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PubMed1978

Protist classification and the kingdoms of organisms.

Traditional classification imposed a division into plant-like and animal-like forms on the unicellular eukaryotes, or protists; in a current view the protists are a diverse assemblage of plant-, animal- and fungus-like groups. Classification of these into phyla is difficult because of their relatively simple structure and limited geological record, but study of ultrastructure and other characteristics is providing new insight on protist classification. Possible classifications are discussed, and a summary classification of the living world into kingdoms (Monera, Protista, Fungi, Animalia, Plantae) and phyla is suggested. This classification also suggests groupings of phyla into superphyla and form-superphyla, and a broadened kingdom Protista (including green algae, oomycotes and slime molds but excluding red and brown algae). The classification thus seeks to offer a compromise between the protist and protoctist kingdoms of Whittaker and Margulis and to combine a full listing of phyla with grouping of these for synoptic treatment.

Animals↗

PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗

PubMed2026

IASLC Update on Classification of Pulmonary Neuroendocrine Neoplasms.

Since the publication of the 2021 WHO classification of thoracic tumors, our knowledge of pulmonary neuroendocrine neoplasms (NENs) has expanded significantly, particularly through the elucidation of molecular pathways and proposals to refine histopathologic classification. This expanded knowledge across all aspects of pulmonary NENs holds promise for more precise stratification of neuroendocrine tumors (NETs) and the potential development of novel, subtype-specific therapeutic strategies for all NENs. Based on our comprehensive review of the current pulmonary NEN landscape, our multidisciplinary expert panel has deliberated on the modification and updating of the 2021 classification, resulting in the proposal of a new pulmonary carcinoid/NET classification presented in this position paper, which incorporates the following three major points: (1) The proposed framework continues the shift from the traditional carcinoid terminology toward broader adoption of the "NET" nomenclature as found in other organ systems while retaining the term "carcinoid" as the primary diagnostic term to ensure clear communication with thoracic clinical providers. (2) Ki-67 has been incorporated as a diagnostic criterion, aligning with practices in other NET classifications. (3) There is formal recognition of the concept of "carcinoid/NET G3," a rare subset of lung carcinoids characterized by increased proliferative activity but with molecular features more aligned with pulmonary NETs than with high-grade neuroendocrine carcinomas. This position paper on the current knowledge of pulmonary NENs, including the proposed carcinoid/NET classification, will aid in accurate tumor categorization and guide treatment strategies.

Carcinoid tumor↗

PubMed2026

A module-based approach for post-omics, post-GWAS network-based gene classification.

MOTIVATION: Complex traits and diseases are highly polygenic and understanding the full set of genes involved is a central challenge in biomedicine. However, due to sample size limitations and noise (technical and biological), experimental approaches for disease-gene discovery such as transcriptomics and GWAS result in long, noisy, heterogeneous gene lists, which may be trimmed to a subset of likely relevant genes while leaving several false negatives. Computational gene classification approaches, especially those using genome-scale molecular interaction networks, are promising avenues for complementing such experimental findings by analytically expanding observed gene lists based on the functional relatedness between genes. We previously introduced the network-based gene classification approach, GenePlexus, which was rigorously benchmarked to show state-of-the-art performance, especially for predicting novel genes associated with biological processes and fine-grained phenotypes. Network-based gene classification performance,however, declines for diseases, especially when the inputs are omics and GWAS-based long gene lists. RESULTS: Here, we show that these disease gene lists span multiple biological processes spread across the molecular network, and we propose ModGenePlexus, a new network-based gene classification method that takes a two-stage approach. First, clustering and semi-supervised learning decomposes the input gene list into coherent, denoised network gene modules. Then, ModGenePlexus trains supervised (GenePlexus) classifiers for each module and aggregates predictions to return genome-wide rankings. We benchmarked ModGenePlexus across simulated data, transcriptomic signatures, and GWAS datasets (together spanning hundreds of diseases), showing improved recovery of known disease genes compared to GenePlexus. Beyond improved classification, the results of enrichment analysis of ModGenePlexus outputs are much more interpretable by virtue of revealing nuanced biological processes. Together, these results establish ModGenePlexus as a scalable, interpretable tool for gene classification of GWAS- and omics-derived gene lists across diverse biological contexts. AVAILABILITY AND IMPLEMENTATION: ModGenePlexus is freely available on GitHub at https://github.com/krishnanlab/ModGenePlexus, and the full source code and results supporting this study are available on Zenodo at https://zenodo.org/records/19857910.

