systematically examined. Additionally, the review presented clinical applications, case studies of approved nanocarrier-based cancer therapies, and emerging formulations in clinical trials. Future directions and obstacles in the field, such as advancements in nanocarrier engineering, personalized nanomedicine, regulatory considerations, and ethical implications, were discussed in detail. The review concluded by summarizing key findings and emphasizing the transformative potential of stealth nanocarriers in revolutionizing cancer therapy. This review enhanced the comprehension of nanocarrier-based cancer therapies and their potential impact by providing insights into advanced studies, clinical applications, and regulatory considerations.

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Unveiling innovative therapeutic strategies and future trajectories on stimuli-responsive drug delivery systems for targeted treatment of breast carcinoma

This comprehensive review delineates the latest advancements in stimuli-responsive drug delivery systems engineered for the targeted treatment of breast carcinoma. The manuscript commences by introducing mammary carcinoma and the current therapeutic methodologies, underscoring the urgency for innovative therapeutic strategies. Subsequently, it elucidates the logic behind the employment of stimuli-responsive drug delivery systems, which promise targeted drug administration and the minimization of adverse reactions. The review proffers an in-depth analysis of diverse types of stimuli-responsive systems, including thermoresponsive, pH-responsive, and enzyme-responsive nanocarriers. The paramount importance of material choice, biocompatibility, and drug loading strategies in the design of these systems is accentuated. The review explores characterization methodologies for stimuli-responsive nanocarriers and probes preclinical evaluations of their efficacy, toxicity, pharmacokinetics, and biodistribution in mammary carcinoma models. Clinical applications of stimuli-responsive systems, ongoing clinical trials, the potential of combination therapies, and the utility of multifunctional nanocarriers for the co-delivery of assorted drugs and therapies are also discussed. The manuscript addresses the persistent challenge of drug resistance in mammary carcinoma and the potential of stimuli-responsive systems in surmounting it. Regulatory and safety considerations, including FDA guidelines and biocompatibility assessments, are outlined. The review concludes by spotlighting future trajectories and emergent technologies in stimuli-responsive drug delivery, focusing on pioneering approaches, advancements in nanotechnology, and personalized medicine considerations. This review aims to serve as a valuable compendium for researchers and clinicians interested in the development of efficacious and safe stimuli-responsive drug delivery systems for the treatment of breast carcinoma.

An optimal method for diagnosing heart disease using combination of grasshopper evalutionary algorithm and support vector machines

Due to the importance of accurate diagnosis and prompt treatment of this condition, the medical world is searching for a solution for its early detection and efficient treatment. Heart disease is one of the leading causes of death in modern society. With the development of computer science today, this issue can be resolved using computers. Data mining is one of the solutions for diagnosing this illness. One of the cutting-edge disciplines, data mining, can aid in better decision-making in many areas of medicine, including disease diagnosis and treatment. In order to improve diagnosis accuracy, a combination method using the evolutionary algorithms locust and support vector machine has been tested in this study. Use should be made of heart disease. Because of the hybrid nature of this approach, normalization is actually carried out in three steps: first, by using pre-processing operations to remove unknown and outlier data from the data set; second, by using the locust evolutionary algorithm to choose the best features from the available features; and third, by classifying the data set using a support vector machine. The accuracy criterion for the proposed method compared to Niobizin methods, neural networks, and J48 trees improved by 18 %, 30 %, and 24 %, respectively, after implementing it on the data set and comparing it with other algorithms used in the field of heart disease diagnosis.

Electrochemical DNA-nano biosensor for the detection of Goserelin as anticancer drug using modified pencil graphite electrode

Goserelin is an effective anticancer drug, but naturally causes several side effects. Hence the determination of this drug in biological samples, plays a key role in evaluating its effects and side effects. The current studies have concentrated on monitoring Goserelin using an easy and quick DNA biosensor for the first time. In this study, copper(II) oxide nanoparticles were created upon the surface of multiwalled carbon nanotubes (CuO/MWCNTs) as a conducting mediator. The modified pencil graphite electrode (ds-DNA/PA/CuO/MWCNTs/PGE) has been modified with the help of polyaniline (PA), ds-DNA, and CuO/MWCNTs nanocomposite. Additionally, the issue with the bio-electroanalytical guanine oxidation signal in relation to ds-DNA at the surface of PA/CuO/MWCNTs/PGE has been examined to determination Goserelin for the first time. It also, established a strong conductive condition to determination Goserelin in nanomolar concentration. Thus, Goserelin's determining, however, has a 0.21 nM detection limit and a 1.0 nM-110.0 µM linear dynamic range according to differential pulse voltammograms (DPV) of ds-DNA/PA/CuO/MWCNTs/PGE. Furthermore, the molecular docking investigation highlighted that Goserelin is able to bind ds-DNA preferentially and supported the findings of the experiments. The determining of Goserelin in real samples has been effectively accomplished in the last phase using ds-DNA/PA/CuO/MWCNTs/PGE.

Revolutionizing Infection Control: Harnessing MXene-Based Nanostructures for Versatile Antimicrobial Strategies and Healthcare Advancements

The escalating global health challenge posed by infections prompts the exploration of innovative solutions utilizing MXene-based nanostructures. Societally, the need for effective antimicrobial strategies is crucial for public health, while scientifically, MXenes present promising properties for therapeutic applications, necessitating scalable production and comprehensive characterization techniques. Here we review the versatile physicochemical properties of MXene materials for combatting microbial threats and their various synthesis methods, including etching and top-down or bottom-up techniques. Crucial characterization techniques such as XRD, Raman spectroscopy, SEM/TEM, FTIR, XPS, and BET analysis provide insightful structural and functional attributes. The review highlights MXenes' diverse antimicrobial mechanisms, spanning membrane disruption and oxidative stress induction, demonstrating efficacy against bacterial, viral, and fungal infections. Despite translational hurdles, MXene-based nanostructures offer broad-spectrum antimicrobial potential, with applications in drug delivery and diagnostics, presenting a promising path for advancing infection control in global healthcare.

Exploring the Therapeutic Potential of Lawsone and Nanoparticles in Cancer and Infectious Disease Management

Lawsone, a naturally occurring compound found in henna, has been used in traditional medicine for centuries due to its diverse biological activities. In recent years, its nanoparticle-based structure has gained attention in cancer and infectious disease research. This review explores the therapeutic potential of lawsone and its nanoparticles in the context of cancer and infectious diseases. Lawsone exhibits promising anticancer properties by inducing apoptosis and inhibiting cell proliferation, while its nanoparticle formulations enhance targeted delivery and efficacy. Moreover, lawsone demonstrates significant antimicrobial effects against various pathogens. The unique physicochemical properties of lawsone nanoparticles enable efficient cellular uptake and targeted delivery. Potential applications in combination therapy and personalized medicine open new avenues for cancer and infectious disease treatment. While clinical trials are needed to validate their safety and efficacy, lawsone-based nanoparticles offer hope in addressing unmet medical needs and revolutionizing therapeutic approaches.

