Heterogeneity of mesencephalic dopaminergic neurons: From molecular classifications, electrophysiological properties to functional connectivity.

The mesencephalic dopamine (DA) system is composed of neuronal subtypes that are molecularly and functionally distinct, are responsible for specific behaviors, and are closely associated with numerous brain disorders. Existing research has made significant advances in identifying the heterogeneity of mesencephalic DA neurons, which is necessary for understanding their diverse physiological functions and disease susceptibility. Moreover, there is a conflict regarding the electrophysiological properties of the distinct subsets of midbrain DA neurons. This review aimed to elucidate recent developments in the heterogeneity of midbrain DA neurons, including subpopulation categorization, electrophysiological characteristics, and functional connectivity to provide new strategies for accurately identifying distinct subtypes of midbrain DA neurons and investigating the underlying mechanisms of these neurons in various diseases.

Evaluation of European LeukemiaNet 2022 risk classification in patients undergoing allogeneic haematopoietic stem cell transplantation for acute myeloid leukaemia: Identification of a very poor prognosis genetic group.

European LeukemiaNet refined their risk classification of acute myeloid leukaemia (AML) in 2022 (ELN 2022) according to the two new myeloid classifications published the same year. We have retrospectively assessed the prognostic value of the ELN 2022 in 120 AML patients undergoing allogeneic haematopoietic cell transplantation (allo-HCT), including 99 in first complete response (CR1) from 2011 to 2021 in our centre. Adverse risk patients (Adv) presented inferior outcome in terms of overall survival (OS) and leukaemia-free survival (LFS) (OS [p = 0.003], LFS [p = 0.02]), confirmed in multivariate analysis (hazard ratio [HR] for OS = 2.00, p = 0.037). These results were also seen in patients allografted in CR1. Further analysis identified a subgroup named adverse-plus (AdvP), including complex karyotype, MECOM(EVI1) rearrangements and TP53 mutations, with worse outcomes than the rest of groups of patients, including the Adv (HR for OS: 3.14, p < 0.001, HR for LFS: 3.36, p < 0.001), with higher 2-year cumulative incidence of relapse (p < 0.001). Notably, within this analysis, the outcome of Adv and intermediate patients were similar. These findings highlight the prognostic value of ELN 2022 in patients undergoing allo-HCT, which can be improved by the recognition of a poor genetic subset (AdvP) within the Adv risk group.

Multidimensional Applications and Challenges of Riboswitches in Biosensing and Biotherapy.

Riboswitches have received significant attention over the last two decades for their multiple functionalities and great potential for applications in various fields. This article highlights and reviews the recent advances in biosensing and biotherapy. These fields involve a wide range of applications, such as food safety detection, environmental monitoring, metabolic engineering, live cell imaging, wearable biosensors, antibacterial drug targets, and gene therapy. The discovery, origin, and optimization of riboswitches are summarized to help readers better understand their multidimensional applications. Finally, this review discusses the multidimensional challenges and development of riboswitches in order to further expand their potential for novel applications.

Retrospective cohort evaluation of non-HIV Castleman disease from a single academic center in Beijing, China.

The purpose is to ascertain the clinical impact of Castleman disease (CD) by reassessment of the real-world data from Peking University First Hospital (PKUFH). The results will contribute to the standardization of diagnosis and treatment on CDs. Based on the last 15-year retrospective real-world data from Peking University First Hospital (PKUFH), we reclassified and re-evaluated the clinical and pathological information of patients with pathologically suspected diagnosis of CD. A total of 203 patients were included in our study, in which the diagnosis of CD was confirmed in 189 cases, including 118 patients with unicentric CD (UCD, n = 118, 62.4%) and 71 patients with multicentric CD (MCD, n = 71, 37.6%). A total of 44.1% (n = 52) of UCDs in our cohort were complicated with paraneoplastic pemphigus (PNP). The treatment of UCD is primarily surgical, with a 5-year overall survival (OS) of 88.1%. Patients with PNP had a poorer prognosis than those without PNP (82.9% (95% CI 123-178) vs 92.8% (95% CI 168-196), log-rank p = 0.041). The rate of concurrent systemic symptoms was 74.6% (n = 53), and renal involvement occurred in 49.3% (n = 35) MCD patients. The MCD treatments were mainly chemotherapy regimens, with a 5-year OS of 77.6% (95% CI, 143-213). Patients with UCD demonstrate a better overall prognosis than patients with MCD. But the prognosis of those complicated with PNP was poor. The differential diagnosis of MCD is extensive. MCD treatment in China is heterogeneous. The inaccessibility of anti-IL-6-targeted drugs in China may contribute to the poor prognosis for patients with MCD.A preprint has previously been published (Guo et al. 34).

