Endoscope image mosaic based on pyramid ORB

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.

3D reconstruction from endoscopy images: A survey

Three-dimensional reconstruction of images acquired through endoscopes is playing a vital role in an increasing number of medical applications. Endoscopes used in the clinic are commonly classified as monocular endoscopes and binocular endoscopes. We have reviewed the classification of methods for depth estimation according to the type of endoscope. Basically, depth estimation relies on feature matching of images and multi-view geometry theory. However, these traditional techniques have many problems in the endoscopic environment. With the increasing development of deep learning techniques, there is a growing number of works based on learning methods to address challenges such as inconsistent illumination and texture sparsity. We have reviewed over 170 papers published in the 10 years from 2013 to 2023. The commonly used public datasets and performance metrics are summarized. We also give a taxonomy of methods and analyze the advantages and drawbacks of algorithms. Summary tables and result atlas are listed to facilitate the comparison of qualitative and quantitative performance of different methods in each category. In addition, we summarize commonly used scene representation methods in endoscopy and speculate on the prospects of deep estimation research in medical applications. We also compare the robustness performance, processing time, and scene representation of the methods to facilitate doctors and researchers in selecting appropriate methods based on surgical applications.

Polymeric nanoparticles delivery circumvents bacterial resistance to ciprofloxacin

OBJECTIVE

The efficient inhibition of bacteria and their by-products from infected root canals is hampered by the limitations of traditional root canal disinfection strategies, bacterial resistance to antibiotic drugs, and regenerative endodontics. Polymeric nanoparticles nanocarrier for controlling antibiotic drug delivery were used to overcome the limitations encountered in endodontics treatment.

BACKGROUND

Several polymeric nanoparticles have been used for the delivery of ciprofloxacin drug. The application of poly (ethylene glycol) methyl ether-block-poly(lactide-co-glycolide) (PEG-PLGA) nanoparticles has highlighted the clean and safe delivery of ciprofloxacin (CIP) hydrophilic drug for endodontics treatment. PEG/PLGA was prepared using the solid/oil/water method and the CIP was loaded into polymeric nanoparticles via an ion pairing agent.

RESULTS

The CIP-loaded PEG-PLGA nanoparticles have a spherical shape with a 120 ± 0.43 nm size, the CIP encapsulating efficiency was 63.26 ± 9.24% with a loading content of 7.75 ± 1.13%, and sustained release was achieved over 168 h which followed Higuchi model with a non-Fickian mechanism. Moreover, CIP-loaded PEG-PLGA had low cytotoxicity to the stem cells of the apical papilla.

CONCLUSION

The results conclude invigorating future perspectives of polymeric nanoparticles for a wide range of drug delivery for various disease treatments. It's anticipated that these polymeric nanoparticles may divert new expectations in the future for topical antibiotic drug delivery with discrete intracellular medicament, and a safe and clean environment.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

Single-cell RNA Sequencing (scRNA-seq): Advances and Challenges for Cardiovascular Diseases (CVDs)

Implementing Single-cell RNA sequencing (scRNA-seq) has significantly enhanced our comprehension of cardiovascular diseases (CVDs), providing new opportunities to strengthen the prevention of CVDs progression. Cardiovascular diseases continue to be the primary cause of death worldwide. Improving treatment strategies and patient risk assessment requires a deeper understanding of the fundamental mechanisms underlying these disorders. The advanced and widespread use of Single-cell RNA sequencing enables a comprehensive investigation of the complex cellular makeup of the heart, surpassing essential descriptive aspects. This enhances our understanding of disease causes and directs functional research. The significant advancement in understanding cellular phenotypes has enhanced the study of fundamental cardiovascular science. scRNA-seq enables the identification of discrete cellular subgroups, unveiling previously unknown cell types in the heart and vascular systems that may have relevance to different disease pathologies. Moreover, scRNA-seq has revealed significant heterogeneity in phenotypes among distinct cell subtypes. Finally, we will examine current and upcoming scRNA-seq studies about various aspects of the cardiovascular system, assessing their potential impact on our understanding of the cardiovascular system and offering insight into how these technologies may revolutionise the diagnosis and treatment of cardiac conditions.

Cardiovascular diseases crossroads: cGAS-STING signaling and disease progression

It is now widely accepted that inflammation is critical in cardiovascular diseases (CVD). Here, studies are being conducted on how cyclic GMP-AMP synthase (cGAS), a component of innate immunity's DNA-sensing machinery, communicates with the STING receptor, which is involved in activating the immune system's antiviral response. Significantly, a growing body of research in recent years highlights the strong activation of the cGAS-STING signalling pathways in several cardiovascular diseases, such as myocardial infarction, heart failure, and myocarditis. This developing collection of research emphasises these pathways' crucial role in initiating and advancing cardiovascular disease. In this extensive narrative, we explore the role of the cGAS-STING pathway in the development of CVD. We elaborate on the basic mechanisms involved in the onset and progression of CVD. This review explores the most recent developments in the recognition and characterization of cGAS-STING pathway. Additionally, it considers the field's future prospects while examining how cGAS-STING pathway might be altered and its clinical applications for cardiovascular diseases.

CHST12: a potential prognostic biomarker related to the immunotherapy response in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is characterized by lower immunogenicity with a poor response rate to immune checkpoint inhibitors (ICIs) and exhibits the poorest prognosis of all solid tumors, which results in the highest tumor-related mortality among malignancies. However, the underlying mechanisms are poorly understood. In addition, diverse carbohydrate sulfotransferases (CHSTs), which are involved in the sulfation process of these structures, play an important role in the metastatic spread of tumor cells. Aberrant glycosylation is beginning to emerge as an influencing factor in tumor immunity and immunotherapy. Therefore, it might serve as a biomarker of the immunotherapeutic response in tumors. The purpose of the study was to evaluate the role of CHST12 in PAAD prognosis and its relevance to the immunotherapeutic response.

Methods

A comprehensive investigation of the interactions between CHST12 expression and the immune microenvironment as well as the clinical significance of CHST12 in PAAD was conducted. Data derived from the Cancer Genome Atlas (TCGA) database were analyzed using univariate and multivariate approaches, the Tumor Immune Estimation Resource (TIMER), and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms. Publicly available datasets were analyzed in this study. These data can be found on websites such as http://www.xiantao.love and https://www.proteinatlas.org. An assessment of the predictive value of CHST12 for PAAD prognosis was conducted using univariate and multivariate Cox regression analysis, Kaplan-Meier analysis, and nomograms. The TIMER algorithm calculates the proportions of six types of immune cells. The TIDE algorithm was used to indicate the characteristics of tumors that respond to ICI therapy.