Genome-Wide Association Study↗

PubMed1978

Utilization of the Goldberg MMPI profile classification rules for the assessment of psychopathology in different clinical populations.

MMPI profiles of 545 psychiatric inpatients and 560 incarcerated offenders were separated sequentially into normal, sociopathic, neurotic and psychotic groups by means of Goldberg's profile classification rules. Patient-prisoner differences in both rates of classification and profile patterns of groups within diagnostic categories were assessed quantitatively, and profiles were interpreted by use of standard MMPI codebooks. For the hospitalized Ss comparisons also were made between clinical diagnoses and Goldberg-MMPI classifications. The resulting differences in classification rates, similarities of profiles within diagnostic categories, correspondence between obtained and codebook-expected profiles, and agreement between clinical and Goldberg-MMPI classifications were not such that this approach could be used with confidence as a basis for establishing diagnostic group membership. Although Goldberg's system appears to represent certain improvements over previous criterion-related methods of profile classification, it was concluded that its value nonetheless is limited by the assumption of an invariant relationship between test patterns and nontest variables.

Adult↗

PubMed1978

Clinico-pathological correlations in the Kiel classification of non-Hodgkin's lymphomata in children.

The rapid development of chemotherapy and radiotherapy during the last decade makes an increasing demand for a reliable classification of malignant non-Hodgkin's lymphomas. This is especially important in children since in this age group the lymphomas show a much worse prognosis than in adults. An attempt at a modern classification is offered by the so-called Kiel classification. 38 children with non-Hodgkin's lymphomas previously classified according to Rappaport were re-evaluated according to the Kiel system. Reclassification was technically feasible in 26 patients. There was good agreement on typing between the two independently working cytopathologists. 3 patients proved to be cases of histiocytic medullary reticulosis. Among the remaining patients, a much larger variety of histological subgroups was seen than in the one published paediatric series of Lennert. 17 patients had high-grade malignant lymphomas with lymphoblastic lymphomas predominating. Some clinical correlations not detectable with the Rappaport classification were found using the Kiel system. 6 patients were judged as having low-grade malignant lymphomas but in this group the survival was poorer than expected and 1 patient showed leukaemic transformation. The Kiel classification makes high demands for adequate surgical techniques and preparatory routines. It seems to represent a step forward in the classification of non-Hodgkin's lymphomas but much more experience is needed, especially in children, in order to evaluate its role as a guide to differentiated therapy.

Child↗

PubMed1978

Classification of lymphomas.

Malignant lymphomas are neoplasms of cells of the lymphoreticular or immune system. Classification of these neoplasms has long been controversial and confusing. In recent years, considerable progress has been made in establishing useful and prognostically significant classifications of lymphomas. Currently, lymphomas may be divided into two main groups: Hodgkin's disease and non-Hodgkin's lymphomas. The Rye classification of Hodgkin's disease is now widely accepted and used throughout most of the world. In contrast, considerable conflict exists about the schemes of non-Hodgkin's lymphomas. The traditional classifications of non-Hodgkin's lymphomas currently used by most pathologists are based purely on morphologic grounds, and, despite the fact that they may be conceptually incorrect, they have often been shown to be useful for clinicopathologic studies. New or modern but yet untested schemes based not only on morphologic criteria, but also on recent immunologic techniques, have been proposed. This work will review the classifications of Hodgkin's disease and the non-Hodgkin's lymphomas, emphasizing the currently used schemes, describe the major modern classifications of lymphomas, and discuss and illustrate the subclasses of lymphomas and the differential diagnoses of the various types of lymphomas from nonlymphomatous proliferations which may mimic them.

Diagnosis, Differential↗

PubMed1975

[Study of interventricular septal defects with equal aortic and pulmonary artery pressures. Classification by clinical and computer methods of 70 cases].

