Gli1 promotes epithelial–mesenchymal transition and metastasis of non-small cell lung carcinoma by regulating Snail transcriptional activity and stability

Optimized Feature Selection and Deep Neural Networks to Improve Heart Disease Prediction

Heart disease remains a significant health threat due to its high mortality rate and increasing prevalence. Early prediction using basic physical markers from routine exams is crucial for timely diagnosis and intervention. However, manual analysis of large datasets can be labor-intensive and error-prone. Our goal is to rapidly and reliably anticipate cardiac disease using a variety of body signs. This research presents a unique model for heart disease prediction. We provide a system for predicting cardiac disease that blends the deep convolutional neural network with a feature selection technique based on the LinearSVC. This integrated feature selection method selects a subset of characteristics that are strongly linked with heart disease. We feed these features into the deep conventual neural network that we constructed. Also to improve the speed of the predictor and avoid gradient varnishing or explosion, the network's hyperparameters were tuned using the random search algorithm. The proposed method was evaluated using the UCI and MIT datasets. The predictor is evaluated using a number of indicators, such as accuracy, recall, precision, and F1 score. The results demonstrate that our model attains accuracy rates of 98.16%, 98.2%, 95.38%, and 97.84% in the UCI dataset, with an average MCC score of 90%. These results affirm the efficacy and reliability of the proposed technique to predict heart disease.

New Machine Learning Method for Medical Image and Microarray Data Analysis for Heart Disease Classification

Microarray technology has become a vital tool in cardiovascular research, enabling the simultaneous analysis of thousands of gene expressions. This capability provides a robust foundation for heart disease classification and biomarker discovery. However, the high dimensionality, noise, and sparsity of microarray data present significant challenges for effective analysis. Gene selection, which aims to identify the most relevant subset of genes, is a crucial preprocessing step for improving classification accuracy, reducing computational complexity, and enhancing biological interpretability. Traditional gene selection methods often fall short in capturing complex, nonlinear interactions among genes, limiting their effectiveness in heart disease classification tasks. In this study, we propose a novel framework that leverages deep neural networks (DNNs) for optimizing gene selection and heart disease classification using microarray data. DNNs, known for their ability to model complex, nonlinear patterns, are integrated with feature selection techniques to address the challenges of high-dimensional data. The proposed method, DeepGeneNet (DGN), combines gene selection and DNN-based classification into a unified framework, ensuring robust performance and meaningful insights into the underlying biological mechanisms. Additionally, the framework incorporates hyperparameter optimization and innovative U-Net segmentation techniques to further enhance computational performance and classification accuracy. These optimizations enable DGN to deliver robust and scalable results, outperforming traditional methods in both predictive accuracy and interpretability. Experimental results demonstrate that the proposed approach significantly improves heart disease classification accuracy compared to other methods. By focusing on the interplay between gene selection and deep learning, this work advances the field of cardiovascular genomics, providing a scalable and interpretable framework for future applications.

GLI2 inhibits cisplatin sensitivity in gastric cancer through DEC1/ZEB1 mediated EMT

Cisplatin (CDDP) based chemotherapy has emerged as the predominant therapeutic regimen for patients with advanced gastric cancer (GC). However, its efficacy is dampened by the development of chemoresistance, which results in poor prognosis of patients. GLI2, a key transcription factor in the Hedgehog (Hh) signaling pathway, is regarded as a target for cancer therapy. However, the significance of GLI2 for CDDP resistance in GC has not been well established. Here, we show that GLI2 expression was upregulated in EMT-type GC and associated with poor prognosis. GLI2 promotes proliferation, migration, and CDDP resistance of GC cells by inducing EMT. In terms of mechanism, GLI2 binds to the promoter region of DEC1 and enhances its expression, thereby co-transcriptionally regulating ZEB1 expression. Animal experiments have demonstrated that both GLI2 knockdown and GLI2 inhibitor significantly enhance CDDP sensitivity in GC. Our data not only identify a novel GLI2/DEC1/ZEB1/EMT pathway in GC CDDP resistance but also provide novel strategies to treat GC in the future.

