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To improve the translation of preclinical cancer research data to successful clinical effect, there is an increasing focus on the use of primary patient‐derived cancer cells with limited growth in culture to reduce genetic and phenotype drift. However, these primary lines are less amenable to standardly used methods of exogenous DNA introduction. Adeno‐associated viral (AAV) vectors display tropism for a wide range of human tissues, avidly infect primary cells and have a good safety profile. In the present study, we therefore used a next‐generation sequencing (NGS) barcoded AAV screening method to assess transduction capability of a panel of 36 AAVs in primary cell lines representing high‐grade glioma (HGG) brain tumours including glioblastoma (GBM) and diffuse intrinsic pontine glioma (DIPG)/diffuse midline glioma (DMG). As proof of principle, we created a reporter construct to analyse activity of the transcriptional co‐activators yes‐associated protein (YAP) and transcriptional co‐activator with PDZ‐binding motif (TAZ). Transcriptional activation was monitored by promoter‐driven expression of the Timer fluorescent tag, a protein that fluoresces green immediately after transcription and transitions to red fluorescence over time. As expected, attempts to express the reporter in primary HGG cells from plasmid expression vectors were unsuccessful. Using the top candidate from the AAV screen, we demonstrate successful AAV‐mediated transduction of HGG cells with the YAP/TAZ dynamic activity reporter. In summary, the NGS‐screening approach facilitated screening of many potential AAVs, identifying vectors that can be used to study the biology of primary HGG cells. [ABSTRACT FROM AUTHOR]

• Unbiased proteomic analysis is consistent with the inhibition of Src as the mechanism of action of TAT-Cx43 266-283 and unveils crucial proteins, such as p120 catenin, as effectors of the antitumor effect of TAT-Cx43 266-283. • TAT-Cx43 266-283 might affect not only in tumor cells but also the brain microenvironment, by regulating the composition of the extracellular matrix, cell-cell interactions, or the immune system response. • The proteome modulation exerted by TAT-Cx43 266-283 in GSCs correlates with an improved survival of GBM patients in clinical datasets, supporting the use of TAT-Cx43 266-283 in GBM clinical trials. Glioblastoma (GBM) is the most frequent and aggressive primary brain cancer. The Src inhibitor, TAT-Cx43 266-283 , exerts antitumor effects in in vitro and in vivo models of GBM. Because addressing the mechanism of action is essential to translate these results to a clinical setting, in this study we carried out an unbiased proteomic approach. Data-independent acquisition mass spectrometry proteomics allowed the identification of 190 proteins whose abundance was modified by TAT-Cx43 266-283. Our results were consistent with the inhibition of Src as the mechanism of action of TAT-Cx43 266-283 and unveiled antitumor effectors, such as p120 catenin. Changes in the abundance of several proteins suggested that TAT-Cx43 266-283 may also impact the brain microenvironment. Importantly, the proteins whose abundance was reduced by TAT-Cx43 266-283 correlated with an improved GBM patient survival in clinical datasets and none of the proteins whose abundance was increased by TAT-Cx43 266-283 correlated with shorter survival, supporting its use in clinical trials. [ABSTRACT FROM AUTHOR]

Brain tumors, characterized by uncontrollable cellular growths, are a significant global health challenge. Navigating the complexities of tumor identification due to their varied dimensions and positions, our research introduces enhanced methods for precise detection. Utilizing advanced learning techniques, we've improved early identification by preprocessing clinical dataset-derived images, augmenting them via a Generative Adversarial Network, and applying an Improved Privacy-Preserving Collaborative Convolutional Neural Network (IPC-CNN) for segmentation. Recognizing the critical importance of data security in today's digital era, our framework emphasizes the preservation of patient privacy. We evaluated the performance of our proposed model on the Figshare and BRATS 2018 datasets. By facilitating a collaborative model training environment across multiple healthcare institutions, we harness the power of distributed computing to securely aggregate model updates, ensuring individual data protection while leveraging collective expertise. Our IPC-CNN model achieved an accuracy of 99.40%, marking a notable advancement in brain tumor classification and offering invaluable insights for both the medical imaging and machine learning communities. [ABSTRACT FROM AUTHOR]

Brain tumors refer to abnormal cell proliferation formed in brain tissue, which can cause neurological dysfunction and cognitive impairment, posing a serious threat to human health. Therefore, it becomes a very challenging work to full-automaticly segment brain tumors using computers because of the mutual infiltration and fuzzy boundary between the focus areas and the normal brain tissue. To address the above issues, a segmentation method which integrates edge features is proposed in this paper. The overall segmentation architecture follows the encoder decoder structure, extracting rich features from the encoder. The first two layers of features are input to the edge attention module, and to extract tumor edge features which are fully fused with the features of the decoder segment. At the same time, an adaptive weighted mixed loss function is introduced to train the network by adaptively adjusting the weights of different loss parts in the training process. Relevant experiments were carried out using the public brain tumor data set. The Dice mean values of the proposed segmentation model in the whole tumor area (WT), the core tumor area (TC), and the enhancing tumor area (ET) reach 91.10%, 87.16%, and 88.86%, respectively, and the mean values of Hausdorff distance are 3.92, 5.12, and 1.92 mm, respectively. The experimental results showed that the proposed method can significantly improve segmentation accuracy, especially the segmentation effect of the edge part. [ABSTRACT FROM AUTHOR]

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers. [ABSTRACT FROM AUTHOR]

Multicompartmental lesions within the central nervous system are challenging due to their complex anatomy. This study evaluates the efficacy, safety, and utility of hybrid endoscopic and microsurgery versus endoscope-assisted microsurgery(EAM) for excising these lesions. A retrospective comparative analysis was conducted on patients who underwent multicompartmental brain tumor surgery, utilizing either hybrid endoscopic and microsurgical techniques with the Endocameleon Hopkins telescope featuring a rotating lens system and knob (Karl Storz GmbH & Co., Tuttlingen, Germany), alternately used with a microscope (ZEISS PENTERO 800 S) (Group 1, n = 69), or endoscope-assisted microsurgery employing a fully high-definition, 45° angled endoscopic tool, QEVO®, integrated into the digital surgical microscope KINEVO 900 (Carl Zeiss Meditec, Oberkochen, Germany) as a plug-in feature (Group 2, n = 63), from July 2018 to March 2024. Data on demographics, clinical presentation, lesion characteristics, surgical details, and outcomes were meticulously collected and analyzed using rigorous statistical methods, including t-tests and chi-square tests. Compared to Group 2, Group 1 had better ease of dissection and visualization of bleeders (p = 0.01) and fewer postoperative hematomas (p = 0.04). Surgical times were similar (p = 0.134). Postoperative follow-up revealed fewer recurrences in Group 1, though not statistically significant (p = 0.33). Group 1 patients reported higher cosmetic satisfaction and shorter hospital stays (p = 0.002). Logistic regression identified tumor vascularity(p = 0.001) and ease of dissection(p = 0.008) as significant factors for recurrence. Hybrid endoscopic and microsurgery demonstrated superior intraoperative visualization, ease of dissection, and postoperative outcomes compared to endoscope-assisted microsurgery with the Quevo device. These findings suggest that the integrated approach may offer better outcomes for multicompartmental lesion excision regarding safety, efficacy, and patient satisfaction. [ABSTRACT FROM AUTHOR]