The International Classification of Headache Disorders: History and future perspectives.

PURPOSE: The International Classification of Headache Disorders (ICHD) is universally accepted and forms the basis of headache management and clinical, experimental and pharmacological headache research. The present review summarizes the history of the three different editions of the classification, concentrating on aspects of general interest that are still valid today. METHODS: The article is based on the memory of the chairperson of three editions of the International Classification and on his notes in the work copies and published scientific classification studies. RESULTS: Many of the crucial issues in headache classification are discussed in the review of the different editions. Some have been resolved and some remain unresolved. The 11th edition of the World Health Organization's International Classification of Diseases (ICD) has been developed in close contact with the International Headache Society classification committee and is in fact an abbreviated version of ICHD-3. The principles of the ICHD have also been used by the International Association for Study of Pain in developing a pain classification now included in ICD-11. The many points of discussion of each of the three editions are still relevant for headache experts and all those who care for headache patients. CONCLUSION: Headache classification is a living and developing discipline of research. Here, the gradual expansion and refinement of the classification through 3 different editions are discussed with a view to present day relevance.

Tropical Logistic Regression Model on Space of Phylogenetic Trees.

Classification of gene trees is an important task both in the analysis of multi-locus phylogenetic data, and assessment of the convergence of Markov Chain Monte Carlo (MCMC) analyses used in Bayesian phylogenetic tree reconstruction. The logistic regression model is one of the most popular classification models in statistical learning, thanks to its computational speed and interpretability. However, it is not appropriate to directly apply the standard logistic regression model to a set of phylogenetic trees, as the space of phylogenetic trees is non-Euclidean and thus contradicts the standard assumptions on covariates. It is well-known in tropical geometry and phylogenetics that the space of phylogenetic trees is a tropical linear space in terms of the max-plus algebra. Therefore, in this paper, we propose an analogue approach of the logistic regression model in the setting of tropical geometry. Our proposed method outperforms classical logistic regression in terms of Area under the ROC Curve in numerical examples, including with data generated by the multi-species coalescent model. Theoretical properties such as statistical consistency have been proved and generalization error rates have been derived. Finally, our classification algorithm is proposed as an MCMC convergence criterion for Mr Bayes. Unlike the convergence metric used by Mr Bayes which is only dependent on tree topologies, our method is sensitive to branch lengths and therefore provides a more robust metric for convergence. In a test case, it is illustrated that the tropical logistic regression can differentiate between two independently run MCMC chains, even when the standard metric cannot.

A close look at sociality in DSM criteria.

PURPOSE: The importance of sociality in psychology and psychotherapy is quite undisputed; however, this construct risks being underestimated in psychiatric nosography. The aim of the review was to assess the relevance of sociality in DSM 5 criteria. METHOD: Sociality-laden criteria of 192 selected DSM categories have been identified through a textual grid. Second, the criteria have been classified into 6 categories, i.e., (1) Affiliation and Attachment (AA), (2) Social Communication (SC), (3) Perception and Understanding of Others (PUO), (4) Culture, (5) Clinical Significance Criterion (CSC) (6), and No Specific Construct (NSC). RESULTS: 13% of all mental disorders mention AA in their criteria. 8.8% of all mental disorders mention SC; 8.8% of all mental disorders mention PUO in their criteria. 15% of all mental disorders mention culture in their criteria (exclusively ex negativo though). 40% of mental disorders mention non-specific sociality (NSC) in their criteria. CSC is mentioned in 85% of mental disorders. Personality disorders have the highest "concentration" of sociality mentions throughout the DSM categories. CONCLUSIONS: The overall results suggest that DSM criteria offer a confused account of sociality. We believe that the descriptive approach is the underlying reason. We suggest that in the long run a theory-laden approach to sociality, informed by evolutionary insights about motivations, could be of help.

Natural hypoglycaemic bioactives: Newer avenues and newer possibilities.