Results

The mRNA and protein levels of CHST12 showed the opposite trend. CHST12 mRNA expression was significantly upregulated in PAAD. According to Cox regression analysis, CHST12 RNA expression acts as a protective factor for overall survival [hazard ratio (HR), 0.617, P < 0.04]. Functional annotation indicated that CHST12-associated differentially expressed genes (DEGs) were related to the signaling activity of receptor tyrosine kinases and the regulation of ubiquitin-protein transferase. These are usually involved in tumor development and may be related to the treatment responses of immune checkpoint inhibitors (ICIs). There was significantly higher CHST12 mRNA expression in PAAD samples than in non-malignant samples.

Conclusions

In PAAD, elevated CHST12 mRNA expression might regulate immune cell infiltration into the tumor microenvironment (TME) and may predict clinical outcomes.

The multifaceted role of Fragile X-Related Protein 1 (FXR1) in cellular processes: an updated review on cancer and clinical applications

RNA-binding proteins (RBPs) modulate the expression level of several target RNAs (such as mRNAs) post-transcriptionally through interactions with unique binding sites in the 3'-untranslated region. There is mounting information that suggests RBP dysregulation plays a significant role in carcinogenesis. However, the function of FMR1 autosomal homolog 1(FXR1) in malignancies is just beginning to be unveiled. Due to the diversity of their RNA-binding domains and functional adaptability, FXR1 can regulate diverse transcript processing. Changes in FXR1 interaction with RNA networks have been linked to the emergence of cancer, although the theoretical framework defining these alterations in interaction is insufficient. Alteration in FXR1 expression or localization has been linked to the mRNAs of cancer suppressor genes, cancer-causing genes, and genes involved in genomic expression stability. In particular, FXR1-mediated gene regulation involves in several cellular phenomena related to cancer growth, metastasis, epithelial-mesenchymal transition, senescence, apoptosis, and angiogenesis. FXR1 dysregulation has been implicated in diverse cancer types, suggesting its diagnostic and therapeutic potential. However, the molecular mechanisms and biological effects of FXR1 regulation in cancer have yet to be understood. This review highlights the current knowledge of FXR1 expression and function in various cancer situations, emphasizing its functional variety and complexity. We further address the challenges and opportunities of targeting FXR1 for cancer diagnosis and treatment and propose future directions for FXR1 research in oncology. This work intends to provide an in-depth review of FXR1 as an emerging oncotarget with multiple roles and implications in cancer biology and therapy.

Automatization of CT Annotation: Combining AI Efficiency with Expert Precision

The integration of artificial intelligence (AI), particularly through machine learning (ML) and deep learning (DL) algorithms, marks a transformative progression in medical imaging diagnostics. This technical note elucidates a novel methodology for semantic segmentation of the vertebral column in CT scans, exemplified by a dataset of 250 patients from Riga East Clinical University Hospital. Our approach centers on the accurate identification and labeling of individual vertebrae, ranging from C1 to the sacrum-coccyx complex. Patient selection was meticulously conducted, ensuring demographic balance in age and sex, and excluding scans with significant vertebral abnormalities to reduce confounding variables. This strategic selection bolstered the representativeness of our sample, thereby enhancing the external validity of our findings. Our workflow streamlined the segmentation process by eliminating the need for volume stitching, aligning seamlessly with the methodology we present. By leveraging AI, we have introduced a semi-automated annotation system that enables initial data labeling even by individuals without medical expertise. This phase is complemented by thorough manual validation against established anatomical standards, significantly reducing the time traditionally required for segmentation. This dual approach not only conserves resources but also expedites project timelines. While this method significantly advances radiological data annotation, it is not devoid of challenges, such as the necessity for manual validation by anatomically skilled personnel and reliance on specialized GPU hardware. Nonetheless, our methodology represents a substantial leap forward in medical data semantic segmentation, highlighting the potential of AI-driven approaches to revolutionize clinical and research practices in radiology.

Hemophilia Healing with AAV: Navigating the Frontier of Gene Therapy

Gene therapy for hemophilia has advanced tremendously after thirty years of continual study and development. Advancements in medical science have facilitated attaining normal levels of Factor VIII (FVIII) or Factor IX (FIX) in individuals with haemophilia, thereby offering the potential for their complete recovery. Despite the notable advancements in various countries, there is significant scope for further enhancement in haemophilia gene therapy. Adeno-associated virus (AAV) currently serves as the primary vehicle for gene therapy in clinical trials targeting haemophilia. Subsequent investigations will prioritize enhancing viral capsid structures, transgene compositions, and promoters to achieve heightened transduction efficacy, diminished immunogenicity, and more predictable therapeutic results. The present study indicates that whereas animal models have transduction efficiency that is over 100% high, human hepatocytes are unable to express clotting factors and transduction efficiency to comparable levels. According to the current study, achieving high transduction efficiency and high levels of clotting factor expression in human hepatocytes is still insufficient. It is also crucial to reduce the risk of cellular stress caused by protein overload. Despite encountering various hurdles, the field of haemophilia gene therapy holds promise for the future. As technology continues to advance and mature, it is anticipated that a personalized therapeutic approach will be developed to cure haemophilia effectively.

The Multifunctional TRPC6 Protein: Significance in the Field of Cardiovascular Studies

Cardiovascular disease is the leading cause of death, medical complications, and healthcare costs. Although recent advances have been in treating cardiovascular disorders linked with a reduced ejection fraction, acutely decompensate cardiac failure remains a significant medical problem. The transient receptor potential cation channel (TRPC6) family responds to neurohormonal and mechanical stress, playing critical roles in cardiovascular diseases. Therefore, TRP C6 channels have great promise as therapeutic targets. Numerous studies have investigated the roles of TRP C6 channels in pain neurons, highlighting their significance in cardiovascular research. The TRPC6 protein exhibits a broad distribution in various organs and tissues, including the brain, nerves, heart, blood vessels, lungs, kidneys, gastrointestinal tract, and other bodily structures. Its activation can be triggered by alterations in osmotic pressure, mechanical stimulation, and diacylglycerol. Consequently, TRPC6 plays a significant role in the pathophysiological mechanisms underlying diverse diseases within living organisms. A recent study has indicated a strong correlation between the disorder known as TRPC6 and the development of cardiovascular diseases. Consequently, investigations into the association between TRPC6 and cardiovascular diseases have gained significant attention in the scientific community. This review explores the most recent developments in the recognition and characterization of TRPC6. Additionally, it considers the field's prospects while examining how TRPC6 might be altered and its clinical applications.

