Meningioma Tumor Research Articles

Targeting MERTK on tumour cells and macrophages: a potential intervention for sporadic and NF2-related meningioma and schwannoma tumours

Meningioma and schwannoma are common tumours of the nervous system. They occur sporadically or as part of the hereditary NF2-related schwannomatosis syndrome. There is an unmet need for new effective drug treatments for both tumour types. In this paper, we demonstrate overexpression/activation of TAM (TYRO3/AXL/MERTK) receptors (TAMs) and overexpression/release of ligand GAS6 in patient-derived meningioma tumour cells and tissue. For the first time, we reveal the formation of MERTK/TYRO3 heterocomplexes in meningioma and schwannoma tissue. We demonstrate the dependence of AXL and TYRO3 expression on MERTK in both tumour types, as well as interdependency of MERTK and AXL expression in meningioma. We show that MERTK and AXL contribute to increased proliferation and survival of meningioma and schwannoma cells, which we inhibited in vitro using the MERTK/FLT3 inhibitor UNC2025 and the AXL inhibitor BGB324. UNC2025 was effective in both tumour types with superior efficacy over BGB324. Finally, we found that TAMs are expressed by tumour-associated macrophages in meningioma and schwannoma tumours and that UNC2025 strongly depleted macrophages in both tumour types.

Development of a Plasma-Based Nanosensor Platform for Triage of Neurological Tumors

Neurological tumors have diverse etiologies and treatments. Presently, diagnostic technologies such as MRI or CT scans are limited by poor resolution, and thus imprecise identification of tumor classes. As a result, diagnostic methods rely on highly invasive intracranial biopsies for conclusive tumor identification. Development of non-invasive methods to accurately identify tumor class may lead to earlier and more accurate diagnoses, enabling prompt and targeted treatment strategies. To address this unmet clinical need, we report the development a molecular-recognition-based nanosensor array that can accurately identify a molecular fingerprint of glioma and meningioma from plasma. We developed this capability using sensor arrays of chemically modified carbon nanotubes that can transduce subtle differences in the physicochemical properties of molecules in biofluids via sensitive molecular binding interactions. Using high-throughput near-infrared fluorescence spectroscopy, we screened 23 chemically modified (6,5) carbon nanotubes sensors in 324 patient plasma samples. We trained machine learning models to classify sensor responses as coming from either glioma or meningioma tumors. The best performing models were able to achieve a positive predictive value of 0.75. Sensor feature analysis revealed a set of spectral features positively associated with class differentiation. This work has the potential to aid clinical care by facilitating the early triage of different types of tumors to provide better treatment options for patients.

Brain tumor detection using a MobileNetV2-SSD model with modified feature pyramid network levels

Brain tumors, a subset of these malignancies, demand accurate and efficient diagnosis. Traditional methods use non-invasive medical imaging like magnetic resonance imaging (MRI) and computed tomography (CT). Although necessary for diagnosis, manual brain MRI picture segmentation is tedious and time-consuming. Using deep learning is a promising solution. This study proposes an innovative approach for brain tumor detection, focusing on meningioma tumors. Utilizing threshold-based segmentation, the MobileNetV2 architecture, a modified feature pyramid network (FPN), and single shot MultiBox detector (SSD), our model achieves precise localization and object detection. Pre-processing techniques such as grayscale conversion, histogram equalization, and Gaussian filtering enhance the MRI image quality. Morphological operations and thresholding facilitate tumor segmentation. Data augmentation and a meticulous dataset division aid in model generalization. The architecture combines MobileNetV2 as a feature extractor, SSD for object detection, and FPN for detecting small objects. Modifications, including lowering the minimum FPN level, enhance small object detection accuracy. The proposed model achieved a recall value of around 98% and a precision value of around 89%. Additionally, the proposed model achieved approximately 93% on both the dice similarity coefficient (DSC) value and the index of similarity. Based on the promising results, our research holds significant advancements for the field of medical imaging and tumor detection.

