Homotypic Targeting Delivery of siRNA with Artificial Cancer Cells

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.

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.

Bioengineered Abiotic Nanomaterials Through Cell Membrane-Camouflaging: Advancements and Challenges in Lung Cancer

Lung cancer remains a major global health concern with high mortality rates and poor prognosis. Bridging the gap between the chemical and cellular understanding of cell-decorated biomimetic nanocomposites and their clinical translation is crucial for developing effective therapies. Nanocomposites show promise in targeted drug delivery and diagnostics, but their clinical application is hindered by biocompatibility and clearance issues. To overcome these challenges, biomimetic approaches utilizing cell membrane-coated nanomaterials emerge. By camouflaging nanomaterials with cell membranes, the biointerfaces are enhanced, and the inherent properties of the donor cell membranes are acquired. This review provides an overview of recent advancements on cell membrane-coated nanocomposites for lung cancer diagnosis and treatment. It discusses fabrication techniques, biomedical applications, challenges, and future prospects. The incorporation of cell membranes into nanocomposites holds potential for improved lung cancer therapy, but further development and refinement are needed for precise tumor targeting. Addressing the identified challenges will pave the way for clinical translation of these biomimetic nanoplatforms and advance lung cancer diagnosis and treatment.

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.

Biomedical application of metal-organic frameworks (MOFs) in cancer therapy: Stimuli-responsive and biomimetic nanocomposites in targeted delivery, phototherapy and diagnosis

The nanotechnology is an interdisciplinary field that has become a hot topic in cancer therapy. Metal-organic frameworks (MOFs) are porous materials and hybrid composites consisted of organic linkers and metal cations. Despite the wide application of MOFs in other fields, the potential of MOFs for purpose of cancer therapy has been revealed by the recent studies. High surface area and porosity, significant drug loading and encapsulation efficiency are among the benefits of using MOFs in drug delivery. MOFs can deliver genes/drugs with selective targeting of tumor cells that can be achieved through functionalization with ligands. The photosensitizers and photo-responsive nanostructures including carbon dots and gold nanoparticles can be loaded in/on MOFs to cause phototherapy-mediated tumor ablation. The immunogenic cell death induction and increased infiltration of cytotoxic CD8+ and CD4+ T cells can be accelerated by MOF platforms in providing immunotherapy of tumor cells. The stimuli-responsive MOF platforms responsive to pH, redox, enzyme and ion can accelerate release of therapeutics in tumor site. Moreover, MOF nanocomposites can be modified ligands and green polymers to improve their selectivity and biocompatibility for cancer therapy. The application of MOFs for the detection of cancer-related biomarkers can participate in the early diagnosis of patients.

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.

Biomimetic Responsive Nanoconverters with Immune Checkpoint Blockade Plus Antiangiogenesis for Advanced Hepatocellular Carcinoma Treatment

The first-line treatment for advanced hepatocellular carcinoma (HCC) combines immune checkpoint inhibitors and antiangiogenesis agents to prolong patient survival. Nonetheless, this approach has several limitations, including stringent inclusion criteria and suboptimal response rates that stem from the severe off-tumor side effects and the unfavorable pharmacodynamics and pharmacokinetics of different drugs delivered systemically. Herein, we propose a single-agent smart nanomedicine-based approach that mimics the therapeutic schedule in a targeted and biocompatible manner to elicit robust antitumor immunity in advanced HCC. Our strategy employed pH-responsive carriers, poly(ethylene glycol)-poly(β-amino esters) amphiphilic block copolymer (PEG-PAEs), for delivering apatinib (an angiogenesis inhibitor), that were surface-coated with plasma membrane derived from engineered cells overexpressing PD-1 proteins (an immune checkpoint inhibitor to block PD-L1). In an advanced HCC mouse model with metastasis, these biomimetic responsive nanoconverters induced significant tumor regression (5/9), liver function recovery, and complete suppression of lung metastasis. Examination of the tumor microenvironment revealed an increased infiltration of immune effector cells (CD8+ and CD4+ T cells) and reduced immunosuppressive cells (myeloid-derived suppressor cells and T regulatory cells) in treated tumors. Importantly, our nanomedicine selectively accumulated in both small and large HCC occupying >50% of the liver volume to exert therapeutic effects with minimal systemic side effects. Overall, these findings highlight the potential of such multifunctional nanoconverters to effectively reshape the tumor microenvironment for advanced HCC treatment.

