Microsurgical lesionectomy for drug-resistant insular cortex epilepsy in focal cortical dysplasia type IIB: a pediatric case report.

Introduction and importance: Focal cortical dysplasia (FCD) is a significant cause of drug-resistant epilepsy, often necessitating surgical intervention. Type IIb FCD poses challenges due to its strong association with drug-resistant seizures. Effective management involves advanced imaging, intraoperative neurophysiological monitoring, and precise surgical techniques. This case study illustrates these strategies in an 11-year-old female with drug-resistant epilepsy attributed to Type IIb FCD. Case presentation: The patient, an 11-year-old female, had drug-resistant seizures despite various anticonvulsant treatments. Preoperative 3 Tesla (3T) MRI revealed an ill-defined lesion in the right frontal operculum. The surgical team used neuro-navigation for intraoperative guidance and electrocorticography for lesionectomy. Pathology confirmed Type IIb FCD with rare concentric calcifications. Clinical discussion: Drug-resistant seizures in FCD often require surgery when medications fail. This case highlights the importance of comprehensive preoperative evaluations and advanced imaging, such as 3T MRI, to accurately identify lesions. Intraoperative neurophysiological monitoring, including electrocorticography, ensures precise resection of the epileptogenic zone. The unusual finding of concentric calcifications in Type IIb FCD is noteworthy, suggesting the need for further research to understand their impact on the disease. Conclusion: Microsurgical lesionectomy is crucial for managing drug-resistant seizures in Type IIb FCD. Combining advanced imaging with intraoperative monitoring improves surgical precision and outcomes. The rare pathological finding of calcifications highlights the diversity of FCD manifestations, warranting further study. These techniques can significantly enhance seizure control and quality of life in patients with drug-resistant epilepsy.

A cross-shaped terahertz metamaterial absorber for brain cancer detection.

The article presents, for the first time, a terahertz metamaterial absorber (TMA) designed in the shape of a cross consisting of four orthogonally positioned horn-shaped patches in succession, to detect brain cancer cells. The design exhibits the property of mu-negative material, indicating magnetic resonance. The proposed TMA has achieved an impressive absorption rate of 99.43% at 2.334 THz and a high Q-factor of 47.15. The sensing capability has been investigated by altering the refractive index of the surrounding medium in the range of 1.3 to 1.48, resulting in a sensitivity of 0.502 THz/RIU. The proposed TMA exhibits complete polarization insensitivity, highlighting this as one of its advantageous features. The adequate sensing capability of the proposed TMA in differentiating normal and cancerous brain cells makes it a viable candidate for an early and efficient brain cancer detector. This research can be the foundation for future research on using THz radiation for brain cancer detection.

An XAI-enhanced efficientNetB0 framework for precision brain tumor detection in MRI imaging.

BACKGROUND: Accurately diagnosing brain tumors from MRI scans is crucial for effective treatment planning. While traditional methods heavily rely on radiologist expertise, the integration of AI, particularly Convolutional Neural Networks (CNNs), has shown promise in improving accuracy. However, the lack of transparency in AI decision-making processes presents a challenge for clinical adoption. METHODS: Recent advancements in deep learning, particularly the utilization of CNNs, have facilitated the development of models for medical image analysis. In this study, we employed the EfficientNetB0 architecture and integrated explainable AI techniques to enhance both accuracy and interpretability. Grad-CAM visualization was utilized to highlight significant areas in MRI scans influencing classification decisions. RESULTS: Our model achieved a classification accuracy of 98.72 % across four categories of brain tumors (Glioma, Meningioma, No Tumor, Pituitary), with precision and recall exceeding 97 % for all categories. The incorporation of explainable AI techniques was validated through visual inspection of Grad-CAM heatmaps, which aligned well with established diagnostic markers in MRI scans. CONCLUSION: The AI-enhanced EfficientNetB0 framework with explainable AI techniques significantly improves brain tumor classification accuracy to 98.72 %, offering clear visual insights into the decision-making process. This method enhances diagnostic reliability and trust, demonstrating substantial potential for clinical adoption in medical diagnostics.