Generative adversarial network for Multimodal Contrastive Domain Sharing based on efficient invariant feature-centric growth analysis improved brain tumor classification

ABSTRACT Efficient and accurate classification of brain tumor categories remains a critical challenge in medical imaging. While existing techniques have made strides, their reliance on generic features often leads to suboptimal results. To overcome these issues, Multimodal Contrastive Domain Sharing Generative Adversarial Network for Improved Brain Tumor Classification Based on Efficient Invariant Feature Centric Growth Analysis (MCDS-GNN-IBTC-CGA) is proposed in this manuscript.Here, the input imagesare amassed from brain tumor dataset. Then the input images are preprocesssed using Range – Doppler Matched Filter (RDMF) for improving the quality of the image. Then Ternary Pattern and Discrete Wavelet Transforms (TPDWT) is employed for feature extraction and focusing on white, gray mass, edge correlation, and depth features. The proposed method leverages Multimodal Contrastive Domain Sharing Generative Adversarial Network (MCDS-GNN) to categorize brain tumor images into Glioma, Meningioma, and Pituitary tumors. Finally, Coati Optimization Algorithm (COA) optimizes MCDS-GNN’s weight parameters. The proposed MCDS-GNN-IBTC-CGA is empirically evaluated utilizing accuracy, specificity, sensitivity, Precision, F1-score,Mean Square Error (MSE). Here, MCDS-GNN-IBTC-CGA attains 12.75%, 11.39%, 13.35%, 11.42% and 12.98% greater accuracy comparing to the existingstate-of-the-arts techniques, likeMRI brain tumor categorization utilizing parallel deep convolutional neural networks (PDCNN-BTC), attention-guided convolutional neural network for the categorization of braintumor (AGCNN-BTC), intelligent driven deep residual learning method for the categorization of braintumor (DCRN-BTC),fully convolutional neural networks method for the classification of braintumor (FCNN-BTC), Convolutional Neural Network and Multi-Layer Perceptron based brain tumor classification (CNN-MLP-BTC) respectively.

α-Mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues.

Immunopeptides are cell surface-located protein fragments that aid our immune system to recognise and respond to pathogenic insult and malignant transformation. In this two-part communication, we firstly summarise and reflect on our recent discovery documenting that MHC-II-bound immunopeptides from immortalised cell lines prevalently carry N-glycans that differ from the cellular glycoproteome (Goodson, Front Immunol, 2023). These findings are important as immunopeptide glycosylation remains poorly understood in immunosurveillance. The study also opened up new technical and biological questions that we address in the second part of this communication. Our study highlighted that the performance of the search engines used to detect glycosylated immunopeptides from LC-MS/MS data remains untested and, importantly, that little biochemical in vivo evidence is available to document the nature of glycopeptide antigens in tumour tissues. To this end, we compared the N-glycosylated MHC-II-bound immunopeptides that were reported from tumour tissues of 14 meningioma patients in the MSFragger-HLA-Glyco database (Bedran, Nat Commun, 2023) to those we identified with the commercial Byonic software. Encouragingly, the search engines produced similar outputs supporting that N-glycosylated MHC-II-bound immunopeptides are prevalent in meningioma tumour tissues. Consistent also with in vitro findings, the tissue-derived MHC-II-bound immunopeptides were found to predominantly carry hyper-processed (paucimannosidic- and chitobiose core-type) and hypo-processed (oligomannosidic-type) N-glycans that varied in prevalence and distribution between patients. Taken together, evidence is emerging suggesting that α-mannosidic glycoepitopes abundantly decorate MHC-II-bound immunopeptides presented in both immortalised cells and tumour tissues warranting further research into their functional roles in immunosurveillance.

BRAIN TUMOR CLASSIFICATION USING INCEPTIONRESNET-V2 AND TRANSFER LEARNING APPROACH

Brain, a highly intricate organ within the central nervous system, plays a fundamental role in information processing, cognition, motor control, and consciousness. Brain tumors pose severe threats to brain function and overall human well-being. Timely detection of these tumors is imperative for life-saving interventions. A dataset comprising four categories: no tumors, meningioma tumors, glioma tumors, and pituitary tumors was regarded in this research. The employed of the InceptionResNet-V2 architecture combined with Transfer Learning and data augmentation proposed to obtain optimal results on brain tumor classification types. Transfer learning act as fine tuning, enabling the model to acquire fundamental low-level image features from a comprehensive dataset. It then leverages higher-level features to become more tailored to the specific training data. This method is employed to improve the model's adaptability to the training data. The InceptionResNet-V2 architecture utilized in the evaluation using test data, in Scenario 1, achieved 94.18% accuracy. Scenario 2, which combined augmentation with InceptionResNetV2, showed an improvement in accuracy to 95.10%. Furthermore, in Scenario 3, the combination of InceptionResNetV2 with Transfer Learning and augmentation resulted in an impressive accuracy of 96.63%, demonstrating its effectiveness in brain tumor classification. Transfer learning aligns the model by acquiring low-level image features and utilizing higher-level features to improve adaptability to the training data.