SMARCB1-driven EGFR-GLI1 epigenetic alterations in lung cancer progression and therapy are differentially modulated by MEOX2 and GLI-1

Lung cancer remains the leading cause of cancer-related mortality globally, with genes such as SMARCB1, MEOX2, and GLI-1 playing significant roles in its malignancy. Despite their known involvement, the specific molecular contributions of these genes to lung cancer progression, particularly their effects on epigenetic modifications on oncogenes sequences as EGFR and GLI-1, and their influence in the response to EGFR-TKI-based therapies, have not been fully explored. Our study reveals how MEOX2 and GLI-1 are key molecular modulators of the GLI-1 and EGFR-epigenetic patterns, which in turn transcriptionally and epigenetically affect EGFR gene expression in lung cancer. Additionally, MEOX2 was found to significantly promote in vivo lung tumor progression and diminish the effectiveness of EGFR-TKI therapies. Conversely, mSWI/SNF derived subunit SMARCB1 was detected to suppress tumor growth and enhance the oncological therapeutic response in in vivo studies by inducing epigenetic modifications in the GLI-1 and EGFR genetic sequences. Furthermore, our results suggest that BRD9 may contribute to the activation of both lung cancer oncogenes GLI-1 and EGFR. Such findings suggest that SMARCB1 and MEOX2 could serve as important prognosis biomarkers and target genes in human lung cancer therapy, offering new opportunities for the development of more effective and selective treatment strategies in the field of lung malignant diseases.

Gadolinium (Gd)-based nanostructures as dual-armoured materials for microbial therapy and cancer theranostics

Gadolinium (Gd) nanoparticles hold significant promise in medical theranostics due to their unique properties. This review outlines the synthesis, characterisation, and applications of Gd nanostructures in combating microbial threats and advancing cancer theragnostic strategies. Synthesis methods such as co-precipitation, microemulsion, and laser ablation are discussed, alongside TEM, SEM, and magnetic characterisation. The antimicrobial efficacy of Gd nanostructures, their potential in combination therapy, and promising anticancer mechanisms are explored. Biocompatibility, toxicity, and regulatory considerations are also evaluated. Challenges, future perspectives, and emerging trends in Gd nanostructure research are highlighted, emphasising their transformative potential in medical applications.

Heart disease prediction using ECG-based lightweight system in IoT based on meta-heuristic approach

Annually, the proportion of individuals suffering from cardiovascular disease rises significantly. Heart attacks are the most prevalent and unpleasant illness among them. Heart disease (HD) diagnosis can be complicated when there are multiple symptoms. The growing popularity of wearable smart devices has increased the likelihood of providing the Internet of Things (IoT). However, one of the biggest obstacles to overcome in implementing the system under IoT is developing a lightweight model for cardiac diagnosis and categorization. In this paper, we have presented a two-step heart disease classification method. This method includes demarcation of classes with the help of optimized non-linear support vector machine technique in the first step and determining the modified fuzzy class in the second step. Initially, pre-processing is accomplished using the ECG signals to eliminate noise and improve signal smoothness. Subsequently, features such as PQRS wave, linear characteristics, and reciprocal information are extracted from pre-processed signals. At the classification stage, the two-stage learning system is used to classify cardiac arrhythmias. First, using the wild horse optimization (WHO) technique (WHO-sigmoid-TH-NL-demarcation), each class is subjected to a binary classification based on feature demarcation, thresholding, and weighting of the sigmoid function. The information from the first stage will be transferred into the subsequent stage for an equal number of heart disease classifications. In the second step, a TS fuzzy logic system optimized by the Giza Pyramids Construction (GPC) approach (GPC-TS-Fuzzy) is utilized to classify each signal. The MIT-BIH arrhythmia dataset is used to assess the suggested approach. In a comprehensive evaluation of the suggested method, performance metrics including "accuracy, sensitivity, and specificity" yielded average values of 98.58 %, 98.13 %, and 96.47 %, respectively. The MATLAB platform is utilized to accomplish the proposed methodology.