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

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

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging. Traditional methodologies primarily rely on manual interpretation of MRI images, supplemented by conventional machine learning techniques. These approaches often lack the robustness and scalability needed for precise and automated tumor classification. The major limitations include a high degree of manual intervention, potential for human error, limited ability to handle large datasets, and lack of generalizability to diverse tumor types and imaging conditions.To address these challenges, we propose a federated learning-based deep learning model that leverages the power of Convolutional Neural Networks (CNN) for automated and accurate brain tumor classification. This innovative approach not only emphasizes the use of a modified VGG16 architecture optimized for brain MRI images but also highlights the significance of federated learning and transfer learning in the medical imaging domain. Federated learning enables decentralized model training across multiple clients without compromising data privacy, addressing the critical need for confidentiality in medical data handling. This model architecture benefits from the transfer learning technique by utilizing a pre-trained CNN, which significantly enhances its ability to classify brain tumors accurately by leveraging knowledge gained from vast and diverse datasets.Our model is trained on a diverse dataset combining figshare, SARTAJ, and Br35H datasets, employing a federated learning approach for decentralized, privacy-preserving model training. The adoption of transfer learning further bolsters the model's performance, making it adept at handling the intricate variations in MRI images associated with different types of brain tumors. The model demonstrates high precision (0.99 for glioma, 0.95 for meningioma, 1.00 for no tumor, and 0.98 for pituitary), recall, and F1-scores in classification, outperforming existing methods. The overall accuracy stands at 98%, showcasing the model's efficacy in classifying various tumor types accurately, thus highlighting the transformative potential of federated learning and transfer learning in enhancing brain tumor classification using MRI images.

A Randomized, Double-Blind, Placebo-Controlled Trial Evaluating the Effect of Topical Urea for Secondary Prophylaxis of Hand Foot Skin Reaction in Renal Cell Cancer Patients on Sunitinib Therapy.

INTRODUCTION: Hand foot skin reaction (HFSR) is a common dose-limiting adverse effect of multi kinase inhibitors (MKI) whose mechanism is not fully understood, and the prophylaxis is inadequate. OBJECTIVE: In this pilot study, a double-blind, randomized placebo-controlled trial was conducted to evaluate the effect of topical urea in secondary prevention of sunitinib-induced HFSR in renal cell cancer patients. METHODS: Out of 55 screened patients, 14 were randomized to receive topical urea or placebo for four weeks. The association of HFSR with drug levels of sunitinib and its metabolite (n-desethyl sunitinib), genetic polymorphism of VEGFR2 gene, quality of life (QOL) and biochemical markers was also assessed. RESULTS: The results showed that urea-based cream was not superior to placebo (P = .075). There was no change in the QOL in both the groups. Single nucleotide polymorphism was checked for two nucleotides rs1870377 and rs2305948 located in VEGFR2 gene on chromosome 4. SNP (variant T > A) at rs1870377 was associated with appearance of new HFSR as compared to the wild type, although the association was not statistically significant (OR 0.714). There was no statistically significant difference between mean plasma levels of sunitinib and N-desethyl sunitinib in urea arm as compared to placebo arm as compared to placebo. The best fit population pharmacokinetic model for sunitinib was one compartment model with first order absorption and linear elimination. The median (IQR) of population parameters calculated from the population pharmacokinetics model for Ka, V and Cl was 0.22 (0.21-0.24) h-1, 4.4 (4.09-4.47) L, 0.049 (0.042-0.12) L/hr, respectively. CONCLUSION: The study suggested that the urea-based cream was not superior to placebo in decreasing the appearance of new HFSR in renal cancer patients receiving 4:2 regimen of sunitinib.

Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering.

Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI). Current methodologies reliant on traditional image processing and conventional machine learning encounter hurdles in accurately discerning tumor regions within intricate MRI scans, often susceptible to noise and varying image quality. The advent of artificial intelligence (AI) has revolutionized various aspects of healthcare, providing innovative solutions for diagnostics and treatment strategies. This paper introduces a novel AI-driven methodology for brain tumor detection from MRI images, leveraging the EfficientNetB2 deep learning architecture. Our approach incorporates advanced image preprocessing techniques, including image cropping, equalization, and the application of homomorphic filters, to enhance the quality of MRI data for more accurate tumor detection. The proposed model exhibits substantial performance enhancement by demonstrating validation accuracies of 99.83%, 99.75%, and 99.2% on BD-BrainTumor, Brain-tumor-detection, and Brain-MRI-images-for-brain-tumor-detection datasets respectively, this research holds promise for refined clinical diagnostics and patient care, fostering more accurate and reliable brain tumor identification from MRI images. All data is available on Github: https://github.com/muskan258/Brain-Tumor-Detection-from-MRI-Images-Utilizing-EfficientNetB2 ).

Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.

Breast Cancer is a significant global health challenge, particularly affecting women with higher mortality compared with other cancer types. Timely detection of such cancer types is crucial, and recent research, employing deep learning techniques, shows promise in earlier detection. The research focuses on the early detection of such tumors using mammogram images with deep-learning models. The paper utilized four public databases where a similar amount of 986 mammograms each for three classes (normal, benign, malignant) are taken for evaluation. Herein, three deep CNN models such as VGG-11, Inception v3, and ResNet50 are employed as base classifiers. The research adopts an ensemble method where the proposed approach makes use of the modified Gompertz function for building a fuzzy ranking of the base classification models and their decision scores are integrated in an adaptive manner for constructing the final prediction of results. The classification results of the proposed fuzzy ensemble approach outperform transfer learning models and other ensemble approaches such as weighted average and Sugeno integral techniques. The proposed ResNet50 ensemble network using the modified Gompertz function-based fuzzy ranking approach provides a superior classification accuracy of 98.986%.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer.

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among different observers. Employing machine learning to automate the diagnosis of breast cancer presents a viable option, striving to improve both precision and speed. Previous studies have primarily focused on applying various machine learning and deep learning models for the classification of breast cancer images. These methodologies leverage convolutional neural networks (CNNs) and other advanced algorithms to differentiate between benign and malignant tumors from histopathological images. Current models, despite their potential, encounter obstacles related to generalizability, computational performance, and managing datasets with imbalances. Additionally, a significant number of these models do not possess the requisite transparency and interpretability, which are vital for medical diagnostic purposes. To address these limitations, our study introduces an advanced machine learning model based on EfficientNetV2. This model incorporates state-of-the-art techniques in image processing and neural network architecture, aiming to improve accuracy, efficiency, and robustness in classification. We employed the EfficientNetV2 model, fine-tuned for the specific task of breast cancer image classification. Our model underwent rigorous training and validation using the BreakHis dataset, which includes diverse histopathological images. Advanced data preprocessing, augmentation techniques, and a cyclical learning rate strategy were implemented to enhance model performance. The introduced model exhibited remarkable efficacy, attaining an accuracy rate of 99.68%, balanced precision and recall as indicated by a significant F1 score, and a considerable Cohen's Kappa value. These indicators highlight the model's proficiency in correctly categorizing histopathological images, surpassing current techniques in reliability and effectiveness. The research emphasizes improved accessibility, catering to individuals with disabilities and the elderly. By enhancing visual representation and interpretability, the proposed approach aims to make strides in inclusive medical image interpretation, ensuring equitable access to diagnostic information.

Horizontal transfer of miR-383 sensitise cells to cisplatin by targeting VEGFA-Akt signalling loop.

BACKGROUND: Cellular resistance to cisplatin has been one of the major obstacles in the success of combination therapy for many types of cancers. Emerging evidences suggest that exosomes released by drug resistant tumour cells play significant role in conferring resistance to drug sensitive cells by means of horizontal transfer of genetic materials such as miRNAs. Though exosomal miRNAs have been reported to confer drug resistance, the exact underlying mechanisms are still unclear. METHODS AND RESULTS: In the present study, mature miRNAs secreted differentially by cisplatin resistant and cisplatin sensitive HepG2 cells were profiled and the effect of most significantly lowered miRNA in conferring cisplatin resistance when horizontally transferred, was analysed. we report miR-383 to be present at the lowest levels among the differentially abundant miRNAs expressed in exosomes secreted by cisplatin resistant cells compared to that that of cisplatin sensitive cells. We therefore, checked the effect of ectopic expression of miR-383 in altering cisplatin sensitivity of Hela cells. Drug sensitivity assay and apoptotic assays revealed that miR-383 could sensitise cells to cisplatin by targeting VEGF and its downstream Akt mediated pathway. CONCLUSION: Results presented here provide evidence for the important role of miR-383 in regulating cisplatin sensitivity by modulating VEGF signalling loop upon horizontal transfer across different cell types.