In this study, we introduce an innovative approach to significantly enhance the precision and interpretability of brain tumor detection and segmentation. Our method ingeniously integrates the cutting-edge capabilities of the ChatGPT chatbot interface with a state-of-the-art multi-modal convolutional neural network (CNN). Tested rigorously on the BraTS dataset, our method showcases unprecedented performance, outperforming existing techniques in terms of both accuracy and efficiency, with an impressive Dice score of 0.89 for tumor segmentation. By seamlessly integrating ChatGPT, our model unveils deep-seated insights into the intricate decision-making processes, providing researchers and physicians with invaluable understanding and confidence in the results. This groundbreaking fusion holds immense promise, poised to revolutionize the landscape of medical imaging, with far-reaching implications for clinical practice and research. Our study exemplifies the transformative potential achieved through the synergistic combination of multi-modal CNNs and natural language processing, paving the way for remarkable advancements in brain tumor detection and segmentation. [ABSTRACT FROM AUTHOR]

Manual segmentation of brain tumors using structural magnetic resonance imaging (MRI) is an arduous and time-consuming task. Therefore, automatic and robust segmentation will considerably influence neuro-oncological clinical trials by reducing excessive manual annotation time. Herein, we propose a deep learning model that automatically segments brain tumors even in cases of missing MRI sequences, which are common in practical clinical settings. To address this issue, we enhance a generative adversarial network (GAN) by incorporating a squeeze-and-excitation (SE) attention module into its generator and a PatchGAN into its discriminator. The SE module recalibrates channel responses by explicitly modeling interdependencies, enabling the network to focus on critical regions such as tumor areas. Our proposed generative model is optimized using a combination of adversarial, structural similarity, and mean absolute error losses to synthesize missing MRI sequences more effectively. This enhancement allows our model to synthesize the missing MRI sequence (fluid attenuated inversion recovery [FLAIR]) by leveraging information from other available sequences (T1-weighted, T2-weighted, or contrast-enhanced T1-weighted [T1ce]). For the segmentation task, we employ an optimized nnU-Net model, which is trained using existing sequences and evaluated using both available and synthesized sequences (including missing ones), mimicking real-world scenarios where often only limited MRI sequences are available or usable. Our findings reveal a notable enhancement in brain tumor segmentation, as indicated by a significant increase in overall the Dice similarity coefficient (DSC) from 0.688% (when FLAIR is missing) to 0.873% (when using synthesized FLAIR derived from T2). This improvement brings the segmentation performance closer to what was achieved when real FLAIR was available, where the DSC reaches 0.901%. Moreover, our synthesizing model was also tested on two additional datasets: the BraTS 2020 validation set and BraTS Africa 2023 training set, which produces results comparable to those of BraTS 2021, thereby proving its robustness and generalizability. In addition, the resulting tumor segmentations are subsequently employed to assess the response to treatment in cases where all sequences were available and when synthesis was employed, according to response assessment in neuro-oncology criteria. [Display omitted] [ABSTRACT FROM AUTHOR]

Brain cancer, one of the leading causes of mortality worldwide, is caused by brain tumors. Early diagnosis of tumors and predicting their progression can help doctors to save lives. In this article, we have designed an automated approach for locating and classifying tumors from MRI images. The novelties of the research work include the following two stages: Developing an encoder-decoder type 20-Layered deep neural network (DNN) named MultiTumor Analyzer (MTA-20) with 15 down-sampling layers and 4 up-sampling layers, the segmentation is performed in the initial stage. Here, we have adhered a Leaky ReLU activation function instead of ReLU which learn a parameter with negative values that may have valuable information which is essential specifically for image segmentation. Further, a 55-layered DNN using multistage feature fusion is developed in the second stage of the work for the classification of localized tumors. The classification is performed using developed MultiTumor Analyzer (MTA-55) DNN with Softmax classifier. The efficacy of the designed network is validated using highly cited quantitative measures such as accuracy, sensitivity, specificity, dice similarity coefficient (DSC), precision, and F1-measure. It is observed that the proposed MTA-20 DNN attains the average accuracy, sensitivity, specificity, DSC, and precision of 99.2 %, 94.6 %, 99.3 %, 88 %, and 82.5 % respectively against seven state-of-the-art techniques. Also, it is found that, the proposed MTA-55 DNN provides the overall accuracy, recall, specificity, F1-measure, precision, and DSC of 99.8 %, 99.633 %, 99.844 %, 99.659 %, 99.689 %, and 99.656 % respectively as compared to thirteen state-of-the-art techniques. These results corroborate the superiority of the proposed technique. [ABSTRACT FROM AUTHOR]

Background: Appropriate exploratory efficacy data from Phase I trials are vital for subsequent phases. Owing to the uniqueness of brain tumors (BTs), use of different strategies to evaluate efficacy is warranted. We studied exploratory efficacy evaluation in Phase I trials involving BTs. Methods: Using Clarivate's Cortellis™, 42 Phase I trials of BT interventions conducted from 2020 to 2022 were analyzed for efficacy endpoints, which were set as primary endpoints (PEs) or secondary endpoints (SEs). Additionally, these metrics were compared in two subgroups: trials including only BTs (Group-A) and those including BTs among mixed solid tumors (Group-B). Results: Selected studies included a median of 1.5 PEs (range, 1–6) and 5 SEs (range, 0–19). Efficacy endpoints were included as PEs and SEs in 2 (5%) and 31 (78%) trials, respectively. Among the latter 31 trials that included 94 efficacy endpoints, 24, 22, 20, 9, and 8 reflected overall response rate (ORR), progression-free survival (PFS), overall survival (OS), duration of response (DOR), and disease control rate (DCR), respectively. ORR for BT was determined using various methods; however, the Response Evaluation Criteria in Solid Tumors (RECIST) was used less frequently in Group-A than in Group-B (p = 0.0039). Conclusions: Recent Phase I trials included efficacy endpoints as SEs, with ORR, PFS, or OS included in ~ 50% trials and DOR or DCR in ~ 25%. No established criteria exist for imaging evaluation of BTs. Phase I trials involving mixed solid tumor cohorts revealed challenges in designing methods to assess the exploratory efficacy of BTs. [ABSTRACT FROM AUTHOR]