Application of various methods of classification to a group of 70 cases of ventricular septal defect with high pulmonary artery hypertension allowed a comparative study between the various methods aiming at distinguishing the forms with low from high pulmonary artery resistance. The reference clinical classification provides supplementary informations derived from the natural or post-operative course and eventually from the microscopic examination. The first automatic classification relies on the study of a single criterion: the pulmonary arteriolar resistance and the systemic resistance ratio. A second classification is based on the attribution of points to some clinical or haemodynamic signs resulting in a score orienting the classification of every individual. Multifactorial analysis methods deal with all the available informations for the overall group, and suppose the use of a computer. The informatic methods make it possible to study the classifying value of every sign. Correlations were established between these various techniques and the medical classification.

Adolescent↗

PubMed2026

Gastric carcinoma classification in the WHO 6th edition (2026): Updated framework and emerging entities.

The sixth edition of the WHO Classification of Digestive System Tumours (2026) represents an important step in the continuing evolution of gastric carcinoma classification. While preserving morphology as the foundation of diagnosis, it incorporates advances in molecular pathology, genotype-phenotype correlations, tumour evolution, and predictive biomarker assessment. This review summarizes the development of the WHO classification from the third edition (2000) to the sixth edition (2026) and highlights its relationship with other major classification systems, including those of Laurén, Nakamura, and the Japanese Gastric Carcinoma Association (JGCA). Major histological categories remain largely unchanged; however, several important conceptual and diagnostic refinements have been introduced. These include recognition of crawling-type adenocarcinoma as a distinctive variant of tubular adenocarcinoma, subclassification of poorly cohesive carcinoma into signet-ring cell and non-signet-ring cell subtypes, introduction of the concept of pure signet-ring cell carcinoma, and increased emphasis on tumour evolution. The sixth edition also expands and refines the spectrum of uncommon gastric carcinoma subtypes, including gastric carcinoma with lymphoid stroma, AFP-producing carcinoma, micropapillary adenocarcinoma, gastric adenocarcinoma of fundic-gland type, and gastric sarcomatoid carcinoma. Crucially, molecular subgroups originally proposed by The Cancer Genome Atlas (TCGA) and actionable biomarkers-including HER2 (ERBB2), Claudin 18.2, mismatch repair deficiency/microsatellite instability (dMMR/MSI), and programmed death-ligand 1 (PD-L1)-have transitioned from research-based categories into essential tools for precision oncology. Rather than providing exhaustive diagnostic criteria, this review offers a conceptual framework and encourages consultation of the original WHO text for full details. These advances illustrate the transition of gastric carcinoma classification from a predominantly morphology-based system toward an integrated histomolecular framework that more closely links pathological diagnosis with tumour biology, prognostication, and therapeutic stratification.

Crawling-type adenocarcinoma↗

PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗

PubMed1979

Comparison between clinical and radiological classification of infants with the respiratory distress syndrome (RDS).

Clinical and radiological classifications of the severity of the respiratory distress syndrome (RDS) were made in 55 infants. According to the clinical classification 17 infants belonged to the first class (mild RDS), 22 to the second (moderate RDS), and 16 to the third class (severe RDS). In the classification based on radiological findings the numbers of infants in classes 1, 2 and 3 were 18, 19 and 18 respectively. On the basis of both the clinical and radiological findings, 11 infants belonged to the mild RDS class, 11 to the moderate, and 12 to the severe RDS class. Thus, 34 infants had the same clinical and radiological classification. In 21 infants there were discrepancies between the clinical and the radiological classifications, but only one infant with the most severe radiological findings belonged to the mild RDS class and only one infant with mild radiological findings belonged to the worst RDS class.

Humans↗

PubMed1975

Proceedings: Prognosis of non-Hodgkin's lymphomas with special emphasis on the staging classification.

The prognosis of the non-Hodgkin's lymphomas is determined by 1. the pattern of origin and spread which can be demonstrated in a staging classification, 2, the histopathological type, and 3. the effectiveness and scope of the treatment methods, particularly radio- and chemo-therapy. In the following paper the Ann Arbor Classification, which was originally conceived of for both disease groups (Hodgkin's and non-Hodgkin's lymphomas), is discussed particularly with respect to the applicability and prognostic evaluation for the non-Hodgkin's lymphomas. The Ann Arbor Classification may in essence reflect the oncological characteristics of the non-Hodgkin's accurately; there are, however, a number of findings with qualitative and quantitative differences which defy integration into the Ann Arbor Classification. The qualitative differences consist of the differing lymphatic and extralymphatic origins and their consequence for spread and prognosis. The quantitative differences refer to the varying patterns of distribution of the different stages of spreading, whereby the dissemination stages in the non-Hodgkin's lymphomas are more dependent on the histological form than is the case with the Hodgkin's lymphomas, and thus must play a greater role in the prognostic evaluation and indication for treatment. Suggestions have been made for a modification of the Ann Arbor Staging Classification for the non-Hodgkin's lymphomas.