Microfluidic-based nanoemulsion of Ocimum basilicum extract: Constituents, stability, characterization, and potential biomedical applications for improved antimicrobial and anticancer properties

This paper reports on the findings from a study that aimed to identify and characterize the constituents of Ocimum basilicum extract using gas chromatography-mass spectrometry (GC-MS) analysis, as well as assess the physicochemical properties and stability of nanoemulsions formulated with O. basilicum extract. The GC-MS analysis revealed that the O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for use in the biomedical field, with a small and uniform particle size distribution, a negative zeta potential, and high encapsulation efficiency for the O. basilicum extract. The nanoemulsions exhibited spherical morphology and remained physically stable for up to 6 months. In vitro release studies indicated sustained release of the extract from the nanoemulsion formulation compared to the free extract solution. Furthermore, the developed nanoformulation exhibited enhanced anticancer properties against K562 cells while demonstrating low toxicity in normal cells (HEK293). The O. basilicum extract demonstrated antimicrobial activity against Pseudomonas aeruginosa, Candida albicans, and Staphylococcus epidermidis, with a potential synergistic effect observed when combined with the nanoemulsion. These findings contribute to the understanding of the constituents and potential applications of O. basilicum extract and its nanoemulsion formulation in various fields, including healthcare and pharmaceutical industries. Further optimization and research are necessary to maximize the efficacy and antimicrobial activity of the extract and its nanoformulation. RESEARCH HIGHLIGHTS: This study characterized the constituents of O. basilicum extract and assessed the physicochemical properties and stability of its nanoemulsion formulation. The O. basilicum extract contained 22 components, with Caryophyllene and Naringenin identified as the primary active constituents. The nanoemulsion formulation demonstrated excellent potential for biomedical applications, with sustained release of the extract, low toxicity, and enhanced anticancer and antimicrobial properties. The findings contribute to the understanding of the potential applications of O. basilicum extract and its nanoemulsion formulation in healthcare and pharmaceutical industries, highlighting the need for further optimization and research.

Role of Metal-Organic Frameworks (MOFs) in treating and diagnosing microbial infections

This review article highlights the innovative role of metal-organic frameworks (MOFs) in addressing global healthcare challenges related to microbial infections. MOFs, comprised of metal nodes and organic ligands, offer unique properties that can be applied in the treatment and diagnosis of these infections. Traditional methods, such as antibiotics and conventional diagnostics, face issues such as antibiotic resistance and diagnostic limitations. MOFs, with their highly porous and customizable structure, can encapsulate and deliver therapeutic or diagnostic molecules precisely. Their large surface area and customizable pore structures allow for sensitive detection and selective recognition of microbial pathogens. They also show potential in delivering therapeutic agents to infection sites, enabling controlled release and possible synergistic effects. However, challenges like optimizing synthesis techniques, enhancing stability, and developing targeted delivery systems remain. Regulatory and safety considerations for clinical translation also need to be addressed. This review not only explores the potential of MOFs in treating and diagnosing microbial infections but also emphasizes their unique approach and discusses existing challenges and future directions.

Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization

Hepato-Cellular Carcinoma (HCC) is the most common type of liver cancer that often occurs in people with chronic liver diseases such as cirrhosis. Although HCC is known as a fatal disease, early detection can lead to successful treatment and improve survival chances. In recent years, the development of computer recognition systems using machine learning approaches has been emphasized by researchers. The effective performance of these approaches for the diagnosis of HCC has been proven in a wide range of applications. With this motivation, this paper proposes a hybrid machine learning approach including effective feature selection and ensemble classification for HCC detection, which is developed based on the Harris Hawks Optimization (HHO) algorithm. The proposed ensemble classifier is based on the bagging technique and is configured based on the decision tree method. Meanwhile, HHO as an emerging meta-heuristic algorithm can select a subset of the most suitable features related to HCC for classification. In addition, the proposed method is equipped with several strategies for handling missing values and data normalization. The simulations are based on the HCC dataset collected by the Coimbra Hospital and University Center (CHUC). The results of the experiments prove the acceptable performance of the proposed method. Specifically, the proposed method with an accuracy of 97.13 % is superior in comparison with the equivalent methods such as LASSO and DTPSO.

A semi-supervised ensemble clustering algorithm for discovering relationships between different diseases by extracting cell-to-cell biological communications

INTRODUCTION

In recent decades, many theories have been proposed about the cause of hereditary diseases such as cancer. However, most studies state genetic and environmental factors as the most important parameters. It has been shown that gene expression data are valuable information about hereditary diseases and their analysis can identify the relationships between these diseases.

OBJECTIVE

Identification of damaged genes from various diseases can be done through the discovery of cell-to-cell biological communications. Also, extraction of intercellular communications can identify relationships between different diseases. For example, gene disorders that cause damage to the same cells in both breast and blood cancers. Hence, the purpose is to discover cell-to-cell biological communications in gene expression data.

METHODOLOGY

The identification of cell-to-cell biological communications for various cancer diseases has been widely performed by clustering algorithms. However, this field remains open due to the abundance of unprocessed gene expression data. Accordingly, this paper focuses on the development of a semi-supervised ensemble clustering algorithm that can discover relationships between different diseases through the extraction of cell-to-cell biological communications. The proposed clustering framework includes a stratified feature sampling mechanism and a novel similarity metric to deal with high-dimensional data and improve the diversity of primary partitions.

RESULTS

The performance of the proposed clustering algorithm is verified with several datasets from the UCI machine learning repository and then applied to the FANTOM5 dataset to extract cell-to-cell biological communications. The used version of this dataset contains 108 cells and 86,427 promoters from 702 samples. The strength of communication between two similar cells from different diseases indicates the relationship of those diseases. Here, the strength of communication is determined by promoter, so we found the highest cell-to-cell biological communication between "basophils" and "ciliary.epithelial.cells" with 62,809 promoters.

CONCLUSION

The maximum cell-to-cell biological similarity in each cluster can be used to detect the relationship between different diseases such as cancer.

