Biology subjects

Ibrahim, A.

Publications and source records attributed to Ibrahim, A..

At least 19 recordsLinked to original sources

bioRxiv2026

CARD:Epi - Contextualizing Antimicrobial Resistance Determinants Using Deep Learning Language Models

Bacterial outbreak publications outline the key factors involved in the uncontrolled spread of infection. Such factors include the environment, pathogens, hosts, and antimicrobial resistance genes (ARGs). Individually, each paper published in this area gives a glimpse into the devastating impact drug resistant infections have on healthcare, agriculture, and livestock. When examined together, these publications provide contextual information on ARG transmission, from the discovery of new resistance genes to their dissemination to different pathogens, hosts, and environments. We have extracted this information from publications in PubMed by using the biomedical deep-learning language model, BioBERT. We trained BioBERT on two tasks: entity recognition to identify AMR-relevant terms (i.e., ARGs, taxonomy, environments, geographical locations, etc.) and relation extraction to determine which terms identified through entity recognition contextualize ARGs. By collating results from 204,094 antimicrobial resistance publications worldwide, we have generated interpretable results about the sources where genes are commonly found. To visualize the dataset, we have created two pipelines to analyze transmission patterns of ARGs across agriculture, environments, and human populations using a Confusogram and Uniform Manifold Approximation and Projection. Overall, we have taken a large-scale approach to collect antimicrobial resistance data from a commonly overlooked resource, i.e., the systematic examination of the large body of AMR literature and have visualized how scientific literature can be used to assess transmission patterns of ARGs.

genomics↗

bioRxiv2026

AI semantics for biomedical data integration

Researchers increasingly need to explore hypotheses that span multimodal data across different scales, organisms, and domains. In practice, this requires connecting knowledge across fragmented databases with incompatible APIs and heterogeneous annotation practices. Large language model (LLM) agents can automate this data integration process, but grounding LLM agent outputs in scientifically correct sources of truth remains a significant challenge. Here we describe our deployment of a novel AI semantics workflow using LLM agents to enable scalable data integration, grounded in biological knowledge in the form of ontologies. Our workflow comprises (1) a multi-agent system curating scientific knowledge across ontologies using the Ontology Lookup Service (OLS) as grounding; (2) an LLM embedding service to enable interoperability between scientific databases by mapping ontology terms; and (3) GrEBI, a knowledge graph and Model Context Protocol (MCP) server enabling LLM agents to conduct cross-cutting, multi-omic biomedical queries.

bioinformatics↗

bioRxiv2026

PREVALENCE OF FUSARIUM WILT (Fusarium oxysporum f. sp. Lycopersici) on TOMATO (Solanum lycopersicum L.) IN CHIKUN LGA, KADUNA STATE

Tomato (Solanum lycopersicum L.) is one of the most important vegetable crops in Nigeria, serving as a major source of income, nutrition, and raw material for food industries. However, its production is severely constrained by Fusarium wilt, a destructive soil-borne disease caused by Fusarium oxysporum f. sp. lycopersici. This study investigated the prevalence and severity of Fusarium wilt on tomato in Chikun Local Government Area (LGA) of Kaduna State, Nigeria. Field survey and laboratory analyses were conducted on forty-five tomato samples from three tomato farms Kujama, Kakau, and Rido. The samples were examined for disease incidence and severity. Data were analyzed using descriptive statistics and Chi-square tests. The overall disease incidence was with Rido recording the highest infection rate (80.0%), followed by Kujama (60.0%) and Kakau (40.0%). Among plant parts, the stem exhibited the highest infection frequency (80.0%), while leaves and fruits had 60.0% and 40.0% incidence respectively. Chi-square analysis indicated no significant difference (p > 0.05) in disease incidence among farms and plant parts, suggesting uniform pathogen distribution. The research recommends the adoption of integrated disease management strategies and improved farmer awareness to mitigate the impact of the disease and ensure sustainable tomato production.