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Semantic segmentation of glioma and its subregions plays a critical role in the entirely clinical workflow of brain cancer diagnosis, monitoring, and treatment planning. Recently, automatic tumor segmentation has attracted a lot of attention, especially supervised learning methods based on neural networks, and the popular “Ushaped” network architecture has achieved state-of-the-art performance in many fields of medical image segmentation. Despite the success of these models, the commonly used small convolution kernel can only extract local features, and more global contextual features cannot be learned, resulting in the disappointing performance of modeling long-range information. At the same time, due to the difficulty of obtaining medical image data, and the imbalance of tumor data in which tumor usually occupies a relatively small volume compared with the background, the adverse influence on the training of the model occurs. In this paper, a novel segmentation framework including TensorMixup data augmentation, improved Receptive Field Expansion UNet (RFE-UNet) and hybrid loss function is designed. Specifically, the TensorMixup algorithm in the data preprocessing phase is used to provide more high-quality training data. In the training phase, both a RFE-UNet network and a hybrid loss function are proposed respectively. RFE-UNet network adds Receptive field expansion module based on Dilated convolution in the first three stages of skip connection, which is used to learn more local and global features. In addition, hybrid loss function is mainly composed of focal loss and focal Tversky loss, focal loss increasing the weight of fewer samples and focal Tversky loss focusing on learning the characteristics of samples with incorrect predictions, which is adopted to alleviate data imbalance. The experimental results on the BraTs2019 dataset show that the average Dice value of the proposed algorithm in the intact tumor, tumor core, and enhanced tumor region can reach 91.55%, 89.23%, and 84.16% respectively, which proves the feasibility and effectiveness of using the proposed architecture. [ABSTRACT FROM AUTHOR]

The current status of clinical trials utilizing nanoparticle drug delivery system (NDDS) for brain tumors was summarized. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre- and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS'20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the model's superior segmentation performance, marking a significant advancement in brain tumor analysis across pre- and post-operative MRI scans. [ABSTRACT FROM AUTHOR]

Background: Primary intracranial Ewing's sarcoma (ES) is a type of primitive neuroectodermal tumour and is a rare malignant tumour in children and adolescents. The imaging features of ES overlap with other central nervous system embryonal tumours, making it difficult to pinpoint a specific diagnosis. We aim to explore the clinical, neuroimaging and differential diagnoses of this entity. Case presentation: We describe a 6-month-old infant who presented with complaints of enlarging the head size and poor feeding. Imaging revealed a contrast-enhancing large solid-cystic mass lesion with internal calcification, focal bone erosion and haemorrhage in the posterior fossa. Histopathological examinations, immunohistochemistry, and molecular analysis confirmed ES. Conclusions: The confirmative diagnosis of primary intracranial ES requires histological examination, immunohistochemical analysis, and genetic detection, along with radiological findings. Surgical excision followed by combined radiotherapy and chemotherapy is the treatment of choice. [ABSTRACT FROM AUTHOR]

Background: Pleomorphic xanthoastrocytoma is a rare astrocytic tumor often diagnosed at a young age. Typically, they appear as supratentorial cortical tumors, frequently involving the temporal lobe with few reported rare locations. The prognosis is favorable following surgical excision; however, recurrence, dissemination, and anaplastic transformation occurred in some cases. Case presentation: A 50-year-old female presented with convulsions and an altered consciousness. Imaging showed a periventricular mixed solid and cystic lesion. Histopathological examination revealed features of pleomorphic xanthoastrocytoma WHO grade 2 without necrosis or mitotic activity. Conclusions: This report highlights the classic imaging findings of pleomorphic xanthoastrocytoma but in an atypical periventricular location. Although rare, pleomorphic xanthoastrocytoma should be considered in the differential diagnosis of mixed solid and cystic periventricular lesions. [ABSTRACT FROM AUTHOR]

Objective: To assess postacute rehabilitation service use and length of stay among a national sample of patients with brain tumors after surgery. Design: A retrospective review was conducted of health care claims data of a national sample of patients via The Optum Clinformatics DataMart. Setting and Participants: This study included adult individuals (≥18 years of age) who were diagnosed with a brain tumor between 2015 and 2019 and underwent a craniotomy or craniectomy within 180 days of diagnosis. Methods: Descriptive statistics were used to characterize patients by tumor type. Multivariate models assessed factors associated with discharge setting and length of stay. Results: Of the 10,275 individuals identified, 69% had malignant tumors. Over two thirds of patients were discharged directly home (with or without home health care) and 9.3% and 9.5% were discharged to acute rehabilitation facilities (inpatient rehabilitation facilities [IRF]) and skilled nursing facilities (SNF/ICF), respectively. About 13.5% were discharged to other settings. The average length of stay during the episode of care was 8.6 (SD = 9.6) days. After adjusting for confounders, individuals with benign brain tumors were more likely to be discharged to either IRF or SNF/ICF than return home after acute care stay, as were those with greater comorbidities, older age, fee‐for‐service and health maintenance organization insurance. Wealthier patients were less likely to be discharged to a SNF/ICF than home, although income was not a factor affecting discharge to an IRF. Patients with benign tumors, the oldest old (80+), those with more comorbidities as well as Black and Hispanic patients had a longer length of stay during the acute hospitalization. Conclusions: Individuals with brain tumors have deficits amenable to rehabilitation; however, this study finds that service use differs by tumor type and demographic and socioeconomic factors. Further study is needed to identify if there are barriers to access and use of rehabilitation services in this population. [ABSTRACT FROM AUTHOR]

Drug repurposing is promising because approving a drug for a new indication requires fewer resources than approving a new drug. Signature reversion detects drug perturbations most inversely related to the disease‐associated gene signature to identify drugs that may reverse that signature. We assessed the performance and biological relevance of three approaches for constructing disease‐associated gene signatures (i.e., limma, DESeq2, and MultiPLIER) and prioritized the resulting drug repurposing candidates for four low‐survival human cancers. Our results were enriched for candidates that had been used in clinical trials or performed well in the PRISM drug screen. Additionally, we found that pamidronate and nimodipine, drugs predicted to be efficacious against the brain tumor glioblastoma (GBM), inhibited the growth of a GBM cell line and cells isolated from a patient‐derived xenograft (PDX). Our results demonstrate that by applying multiple disease‐associated gene signature methods, we prioritized several drug repurposing candidates for low‐survival cancers. [ABSTRACT FROM AUTHOR]

