A case of myxopapillary ependymoma with predominant giant cell morphology: A rare entity with comprehensive genomic profiling and review of literature.

In the evolving landscape of ependymoma classification, which integrates histological, molecular, and anatomical context, we detail a rare case divergent from the usual histopathological spectrum. We present the case of a 37-year-old man with symptomatic spinal cord compression at the L3-L4 level. Neuroradiological evaluation revealed an intradural, encapsulated mass. Histologically, the tumor displayed atypical features: bizarre pleomorphic giant cells, intranuclear inclusions, mitotic activity, and a profusion of eosinophilic cytoplasm with hyalinized vessels, deviating from the characteristic perivascular pseudorosettes or myxopapillary patterns. Immunohistochemical staining bolstered this divergence, marking the tumor cells positive for glial fibrillary acidic protein and epithelial membrane antigen with a characteristic ring-like pattern, and CD99 but negative for Olig-2. These markers, alongside methylation profiling, facilitated its classification as a myxopapillary ependymoma (MPE), despite the atypical histologic features. This profile underscores the necessity of a multifaceted diagnostic process, especially when histological presentation is uncommon, confirming the critical role of immunohistochemistry and molecular diagnostics in classifying morphologically ambiguous ependymomas and exemplifying the histological diversity within MPEs.

Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment.

This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses. The review covers AI techniques, from classical machine learning to deep learning, highlighting current applications and challenges. Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes. Ethical, legal, and social implications are discussed, advocating for transparency and fairness in AI integration for neuro-oncology and providing a holistic understanding of its transformative impact on patient care.

Extracavitary primary effusion lymphoma presenting as a solitary brain mass.

Primary effusion lymphoma (PEL) is an uncommon B-cell lymphoma associated with human herpesvirus 8 and comprises 3-4% of all HIV-related lymphomas. It traditionally presents as a pleural, pericardial, and/or peritoneal effusion, though it can occasionally manifest as an extracavitary or solid mass in the absence of an effusion. The extracavitary or solid variant of primary effusion lymphoma has been reported in the skin, gastrointestinal tract, lung, and lymph nodes. However, very few cases have been reported in the central nervous system. We describe a case of extracavitary or solid variant of primary effusion lymphoma presenting as a brain mass in an HIV-positive man, highlighting the clinicopathologic and immunophenotypic findings of a rare entity.

Primary effusion lymphoma (PEL) is an uncommon and aggressive form of large B-cell lymphoma with a grim outlook, making up less than 1% of all lymphomas. PEL is linked to human herpesvirus 8 and predominantly impacts individuals with HIV or weakened immune systems. The typical presentation of PEL involves cancerous fluid accumulating in the chest or abdominal cavities. Occasionally, PEL can appear as a solid mass outside these cavities, termed extracavitary PEL (EC-PEL). The case we are describing highlights the difficulties in diagnosing PEL/EC-PEL. It is crucial for healthcare providers to consider EC-PEL when dealing with human herpesvirus 8-positive B-cell lymphomas, especially when patients have weakened immune systems and an unusual clinical scenario involving a solid mass, as seen in this case.

Association between human leukocyte antigen alleles and COVID-19 disease severity.

BACKGROUND: the human leukocyte antigen (HLA) loci have been widely characterized to be associated with viral infectious diseases. Several studies including various ethnic groups and populations suggested associations between certain HLA alleles and SARS-CoV-2 infection. Despite the numerous associations identified, the role of HLA polymorphisms in determining the individual response to SARS-CoV-2 infection is controversial among different Saudi populations. METHOD: Here, we performed HLA typing by next-generation sequencing to investigate if variations in polymorphic HLA genes are linked to COVID-19 severity in the Saudi population. Namely, we analyzed HLA loci at allele level in 575 Saudi patients with SARS-CoV-2 infection. HLA class I and class II frequencies in patients were compared with allele frequency data from healthy Saudi population. RESULTS: in our cohort HLA-A* 02:01:01 G was associated with mild disease but was not associated with moderate and severe disease. HLA-B* 51:01:01 G was protective from severe disease while HLA-B* 50:01:01 G, HLA-C* 06:02:01 G and HLA-DRB1 * 07:01:01 G were associated with risk to severe disease as well as the total COVID-19 cohort. HLA-DRB1 * 15:01:01 G was associated with risk to all severity groups. CONCLUSION: in conclusion, we found significant associations between HLA alleles and COVID-19 disease severity in Saudis. Further studies are warranted to include HLA typing in the workup for any new COVID-19 patients.

CffDNA screening for Niemann-pick disease, type C1: a case series.