Recommended Resting-State fMRI Acquisition and Preprocessing Steps for Preoperative Mapping of Language and Motor and Visual Areas in Adult and Pediatric Patients with Brain Tumors and Epilepsy.

Resting-state (rs) fMRI has been shown to be useful for preoperative mapping of functional areas in patients with brain tumors and epilepsy. However, its lack of standardization limits its widespread use and hinders multicenter collaboration. The American Society of Functional Neuroradiology, American Society of Pediatric Neuroradiology, and the American Society of Neuroradiology Functional and Diffusion MR Imaging Study Group recommend specific rs-fMRI acquisition approaches and preprocessing steps that will further support rs-fMRI for future clinical use. A task force with expertise in fMRI from multiple institutions provided recommendations on the rs-fMRI steps needed for mapping of language, motor, and visual areas in adult and pediatric patients with brain tumor and epilepsy. These were based on an extensive literature review and expert consensus.Following rs-fMRI acquisition parameters are recommended: minimum 6-minute acquisition time; scan with eyes open with fixation; obtain rs-fMRI before both task-based fMRI and contrast administration; temporal resolution of ≤2 seconds; scanner field strength of 3T or higher. The following rs-fMRI preprocessing steps and parameters are recommended: motion correction (seed-based correlation analysis [SBC], independent component analysis [ICA]); despiking (SBC); volume censoring (SBC, ICA); nuisance regression of CSF and white matter signals (SBC); head motion regression (SBC, ICA); bandpass filtering (SBC, ICA); and spatial smoothing with a kernel size that is twice the effective voxel size (SBC, ICA).The consensus recommendations put forth for rs-fMRI acquisition and preprocessing steps will aid in standardization of practice and guide rs-fMRI program development across institutions. Standardized rs-fMRI protocols and processing pipelines are essential for multicenter trials and to implement rs-fMRI as part of standard clinical practice.

Abdominal wall calcification in a peritoneal dialysis patient.

Abdominal wall calcification in a peritoneal dialysis patient has not previously been reported. We describe a 40-year-old lady, a type 2 diabetic and hypertensive for the past 14 years, who did not have any history, clinical features or laboratory results suggesting autoimmune disease, and had not suffered from tuberculosis in the past, but who had been diagnosed with chronic kidney disease in 2016. She had initiated peritoneal dialysis in December 2018.

The Role of BRAF Inhibitors in the Management of Ameloblastoma: A Literature Review.

Ameloblastoma is one of the most prevalent but enigmatic benign odontogenic tumors of the jaw, accounting for approximately 10% of all maxillary and mandibular tumors. This neoplasia is distinguished by exhibiting several clinical and histological variants along with several mutations that affect its behavior. The ameloblastoma treatment plan is determined by the tumor's size, anatomical location, histologic variant, and anatomical involvement. On chromosome 7, there is a proto-oncogene called BRAF. When BRAF is mutated, it becomes an oncogene and continuously produces proteins like MEK and ERK, members of mitogen-activated protein kinase (MAPK). In the signaling pathway, these proteins activate transcription factor inside the nucleus that helps in cell division and growth. Numerous neoplasms have been linked to more than 40 BRAF mutations. The most common one is BRAF proto-oncogene serine/threonine kinase (BRAF) V600E, whose treatment has been linked to a positive outcome. BRAF inhibitors like vemurafenib, dabrafenib, and sorafenib have shown excellent results, especially in metastatic ameloblastoma. BRAF, particularly in the case of metastatic ameloblastoma, inhibitors such as vemurafenib, dabrafenib, and sorafenib, has demonstrated outstanding results. Targeted therapies have been employed as adjuvant therapies to enhance cosmetic outcomes, even though no reports of serial cases demonstrate their effectiveness in ameloblastomas. In the treatment of ameloblastomas, the identification of BRAF V600E and additional mutations as the prime targeted therapies has proven to be a significant breakthrough where surgical treatment was contraindicated. In this article, we review the presence of BRAF V600E mutations, their inhibitors, and targeted therapies in ameloblastoma.