Achieving enhanced sensitivity and accuracy in carcinoembryonic antigen (CEA) detection as an indicator of cancer monitoring using thionine/chitosan/graphene oxide nanocomposite-modified electrochemical immunosensor

The current study has focused on electrochemical immunosensing of carcinoembryonic antigen (CEA) employing an immobilized antibody on a thionine, chitosan, or graphene oxide nanocomposite modified glassy carbon electrode (anti-CEA/THi-CS-GO/GCE) as an indicator of cancer monitoring. THi-CS-GO nanocomposites were made using ultrasonication, and analyses of their morphology and crystal structure using SEM, FTIR, and XRD showed that thionine and chitosan molecules were intercalated with stacking interactions with both the top and bottom of GO nanosheets. Electrochemical experiments revealed anti-CEA, THi-CS-GO/GCE to have exceptional sensitivity and selectivity towards CEA compounds. The detection limit value was established to be 0.8 pg/mL when it was discovered that variations in the decrease peak current were directly proportional to the logarithm concentration of CEA over a wide range from 10-3 to 104 ng/mL. Results of testing the immunosensor's application capability for detecting CEA in a sample of human serum show that ELISA and DPV results are very congruent. The produced immunosensor demonstrated adequate immunosensor precision in determining CEA in prepared genuine samples of human serum and clinical applications.

Deeper insight into ferroptosis: association with Alzheimer's, Parkinson's disease, and brain tumors and their possible treatment by nanomaterials induced ferroptosis

Ferroptosis is an emerging and novel type of iron-dependent programmed cell death which is mainly caused by the excessive deposition of free intracellular iron in the brain cells. This deposited free iron exerts a ferroptosis pathway, resulting in lipid peroxidation (LiPr). There are mainly three ferroptosis pathways viz. iron metabolism-mediated cysteine/glutamate, and LiPr-mediated. Iron is required by the brain as a redox metal for several physiological activities. Due to the iron homeostasis balance disruption, the brain gets adversely affected which further causes neurodegenerative diseases (NDDs) like Parkinson's and Alzheimer's disease, strokes, and brain tumors like glioblastoma (GBS), and glioma. Nanotechnology has played an important role in the prevention and treatment of these NDDs. A synergistic effect of nanomaterials and ferroptosis could prove to be an effective and efficient approach in the field of nanomedicine. In the current review, the authors have highlighted all the latest research in the field of ferroptosis, specifically emphasizing on the role of major molecular key players and various mechanisms involved in the ferroptosis pathway. Moreover, here the authors have also addressed the correlation of ferroptosis with the pathophysiology of NDDs and theragnostic effect of ferroptosis and nanomaterials for the prevention and treatment of NDDs.

Automatic deep learning-based pleural effusion segmentation in lung ultrasound images

BACKGROUND

Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from the clinician to accurately interpret the images. To address this limitation, we previously demonstrated a deep-learning model capable of detecting the presence of PE on LUS at an accuracy greater than 90%, when compared to an experienced LUS operator.

METHODS

This follow-up study aimed to develop a deep-learning model to provide segmentations for PE in LUS. Three thousand and forty-one LUS images from twenty-four patients diagnosed with PE were selected for this study. Two LUS experts provided the ground truth for training by reviewing and segmenting the images. The algorithm was then trained using ten-fold cross-validation. Once training was completed, the algorithm segmented a separate subset of patients.

RESULTS

Comparing the segmentations, we demonstrated an average Dice Similarity Coefficient (DSC) of 0.70 between the algorithm and experts. In contrast, an average DSC of 0.61 was observed between the experts.

CONCLUSION

In summary, we showed that the trained algorithm achieved a comparable average DSC at PE segmentation. This represents a promising step toward developing a computational tool for accurately augmenting PE diagnosis and treatment.

Usage of computational method for hemodynamic analysis of intracranial aneurysm rupture risk in different geometrical aspects

The importance of the parent vessel geometrical feature on the risk of cerebral aneurysm rupture is unavoidable. This study presents inclusive details on the hemodynamics of Internal carotid artery (ICA) aneurysms with different parent vessel mean diameters. Different aspects of blood hemodynamics are compared to find a reasonable connection between parent vessel mean diameter and significant hemodynamic factors of wall shear stress (WSS), oscillatory shear index (OSI), and pressure distribution. To access hemodynamic data, computational fluid dynamics is used to model the blood stream inside the cerebral aneurysms. A hemodynamic comparison of the selected cerebral aneurysm shows that the minimum WSS is reduced by about 71% as the parent vessel's mean diameter is increased from 3.18 to 4.48 mm.

Influence of parent vessel feature on the risk of internal carotid artery aneurysm rupture via computational method

In this study, the role of sac section area and parent vessel diameter on the hemodynamic feature of the blood flow in selected internal carotid artery (ICA) aneurysms is comprehensively investigated. The changes of wall shear stress, pressure, and oscillatory shear index (OSI) of blood stream on the vessel for various aneurysms with coiling treatment. To attain hemodynamic factors, computational technique is used for the modeling of non-Newtonian transient blood flow inside the three different ICA aneurysms. Three different saccular models with various Parent vessel mean Diameter is investigated in this study. The achieved outcomes show that increasing the diameter of the parent vessel directly decreases the OSI value on the sac surface. In addition, the mean wall shear stress decreases with the increase of the parent vessel diameter.