Cell-free fetal DNA (cffDNA) screening is a valuable tool in clinical practice for detecting chromosomal abnormalities and autosomal dominant (AD) conditions. This study introduces a novel proof-of-concept assay designed for autosomal recessive (AR) cffDNA screening, focusing on cases involving the NPC1 gene. We aim to illustrate the significant benefits of AR cffDNA screening in managing high-risk pregnancies, specifically where biallelic pathogenic variants in NPC1 cause Niemann-Pick disease, type C1 (NPC), a disorder marked by progressive neurodegeneration. Three participants for this study were recruited and gave consent to a hospital in Saudi Arabia. These participants were either carriers of NPC or had a first- or second-degree relative affected by the disorder. No specific criteria were set for the age of the participants. All were between 15 and 18 weeks of gestation. Using amplicon-based next-generation sequencing (NGS), we analyzed the zygosity and variants in cffDNA extracted from maternal peripheral blood. After amplicon NGS, analysis was completed by a custom data analysis pipeline that included in-house-built data processing scripts and commonly used software packages. Importantly, the results were not disclosed to the patients. Our findings showed that in all three cases, AR cffDNA screening results were consistent with standard invasive diagnostic testing. This screening method offers several advantages: it provides critical information to families earlier in the pregnancy compared to invasive diagnostic tests, and it helps to alleviate parental anxiety. Moreover, this non-invasive method can determine pregnancy status in the first trimester for known familial variants. Future research may extend this approach to screen for known disease-causing variants in common AR conditions.

Analytical code sharing practices in biomedical research.

Data-driven computational analysis is becoming increasingly important in biomedical research, as the amount of data being generated continues to grow. However, the lack of practices of sharing research outputs, such as data, source code and methods, affects transparency and reproducibility of studies, which are critical to the advancement of science. Many published studies are not reproducible due to insufficient documentation, code, and data being shared. We conducted a comprehensive analysis of 453 manuscripts published between 2016-2021 and found that 50.1% of them fail to share the analytical code. Even among those that did disclose their code, a vast majority failed to offer additional research outputs, such as data. Furthermore, only one in ten articles organized their code in a structured and reproducible manner. We discovered a significant association between the presence of code availability statements and increased code availability. Additionally, a greater proportion of studies conducting secondary analyses were inclined to share their code compared to those conducting primary analyses. In light of our findings, we propose raising awareness of code sharing practices and taking immediate steps to enhance code availability to improve reproducibility in biomedical research. By increasing transparency and reproducibility, we can promote scientific rigor, encourage collaboration, and accelerate scientific discoveries. We must prioritize open science practices, including sharing code, data, and other research products, to ensure that biomedical research can be replicated and built upon by others in the scientific community.

Packaging and containerization of computational methods.

Methods for analyzing the full complement of a biomolecule type, e.g., proteomics or metabolomics, generate large amounts of complex data. The software tools used to analyze omics data have reshaped the landscape of modern biology and become an essential component of biomedical research. These tools are themselves quite complex and often require the installation of other supporting software, libraries and/or databases. A researcher may also be using multiple different tools that require different versions of the same supporting materials. The increasing dependence of biomedical scientists on these powerful tools creates a need for easier installation and greater usability. Packaging and containerization are different approaches to satisfy this need by delivering omics tools already wrapped in additional software that makes the tools easier to install and use. In this systematic review, we describe and compare the features of prominent packaging and containerization platforms. We outline the challenges, advantages and limitations of each approach and some of the most widely used platforms from the perspectives of users, software developers and system administrators. We also propose principles to make the distribution of omics software more sustainable and robust to increase the reproducibility of biomedical and life science research.

Fusing Peptide Epitopes for Advanced Multiplex Serological Testing for SARS-CoV-2 Antibody Detection.

The tragic COVID-19 pandemic, which has seen a total of 655 million cases worldwide and a death toll of over 6.6 million seems finally tailing off. Even so, new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continue to arise, the severity of which cannot be predicted in advance. This is concerning for the maintenance and stability of public health, since immune evasion and increased transmissibility may arise. Therefore, it is crucial to continue monitoring antibody responses to SARS-CoV-2 in the general population. As a complement to polymerase chain reaction tests, multiplex immunoassays are elegant tools that use individual protein or peptide antigens simultaneously to provide a high level of sensitivity and specificity. To further improve these aspects of SARS-CoV-2 antibody detection, as well as accuracy, we have developed an advanced serological peptide-based multiplex assay using antigen-fused peptide epitopes derived from both the spike and the nucleocapsid proteins. The significance of the epitopes selected for antibody detection has been verified by in silico molecular docking simulations between the peptide epitopes and reported SARS-CoV-2 antibodies. Peptides can be more easily and quickly modified and synthesized than full length proteins and can, therefore, be used in a more cost-effective manner. Three different fusion-epitope peptides (FEPs) were synthesized and tested by enzyme-linked immunosorbent assay (ELISA). A total of 145 blood serum samples were used, compromising 110 COVID-19 serum samples from COVID-19 patients and 35 negative control serum samples taken from COVID-19-free individuals before the outbreak. Interestingly, our data demonstrate that the sensitivity, specificity, and accuracy of the results for the FEP antigens are higher than for single peptide epitopes or mixtures of single peptide epitopes. Our FEP concept can be applied to different multiplex immunoassays testing not only for SARS-CoV-2 but also for various other pathogens. A significantly improved peptide-based serological assay may support the development of commercial point-of-care tests, such as lateral-flow-assays.