plant biology↗

bioRxiv2026

Proteomic and Metabolomic Profiling of Transgenic Pod Borer-Resistant Cowpea: Assessing Unintended Molecular Changes and Their Implications for Ecosystem Resilience

BackgroundThe commercialization of Nigerias single-line pod borer-resistant (PBR) cowpea (IT97KT), the first transgenic cowpea variety in the world expressing Cry1Ab gene, has raised questions about potential unintended molecular changes and their ecological implications. This study employed integrated proteomic and metabolomic profiling to compare the transgenic line with its non-transgenic isoline (IT97KN) and assess molecular indicators associated with ecosystem resilience. MethodsProteomic analyses were conducted using LC-MS/MS following filter-assisted sample preparation, while metabolomic profiling employed GC-MS and UHPLC-MS/MS platforms. Differential protein and metabolite abundance were assessed using label-free quantification, volcano plot analysis, principal component analysis (PCA), hierarchical clustering, and Gene Ontology (GO) enrichment analyses. ResultsProteomic profiling revealed substantial overlap between IT97KT and IT97KN, with only a limited subset of proteins exhibiting significant differential abundance. Upregulated proteins in IT97KT were primarily associated with seed storage, redox regulation, oxidative stress mitigation, and defense-related functions, including Late Embryogenesis Abundant Protein 1 (LEA1), vicilins, thioredoxin, and iron superoxide dismutase. Among 37 proteins linked to ecological adaptation, only LEA1, CPRD22, and Bg7S showed significant differences. Similarly, only carbonic anhydrase II displayed differential abundance among proteins associated with potential ecological risk. PCA and clustering analyses demonstrated high proteomic similarity between genotypes. Metabolomic analyses identified sixteen major metabolites, predominantly fatty acids, with no statistically significant differences in abundance or composition between transgenic and non-transgenic lines ConclusionsThe transgenic PBR cowpea exhibited minimal unintended proteomic and metabolomic alterations relative to its non-transgenic isoline. These findings indicate that Cry1Ab insertion did not substantially disrupt molecular pathways associated with ecological adaptation, environmental risk, or metabolic homeostasis, providing molecular evidence supporting the environmental and biosafety equivalence of PBR cowpea.

Plant Biology↗

bioRxiv2026

AI-based Psychiatric Prediction in Youth: Neuroimaging Provides Minimal Gains Beyond Confounds

Recent advances in artificial intelligence (AI) have raised interest in its potential to similarly progress biological psychiatry. This study investigates the current utility of AI models in predicting psychiatric phenotypes in youth - a critical window for psychiatric diagnosis - using neuroimaging data from a large developmental clinical cohort. We assessed the predictive performance of machine learning models on diverse psychiatric and non-psychiatric targets. We show that while models are able to predict various targets from EEG and fMRI data, simple models using only readily-available factors such as demographics and recording site match their performance for clinical phenotypes. This pattern holds across clinical targets and replicates for state-of-the-art deep neural networks, suggesting that either neuroimaging data contains limited disease-specific information or that current methods cannot reliably identify such patterns. These benchmarking results provide important context regarding promises of modern AI in the field of biological psychiatry with a focus on youth.

neuroscience↗

bioRxiv2026

Functional Exploration of African Colorectal Cancer Patients Using Personalised Drosophila Avatars