Dispersive Modeling of Normal and Cancerous Cervical Cell Responses to Nanosecond Electric Fields in Reversible Electroporation Using a Drift-Step Rectifier Diode Generator

This paper creates an approximate three-dimensional model for normal and cancerous cervical cells using image processing and computer-aided design (CAD) tools. The model is then exposed to low-frequency electric pulses to verify the work with experimental data. The transmembrane potential, pore density, and pore radius evolution are analyzed. This work adds a study of the electrodeformation of cells under an electric field to investigate cytoskeleton integrity. The Maxwell stress tensor is calculated for the dispersive bi-lipid layer plasma membrane. The solid displacement is calculated under electric stress to observe cytoskeleton integrity. After verifying the results with previous experiments, the cells are exposed to a nanosecond pulsed electric field. The nanosecond pulse is applied using a drift-step rectifier diode (DSRD)-based generator circuit. The cells' transmembrane voltage (TMV), pore density, pore radius evolution, displacement of the membrane under electric stress, and strain energy are calculated. A thermal analysis of the cells under a nanosecond pulse is also carried out to prove that it constitutes a non-thermal process. The results showed differences in normal and cancerous cell responses to electric pulses due to changes in morphology and differences in the cells' electrical and mechanical properties. This work is a model-driven microdosimetry method that could be used for diagnostic and therapeutic purposes.

Computational and statistical analyses of blood hemodynamic inside cerebral aneurysms for treatment evaluation of endovascular coiling

Diagnosis of aneurysm and possibility of aneurysm rupture are crucial for avoiding brain hemorrhage. In this work, blood stream inside internal carotid arteries (ICAs) are simulated in diverse working conditions to disclose the importance of hemodynamic factors on the rupture of aneurysm. The main attention of this study is to investigate the role of hemodynamic on the aneurysm rupture. Statistical and computational methods are applied to investigate coiling porosity and blood hematocrit in 9 specific real ICA geometries. Response surface model (RSM) develops 25 runs to investigate all features of selected geometrical parameters and treatment factors. Computational fluid dynamic is used for the simulation of the blood stream in the selected aneurysms. The effects of sac section area and mean radius of parent vessel on blood hemodynamics are fully investigated. Hemodynamic factors are examined and compared at the peak systolic time instant, including pressure distributions, and velocity. Achieved results indicate that the increasing sac section area (from 36.6 to 75.4 mm2) results in 20% pressure reduction on the sac wall.

Trends in using deep learning algorithms in biomedical prediction systems

In the domain of using DL-based methods in medical and healthcare prediction systems, the utilization of state-of-the-art deep learning (DL) methodologies assumes paramount significance. DL has attained remarkable achievements across diverse domains, rendering its efficacy particularly noteworthy in this context. The integration of DL with health and medical prediction systems enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This comprehensive literature review systematically investigates the latest DL solutions for the challenges encountered in medical healthcare, with a specific emphasis on DL applications in the medical domain. By categorizing cutting-edge DL approaches into distinct categories, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), long short-term memory (LSTM) models, support vector machine (SVM), and hybrid models, this study delves into their underlying principles, merits, limitations, methodologies, simulation environments, and datasets. Notably, the majority of the scrutinized articles were published in 2022, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical prediction systems, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image segmentation within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of using DL-based methods in medical and health prediction systems. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, precision, specificity, F-score, adoptability, adaptability, and scalability.

Medical image analysis using deep learning algorithms

In the field of medical image analysis within deep learning (DL), the importance of employing advanced DL techniques cannot be overstated. DL has achieved impressive results in various areas, making it particularly noteworthy for medical image analysis in healthcare. The integration of DL with medical image analysis enables real-time analysis of vast and intricate datasets, yielding insights that significantly enhance healthcare outcomes and operational efficiency in the industry. This extensive review of existing literature conducts a thorough examination of the most recent deep learning (DL) approaches designed to address the difficulties faced in medical healthcare, particularly focusing on the use of deep learning algorithms in medical image analysis. Falling all the investigated papers into five different categories in terms of their techniques, we have assessed them according to some critical parameters. Through a systematic categorization of state-of-the-art DL techniques, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), Long Short-term Memory (LSTM) models, and hybrid models, this study explores their underlying principles, advantages, limitations, methodologies, simulation environments, and datasets. Based on our results, Python was the most frequent programming language used for implementing the proposed methods in the investigated papers. Notably, the majority of the scrutinized papers were published in 2021, underscoring the contemporaneous nature of the research. Moreover, this review accentuates the forefront advancements in DL techniques and their practical applications within the realm of medical image analysis, while simultaneously addressing the challenges that hinder the widespread implementation of DL in image analysis within the medical healthcare domains. These discerned insights serve as compelling impetuses for future studies aimed at the progressive advancement of image analysis in medical healthcare research. The evaluation metrics employed across the reviewed articles encompass a broad spectrum of features, encompassing accuracy, sensitivity, specificity, F-score, robustness, computational complexity, and generalizability.

Impacts of morphology parameters on the risk of rupture in intracranial aneurysms: statistical and computational analyses

The hemodynamic analysis of the blood stream inside the cerebral aneurysms reveals the risk of the aneurysm rupture. In addition, the high risk region prone to rupture would be determined by the hemodynamic analysis of the blood. In present article, computational fluid dynamic is used for the investigation of the hemodynamic effects on the aneurysm wall and risk of rupture. This study tries to find the connection between the risk of rupture with three geometrical features of aneurysm i.e., Ellipsoid Max semi-axis, Size ratio and Tortuosity. Statistical analysis is done over 30 different ruptured /unruptured ICA aneurysms to find meaningful relation between selected geometrical factors and rupture risk. The hemodynamic analysis is done over four distinct aneurysm models to attain more details on effects of chosen geometrical factors. The results of simulations indicate that the Ellipsoid Max semi-axis have meaningful impacts on the risk of rupture.

Evaluation of common protein biomarkers involved in the pathogenesis of respiratory diseases with proteomic methods: A systematic review

AIM

Respiratory disease (RD) is one of the most common diseases characterized by lung dysfunction. Many diagnostic mechanisms have been used to identify the pathogenic agents of responsible for RD. Among these, proteomics emerges as a valuable diagnostic method for pinpointing the specific proteins involved in RD pathogenesis. Therefore, in this study, for the first time, we examined the protein markers involved in the pathogenesis of chronic obstructive pulmonary disease (COPD), idiopathic pulmonary fibrosis (IPF), asthma, bronchiolitis obliterans (BO), and chemical warfare victims exposed to mustard gas, using the proteomics method as a systematic study.