Brain tumors, particularly gliomas, pose a significant threat to global health, necessitating accurate and efficient diagnostic methods. Magnetic Resonance Imaging (MRI) serves as a crucial tool for diagnosing glioma grades, but interpretation is subject to variability, hindering treatment planning. Intra and inter-observer variability in radiological image interpretation impede effective therapeutic strategies for brain tumor patients. Accessing relevant images from vast medical databases for comparison and treatment planning is cumbersome and time-consuming. This paper proposes a ContentBased Medical Image Retrieval (CBIR) system utilizing Convolutional Neural Network (CNN)-based feature extraction, specifically employing the AlexNet architecture. The system employs KNN clustering for indexing the feature map database and implements Gainbased feature selection to reduce feature vector dimensionality. The proposed system underwent evaluation using BraTS 2018 and 2020 datasets with five-fold cross-validation. Achieving state-of-the-art performance, the system demonstrated a mean Average Precision of 98% and Precision of 97%, showcasing its efficacy in accurately retrieving similar pathological MRI brain images. [ABSTRACT FROM AUTHOR]

Calcium flux has been successfully verified to play an important role in the malignant proliferation and progression of brain tumors, which can serve as an important diagnosis guide. However, clinical diagnosis based on calcium information remains challenging because of the highly complex and heterogeneous features in calcium signals. Here we propose a calcium feature-based tumor diagnosis and treatment guidance platform (CA-TDT-GP) using random forest analysis framework for the efficient prediction of complex tumor behaviors for clinical therapy guidance. Multiple important features associated with brain tumor biological malignancy were screened out through comprehensive feature importance analysis. It provided useful guidance for understanding the biological process and the selection of drugs of brain tumors. Further clinical validation confirmed the accurate prediction of tumor biological characteristics by the model, with a coefficient of determination of over 0.86 in the same cohort of patients and over 0.77 for the new cohort of patients. We further verified the clinical malignant assessment by this model, which performed a 100% prediction match with diagnosed WHO grades, indicating great potential of the platform for clinical guidance. This promising model provides a new diagnostic and therapeutic tool for brain tumor research and preclinical treatment. [ABSTRACT FROM AUTHOR]

The urge to unveil the secrets of digital visual enhancement has always been a dream for mankind. It has always been an expanding realm of research that has never failed to surprise humanity. In this paper, we have proposed a modified Clustering technique in Fuzzy C-Means named Narrow Fuzzy C-Means Clustering. This clustering method is implemented and fused with U-Net Convolution. The proposed segmentation algorithm uses this unique technique which assists in providing elevated and enhanced outcomes. The suggested approach helps to precisely segment the area of interest from the provided input images. The novel proposal provides an accuracy of 96.5% with a Dice Similarity Co-Efficient (DSC) of 0.94 which tends to determine the exact segmentation of the area of interest with a low false positive rate. [ABSTRACT FROM AUTHOR]

Segmentation of tissues in magnetic resonance (MR) images is vital for effective analysis of brain pathology. However, existing approaches for brain MR image segmentation and feature extraction have limitations such as high computational costs and lower accuracy. To overcome these challenges, this paper presents an integrated approach, termed PSO-Guided Segmentation with U-Net and CNN Classification, for the detection and quantification of brain pathology in MR images. The proposed algorithm combines Particle Swarm Optimization (PSO) for enhanced feature extraction, for the classification CNN while segmentation can be done through U-Net. Numerous tests show that the suggested algorithm performs more effectively and accurately than existing techniques in terms of sensitivity and accuracy. By utilizing cutting-edge technologies like Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO), and U-Net, significant progress has been made in enhancing the accuracy of MR image segmentation and tumor detection. Compared to traditional methods, our proposed methodology outperforms, enhancing accuracy and sensitivity by approximately 0.44%, significantly decreasing computational expenses while maintaining high precision. However, due to the dynamic nature of technology advancements, it remains imperative to continually explore and develop novel frameworks and algorithms to further improve accuracy and achieve the highest level of performance. [ABSTRACT FROM AUTHOR]

Assessment of brain tumour using Three-Dimensional Magnetic Resonance Imaging (3D MRI) is computationally multifaceted task. Currently, hospitals employ 2D MRI scans, followed by manual evaluation by experienced doctors, aided by a Computerized Diagnostic Tool (CDT). This research aims to develop an advanced CDT to significantly enhance the accuracy of brain tumor assessment. The CDT presented in this study evaluates Axial-View (AV), Coronal-View (CV), and Sagittal-View (SV) MRI images. It encompasses a comprehensive pipeline, including pre-processing, post-processing, feature extraction, feature selection, and categorization phases. Various tumor segmentation techniques, including active contour, level-set, watershed, and region growing, are thoroughly explored. Additionally, a comparative analysis of classification methods such as SVM, ANFIS, k-NN, Random Forest, and Adaboost is conducted. Experimental validation using the BRATS 2016 dataset and real-time 2D MRI data demonstrates that the proposed CDT consistently achieves an average classification accuracy exceeding 95% in tumor-based categorization. This research represents a significant advancement in brain tumor assessment, leveraging machine learning and advanced MRI techniques to improve diagnostic precision. [ABSTRACT FROM AUTHOR]

Cancer, regardless of its type, represents a formidable threat to human life and disrupts the delicate balance of normal bodily functions. Among the various forms of cancer, malignant brain tumors stand out as a leading cause of mortality in both adult and pediatric populations. The timely identification of these brain tumors is crucial for attaining precise diagnoses. Brain tumour identification and diagnosis are now made possible by the use of magnetic resonance imaging (MRI). However, the intricate and irregular shapes and locations of these tumors often pose challenges for complete comprehension. Typically, the expertise of neurosurgical specialists is required for the precise analysis of MRI scans. Unfortunately, in many developing countries, a shortage of skilled medical professionals and limited awareness about brain tumors compound the difficulties associated with obtaining timely and accurate MRI results. To address these notable challenges, this research introduces BrainNet, an innovative Convolutional Neural Network (CNN) architecture specifically designed for the classification of brain tumors into distinct categories. The established transfer learning models VGG13, VGG19, VGG16, InceptionResV2, and Squeeznet, all of which were pretrained on the Imagenet dataset, are outperformed by BrainNet in both how well it handles these problems and how well it outperforms them. The performance of the BrainNet CNN architecture is particularly impressive, with a precision score of 94.75 percent and accuracy rates of 99.96 percent during training and 97.71 percent during testing. This accomplishment has the potential to significantly improve brain tumour diagnosis and classification, particularly in areas with limited access to medical resources and knowledge. [ABSTRACT FROM AUTHOR]