Composition, preparation methods, and applications of nanoniosomes as codelivery systems: a review of emerging therapies with emphasis on cancer

Nanoniosome-based drug codelivery systems have become popular therapeutic instruments, demonstrating tremendous promise in cancer therapy, infection treatment, and other therapeutic domains. An emerging form of vesicular nanocarriers, niosomes are self-assembling vesicles composed of nonionic surfactants, along with cholesterol or other amphiphilic molecules. This comprehensive review focuses on how nanosystems may aid in making anticancer and antibacterial pharmaceuticals more stable and soluble. As malleable nanodelivery instruments, the composition, types, preparation procedures, and variables affecting the structure and stability of niosomes are extensively investigated. In addition, the advantages of dual niosomes for combination therapy and the administration of multiple medications simultaneously are highlighted. Along with categorizing niosomal drug delivery systems, a comprehensive analysis of various preparation techniques, including thin-layer injection, ether injection, and microfluidization, is provided. Dual niosomes for cancer treatment are discussed in detail regarding the codelivery of two medications and the codelivery of a drug with organic, plant-based bioactive compounds or gene agents. In addition, niogelosomes and metallic niosomal carriers for targeted distribution are discussed. The review also investigates the simultaneous delivery of bioactive substances and gene agents, including siRNA, microRNA, shRNA, lncRNA, and DNA. Additional sections discuss the use of dual niosomes for cutaneous drug delivery and treating leishmanial infections, Pseudomonas aeruginosa, and Mycobacterium tuberculosis. The study concludes by delineating the challenges and potential routes for nanoniosome-based pharmaceutical codelivery systems, which will be useful for nanomedicine practitioners and researchers.

Trends in the synthetic polymer delivery of RNA

Ribonucleic acid (RNA) has emerged as one of the most promising therapeutic payloads in the field of gene therapy. There are many unique types of RNA that allow for a range of applications including vaccination, protein replacement therapy, autoimmune disease treatment, gene knockdown and gene editing. However, RNA triggers the host immune system, is vulnerable to degradation and has a low proclivity to enter cells spontaneously. Therefore, a delivery vehicle is required to facilitate the protection and uptake of RNA therapeutics into the desired host cells. Lipid nanoparticles have emerged as one of the only clinically approved vehicles for genetic payloads, including in the COVID-19 messenger RNA vaccines. While lipid nanoparticles have distinct advantages, they also have drawbacks, including strong immune stimulation, complex manufacturing and formulation heterogeneity. In contrast, synthetic polymers are a widely studied group of gene delivery vehicles and boast distinct advantages, including biocompatibility, tunability, inexpensiveness, simple formulation and ease of modification. Some classes of polymers enhance efficient transfection efficiency, and lead to lower stimulation of the host immune system, making them more viable candidates for non-vaccine-related applications of RNA medicines. This review aims to identify the most promising classes of synthetic polymers, summarize recent research aimed at moving them into the clinic and postulate the future steps required for unlocking their full potential.