MATERIALS AND METHODS

A systematic search was performed up to September 2023 on several databases, including PubMed, Scopus, ISI Web of Science, and Cochrane. In total, selected 4246 articles were for evaluation according to the criteria. Finally, 119 studies were selected for this systematic review.

RESULTS

A total of 13,806 proteins were identified, 6471 in COPD, 1603 in Asthma, 5638 in IPF, three in BO, and 91 in mustard gas exposed victims. Alterations in the expression of these proteins were observed in the respective diseases. After evaluation, the results showed that 31 proteins were found to be shared among all five diseases.

CONCLUSION

Although these 31 proteins regulate different factors and molecular pathways in all five diseases, they ultimately lead to the regulation of inflammatory pathways. In other words, the expression of some proteins in COPD and mustard-exposed patients increases inflammatory reactions, while in IPF, they cause lung fibrosis. Asthma, causes allergic reactions due to T-cell differentiation toward Th2.

The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things

Medical data processing has grown into a prominent topic in the latest decades with the primary goal of maintaining patient data via new information technologies, including the Internet of Things (IoT) and sensor technologies, which generate patient indexes in hospital data networks. Innovations like distributed computing, Machine Learning (ML), blockchain, chatbots, wearables, and pattern recognition can adequately enable the collection and processing of medical data for decision-making in the healthcare era. Particularly, to assist experts in the disease diagnostic process, distributed computing is beneficial by digesting huge volumes of data swiftly and producing personalized smart suggestions. On the other side, the current globe is confronting an outbreak of COVID-19, so an early diagnosis technique is crucial to lowering the fatality rate. ML systems are beneficial in aiding radiologists in examining the incredible amount of medical images. Nevertheless, they demand a huge quantity of training data that must be unified for processing. Hence, developing Deep Learning (DL) confronts multiple issues, such as conventional data collection, quality assurance, knowledge exchange, privacy preservation, administrative laws, and ethical considerations. In this research, we intend to convey an inclusive analysis of the most recent studies in distributed computing platform applications based on five categorized platforms, including cloud computing, edge, fog, IoT, and hybrid platforms. So, we evaluated 27 articles regarding the usage of the proposed framework, deployed methods, and applications, noting the advantages, drawbacks, and the applied dataset and screening the security mechanism and the presence of the Transfer Learning (TL) method. As a result, it was proved that most recent research (about 43%) used the IoT platform as the environment for the proposed architecture, and most of the studies (about 46%) were done in 2021. In addition, the most popular utilized DL algorithm was the Convolutional Neural Network (CNN), with a percentage of 19.4%. Hence, despite how technology changes, delivering appropriate therapy for patients is the primary aim of healthcare-associated departments. Therefore, further studies are recommended to develop more functional architectures based on DL and distributed environments and better evaluate the present healthcare data analysis models.

A new conceptual model to investigate the role of hospital's capabilities on sustainable learning

The health-care industry is in a state of constant flux, with new challenges and opportunities emerging regularly. Hospitals, as the cornerstone of health-care delivery, must adapt and embrace change to provide optimal patient care. One crucial aspect that plays a significant role in the success of hospitals is sustainable learning. Sustainable learning refers to acquiring knowledge, skills, and competencies that enable health-care professionals to adapt to changes, implement best practices, and deliver high-quality care. Sustainable learning, a concept gaining prominence, emphasizes the ability of hospitals to learn from experiences and adapt to changing circumstances while maintaining quality health-care delivery. This article aims to investigate the role of hospital capabilities in sustainable learning and explore how hospitals can foster an environment that promotes continuous learning and development. Another goal of the paper is to test the relationships between cultural capabilities, structural capabilities, knowledge management capabilities, Information Technology (IT) infrastructure, top management support, application capabilities, and sustainable learning. The Partial Least-Squares (PLS) algorithm was performed using SmartPLS 3.0 to attain this goal. The results successfully support the study goals. This study verified that cultural capability, structural capabilities, knowledge management capabilities, IT infrastructure, top management support, and application capabilities positively affected sustainable learning. This investigation contributes to hospital, management, and education research by developing an integrated paradigm for sustainable learning. In conclusion, the new conceptual model presented here provides a robust framework for investigating the role of hospital capabilities in sustainable learning. By understanding and improving their capabilities, hospitals can not only adapt to change but also thrive in an ever-changing health-care landscape.

IDRM: Brain tumor image segmentation with boosted RIME optimization

Timely diagnosis of medical conditions can significantly mitigate the risks they pose to human life. Consequently, there is an urgent demand for an effective auxiliary model that assists physicians in accurately diagnosing medical conditions based on imaging data. While multi-threshold image segmentation models have garnered considerable attention due to their simplicity and ease of implementation, the selection of threshold combinations greatly influences the segmentation performance. Traditional optimization algorithms often require substantial time to address multi-threshold image segmentation problems, and their segmentation accuracy is frequently unsatisfactory. As a result, metaheuristic algorithms have been employed in this domain. However, several algorithms suffer from drawbacks such as premature convergence and inadequate exploration of the solution space when it comes to threshold selection. For instance, the recently proposed optimization algorithm RIME, inspired by the physical phenomenon of rime-ice, falls short in terms of avoiding local optima and fully exploring the solution space. Therefore, this study introduces an enhanced version of RIME, called IDRM, which incorporates an interactive mechanism and Gaussian diffusion strategy. The interactive mechanism facilitates information exchange among agents, enabling them to evolve towards more promising directions and increasing the likelihood of discovering the optimal solution. Additionally, the Gaussian diffusion strategy enhances the agents' local exploration capabilities and expands their search within the solution space, effectively preventing them from becoming trapped in local optima. Experimental results on 30 benchmark test functions demonstrate that IDRM exhibits favorable optimization performance across various optimization functions, showcasing its robustness and convergence properties. Furthermore, the algorithm is applied to select threshold combinations for brain tumor image segmentation, and the results are evaluated using metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The overall findings consistently highlight the exceptional performance of this approach, further validating the effectiveness of IDRM in addressing image segmentation problems.