Colorectal cancer across sub-Saharan Africa presents a growing global health burden, with increasing cases and mortality linked to late diagnosis, limited healthcare access and lack of effective treatments. African patients typically present with aggressive disease marked by distinct genomic signatures, indicating the need for targeted treatment approaches. We integrated genetic modelling, phenotypic scoring, imaging and biochemical analysis to explore how mutations found in individual Nigerian colorectal cancer patients influence drug responsiveness. We used the data from Cancer Genome Atlas to identify mutation profiles specific to Nigerian patients. We then generated ten stable Drosophila melanogaster personalised patient avatar lines designed to model patient genomic profiles. This study focused on three lines; each line included oncogenic RAS plus targeting patient-specific variants. These models exhibited various phenotypes including altered larval size, gut size and reduced survival. Two of the three avatar lines showed improved survival, reduced hindgut proliferation zone expansion and restored redox balance after treatment with regorafenib and trametinib. Mirroring clinical patient responses, we found that response to therapy is dependent on the specific genetic profile of the tumour. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/714433v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@db03forg.highwire.dtl.DTLVardef@ae2750org.highwire.dtl.DTLVardef@196f89dorg.highwire.dtl.DTLVardef@9c1577_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIAfrican colorectal cancer showed distinct mutation patterns that contribute to tumour heterogeneity. C_LIO_LIPatient-derived Drosophila avatars were engineered using tumour-specific genetic mutations with key features of human colorectal cancer. C_LIO_LITreatment with targeted therapies showed responses patterned by tumour genotype. C_LIO_LIResponse patterns indicated the need for personalised for colorectal cancer therapies among diverse populations. C_LI

cancer biology↗

bioRxiv2026

GAPMs form a heterotrimeric complex bridging the gliding machinery and the cytoskeleton across Plasmodium species

Apicomplexan parasites, such as malaria-causing Plasmodium spp., use a specialised actomyosin motor system known as the glideosome to drive movement through host tissue and invade host cells. This system is anchored to the inner membrane complex (IMC), a series of flattened vesicles located beneath the plasma membrane, and thought to be linked to the underlying cytoskeleton by the GAPM protein family. However, it is not known how these GAPM proteins are localised across the Plasmodium life cycle, and whether different family members function alone or together. Here, we show that in two Plasmodium species GAPM2 is an IMC component whose recruitment and organisation are tightly coordinated with nuclear and cytoskeletal dynamics during parasite replication and differentiation. We find that the GAPM2 interactome remodels between asexual and sexual stages using mass spectrometry. To understand the molecular relationship between three GAPM paralogues, we solved a cryo-electron microscopy structure of the GAPM complex. This revealed an obligate heterotrimeric architecture that forms an asymmetric platform, likely to serve as a docking interface for other components of the glideosome. Finally integrating our GAPM heterotrimer structure with mass spectrometry data allowed us to propose a unified structural model of the glideosome that is conserved across apicomplexan parasites.

microbiology↗

bioRxiv2026

Orthogonal cell division organizes surface virulence factors to drive staphylococcal abscess community formation

During infection, Staphylococcus aureus forms dense multicellular structures, called staphylococcal abscess communities (SACs), that are encased in a capsule made of host fibrin to evade host immune defenses. S. aureus cells divide characteristically along successive orthogonal planes, but the contribution of this division geometry to infection is unclear. Here, we show that disrupting orthogonal cell division by deleting the cell division septum placement factor PcdA impairs SAC formation in vivo and in a three-dimensional in vitro model. Loss of PcdA leads to uneven surface distribution of adhesins containing the YSIRK signal sequence that directs their insertion into the division septum, thereby resulting in uneven interaction with fibrin fibers. Consequently, bacterial communities fail to establish a robust fibrin pseudocapsule and remain accessible to immune cells. We propose that orthogonal cell division coordinates cell cycle progression with extracellular matrix engagement, SAC architecture, and persistence within host tissues. HIGHLIGHTSO_LIOrthogonal cell division promotes staphylococcal abscess community formation C_LIO_LILoss of PcdA disrupts fibrin pseudocapsule assembly in 3D models C_LIO_LIDivision geometry ensures uniform surface deployment of adhesins C_LIO_LICell division plane selection links bacterial cell cycle control to virulence C_LI

microbiology↗

bioRxiv2026

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language SummaryTemporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

plant biology↗

bioRxiv2026

Hierarchical Deep Learning Framework Integrating Structural Interaction Potentials and Evolutionary Information for Protein-Protein Interaction Affinity Prediction