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 phenanthroline-based erbium (III) complex: molecular docking, DNA/BSA -binding and biological evaluation

With the help of both theoretical as well as experimental research, in vitro binding research with CT-DNA (calf thymus) and BSA (bovine serum albumin) were carefully examined to figure out the chemotherapeutic and pharmacokinetic facets of the Erbium complex, which contains 1,10-phenanthroline (Phen). The binding characteristics and the mechanism of complex's interaction with DNA as well as the protein were determined utilizing fluorescence quenching method. Findings indicated that the complex's interaction with DNA via groove binding into DNA's minor grooves, with their binding constants falling within the 104 M-1 range. Furthermore, thermodynamic characteristics and the fluorescence emission of the tryptophan residues of the protein were obtained through fluorescence quenching studies at different temperatures. According to the results of the binding constants, the protein's interactions with the Er- complex were moderate, demonstrating that the compound may be transported effectively by the protein. Molecular docking results supported that of the experimental research. The HeLa and MCF-7 cancer cell lines, along with the normal human fibroblast cell line, were used in an MTT assay evaluation of the Er-complex cytotoxicity. The Er-complex displayed a selective inhibitory effect on the proliferation of different cancer cells.Communicated by Ramaswamy H. Sarma.

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.

Green synthesis of Ag and Cu-doped Bismuth oxide nanoparticles: Revealing synergistic antimicrobial and selective cytotoxic potentials for biomedical advancements

BACKGROUND

Nanotechnology has emerged as a transformative realm of exploration across diverse scientific domains. A particular focus lies on metal oxide nanoparticles, which boast distinctive physicochemical attributes on the nanoscale. Of note, green synthesis has emerged as a promising avenue, leveraging plant extracts as both reduction and capping agents. This approach offers environmentally friendly and cost-effective avenues for generating monodispersed nanoparticles with precise morphologies.

METHODS

In this investigation, we embarked on the synthesis of Bismuth oxide nanoparticles, both in their pure form and doped with silver (Ag) and copper (Cu). This synthesis harnessed the potential of Biebersteinia multifida extract as a versatile reducing agent. To comprehensively characterize the synthesized nanoparticles, a suite of analytical techniques was employed, including energy-dispersive X-ray spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), UV-Vis spectroscopy, and Raman spectroscopy.

RESULTS

The synthesized nanoparticles underwent a rigorous assessment. Their antibacterial attributes were probed, revealing a pronounced enhancement in antibiofilm activity against Pseudomonas aeruginosa and Staphylococcus aureus bacteria upon metal nanoparticle doping. Furthermore, their potential for combating cancer was scrutinized, with the nanoparticles exhibiting selective cytotoxicity towards cancer cells, U87, compared to normal 3T3 cells. Notably, among the doped nanoparticles, Cu-doped variants demonstrated the highest potency, further underscoring their promising potential.

CONCLUSION

In conclusion, the present study underscores the efficacy of green synthesized Bismuth oxide nanoparticles, particularly those doped with Ag and Cu, in augmenting antibacterial efficacy, bolstering biofilm inhibition, and manifesting selective cytotoxicity against cancer cells. These findings portend a promising trajectory for these nanoparticles in the spheres of biomedicine and therapeutics. As we look ahead, a deeper elucidation of their mechanistic underpinnings and in vivo investigations are essential to fully unlock their potential for forthcoming biomedical applications.

Cell Membrane-Camouflaged Nanoparticles Mediated Nucleic Acids Delivery

Nucleic acids have emerged as promising therapeutic agents for many diseases because of their potential in modulating gene expression. However, the delivery of nucleic acids remains a significant challenge in gene therapy. Although viral vectors have shown high transfection efficiency, concerns regarding teratogenicity or carcinogenicity have been raised. Non-viral vehicles, including cationic polymers, liposomes, and inorganic materials possess advantages in terms of safety, ease of preparation, and low cost. Nevertheless, they also face limitations related to immunogenicity, quick clearance in vivo, and lack of targeting specificity. On the other hand, bioinspired strategies have shown increasing potential in the field of drug delivery, yet there is a lack of comprehensive reviews summarizing the rapid development of bioinspired nanoparticles based on the cell membrane camouflage to construct the nucleic acids vehicles. Herein, we enumerated the current difficulties in nucleic acid delivery with various non-viral vehicles and provided an overview of bioinspired strategies for nucleic acid delivery.