Gastrointestinal tract disorders classification using ensemble of InceptionNet and proposed GITNet based deep feature with ant colony optimization

Computer-aided classification of diseases of the gastrointestinal tract (GIT) has become a crucial area of research. Medical science and artificial intelligence have helped medical experts find GIT diseases through endoscopic procedures. Wired endoscopy is a controlled procedure that helps the medical expert in disease diagnosis. Manual screening of the endoscopic frames is a challenging and time taking task for medical experts that also increases the missed rate of the GIT disease. An early diagnosis of GIT disease can save human beings from fatal diseases. An automatic deep feature learning-based system is proposed for GIT disease classification. The adaptive gamma correction and weighting distribution (AGCWD) preprocessing procedure is the first stage of the proposed work that is used for enhancing the intensity of the frames. The deep features are extracted from the frames by deep learning models including InceptionNetV3 and GITNet. Ant Colony Optimization (ACO) procedure is employed for feature optimization. Optimized features are fused serially. The classification operation is performed by variants of support vector machine (SVM) classifiers, including the Cubic SVM (CSVM), Coarse Gaussian SVM (CGSVM), Quadratic SVM (QSVM), and Linear SVM (LSVM) classifiers. The intended model is assessed on two challenging datasets including KVASIR and NERTHUS that consist of eight and four classes respectively. The intended model outperforms as compared with existing methods by achieving an accuracy of 99.32% over the KVASIR dataset and 99.89% accuracy using the NERTHUS dataset.

Evaluation of Long Non-coding RNA (LncRNA) in the Pathogenesis of Chemotherapy Resistance in Cervical Cancer: Diagnostic and Prognostic Approach

Cervical cancer (CC), caused by human papillomavirus (HPV), is a leading cause of female malignancies worldwide. Therefore, understanding the underlying mechanisms of CC development and identifying novel therapeutic targets are significantly important. Cisplatin resistance is a significant challenge in the management of CC. Recent studies highlighted the critical role of long non-coding RNAs (lncRNAs) in modulation of cisplatin resistance. This comprehensive review aims to collect the current understanding roles of lncRNAs and their involvement in cisplatin resistance in CC by highlighting key processes of cancer progression, including apoptosis, proliferation, angiogenesis and epithelial-to-mesenchymal transition (EMT). We discussed the role of lncRNA in CC resistance to cisplatin through molecular pathways and examined gene expression changes. We also discussed treatment strategies and factors that reduce CC resistance to cisplatin by targeting them.

Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images

Diabetic retinopathy (DR) is one of the main causes of blindness in people around the world. Early diagnosis and treatment of DR can be accomplished by organizing large regular screening programs. Still, it is difficult to spot diabetic retinopathy timely because the situation might not indicate signs in the primary stages of the disease. Due to a drastic increase in diabetic patients, there is an urgent need for efficient diabetic retinopathy detecting systems. Auto-encoders, sparse coding, and limited Boltzmann machines were used as a few past deep learning (DL) techniques and features for the classification of DR. Convolutional Neural Networks (CNN) have been identified as a promising solution for detecting and classifying DR. We employ the deep learning capabilities of efficient net batch normalization (BNs) pre-trained models to automatically acquire discriminative features from fundus images. However, we successfully achieved F1 scores above 80% on all efficient net BNs in the EYE-PACS dataset (calculated F1 score for DeepDRiD another dataset) and the results are better than previous studies. In this paper, we improved the accuracy and F1 score of the efficient net BNs pre-trained models on the EYE-PACS dataset by applying a Gaussian Smooth filter and data augmentation transforms. Using our proposed technique, we have achieved F1 scores of 84% and 87% for EYE-PACS and DeepDRiD.

4-Dimensional printing: exploring current and future capabilities in biomedical and healthcare systems-a Concise review

4-Dimensional Printing (4DP) is the latest concept in the pharmacy and biomedical segment with enormous potential in dosage from personalization and medication designing, which adopts time as the fourth dimension, giving printed structures the flexibility to modify their morphology. It can be defined as the fabrication in morphology with the help of smart/intelligent materials like polymers that permit the final object to alter its properties, shape, or function in response to external stimuli such as heat, light, pH, and moisture. The applications of 4DP in biomedicines and healthcare are explored with a focus on tissue engineering, artificial organs, drug delivery, pharmaceutical and biomedical field, etc. In the medical treatments and pharmaceutical field 4DP is paving the way with unlimited potential applications; however, its mainstream use in healthcare and medical treatments is highly dependent on future developments and thorough research findings. Therefore, previous innovations with smart materials are likely to act as precursors of 4DP in many industries. This review highlights the most recent applications of 4DP technology and smart materials in biomedical and healthcare fields which can show a better perspective of 4DP applications in the future. However, in view of the existing limitations, major challenges of this technology must be addressed along with some suggestions for future research. We believe that the application of proper regulatory constraints with 4DP technology would pave the way for the next technological revolution in the biomedical and healthcare sectors.

An Approach to Binary Classification of Alzheimer's Disease Using LSTM

In this study, we use LSTM (Long-Short-Term-Memory) networks to evaluate Magnetic Resonance Imaging (MRI) data to overcome the shortcomings of conventional Alzheimer's disease (AD) detection techniques. Our method offers greater reliability and accuracy in predicting the possibility of AD, in contrast to cognitive testing and brain structure analyses. We used an MRI dataset that we downloaded from the Kaggle source to train our LSTM network. Utilizing the temporal memory characteristics of LSTMs, the network was created to efficiently capture and evaluate the sequential patterns inherent in MRI scans. Our model scored a remarkable AUC of 0.97 and an accuracy of 98.62%. During the training process, we used Stratified Shuffle-Split Cross Validation to make sure that our findings were reliable and generalizable. Our study adds significantly to the body of knowledge by demonstrating the potential of LSTM networks in the specific field of AD prediction and extending the variety of methods investigated for image classification in AD research. We have also designed a user-friendly Web-based application to help with the accessibility of our developed model, bridging the gap between research and actual deployment.