Protein-protein interactions (PPIs) are fundamental to cellular biology, making accurate prediction of protein-protein interaction affinity (PPIA) crucial for drug discovery and disease mechanism elucidation. Existing computational methods often lack explicit physical constraints or operate as "black boxes," hindering interpretability and generalization. To address these limitations, we introduce StructFuncNet, a novel hierarchical deep learning framework designed for highly accurate, generalizable, and interpretable PPIA prediction. Our approach uniquely integrates multi-level Structural Interaction Potentials (SIP) with rich evolutionary information and advanced graph neural networks (GNNs). This dual paradigm leverages SIP for physical and geometric realism, while GNNs and Transformer-like components learn synergistic dynamics and contextual corrections. StructFuncNet incorporates comprehensive input feature categories, including pre-trained protein residue features, SIP interface energies, evolutionary conservation scores, global molecular descriptors, and interface interaction counts. We extensively evaluated StructFuncNet on diverse and challenging benchmarks, including those for mutation affinity, multi-domain complexes, and intrinsically disordered proteins, as well as complexes derived from predicted structures. Our framework consistently achieves state-of-the-art performance, demonstrating high correlations across all tested scenarios. Furthermore, StructFuncNet proves robust on complexes derived from predicted structures. Ablation studies confirm the synergistic contributions of our multi-modal features and architectural components, underscoring StructFuncNets capacity to provide a robust, interpretable, and efficient solution for complex PPIA prediction challenges.

bioinformatics↗

bioRxiv2025

Mucoricin binding to β-glucan sites on germinating Mucorales spores disrupts neutrophil swarming to promote pathogenicity

Mucormycosis is an emerging, life-threatening infection caused by Mucorales fungi with incompletely understood immunopathogenesis. We found that unlike other fungal pathogens, Mucorales trigger coordinated neutrophil clustering (swarming) upon swelling of fungal spores. Neutrophil swarms efficiently eliminate germinating Mucorales via ROS production, upon sensing of {beta}-glucan by complement receptor 3 (CR3). Impaired neutrophil clustering in mucormycosis-predisposing conditions, allows for mucoricin toxin production by germinating Mucorales. Released mucoricin selectively binds to {beta}-glucan on the surface of Mucorales, creating a molecular trap that induces neutrophil apoptosis and swarming disruption, leading to invasive fungal disease. Suppression of mucoricin, either genetically or physiologically by albumin-bound free fatty acids, stabilizes neutrophil clusters leading to abrogation of fungal pathogenicity. Additionally, prophylactic GM-CSF administration prevents mucoricin-induced neutrophil death, and protects mice from mucormycosis. Our work identifies neutrophil swarming as the primary target of mucoricin and provides compelling evidence for therapeutic harnessing of this pathway to improve mucormycosis outcome.

immunology↗

bioRxiv2025

PanScan: A Tool for Tertiary Analysis of Human Pangenome Graphs

The genomic representation of populations across the globe is critical to ensuring a comprehensive and equitable human reference. Constructing a pangenome graph reference for different populations is the best approach to addressing local genomic diversities. Although major initiatives across continents are underway to construct pangenome graph references, the field lacks the necessary toolsets for tertiary analysis to characterize telomere-to-telomere (T2T) assemblies and the complexity of haplotypes. PanScan is a bioinformatics software package developed for human pangenome tertiary analysis. It includes multiple modules designed to detect duplicated gene sets from T2T assemblies, identify novel variants and sequences, as well as detect and visualize complex genomic regions through pangenome graph haplotype loops. We have used multiple pangenomes across different populations to assess the tertiary analysis and their accuracy. The tool is designed to streamline tertiary analysis and is compatible with multiple pangenome graph construction algorithms. PanScan is freely available on GitHub (https://github.com/CATG-Github/panscan), where users can provide human pangenome assemblies or VCF files as inputs for automated analyses through command-line operations on Linux systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/651685v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@16cc212org.highwire.dtl.DTLVardef@13961d0org.highwire.dtl.DTLVardef@44d194org.highwire.dtl.DTLVardef@1b72aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2025