Increased NOTCH1 expression is associated with low survival in moderate/ poor differentiated human oral squamous cell carcinoma patients.

Notch deregulation has been reported in various types of cancers, including Oral squamous cell carcinomas (OSCCs). The role of Notch1 signaling in oral squamous cell carcinoma (OSCC) remains poorly understood. In this study, NOTCH1 was aberrantly expressed in human oral cancer tissues compared with that in normal marginal tissues and was associated with poor prognosis. The positive Notch 1 expression was significantly associated with poor tumor differentiation status. Kaplan-Meier survival curves revealed that elevated cytoplasmic NOTCH1 expression levels in OSCC patients were associated with poor overall survival. Moreover, multivariate COX proportional hazard models revealed that T N status, AJCC stage histological grade were independent prognostic factors for survival. Our result clearly demonstrates the oncogenic role of Notch1 in oral cancer and Notch1 may be a useful biomarker to target oral cancer patients.

THE MICROBIOME AND METABOLIC DISORDERS: THE LINK BETWEEN THE GUT MICROBIOTA AND METABOLIC SYNDROME.

The diverse population of microbes that live in our digestive system, known as the gut microbiota, remains essential for many physiological processes. It plays a role in obtaining energy from food and controls both regional and overall immunity. In addition, changes in the microbiota of the digestive tract are connected to the emergence of an extensive variety of illnesses, such as cancer, gastrointestinal problems, and metabolic disorders. From a metabolic perspective, the gut microbiota can affect processes like lipid accumulation, lipopolysaccharide satisfied, and short-chain fatty acid synthesis, all of which have an effect on food intake, inflammatory reactions, and insulin signaling. Prebiotics, probiotics, specialized anti-diabetic medications, and faecalmicrobiota implantation are a few of the ways that have been discovered to alter the gut microbiota; each has a different influence the human body's metabolism and the emergence of metabolic disorders. These therapies have been reported to be therapeutic strategies for enhancing general wellness and reestablishing a balanced gut flora.

Arachnoid Welding-A Simple and Economical Method of Arachnoid Closure to Prevent Cerebrospinal Fluid Leak.

BACKGROUND: Collection of cerebrospinal fluid (CSF) in the subdural compartment is a major cause of postoperative morbidity, especially for posterior fossa surgeries. Arachnoid closure techniques, including suturing of the arachnoid and use of synthetic sealants, have been described in the literature. However, they are not always feasible or effective and have not been universally adopted. METHODS: We describe the technique of arachnoid welding for a case of brainstem cavernoma. This is a simple, cost-effective, and easily reproducible technique using readily available bipolar cautery kept at a low-current setting. At the end of surgery, the arachnoid leaflets are closely approximated, and bipolar cautery is used to seal the edges together. An illustrative video shows the technical nuances of this procedure. This technique can also be applied for arachnoid closure at other cranial and spinal sites. RESULTS: Arachnoid closure can act as an effective natural barrier to keep CSF in its physiological subarachnoid compartment. It provides an additional barrier to prevent CSF leak. It also prevents morbidity associated with adhesions and arachnoiditis. Proper closure of arachnoid makes durotomy during repeat surgery much easier and avoids injury to the underlying pia. A brief review of related literature shows the benefits of closing the arachnoid before dural closure and the different techniques that have been described so far. CONCLUSIONS: The arachnoid welding technique has a wide application, is easy to learn, and can be used especially for posterior fossa surgeries in which rates of CSF leak are the highest.

Integrin ß4 induced epithelial-to-mesenchymal transition involves miR-383 mediated regulation of GATA6 levels.

