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Glioma represents the most common central nervous system neoplasm in adults. Current classification scheme utilizes molecular alterations, particularly IDH1.R132H, to stratify lesions into distinct prognostic groups. Identification of the single nucleotide variant through traditional tissue biopsy assessment poses procedural risks and does not fully reflect the heterogeneous and evolving tumor landscape. Here, we introduce a liquid biopsy assay, mt- IDH1 dx . The blood-based test allows minimally invasive detection of tumor-derived extracellular vesicle RNA using only 2 ml plasma volume. We perform rigorous, blinded validation testing across the study population ( n  = 133), comprising of IDH1.R132H patients ( n  = 80), IDH1 wild-type gliomas ( n  = 44), and age matched healthy controls ( n  = 9). Results from our plasma testing demonstrate an overall sensitivity of 75.0% (95% CI: 64.1%–84.0%), specificity 88.7% (95% CI: 77.0%–95.7%), positive predictive value 90.9%, and negative predictive value 70.1% compared to the tissue gold standard. In addition to fundamental diagnostic applications, the study also highlights the utility of mt- IDH1 dx platform for blood-based monitoring and surveillance, offering valuable prognostic information. Finally, the optimized workflow enables rapid and efficient completion of both tumor tissue and plasma testing in under 4 hours from the time of sampling. Efficient and non-invasive, liquid biopsy methods could greatly improve the molecular classification of gliomas. Here, the authors develop an RNA-based Droplet Digital PCR assay to detect the key IDH1.R132H mutation in plasma-derived extracellular vesicles from glioma patients with high sensitivity, allowing accurate diagnosis, prognostication and longitudinal monitoring.

Over the last 20 years, gliomas have made up over 89% of malignant CNS tumor cases in the American population (NIH SEER). Within this, glioblastoma is the most common subtype, comprising 57% of all glioma cases. Being highly aggressive, this deadly disease is known for its high genetic and phenotypic heterogeneity, rendering a complicated disease course. The current standard of care consists of maximally safe tumor resection concurrent with chemoradiotherapy. However, despite advances in technology and therapeutic modalities, rates of disease recurrence are still high and survivability remains low. Given the delicate nature of the tumor location, remaining margins following resection often initiate disease recurrence. Photodynamic therapy (PDT) is a therapeutic modality that, following the administration of a non-toxic photosensitizer, induces tumor-specific anti-cancer effects after localized, wavelength-specific illumination. Its effect against malignant glioma has been studied extensively over the last 30 years, in pre-clinical and clinical trials. Here, we provide a comprehensive review of the three generations of photosensitizers alongside their mechanisms of action, limitations, and future directions.

Abstract m6AnetAnalyzer is an R package that streamlines post-processing and interpretation of site-level m6A predictions from m6Anet. It summarizes m6A distributions across transcripts, genes, biotypes, and transcript regions, and enables functional annotation using user-provided BED files or built-in datasets, including RNA-binding proteins and SNPs. Condition-specific changes in m6A methylation are quantified using the log2-transformed weighted modification ratio, with statistical tests applied when appropriate to identify significant differential methylation. By integrating differential gene expression data, m6AnetAnalyzer links methylation changes with expression differences, offering biotype- and region-specific insights into how m6A localization patterns relate to transcriptional regulation. Availability  and implementation m6AnetAnalyzer is freely available at https://github.com/hannalee809/m6AnetAnalyzer. It is compatible with Linux, macOS, and Windows platforms. Detailed installation instructions, example input and output files, and a step-by-step analysis workflow are provided in the package vignette.