Liver-Specific Ionizable Lipid Nanoparticles Mediated Efficient RNA Interference to Clear "Bad Cholesterol"

Background

High-level low-density lipoprotein cholesterol (LDL-C) plays a vital role in the development of atherosclerotic cardiovascular disease. Low-density lipoprotein receptors (LDLRs) are scavengers that bind to LDL-C in the liver. LDLR proteins are regulated by proprotein convertase subtilisin/kexin type 9 (PCSK9), which mediates the degradation of LDLR and adjusts the level of the plasma LDL-C. The low expression of PCSK9 leads to the up-regulation of liver LDLRs and the reduction of plasma LDL-C. Hepatocytes are attractive targets for small interfering RNA (siRNA) delivery to silence Pcsk9 gene, due to their significant role in LDL-C regulation.

Methods

Here, a type of liver-specific ionizable lipid nanoparticles is developed for efficient siRNA delivery. This type of nanoparticles shows high stability, enabling efficient cargo delivery specifically to hepatocytes, and a membrane-active polymer that reversibly masks activity until an acidic environment is reached.

Results

Significantly, the siPcsk9 (siRNA targeting to Pcsk9)-loaded nanoparticles (GLP) could silence 90% of the Pcsk9 mRNA in vitro. In vivo study showed that the improved accumulation of GLP in the liver increased LDLR level by 3.35-fold and decreased plasma LDL-C by 35%.

Conclusion

GLP has shown a powerful effect on reducing LDL-C, thus providing a potential therapy for atherosclerotic cardiovascular disease.

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.

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.

Bioinspired PROTAC-induced macrophage fate determination alleviates atherosclerosis

Atherosclerosis is a major cause of death and disability in cardiovascular disease. Atherosclerosis associated with lipid accumulation and chronic inflammation leads to plaques formation in arterial walls and luminal stenosis in carotid arteries. Current approaches such as surgery or treatment with statins encounter big challenges in curing atherosclerosis plaque. The infiltration of proinflammatory M1 macrophages plays an essential role in the occurrence and development of atherosclerosis plaque. A recent study shows that TRIM24, an E3 ubiquitin ligase of a Trim family protein, acts as a valve to inhibit the polarization of anti-inflammatory M2 macrophages, and elimination of TRIM24 opens an avenue to achieve the M2 polarization. Proteolysis-targeting chimera (PROTAC) technology has emerged as a novel tool for the selective degradation of targeting proteins. But the low bioavailability and cell specificity of PROTAC reagents hinder their applications in treating atherosclerosis plaque. In this study we constructed a type of bioinspired PROTAC by coating the PROTAC degrader (dTRIM24)-loaded PLGA nanoparticles with M2 macrophage membrane (MELT) for atherosclerosis treatment. MELT was characterized by morphology, size, and stability. MELT displayed enhanced specificity to M1 macrophages as well as acidic-responsive release of dTRIM24. After intravenous administration, MELT showed significantly improved accumulation in atherosclerotic plaque of high fat and high cholesterol diet-fed atherosclerotic (ApoE-/-) mice through binding to M1 macrophages and inducing effective and precise TRIM24 degradation, thus resulting in the polarization of M2 macrophages, which led to great reduction of plaque formation. These results suggest that MELT can be considered a potential therapeutic agent for targeting atherosclerotic plaque and alleviating atherosclerosis progression, providing an effective strategy for targeted atherosclerosis therapy.

Design, strategies, and therapeutics in nanoparticle-based siRNA delivery systems for breast cancer

Utilizing small interfering RNA (siRNA) as a treatment for cancer, a disease largely driven by genetic aberrations, shows great promise. However, implementing siRNA therapy in clinical practice is challenging due to its limited bioavailability following systemic administration. An attractive approach to address this issue is the use of a nanoparticle (NP) delivery platform, which protects siRNA and delivers it to the cytoplasm of target cells. We provide an overview of design considerations for using lipid-based NPs, polymer-based NPs, and inorganic NPs to improve the efficacy and safety of siRNA delivery. We focus on the chemical structure modification of carriers and NP formulation optimization, NP surface modifications to target breast cancer cells, and the linking strategy and intracellular release of siRNA. As a practical example, recent advances in the development of siRNA therapeutics for treating breast cancer are discussed, with a focus on inhibiting cancer growth, overcoming drug resistance, inhibiting metastasis, and enhancing immunotherapy.

