A Novel Approach of Surface Texture Mapping for Cone-Beam Computed Tomography in Image-Guided Surgical Navigation

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.

Refining neural network algorithms for accurate brain tumor classification in MRI imagery

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis and delayed treatment. Current approaches primarily include manual examination by radiologists and conventional machine learning techniques. These methods rely heavily on feature extraction and classification algorithms, which may not capture the intricate patterns present in brain MRI images. Conventional techniques often suffer from limited accuracy and generalizability, mainly due to the high variability in tumor appearance and the subjective nature of manual interpretation. Additionally, traditional machine learning models may struggle with the high-dimensional data inherent in MRI images. To address these limitations, our research introduces a deep learning-based model utilizing convolutional neural networks (CNNs).Our model employs a sequential CNN architecture with multiple convolutional, max-pooling, and dropout layers, followed by dense layers for classification. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The proposed model demonstrates a significant improvement in diagnostic accuracy, achieving an overall accuracy of 98% on the test dataset. The precision, recall, and F1-scores ranging from 97 to 98% with a roc-auc ranging from 99 to 100% for each tumor category further substantiate the model's effectiveness. Additionally, the utilization of Grad-CAM visualizations provides insights into the model's decision-making process, enhancing interpretability. This research addresses the pressing need for enhanced diagnostic accuracy in identifying brain tumors through MRI imaging, tackling challenges such as variability in tumor appearance and the need for rapid, reliable diagnostic tools.

Correspondence-based Generative Bayesian Deep Learning for semi-supervised volumetric medical image segmentation

Automated medical image segmentation plays a crucial role in diverse clinical applications. The high annotation costs of fully-supervised medical segmentation methods have spurred a growing interest in semi-supervised methods. Existing semi-supervised medical segmentation methods train the teacher segmentation network using labeled data to establish pseudo labels for unlabeled data. The quality of these pseudo labels is constrained as these methods fail to effectively address the significant bias in the data distribution learned from the limited labeled data. To address these challenges, this paper introduces an innovative Correspondence-based Generative Bayesian Deep Learning (C-GBDL) model. Built upon the teacher-student architecture, we design a multi-scale semantic correspondence method to aid the teacher model in generating high-quality pseudo labels. Specifically, our teacher model, embedded with the multi-scale semantic correspondence, learns a better-generalized data distribution from input volumes by feature matching with the reference volumes. Additionally, a double uncertainty estimation schema is proposed to further rectify the noisy pseudo labels. The double uncertainty estimation takes the predictive entropy as the first uncertainty estimation and takes the structural similarity between the input volume and its corresponding reference volumes as the second uncertainty estimation. Four groups of comparative experiments conducted on two public medical datasets demonstrate the effectiveness and the superior performance of our proposed model. Our code is available on https://github.com/yumjoo/C-GBDL.

Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier

There is no doubt that brain tumors are one of the leading causes of death in the world. A biopsy is considered the most important procedure in cancer diagnosis, but it comes with drawbacks, including low sensitivity, risks during biopsy treatment, and a lengthy wait for results. Early identification provides patients with a better prognosis and reduces treatment costs. The conventional methods of identifying brain tumors are based on medical professional skills, so there is a possibility of human error. The labor-intensive nature of traditional approaches makes healthcare resources expensive. A variety of imaging methods are available to detect brain tumors, including magnetic resonance imaging (MRI) and computed tomography (CT). Medical imaging research is being advanced by computer-aided diagnostic processes that enable visualization. Using clustering, automatic tumor segmentation leads to accurate tumor detection that reduces risk and helps with effective treatment. This study proposed a better Fuzzy C-Means segmentation algorithm for MRI images. To reduce complexity, the most relevant shape, texture, and color features are selected. The improved Extreme Learning machine classifies the tumors with 98.56% accuracy, 99.14% precision, and 99.25% recall. The proposed classifier consistently demonstrates higher accuracy across all tumor classes compared to existing models. Specifically, the proposed model exhibits accuracy improvements ranging from 1.21% to 6.23% when compared to other models. This consistent enhancement in accuracy emphasizes the robust performance of the proposed classifier, suggesting its potential for more accurate and reliable brain tumor classification. The improved algorithm achieved accuracy, precision, and recall rates of 98.47%, 98.59%, and 98.74% on the Fig share dataset and 99.42%, 99.75%, and 99.28% on the Kaggle dataset, respectively, which surpasses competing algorithms, particularly in detecting glioma grades. The proposed algorithm shows an improvement in accuracy, of approximately 5.39%, in the Fig share dataset and of 6.22% in the Kaggle dataset when compared to existing models. Despite challenges, including artifacts and computational complexity, the study's commitment to refining the technique and addressing limitations positions the improved FCM model as a noteworthy advancement in the realm of precise and efficient brain tumor identification.