Direct cellular reprogramming techniques for cardiovascular regenerative therapeutics

Cardiovascular diseases remain a leading cause of hospitalization affecting approximately 38 million people worldwide. While pharmacological and revascularization techniques can improve the patient's survival and quality of life, they cannot help reversing myocardial infarction injury and heart failure. Direct reprogramming of somatic cells to cardiomyocyte and cardiac progenitor cells offers a new approach to cellular reprogramming and paves the way for translational regenerative medicine. Direct reprogramming can bypass the pluripotent stage with the potential advantage of non-immunogenic cell products, reduced carcinogenic risk, and no requirement for embryonic tissue. The process of directly reprogramming cardiac cells was first achieved through the overexpression of transcription factors such as GATA4, MEF2C, and TBX5. However, over the past decade, significant work has been focused on enhancing direct reprogramming using a mixture of transcription factors, microRNAs, and small molecules to achieve cardiac cell fate. This review discusses the evolution of direct reprogramming, recent progress in achieving efficient cardiac cell fate conversion, and describes the reprogramming mechanisms at a molecular level. We also explore various viral and non-viral delivery methods currently being used to aid in the delivery of reprogramming factors to improve efficiency. However, further studies will be needed to overcome molecular and epigenetic barriers to successfully achieve translational cardiac regenerative therapeutics.

Fibroblast Reprogramming in Cardiac Repair

Cardiovascular disease is one of the major causes of death worldwide. Limited proliferative capacity of adult mammalian cardiomyocytes has prompted researchers to exploit regenerative therapy after myocardial injury, such as myocardial infarction, to attenuate heart dysfunction caused by such injury. Direct cardiac reprogramming is a recently emerged promising approach to repair damaged myocardium by directly converting resident cardiac fibroblasts into cardiomyocyte-like cells. The achievement of in vivo direct reprogramming of fibroblasts has been shown, by multiple laboratories independently, to improve cardiac function and mitigate fibrosis post-myocardial infarction, which holds great potential for clinical application. There have been numerous pieces of valuable work in both basic and translational research to enhance our understanding and continued refinement of direct cardiac reprogramming in recent years. However, there remain many challenges to overcome before we can truly take advantage of this technique to treat patients with ischemic cardiac diseases. Here, we review recent progress of fibroblast reprogramming in cardiac repair, including the optimization of several reprogramming strategies, mechanistic exploration, and translational efforts, and we make recommendations for future research to further understand and translate direct cardiac reprogramming from bench to bedside. Challenges relating to these efforts will also be discussed.

Noninvasive grading of glioma brain tumors using magnetic resonance imaging and deep learning methods

PURPOSE

Convolutional Neural Networks (ConvNets) have quickly become popular machine learning techniques in recent years, particularly in the classification and segmentation of medical images. One of the most prevalent types of brain cancers is glioma, and early, accurate diagnosis is essential for both treatment and survival. In this study, MRI scans were examined utilizing deep learning techniques to examine glioma diagnosis studies.

METHODS

In this systematic review, keywords were used to obtain English-language studies from the Arxiv, IEEE, Springer, ScienceDirect, and PubMed databases for the years 2010-2022. The material needed for review was then collected from the articles once they had been chosen based on the entry and exit criteria and in accordance with the research's goal.

RESULTS

Finally, 77 different academic articles were chosen. According to a study of published articles, glioma brain tumors were discovered, categorized, and segmented utilizing a coordinated approach that included image collecting, pre-processing, model design and execution, and model output evaluation. The majority of investigations have used publicly accessible photo databases and already-trained algorithms. The bulk of studies have employed Dice's classification accuracy and similarity coefficient metrics to assess model performance.

CONCLUSION

The results of this study indicate that glioma segmentation has received more attention from researchers than glioma detection and classification. It is advised that more research be done in the areas of glioma detection and, particularly, grading in order to be included in systems that support medical diagnosis.

Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis

INTRODUCTION

Breast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science as one of the most important issues. In addition to reducing costs, the diagnosis of benign or malignant breast cancer is very important in determining the treatment method.

OBJECTIVE

The purpose of this paper is to present a model based on data mining techniques including feature selection and ensemble classification that can accurately predict breast cancer patients in the early stages.

METHODOLOGY

The proposed breast cancer detection model is developed by joining Adaptive Differential Evolution (ADE) algorithm for feature selection and Learning Vector Quantization (LVQ) neural network for classification. Our proposed model as ADE-LVQ has the ability to automatically and quickly diagnose breast cancer patients into two classes, benign and malignant. As a new evolutionary approach, ADE performs optimal configuration for LVQ neural network in addition to selecting effective features from breast cancer data. Meanwhile, we configure an ensemble classification technique based on LVQ, which significantly improves the prediction performance.

RESULTS

ADE-LVQ has been analyzed from different perspectives on different datasets from Wisconsin breast cancer database. We apply different approaches to handle missing values and improve data quality on this database. The results of the simulations showed that the ADE-LVQ model is more successful than the equivalent and state-of-the-art models in diagnosing breast cancer patients. Also, ADE-LVQ provides better performance with less complexity, considering feature selection and ensemble learning. In particular, ADE-LVQ improves accuracy (up to 3.4%) and runtime (up to 2.3%) on average compared to the existing best method.

CONCLUSION

Combined methods based on data mining techniques for breast cancer diagnosis can help doctors in making better decisions for disease treatment.

Can we stop one heart from breaking: triumphs and challenges in cardiac reprogramming

Ischemic cardiac injury causes irreversible muscle loss and scarring, but recent years have seen dramatic advances in cardiac reprogramming, the field focused on regenerating cardiac muscle. With SARS-CoV2 increasing the age-adjusted cardiovascular disease mortality rate, it is worth evaluating the state of this field. Here, we summarize novel innovations in reprogramming strategies, insights into their mechanisms, and technologies for factor delivery. We also propose a broad model of reprogramming to suggest directions for future research. Poet Emily Dickinson wrote, "If I can stop one heart from breaking, I shall not live in vain." Today, researchers studying cardiac reprogramming view this line as a call to action to translate this revolutionary approach into life-saving treatments for patients with cardiovascular diseases.

Data mining techniques in breast cancer diagnosis at the cellular-molecular level

INTRODUCTION

Studies in the field of better diagnosis of breast cancer using machine learning and data mining techniques have always been promising. A new diagnostic method can detect the characteristics of breast cancer in the early stages and help in better treatment. The aim of this study is to provide a method for early detection of breast cancer by reducing human errors based on data mining techniques in medicine using accurate and rapid screening.

METHODOLOGY

The proposed method includes data pre-processing and image quality improvement in the first step. The second step consists of separating cancer cells from healthy breast tissue and removing outliers using image segmentation. Finally, a classification model is configured by combining deep neural networks in the third phase. The proposed ensemble classification model uses several effective features extracted from images and is based on majority vote. This model can be used as a screening system to diagnose the grade of invasive ductal carcinoma of the breast.

RESULTS

Evaluations have been done using two histopathological microscopic datasets including patients with invasive ductal carcinoma of the breast. With extracting high-level features with average accuracies of 92.65% and 93.34% in these two datasets, the proposed method has succeeded in quickly diagnosing and classifying breast cancer with high performance.

CONCLUSION

By combining deep neural networks and extracting features affecting breast cancer, the ability to diagnose with the highest accuracy is provided, and this is a step toward helping specialists and increasing the chances of patients' survival.