Whale optimization with random contraction and Rosenbrock method for COVID-19 disease prediction

Coronavirus Disease 2019 (COVID-19), instigated by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has hugely impacted global public health. To identify and intervene in critically ill patients early, this paper proposes an efficient, intelligent prediction model based on the machine learning approach, which combines the improved whale optimization algorithm (RRWOA) with the k-nearest neighbor (KNN) classifier. In order to improve the problem that WOA is prone to fall into local optimum, an improved version named RRWOA is proposed based on the random contraction strategy (RCS) and the Rosenbrock method. To verify the capability of the proposed algorithm, RRWOA is tested against nine classical metaheuristics, nine advanced metaheuristics, and seven well-known WOA variants based on 30 IEEE CEC2014 competition functions, respectively. The experimental results in mean, standard deviation, the Friedman test, and the Wilcoxon signed-rank test are considered, proving that RRWOA won first place on 18, 24, and 25 test functions, respectively. In addition, a binary version of the algorithm, called BRRWOA, is developed for feature selection problems. An efficient prediction model based on BRRWOA and KNN classifier is proposed and compared with seven existing binary metaheuristics based on 15 datasets of UCI repositories. The experimental results show that the proposed algorithm obtains the smallest fitness value in eleven datasets and can solve combinatorial optimization problems, indicating that it still performs well in discrete cases. More importantly, the model was compared with five other algorithms on the COVID-19 dataset. The experiment outcomes demonstrate that the model offers a scientific framework to support clinical diagnostic decision-making. Therefore, RRWOA is an effectively improved optimizer with efficient value.

Defect Detection for Metal Shaft Surfaces Based on an Improved YOLOv5 Algorithm and Transfer Learning

To address the problem of low efficiency for manual detection in the defect detection field for metal shafts, we propose a deep learning defect detection method based on the improved YOLOv5 algorithm. First, we add a Convolutional Block Attention Module (CBAM) mechanism layer to the last layer of the backbone network to improve the feature extraction capability. Second, the neck network introduces the Bi-directional Feature Pyramid Network (BiFPN) module to replace the original Path-Aggregation Network (PAN) structure and enhance the multi-scale feature fusion. Finally, we use transfer learning to pre-train the model and improve the generalization ability of the model. The experimental results show that the method achieves an average accuracy of 93.6% mAP and a detection speed of 16.7 FPS for defect detection on the dataset, which can identify metal shaft surface defects quickly and accurately, and is of reference significance for practical industrial applications.

Boosted crow search algorithm for handling multi-threshold image problems with application to X-ray images of COVID-19

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC'21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi's entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi's entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available extra info can be found at https://github.com/1234zsw/VMCSA.

The various role of microRNAs in breast cancer angiogenesis, with a special focus on novel miRNA-based delivery strategies

After skin malignancy, breast cancer is the most widely recognized cancer detected in women in the United States. Breast cancer (BCa) can happen in all kinds of people, but it's much more common in women. One in four cases of cancer and one in six deaths due to cancer are related to breast cancer. Angiogenesis is an essential factor in the growth of tumors and metastases in various malignancies. An expanded level of angiogenesis is related to diminished endurance in BCa patients. This function assumes a fundamental part inside the human body, from the beginning phases of life to dangerous malignancy. Various factors, referred to as angiogenic factors, work to make a new capillary. Expanding proof demonstrates that angiogenesis is managed by microRNAs (miRNAs), which are small non-coding RNA with 19-25 nucleotides. MiRNA is a post-transcriptional regulator of gene expression that controls many critical biological processes. Endothelial miRNAs, referred to as angiomiRs, are probably concerned with tumor improvement and angiogenesis via regulation of pro-and anti-angiogenic factors. In this article, we reviewed therapeutic functions of miRNAs in BCa angiogenesis, several novel delivery carriers for miRNA-based therapeutics, as well as CRISPR/Cas9 as a targeted therapy in breast cancer.

Design of erythrocyte-derived carriers for bioimaging applications

Erythrocytes are physiological entities that have been exploited in both preclinical and clinical trials for the delivery of exogenous agents. Over the years, diverse erythrocyte-derived carriers (ECs) have been developed with related patents granted for industrial and commercial purposes. However, most ECs have only been exploited for drug delivery. Serious discussions regarding their applications in imaging are scarce. This article reviews the role of ECs in enhancing imaging efficiency and subsequently delineates strategies for engineering and optimising their preclinical and clinical performance. With a snapshot of the latest developments and use of ECs in imaging, directions to streamline the clinical translation of related technologies can be attained for future research.

bSRWPSO-FKNN: A boosted PSO with fuzzy K-nearest neighbor classifier for predicting atopic dermatitis disease

Introduction

Atopic dermatitis (AD) is an allergic disease with extreme itching that bothers patients. However, diagnosing AD depends on clinicians' subjective judgment, which may be missed or misdiagnosed sometimes.

Methods

This paper establishes a medical prediction model for the first time on the basis of the enhanced particle swarm optimization (SRWPSO) algorithm and the fuzzy K-nearest neighbor (FKNN), called bSRWPSO-FKNN, which is practiced on a dataset related to patients with AD. In SRWPSO, the Sobol sequence is introduced into particle swarm optimization (PSO) to make the particle distribution of the initial population more uniform, thus improving the population's diversity and traversal. At the same time, this study also adds a random replacement strategy and adaptive weight strategy to the population updating process of PSO to overcome the shortcomings of poor convergence accuracy and easily fall into the local optimum of PSO. In bSRWPSO-FKNN, the core of which is to optimize the classification performance of FKNN through binary SRWPSO.

Results

To prove that the study has scientific significance, this paper first successfully demonstrates the core advantages of SRWPSO in well-known algorithms through benchmark function validation experiments. Secondly, this article demonstrates that the bSRWPSO-FKNN has practical medical significance and effectiveness through nine public and medical datasets.

Discussion

The 10 times 10-fold cross-validation experiments demonstrate that bSRWPSO-FKNN can pick up the key features of AD, including the content of lymphocytes (LY), Cat dander, Milk, Dermatophagoides Pteronyssinus/Farinae, Ragweed, Cod, and Total IgE. Therefore, the established bSRWPSO-FKNN method practically aids in the diagnosis of AD.