Humans↗

PubMed1977

Development of a chemical use classification system to facilitate reporting under the Toxic Substances Control Act.

A classification system was developed to enable manufacturers and processors of industrial chemicals to report categories of proposed categories of use of such chemicals to the Environmental Protection Agency in accordance with the Toxic Substances Control Act. To accommodate the two aspects of chemical use (i.e., function and application), a faceted classification scheme was designed. The function facet contains categories denoting the action for which a chemical is specially fitted or used, for example, adhesives or fuels. The application facet contains categories denoting the process or product in which a chemical is used, such as synthetic rubber manufacture. Linking these two facets in a single notation code provides a comprehensive indication of a chemical's use or uses. A variety of existing relevant classification schemes and reference tools were used as input sources for the chemical use classification. The practicability of the classification system was tested using a small sample of manufacturing and processing companies.

Chemical Phenomena↗

PubMed2026

MegaPlantTF: a machine learning framework for comprehensive identification and classification of plant transcription factors.

MOTIVATION: Understanding the role of transcription factors (TFs) in plants is essential for the study of gene regulation and various biological processes. However, both TF detection and classification remain challenging due to the great diversity and complexity of these proteins. Conventional approaches, such as BLAST, often suffer from high computational complexity and limited performance on less common TF families. RESULTS: We introduce MegaPlantTF, the first comprehensive machine learning and deep learning framework for the prediction (TF versus non-TF) and classification (family-level) of plant TFs. Our method employs k-mer-based protein representations and a two-stage architecture combining a deep feed-forward neural network with a stacking ensemble classifier. To ensure robust performance assessment, we report micro-, macro-, and weighted-average performance metrics, providing a holistic evaluation of both frequent and underrepresented TF families. Additionally, we employ threshold-based evaluation to calibrate confidence in TF detection. The results show that MegaPlantTF achieves strong accuracy and precision, particularly with a k-mer size of 3 and a classification threshold of 0.5, and maintains stable performance even under stringent thresholds. In addition to the standard cross-validation tests, a use case study on Sorghum bicolor confirms that our method performs strongly in the genome-wide analysis, making it highly suitable for large-scale TF identification and classification tasks. MegaPlantTF represents a novel contribution by integrating k-mer encoding, binary family-specific classifiers, and a two-stage stacking ensemble into a unified, reproducible framework for large-scale plant TF identification and classification. AVAILABILITY AND IMPLEMENTATION: MegaPlantTF is freely accessible through a public web server available at https://bioinformatics.um6p.ma/MegaPlantTF. The complete source code, including pretrained models and example datasets, is available at https://github.com/Bioinformatics-UM6P/MegaPlantTF.

Transcription Factors↗

PubMed2026

Advances in the diagnosis and classification of B-ALL: comparative insights from updated guidelines.

Accurate molecular classification is essential for diagnosis, risk stratification, and treatment selection in B-cell lymphoblastic leukemia (B-ALL). In this study, we performed a comprehensive, real-world reclassification of 1015 consecutively diagnosed B-ALL patients using the fifth edition of the World Health Organization Classification of Haematolymphoid Tumours (WHO-HAEM5) and the International Consensus Classification (ICC). An integrative genomic strategy that combined whole transcriptome sequencing, fusion detection, mutational analysis, and cytogenetics enabled reclassification according to both the WHO-HAEM5 and ICC frameworks, thereby substantially reducing the proportion of unclassifiable B-ALL from 41.9% (2016 WHO revision [WHO-HAEM4R]) to 15.9% (WHO-HAEM5) and 11.9% (ICC). Distinct clinical and prognostic features were identified across newly defined subtypes. Multivariable analysis confirmed that this genomic classification is a robust, independent predictor of survival after adjusting for age, minimal residual disease status, and transplant intervention. Specifically, HLF-rearranged and MEF2D-rearranged B-ALL conferred a persistently poor prognosis across all age groups despite allogeneic hematopoietic stem cell transplantation, highlighting an urgent need for novel therapeutic strategies. Gene expression profiling resolved cryptic subtypes, including ETV6::RUNX1-like, ZNF384-rearranged-like, and BCR::ABL1-like B-ALL, and uncovered diagnostic ambiguity in patients with concurrent lesions. In addition, we report emerging high-risk groups, including IDH1/2- and ZEB2 Q1072-mutated B-ALL, that may warrant recognition as distinct molecular entities. Our findings demonstrate the clinical use of integrative transcriptomic profiling in refining B-ALL taxonomy in guiding risk-adapted therapies and informing future revisions of diagnostic standards. This study supports the incorporation of high-throughput molecular diagnostics into routine leukemia classification and precision treatment planning.