Cancer detection in breast cells using a hybrid method based on deep complex neural network and data mining

INTRODUCTION

Diagnosis of cancer in breast cells is an important and vital issue in the field of medicine. In this context, the use of advanced methods such as deep complex neural networks and data mining can significantly improve the accuracy and speed of diagnosis. A hybrid approach that can be effective in breast cancer diagnosis is the use of deep complex neural networks and data mining. Due to their powerful nonlinear capabilities in extracting complex features from data, deep neural networks have a very good ability to detect patterns related to cancer. By analyzing millions of data related to breast cells and recognizing common and unusual patterns in them, these networks are able to diagnose cancer with high accuracy. Also, the use of data mining method plays an important role in this process.

METHODOLOGY

Using data mining algorithms and techniques, useful information can be extracted from the available data and the characteristics of healthy and cancerous cells can be separated. This information can be given as input to the deep neural network to achieve more accurate diagnosis. Another method to diagnose breast cancer is the use of thermography, which we use in this research along with data mining and deep learning.

RESULTS

Thermography uses an infrared camera to record the temperature of the target area. This method of breast cancer imaging is less expensive and completely safe compared to other methods. A total of 187 volunteers including 152 healthy people and 35 cancer patients were evaluated. Each person had ten thermographic images, resulting in a total of 1870 thermographic images. Four alternative deep complex neural network models, namely ResNet18, ResNet50, VGG19, and Xception, were used to identify thermal images, including benign and malignant images.

CONCULSION

The evaluation results showed that the use of a combined method based on deep complex neural network and data mining in the diagnosis of cancer in breast cells can bring a significant improvement in the accuracy and speed of diagnosis of this important disease.

Classification of cancer cells and gene selection based on microarray data using MOPSO algorithm

PURPOSE

Microarray information is crucial for the identification and categorisation of malignant tissues. The very limited sample size in the microarray has always been a challenge for classification design in cancer research. As a result, by pre-processing gene selection approaches and genes lacking their information, the microarray data are deleted prior to categorisation. In essence, an appropriate gene selection technique can significantly increase the accuracy of illness (cancer) classification.

METHODS

For the classification of high-dimensional microarray data, a novel approach based on the hybrid model of multi-objective particle swarm optimisation (MOPSO) is proposed in this research. First, a binary vector representing each particle's position is presented at random. A gene is represented by each bit. Bit 0 denotes the absence of selection of the characteristic (gene) corresponding to it, while bit 1 denotes the selection of the gene. Therefore, the position of each particle represents a set of genes, and the linear Bayesian discriminant analysis classification algorithm calculates each particle's degree of fitness to assess the quality of the gene set that particle has chosen. The suggested methodology is applied to four different cancer database sets, and the results are contrasted with those of other approaches currently in use.

RESULTS

The proposed algorithm has been applied on four sets of cancer database and its results have been compared with other existing methods. The results of the implementation show that the improvement of classification accuracy in the proposed algorithm compared to other methods for four sets of databases is 25.84% on average. So that it has improved by 18.63% in the blood cancer database, 24.25% in the lung cancer database, 27.73% in the breast cancer database, and 32.80% in the prostate cancer database. Therefore, the proposed algorithm is able to identify a small set of genes containing information in a way choose to increase the classification accuracy.

CONCLUSION

Our proposed solution is used for data classification, which also improves classification accuracy. This is possible because the MOPSO model removes redundancy and reduces the number of redundant and redundant genes by considering how genes are correlated with each other.

A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis

INTRODUCTION

Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning approaches cannot replace professional humans, but they can change the treatment of diseases such as cancer and be used as medical assistants.

BACKGROUND

Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. Feature selection and classification are common data mining techniques in machine learning that can provide breast cancer diagnosis with high speed, low cost and high precision.

METHODOLOGY

This paper proposes a new intelligent approach using an integrated filter-evolutionary search-based feature selection and an optimized ensemble classifier for breast cancer diagnosis. The selected features mainly relate to the viable solution as the selected features are successfully used in the breast cancer disease classification process. The proposed feature selection method selects the most informative features from the original feature set by integrating adaptive thresholder information gain-based feature selection and evolutionary gravity-search-based feature selection. Meanwhile, classification model is done by proposing a new intelligent multi-layer perceptron neural network-based ensemble classifier.

RESULTS

The simulation results show that the proposed method provides better performance compared to the state-of-the-art algorithms in terms of various criteria such as accuracy, sensitivity and specificity. Specifically, the proposed method achieves an average accuracy of 99.42% on WBCD, WDBC and WPBC datasets from Wisconsin database with only 56.7% of features.

CONCLUSION

Systems based on intelligent medical assistants configured with machine learning approaches are an important step toward helping doctors to detect breast cancer early.

Antibacterial activity and DNA interaction of triazine iron and ruthenium complexes: spectroscopic, voltammetric and theoretical studies

The 2,4,6-tris(2-pyridyl)-1,3,5-triazine (tptz), [Ru(μ-tptz)2]Cl2 and [Fe(μ-tptz)2]Cl2, complexes containing Ru (1) and Fe (2) are created. Using electronic absorption spectroscopy, fluorescence spectroscopy, circular dichroism spectroscopy, viscosity measurement and electrochemistry, as well as two complexes with Fish Salmon DNA (FS-DNA), the binding interactions of these complexes were investigated. According to binding assays, complexes bind to DNA through a mild intercalation mechanism, most likely via the DNA helix's base pairs being intercalated by the tptz ligand. Additionally, complex (2) is more capable of binding than complex (1). The electrochemical method offers a quick and easy way to determine the binding constant (Kb). The antibacterial performance of these complexes versus Gram-positive and Gram-negative bacteria was examined using the zone of inhibition test, MIC, and MBC method, and the results revealed that complex (2) exhibits strong antibacterial activity against these bacteria. The outcomes of this investigation will help in understanding DNA interaction mechanisms as well as the creation of a prospective one. Additionally, the density functional theory (DFT) computation included probes of DNA structure and conformation as well as potential pharmacological regulators for particular disorders to fully explain the experimental results.

Combining ensemble classification and integrated filter-evolutionary search for breast cancer diagnosis

INTRODUCTION

Breast cancer is one of the most common chronic diseases and the second cause of death among women, where its timely diagnosis plays an important role in survival and treatment. Advances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. In recent years, the development of these systems with data mining techniques and machine learning approaches has attracted the attention of researchers.

METHODOLOGY

This study presents a new hybrid approach using data mining techniques including feature selection and classification. Feature selection is configured using a method based on integrated filter-evolutionary search, where this method includes an evolutionary algorithm and information gain. The proposed feature selection method can provide the most suitable features by reducing dimensions for breast cancer classification. Meanwhile, we introduce an ensemble classification approach based on neural networks whose parameters are adjusted by an evolutionary algorithm.