BACKGROUND: The process of transdifferentiating epithelial cells to mesenchymal-like cells (EMT) involves cells gradually taking on an invasive and migratory phenotype. Many cell adhesion molecules are crucial for the management of EMT, integrin ß4 (ITGB4) being one among them. Although signaling downstream of ITGB4 has been reported to cause changes in the expression of several miRNAs, little is known about the role of such miRNAs in the process of EMT. METHODS AND RESULTS: The cytoplasmic domain of ITGB4 (ITGB4CD) was ectopically expressed in HeLa cells to induce ITGB4 signaling, and expression analysis of mesenchymal markers indicated the induction of EMT. ß-catenin and AKT signaling pathways were found to be activated downstream of ITGB4 signaling, as evidenced by the TOPFlash assay and the levels of phosphorylated AKT, respectively. Based on in silico and qRT-PCR analysis, miR-383 was selected for functional validation studies. miR-383 and Sponge were ectopically expressed in HeLa, thereafter, western blot and qRT-PCR analysis revealed that miR-383 regulates GATA binding protein 6 (GATA6) post-transcriptionally. The ectopic expression of shRNA targeting GATA6 caused the reversal of EMT and ß catenin activation downstream of ITGB4 signaling. Cell migration assays revealed significantly high cell migration upon ectopic expression ITGB4CD, which was reversed upon ectopic co-expression of miR-383 or GATA6 shRNA. Besides, ITGB4CD promoted EMT in in ovo xenograft model, which was reversed by ectopic expression of miR-383 or GATA6 shRNA. CONCLUSION: The induction of EMT downstream of ITGB4 involves a signaling axis encompassing AKT/miR-383/GATA6/ß-catenin.

Prognostic and Clinical Implications of UNC13C expression in Hepatocellular Carcinoma Patients.

Aberrant expression of UNC13C (Unc-13 Homolog C) has been observed during the progression of oral squamous cell carcinoma. However, the expression pattern and clinical relevance of UNC13C in Hepatocellular carcinoma (HCC) remain to be elucidated. The purpose of this study is to examine UNC13C expression in HCC and explore its role in clinicopathological factor or prognosis in HCC. Two hundred and sixty-five patients diagnosed with HCC were included in the present study. The expression of UNC13C in HCC tissues was analyzed by immunohistochemistry analysis. The relationship between UNC13C protein and clinicopathological characteristics in HCC was investigated. Moreover, the high expression of UNC13C was significantly correlated with T stage, AJCC stage and overall survival rates. Cox regression analysis identified UNC13C as an independent prognostic indicator for HCC patients. UNC13C might be a prognostic biomarker and therapeutic target in HCC. Further studies with larger sample sets are needed to understand the clinical implications of UNC13C in hepatocellular carcinoma.

Targeting Histone 3 Variants Epigenetic Landscape and Inhibitory Immune Checkpoints: An Option for Paediatric Brain Tumours Therapy.

Despite little progress in survival rates with regular therapies, which do not provide complete care for curing pediatric brain tumors (PBTs), there is an urgent need for novel strategies to overcome the toxic effects of conventional therapies to treat PBTs. The co-inhibitory immune checkpoint molecules, e.g., CTLA-4, PD-1/PD-L1, etc., and epigenetic alterations in histone variants, e.g., H3K27me3 that help in immune evasion at tumor microenvironment have not gained much attention in PBTs treatment. However, key epigenetic mechanistic alterations, such as acetylation, methylation, phosphorylation, sumoylation, poly (ADP)-ribosylation, and ubiquitination in histone protein, are greatly acknowledged. The crucial checkpoints in pediatric brain tumors are cytotoxic T lymphocyte antigen-4 (CTLA-4), programmed cell death protein-1 (PD-1) and programmed death-ligand 1 (PDL1), OX-2 membrane glycoprotein (CD200), and indoleamine 2,3-dioxygenase (IDO). This review covers the state of knowledge on the role of multiple co-inhibitory immunological checkpoint proteins and histone epigenetic alterations in different cancers. We further discuss the processes behind these checkpoints, cell signalling, the current scenario of clinical and preclinical research and potential futuristic opportunities for immunotherapies in the treatment of pediatric brain tumors. Conclusively, this article further discusses the possibilities of these interventions to be used for better therapy options.