The incidences of endocrine and metabolic disorders like diabetes have increased worldwide. Several proposed molecular pathways mechanisms for the management of diabetes have been identified, but glycaemic control is still a challenging task in the drug discovery process. Most of the drug discovery processes lead to numerous scaffolds that are prominent in natural products. The review deals with the natural bioactives as an α-amylase inhibitors, α-glucosidase inhibitors, protein tyrosine phosphatase-1B inhibitors, dipeptidyl peptidase-IV inhibitors, G-protein coupled receptors-40 agonists, PPAR-γ agonists and the activators of 5'-adenosine monophosphate-activated protein kinase and glucokinase. So, in this review, we focused on the hypoglycaemic bioactives, which will assist scientific developers, traditional medicinal practitioners, and readers to discover some potent antidiabetic molecules. Strategies like chemometric approaches, scaffold hopping, and total synthesis of natural products by group modification or ring opening/closing mechanism could be useful for the development of novel hit/lead antidiabetic molecules. The study concludes that each phyto molecule inherits a potential to get explored by repurposing techniques for various antidiabetic targets and offer an alternative antidiabetic therapeutic medicinal potential.

Meniscal ramp lesion classification systems: A systematic review.

PURPOSE: To describe the proposed classification systems for meniscal ramp lesions (RLs) in the literature and evaluate their accuracy and reliability. METHODS: A systematic search was conducted according to the Preferred Reporting Items for Systematic reviews and Meta-analyses guidelines utilising PubMed, Embase and Cochrane Library databases. Level I-IV studies referencing RLs along with either an arthroscopic- or magnetic resonance imaging (MRI)-based classification system used to describe RL subtypes were included. RESULTS: In total, 21 clinical studies were included. Twenty-seven (79%) of the included studies were published in 2020 or later. There were four main classification systems proposed within the literature (two arthroscopic-, two MRI-based), describing tear patterns, mediolateral extent, associated ligament disruption and stability of the lesion. The first classification was proposed in 2015 by Thaunat et al. and is referenced in 22 (64.7%) of the included studies. The application of the Thaunat et al. criteria to MRI showed variable sensitivity (31.70%-93.8%) and interobserver agreement (k = 0.55-0.80). The Greif et al. modification to the Thaunat et al. system was referenced in 32.4% of the included studies and had a substantial interobserver agreement (k = 0.8). Stability to probing and specific tear location were each used to classify RLs in 28.6% and 23.8% of the included clinical studies, respectively. CONCLUSION: Although there has been a recent increase in the recognition and treatment of meniscal RLs, there is limited consistency in descriptive classifications used for this pathology. Current RL classification systems based on preoperative MRI have variable reliability, and arthroscopic examination remains the gold standard for diagnosis and classification. LEVEL OF EVIDENCE: Level IV.

A Novel Approach to Detect Drones Using Deep Convolutional Neural Network Architecture.

Over the past decades, drones have become more attainable by the public due to their widespread availability at affordable prices. Nevertheless, this situation sparks serious concerns in both the cyber and physical security domains, as drones can be employed for malicious activities with public safety threats. However, detecting drones instantly and efficiently is a very difficult task due to their tiny size and swift flights. This paper presents a novel drone detection method using deep convolutional learning and deep transfer learning. The proposed algorithm employs a new feature extraction network, which is added to the modified YOU ONLY LOOK ONCE version2 (YOLOv2) network. The feature extraction model uses bypass connections to learn features from the training sets and solves the "vanishing gradient" problem caused by the increasing depth of the network. The structure of YOLOv2 is modified by replacing the rectified linear unit (relu) with a leaky-relu activation function and adding an extra convolutional layer with a stride of 2 to improve the small object detection accuracy. Using leaky-relu solves the "dying relu" problem. The additional convolution layer with a stride of 2 reduces the spatial dimensions of the feature maps and helps the network to focus on larger contextual information while still preserving the ability to detect small objects. The model is trained with a custom dataset that contains various types of drones, airplanes, birds, and helicopters under various weather conditions. The proposed model demonstrates a notable performance, achieving an accuracy of 77% on the test images with only 5 million learnable parameters in contrast to the Darknet53 + YOLOv3 model, which exhibits a 54% accuracy on the same test set despite employing 62 million learnable parameters.

The reliability of posterior malleolar ankle fracture assessment: a unique perspective.