Experimental and theoretical investigations of Erbium complex: DNA/BSA interaction, anticancer and antibacterial studies

To assess the biological potential of an Er complex that contains a 2,2'-bipyridine ligand, various techniques such as multispectral and molecular modeling procedures were utilized to examine its DNA-binding ability, BSA binding affinity, antimicrobial effects, and anticancer properties. By analyzing fluorescent information and employing the vant' Hoff equation, important parameters such as the innate docking coefficient (Kb), Stern-Volmer coefficient (KSV), and thermodynamic properties including modifications in liberated energy (ΔG°), enthalpy (∆H°), and entropy (∆S°) were determined. The trial findings suggest that the compound can bind to DNA, primarily through groove binding. Additionally, the engagement between the Er compound and the protein BSA was examined using emission spectroscopy technique, revealing a powerful binding affinity between the compound and BSA. The Er complex binds to BSA primarily via hydrogen links and van der Waals forces, as indicated by the adverse values of ΔH° and ∆S°. Through a static quenching process, the complex significantly reduces the intrinsic fluorescence of BSA. Molecular binding calculations and rivalrous binding trials confirm that this compound dock to hydrophobic remains found in site III of BSA. Additionally, the Er complex demonstrates promising results in terms of its anticancer and antimicrobial activities based on screening tests.

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.

Biomembrane-wrapped gene delivery nanoparticles for cancer therapy

As a promising strategy, gene delivery for cancer treatment accepts encouraging progress due to its high efficacy, low toxicity, and exclusive selectivity. However, the delivery efficiency, specific biological distribution, targeted uptake, and biosafety of naked nucleic acid agents still face serious challenges, which limit further clinical application. To overcome the above bottleneck, safe and efficient functional nanovectors are developed to improve the delivery efficiency of nucleic acid agents. In recent years, emerging membrane-wrapped biomimetic nanoparticles (MBNPs) based on the concept of "imitating nature" are well known for their advantages, such as low immunogenicity and long cycle time, and especially play a crucial role in improving the overall efficiency of gene delivery and reducing adverse reactions. Therefore, combining MBNPs and gene delivery is an effective strategy to enhance tumor treatment efficiency. This review presents the mechanism of gene therapy and the current obstacles to gene delivery. Remarkably, the latest development of gene delivery MBNPs and the strategies to overcome these obstacles are summarized. Finally, the future challenges and prospects of gene delivery MBNPs toward clinical transformation are introduced. The principal purpose of this review is to discuss the biomedical potential of gene delivery MBNPs for cancer therapy and to provide guidance for further enhancing the efficiency of tumor gene therapy.

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.

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.

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.

Biomimetic cell membrane-coated nanocarriers for targeted siRNA delivery in cancer therapy

Small interfering RNA (siRNA) therapeutics for cancer are a focus of increasing research interest. However, the major obstacle to their clinical application is the targeted delivery of siRNA to cancer cells at desirable levels. Cell membrane-coated nanocarriers have the advantage of combining the properties of both cell membranes and nanoparticles (NPs). In this review, we highlight the most common RNAi therapeutics and the extracellular and intracellular barriers to siRNA delivery. Moreover, we discuss clinical applications of different cell membrane-coated nanocarriers for targeted siRNA delivery, including cancer cell membranes (CCMs), platelet membranes, erythrocyte membranes, stem cell membranes, exosome membranes, and hybrid membranes. Taken together, biomimetic cell membrane-coated nanotechnology is a promising strategy for targeted siRNA delivery for cancer treatment.