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models' accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results. [ABSTRACT FROM AUTHOR]

Brain tumors pose a significant medical challenge, demanding early detection and diagnosis to enhance patient outcomes. However, existing brain tumor detection methods often suffer from inefficiency and inaccuracy. Presently, deep learning models have gained prominence in classifying brain MRI images due to their superior accuracy compared to traditional classifiers. Nonetheless, optimizing hyperparameters manually to achieve precise image classification is a daunting and time-consuming task. To address these challenges, we present a novel approach in this paper—the Manta Ray Foraging Optimized Convolutional Neural Network (MRFO-CNN) model to classify brain MRIs into tumorous and non-tumorous categories. We leverage the Manta Ray Foraging Optimization technique to automatically determine the optimal hyperparameters, specifically batch size and epoch settings. Our model is trained on a dataset comprising 3000 brain MR images and validated on an additional 351 brain MR images. The results unequivocally demonstrate the superiority of our proposed MRFO-CNN model over conventional CNN approaches, achieving an impressive training accuracy of 99.3% and a validation accuracy of 98.7%. This research not only showcases the potential of deep learning in brain tumours classification but also underscores the efficiency and effectiveness of automated hyperparameter optimization in medical image analysis. [ABSTRACT FROM AUTHOR]

Recent strides in artificial intelligence (AI) and deep learning techniques have propelled the development of an AI-powered brain tumour detection model. This study blends multilevel thresholding, neural network optimisation, and image preprocessing to craft a robust AI model capable of accurately categorising diverse brain tumour types and normal cases. Through rigorous testing with a comprehensive dataset of 1747 images, the model achieves an accuracy of 92%. Its integration into a user-friendly smartphone app, MediScan, enhances accessibility and practicality. The app provides heatmap visualisations and generates diagnostic reports, supporting medical professionals in making swift decisions. The model prioritises interpretability enhancement and has the potential to cultivate collaboration between AI experts and medical practitioners, thus advancing the field of brain tumour detection and diagnosis. While promising, the model demands computational resources and diverse datasets. This research also highlights AI's potential to transform healthcare diagnostics, ensuring precise and efficient brain tumour identification. [ABSTRACT FROM AUTHOR]

Segmentation of tissues in magnetic resonance (MR) images is vital for effective analysis of brain pathology. However, existing approaches for brain MR image segmentation and feature extraction have limitations such as high computational costs and lower accuracy. To overcome these challenges, this paper presents an integrated approach, termed PSO-Guided Segmentation with U-Net and CNN Classification, for the detection and quantification of brain pathology in MR images. The proposed algorithm combines Particle Swarm Optimization (PSO) for enhanced feature extraction, for the classification CNN while segmentation can be done through U-Net. Numerous tests show that the suggested algorithm performs more effectively and accurately than existing techniques in terms of sensitivity and accuracy. By utilizing cutting-edge technologies like Convolutional Neural Networks (CNN), Particle Swarm Optimization (PSO), and U-Net, significant progress has been made in enhancing the accuracy of MR image segmentation and tumor detection. Compared to traditional methods, our proposed methodology outperforms, enhancing accuracy and sensitivity by approximately 0.44%, significantly decreasing computational expenses while maintaining high precision. However, due to the dynamic nature of technology advancements, it remains imperative to continually explore and develop novel frameworks and algorithms to further improve accuracy and achieve the highest level of performance. [ABSTRACT FROM AUTHOR]

Assessment of brain tumour using Three-Dimensional Magnetic Resonance Imaging (3D MRI) is computationally multifaceted task. Currently, hospitals employ 2D MRI scans, followed by manual evaluation by experienced doctors, aided by a Computerized Diagnostic Tool (CDT). This research aims to develop an advanced CDT to significantly enhance the accuracy of brain tumor assessment. The CDT presented in this study evaluates Axial-View (AV), Coronal-View (CV), and Sagittal-View (SV) MRI images. It encompasses a comprehensive pipeline, including pre-processing, post-processing, feature extraction, feature selection, and categorization phases. Various tumor segmentation techniques, including active contour, level-set, watershed, and region growing, are thoroughly explored. Additionally, a comparative analysis of classification methods such as SVM, ANFIS, k-NN, Random Forest, and Adaboost is conducted. Experimental validation using the BRATS 2016 dataset and real-time 2D MRI data demonstrates that the proposed CDT consistently achieves an average classification accuracy exceeding 95% in tumor-based categorization. This research represents a significant advancement in brain tumor assessment, leveraging machine learning and advanced MRI techniques to improve diagnostic precision. [ABSTRACT FROM AUTHOR]

Cancer, regardless of its type, represents a formidable threat to human life and disrupts the delicate balance of normal bodily functions. Among the various forms of cancer, malignant brain tumors stand out as a leading cause of mortality in both adult and pediatric populations. The timely identification of these brain tumors is crucial for attaining precise diagnoses. Brain tumour identification and diagnosis are now made possible by the use of magnetic resonance imaging (MRI). However, the intricate and irregular shapes and locations of these tumors often pose challenges for complete comprehension. Typically, the expertise of neurosurgical specialists is required for the precise analysis of MRI scans. Unfortunately, in many developing countries, a shortage of skilled medical professionals and limited awareness about brain tumors compound the difficulties associated with obtaining timely and accurate MRI results. To address these notable challenges, this research introduces BrainNet, an innovative Convolutional Neural Network (CNN) architecture specifically designed for the classification of brain tumors into distinct categories. The established transfer learning models VGG13, VGG19, VGG16, InceptionResV2, and Squeeznet, all of which were pretrained on the Imagenet dataset, are outperformed by BrainNet in both how well it handles these problems and how well it outperforms them. The performance of the BrainNet CNN architecture is particularly impressive, with a precision score of 94.75 percent and accuracy rates of 99.96 percent during training and 97.71 percent during testing. This accomplishment has the potential to significantly improve brain tumour diagnosis and classification, particularly in areas with limited access to medical resources and knowledge. [ABSTRACT FROM AUTHOR]

