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Journal of Biomedical and Techno Nanomaterials
Published by Yayasan Adra Karima Hubbi
ISSN : 30481120     EISSN : 30481155     DOI : 10.70177/jbtn
Core Subject : Science,
Biochemistry, Genetics & Molecular Biology
Journal of Biomedical and Techno Nanomaterials is an international forum for the publication of peer-reviewed integrative review articles, special thematic issues, reflections or comments on previous research or new research directions, interviews, replications, and intervention articles - all pertaining to the research fields of medicine, pharmaceuticals, biomaterials, biotechnology, diagnosis and prevention of diseases, biomedical devices, bioinformatics, and all other related fields of biomedical and life sciences. All publications provide breadth of coverage appropriate to a wide readership in Biomedical and Techno Nanomaterials research depth to inform specialists in that area. We feel that the rapidly growing Journal of Biomedical and Techno Nanomaterials community is looking for a journal with this profile that we can achieve together. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Biokimia, Genetika dan Biologi Molekuler - Biokimia, Genetika dan Biologi Molekuler (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "vol. 2 no. 3 (2025)" : 5 Documents clear
AI POWERED DIGITAL HISTOPATHOLOGY: PREDICTING IMMUNOTHERAPY RESPONSE USING DEEP LEARNING Loso Judijanto; Som Chai; Ming Pong; Justam Justam; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2379

Abstract

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.
AI ASSISTED PERSONALIZED VACCINE DESIGN USING MULTI-OMICS CANCER DATA Khalil Zaman; Shazia Akhtar; Sofia Lim; Ardi Azhar Nampira
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2381

Abstract

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.
DEVELOPMENT OF PH-RESPONSIVE POLYMERIC MICELLES FOR TARGETED DOXORUBICIN DELIVERY TO HYPOXIC TUMOR MICRO-ENVIRONMENTS Fathimath Saida; Jigme Dorji; David Ríos
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2973

Abstract

Hypoxic tumor micro-environments are characterized by abnormal vascularization and acidic extracellular pH, which significantly reduce the effectiveness of conventional chemotherapy and contribute to therapeutic resistance. Doxorubicin, although widely used, suffers from severe systemic toxicity and limited selectivity toward hypoxic tumor regions. This study aims to develop pH-responsive polymeric micelles capable of selectively delivering doxorubicin to hypoxic tumor micro-environments by exploiting endogenous acidity as a biological trigger. An experimental laboratory-based design was employed involving the synthesis of amphiphilic block copolymers, micelle self-assembly, physicochemical characterization, and in vitro biological evaluation under normoxic and hypoxic conditions. Particle size, stability, drug loading, and pH-dependent release behavior were systematically assessed, followed by cytotoxicity, cellular uptake, and three-dimensional tumor spheroid studies. The developed micelles exhibited uniform nanoscale size, high encapsulation efficiency, minimal drug leakage at physiological pH, and accelerated drug release under mildly acidic conditions representative of hypoxic tumors. Enhanced intracellular doxorubicin accumulation, deeper tumor penetration, and significantly increased cytotoxicity under hypoxia were observed compared to non-responsive micelles and free drug. These findings demonstrate that pH-responsive polymeric micelles provide an effective and biologically informed platform for targeted chemotherapy in hypoxic tumor micro-environments, offering promising potential for improving therapeutic efficacy while reducing systemic toxicity.  
QUANTUM DOTS AS NEAR-INFRARED FLUORESCENT PROBES FOR REAL-TIME IN VIVO BIOIMAGING OF CANCER CELL METASTASIS Oumar Traore; Binta Konate; Fatiata Diarra
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2978

Abstract

Cancer metastasis is the primary cause of cancer-related mortality, yet its dynamic progression in living systems remains difficult to visualize due to limitations of existing imaging probes. Conventional fluorescent dyes used for in vivo bioimaging often suffer from poor photostability, limited brightness, and insufficient tissue penetration, restricting their ability to capture metastatic events in real time. This study aims to develop and evaluate near-infrared-emitting quantum dots as fluorescent probes for real-time in vivo bioimaging of cancer cell metastasis. An experimental nanobiotechnology approach was employed, involving the synthesis of near-infrared quantum dots, surface functionalization to enhance biocompatibility, physicochemical and optical characterization, and biological evaluation using metastatic cancer cell lines and small animal models. Optical analysis demonstrated high quantum yield, narrow emission bandwidth, and excellent photostability within the near-infrared window. In vitro assays confirmed high cell-labeling efficiency with minimal cytotoxicity, while in vivo imaging revealed sustained and high-contrast fluorescence signals that enabled continuous tracking of cancer cell migration and organ colonization. Ex vivo validation corroborated in vivo imaging findings. These results indicate that near-infrared quantum dots provide superior performance compared to conventional fluorescent probes for dynamic metastasis imaging. In conclusion, quantum dot–based near-infrared probes represent a powerful and versatile platform for real-time in vivo visualization of cancer metastasis, offering significant potential for advancing cancer research and diagnostic imaging.
A LAB-ON-A-CHIP DEVICE WITH INTEGRATED GOLD NANOSENSORS FOR THE ULTRASENSITIVE ELECTROCHEMICAL DETECTION OF DENGUE VIRUS BIOMARKERS Andri Setiawan; Luis Santos; Josefa Flores
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i3.2479

Abstract

Dengue fever is a significant global health threat, where rapid and accurate diagnosis is crucial for timely clinical intervention and outbreak management. Conventional diagnostic methods are often centralized, time-consuming, and require sophisticated equipment, limiting their utility in point-of-care (POC) settings. This study reports the development and validation of a novel lab-on-a-chip (LoC) device integrating gold nanosensors for the ultrasensitive electrochemical detection of the Dengue virus non-structural protein 1 (NS1). The microfluidic device was fabricated using soft lithography, and the gold electrode surfaces were functionalized with specific anti-NS1 monoclonal antibodies. Detection was performed using differential pulse voltammetry (DPV), measuring the change in current response upon immunocomplex formation. The developed immunosensor exhibited a wide linear dynamic range and an exceptionally low limit of detection (LoD) of 1.5 pg/mL for the NS1 antigen. Furthermore, the device demonstrated high selectivity against other interfering proteins. Our findings successfully establish a robust, miniaturized LoC platform for the rapid and highly sensitive detection of dengue biomarkers. This device holds significant potential as a powerful POC diagnostic tool for the early detection of dengue fever, particularly in resource-limited environments.  

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