Humans↗

PubMed1976

[Classification and diagnosis of ankle injuries].

A new method for the classification of the injuries of the ankle is recommended by the author. The main types according to his classifixation are the following: pronation-flexion, pronation-extension, supination-extension, supination-flexion and supination-extension types. His classification is compared with Lauge-Hansen's and Weber's classification. Critical analysis of these two last classifications is given. The aim of the author's classification is to render help to the doctors for their every-days' curative work. The characteristic symptoms of the pronation and supination, resp., injuries are described. Attention is drawn to "Weber's lace"--this denomination is proposed by the author, since the first description is due to Weber. On the basis of the author's examinations described in his candidate's dissertation "syndesmolysis trigonum"--pathognostic for syndesmolysis--is dealt with. The "reclined" roentgenograms are dealt with. The so-called pronation reclined roentgenogram visualize the rupture of the deltoid ligament and the syndesmolysis in the same time. The sagittal reclined roentgenogram is dealth with separately, by means of which the "table-drawer" symptom may be produced.

Ankle Injuries↗

PubMed1976

[TNM-classification of oral cavity neoplasms the value of clinically measurable factors (TN)].

Problems associated with the classification of cavum oris and labial carcinomata were discussed with particular reference to patients admitted to and treated in seven different clinical hospitals where identical methods of diagnosis and case history evaluation had been used. Using electronic data processing and biostatistical methods it was possible to study the effects of two clinically detectable factors (growth of primary tumor and degree of regional metastasizing) on both the prognosis and classification according to UICC rules. It was possible to show that a determination of the size of primary tumor (T) alone was not sufficient for three homogeneous, prognostically different collectives of tumors to be satisfactorily classified by the new UICC rules. It has been shown that a classification of three collectives of tumors (new classification according to UICC rules), especially as regards the proportion of N3 metastases within the collectives of tumors, was made possible. Therefore, it will be necessary to study the prognostic influences of additional clinically determinable factors with a view to arriving at a useful and practicable classification of oral cavity carcinomata.

Aged↗

PubMed1977

Rappaport classification of non-Hodgkin's lymphoma: histologic features and clinical significance.

The architectural arrangement of the neoplastic cells and their cytologic identification form the histologic basis of the Rappaport classification of non-Hodgkin's lymphomas clinical studies have shown the favorable prognosis of the nodular lymphomas while the diffuse lymphomas irrespective of cell type have a poor prognosis. Several recent studies have shown that pathologists can identify the nodular and diffuse patterns with a high degree of reproducibility. The cytologic subclassification has, however, not achieved a similar high degree of reproducibility. The Southwest Oncology Group study has shown the most reproducible subgroups to be the nodular poorly differentiated lymphocytic malignant lymphoma (ML) and the diffuse histiocytic ML. The clinical significance of the Rappaport classification when applied to childhood lymphomas is not as clear as in adult lymphomas. In view of the recent description of a new clinicopathologic entity primarily in children and adolescents (ie, lymphoblastic ML), IT IS APPARENT THAT THE CHILDHOOD LYMPHOMAS Will have to be examined more critically in order to determine the clinical significance of this classification. Although some have proposed new classifications of these lymphomas based upon immunologic identification of cell origin, none have been shown to be of clinical significance. Based on recent immunologic and clinical studies, a modified classification of the non-Hodgkin's lymphoma is proposed which does not alter its clinical usefulness.

Burkitt Lymphoma↗