Eres-UNet++: Liver CT image segmentation based on high-efficiency channel attention and Res-UNet+

Computerized tomography (CT) is of great significance for the localization and diagnosis of liver cancer. Many scholars have recently applied deep learning methods to segment CT images of liver and liver tumors. Unlike natural images, medical image segmentation is usually more challenging due to its nature. Aiming at the problem of blurry boundaries and complex gradients of liver tumor images, a deep supervision network based on the combination of high-efficiency channel attention and Res-UNet++ (ECA residual UNet++) is proposed for liver CT image segmentation, enabling fully automated end-to-end segmentation of the network. In this paper, the UNet++ structure is selected as the baseline. The residual block feature encoder based on context awareness enhances the feature extraction ability and solves the problem of deep network degradation. The introduction of an efficient attention module combines the depth of the feature map with spatial information to alleviate the uneven sample distribution impact; Use DiceLoss to replace the cross-entropy loss function to optimize network parameters. The liver and liver tumor segmentation accuracy on the LITS dataset was 95.8% and 89.3%, respectively. The results show that compared with other algorithms, the method proposed in this paper achieves a good segmentation performance, which has specific reference significance for computer-assisted diagnosis and treatment to attain fine segmentation of liver and liver tumors.

Applications of Extracellular Vesicles in Nervous System Disorders: An Overview of Recent Advances

Diseases affecting the brain and spinal cord fall under the umbrella term "central nervous system disease". Most medications used to treat or prevent chronic diseases of the central nervous system cannot cross the blood-brain barrier (BBB) and hence cannot reach their intended target. Exosomes facilitate cellular material movement and signal transmission. Exosomes can pass the blood-brain barrier because of their tiny size, high delivery efficiency, minimal immunogenicity, and good biocompatibility. They enter brain endothelial cells via normal endocytosis and reverse endocytosis. Exosome bioengineering may be a method to produce consistent and repeatable isolation for clinical usage. Because of their tiny size, stable composition, non-immunogenicity, non-toxicity, and capacity to carry a wide range of substances, exosomes are indispensable transporters for targeted drug administration. Bioengineering has the potential to improve these aspects of exosomes significantly. Future research into exosome vectors must focus on redesigning the membrane to produce vesicles with targeting abilities to increase exosome targeting. To better understand exosomes and their potential as therapeutic vectors for central nervous system diseases, this article explores their basic biological properties, engineering modifications, and promising applications.

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques

Introduction

Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients.

Methods

Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed.

Results

To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital.

Discussion

The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

Blockchain in Healthcare: A Decentralized Platform for Digital Health Passport of COVID-19 Based on Vaccination and Immunity Certificates

COVID-19 has become a very transmissible disease that has had a worldwide impact, resulting in a huge number of infections and fatalities. Testing is critical to the pandemic's successful response because it helps detect illnesses and so attenuate (isolate/cure) them and now vaccination is a life-safer innovation against the pandemic which helps to make the immunity system stronger and fight against this infection. Patient-sensitive information, on the other hand, is now held in a centralized or third-party storage paradigm, according to COVID-19. One of the most difficult aspects of using a centralized storage strategy is maintaining patient privacy and system transparency. The application of blockchain technology to support health initiatives that can minimize the spread of COVID-19 infections in the context of accessibility of the system and for verification of digital passports. Only by combining blockchain technology with advanced cryptographic algorithms can a secure and privacy-preserving solution to COVID-19 be provided. In this article, we investigate the issue and propose a blockchain-based solution incorporating conscience identity, encryption, and decentralized storage via interplanetary file systems (IPFS). For COVID-19 test takers and vaccination takers, our solution includes digital health passports (DHP) as a certification of test or vaccination. We explain smart contracts constructed and tested with Ethereum to preserve a DHP for test and vaccine takers, allowing for a prompt and trustworthy response from the necessary medical authorities. We use an immutable trustworthy blockchain to minimize medical facility response times, relieve the transmission of incorrect information, and stop the illness from spreading via DHP. We give a detailed explanation of the proposed solution's system model, development, and assessment in terms of cost and security. Finally, we put the suggested framework to the test by deploying a smart contract prototype on the Ethereum TESTNET network in a Windows environment. The study's findings revealed that the suggested method is effective and feasible.

Polyphenols and inflammatory bowel disease: Natural products with therapeutic effects?

Inflammatory bowel disease (IBD) is a long-life disease with periods of recurrence and relief. Oxidative stress plays an important role in the pathogenesis of this disease. Recent years' studies in the field of IBD treatment mostly have focused on targeting cytokines and immune cell trafficking using antibodies and inhibitors, altering the composition of intestinal bacteria in the line of attenuation of inflammation using probiotics and prebiotics, and attenuating oxidative stress through antioxidant supplementation. Studies in animal models of IBD have shown that some polyphenolic compounds including curcumin, quercetin, resveratrol, naringenin, and epigallocatechin-3-gallate can affect almost all of the above aspects and are useful compounds in the treatment of IBD. Clinical studies performed on IBD patients have also confirmed the findings of animal model studies and have shown that supplementation with some of the above-mentioned polyphenolic compounds has positive effects in reducing disease clinical and endoscopic activity, inducing and maintaining remission, and improving quality of life. In this review article, in addition to a detailed reviewing the effects of the above-mentioned polyphenolic compounds on the events involved in the pathogenesis of IBD, the results of these clinical studies will also be reviewed.

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.

DAD-Net: Classification of Alzheimer’s Disease Using ADASYN Oversampling Technique and Optimized Neural Network

Alzheimer’s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model’s prediction accuracy depends on the dataset’s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer’s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer’s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer’s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer’s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer’s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care

There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

Nutritional Management Mode of Early Cardiac Rehabilitation in Patients with Stanford Type A Aortic Dissection

Malnutrition and metabolic disorders are common problems faced by patients with Stanford type A aortic dissection after surgery. Some patients have dietary problems such as malnutrition, unbalanced diet, and poor eating habits before surgery. Therefore, the nutritional management of early heart health can improve the nutritional support for perioperative recovery, to improve the pertinence. Therefore, active nutritional support after surgery will help to change malnutrition and metabolism and is of great significance to postoperative recovery and quality of life. This paper is aimed at studying the nutritional management mode of early cardiac rehabilitation in patients with Stanford type A aortic dissection. Based on the analysis of the pathogenesis of aortic dissection and the diagnosis of aortic dissection, two groups of patients were given individualized nutritional management scheme and routine nutritional scheme, respectively, and the nutritional risk differences between the two groups under different schemes were compared. The results showed that there was a statistical difference between the two groups at discharge. The NRS-2002 score of 14 cases in the observation group was less than 3 after nutritional intervention, indicating that there was no nutritional risk at discharge.

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.