RESULTS

The effectiveness of the proposed method has been evaluated by several real datasets from the UCI machine learning repository. The results of simulations in terms of various metrics such as accuracy, precision and recall show that the proposed method is 12% better than the best existing methods on average.

CONCLUSION

The evaluation of the proposed method confirms its effectiveness for breast cancer diagnosis as an intelligent medical assistant.

Spectroscopic and molecular modeling studies of binding interaction between the new complex of yttrium and 1,10-phenanthroline derivatives with DNA and BSA

In this study, the 4,9 diazafluoren-9-one ligand and [Y(Daf)2Cl3.OH2] complex were synthesized. The interaction of this complex with DNA and bovine serum albumin (BSA) was investigated by UV-vis and fluorescence spectroscopy. The molecular docking method was used to confirm the experimental results, investigate the type of interaction, and determine the binding site. The binding constant and Stern-Volmer constant were calculated using spectroscopy techniques. The binding constant of the Y-complex with DNA and BSA obtained using the UV-vis technique was 1.61 × 105 M-1 and 0.49 × 105 M-1, while that obtained using the fluorescence method was 3.39 × 105 M-1 and 3.63 × 105 M-1, respectively. The results of experimental and theoretical data showed that the interaction between the yttrium complex and DNA and BSA is driven by the hydrogen bond and van der Waals interaction, respectively. The yttrium complex communicates with DNA via the groove interaction. This complex has high binding energy with bovine serum albumin. In addition, the molecular docking results showed that the complex binds to the IIA subdomain of BSA (site I). Finally, anticancer activity of the yttrium complex was studied on MCF-7 and A549 cell lines by using the MTT method. The IC50 values obtained showed that the yttrium complex possesses anticancer activity.

In vitro BSA-binding, antimicrobial, and antitumor activity against human cancer cell lines of two lanthanide (III) complexes

The investigation involved examining the binding of two lanthanide complexes, specifically those containing Holmium (Ho) and Dysprosium (Dy), with a ligand called 1, 10-phenanthroline (phen), and bovine serum albumin (BSA). The evaluation was carried out utilizing fluorescence measurements, Förster theory, and docking studies. The findings indicated that both the Ho-complex and Dy-complex possessed a significant ability to quench the emission of the protein. Furthermore, the primary mechanism of interaction was identified as a static process. The K b values indicate a strong tendency of these complexes for binding with BSA. The Kb values show the strangely high affinity of BSA to complexes and the following order for binding affinity: Ho-complex > Dy-complex. The thermodynamic parameters were found to be negative, affirming that the main forces driving the interaction between BSA and the lanthanide complexes are van der Waals engagement and hydrogen bonds. Additionally, the investigation included the examination of competition site markers, and molecular docking proposed that the engagement sites of the Ho-complex and Dy-complex with BSA were predominantly located in site 3 (specifically, subdomain IB). Moreover, the Ho-complex and Dy-complex were specifically chosen for their potential anticancer and antimicrobial properties. Consequently, these complexes could present promising prospects as novel candidates for anti-tumor and antibacterial applications.

Automatic breast cancer diagnosis based on hybrid dimensionality reduction technique and ensemble classification

INTRODUCTION

Feature selection in the face of high-dimensional data can reduce overfitting and learning time, and at the same time improve the accuracy and efficiency of the system. Since there are many irrelevant and redundant features in breast cancer diagnosis, removing such features leads to more accurate prediction and reduced decision time when dealing with large-scale data. Meanwhile, ensemble classifiers are powerful techniques to improve the prediction performance of classification models, where several individual classifier models are combined to achieve higher accuracy.

METHODS

In this paper, an ensemble classifier algorithm based on multilayer perceptron neural network is proposed for the classification task, in which the parameters (e.g., number of hidden layers, number of neurons in each hidden layer, and weights of links) are adjusted based on an evolutionary approach. Meanwhile, this paper uses a hybrid dimensionality reduction technique based on principal component analysis and information gain to address this problem.

RESULTS

The effectiveness of the proposed algorithm was evaluated based on the Wisconsin breast cancer database. In particular, the proposed algorithm provides an average of 17% better accuracy compared to the best results obtained from the existing state-of-the-art methods.

CONCLUSION

Experimental results show that the proposed algorithm can be used as an intelligent medical assistant system for breast cancer diagnosis.

Synthesis and characterization of novel denosumab/magnesium-based metal organic frameworks nanocomposite prepared by ultrasonic route as drug delivery system for the treatment of osteoporosis

Introduction: The metal-organic frameworks (MOF) have shown fascinating possibilities in biomedical applications, and designing a drug delivery system (DDS) based on the MOF is important. This work aimed at developing a suitable DDS based on Denosumab-loaded Metal Organic Framework/Magnesium (DSB@MOF (Mg)) for attenuating osteoarthritis. Materials and Methods: The MOF (Mg) (Mg3(BPT)2(H2O)4) was synthesized using a sonochemical protocol. The efficiency of MOF (Mg) as a DDS was evaluated by loading and releasing DSB as a drug. In addition, the performance of MOF (Mg) was evaluated by releasing Mg ions for bone formation. The MOF (Mg) and DSB@MOF (Mg) cytotoxicity towards the MG63 cells were explored by MTT assay. Results: MOF (Mg) characterized by using XRD, SEM, EDX, TGA, and BET. Drug loading, and releasing experiments proved that DSB was loaded on the MOF (Mg) and approximately 72% DSB was released from it after 8 h. The characterization techniques showed that MOF (Mg) was successfully synthesized with good crystal structure and thermal stability. The result of BET showed that MOF (Mg) had high surface areas and pore volume. This is the reason why its 25.73% DSB was loaded in the subsequent drug-loading experiment. Drug release and ion release experiments indicated DSB@MOF (Mg) had a good controlled release of DSB and Mg ions in solution. Cytotoxicity assay confirmed that the optimum dose of it had excellent biocompatibility and could stimulate the proliferation of MG63 cells as time went on. Conclusion: Due to the high loading amount of DSB and releasing time, DSB@MOF (Mg) can be promising as a suitable candidate for relieving bone pain caused by osteoporosis, with ossification-reinforcing functions.

Investigation of kinetic, isotherm and adsorption efficacy of thorium by orange peel immobilized on calcium alginate

In this research work the thorium uptake on immobilized protonated orange peel was studied in a batch system. The effects of effective parameters such as biosorbent dosage, initial metal ion concentration, and contact time on the biosorption of thorium were analyzed. The biosorption capacity of the immobilized orange peel for thorium at optimal conditions of initial pH 3.8, biosorbent dosage 8 g/L, and initial thorium concentration 170 mg/L was found to be 18.65 mg/g. According to the results of contact time, the biosorption process reached equilibrium after around 10 h of contact. Investigation of the kinetics showed that the biosorption of thorium onto immobilized orange peel follows the pseudo-second-order model. The Langmuir and Freundlich isotherms were used to model the experimental equilibrium data. The results showed better agreement by the Langmuir isotherm. The maximum absorption capacity of immobilized protonated orange peel for thorium adsorption was predicted by the Langmuir isotherm at 29.58 mg/g.

