Biology subjects

Neupane, B.

Publications and source records attributed to Neupane, B..

3 recordsLinked to original sources

bioRxiv2026

Comparative Evaluation of Commercial Iron Oxide Particles for Magnetic Particle Imaging Using Relaxometry and Image-Based Metrics

In magnetic particle imaging (MPI), signal is generated from the non-linear magnetic response of superparamagnetic iron oxide (SPIO) nanoparticles. The optimization of SPIOs for MPI is an active area of investigation. Tracer performance can be evaluated by magnetic particle relaxometry (MPR) and image-based metrics. This study characterized and compared the performance of ten commercially available tracers, spanning a broad range of magnetic core sizes, hydrodynamic diameters and surface coatings, using the peak signal intensity measured from MPR, and the total signal and maximum signal intensity measured from 2D MPI images. Synomag-D tracers exhibited the highest MPR peak signal intensities and highest maximum signal per {micro}g iron. ProMag, a micron-sized iron oxide particle (MPIO), yielded a low MPR peak signal intensity but had the highest total MPI signal per {micro}g of iron. A strong correlation was observed between MPR peak signal intensity and MPI maximum signal intensity (R2=0.94). In contrast, there was a weak correlation between MPR peak signal intensity and MPI total signal intensity (R2=0.38), though this correlation improved when MPIOs were excluded from the analysis (R2=0.91). While MPR generally predicts the tracer performance, it does not completely replicate the complex imaging conditions. As such, comprehensive tracer evaluation requires combining both MPR and image-based metrics.

biophysics↗

bioRxiv2025

Moving Beyond Binary Biomarkers: Machine Learning Model Resolves Concurrent and Molecularly Heterogeneous Mismatch Repair and Homologous Recombination Deficiencies in Prostate Cancer

Current DNA damage repair (DDR) biomarkers employ binary classifications that fail to capture the molecular complexity of tumors with concurrent repair deficiencies. We used genomics analysis to stratify 672 metastatic prostate cancer patients into 11 DDR subgroups, identifying 51 molecular signatures with weighted roles in class identity. We identified a tumor-mutational-burden very-high subset, characterized by 19 mutations/Mb or more, as a molecularly distinct group characterized by preserved genomic integrity and enhanced immunogenicity. Critically, 2.3 percent of tumors exhibited concurrent TMB-High and HRR mutant phenotypes, while 1.5 percent harbored MMR bi-allelic loss without MMRd (mismatch-repair-deficiency) signatures. Clinical validation in 130 patients demonstrated superior immunotherapy responses in tumors with very high TMB levels. We developed CHIMERA DDR, a probabilistic machine learning tool that integrates these 51 genomic features using a nested Random Forest architecture to infer seven clinically relevant DDR subgroups. After negating model overfit concerns, CHIMERA-DDR showed exceptional classification performance (AUCs 0.919-0.999) to accurately detect MMRd and HRR mutant molecular subtypes with or without concurrent DDR deficiencies, resolving admixed phenotypes to enable precision therapeutic stratification beyond binary methods.

genomics↗

bioRxiv2025

Harnessing Lytic Phages for Biofilm Control in Carbapenem-Resistant Klebsiella pneumoniae Causing Urinary Tract Infection

BackgroundKlebsiella pneumoniae is a major opportunistic pathogen with rising multidrug resistance and biofilm-related infections. Molecular and phage characterization is crucial to understand resistance mechanisms and explore alternative therapies such as phage therapy. MethodsWe performed whole-genome sequencing and antibiotic susceptibility testing of hospital-isolated Klebsiella pneumoniae (KP6697). MLST, plasmid replicon analysis, and resistance gene identification were conducted using bioinformatics. Phage isolation, electron microscopy-based morphological and biofilm analysis, and evaluation of lytic activity, stability, and host range were performed. Phage genome sequencing and annotation identified functional genes. ResultsThe host strain Klebsiella pneumoniae (KP6697) was multidrug-resistant, exhibiting resistance to 18 of 22 tested antibiotics, and genome analysis identified ST16 with eight plasmid replicons and 23 resistance genes, including blaCTX-M-15, blaNDM-5, and blaOXA-181. Functional annotations revealed extensive metabolic versatility and a rich repertoire of genes for biofilm formation, quorum sensing, secretion systems, and stress response. A lytic phage, Phage_KP6697_Omshanti, was isolated and classified as a Caudoviricetes member with a 45.3kb genome encoding lysis, replication, and structural genes. It demonstrated short latency, high burst size, thermal and pH stability, and broad host range against CRKP and other MDR strains. Importantly, microscopy confirmed its ability to inhibit and degrade biofilms at multiple stages, highlighting strong therapeutic potential. ConclusionComprehensive analysis of carbapenem-resistant K. pneumoniae (KP6697) revealed multidrug resistance and strong biofilm formation. The lytic phage Phage_KP6697_Omshanti, with depolymerase and endolysin activity, disrupted biofilms, and its stability, high burst size, and genomic traits suggest potential as an anti-CRKP agent, especially with antibiotics IMPORTANCEKlebsiella pneumoniae is increasing multidrug resistance and robust biofilm formation pose severe clinical challenges, limiting treatment options. Understanding the molecular basis of its resistance and exploiting bacteriophages with strong biofilm-disrupting properties provide promising alternative therapeutic strategies. This study highlights the isolation and genomic characterization of a lytic phage with potent anti-biofilm activity against carbapenem-resistant K. pneumoniae, underscoring its potential in combating resistant infections.

microbiology↗