Combination of Deep and Statistical Features of the Tissue of Pathology Images to Classify and Diagnose the Degree of Malignancy of Prostate Cancer

Prostate cancer is one of the most prevalent male-specific diseases, where early and accurate diagnosis is essential for effective treatment and preventing disease progression. Assessing disease severity involves analyzing histological tissue samples, which are graded from 1 (healthy) to 5 (severely malignant) based on pathological features. However, traditional manual grading is labor-intensive and prone to variability. This study addresses the challenge of automating prostate cancer classification by proposing a novel histological grade analysis approach. The method integrates the gray-level co-occurrence matrix (GLCM) for extracting texture features with Haar wavelet modification to enhance feature quality. A convolutional neural network (CNN) is then employed for robust classification. The proposed method was evaluated using statistical and performance metrics, achieving an average accuracy of 97.3%, a precision of 98%, and an AUC of 0.95. These results underscore the effectiveness of the approach in accurately categorizing prostate tissue grades. This study demonstrates the potential of automated classification methods to support pathologists, enhance diagnostic precision, and improve clinical outcomes in prostate cancer care.

Advancements in Exosome Proteins for Breast Cancer Diagnosis and Detection: With a Focus on Nanotechnology

Breast cancer, a leading cause of mortality among women, has been recognized as requiring improved diagnostic methods. Exosome proteins, found in small extracellular vesicles, have emerged as a promising solution, reflecting the state of their cell of origin and playing key roles in cancer progression. This review examines their potential in breast cancer diagnosis, discussing advanced isolation and characterization techniques such as ultracentrifugation and microfluidic-based approaches. Various detection methods-including electrochemical, nano-based, optical, and machine learning platforms-were evaluated for their high sensitivity, specificity, and non-invasive capabilities. Electrochemical methods were used to identify unique protein signatures for rapid, cost-effective diagnosis, while machine learning enhanced the classification of exosome proteins. Nano-based techniques leveraged nanomaterials to detect low-abundance proteins, and optical methods offered real-time, label-free monitoring. Despite their promise, challenges in standardizing protocols and integrating these diagnostics into clinical practice remain. Future directions include technological advancements, personalized medicine, and exploring the therapeutic potential of exosome proteins.

Synthesis, characterization, and applications of starch-based nano drug delivery systems for breast cancer therapy: A review

The review examined the potential of starch-based drug delivery systems for managing breast cancer efficiently. It covered the background of breast cancer and the significance of drug delivery systems in treatment enhancement. Starch, known for its versatile physicochemical properties, was explored as a promising biopolymer for drug delivery. The review detailed the properties of starch relevant to drug delivery, synthesis methods, and characterization approaches. It discussed the application of starch-based systems in breast cancer treatment, focusing on their role in improving chemotherapy delivery. The advantages and limitations of these systems, such as biocompatibility and drug loading capacity, were evaluated, along with future research directions in starch modification and emerging technologies. The review concluded by emphasizing the potential of starch-based drug delivery systems in improving breast cancer treatment outcomes.

Pneumolysin contributes to dysfunction of nasal epithelial barrier for promotion of pneumococcal dissemination into brain tissue