An ensemble classifier method based on teaching-learning-based optimization for breast cancer diagnosis

INTRODUCTION

Epidemiological studies show that breast cancer is the most common cancer in women in the world. Breast cancer treatment can be very effective, especially when the disease is detected in the early stages. The goal can be achieved by using large-scale breast cancer data with the machine learning models METHODS: This paper proposes a new intelligent approach using an optimized ensemble classifier for breast cancer diagnosis. The classification is done by proposing a new intelligent Group Method of Data Handling (GMDH) neural network-based ensemble classifier. This method improves the performance of the machine learning technique by using a Teaching-Learning-Based Optimization (TLBO) algorithm to optimize the hyperparameters of the classifier. Meanwhile, we use TLBO as an evolutionary method to address the problem of appropriate feature selection in breast cancer data.

RESULTS

The simulation results show that the proposed method has a better accuracy between 7 and 26% compared to the best results of the existing equivalent algorithms.

CONCLUSION

According to the obtained results, we suggest the proposed algorithm as an intelligent medical assistant system for breast cancer diagnosis.

Reduction of rupture risk in ICA aneurysms by endovascular techniques of coiling and stent: numerical study

The initiation, growth, and rupture of cerebral aneurysms are directly associated with Hemodynamic factors. This report tries to disclose effects of endovascular technique (coiling and stenting) on the quantitative intra-aneurysmal hemodynamic and the rupture of cerebral aneurysms. In this paper, Computational Fluid Dynamic are done to investigate and compare blood hemodynamic inside aneurysm under effects of deformation (due to stent) and coiling of aneurysm. The blood stream inside the sac of aneurysm as well as pressure and OSI distribution on the aneurysm wall are compared in nine cases and results of two distinctive cases are compared and reported. Obtained results specifies that the mean WSS is reduced up to 20% via coiling of the aneurysm while the deformation of the aneurysm (applying stent) could reduce the mean WSS up to 71%. In addition, comparison of the blood hemodynamic shows that the blood bifurcation occurs in the dome of aneurysm when endovascular technique for the treatment is not applied. It is found that the bifurcation occurs at ostium section when ICA aneurysm is deformed by the application of stent. The impacts of coiling are mainly limited since the blood flow entrance is not limited in this technique and WSS is not reduced substantial. However, usage of stent deforms the aneurysm angle with the orientation of parent vessel and this reduces blood velocity at entrance of the ostium and consequently, WSS is decreased when deformation of the aneurysm fully occurs. These qualitative procedures provide a preliminary idea for more profound quantitative examination intended for assigning aneurysm risk of upcoming rupture.

Investigation of the stent induced deformation on hemodynamic of internal carotid aneurysms by computational fluid dynamics

Application of the stent for treatment of the internal carotid artery (ICA) aneurysms has been extensively increased in recent decades. In the present work, stent-induced deformations of the parent vessel of ICA aneurysms are fully investigated. This study tries to visualize blood stream and calculated hemodynamic factors inside the four ICA aneurysms after deformations of parent vessel. For the simulation of the non-Newtonian blood stream, computational fluid dynamic is applied with one-way Fluid-Solid interaction (FSI) approach. Four ICA aneurysms with different ostium sizes and neck vessel angle are selected for this investigation. Wall shear stress on wall of aneurysm is analyzed in two angles of deformation due to application of the stent. Blood flow investigation shows that the deformation of the aneurysm limited blood entrance to the sac region and this decreases the blood velocity and consequently oscillatory shear index (OSI) on the sac wall. It is also observed that the stent-induced deformation is more effective on those cases with extraordinary OSI values on aneurysm wall.

Classification of skin cancer stages using a AHP fuzzy technique within the context of big data healthcare

BACKGROUND AND OBJECTIVES

Skin conditions in humans can be challenging to diagnose. Skin cancer manifests itself without warning. In the future, these illnesses, which have been an issue for many, will be identified and treated. With the rapid expansion of big data healthcare framework summarization and precise prediction in early stage skin cancer diagnosis, the fuzzy AHP technique produces the best results in both of these fields. Big data is a potent technology that enhances the standard of research and generates better results more rapidly. This essay gives a way to group the stages of skin cancer treatment based on this information. The combination of support vector machine multi-class classification and fuzzy selector with radial basis function-based binary migration classification of virtual machines is put through a number of experiments. The connections have been categorized.

ANALYSIS METHOD

These examinations have determined whether the tumors are malignant or benign and how malignant they are. The images of spots on the skin acquired from laboratory images make up the data set used for processing. We have talked about how to handle and process large datasets in the area of classification using MATLAB, like skin spot images.

FINDINGS

Our technique outperforms competing approaches by maintaining stability even as the size of the data set grows rapidly and with little error. In comparison to other methods, the suggested approach meets the accuracy criterion for correct classifications with a score of 90.86%. As a result, the proposed solution is viewed as a potentially useful tool for identifying mass stages and categorizing skin cancer severity.

Direct cardiac reprogramming: A new technology for cardiac repair

Cardiovascular disease is one of the leading causes of morbidity and mortality worldwide, with myocardial infarctions being amongst the deadliest manifestations. Reduced blood flow to the heart can result in the death of cardiac tissue, leaving affected patients susceptible to further complications and recurrent disease. Further, contemporary management typically involves a pharmacopeia to manage the metabolic conditions contributing to atherosclerotic and hypertensive heart disease, rather than regeneration of the damaged myocardium. With modern healthcare extending lifespan, a larger demographic will be at risk for heart disease, driving the need for novel therapeutics that surpass those currently available in efficacy. Transdifferentiation and cellular reprogramming have been looked to as potential methods for the treatment of diseases throughout the body. Specifically targeting the fibrotic cells in cardiac scar tissue as a source to be reprogrammed into induced cardiomyocytes remains an appealing option. This review aims to highlight the history of and advances in cardiac reprogramming and describe its translational potential as a treatment for cardiovascular disease.

dCas9-Based PDGFR-β Activation ADSCs Accelerate Wound Healing in Diabetic Mice through Angiogenesis and ECM Remodeling