AIM: This study aims to elucidate the pathology of PMFs in the South African population, establish correlations between fracture patterns and international classification guidelines and evaluate the interobserver reliability of current classifications. METHODS: A retrospective review was conducted in a multicentre analysis over a one-year period from January 2019 to December 2019 at our institution. Computer tomography scans for foot and ankle injuries were reviewed, and posterior malleolus fractures were included. Pathoanatomical data was collected and analysed according to known classification systems and subsequent treatment modalities evaluated. A panel of observers individually reviewed radiographic data to determine interobserver reliability. RESULTS: A total of 71 patients were included with a mean age of 41 ± 13.4 years (range 18-78) and a female predominant population (69%). A greater proportion of injuries were high energy (23.9%), with significant fragment comminution (53.5%), and half (52.1%) of all injuries were subluxated/dislocated at presentation. A total of 93% of injuries were managed operatively, despite theatre access limitations resulting in significant delays to fixation (19.1 days). Despite good pathoanatomical agreement with most international classifications, interobserver reliability was poor (Krippendorff α-coefficient < 0.667). Inconsistent treatment patterns in operative and non-operative strategies are reported. CONCLUSION: A unique patient population of younger, female individuals incurred posterior malleolar fractures due to higher energy mechanisms of injury. Whilst injury patterns were mostly comparable, significant interobserver variability was noted. Resource limitations, diagnostic challenges, poorly defined and inconsistent treatment strategies, inevitably impact outcomes within the South African population. LEVEL OF EVIDENCE: Level III.

Rio de Janeiro Global Consensus on Landmarks, Definitions, and Classifications in Barrett's Esophagus: World Endoscopy Organization Delphi Study.

BACKGROUND & AIMS: Despite the significant advances made in the diagnosis and treatment of Barrett's esophagus (BE), there is still a need for standardized definitions, appropriate recognition of endoscopic landmarks, and consistent use of classification systems. Current controversies in basic definitions of BE and the relative lack of anatomic knowledge are significant barriers to uniform documentation. We aimed to provide consensus-driven recommendations for uniform reporting and global application. METHODS: The World Endoscopy Organization Barrett's Esophagus Committee appointed leaders to develop an evidence-based Delphi study. A working group of 6 members identified and formulated 23 statements, and 30 internationally recognized experts from 18 countries participated in 3 rounds of voting. We defined consensus as agreement by ≥80% of experts for each statement and used the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) tool to assess the quality of evidence and the strength of recommendations. RESULTS: After 3 rounds of voting, experts achieved consensus on 6 endoscopic landmarks (palisade vessels, gastroesophageal junction, squamocolumnar junction, lesion location, extraluminal compressions, and quadrant orientation), 13 definitions (BE, hiatus hernia, squamous islands, columnar islands, Barrett's endoscopic therapy, endoscopic resection, endoscopic ablation, systematic inspection, complete eradication of intestinal metaplasia, complete eradication of dysplasia, residual disease, recurrent disease, and failure of endoscopic therapy), and 4 classification systems (Prague, Los Angeles, Paris, and Barrett's International NBI Group). In round 1, 18 statements (78%) reached consensus, with 12 (67%) receiving strong agreement from more than half of the experts. In round 2, 4 of the remaining statements (80%) reached consensus, with 1 statement receiving strong agreement from 50% of the experts. In the third round, a consensus was reached on the remaining statement. CONCLUSIONS: We developed evidence-based, consensus-driven statements on endoscopic landmarks, definitions, and classifications of BE. These recommendations may facilitate global uniform reporting in BE.

A Dual-Responsive Artificial Skin for Tactile and Touchless Interfaces.

The progress from intelligent interactions and supplemented/augmented reality requires artificial skins to shift from the single-functional tactile paradigm. Dual-responsive sensors that can both detect pre-contact proximal events and tactile pressure levels enrich the perception dimensions and deliver additional cognitive information. Previous dual-responsive sensors show very limited utilizations only in proximity perception or approaching switches. Whereas, the approaching inputs from the environment should be able to convey more valuable messages. Herein, a flexible iontronic dual-responsive artificial skin is present. The artificial skin is sensitive to external object's applied pressure as well as its approaching, and can elicit information of target material categories encoded in the proximal inputs. Versatile applications are then demonstrated. Dual-mode human-machine interfaces are developed based on the devices, including a manipulation of virtual game characters, navigation and zooming in of electronic maps, and scrolling through electronic documents. More importantly, the proof-of-concept application of an entirely touchless material classification system is demonstrated. Three types of materials (metals, polymers, and human skins) are classified and predicted accurately. These features of the artificial skin make it highly promising for next-generation smart engineered electronics.

Empirico-inductive and/or hypothetico-deductive methods in food science and nutrition research: which one to favor for a better global health?