Streptococcus pneumoniae is one of the major pathogens responsible for bacterial meningitis and neurological sequelae. The present study was conducted to identify a non-hematogenous route used by S. pneumoniae to gain access to brain tissue without causing bacteremia or pneumonia, as well as bacterial and host factors involved in this process. To investigate the molecular mechanisms and dissemination pathways of pneumococcal infection in brain tissue, mice were intranasally inoculated with S. pneumoniae strain EF3030, a clinical isolate from a patient with otitis media. Pneumococci were isolated from the frontal olfactory bulb, caudal cerebrum, and cerebellum, with neither bacteremia nor pneumonia observed in the present model. Immunostaining imaging revealed the presence of S. pneumoniae organisms in olfactory nerve fibers. Knockout of the ply gene encoding pneumolysin (PLY) markedly compromised the ability of the bacterial organisms to disseminate into brain tissue, whereas the dissemination efficiency of the complemented strain was restored to nearly the same level as the wild type. Notably, distinct upregulation of Gli1 and Snail1, which are involved in the transcriptional repression of junctional proteins, along with downregulation of E-cadherin, was detected in nasal lavage samples from mice infected with the wild-type or complemented strain, but not in those from mice infected with the ply mutant. Taken together, the present findings indicate that PLY induces Gli1-Snail1-dependent dysfunction of the nasal epithelial barrier, thus allowing pneumococcal dissemination to brain tissue that occurs in a non-hematogenous manner.IMPORTANCEBacterial meningitis, considered to be caused by bacteremia, can lead to blood-brain barrier disruption and bacterial dissemination into the central nervous system. Despite the availability of intravenously administered antibiotics with cerebrospinal fluid transferability, bacterial meningitis remains associated with high rates of morbidity and mortality. Here, we utilized Streptococcus pneumoniae strain EF3030, clinically isolated from otitis media, for the construction of a murine infection model to investigate the molecular mechanisms by which nasally colonized pneumococci disseminate into brain tissue. The obtained findings indicate that pneumolysin (PLY) induces Gli1-Snail1-dependent dysfunction of the nasal epithelial barrier, which facilitates pneumococcal dissemination to brain tissue in a non-hematogenous manner. Our results support the existence of an alternative route by which S. pneumoniae can reach the central nervous system and indicate the need for the development of novel therapeutic strategies, which would be an important contribution to the clinical management of bacterial meningitis.

A comprehensive study of pharmaceutics solubility in supercritical solvent through diverse thermodynamic and hybrid Machine learning approaches

The pharmaceutical industry is increasingly drawn to the research of innovative drug delivery systems through the use of supercritical CO2 (scCO2)-based techniques. Measuring the solubility of drugs in scCO2 at varying conditions is a crucial parameter in this context. In this research, the supercritical solubility of two pharmaceutical ingredients, namely Febuxostat and Chlorpromazine, has been assessed theoretically using various thermodynamic approaches, including PR, SRK, UNIQUAC, and Wilson models. Additionally, hybrid machine learning models of PO-GPR, and PO-KNN were applied to anticipate the supercritical solubility of these medicines. Verification of the accuracy of each model for each pharmaceutical substance is conducted against previously reported experimental solubility data. In the comparison between the SRK and PR models, it is observed that the SRK model displays greater precision in correlating the solubility of both drugs. It consistently achieves a mean Radj value of 0.995 across all cases and mean AARD% values of 14.47 and 9.30 for Febuxostat and Chlorpromazine, respectively. Furthermore, the findings indicate that the UNIQUAC model surpasses the Wilson model in precisely representing the solubility of both medicines. It consistently achieves a mean Radj value higher than 0.985 across both cases and mean AARD% values of 11.39 and 7.08 for Febuxostat and Chlorpromazine, respectively. Additionally, the performance of both hybrid machine learning models proved to be excellent in anticipating the supercritical solubility of both compounds.

Detection and isolation of brain tumors in cancer patients using neural network techniques in MRI images