Nanoscale coordination polymers enabling antioxidants inhibition for enhanced chemodynamic therapy

Reactive oxygen species (ROS) generation to induce cell death is an effective strategy for cancer therapy. In particular, chemodynamic therapy (CDT), using Fenton-type reactions to generate highly cytotoxic hydroxyl radical (•OH), is a promising treatment modality. However, the therapeutic efficacy of ROS-based cancer treatment is still limited by some critical challenges, such as overexpression of enzymatic and non-enzymatic antioxidants by tumor cells, as well as the low tumor targeting efficiency of therapeutic agents. To address those problems, biomimetic CuZn protoporphyrin IX nanoscale coordination polymers have been developed, which significantly amplify oxidative stress against tumors by simultaneously inhibiting enzymatic and non-enzymatic antioxidants and initiating the CDT. In this design, cancer cell membrane camouflaged nanoparticle exhibits an excellent homotypic targeting effect. After being endocytosed into tumor cells, the nanoparticles induce depletion of the main non-enzymatic antioxidant glutathione (GSH) by undergoing a redox reaction with GSH. Afterward, the redox reaction generated cuprous ion (Cu⁺) works as a CDT agent for •OH generation. Furthermore, the released Zn protoporphyrin IX strongly inhibits the activity of the typical enzymatic antioxidant heme oxygenase-1. This tetra-modal synergistic strategy endows the biomimetic nanoparticles with great capability for anticancer therapy, which has been demonstrated in both in vitro and in vivo studies.

Biopolymer-Based Nanosystems for siRNA Drug Delivery to Solid Tumors including Breast Cancer

Nanobiopolymers such as chitosan, gelatin, hyaluronic acid, polyglutamic acid, lipids, peptides, exosomes, etc., delivery systems have prospects to help overwhelmed physiological difficulties allied with the delivery of siRNA drugs to solid tumors, including breast cancer cells. Nanobiopolymers have favorable stimuli-responsive properties and therefore can be utilized to improve siRNA delivery platforms to undruggable MDR metastatic cancer cells. These biopolymeric siRNA drugs can shield drugs from pH degradation, extracellular trafficking, and nontargeted binding sites and are consequently suitable for drug internalization in a controlled-release fashion. In this review, the utilization of numerous biopolymeric compounds such as siRNA drug delivery systems for MDR solid tumors, including breast cancers, will be discussed.

Current Advancements in Self-assembling Nanocarriers-Based siRNA Delivery for Cancer Therapy

Different therapeutic practices for treating cancers have significantly evolved to compensate and/or overcome the failures in conventional methodologies. The demonstrated potentiality in completely inhibiting the tumors and in preventing cancer relapse has made nucleic acids therapy (NAT)/gene therapy as an attractive practice. This has been made possible because NAT-based cancer treatments are highly focused on the fundamental mechanisms - i.e., silencing the expression of oncogenic genes responsible for producing abnormal proteins (via messenger RNAs (mRNAs)). However, the future clinical translation of NAT is majorly dependent upon the effective delivery of the exogenous nucleic acids (especially RNAs - e.g., short interfering RNAs (siRNAs) - herein called biological drugs). Moreover, nano-based vehicles (i.e., nanocarriers) are involved in delivering them to prevent degradation and undesired bioaccumulation while enhancing the stability of siRNAs. Herein, we have initially discussed about three major types of self-assembling nanocarriers (liposomes, polymeric nanoparticles and exosomes). Later, we have majorly reviewed recent developments in non-targeted/targeted nanocarriers for delivery of biological drugs (individual/dual) to silence the most important genes/mRNAs accountable for inducing protein abnormality. These proteins include polo-like kinase 1 (PLK1), survivin, vascular endothelial growth factor (VEGF), B-cell lymphoma/leukaemia-2 (Bcl-2) and multi-drug resistance (MDR). Besides, the consequent therapeutic effects on cancer growth, invasion and/or metastasis have also been discussed. Finally, we have comprehensively reviewed the improvements achieved in the cutting-edge cancer therapeutics while delivering siRNAs in combination with clinically approved chemotherapeutic drugs.