The chronic wound represents a serious disease characterized by a failure to heal damaged skin and surrounding soft tissue. Mesenchymal stem cells (MSCs) derived from adipose tissue (ADSCs) are a promising therapeutic strategy, but their heterogeneity may result in varying or insufficient therapeutic capabilities. In this study, we discovered that all ADSCs populations expressed platelet-derived growth factor receptor β (PDGFR-β), while the expression level decreased dynamically with passages. Thus, using a CRISPRa-based system, we endogenously overexpressed PDGFR-β in ADSCs. Moreover, a series of in vivo and in vitro experiments were conducted to determine the functional changes in PDGFR-β activation ADSCs (AC-ADSCs) and to investigate the underlying mechanisms. With the activation of PDGFR-β, AC-ADSCs exhibited enhanced migration, survival, and paracrine capacity relative to control ADSCs (CON-ADSCs). In addition, the secretion components of AC-ADSCs contained more pro-angiogenic factors and extracellular matrix-associated molecules, which promoted the function of endothelial cells (ECs) in vitro. Additionally, in in vivo transplantation experiments, the AC-ADSCs transplantation group demonstrated improved wound healing rates, stronger collagen deposition, and angiogenesis. Consequently, our findings revealed that PDGFR-β overexpression enhanced the migration, survival, and paracrine capacity of ADSCs and improved therapeutic effects after transplantation to diabetic mice.

Computational study of blood flow inside MCA aneurysm with/without endovascular coiling

The simulation of blood hemodynamics inside the MCA aneurysm is done to investigate the potential region for rupture and hemorrhage. The main focus of this work is to disclose the impacts of endovascular coiling on blood hemodynamics and the risk of aneurysm rupture. Navier-stokes equations are solved for the computational study of blood flow while it is assumed that flow remains laminar, unsteady, and non-Newtonian. Influences of blood hematocrits and coiling porosity are also examined in this work. Obtained results show that impacts of blood hematocrit on the maximum OSI are limited in the MCA case.

Computational study of the blood hemodynamic inside the cerebral double dome aneurysm filling with endovascular coiling

The rupture of the aneurysm wall is highly associated with the hemodynamic feature of bloodstream as well as the geometrical feature of the aneurysm. Coiling is known as the most conventional technique for the treatment of intracranial cerebral aneurysms (ICA) in which blood stream is obstructed from entering the sac of the aneurysm. In this study, comprehensive efforts are done to disclose the impacts of the coiling technique on the aneurysm progress and risk of rupture. The computational fluid dynamic method is used for the analysis of the blood hemodynamics in the specific ICA. The impacts of the pulsatile blood stream on the high-risk region are also explained. Wall shear Stress (WSS) and Oscillatory shear index (OSI) factors are also compared in different blood viscosities and coiling conditions. According to our study, the hematocrit test (Hct) effect is evident (25% reduction in maximum WSS) in the two first stages (maximum acceleration and peak systolic). Our findings present that reduction of porosity from 0.89 to 0.79 would decrease maximum WSS by about 8% in both HCT conditions.

CRISPR activation and interference as investigative tools in the cardiovascular system

CRISPR activation and interference (CRISPRa/i) technology offers the unprecedented possibility of achieving regulated gene expression both in vitro and in vivo. The DNA pairing specificity of a nuclease dead Cas9 (dCas9) is exploited to precisely target a transcriptional activator or repressor in proximity to a gene promoter. This permits both the study of phenotypes arising from gene modulation for investigative purposes, and the development of potential therapeutics. As with virtually all other organ systems, the cardiovascular system can deeply benefit from a broader utilisation of CRISPRa/i. However, application of this technology is still in its infancy. Significant areas for improvement include the identification of novel and more effective transcriptional regulators that can be docked to dCas9, and the development of more efficient methods for their delivery and expression in vivo.

Direct Cardiac Reprogramming: Current Status and Future Prospects

Advances in cellular reprogramming articulated the path for direct cardiac lineage conversion, bypassing the pluripotent state. Direct cardiac reprogramming attracts major attention because of the low or nil regenerative ability of cardiomyocytes, resulting in permanent cell loss in various heart diseases. In the field of cardiology, balancing this loss of cardiomyocytes was highly challenging, even in the modern medical world. Soon after the discovery of cell reprogramming, direct cardiac reprogramming also became a promising alternative for heart regeneration. This review mainly focused on the various direct cardiac reprogramming approaches (integrative and non-integrative) for the derivation of induced autologous cardiomyocytes. It also explains the advancements in cardiac reprogramming over the decade with the pros and cons of each approach. Further, the review highlights the importance of clinically relevant (non-integrative) approaches and their challenges for the prospective applications for personalized medicine. Apart from direct cardiac reprogramming, it also discusses the other strategies for generating cardiomyocytes from different sources. The understanding of these strategies could pave the way for the efficient generation of integration-free functional autologous cardiomyocytes through direct cardiac reprogramming for various biomedical applications.

Transcription Factors - the Essence of Heart Regeneration: A Potential Novel Therapeutic Strategy

Myocardial cell injury and following sequelae are the primary reasons for death globally. Unfortunately, myocardiocytes in adults have limited regeneration capacity. Therefore, the generation of neo myocardiocytes from non-myocardial cells is a surrogate strategy. Transcription factors (TFs) can be recruited to achieve this tremendous goal. Transcriptomic analyses have suggested that GATA, Mef2c, and Tbx5 (GMT cocktail) are master TFs to transdifferentiate/reprogram cell linage of fibroblasts, somatic cells, mesodermal cells into myocardiocytes. However, adding MESP1, MYOCD, ESRRG, and ZFPM2 TFs induces the generation of more efficient and physiomorphological features for induced myocardiocytes. Moreover, the same cocktail of transcription factors can induce the proliferation and differentiation of induced/pluripotent stem cells into myocardial cells. Amelioration of impaired myocardial cells involves the activation of healing transcription factors, which are induced by inflammation mediators; IL6, tumor growth factor β, and IL22. Transcription factors regulate the cellular and subcellular physiology of myocardiocytes to include mitotic cell cycling regulation, karyokinesis and cytokinesis, hypertrophic growth, adult sarcomeric contractile protein gene expression, fatty acid metabolism, and mitochondrial biogenesis and maturation. Cell therapy by transcription factors can be applied to cardiogenesis and ameliorating impaired cardiocytes. Transcription factors are the cornerstone in cell differentiation.