MRI imaging primarily focuses on the soft tissues of the human body, typically performed prior to a patient's transfer to the surgical suite for a medical procedure. However, utilizing MRI images for tumor diagnosis is a time-consuming process. To address these challenges, a new method for automatic brain tumor diagnosis was developed, employing a combination of image segmentation, feature extraction, and classification techniques to isolate the specific region of interest in an MRI image corresponding to a brain tumor. The proposed method in this study comprises five distinct steps. Firstly, image pre-processing is conducted, utilizing various filters to enhance image quality. Subsequently, image thresholding is applied to facilitate segmentation. Following segmentation, feature extraction is performed, analyzing morphological and structural properties of the images. Then, feature selection is carried out using principal component analysis (PCA). Finally, classification is performed using an artificial neural network (ANN). In total, 74 unique features were extracted from each image, resulting in a dataset of 144 observations. Principal component analysis was employed to select the top 8 most effective features. Artificial Neural Networks (ANNs) leverage comprehensive data and selective knowledge. Consequently, the proposed approach was evaluated and compared with alternative methods, resulting in significant improvements in precision, accuracy, and F1 score. The proposed method demonstrated notable increases in accuracy, with improvements of 99.3%, 97.3%, and 98.5% in accuracy, Sensitivity and F1 score. These findings highlight the efficiency of this approach in accurately segmenting and classifying MRI images.

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.

Recent advances to address challenges in extracellular vesicle-based applications for lung cancer

Lung cancer, highly prevalent and the leading cause of cancer-related death globally, persists as a significant challenge due to the lack of definitive tumor markers for early diagnosis and personalized therapeutic interventions. Recently, extracellular vesicles (EVs), functioning as natural carriers for intercellular communication, have received increasing attention due to their ability to traverse biological barriers and deliver diverse biological cargoes, including cytosolic proteins, cell surface proteins, microRNA, lncRNA, circRNA, DNA, and lipids. EVs are increasingly recognized as a valuable resource for non-invasive liquid biopsy, as well as drug delivery platforms, and anticancer vaccines for precision medicine in lung cancer. Herein, given the diagnostic and therapeutic potential of tumor-associated EVs for lung cancer, we discuss this topic from a translational standpoint. We delve into the specific roles that EVs play in lung cancer carcinogenesis and offer a particular perspective on how advanced engineering technologies can overcome the current challenges and expedite and/or enhance the translation of EVs from laboratory research to clinical settings.

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.

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.

TRIM34 suppresses non-small-cell lung carcinoma via inducing mTORC1-dependent glucose utilization and promoting cellular death

Non-small-cell lung carcinoma (NSCLC) is a type of pernicious tumor, which owns high morbidity and mortality. TRIM34 has a stimulative role in cell apoptosis and a suppressive role in inflammation. However, no studies were focused on the regulatory impacts of TRIM34 in NSCLC. This study aimed to examine the underlying regulatory effects of TRIM34 in NSCLC. TRIM34 exhibited lower expression in NSCLC. TRIM34 facilitated mitochondrial damage and apoptosis in NSCLC. TRIM34 induced the increased activity of mTORC1 and accelerated glycolysis in NSCLC. Enhanced mitochondrial damage induced by TRIM34 overexpression was reversed after rapamycin (mTORC1 inhibitor) treatment in NSCLC. The strengthened cell apoptosis stimulated by TRIM34 overexpression was rescued after rapamycin treatment. TRIM34 activated mTORC1 to suppress NSCLC progression in vivo. TRIM34 suppressed NSCLC via inducing mTORC1-dependent glucose utilization and promoting cellular death. The results suggest that TRIM34 can be a useful therapeutic biomarker for NSCLC patients.

Gli1-mediated tumor cell-derived bFGF promotes tumor angiogenesis and pericyte coverage in non-small cell lung cancer

BACKGROUND

Tumor angiogenesis inhibitors have been applied for non-small cell lung cancer (NSCLC) therapy. However, the drug resistance hinders their further development. Intercellular crosstalk between lung cancer cells and vascular cells was crucial for anti-angiogenenic resistance (AAD). However, the understanding of this crosstalk is still rudimentary. Our previous study showed that Glioma-associated oncogene 1 (Gli1) is a driver of NSCLC metastasis, but its role in lung cancer cell-vascular cell crosstalk remains unclear.