Effective treatment of brain tumours depends on early identification of tumour tissues. Categorizing tumors is essential for its early identification. In order to split and categorise brain tumours using MRI images, various machine learning algorithms are examined. Preprocessing methods including image augmentation, rotation flipping, and adaptive deformation are used on the BRATs dataset to improve the data quality. Convolutional neural network (CNN) feature extraction techniques like VGG19 and InceptionV3 are applied. K-Nearest Neighbours (KNN) and logistic regression techniques are used to categorise the features. The proposed models' accuracy for Logistic Regression, KNN, VGG19, Inception V3 and VGG19 with augmentation is 84.66%, 90.68%, 92.17%, 91.56% and 95.43% respectively. These findings imply that CNN-based feature extraction and picture augmentation techniques greatly improves the accuracy of brain tumour identification, Overall thecInception V3 with augmentation produces the best results. [ABSTRACT FROM AUTHOR]

Brain tumors arise from the emergence of abnormal cells in brain tissue and are considered one of the most perilous conditions affecting individuals of all ages, including both children and adults. The disease advances swiftly, and the likelihood of survival diminishes significantly without prompt and adequate treatment. Hence, accurate diagnosis and meticulous treatment planning play a pivotal role in improving the patient's life expectancy. Neurologists and radiologists play a crucial role in the early detection of brain tumors. However, manually identifying and segmenting brain tumors from Magnetic Resonance Imaging (MRI) data poses significant challenges and is susceptible to inaccuracies. The need for an automated brain tumor detection method becomes imperative to achieve early detection of brain tumors. Objective: The objective of the paper is to measure the capability of You Only Look Once version 5 (YOLOv5x) in brain tumor detection in the early stage so that patients can be treated accordingly. Methods: YOLOv5x is examined; Brats (Brain Tumor Segmentation) image and roboflow dataset has been used. Model performance is evaluated using precision, recall rate, F1 score, and Mean Average Precision (mAP). Results: YOLOv5x exhibited precision (98.7), recall (95.6), F1-score (93), mAP at a learning rate of 0.5 (98.4), and the total time taken for implementation of work is 193.20 minutes. Conclusion: YOLOv5x showed improved performance for the detection of brain tumors on dynamic contrast-enhanced MRI when compared with state of art existing work. It is also the fastest and accurate method indicating a greater potential for clinical application. [ABSTRACT FROM AUTHOR]

A brain tumor is a critically severe health disorder that requires an accurate and timely diagnosis for effective treatment. Advances in medical imaging and deep learning methods have shown potential for enhancing the identification and categorization of brain cancers throughout the years. In the present research, our study compares the accuracy of eight different deep learning models in the classification of brain tumors employing brain MRI data that involve Densenet121, EfficientNet B7, InceptionResNetV2, Inception_V3, RestNet50V2, VGG16, VGG19, and Xception. To further improve performance, we propose integrating a hybrid deep learning technique. Efficient and timely diagnosis of brain tumors is critical for the treatment of patients, and our study aims to achieve high recall, accuracy, and F1-score in this context. With a precision of 96.63%, our innovative convolutional neural network (CNN) technique achieved outstanding results in brain tumor diagnosis. Also, our study investigates the unique capabilities of certain models, such as VGG19 and InceptionResNetV2, and their possibilities for better glioma tumor detection efficiency. Our results, in particular, provide insight into possible uses of deep learning frameworks, including the integration of hybrid techniques, in medical imaging, offering an innovative approach for increased brain tumor detection and identification. [ABSTRACT FROM AUTHOR]

[Display omitted] This article addresses the management of venous thromboembolism in patients with malignant brain tumours, including both primary and secondary (metastatic) tumours. The available data on patients on venous thromboembolism recurrence and bleeding risks in patients with brain tumours is limited, since these patients have been excluded from most randomised, interventional, head-to-head, clinical trials comparing low molecular weight heparins to vitamin K antagonists or to direct oral Factor Xa inhibitors. More information is available from retrospective observational studies, which however were generally small, and carried a high risk of confounding. Their findings suggest that direct Factor Xa inhibitor use is associated with lower rates of intracranial haemorrhage compared with low molecular weight heparins. Overall, the safety profile of direct oral Factor Xa inhibitors when used to prevent venous thromboembolism recurrence in patients with either primary or secondary brain tumours appears to be favourable. The available data are in favour of using an anticoagulant at a full therapeutic dose in patients with primary and secondary brain tumours experiencing a venous thromboembolism, although they are not yet sufficiently robust to permit recommending a direct Factor Xa inhibitor over low-molecular weight heparin. [ABSTRACT FROM AUTHOR]

One of the important causes of death in cancer patients is malignant metastasis, invasion, and metastasis of tumor cells. Metastasis is also the most basic physiological characteristics and pathogenesis of various tumors. Previously published studies have suggested that autocrine motor factor receptor (AMFR) is the key regulator of tumor cell migration and invasion. Meanwhile, AMFR is highly expressed in esophageal tumors, gastrointestinal tumors, and bladder cancer, and it is also involved in its pathogenesis. However, the role of AMFR in glioblastoma has not been reported. In order to study the role of AMFR in the cell migration and invasion of glioblastoma, AMFR was silenced using siRNA and overexpressed using cDNA. Immunoblotting analysis and real-time quantitative polymerase chain reaction (PCR) were employed to assess the expression of AMFR. We conducted wound healing assay, cell migration assay, and tumorsphere formation assay to detect the invasion and metastatic ability of glioblastoma. This study found that the level of AMFR expression was significantly correlated with the malignant degree of glioma tissue in clinic samples. AMFR silencing decreased cell migration and invasion of LN229. Overexpression of AMFR significantly increased cell migration and invasion of U251. This study suggests that AMFR could be used as a therapeutic strategy for the clinical treatment of glioblastoma. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Amidst the evident challenges posed by brain tumors and the evident limitations of conventional treatment methodologies like surgery, radiotherapy, and chemotherapy, our primary objective was to probe the therapeutic potential of high-intensity therapeutic ultrasound (HITU). The aim was to introduce a safer, cost-effective, and efficient alternative to existing treatments, especially beneficial for inaccessible brain tumor sites and resource-constrained medical facilities. MATERIALS AND METHODS: Leveraging post-1990s MR technology advancements, we employed the non-invasive HITU technique, akin to high-intensity focused ultrasound. This method directs acoustic energy to tissues, primarily inducing coagulation necrosis by absorbing energy and elevating tissue temperatures. Glial tumor cells were subjected to HITU to assess its effects. RESULTS: Upon applying HITU to glial tumor cells, significant alterations in cellular structural integrity were evident. The main action of HITU was the absorption of acoustic energy, leading to a notable temperature rise and coagulation necrosis. Flow cytometry indicated significant cellular changes post-HITU. ANOVA and t-test analyses showed a significant relationship between HITU application and time (p<0.05). The Shapiro-Wilk test revealed non-normal data distribution (p<0.05), leading to the use of nonparametric methods. The t-test results after HITU displayed significant differences (p<0.05) in cell counts and fluorescence intensity between control and treated groups. This result was consistent across multiple tests, indicating the reliability of the method in causing cellular damage to the tumor cells. CONCLUSIONS: Our laboratory analyses offer compelling evidence that HITU is not merely feasible but is also a promising non-invasive approach in the treatment paradigm of brain tumors. Standing distinctively apart from radio therapy, HITU averts early, or late complications commonly associated with the former. While the path ahead mandates comprehensive research to ascertain its clinical utility, preliminary indications firmly posit HITU as a groundbreaking prospect in the management of brain tumors. [ABSTRACT FROM AUTHOR]