Candida auris skin colonization is mediated by Als4112 and interactions with host extracellular matrix proteins

Candida auris is an often multidrug-resistant fungal pathogen notorious for persistent skin colonization and transmission in healthcare settings. However, the mechanisms driving its adherence to skin remain poorly understood. Here, we developed in vitro systems to allow for detailed analysis of early skin colonization events and identified critical host and pathogen mediators of attachment. Across multiple strains and clades of C. auris, we identified that Als4112, a conserved adhesin, is required for skin colonization via keratinocyte attachment and direct interactions with host extracellular matrix (ECM) proteins, especially basement membrane proteins such as laminin. In a murine epicutaneous infection and human skin explants, deletion of ALS4112 significantly reduced skin colonization, underscoring its essential role in establishing cutaneous persistence. Als4112 also contributes to systemic infection, highlighting the connection between adherence and pathogenicity in this organism. Finally, coating plastic and catheter surfaces with collagen I or III markedly inhibited C. auris attachment and biofilm formation, offering an approach to curb nosocomial transmission. Our study highlights the critical role of Als4112 in C. auris colonization and virulence in vivo, making it an attractive target for future vaccine development. This study also explores the potential of specific collagen coatings as a novel strategy to prevent C. auris adherence to abiotic surfaces, offering new therapeutic avenues to control the spread of C. auris in healthcare settings.

microbiology↗

bioRxiv2024

The NHGRI-EBI GWAS Catalog: standards for reusability, sustainability and diversity

The NHGRI-EBI GWAS Catalog serves as a vital resource for the genetic research community, providing access to the most comprehensive database of human GWAS results. Currently, it contains close to 7,000 publications for more than 15,000 traits, from which more than 625,000 lead associations have been curated. Additionally, 85,000 full genome-wide summary statistics datasets - containing association data for all variants in the analysis - are available for downstream analyses such as meta-analysis, fine-mapping, Mendelian randomisation or development of polygenic risk scores. As a centralised repository for GWAS results, the GWAS Catalog sets and implements standards for data submission and harmonisation, and encourages the use of consistent descriptors for traits, samples and methodologies. We share processes and vocabulary with the PGS Catalog, improving interoperability for a growing user group. Here, we describe the latest changes in data content, improvements in our user interface, and the implementation of the GWAS-SSF standard format for summary statistics. We address the challenges of handling the rapid increase in large-scale molecular quantitative trait GWAS and the need for sensitivity in the use of population and cohort descriptors while maintaining data interoperability and reusability.

genetics↗

bioRxiv2024

Identification of leaf rust resistance loci in hard winter wheat using genome-wide association mapping

Leaf rust, caused by Puccinia triticina (Pt), is a serious constraint to wheat production. Developing resistant varieties is the best approach to managing this disease. Wheat leaf rust resistance (Lr) genes have been classified into either all-stage resistance (ASR) or adult-plant resistance (APR). The objectives of this study were to identify sources of leaf rust resistance in contemporary U.S. hard winter wheat (HWW) and to dissect the genetic basis underlying leaf rust resistance in HWW. A panel of 732 elite HWW genotypes was evaluated for response to U.S. Pt races at the seedling stage and at the adult plant stage in leaf rust nurseries in Oklahoma, Texas, and Kansas. Further, the panel was genotyped using Multiplex Restriction Amplicon Sequencing (MRA-Seq) and DNA markers linked to the known ASR genes Lr18, Lr19, Lr21, Lr24, Lr37, and Lr42 and APR genes Lr34, Lr46, Lr67, Lr68, Lr77, and Lr78. Single nucleotide polymorphism (SNP) markers derived from MRA-Seq, DNA markers linked to the known Lr genes, and the phenotypic data were used for genome-wide association study (GWAS) to identify markers associated with leaf rust response. Gene postulation based on leaf rust reactions, DNA markers, and GWAS suggested the presence of Lr1, Lr2a, Lr10, Lr14a, Lr16, Lr18, Lr19, Lr21, Lr24, Lr26, Lr34, Lr37, Lr39, Lr42, Lr46, Lr68, Lr77, and Lr78 in the HWW panel. The GWAS identified 59 SNPs significantly associated with leaf rust response, of which 20 were likely associated with novel resistance loci and can be used to enhance wheat leaf rust resistance.