Advances in Cellular Reprogramming-Based Approaches for Heart Regenerative Repair

Continuous loss of cardiomyocytes (CMs) is one of the fundamental characteristics of many heart diseases, which eventually can lead to heart failure. Due to the limited proliferation ability of human adult CMs, treatment efficacy has been limited in terms of fully repairing damaged hearts. It has been shown that cell lineage conversion can be achieved by using cell reprogramming approaches, including human induced pluripotent stem cells (hiPSCs), providing a promising therapeutic for regenerative heart medicine. Recent studies using advanced cellular reprogramming-based techniques have also contributed some new strategies for regenerative heart repair. In this review, hiPSC-derived cell therapeutic methods are introduced, and the clinical setting challenges (maturation, engraftment, immune response, scalability, and tumorigenicity), with potential solutions, are discussed. Inspired by the iPSC reprogramming, the approaches of direct cell lineage conversion are merging, such as induced cardiomyocyte-like cells (iCMs) and induced cardiac progenitor cells (iCPCs) derived from fibroblasts, without induction of pluripotency. The studies of cellular and molecular pathways also reveal that epigenetic resetting is the essential mechanism of reprogramming and lineage conversion. Therefore, CRISPR techniques that can be repurposed for genomic or epigenetic editing become attractive approaches for cellular reprogramming. In addition, viral and non-viral delivery strategies that are utilized to achieve CM reprogramming will be introduced, and the therapeutic effects of iCMs or iCPCs on myocardial infarction will be compared. After the improvement of reprogramming efficiency by developing new techniques, reprogrammed iCPCs or iCMs will provide an alternative to hiPSC-based approaches for regenerative heart therapies, heart disease modeling, and new drug screening.

Activation of melatonin receptor 1 by CRISPR-Cas9 activator ameliorates cognitive deficits in an Alzheimer's disease mouse model

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by the presence of neurotoxic beta-amyloid (Aβ) in the brain. Melatonin receptors have been reported to associate with aging and AD, and their expression decreased with the progression of AD. As an alternative to AD treatment, overexpression of melatonin receptors may lead to melatonin-like effects to treat alleviate the symptoms of AD. Here, we successfully activated the type 1 melatonin receptor (Mt1) in vivo brain using a Cas9 activator as a novel AD therapeutic strategy. The Cas9 activator efficiently activated the endogenous Mt1 gene in the brain. Activation of Mt1 via Cas9 activators modulated anti-amyloidogenic and anti-inflammatory roles in 5xFAD AD mice brain. Moreover, activation of Mt1 with the CRISPR/Cas9 activator improved cognitive deficits in an AD model. These results demonstrated the therapeutic potential of melatonin receptor activation via CRISPR/Cas9 activator for AD.

CRISPR activation of endogenous genes reprograms fibroblasts into cardiovascular progenitor cells for myocardial infarction therapy

Fibroblasts can be reprogrammed into cardiovascular progenitor cells (CPCs) using transgenic approaches, although the underlying mechanism remains unclear. We determined whether activation of endogenous genes such as Gata4, Nkx2.5, and Tbx5 can rapidly establish autoregulatory loops and initiate CPC generation in adult extracardiac fibroblasts using a CRISPR activation system. The induced fibroblasts (>80%) showed phenotypic changes as indicated by an Nkx2.5 cardiac enhancer reporter. The progenitor characteristics were confirmed by colony formation and expression of cardiovascular genes. Cardiac sphere induction segregated the early and late reprogrammed cells that can generate functional cardiomyocytes and vascular cells in vitro. Therefore, they were termed CRISPR-induced CPCs (ciCPCs). Transcriptomic analysis showed that cell cycle and heart development pathways were important to accelerate CPC formation during the early reprogramming stage. The CRISPR system opened the silenced chromatin locus, thereby allowing transcriptional factors to access their own promoters and eventually forming a positive feedback loop. The regenerative potential of ciCPCs was assessed after implantation in mouse myocardial infarction models. The engrafted ciCPCs differentiated into cardiovascular cells in vivo but also significantly improved contractile function and scar formation. In conclusion, multiplex gene activation was sufficient to drive CPC reprogramming, providing a new cell source for regenerative therapeutics.

Transcriptional activation with Cas9 activator nanocomplexes rescues Alzheimer's disease pathology

CRISPR/Cas9-mediated gene activation is a potential therapeutic strategy that does not induce double-strand break (DSB) DNA damage. However, in vivo gene activation via a Cas9 activator remains a challenge, currently limiting its therapeutic applications. We developed a Cas9 activator nanocomplex that efficiently activates an endogenous gene in the brain in vivo, suggesting its possible application in novel therapeutics. We demonstrated a potential treatment application of the Cas9 activator nanocomplex by activating Adam10 in the mouse brain without introducing insertions and deletions (inDels). Remarkably, in vivo activation of Adam10 with the Cas9 activator nanocomplex improved cognitive deficits in an Alzheimer's disease (AD) mouse model. These results demonstrate the therapeutic potential of Cas9 activator nanocomplexes for a wide range of neurological diseases.

Direct reprogramming of somatic cells into induced hepatocytes: Cracking the Enigma code

There is an unmet need for functional primary human hepatocytes to support the pharmaceutical and (bio)medical demand. The unique discovery, a decade ago, that somatic cells can be drawn out of their apparent biological lockdown to reacquire a pluripotent state has revealed a completely new avenue of possibilities for generating surrogate human hepatocytes. Since then, the number of papers reporting the direct conversion of somatic cells into induced hepatocytes (iHeps) has burgeoned. A hepatic cell fate can be established via the ectopic expression of native liver-enriched transcription factors in somatic cells, thereby bypassing the need for an intermediate (pluripotent) stem cell state. That said, understanding and eventually controlling the processes that give rise to functional iHeps remains challenging. In this review, we provide an overview of the state-of-the-art reprogramming cocktails and techniques, as well as their corresponding conversion efficiencies. Special attention is paid to the role of liver-enriched transcription factors as hepatogenic reprogramming tools and small molecules as facilitators of hepatic transdifferentiation. To conclude, we formulate recommendations to optimise, standardise and enrich the in vitro production of iHeps to reach clinical standards, and propose minimal criteria for their characterisation.

Direct Reprogramming of Cardiac Fibroblasts to Repair the Injured Heart

Coronary heart disease is a leading cause of mortality and morbidity. Those that survive acute myocardial infarction are at significant risk of subsequent heart failure due to fibrotic remodelling of the infarcted myocardium. By applying knowledge from the study of embryonic cardiovascular development, modern medicine offers hope for treatment of this condition through regeneration of the myocardium by direct reprogramming of fibrotic scar tissue. Here, we will review mechanisms of cell fate specification leading to the generation of cardiovascular cell types in the embryo and use this as a framework in which to understand direct reprogramming. Driving expression of a network of transcription factors, micro RNA or small molecule epigenetic modifiers can reverse epigenetic silencing, reverting differentiated cells to a state of induced pluripotency. The pluripotent state can be bypassed by direct reprogramming in which one differentiated cell type can be transdifferentiated into another. Transdifferentiating cardiac fibroblasts to cardiomyocytes requires a network of transcription factors similar to that observed in embryonic multipotent cardiac progenitors. There is some flexibility in the composition of this network. These studies raise the possibility that the failing heart could one day be regenerated by directly reprogramming cardiac fibroblasts within post-infarct scar tissue.