Characterizing the m6A epigenetic landscape is essential for understanding glioma biology, yet transcriptome-wide mapping of these modifications at isoform resolution across specific tumor subtypes has remained limited. Conventional short-read approaches lack the capacity to resolve full-length transcript isoforms or assign m6A modifications to individual transcripts, representing a critical gap in glioma where alternative splicing is pervasive. We performed direct RNA nanopore sequencing and transcriptome-wide m6A analysis in 14 glioma tumor tissues, including IDH1-mutant astrocytoma, oligodendroglioma, and IDH1 wild-type glioblastoma, enabling isoform-resolved profiling not accessible by conventional short-read approaches. m6A sites were predicted computationally using the m6Anet deep learning framework, which has been independently benchmarked against MeRIP-seq-derived sites, and high-confidence calls were defined at a probability threshold of ≥0.9 and required detection across multiple patients within each subtype. IDH1-mutant gliomas showed a higher overall burden of computationally inferred m6A-modified sites, transcripts, and genes than IDH1 wild-type glioblastoma, along with variation in transcript biotypes, regional distribution of m6A sites, and extent of isoform methylation. Differential methylation analysis identified subtype-specific patterns of m6A localization, many of which were observed without corresponding changes in gene-level expression, indicating that m6A variation represents a post-transcriptional regulatory layer not captured by gene-level analysis alone. Integration of gene expression, isoform usage, and m6A status further identified variation in isoform composition and transcript features between astrocytoma and glioblastoma. Analysis of m6A regulators showed subtype-associated expression patterns among readers, writers, and erasers, and exploratory analyses identified isoform-level associations with survival that were not apparent at the gene level. Overall, these data describe subtype-specific patterns of m6A marking and isoform architecture across glioma tissues, derived from computational inference using direct RNA sequencing in a modestly sized cohort and warrant validation by orthogonal methods in larger studies. These findings are consistent with concurrent independent evidence that isoform-specific m6A deposition is evolutionarily conserved across mammals and that long-read isoform resolution reveals transcript diversity in glioma not captured by gene-level analysis. While cohort size and the absence of orthogonal site-level validation suggest that the data require cautious interpretation, this work provides a hypothesis-generating resource and methodological framework for future mechanistic and translational investigation of the glioma epitranscriptome.

m6ASeqTools is an R package designed to streamline the post-processing and interpretation of site-level m6A predictions from m6Anet. It provides descriptive summaries of m6A distribution across transcripts, genes, biotypes, and transcript regions, and enables condition comparisons through a calculated weighted modification ratio. By integrating differential gene expression data, the package links methylation changes with expression differences, providing biotype-specific and region-specific insights into how m6A localization patterns relate to transcriptional regulation.

N6-methyladenosine (m6A) is the most prevalent internal mRNA modification, enriched in the CNS yet poorly characterized in glioma. Using long-read RNA sequencing, we mapped m6A in an glioma model following knockdown (KD) of the reader IGF2BP2, writer METTL3, and eraser ALKBH5, with naive glioma cells and astrocytes as controls. Glioma cells exhibited a two-fold reduction in global m6A, suggesting progressive loss from healthy to malignant states. Integrated analysis revealed that m6A mediated control of gene expression is influenced by modification topology (CDS:3'UTR), transcript biotype, and length. Regulator KD, particularly ALKBH5 induced redistribution of m6A toward 3'UTR with consequent gene upregulation. We also identified m6A-mediated isoform switching, with a higher usage of retained intron and nonsense-mediated decay isoforms. Structural and splicing alterations at the isoform level were identified unique to each KD condition indicating m6A driven aberrant alternative splicing. At the functional level, KD specific remodeling of oncogenic signaling was also observed. ALKBH5 KD suppressed MYC targets and pro-apoptotic signaling while METTL3 KD enhanced mTOR and PI3K-AKT signaling. Collectively, these results demonstrate that m6A mediated regulation in glioma is highly context-dependent, defining distinct clinically relevant phenotypes. This has implications for future biomarker discovery and development of targeted therapeutics.

In this study, a drug repurposing strategy was implemented with the aim of identifying new trypanocidal agents against Trypanosoma cruzi (T. cruzi). A total of 924 Food and Drug Administration (FDA)-approved drugs were screened by molecular docking on three sites of trypanothione synthetase (TS), including the catalytic site, a blind docking site, and a potential allosteric site. Selected compounds were further evaluated through in vitro and in vivo assays. Tadalafil, Zafirlukast, Raltegravir, and Olmesartan had better trypanocidal activity than the reference drugs Benznidazole and Nifurtimox in the in vitro evaluation against the trypomastigote form. Additionally, these drugs were able to decrease parasitemia by 20–50% in mice in an acute treatment. Molecular dynamics simulations (MDS) at 120 ns helped link findings from in vitro/in vivo experiments to a potential mechanism of action targeting T. cruzi trypanothione synthetase (TcTS). Therefore, the results encourage the use of these drugs to develop new anti-T. cruzi agents.

