Cyclodextrin nanoparticles in targeted cancer theranostics

The field of cancer nanotheranostics is rapidly evolving, with cyclodextrin (CD)-based nanoparticles emerging as a promising tool. CDs, serving as nanocarriers, have higher adaptability and demonstrate immense potential in delivering powerful anti-cancer drugs, leading to promising and specific therapeutic outcomes for combating various types of cancer. The unique characteristics of CDs, combined with innovative nanocomplex creation techniques such as encapsulation, enable the development of potential theranostic treatments. The review here focuses mainly on the different techniques administered for effective nanotheranostics applications of CD-associated complex compounds in the domain of cancer treatments. The experimentations on various loaded drugs and their complex conjugates with CDs prove effective in in vivo results. Various cancers can have potential nanotheranostics cures using CDs as nanoparticles along with a highly efficient process of nanocomplex development and a drug delivery system. In conclusion, nanotheranostics holds immense potential for targeted drug delivery and improved therapeutic outcomes, offering a promising avenue for revolutionizing cancer treatments through continuous research and innovative approaches

Carotenoids: Role in Neurodegenerative Diseases Remediation

Numerous factors can contribute to the development of neurodegenerative disorders (NDs), such as Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, and multiple sclerosis. Oxidative stress (OS), a fairly common ND symptom, can be caused by more reactive oxygen species being made. In addition, the pathological state of NDs, which includes a high number of protein aggregates, could make chronic inflammation worse by activating microglia. Carotenoids, often known as “CTs”, are pigments that exist naturally and play a vital role in the prevention of several brain illnesses. CTs are organic pigments with major significance in ND prevention. More than 600 CTs have been discovered in nature, and they may be found in a wide variety of creatures. Different forms of CTs are responsible for the red, yellow, and orange pigments seen in many animals and plants. Because of their unique structure, CTs exhibit a wide range of bioactive effects, such as anti-inflammatory and antioxidant effects. The preventive effects of CTs have led researchers to find a strong correlation between CT levels in the body and the avoidance and treatment of several ailments, including NDs. To further understand the connection between OS, neuroinflammation, and NDs, a literature review has been compiled. In addition, we have focused on the anti-inflammatory and antioxidant properties of CTs for the treatment and management of NDs

Non-Contrast MR-Lymphography: New Tool for Evaluating Lymphedema Pre and Post-Lymph nodal Autotransplantation

MR lymphangiography using dynamic contrast-enhanced images is useful in providing high quality images to diagnose many clinical conditions. However, this procedure cannot be used for traumatic patients or patients with severe side effect of using contrast media. Thus the only option available is to develop a protocol for lymphedema without contrast agents in order to reduce the contra indication of the procedure and to extract diagnostic information without contrast medium. The objectives of this study are to evaluate the role of MR- lymphographic for the assessment of lymphedema before and after lymph node self-transplantation. This study was conducted at IRCCS Policlinico San Matteo (PV), Pavia, Italy. A total of 17 patients were investigated for lymphedema evaluation due to primary or secondary lymphedema. All procedures were performed due to justified clinical conditions according to the ethical guidelines. All procedures were performed using two MRI machines: Siemens Magnetom Aera 1.5T and Philips ingenia 1.5 T. Early Lymphedema stages diagnosis represents great challenges. Non contrast MRL is used to diagnose accurately the lymphatic system disorder. From studies, the researchers have found non contrast MRL is as a promising methodology in the diagnosis of lymphatic system disorders with accuracy up to 90%. The study revealed that non-contrast MRL imaging technique can increase the accuracy of lymphedema diagnosis, improve disease prognostication, and provide a more robust marker of treatment response.MR lymphangiography using dynamic contrast-enhanced images is useful in providing high quality images to diagnose many clinical conditions. However, this procedure cannot be used for traumatic patients or patients with severe side effect of using contrast media. Thus the only option available is to develop a protocol for lymphedema without contrast agents in order to reduce the contra indication of the procedure and to extract diagnostic information without contrast medium. The objectives of this study are to evaluate the role of MR- lymphographic for the assessment of lymphedema before and after lymph node self-transplantation. This study was conducted at IRCCS Policlinico San Matteo (PV), Pavia, Italy. A total of 17 patients were investigated for lymphedema evaluation due to primary or secondary lymphedema. All procedures were performed due to justified clinical conditions according to the ethical guidelines. All procedures were performed using two MRI machines: Siemens Magnetom Aera 1.5T and Philips ingenia 1.5 T. Early Lymphedema stages diagnosis represents great challenges. Non contrast MRL is used to diagnose accurately the lymphatic system disorder. From studies, the researchers have found non contrast MRL is as a promising methodology in the diagnosis of lymphatic system disorders with accuracy up to 90%. The study revealed that non-contrast MRL imaging technique can increase the accuracy of lymphedema diagnosis, improve disease prognostication, and provide a more robust marker of treatment response

B2C3NetF2: Breast cancer classification using an end‐to‐end deep learning feature fusion and satin bowerbird optimization controlled Newton Raphson feature selection