genomics↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2024

Filter bank common spatial pattern and envelope-based features in multimodal EEG-fTCD brain-computer interfaces

Brain-computer interfaces (BCIs) exploit brain activity to bypass neuromuscular control with the aim of providing alternative means of communication with the surrounding environment. Such systems can significantly improve the quality of life for patients suffering from severe motor or speech impairment. Multimodal BCIs have been introduced recently to enhance the performance of BCIs utilizing single modality. In this paper, we aim to improve the performance of multimodal BCIs combining Electroencephalography (EEG) and functional transcranial Doppler ultrasound (fTCD). The BCIs included in the study utilized two different paradigms to infer user intent including motor imagery (MI) and flickering mental rotation (MR)/word generation (WG) paradigms. Filter Bank Common Spatial Pattern (FBCSP) algorithm was used to extract features from the EEG data. Several time series features were extracted from the envelope of the fTCD signals. Wilcoxon rank sum test and linear kernel Support vector machines (SVM) were used for feature selection and classification respectively. Additionally, a probabilistic Bayesian fusion approach was used to fuse the information from EEG and fTCD modalities. Average accuracies of 94.53%, 94.9% and 96.29% were achieved for right arm MI versus baseline, left arm MI versus baseline, and right arm MI versus left arm MI respectively. Whereas average accuracies of 95.27%, 85.93% and 96.97% were achieved for MR versus baseline, WG versus baseline, and MR versus WG respectively. Our results show that EEG- fTCD BCIs with the proposed analysis techniques outperformed the multimodal EEG-fNRIS BCIs in comparison.

bioengineering↗

bioRxiv2023

Efficacy of an oral lipid nanocrystal (LNC) formulation of amphotericin B (MAT2203) in the neutropenic mouse model of pulmonary mucormycosis

Invasive mucormycosis (IM) is associated with high mortality and morbidity and commonly afflicts patients with weakened immune systems. MAT2203 is an orally administered lipid nanocrystal (LNC) formulation of amphotericin B, which has been shown to be safe and effective against other fungal infections. We sought to compare the efficacy of MAT2203 to liposomal amphotericin B (LAMB) treatment in a neutropenic mouse model of IM due to R. arrhizus var. delemar or Mucor circinelloides f. jenssenii DI15-131. Treatment with placebo (diluent control), oral MAT2203 administered as BID and QD or intravenous LAMB for 4 days, began 16 h post infection and continued for 7 and 4 days, respectively. Survival through Day +21 and tissue fungal burden of lung or brain in animals euthanized on Day +4 served as a primary and secondary endpoint, respectively. In both infection types, MAT2203 was as effective as LAMB in prolonging median survival time (MST) and enhancing overall survival vs. placebo-treated mice (P<0.05 by Log-Rank). Furthermore, both MAT2203 and LAMB treatment resulted in significant [~]1.0-1.5-log reduction and [~]2.0-2.2-log in R. delemar or M. circinelloides lung and brain burden, vs. placebo mice, respectively. These results support the potential efficacy of oral MAT2203 as an alternative to LAMB. Continued investigation and development of this novel oral formulation of the amphotericin B for the treatment of mucormycosis is warranted.

microbiology↗