Scientific research generally follows two main methods: empirico-inductive (EI), gathering scattered, real-world qualitative/quantitative data to elaborate holistic theories, and the hypothetico-deductive (HD) approach, testing the validity of hypothesized theory in specific conditions, generally according to reductionist methodologies or designs, with the risk of over simplifying the initial complexity empirically perceived in its holistic view. However, in current food and nutrition research, new hypotheses are often elaborated from reductionist data obtained with the HD approach, and aggregated to form (ultra)reductionist theories, with no application of EI observations, limiting the applicability of these hypotheses in real life. This trend and the application of the EI method are illustrated as regards with the global health issue through the examples of food classifications/scoring, clinical studies, the definition of a sustainable diet, the "matrix effect"-related hypothesis, the concept of healthy core metabolism, and obesity prevention within the perspective of social sciences. To be efficient for producing food and nutritional data appropriable by the society, it finally appears that not only both approaches are necessary, starting with the EI method then the HD one, but also a back and forth between the two, this being not always realized, potentially leading to confusion and misunderstanding in society.

The impact of the time to last seizure before admission to the epilepsy monitoring unit (EMU) on epilepsy classifications.

INTRODUCTION AND BACKGROUND: The impact of the timing of the last seizure (TTLS) prior to admission to the epilepsy monitoring unit (EMU) on epilepsy classification is unclear for which we conducted this study. METHODS: We reviewed patients with epilepsy admitted to EMU between January 2021 and April 2022 and identified TTLS before EMU admission. We considered EMU yield as whether; it confirmed epilepsy classification, added new knowledge to the classification, or failed to classify epilepsy. RESULTS: We studied 156 patients. There were 72 (46%) men, with a mean age of 30. TTLS was divided according to a one- or three-month cutoff. We confirmed the pre-EMU epilepsy classification in 52 (33%) patients, learned new findings on epilepsy classification in 80 (51%) patients, and failed to classify epilepsy in 24 (15%) patients. Patients with "confirmed epilepsy classifications" reported seizures sooner to EMU admission than other groups (0.7 vs. 2.3 months, p-value = 0.02, 95% CI; -1.8, -1.3). Also, the odds of confirming epilepsy classification were more than two times in patients with TTLS within a month compared to those with TTLS of more than a month (OR = 2.4, p-value = 0.04, 95% CI; 1.1, 5.9). The odds were also higher when the 3-month TTLS cutoff was considered (OR = 6.2, p-value = 0.002, 95% CI; 1.6, 40.2). Confirming epilepsy classification was also associated with earlier seizures recorded at one- or three-month cutoff (OR = 2.1 and OR = 2.3, respectively, p-value = 0.05). We did not observe similar findings when we modified the classification or failed to reach a classification. CONCLUSIONS: The timing of the last seizure before EMU admission appeared to influence the yield of EMU and enhanced the confirmation of epilepsy classifications. Such findings can improve the utilization of EMU in the presurgical evaluation of patients with epilepsy.

Heart failure classifications via non-invasive pressure volume loops from echocardiography.

BACKGROUND: Left ventricular pressure-volume (LV-PV) loops provide comprehensive characterization of cardiovascular system in both health and disease, which are the essential element of the hemodynamic evaluation of heart failure (HF). This study attempts to achieve more detailed HF classifications by non-invasive LV-PV loops from echocardiography and analyzes contribution of parameters to HF classifications. METHODS: Firstly, non-invasive PV loops are established by time-varying elastance model where LV volume curves were extracted from apical-four-chambers view of echocardiographic videos. Then, 16 parameters related to cardiac structure and functions are automatically acquired from PV loops. Next, we applied six machine learning (ML) methods to divide four categories. On this premise, we choose the best performing classifier among machine learning approaches for feature ranking. Finally, we compare the contributions of different parameters to HF classifications. RESULTS: By the experimental, the PV loops were successfully acquired in 1076 cases. When single left ventricular ejection fraction (LVEF) is used for HF classifications, the accuracy of the model is 91.67%. When added parameters extracted from ML-derived LV-PV loops, the classification accuracy is 96.57%, which improved by 5.1%. Especially, our parameters have a great improvement in the classification of non-HF controls and heart failure with preserved ejection fraction (HFpEF). CONCLUSIONS: We successfully presented the classification of HF by machine derived non-invasive LV-PV loops, which has the potential to improve the diagnosis and management of heart failure in clinic. Moreover, ventriculo-arterial (VA) coupling and ventricular efficiency were demonstrated important factors for ML-based HF classification model besides LVEF.

Medical Imaging Technology and Imaging Agents.

