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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 48 Documents
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Hybrid Nanozyme-Enabled Biosensors for Real-Time Detection of Multi-Disease Biomarkers Judijanto, Loso; Rahman, Rashid; Anis, Nina
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.v2i3.2378

Abstract

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 Judijanto, Loso; Chai, Som; Pong, Ming; Justam, Justam; Nampira, Ardi Azhar
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.
Peptide-Functionalized Magnetic Nanoparticles for Early-Stage Pathogen Detection Judijanto, Loso; Sok, Vann; Dara, Chenda
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.v2i3.2380

Abstract

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 Zaman, Khalil; Akhtar, Shazia; Lim, Sofia; Nampira, Ardi Azhar
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.
ARTIFICIAL INTELLIGENCE IN MEDICINE: A DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK FOR PATHOLOGICAL IMAGE ANALYSIS AND CANCER GRADING Smith, James; Harris, Oliver; Anurogo, Dito
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2480

Abstract

The histopathological analysis of tissue slides is the gold standard for cancer diagnosis and grading. However, this process is labor-intensive, time-consuming, and prone to inter-observer variability, which can affect clinical outcomes. The advent of artificial intelligence (AI), particularly deep learning, presents a transformative opportunity to enhance diagnostic precision and efficiency in pathology. This study aimed to develop, train, and validate a deep learning convolutional neural network (CNN) for the automated analysis of pathological images to accurately classify malignancies and provide reliable cancer grading. A robust CNN model was trained on a comprehensive, curated dataset of thousands of annotated digital histopathology slides from multiple cancer types. The model’s performance was rigorously evaluated against the consensus diagnoses of expert pathologists using key metrics, including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Our developed CNN model demonstrated exceptional performance, achieving an overall accuracy of 98.7% in distinguishing malignant from benign tissues. For cancer grading, the model yielded a Cohen’s Kappa score of 0.92, indicating almost perfect agreement with expert pathologists. The model also showed high robustness to variations in staining and image acquisition protocols. This research confirms that a deep learning CNN can function as a highly accurate and reliable tool for automated pathological image analysis and cancer grading. Integrating such AI systems into clinical workflows could significantly augment the capabilities of pathologists, leading to improved diagnostic consistency, reduced workload, and ultimately, better patient care.
TARGETED GENE SILENCING OF KRAS ONCOGENES IN PANCREATIC CANCER USING SIRNA-LOADED GOLD NANOPARTICLES Ahmed, Shakib; Islam, Zahidul; El Balqis, Fatimah
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2516

Abstract

Pancreatic cancer, predominantly driven by mutations in the KRAS oncogene, remains one of the most lethal malignancies due to its resistance to conventional therapies. RNA interference (RNAi) using small interfering RNA (siRNA) presents a powerful strategy to silence oncogenes, but its clinical application is liited by the poor stability and inefficient delivery of naked siRNA. This study aimed to develop and validate a targeted nanodelivery system using gold nanoparticles (AuNPs) to efficiently deliver KRAS-specific siRNA and induce potent gene silencing in pancreatic cancer cells. A nanoconjugate was synthesized by attaching thiol-modified siRNA targeting the G12D-mutant KRAS gene to PEGylated gold nanoparticles. The physicochemical properties of the siRNA-AuNPs were characterized. The platform’s efficacy was evaluated in vitro using the PANC-1 human pancreatic cancer cell line. KRAS expression was quantified via qRT-PCR and Western blot, while cellular viability and apoptosis were assessed using MTT and flow cytometry assays, respectively. The synthesized siRNA-AuNPs exhibited excellent stability and were efficiently internalized by the cancer cells. This targeted delivery resulted in a significant downregulation of KRAS mRNA and protein expression by over 75% (p < 0.01) compared to controls. Consequently, this oncogene silencing led to a substantial inhibition of cancer cell proliferation and a marked increase in apoptosis. Gold nanoparticles serve as a highly effective and robust vector for the targeted delivery of siRNA. This nanomedicine platform successfully silences the critical KRAS oncogene, inducing cell death in pancreatic cancer cells and representing a promising new avenue for targeted cancer therapy.
PHARMACEUTICAL NANOTECHNOLOGY: FORMULATION AND IN VIVO EVALUATION OF CURCUMIN-LOADED NANOSUSPENSIONS FOR ENHANCED ANTI-INFLAMMATORY EFFICACY Issusilaningtyas, Elisa; Williams, Sarah; Muntasir, Muntasir
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2523