METHODS

Conditioned medium (CM) from Gli1-overexpressing or Gli1-knockdown NSCLC cells was used to educate endothelia cells and pericytes, and the effects of these media on angiogenesis and the maturation of new blood vessels were evaluated via wound healing assays, Transwell migration and invasion assays, tube formation assays and 3D coculture assays. The xenograft model was conducted to establish the effect of Gli1 on tumor angiogenesis and growth. Angiogenic antibody microarray analysis, ELISA, luciferase reporte, chromatin immunoprecipitation (ChIP), bFGF protein stability and ubiquitination assay were performed to explore how Gli1 regulate bFGF expression.

RESULTS

Gli1 overexpression in NSCLC cells enhanced the endothelial cell and pericyte motility required for angiogenesis required for angiogenesis. However, Gli1 knockout in NSCLC cells had opposite effect on this process. bFGF was critical for the enhancement effect on tumor angiogenesis. bFGF treatment reversed the Gli1 knockdown-mediated inhibition of angiogenesis. Mechanistically, Gli1 increased the bFGF protein level by promoting bFGF transcriptional activity and protein stability. Importantly, suppressing Gli1 with GANT-61 obviously inhibited angiogenesis.

CONCLUSION

The Gli1-bFGF axis is crucial for the crosstalk between lung cancer cells and vascular cells. Targeting Gli1 is a potential therapeutic approach for NSCLC angiogenesis.

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.

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.

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.

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.

Revisiting ameloblastin; addressing the EMT-ECM axis above and beyond oral biology

Ameloblastin (AMBN) is best characterized for its role in dental enamel formation, regulating cell differentiation and mineralization, and cell matrix adhesion. However, AMBN has also been detected in mesenchymal stem cells in addition to bone, blood, and adipose tissue. Using immunofluorescence in a pilot scheme, we identified that AMBN is expressed in different parts of the gastrointestinal (GI) tract. AMBN mRNA and protein detection in several tissues along the length of the GI tract suggests a role for AMBN in the structure and tissue integrity of the extracellular matrix (ECM). Intracellular AMBN expression in subsets of cells indicates a potential alternative role in signaling processes. Of note, our previous functional AMBN promoter analyses had shown that it contains epithelial-mesenchymal transition (EMT) regulatory elements. ΑΜΒΝ is herein presented as a paradigm shift of the possible associations and the spatiotemporal regulation of the ECM regulating the EMT and vice versa, using the example of AMBN expression beyond oral biology.

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.

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.

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.

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.

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.

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.

Isotoosendanin exerts inhibition on triple-negative breast cancer through abrogating TGF-β-induced epithelial-mesenchymal transition via directly targeting TGFβR1

As the most aggressive breast cancer, triple-negative breast cancer (TNBC) is still incurable and very prone to metastasis. The transform growth factor β (TGF-β)-induced epithelial-mesenchymal transition (EMT) is crucially involved in the growth and metastasis of TNBC. This study reported that a natural compound isotoosendanin (ITSN) reduced TNBC metastasis by inhibiting TGF-β-induced EMT and the formation of invadopodia. ITSN can directly interact with TGF-β receptor type-1 (TGFβR1) and abrogated the kinase activity of TGFβR1, thereby blocking the TGF-β-initiated downstream signaling pathway. Moreover, the ITSN-provided inhibition on metastasis obviously disappeared in TGFβR1-overexpressed TNBC cells in vitro as well as in mice bearing TNBC cells overexpressed TGFβR1. Furthermore, Lys232 and Asp351 residues in the kinase domain of TGFβR1 were found to be crucial for the interaction of ITSN with TGFβR1. Additionally, ITSN also improved the inhibitory efficacy of programmed cell death 1 ligand 1 (PD-L1) antibody for TNBC in vivo via inhibiting the TGF-β-mediated EMT in the tumor microenvironment. Our findings not only highlight the key role of TGFβR1 in TNBC metastasis, but also provide a leading compound targeting TGFβR1 for the treatment of TNBC metastasis. Moreover, this study also points out a potential strategy for TNBC treatment by using the combined application of anti-PD-L1 with a TGFβR1 inhibitor.

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.

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.