Medical imaging is a technology that studies the interaction between human body and irradiations of X-ray, ultrasound, magnetic field, etc. and represents anatomical structures of human organs/tissues with the implication of irradiation attenuation in the form of grayscales. With these medical images, detailed information on health status and disease diagnosis may be judged by clinical physicians to determine an appropriate therapy approach. This chapter will give a systematic introduction on the modalities, classifications, basic principles, and biomedical applications of traditional medical imaging along with the types, construction, and major features of the corresponding contrast agents or imaging probes.

The Role of Epigenetics in Brain and Spinal Cord Tumors.

Identification of distinct genetic and epigenetic profiles in various neuroepithelial tumors has improved the classification and uncovered novel diagnostic, prognostic, and predictive molecular biomarkers for improved prediction of treatment response and outcome. Especially, in pediatric high-grade brain tumors, such as diffuse midline glioma, H3K27M-altered and posterior fossa group A-ependymoma, epigenetic changes predominate, along with changes in expression of known oncogenes and tumor suppressor genes induced by histone modifications and DNA methylation. The precise role of epigenetic abnormalities is important for understanding tumorigenesis and the establishment of brain tumor treatment strategies. Using powerful epigenetic-based therapies for cancer cells, the aberrantly regulated epigenome can be restored to a more normal state through epigenetic reprogramming. Combinations of agents targeting DNA methylation and/or other epigenetic modifications may be a promising cancer treatment. Therefore, the integration of multi-omics data including epigenomics is now important for classifying primary brain tumors and predicting their biological behavior. Recent advances in molecular genetics and epigenetic integrated diagnostics of brain tumors influence new strategies for targeted therapy.

Outpatient Opioid Dispensing Patterns for SC Medicaid Children 1-36 Months Old.

OBJECTIVES: We sought to identify the most common diagnostic categories linked to dispensed opioid prescriptions among children 1-36 months old and changes in patterns over the years 2000 to 2017. METHODS: This study used South Carolina's Medicaid claims data of pediatric dispensed outpatient opioid prescriptions between 2000 and 2017. The major opioid-related diagnostic category (indication) for each prescription was identified using visit primary diagnoses and the Clinical Classification System (AHRQ-CCS) software. The variables of interest were the rate of opioid prescriptions per 1,000 visits for each diagnostic category and the relative percentage of opioid prescriptions assigned to each category compared to all categories. RESULTS: Six major diagnostic categories were identified; Diseases of the respiratory system (RESP), Congenital anomalies (CONG), Injury (INJURY), Diseases of the nervous system and sense organs (NEURO), Diseases of the digestive system (GI), and Diseases of the genitourinary system (GU). The overall rate of dispensed opioid prescriptions per category declined significantly for four diagnostic categories throughout the study period, RESP by 15.13, INJURY by 8.49, NEURO by 7.33, and GI by 5.93. Two categories increased during the same time, CONG (by 9.47) and GU (by 6.98). RESP was the most prevalent category linked to a dispensed opioid prescription within 2010-2012 (almost 25%) but CONG was the most prevalent by 2014 (17.77%). CONCLUSIONS FOR PRACTICE: Among Medicaid children 1-36 months old, annual dispensed opioid prescription rates declined for most major diagnostic categories (RESP, INJURY, NEURO, and GI). Future studies should explore alternatives to current opioid dispensing practices for GU and CONG cases.

Brain Tumor Classification by Methylation Profile.

The goal of the methylation classifier in brain tumor classification is to accurately classify tumors based on their methylation profiles. Accurate brain tumor diagnosis is the first step for healthcare professionals to predict tumor prognosis and establish personalized treatment plans for patients. The methylation classifier can be used to perform classification on tumor samples with diagnostic difficulties due to ambiguous histology or mismatch between histopathology and molecular signatures, i.e., not otherwise specified (NOS) cases or not elsewhere classified (NEC) cases, aiding in pathological decision-making. Here, the authors elucidate upon the application of a methylation classifier as a tool to mitigate the inherent complexities associated with the pathological evaluation of brain tumors, even when pathologists are experts in histopathological diagnosis and have access to enough molecular genetic information. Also, it should be emphasized that methylome cannot classify all types of brain tumors, and it often produces erroneous matches even with high matching scores, so, excessive trust is prohibited. The primary issue is the considerable difficulty in obtaining reference data regarding the methylation profile of each type of brain tumor. This challenge is further amplified when dealing with recently identified novel types or subtypes of brain tumors, as such data are not readily accessible through open databases or authors of publications. An additional obstacle arises from the fact that methylation classifiers are primarily research-based, leading to the unavailability of charging patients. It is important to note that the application of methylation classifiers may require specialized laboratory techniques and expertise in DNA methylation analysis.