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.

CDRIME-MTIS: An enhanced rime optimization-driven multi-threshold segmentation for COVID-19 X-ray images

To improve the detection of COVID-19, this paper researches and proposes an effective swarm intelligence algorithm-driven multi-threshold image segmentation (MTIS) method. First, this paper proposes a novel RIME structure integrating the Co-adaptive hunting and dispersed foraging strategies, called CDRIME. Specifically, the Co-adaptive hunting strategy works in coordination with the basic search rules of RIME at the individual level, which not only facilitates the algorithm to explore the global optimal solution but also enriches the population diversity to a certain extent. The dispersed foraging strategy further enriches the population diversity to help the algorithm break the limitation of local search and thus obtain better convergence. Then, on this basis, a new multi-threshold image segmentation method is proposed by combining the 2D non-local histogram with 2D Kapur entropy, called CDRIME-MTIS. Finally, the results of experiments based on IEEE CEC2017, IEEE CEC2019, and IEEE CEC2022 demonstrate that CDRIME has superior performance than some other basic, advanced, and state-of-the-art algorithms in terms of global search, convergence performance, and escape from local optimality. Meanwhile, the segmentation experiments on COVID-19 X-ray images demonstrate that CDRIME is more advantageous than RIME and other peers in terms of segmentation effect and adaptability to different threshold levels. In conclusion, the proposed CDRIME significantly enhances the global optimization performance and image segmentation of RIME and has great potential to improve COVID-19 diagnosis.

Oral squamous cell carcinoma detection using EfficientNet on histopathological images

Introduction

Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accuracy and efficiency, highlighting the need for more reliable approaches. This study aims to explore the discriminative potential of histopathological images of oral epithelium and OSCC. By utilizing a database containing 1224 images from 230 patients, captured at varying magnifications and publicly available, a customized deep learning model based on EfficientNetB3 was developed. The model's objective was to differentiate between normal epithelium and OSCC tissues by employing advanced techniques such as data augmentation, regularization, and optimization.

Methods

The research utilized a histopathological imaging database for Oral Cancer analysis, incorporating 1224 images from 230 patients. These images, taken at various magnifications, formed the basis for training a specialized deep learning model built upon the EfficientNetB3 architecture. The model underwent training to distinguish between normal epithelium and OSCC tissues, employing sophisticated methodologies including data augmentation, regularization techniques, and optimization strategies.

Results

The customized deep learning model achieved significant success, showcasing a remarkable 99% accuracy when tested on the dataset. This high accuracy underscores the model's efficacy in effectively discerning between normal epithelium and OSCC tissues. Furthermore, the model exhibited impressive precision, recall, and F1-score metrics, reinforcing its potential as a robust diagnostic tool for OSCC.

Discussion

This research demonstrates the promising potential of employing deep learning models to address the diagnostic challenges associated with OSCC. The model's ability to achieve a 99% accuracy rate on the test dataset signifies a considerable leap forward in earlier and more accurate detection of OSCC. Leveraging advanced techniques in machine learning, such as data augmentation and optimization, has shown promising results in improving patient outcomes through timely and precise identification of OSCC.