Versatile Nano-PROTAC-Induced Epigenetic Reader Degradation for Efficient Lung Cancer Therapy

Recent evidence has indicated that overexpression of the epigenetic reader bromodomain-containing protein 4 (BRD4) contributes to a poor prognosis of lung cancers, and the suppression of its expression promotes cell apoptosis and leads to tumor shrinkage. Proteolysis targeting chimera (PROTAC) has recently emerged as a promising therapeutic strategy with the capability to precisely degrade targeted proteins. Herein, a novel style of versatile nano-PROTAC (CREATE (CRV-LLC membrane/DS-PLGA/dBET6)) is developed, which is constructed by using a pH/GSH (glutathione)-responsive polymer (disulfide bond-linked poly(lactic-co-glycolic acid), DS-PLGA) to load BRD4-targeted PROTAC (dBET6), followed by the camouflage with engineered lung cancer cell membranes with dual targeting capability. Notably, CREATE remarkably confers simultaneous targeting ability to lung cancer cells and tumor-associated macrophages (TAMs). The pH/GSH-responsive design improves the release of dBET6 payload from nanoparticles to induce pronounced apoptosis of both cells, which synergistically inhibits tumor growth in both subcutaneous and orthotopic tumor-bearing mouse model. Furthermore, the efficient tumor inhibition is due to the direct elimination of lung cancer cells and TAMs, which remodels the tumor microenvironment. Taken together, the results elucidate the construction of a versatile nano-PROTAC enables to eliminate both lung cancer cells and TAMs, which opens a new avenue for efficient lung cancer therapy via PROTAC.

A zwitterionic polymer-inspired material mediated efficient CRISPR-Cas9 gene editing

The type II prokaryotic CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 (CRISPR/Cas9) adaptive immune system is a cutting-edge genome-editing toolbox. However, its applications are still limited by its inefficient transduction. Herein, we present a novel gene vector, the zwitterionic polymer-inspired material with branched structure (ZEBRA) for efficient CRISPR/Cas9 delivery. Polo-like kinase 1 (PLK1) acts as a master regulator of mitosis and overexpresses in multiple tumor cells. The Cas9 and single guide sgRNA (sgRNA)-encoded plasmid was transduced to knockout Plk1 gene, which was expected to inhibit the expression of PLK1. Our studies demonstrated that ZEBRA enabled to transduce the CRISPR/Cas9 system with large size into the cells efficiently. The transduction with ZEBRA was cell line dependent, which showed ∼10-fold higher in CD44-positive cancer cell lines compared with CD44-negative ones. Furthermore, ZEBRA induced high-level expression of Cas9 proteins by the delivery of CRISPR/Cas9 and efficient gene editing of Plk1 gene, and inhibited the tumor cell growth significantly. This zwitterionic polymer-inspired material is an effective and targeted gene delivery vector and further studies are required to explore its potential in gene delivery applications.

Biomimetic metal-organic frameworks navigated biological bombs for efficient lung cancer therapy

The rising risk of lung cancer has become a primary global concern with high mortality and mobility. Presently, clinically used anticancer drugs show limited efficacy and significant side effects. A new generation of anticancer weapons is in great demand for lung cancer therapy. Herein, we have developed a novel style of biomimetic zeolitic imidazolate framework-8 (ZIF-8) based on the merits of cell membranes derived from human bone marrow mesenchymal stem cells (hBMSCs), which can navigate biological bombs herpes simplex virus type I thymidine kinase-encoded plasmids (pHSVtk) and ganciclovir (GCV) to treat lung cancer. The biological bomb-loaded structure can kill transfected lung cancer cells and neighboring lung cancer cells through the "bystander effect," which induces efficient suppression of lung cancer both in vitro and in vivo. The biomimetic nanoparticles show an enhanced circulation lifetime and drug accumulation in the tumor tissues and significantly inhibit the tumors. We have developed a straightforward approach to deliver biological bombs with biomimetic metal-organic frameworks for efficient lung cancer therapy. To the best of our knowledge, this is the first report of such a strategy for lung cancer therapy.