Deep Learning-Based Detection of Pituitary, Glioma, and Meningioma Tumors from Brain MRIs

Deep Learning-Based Detection of Pituitary, Glioma, and Meningioma Tumors from Brain MRIs

Brain Tumour Detection and Classification Using Deep Learning

Now a day’s tumor is second leading cause of cancer. Currently Doctors locate the positions and area of a brain tumor by looking at the MRI of the brain manually. This project helps to reduce the inaccuracy and time consumption in detection, and it also provides information about brain. This study presents Convolution neural network architecture for brain tumour detection and classification using magnetic resonance imaging (MRI) as datsets. The performance of the model is to predict whether the given image is tumours or non tumours and classify the tumour image and using use classification to classify brain tumors into three categories: glioma, meningioma, and pituitary tumors and implemented in an Android application.

Immune cell infiltration and inflammatory landscape in primary brain tumours

BackgroundPrimary malignant brain tumours are more than one-third of all brain tumours and despite the molecular investigation to identify cancer driver mutations, the current therapeutic options available are challenging due to high intratumour heterogeneity. In addition, an immunosuppressive and inflammatory tumour microenvironment strengthens cancer progression. Therefore, we defined an immune and inflammatory profiling of meningioma and glial tumours to elucidate the role of the immune infiltration in these cancer types.MethodsUsing tissue microarrays of 158 brain tumour samples, we assessed CD3, CD4, CD8, CD20, CD138, Granzyme B (GzmB), 5-Lipoxygenase (5-LOX), Programmed Death-Ligand 1 (PD-L1), O-6-Methylguanine-DNA Methyltransferase (MGMT) and Transglutaminase 2 (TG2) expression by immunohistochemistry (IHC). IHC results were correlated using a Spearman correlation matrix. Transcript expression, correlation, and overall survival (OS) analyses were evaluated using public datasets available on GEPIA2 in Glioblastoma (GBM) and Lower Grade Glioma (LGG) cohorts.ResultsSeven out of ten markers showed a significantly different IHC expression in at least one of the evaluated cohorts whereas CD3, CD4 and 5-LOX were differentially expressed between GBMs and astrocytomas. Correlation matrix analysis revealed that 5-LOX and GzmB expression were associated in both meningiomas and GBMs, whereas 5-LOX expression was significantly and positively correlated to TG2 in both meningioma and astrocytoma cohorts. These findings were confirmed with the correlation analysis of TCGA-GBM and LGG datasets. Profiling of mRNA levels indicated a significant increase in CD3 (CD3D, CD3E), and CD138 (SDC1) expression in GBM compared to control tissues. CD4 and 5-LOX (ALOX5) mRNA levels were significantly more expressed in tumour samples than in normal tissues in both GBM and LGG. In GBM cohort, GzmB (GZMB), SDC1 and MGMT gene expression predicted a poor overall survival (OS). Moreover, in LGG cohort, an increased expression of CD3 (CD3D, CD3E, CD3G), CD8 (CD8A), GZMB, CD20 (MS4A1), SDC1, PD-L1, ALOX5, and TG2 (TGM2) genes was associated with worse OS.ConclusionsOur data have revealed that there is a positive and significant correlation between the expression of 5-LOX and GzmB, both at RNA and protein level. Further evaluation is needed to understand the interplay of 5-LOX and immune infiltration in glioma progression.