Abstract

Curcumin, a natural polyphenol derived from Curcuma longa, is well-regarded for its potent anti-inflammatory properties. However, its therapeutic application is severely hampered by its extremely low aqueous solubility and poor oral bioavailability, which leads to suboptimal absorption and limited clinical efficacy. Pharmaceutical nanotechnology offers a promising strategy to overcome these biopharmaceutical challenges. This research aimed to formulate a stable curcumin nanosuspension to significantly enhance its dissolution rate and bioavailability, and to subsequently evaluate its improved anti-inflammatory efficacy in an in vivo model. A curcumin nanosuspension was prepared using the high-pressure homogenization technique, stabilized with Poloxamer 188. The formulation was characterized for particle size, polydispersity index (PDI), and zeta potential. An in vivo anti-inflammatory study was conducted using the carrageenan-induced paw edema model in Wistar rats, comparing the efficacy of the nanosuspension against a conventional coarse curcumin suspension. The optimized nanosuspension exhibited a narrow particle size distribution with a mean diameter of 210 nm and a zeta potential of -28.5 mV, indicating good physical stability. The in vivo evaluation demonstrated that the curcumin nanosuspension produced a significantly greater inhibition of paw edema (72.4%) compared to the coarse curcumin suspension (28.1%) at the same dose (p < 0.01). Formulating curcumin into a nanosuspension is a highly effective strategy for overcoming its inherent bioavailability limitations. This nanotechnological approach dramatically enhances curcumin’s anti-inflammatory activity, validating its potential as a powerful therapeutic agent for inflammatory conditions.
A 3D-PRINTED, GRAPHENE-REINFORCED HYDROGEL SCAFFOLD FOR ENHANCED OSTEOGENIC DIFFERENTIATION OF MESENCHYMAL STEM CELLS Anurogo, Dito
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i5.2761

Abstract

Bone tissue engineering requires scaffolds that replicate the mechanical stiffness and electroactive properties of native bone, features that conventional hydrogels lack. This study aimed to design, fabricate, and validate a 3D-printed graphene-reinforced hydrogel scaffold that enhances osteogenic differentiation of human mesenchymal stem cells (hMSCs) via combined mechanical and electrical stimulation. A composite bio-ink was developed by incorporating graphene nanoparticles (0, 0.1, 0.2, and 0.5% w/v) into a biocompatible hydrogel matrix, optimized for extrusion-based 3D printing. Scaffolds with a controlled pore size of 300 ?m were fabricated and analyzed for compressive strength, degradation kinetics, and electrical conductivity using a four-point probe. hMSCs were seeded onto the scaffolds and cultured under osteogenic conditions for 28 days. Osteogenic differentiation was assessed by alkaline phosphatase (ALP) activity (day 14), qPCR for RUNX2 and osteocalcin (OCN) (day 21), and Alizarin Red S staining for mineralization (day 28). Data were analyzed using ANOVA and regression modeling. The 0.2% w/v graphene-reinforced scaffolds showed optimal performance, with compressive strength of 35.0 MPa and electrical conductivity of 0.15 S/m, significantly higher than pure hydrogel controls. hMSCs cultured on these scaffolds exhibited increased ALP activity, upregulation of RUNX2 and OCN, and enhanced mineralization. At 0.5% w/v graphene, excessive viscosity hindered printability and reduced cell viability. Overall, the 3D-printed graphene-reinforced hydrogel scaffold at 0.2% w/v creates a synergistic electromechanical microenvironment, robustly promoting hMSC osteogenesis, and offers a scalable platform for next-generation bone tissue engineering.
DEVELOPMENT OF PH-RESPONSIVE POLYMERIC MICELLES FOR TARGETED DOXORUBICIN DELIVERY TO HYPOXIC TUMOR MICRO-ENVIRONMENTS Saida, Fathimath; Dorji, Jigme; Ríos, David
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i4.2973

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.  
LIPID NANOPARTICLE-MEDIATED MRNA DELIVERY FOR A NOVEL UNIVERSAL VACCINE AGAINST INFLUENZA VIRUS SUBTYPES Pradeep, Lakshan; Wijerathna, Kumudu; Perera, Dilshan
Journal of Biomedical and Techno Nanomaterials Vol. 2 No. 5 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jbtn.v2i5.2974

Abstract

Influenza viruses continue to pose a major global health challenge due to rapid antigenic drift and shift, which limit the effectiveness of seasonal, strain-specific vaccines. Current vaccine strategies require frequent reformulation and often fail to provide broad and durable protection against diverse influenza virus subtypes. This study aims to develop a lipid nanoparticle–mediated mRNA delivery platform encoding conserved influenza antigens as a novel universal vaccine strategy. An experimental preclinical design was employed, involving in vitro transcription of mRNA, formulation into lipid nanoparticles, physicochemical characterization, and immunological evaluation in animal models. Particle size, encapsulation efficiency, mRNA expression, and stability were systematically assessed, followed by analysis of humoral and cellular immune responses and heterologous viral challenge studies. The mRNA–LNP vaccine exhibited uniform nanoscale properties, high mRNA integrity, and efficient antigen expression. Immunization induced robust cross-reactive antibody responses and strong CD4? and CD8? T-cell activation against multiple influenza subtypes. Vaccinated subjects demonstrated reduced viral loads, attenuated disease severity, and improved survival following heterologous influenza challenge. These findings indicate that lipid nanoparticle–mediated mRNA delivery of conserved influenza antigens represents a promising and adaptable platform for universal influenza vaccination, with significant potential to enhance pandemic preparedness and long-term influenza control.  

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