The paper presents the most comprehensive and large-scale global study to date on how higher education students perceived the use of ChatGPT in early 2024. With a sample of 23,218 students from 109 countries and territories, the study reveals that students primarily used ChatGPT for brainstorming, summarizing texts, and finding research articles, with a few using it for professional and creative writing. They found it useful for simplifying complex information and summarizing content, but less reliable for providing information and supporting classroom learning, though some considered its information clearer than that from peers and teachers. Moreover, students agreed on the need for AI regulations at all levels due to concerns about ChatGPT promoting cheating, plagiarism, and social isolation. However, they believed ChatGPT could potentially enhance their access to knowledge and improve their learning experience, study efficiency, and chances of achieving good grades. While ChatGPT was perceived as effective in potentially improving AI literacy, digital communication, and content creation skills, it was less useful for interpersonal communication, decision-making, numeracy, native language proficiency, and the development of critical thinking skills. Students also felt that ChatGPT would boost demand for AI-related skills and facilitate remote work without significantly impacting unemployment. Emotionally, students mostly felt positive using ChatGPT, with curiosity and calmness being the most common emotions. Further examinations reveal variations in students’ perceptions across different socio-demographic and geographic factors, with key factors influencing students’ use of ChatGPT also being identified. Higher education institutions’ managers and teachers may benefit from these findings while formulating the curricula and instructions/regulations for ChatGPT use, as well as when designing the teaching methods and assessment tools. Moreover, policymakers may also consider the findings when formulating strategies for secondary and higher education system development, especially in light of changing labor market needs and related digital skills development.

Fungal fermentation of food and agricultural by-products holds promise for improving food sustainability and security. However, the molecular basis of fungal waste-to-food upcycling remains poorly understood. Here we use a multi-omics approach to characterize oncom, a fermented food traditionally produced from soymilk by-products in Java, Indonesia. Metagenomic sequencing of samples from small-scale producers in Western Java indicated that the fungus Neurospora intermedia dominates oncom. Further transcriptomic, metabolomic and phylogenomic analysis revealed that oncom-derived N. intermedia utilizes pectin and cellulose degradation during fermentation and belongs to a genetically distinct subpopulation associated with human-generated by-products. Finally, we found that N. intermedia grew on diverse by-products such as fruit and vegetable pomace and plant-based milk waste, did not encode mycotoxins, and could create foods that were positively perceived by consumers outside Indonesia. These results showcase the traditional significance and future potential of fungal fermentation for creating delicious and nutritious foods from readily available by-products. A multi-omics analysis of oncom, an Indonesian fermented food made from soymilk waste, shows how associated fungi break down food waste to yield nutritious and positively received foods.

Background: Chagas disease is a major public health problem, especially in Latin American countries, and benznidazole and nifurtimox are currently the only drugs available for its treatment. However, they present several disadvantages, such as low availability, high toxicity, and limited efficacy, which often result in treatment discontinuation. In recent decades, bioinformatics studies have accelerated the field of drug repurposing, reducing time and costs. In this study, the aim was to identify novel cruzain inhibitors from the analogs of FDA-approved drugs with trypanocidal activity. Methods: A ligand-based virtual screen, along with molecular docking analysis, was carried out, and the selected compounds were evaluated for their trypanocidal activity against trypomastigotes of two endemic Mexican strains and their inhibitory activity on cysteine proteases. Results: A cefsulodin analog (LC50 = 126.18 and 77.50 µM), two flucloxacillin analogs (LC50 = 94.05 and 101.73 µM; 48.74 and 64.49 µM), and one piperacillin analog (LC50 = 48.46 and 83.68 µM) had better trypanocidal activity and selectivity index against the NINOA and INC-5 strains than the reference drugs. Enzymatic evaluation showed that all four compounds inhibited cysteine proteases (IC50 < 840.03 µM). Furthermore, molecular dynamics simulations predicted the stability of the compound–protein complex, while the docking test on human cathepsin L predicted their potential selectivity. Finally, our in silico analysis of ADMET properties showed that all compounds exhibited favorable profiles. Conclusions: These results encourage the development of new and more potent anti-Trypanosoma cruzi agents using FDA-approved drugs as scaffolds.