Brain tumors are particularly perilous because they form when cells in the brain multiply uncontrollably within the skull. Therefore, a fast and accurate method of diagnosing tumors is crucial for the patient's health. This study proposes a method for evaluating brain cancer images. The phases of implementation for the proposed work are as follows: In the first phase, we compiled a set of specialized feature vector descriptions for advanced classification tasks by employing both deep learning (DL) and conventional feature extraction techniques. In the second phase, we employ a proposed convolutional neural network (CNN) approach and a traditional subset of features from a genetic algorithm (GA) to select our deep features. The third phase involves using the fusion method to merge the prioritized features. Finally, determine whether the brain image is normal or abnormal. The results showed that the proposed method successfully classified objects accurately and revealed their robustness across different ages and acquisition protocols. According to the results, the classification accuracy of the support vector machines (SVM) classifier has significantly improved by combining conventional features and deep learning features (DLF), achieving an accuracy of up to 86.50% using the T1 weighted brain MR image. [ABSTRACT FROM AUTHOR]

A brain tumor is an abnormal collection of tissue in the brain. When tumors form, they are classified as either malignant or benign. It is critical to notice and identify the existence of tumors in brain images since they can be life threatening. This paper illustrates a novel segmentation method in which threshold technique is combined with normalized cut (Ncut) for the segregation of the tumors from brain magnetic resonance (MR) images. Image segmentation is a technique for grouping images. It is a method of splitting an image into sections with comparable attributes such as intensity, texture, colour, and so on. In thresholding, an object is distinguished from the background, and for the proposed segmentation methodology, the threshold value is determined by normalized graph cut. A weighted graph is divided into disjointed sets (groups) in which the similarity within a group is high and the similarity across groups is low. A graph-cut is a grouping approach in which the total weight of edges eliminated between these two pieces is used to calculate the degree of dissimilarity between these two groups. The normalized cut criterion is used to calculate the total likeness within the groups as well as the dissimilarity between the different groups. [ABSTRACT FROM AUTHOR]

Gliomas constitute the prevalently seen brain tumours in humans. The real-time utilization of Computer Aided Diagnosis system depends on brain Magnetic Resonance Imaging (MRIs) has the ability of helping radiologists and professionals to identify the presence of glioma tumours. It is very difficult to segment brain tumours because of the brain image and it has a complex structure. A fully automated, accurate, segmentation and classification model is developed using a modified Graph Neural Network (MGNN) for brain tumours. Proposed work steps are, image registration, Shift-Invariant Shear let Transform (SIST), adaptive segmentation, feature extraction, and categorization of tumours. At first, image registration and SIST are carried out to improve image quality. Adaptive segmentation is then carried out utilizing Improved Fuzzy C-Means clustering. Next, Grey Level Co-occurrence Matrix, Discrete Wavelet Transform is utilized for the extraction of features in brain MRI data. Finally, MGNN is introduced for the detection of anomalous tumour-infected MR and actual MR brain images. The findings are demonstrated that the proposed model leads in higher accuracy levels for both classification and segmentation. [ABSTRACT FROM AUTHOR]

Introduction: Central nervous system (CNS) tumours represent a significant public health issue worldwide, and their incidence and distribution vary across different populations. Although studies on CNS tumours have been conducted in various countries, there is a lack of information regarding their patterns in Macedonia. Therefore, this study is aimed at investigating the distribution, histopathological types and subtypes and demographic features of CNS tumours in our country. Materials and Methods: A cross sectional study was conducted using the electronic database of the Institute of Pathology – Medical Faculty, University "Ss. Cyril and Methodius" in Skopje which contains data from 3286 received and analysed surgical specimens, mainly from the University Clinic of Neurosurgery in Skopje, and a smaller number of surgical specimens from the University Surgical Centre "St. Naum Ohridski" in Skopje between 2012 and 2022. The collected and analysed data includes patient age, sex and histopathological types and subtypes of the tumours. Results: The majority of CNS tumours were diagnosed in adults aged between 50-70, with a male to female ratio of 1.5:1. The most common location of the tumours was the cerebrum, followed by the pituitary gland and cerebellum. The most frequent histological groups were gliomas, with glioblastoma as the most common diagnosis, followed by meningiomas. Conclusion: Following a detailed and thorough review of the CNS tumours in our study, we can conclude that the R. of Macedonia follows global statistics and trends regarding brain tumours. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common and aggressive malignant brain tumor in adults and is poorly controlled. Previous studies have shown that both macrophages and angiogenesis play significant roles in GBM progression, and co-targeting of CSF1R and VEGFR is likely to be an effective strategy for GBM treatment. Therefore, this study developed a novel and selective inhibitor of CSF1R and VEGFR, SYHA1813, possessing potent antitumor activity against GBM. SYHA1813 inhibited VEGFR and CSF1R kinase activities with high potency and selectivity and thus blocked the cell viability of HUVECs and macrophages and exhibited anti-angiogenetic effects both in vitro and in vivo. SYHA1813 also displayed potent in vivo antitumor activity against GBM in immune-competent and immune-deficient mouse models, including temozolomide (TMZ) insensitive tumors. Notably, SYHA1813 could penetrate the blood–brain barrier (BBB) and prolong the survival time of mice bearing intracranial GBM xenografts. Moreover, SYHA1813 treatment resulted in a synergistic antitumor efficacy in combination with the PD-1 antibody. As a clinical proof of concept, SYHA1813 achieved confirmed responses in patients with recurrent GBM in an ongoing first-in-human phase I trial. The data of this study support the rationale for an ongoing phase I clinical study (ChiCTR2100045380). SYHA1813 exerts anti-tumor effects by simultaneously inhibiting angiogenesis and affecting tumor-associated macrophages, and shows the therapeutic potential of glioma by efficiently crossing the blood–brain barrier. [Display omitted] [ABSTRACT FROM AUTHOR]