P.096 Hearing preservation and quality of life outcomes in partial labyrinthectomy petrous apicectomy for microsurgical resection of large posterior fossa skullbase tumors

Background: The Partial Labyrinthectomy Petrous Apicectomy (PLPA) aims to give transpetrosal access whilst preserving hearing for challenging tumors such as petroclival meningioma. There are few studies assessing resection and morbidity and no large studies that document hearing preservation and quality of life (QOL). We present the first large series to do so. Methods: A retrospective review was performed of all PLPA cases between 2005 and 2023 at a tertiary center. Demographics, tumor characteristics, neuromonitoring, hearing and surgical outcomes were collected. QOL was measured with the 36-item short form survey (SF-36). Results: Of 73 PLPAs, data for 56 patients undergoing 57 surgeries was obtained. Petroclival meningioma (57.8%) and epidermoid tumors (21.0%) were common indications . The mean patient age and tumor size were 51.6 years and 44mm. Gross total resection was achieved in 40.3%, near total in 15.8% and subtotal in 43.8% of cases with no perioperative mortality and was not influenced by attempted hearing preservation (p=0.183). Of 39 hearing preservation cases, 27 (69.2%) were preserved, 10 (25.6%) were lost and 2 had unclear outcomes. Conclusions: Improved microsurgery and neuromonitoring during PLPA leads to decreased mortality and morbidity compared to historical cohorts while achieving a high rate of resection, hearing preservation and maintained QOL.

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Brain Tumor and Lung Disease Detection using Deep Learning

Brain Tumor and Lung Disease Detection using Deep Learning stands at the forefront of medical innovation, revolutionizing diagnostics with cutting-edge technology. Leveraging intricate algorithms and convolutional neural networks (CNNs), it automates the analysis of MRI scans for brain tumors and chest CT-Scans for lung diseases. Through meticulous feature extraction, this system precisely identifies diverse tumor types like glioma, meningioma, and pituitary tumors, along with lung pathologies such as bacterial pneumonia, viral pneumonia, and tuberculosis. By harnessing the power of deep learning, this ambitious project aims to significantly enhance diagnostic efficiency and accuracy, ushering in a new era in medical practice. Beyond expediting diagnoses, it holds promise in tailoring treatment strategies for improved patient care and treatment outcomes across neurology and pulmonology. Keywords: Brain Tumor Detection, Lung Disease Identification, Advanced Medical Diagnostics, Convolutional neural networks, Neural Network-based Diagnosis, Medical Diagnostics.

EfficientNet Transfer Learning Approach for Multi-Class Brain Tumor Classification

- It can be difficult for radiologists to accurately diagnose brain tumors in the medical field. When radiologists manually identify tumors, it can result in incorrect treatment planning and medical errors. The Miami Neuroscience Center states that there are 120 different kinds of brain tumors that can impact an individual's brain. Brain tumors that are most common and dangerous include glioma, meningioma, and pituitary tumors. In this work, we developed an online diagnostic tool to accurately classify brain tumors, including Astrocytoma, Glioma, Meningioma, Neurocytoma and Pituitary. Additionally, our program is capable of classifying a normal brain. We added CT and MRI scan images to improve cross-modality and model capability. We employed the CNN based neural network architecture known as EfficientNet. With this model, we achieved over 93.4% accuracy. Our application will help the radiologist identify tumors quickly and promote better treatment planning. Key words: Deep Learning, Transfer Learning, CNN, MRI, CT, Multi-Class Classification.

Diagnosing Brain Tumors: An Artificial Intelligence Modeling Approach

Brain tumour diagnosis poses significant challenges due to the complexity and heterogeneity of tumour characteristics. This study introduces an innovative deep-learning approach that leverages the complementary strengths of weighted magnetic resonance imaging and advanced deep-learning techniques to enhance the accuracy and reliability of brain tumour diagnosis. Historically, diverse methodologies have been employed, often centring on categorizing imaging modalities into binary distinctions, either cancerous versus non-cancerous images or discerning between benign and malignant tumours. In contrast, this study aims to classify multi-class malignant tumours into their specific categories with optimal precision. The proposed research centres on the differentiation of meningioma, glioma, and pituitary adenoma brain tumours, representing the three primary types of malignant brain tumours, along with non-tumorous MRI scans. This study employs a Convolutional Neural Network (CNN) and a Variational Autoencoder for the detection of brain tumours in Magnetic Resonance Imaging (MRI) images. Hyperparameter tuning for the CNN is achieved through a Grid search. The highest-performing model exhibits an accuracy of 95.25%, the other model reaches a peak accuracy of almost 100% in diagnosing cases without tumours.

