Journal of Biomedical and Techno Nanomaterials

Hybrid Nanozyme-Enabled Biosensors for Real-Time Detection of Multi-Disease Biomarkers

Loso Judijanto — 2025-08-30

The early and accurate detection of disease biomarkers is fundamental to timely diagnosis and effective treatment, yet conventional laboratory methods are often slow, costly, and require complex instrumentation. Nanozymes—nanomaterials with intrinsic enzyme-like properties—offer a promising alternative for developing robust biosensors. This study aimed to design, synthesize, and validate a novel hybrid nanozyme-enabled biosensor platform capable of the sensitive, selective, and real-time multiplexed detection of biomarkers for different diseases from a single sample. A hybrid nanozyme was synthesized by integrating platinum nanoparticles with metal-organic frameworks (MOFs) to create a material with superior catalytic activity. This hybrid nanozyme was then immobilized onto a multi-channel electrochemical sensor chip. Each channel was functionalized with specific aptamers targeting three distinct biomarkers: cardiac troponin I (a cardiac marker), prostate-specific antigen (a cancer marker), and glucose (a metabolic marker). The detection was based on the catalytic signal amplification upon biomarker binding. The platform showed excellent selectivity with negligible cross-reactivity between channels and achieved a rapid detection time of under 15 minutes. The multiplexed assay successfully and accurately quantified all three biomarkers simultaneously in complex serum samples. The hybrid nanozyme-enabled electrochemical biosensor represents a significant advancement in diagnostic technology.

AI-Powered Digital Histopathology: Predicting Immunotherapy Response Using Deep Learning

Loso Judijanto — 2025-08-30

Immunotherapy has revolutionized cancer treatment, yet predicting which patients will respond remains a major clinical challenge. Current predictive biomarkers, such as PD-L1 expression, have limited accuracy and fail to capture the complex interplay of cells within the tumor microenvironment. Digital histopathology, the analysis of digitized tissue slides, combined with artificial intelligence (AI), offers a novel approach to identify complex morphological patterns that could serve as more robust predictive biomarkers.

Objective: A deep learning model, specifically a convolutional neural network (CNN), was trained on a large, multi-center cohort of digitized tumor slides from patients with non-small cell lung cancer who had received ICI therapy. The model was trained to identify subtle morphological features and the spatial arrangement of tumor cells and tumor-infiltrating lymphocytes. The model’s predictive performance was rigorously validated on an independent, held-out test cohort, and its performance was compared to the predictive accuracy of PD-L1 staining. The AI-powered model successfully predicted immunotherapy response with a high degree of accuracy, achieving an area under the receiver operating characteristic curve (AUC) of 0.88 in the validation cohort.

Peptide-Functionalized Magnetic Nanoparticles for Early-Stage Pathogen Detection

Loso Judijanto — 2025-08-30

The rapid and sensitive detection of pathogenic bacteria is paramount for preventing infectious disease outbreaks, ensuring food safety, and guiding clinical treatment. This study aimed to develop and validate a novel biosensing platform based on peptide-functionalized magnetic nanoparticles for the rapid, selective, and sensitive detection of a model pathogen, Escherichia coli O157:H7, in its early stages. Superparamagnetic iron oxide nanoparticles were synthesized and subsequently functionalized with a specifically designed, high-affinity peptide that targets an outer membrane protein of E. coli O157:H7. The detection was performed using a simple colorimetric assay based on the peroxidase-like activity of the MNPs, where the signal intensity was proportional to the concentration of captured bacteria. The peptide-functionalized MNPs demonstrated a high capture efficiency of over 95% within 20 minutes. The platform exhibited excellent sensitivity with a low limit of detection of approximately 15 colony-forming units per milliliter (CFU/mL) in buffer and 30 CFU/mL in spiked milk samples. The developed peptide-functionalized magnetic nanoparticle platform is a highly effective and robust system for the early-stage detection of pathogens. Its combination of speed, high sensitivity, and excellent specificity makes it a promising candidate for the development of portable, point-of-care diagnostic tools for applications in food safety, environmental monitoring, and clinical diagnostics, addressing a critical need for rapid and reliable pathogen screening.

AI-Assisted Personalized Vaccine Design Using Multi-Omics Cancer Data

Khalil Zaman — 2025-08-30

The development of personalized cancer vaccines represents a promising frontier in oncology, yet traditional approaches struggle with the complexity and volume of multi-omics data. This study addresses this challenge by introducing an AI-assisted framework for the design of personalized vaccines. The primary objective was to leverage machine learning models to identify and prioritize neoantigens from integrated genomic, transcriptomic, and proteomic data of cancer patients. The methodology involved a deep learning pipeline to analyze multi-omics datasets, predicting tumor-specific mutations and their immunogenicity. This was followed by an algorithm to select the most potent neoantigen peptides for vaccine formulation, optimizing for both MHC binding affinity and T-cell activation potential. Our results demonstrate that the AI-driven approach significantly improved the speed and accuracy of neoantigen identification compared to conventional methods. The framework successfully predicted a set of high-quality vaccine candidates for individual patients, which showed strong in silico binding to patient-specific MHC molecules. We conclude that this AI-assisted methodology provides a powerful and scalable solution for personalized vaccine design, accelerating the translation of multi-omics data into clinically actionable immunotherapies.