Using anti-epileptic drugs in neurosurgery patients is a routine practice. This controlled trial aimed to assess whether prophylaxis with Valproate in brain surgery patients is justified or not. Group A (n=50; controls) patients received valproic acid postoperatively for three months, while group B (n=50; subjects) received a placebo. Serum valproic acid levels between 50-125g/ml were required. Kendall’s Tau was applied to see the correlation between the ‘frequency of seizures’ between different surgical procedures performed and the extent of manipulations-EOMs. A wireless EMOTIV EPOC device was used to visualize the Electroencephalogram patterns. In controls, 12 patients had one seizure and only two patients had 2 seizures. In the placebo group, 13 patients had one and 4 patients had 2 seizures. The seizure frequency was highest amongst brain tumor patients. An insignificant difference was found between the seizure frequencies of the placebo and control groups. A statistically insignificant correlation was found between seizure frequency and independent variables: surgical procedures and EOM (%). Using an AED or not, the frequency of seizures did not substantially reduce over the postoperative period. If not necessary, the anti-epileptic medication that is frequently provided as a prophylactic against seizures in the post-operative period should not be administered. [ABSTRACT FROM AUTHOR]

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results. [ABSTRACT FROM AUTHOR]

BACKGROUND: Ineffective effort (IE) is a frequent patient-ventilator asynchrony in invasive mechanical ventilation. This study aimed to investigate the incidence of IE and to explore its relationship with respiratory drive in subjects with acute brain injury undergoing invasive mechanical ventilation. METHODS: We retrospectively analyzed a clinical database that assessed patient-ventilator asynchrony in subjects with acute brain injury. IE was identified based on airway pressure, flow, and esophageal pressure waveforms collected at 15-min intervals 4 times daily. At the end of each data set recording, airway-occlusion pressure (P0.1) was determined by the airway occlusion test. IE index was calculated to indicate the severity of IE. The incidence of IE in different types of brain injuries as well as its relationship with P0.1 was determined. RESULTS: We analyzed 852 data sets of 71 subjects with P0.1 measured and undergoing mechanical ventilation for at least 3 d after enrollment. IE was detected in 688 (80.8%) data sets, with a median index of 2.2% (interquartile range 0.4-13.1). Severe IE (IE index ≥ 10%) was detected in 246 (28.9%) data sets. The post craniotomy for brain tumor and the stroke groups had higher median IE index and lower P0.1 compared with the traumatic brain injury group (2.6% [0.7--9.7] vs 2.7% [0.3-21] vs 1.2% [0.1--8.5], P = .002; 1.4 [1-2] cm H2O vs 1.5 [1-2.2] cm H2O vs 1.8 [1.1-2.8] cm H2O, P = .001). Low respiratory drive (P0.1 < 1.14 cm H2O) was independently associated with severe IE in the expiratory phase (IEE) even after adjusting for confounding factors by logistic regression analysis (odds ratio 5.18 [95% CI 2.69-10], P < .001). CONCLUSIONS: IE was very common in subjects with acute brain injury. Low respiratory drive was independently associated with severe IEE. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most aggressive malignant brain tumor and has a high mortality rate. Photodynamic therapy (PDT) has emerged as a promising approach for the treatment of malignant brain tumors. However, the use of PDT for the treatment of GBM has been limited by its low blood‒brain barrier (BBB) permeability and lack of cancer-targeting ability. Herein, brain endothelial cell-derived extracellular vesicles (bEVs) were used as a biocompatible nanoplatform to transport photosensitizers into brain tumors across the BBB. To enhance PDT efficacy, the photosensitizer chlorin e6 (Ce6) was linked to mitochondria-targeting triphenylphosphonium (TPP) and entrapped into bEVs. TPP-conjugated Ce6 (TPP-Ce6) selectively accumulated in the mitochondria, which rendered brain tumor cells more susceptible to reactive oxygen species-induced apoptosis under light irradiation. Moreover, the encapsulation of TPP-Ce6 into bEVs markedly improved the aqueous stability and cellular internalization of TPP-Ce6, leading to significantly enhanced PDT efficacy in U87MG GBM cells. An in vivo biodistribution study using orthotopic GBM-xenografted mice showed that bEVs containing TPP-Ce6 [bEV(TPP-Ce6)] substantially accumulated in brain tumors after BBB penetration via transferrin receptor-mediated transcytosis. As such, bEV(TPP-Ce6)-mediated PDT considerably inhibited the growth of GBM without causing adverse systemic toxicity, suggesting that mitochondria are an effective target for photodynamic GBM therapy. Brain endothelial cell-derived extracellular vesicles are used as biocompatible nanoplatforms to transport mitochondria-targeting photosensitizers into glioblastoma across the blood‒brain barrier. Under light irradiation, the mitochondria-targeting photosensitizers trigger the apoptosis of glioblastoma. [Display omitted] [ABSTRACT FROM AUTHOR]

The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFACNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey mass, texture, and shape are extracted from the images. Extracted features are trained with deep learning Convolution Neural Network (CNN). The network has been designed with a single convolution layer towards dimensionality reduction. The texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix, which has been transformed into a single-dimensional feature vector at the convolution layer. The neurons of the intermediate layer are designed to measure White Mass Texture Support (WMTS), GrayMass Texture Support (GMTS), WhiteMass Covariance Support (WMCS), GrayMass Covariance Support (GMCS), and Class Texture Adhesive Support (CTAS). In the test phase, the neurons at the intermediate layer compute the support as mentioned above values towards various classes of images. Based on that, the method adds a Multi-Variate Feature Similarity Measure (MVFSM). Based on the importance of MVFSM, the process finds the class of brain image given and produces an efficient result. [ABSTRACT FROM AUTHOR]

This section explores the decision of the New South Wales Professional Standards Committee, in Re Teo [2023] NSWMPSC 2. The case provides insights into how the Health Practitioner Regulation National Law Act 2009 (Qld) regulates practitioners who practise outside of conventional practice. The section compares the decision to similar cases and then concludes with a proposal that an express policy on unconventional practice is needed in Australia. [ABSTRACT FROM AUTHOR]