Biosensor-Based Multimodal Deep Human Locomotion Decoding via Internet of Healthcare Things

Multiple Internet of Healthcare Things (IoHT)-based devices have been utilized as sensing methodologies for human locomotion decoding to aid in applications related to e-healthcare. Different measurement conditions affect the daily routine monitoring, including the sensor type, wearing style, data retrieval method, and processing model. Currently, several models are present in this domain that include a variety of techniques for pre-processing, descriptor extraction, and reduction, along with the classification of data captured from multiple sensors. However, such models consisting of multiple subject-based data using different techniques may degrade the accuracy rate of locomotion decoding. Therefore, this study proposes a deep neural network model that not only applies the state-of-the-art Quaternion-based filtration technique for motion and ambient data along with background subtraction and skeleton modeling for video-based data, but also learns important descriptors from novel graph-based representations and Gaussian Markov random-field mechanisms. Due to the non-linear nature of data, these descriptors are further utilized to extract the codebook via the Gaussian mixture regression model. Furthermore, the codebook is provided to the recurrent neural network to classify the activities for the locomotion-decoding system. We show the validity of the proposed model across two publicly available data sampling strategies, namely, the HWU-USP and LARa datasets. The proposed model is significantly improved over previous systems, as it achieved 82.22% and 82.50% for the HWU-USP and LARa datasets, respectively. The proposed IoHT-based locomotion-decoding model is useful for unobtrusive human activity recognition over extended periods in e-healthcare facilities.

Simultaneous Study of Analysis of Anti-inflammatory Potential of Dryopteris ramosa (C. Hope) C. Chr. using GC-Mass and Computational Modeling on the Xylene-induced Ear Oedema in Mouse Model

INTRODUCTION

In the present study, we aimed to investigate the extraction and identification of the potential phytochemicals from the Methanolic Extract of Dryopteris ramosa (MEDR) using GC-MS profiling for validating the traditional uses of MEDR its efficacy in inflammations by using in-vitro, in-vivo and in silico approaches in anti-inflammatory models.

METHODS

GC-MS analysis confirmed the presence of a total of 59 phytochemical compounds. The human red blood cells (HRBC) membrane stabilization assay and heat-induced hemolysis method were used as in-vitro anti-inflammatory activity of the extract. The in-vivo analysis was carried out through the Xylene-induced mice ear oedema method. It was found that MEDR at a concentration of 20 μg, 30 μg, and 40 μg showed 35.45%, 36.01%, and 36.33% protection to HRBC in a hypotonic solution, respectively. At the same time, standard Diclofenac at 30 μg showed 45.31% protection of HRBC in a hypotonic solution.

RESULTS

The extract showed inhibition of 25.32%, 26.53%, and 33.31% cell membrane lysis at heating at 20 μg, 30 μg, and 40 μg, respectively. In comparison, standard Diclofenac at 30 μg showed 50.49% inhibition of denaturation to heat. Methanolic extract of the plant exhibited momentous inhibition in xylene-induced ear oedema in mice treated with 30 μg extract were 47.2%, 63.4%, and 78.8%, while inhibition in mice ear oedema treated with 60 μg extract was 34.7%, 43.05%, 63.21% and reduction in ear thickness of standard drug were 57.3%, 59.54%, 60.42% recorded at the duration of 1, 4 and 24 hours of inflammation. Molecular docking and simulations were performed to validate the anti-inflammatory role of the phytochemicals that revealed five potential phytochemicals i.e. Stigmasterol,22,23dihydro, Heptadecane,8methyl, Pimaricacid, Germacrene and 1,3Cyclohexadiene,_5(1,5dimethyl4hexenyl)-2methyl which revealed potential or significant inhibitory effects on cyclooxygenase-2 (COX-2), tumour necrosis factor (TNF-α), and interleukin (IL-6) in the docking analysis.

CONCLUSION

The outcome of the study signifies that MEDR can offer a new prospect in the discovery of a harmonizing and alternative therapy for inflammatory disease conditions.