Optimised ResNet50 for Multi-Class Classification of Brain Tumors

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

302 Targeted Inhibition of the Epigenetically Activated miR-483-5p IGF-2 Pathway as a Novel Therapeutic Strategy for Meningioma

INTRODUCTION: Meningioma is the most common primary central nervous system tumor. These are usually treated by surgical resection with curative intent, however gross total resection is not always feasible and there are many cases of recurrent and refractory disease despite adjuvant radiotherapy. Presently, there is no effective pharmacotherapeutic options for these tumors. METHODS: Small RNA sequencing was performed on meningioma tumor samples to study grade-dependent changes in microRNA expression. Gene expression was analyzed by chromatin marks, qRT-PCR and western blot. miRNA modulation, anti-IGF-2 neutralizing antibodies, and inhibitors against IGF1R were evaluated in a tumor-derived primary cultures of meningioma cells. RESULTS: Meningioma tumor samples showed high, grade-dependent expression of miR-483-5p, associated with high mRNA and protein expression of its host gene IGF-2. Inhibition of miR-483-5p reduced the growth of cultured meningioma cells, whereas a miR-483 mimic increased cell proliferation. Similarly, inhibition of this pathway with anti-IGF-2 neutralizing antibodies reduced meningioma cell proliferation. Small molecule tyrosine kinase inhibitor blockade of the IGF-2 receptor (IGF1R) resulted in rapid loss of viability of cultured meningioma tumor-derived cells, suggesting that autocrine IGF-2 feedback is obligatory for meningioma tumor cell survival and growth. The observed IGF1R-inhibitory IC50 for GSK1838705A and Ceritinib in cell-based assays along with the available pharmacokinetics data predicted that effective drug concentration could be achieved in vivo as a new medical treatment of meningioma. CONCLUSIONS: Meningioma cell growth is critically dependent on autocrine miR-483/IGF-2 stimulation and the IGF-2 pathway provides a new feasible meningioma treatment target.

A Literature Review on Brain Tumor Classification Using Deep Learning

Abstract: Brain tumor detection is a hard process in medicine. These brain tumors were brought on by the development of defective brain cells. It is classified into benign and malignant tumor which is cancerous and non-cancerous. Brain tumors are divided into three types Glioma, Meningioma, and Pituitary Tumor. By using magnetic resonance images, brain tumor prediction is carried out quickly with greater accuracy when these algorithms are applied to MRI scans. This aids inpatient treatment. The radiologist can make speedy decisions thanks to these predictions. Deep learning is used to analyze enormous datasets and to create clear images of the body for brain tumor diagnosis. In the proposed work, the CNN algorithm is applied in the current model for computation accuracy as well as speed.

Detecting Subtypes of Brain Tumors using Resnet50 with Transfer Learning

To classify a Brain Tumor dataset consisting of 3064 T1 weighted contrast-enhanced brain MR (Magnetic Resonance) images, we used the CNN (Convolutional Neural Network) model ResNet50 Neural Network, one of the most popular deep learning architectures, in the proposed framework. We graded (classified) the brain tumors into three classes (Glioma, Meningioma, and Pituitary Tumor). Additionally, we will apply Transfer Learning to reduce the amount of data needed, improve neural network performance (most of the time), and save training time. Using a refined ResNet50 Neural Network architecture, our Brain Tumor Detector achieves over 95% accuracy by using the technique of Transfer Learning.

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

ABSTRACT This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.

Consensus guidelines for the management of intracranial meningioma for low- and middle-income countries.

Intra-cranial meningiomas represent the most common type of extra-axial brain tumour in adults. Characteristically slow-growing and often asymptomatic, these tumours may only require observation in some cases. However, lesions that cause a significant mass effect necessitate intervention, primarily through surgical means. Additionally, in cases of significant unresectable low-grade residual meningioma or high-grade tumours, radiation therapy becomes essential. Notably, current management guidelines predominantly reflect data derived from high-income countries, failing to address constraints prevalent in the developing world, such as limited financial resources and restricted access to advanced surgical facilities. This manuscript introduces guidelines specifically tailored for the management of meningioma in patients from low- and middle-income countries, considering their unique healthcare challenges and resources.