Abstract Currently, the improvement in AI is mainly related to deep learning techniques that are employed for the classification, identification, and quantification of patterns in clinical images. The deep learning models show more remarkable performance than the traditional methods for medical image processing tasks, such as skin cancer, colorectal cancer, brain tumour, cardiac disease, Breast cancer (BrC), and a few more. The manual diagnosis of medical issues always requires an expert and is also expensive. Therefore, developing some computer diagnosis techniques based on deep learning is essential. Breast cancer is the most frequently diagnosed cancer in females with a rapidly growing percentage. It is estimated that patients with BrC will rise to 70% in the next 20 years. If diagnosed at a later stage, the survival rate of patients with BrC is shallow. Hence, early detection is essential, increasing the survival rate to 50%. A new framework for BrC classification is presented that utilises deep learning and feature optimization. The significant steps of the presented framework include (i) hybrid contrast enhancement of acquired images, (ii) data augmentation to facilitate better learning of the Convolutional Neural Network (CNN) model, (iii) a pre‐trained ResNet‐101 model is utilised and modified according to selected dataset classes, (iv) deep transfer learning based model training for feature extraction, (v) the fusion of features using the proposed highly corrected function‐controlled canonical correlation analysis approach, and (vi) optimal feature selection using the modified Satin Bowerbird Optimization controlled Newton Raphson algorithm that finally classified using 10 machine learning classifiers. The experiments of the proposed framework have been carried out using the most critical and publicly available dataset, such as CBIS‐DDSM, and obtained the best accuracy of 94.5% along with improved computation time. The comparison depicts that the presented method surpasses the current state‐of‐the‐art approaches

Gamify4LexAmb: a gamification-based approach to address lexical ambiguity in natural language requirements

© 2024 Dar et al. This is an open access article distributed under the Creative Commons Attribution License, to view a copy of the license, see: https://creativecommons.org/licenses/by/4.0/Ambiguity is a common challenge in specifying natural language (NL) requirements. One of the reasons for the occurrence of ambiguity in software requirements is the lack of user involvement in requirements elicitation and inspection phases. Even if they get involved, it is hard for them to understand the context of the system, and ultimately unable to provide requirements correctly due to a lack of interest. Previously, the researchers have worked on ambiguity avoidance, detection, and removal techniques in requirements. Still, less work is reported in the literature to actively engage users in the system to reduce ambiguity at the early stages of requirements engineering. Traditionally, ambiguity is addressed during inspection when requirements are initially specified in the SRS document. Resolving or removing ambiguity during the inspection is time-consuming, costly, and laborious. Also, traditional elicitation techniques have limitations like lack of user involvement, inactive user participation, biases, incomplete requirements, etc. Therefore, in this study, we have designed a framework, Gamification for Lexical Ambiguity (Gamify4LexAmb), for detecting and reducing ambiguity using gamification. Gamify4LexAmb engages users and identifies lexical ambiguity in requirements, which occurs in polysemy words where a single word can have several different meanings. We have also validated Gamify4LexAmb by developing an initial prototype. The results show that Gamify4LexAmb successfully identifies lexical ambiguities in given requirements by engaging users in requirements elicitation. In the next part of our research, an industrial case study will be performed to understand the effects of gamification on real-time data for detecting and reducing NL ambiguity.Peer reviewe

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

Abstract A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder–Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance

Enhancing Vehicle Detection and Tracking in UAV Imagery: A Pixel Labeling and Particle Filter Approach

Systems must be capable of detecting and tracking autonomous vehicles for intelligent management and control of transportation. Even though several methods are used to create intelligent systems for traffic monitoring, this article explains how to detect and track vehicles using pixel labeling and particle filter algorithms. We suggested a novel technique that segments the image using image segmentation to retrieve the foreground objects. We have divided our proposed model into the following steps: at first, geo-referencing is used to find the exact location; secondly, the images are denoised by using pre-processing; image segmentation is used to separate the background from the foreground; multiple objection detection is performed using the random forest to classify different objects; vehicles are detected through a method called template matching; after this, the vehicles are counted using histogram of oriented gradients (HOG); and after counting, the tracking of vehicles is obtained using particle filter; lastly, the trajectories are predicted by comparing the rectangular centroid of each car against the frame number and using it as a time stamp reference, the last match that the tracking algorithm obtained for each vehicle was recorded and used to estimate the trajectories. Our model outperforms current traffic monitoring approaches in terms of detection and tracking accuracy by 0.87 and 0.92, respectively using the Aerial Car dataset and 0.84 and 0.88, respectively, using the AU-AIR datasets. Vehicle recognition, traffic density detection, traffic flow analysis, and pedestrian route generation are all possible uses for the proposed system. It can transform traffic management and improve overall road safety due to its powerful algorithms and cutting-edge technologies. The system’s adaptability makes it a significant asset in modern transportation, from optimizing signal timing to improving pedestrian navigation

BC²NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework