Accurate quantification of contrast-agent kinetics in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) remains a critical challenge in prostate malignancy assessment, particularly due to signal distortion during the early intravascular phase and heterogeneity in tumor microenvironment dynamics. This study presents a physics-guided computational framework that integrates pharmacokinetic (PK) modeling with Adaptive Complex Independent Component Analysis (ACICA) to enhance intravascular contrast concentration estimation and tissue characterization. The proposed approach combines Tofts-based PK modeling with ACICA-driven arterial input function refinement, enabling correction of early-phase concentration curve distortions and improved estimation of vascular permeability parameters. To capture tissue heterogeneity, statistical distribution metrics including entropy (6.25), skewness (2.56), and kurtosis (169.35) are incorporated as quantitative biomarkers of contrast dispersion complexity. To further support diagnostic decision-making, an interpretable Visual-Based Image Retrieval framework is introduced, leveraging Color Layout Descriptors (CLD) and Edge Histogram Descriptors (EHD) for structured feature representation and similarity-driven retrieval. Experimental validation demonstrates robust performance, achieving a sensitivity of 94.05\% and specificity of 92.86\%, indicating reliable identification of malignant cases while minimizing false positives. By integrating physics-based kinetic modeling, independent component analysis, and interpretable image retrieval, the proposed framework enhances quantitative DCE-MRI analysis and provides clinically meaningful decision support. This approach contributes toward more reliable prostate tumor characterization, improved treatment planning, and scalable deployment of interpretable computational imaging systems in clinical practice.

Recently, the formulation of multifunctional nanoparticles has significantly reduced various limitations associated with drugs and drug carriers. In this study, a novel multilamellar hybrid nanovesicle system was developed using a combination of a polysaccharide and a lipid. Starch forms the core of hybrid nanoparticles, while stearic acid with polyethylene glycol containing calcium ferrite nanoparticles form the shell. The drug, zidovudine (AZT), was encapsulated within the hybrid nanovesicle system for anticancer drug delivery. The calcium ferrite nanoparticles were characterized by transmission electron microscopy (TEM) and x-ray diffraction (XRD) analyses. The morphology of the hybrid nanoparticles was analyzed using TEM, which showed a multilamellar morphology with an average size of 179 +/- 18.23 nm. The drug loading and release behavior of these nanoparticles were assessed, and they showed high encapsulation efficiency (93%) and a sustainable release of AZT over 48 h. The cytocompatibility of the hybrid nanosystem was analyzed in human fibroblasts and keratinocytes, and the nanoparticles demonstrated no significant cytotoxicity toward these cell lines. The anticancer activity was tested against human breast cancer cell lines, which showed excellent cytotoxicity toward cancer cell lines. The investigation is promising for designing a multilamellar hybrid nanosystem-based anticancer delivery system.

In this article, the design of a non-invasive wearable antenna-based fat intra-body communication (Fat-IBC) system is presented for biomedical applications. The Fat-IBC system is used for uninterrupted communication between various wearable, implanted, and semi-implanted devices, facilitating the exchange of data and information within body area networks (BAN). Herein, to eliminate the design complexity, a simple planar-loop antenna is considered to establish the Fat-IBC link. For the numerical analysis, a three-layer human body tissue model (skin, fat, and muscle) is considered to optimize the antenna. A polydimethylsiloxane (PDMS) coating layer is deposited around the wearable antenna to eliminate direct contact with the human body. In addition, the antenna has also been shielded by a ferrite substrate and copper tape to reduce the loss of energy in undesired directions and stop the surface wave propagation over the skin tissue. The Fat-IBC system is constructed by using two identical wearable antennas that act as transmitting (Tx) and receiving (Rx) elements. These antennas have been placed on the three-layer human body tissue models at different distances to demonstrate the data transmission. The concept of the proposed wearable antenna-based Fat-IBC system has been established by numerical simulations and validated by experimental studies using phantoms. The proposed data transmission link was characterized using scattering parameters and the IEEE 802.11n wireless communication standard with combinations of on-skin wearable antennas. To achieve high in-body data rate using on-body antennas through the fat layer, a wireless LAN at the 2.4 GHz band was tested using low-cost Raspberry Pi single-board computers. The phantoms are utilized for measurement purposes to emulate the human body. For the proposed Fat-IBC system, a maximum link speed of 93 Mb/s is achieved using the 40 MHz bandwidth provided by the IEEE 802.11n standard at the frequency of 2.4 GHz. The obtained results demonstrate that the proposed Fat-IBC system, utilizing low-cost off-the-shelf hardware and established IEEE 802.11 wireless communication, can achieve high-speed data communication through three-layer phantom tissue.

Microwave sensing presents a promising alternative to conventional medical imaging techniques such as X-rays, magnetic resonance imaging (MRI), and computed tomography (CT) for medical diagnostics. This study presents a magnetic resonating antenna-based efficient system for assessing local body composition, with a primary focus on muscle quality evaluation. The system is designed using a simple planar circular spiral resonator with a notch excited by a loop antenna to form the wearable muscle analyzer. The wearable resonating antenna is designed by placing it on a three-layer (skin, fat, and muscle) tissue model. The antenna is optimized to operate in the ISM frequency band of 2.40-2.48 GHz. To analyze the muscle quality with the proposed wearable antenna, the dielectric property of muscle tissue is changed in the simulation model. Due to a change in muscle property, there is a change in the resonance frequency and phase in the characteristics of the antenna. From this change in frequency and phase, a prediction about the muscle quality has been made. Finally, the prototype of the wearable antenna is fabricated, and its performance has been measured on the threelayer phantom tissue as well as on the human arm. The obtained result shows a good agreement between simulated and measured results.

This work presents an integrated design framework for compact series-fed microstrip patch antenna arrays (SF-MSPAAs) enabling scalable sub-arrays for planar and MIMO configurations. In the proposed framework, an artificial neural network (ANN) based surrogate model is utilized as a computational accelerator to eliminate repeated full-wave simulations, while the subsequent array design is carried out using deterministic, physics-based electromagnetic design procedures. Phase equalization is achieved through S-parameter based optimization of feed-line lengths using sequential nonlinear programming (SNLP), resulting in quasi-uniform inter-element spacing without additional phase compensation structures. The desired sidelobe level (SLL) is realized via width-scaled Taylor amplitude tapering, and robust input matching is ensured using an adaptive two-section microstrip impedance transformer. Unlike conventional approaches that primarily optimize one or few performance metrics aggressively, for example, maximum sidelobe suppression, the proposed framework provides specification-driven control over key parameters such as operating frequency, SLL, gain and array footprint. This allows designs to precisely meet application-specific requirements within a structured and computationally efficient workflow. A five element prototype is designed using the proposed framework to meet an application requirement of SLL <= -15dB and a gain of >= 10 dBi operating at 6.4 GHz. The designed prototype is fabricated and experimentally validated. The measured results demonstrated a gain of similar to 12 dBi, 75% radiation efficiency, 1.6% bandwidth, -18 dB SLL and return loss better than 35 dB across the bandwidth, showing close agreement with simulations.

In this study, the focus is on developing a compact, multi-band, and flexible metasurface (MS) with low-loss characteristics for microwave applications. Recently, there has been a strong need to explore MS designs that can efficiently operate across multiple resonance bands while maintaining a compact size and low loss for advanced performance in ever-increasing microwave technology. The unit cell configuration of the MS is designed using three split-ring resonators (SRRs) interconnected with a spiral notch to achieve multi-resonance characteristics with low loss. The miniaturization of the proposed MS is achieved through inductive loading, which is enabled by extending the metallic strip length within the unit cell. Increasing the metal length increases the effective inductance-to-capacitance ratio (L/C), resulting not only in greater compactness but also lower loss. Despite its compact size and low loss, the numerical simulation results reveal that the proposed SRR-MS unit cell exhibits hexa-resonance frequencies at 2.7 GHz (S-band), 4.22 GHz, 6.25 GHz (C-band), 8.27 GHz, 9.65 GHz, and 10.95 GHz (X-band), respectively. The proposed MS exhibits six distinct $$\epsilon$$-negative (ENG) regions and one $$\mu$$-negative (MNG) region across the S, C, and X microwave frequency bands with high refractive index (HRI) properties near all six resonances. Importantly, the low-loss behavior is maintained across all six resonance frequencies of the MS. Additionally, the MS is designed on a single-sided flexible dielectric substrate, and its performance characteristics remain stable to some extent under bending and twisting.

Pleural effusion and other high-water lung abnormalities often need to be monitored continuously or repeatedly. However, traditional techniques like chest X-rays and CT scans can be hazardous because they use ionizing radiation. This study presents a microwave-based imaging method that uses a high-gain, wideband directional antenna and a raster scanning method to identify these anomalies. The antenna operates in the 1.5-3.1 GHz range, including the 2.45 GHz ISM band, giving it broad-spectrum coverage. The image reconstruction technique was modified and subsequently compared to assess the accuracy and potential for clinical application. The proposed approach is demonstrated on a realistic human body model, and a comparison with conventional X-ray imaging highlights its potential as a safe, non-invasive, and effective alternative for detecting lung conditions characterized by high water content.

Diabetic wounds, especially diabetic foot ulcers, remain a significant clinical and economic challenge, largely due to compromised angiogenesis, persistent inflammation, and delayed tissue repair. Conventional therapies often fail to address the complex nature of these wounds, which leads to prolonged healing, increased infection risk, and chances of limb amputation. Three-dimensional (3D) bioprinting has emerged as a promising approach in treating diabetic wounds, offering precise fabrication of skin substitutes that recapitulate native skin architecture and function. Recent advances in 3D bioprinting strategies have focused on three critical areas: the development of functional bioinks, promotion of vascularization, and the modulation of immune responses. Functional bioinks typically formulated with combinations of natural and synthetic polymers are designed to support cell viability, simulate the extracellular matrix, and deliver bioactive agents such as growth factors, oxygen-generating materials, or nitric oxide donors. Vascularization strategies frequently involve co-printing with endothelial cells or incorporating angiogenic factors to restore tissue perfusion and facilitate integration. Simultaneously, immune-modulatory approaches are employed to resolve chronic inflammation and create a more regenerative wound microenvironment. This review presents a comprehensive overview of 3D bioprinting strategies for diabetic wound healing by discussing three essential domains: functional bioinks, vascularization strategies, and immune modulation. It provides a unified framework that reflects the complex nature of chronic wound repair. Ongoing progress in biofabrication, material science, and regulatory frameworks will be vital for translating these innovative technologies into effective clinical interventions for diabetic wound healing.

This article introduces a biocompatible, non-invasive intra-body communication (IBC) system that employs a U-shaped, flexible wearable antenna specifically designed for fat-intra-body communication (Fat-IBC) applications. The proposed Fat-IBC system is engineered to function within the industrial, scientific, and medical (ISM) frequency range of 2.40-2.48 GHz. The U-shaped wearable antenna is encased in a biocompatible polydimethylsiloxane (PDMS) coating layer, enhancing durability and safety, making it suitable for continuous use in medical contexts. The U-shaped Fat-IBC antenna leverages the low-loss property of adipose (fat) tissue, positioned between skin and muscle layers, to facilitate efficient signal propagation across the body. The antenna is optimized for transmission through a three-layer tissue model (skin, fat, and muscle), leveraging the favorable transmission properties of adipose tissue to minimize signal loss. To assess performance, detailed numerical simulations and experimental validations were conducted using three-layer tissue models, torso phantoms (obese and athletic), and human volunteer trials. Ethical approvals were obtained for human testing, ensuring compliance with biomedical research standards. The performance of the U-shaped Fat-IBC antenna was benchmarked against a standard Bluetooth low energy (BLE) chip antenna under controlled conditions, with measurements taken in both front-to-back and side-to-side orientations across various body types. Experimental results demonstrated the superior performance of the U-shaped Fat-IBC antenna over BLE, specifically in terms of signal stability and transmission efficiency across body tissue, especially within adipose layers. Specific Absorption Rate (SAR) analysis revealed a peak value of 0.3061 W/kg, which is well within the IEEE safety limits, confirming the system's suitability for biomedical applications. This Fat-IBC platform demonstrates the feasibility of fat tissue as a viable medium for intra-body networks, offering a reliable, power-efficient solution for real-time medical telemetry and patient monitoring, with promising implications for broader health applications.

This research paper presents a comprehensive approach to improving liver cancer tumor identification in ultrasound images using a Support Vector Machine (SVM) classifier. The proposed methodology involves four critical steps: first, a novel ”peak-and-valley” noise-filtering technique is employed, which scans pixels along the Hilbert curve to adaptively reduce noise. This is followed by a ”Windows adaptive threshold” method that optimizes noise reduction and enhances Otsu’s algorithm for precise segmentation thresholding. Next, a ”core area” approach is used to detect disconnected objects, aided by a feature knowledge base. Finally, an SVM classifier is trained on distinct liver tumor types—such as abscesses, cysts, hemangiomas, and hepatocellular carcinoma—using diverse feature sets for accurate classification of liver images into normal or tumor categories. The peak-and-valley filter, grounded in order statistics, shows significant advancements in impulsive noise reduction, outperforming the median filter by relying less on background data and employing conditional rules to efficiently replace noisy pixels. The integration of Otsu’s method further refines the segmentation process due to its simplicity, nonparametric nature, and effectiveness in multi-thresholding tasks. Experimental results demonstrate the model’s ability to achieve an impressive accuracy of 91.8 ± 4.2 %, marking a significant step forward in non-invasive liver cancer diagnosis through enhanced image processing and machine learning techniques.

In-body communication is a key enabler for next-generation healthcare applications, allowing seamless networking of implants. Fat tissue, with its lower water content and reduced signal attenuation compared to other body tissues at microwave frequencies, has emerged as a promising medium for radio-based in-body networks. Despite this advantage, signal leakage through the body can compromise privacy, exposing sensitive data and the mere presence of implants to external adversaries. This paper investigates the feasibility of covert communication in fat tissue-based in-body networks by leveraging the previously unexplored signal attenuation properties of human tissue to transmit data undetectable to adversaries, ensuring privacy beyond encryption. We develop a system in which an implanted transmitter communicates discreetly with an implanted receiver, shielded from external passive eavesdroppers. Our theoretical analysis and experimental results demonstrate that the attenuation properties of human tissues enable covert communication at reduced transmit power levels without requiring friendly jamming, unlike over-the-air systems. To further enhance covertness, we explore the use of an external friendly jammer and show its significant benefits. Experimental results show a 500% increase in the maximum channel capacity of covert communication, from 2.86 bps/Hz at -56 dBm transmit power without jamming, to 17 bps/Hz with no bit errors at 0 dBm transmit power with a friendly jammer, using the IEEE 802.15.4 standard for communication in the 2.45 GHz frequency band. These findings highlight that covert communication is achievable in fat tissue-based in-body networks at low data rates without additional infrastructure such as an external jammer. For applications requiring higher data rates, a friendly jammer offers a scalable solution, making this approach practical for a wide range of implant communication scenarios.

Burns represents a serious clinical problem because the diagnosis and assessment are very complex. This paper proposes a methodology that combines the use of advanced medical imaging with predictive modeling for the improvement of burn injury assessment. The proposed framework makes use of the Adaptive Complex Independent Components Analysis (ACICA) and Reference Region (TBSA) methods in conjunction with deep learning techniques for the precise estimation of burn depth and Total Body Surface Area analysis. It also allows for the estimation of the depth of burns with high accuracy, calculation of TBSA, and non-invasive analysis with 96.7% accuracy using an RNN model. Extensive experimentation on DCE-LUV samples validates enhanced diagnostic precision and detailed texture analysis. These technologies provide nuanced insights into burn severity, improving diagnostic accuracy and treatment planning. Our results demonstrate the potential of these methods to revolutionize burn care and optimize patient outcomes.

Fat-Intra Body Communication (Fat-IBC) leverages the unique dielectric properties of human adipose tissue for low-loss microwave signal propagation. This work presents a comprehensive analysis framework combining interpolation and extrapolation algorithms to predict transmission characteristics (S21) across varying phantom lengths and antenna configurations. Interpolation accurately models intermediate lengths, while extrapolation extends predictions to unmeasured distances up to 100 cm. A software application was developed to integrate these computational models with a user-friendly interface and compatibility with microwave design tools. The results validate the efficiency of Fat-IBC for inbody-to-inbody configurations while highlighting challenges in onbody setups. This study establishes a foundation for optimizing Fat-IBC systems for biomedical and wearable applications.

Nanocellulose-based scaffolds are gaining significant attention in biomedical applications due to their non-toxicity, biocompatibility, biodegradability, high tensile strength, water absorption, and porosity. This study focuses on developing a polyethylene glycol (PEG)-plasticized nanocellulose membrane incorporated with varying concentrations (1%-5%) of zinc oxide (ZnO). The influence of ZnO on fiber morphology, mechanical strength, fiber diameter, antibacterial activity, blood compatibility, and overall biocompatibility was systematically evaluated. Structural, morphological, and thermal characterizations were performed using Scanning Electron Microscopy (SEM), Energy Dispersive Spectroscopy (EDX), Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Thermogravimetric/Differential Thermal Analysis (TGA/DTA), and Differential Scanning Calorimetry (DSC). Antibacterial and biocompatibility assessments included Minimum Inhibitory Concentration (MIC), MTT assay, hemocompatibility assay, and scratch assay. Additionally, in vivo biocompatibility was studied via subcutaneous implantation in a murine model. The incorporation of ZnO nanoparticles significantly enhanced the mechanical strength, antimicrobial efficacy, cytocompatibility, fibroblast proliferation and migration, and cellular metabolic activity of the scaffold. In vivo results indicated no inflammatory response, confirming the scaffold's biocompatibility. Overall, the ZnO-incorporated PEG-plasticized nanocellulose membrane demonstrates promising potential as an effective wound healing material due to its enhanced structural integrity and biological performance.

This study compares multiple image processing and deep learning methods to demonstrate an enhanced approach to glaucoma diagnosis. The approach focuses on noise reduction using median filtering and optic disc segmentation utilizing the U-Net and U-Net+ architectures. Capsule Networks were utilized for feature extraction and Extreme Learning Machines (ELM) for diagnostic classification. Three datasets were evaluated, including DRISHTI-GS, DRIONS-DB, and HRF, utilizing important parameters such as accuracy, sensitivity, and specificity. The findings revealed that median filtering reduced noise by 97.88%, with a peak signal-to-noise ratio of 44.99. U-Net beat U-Net+ in optic disc in the process of segmentation with a Dice coefficient of 0.8557, a Jaccard index of 0.7307, and higher segmentation accuracy. The suggested model has great diagnostic accuracy, scoring 99% for DRISHTI-GS, 99.5% for DRIONS-DB, and 98.5% for HRF. These findings show that using deep learning approaches can increase glaucoma diagnosis accuracy and reliability, with important implications for healthcare applications and patient outcomes.

MNPs have extensive array of healthcare utilization, like targeted drug delivery, magnetic resonance imaging (MRI), hyperthermia therapy, and biosensing. The review paper aims to perform an in-depth detailing of MNPs as a therapeutic and diagonostic platform. The article discusses fundamental law governing magnetic targeting and explores most widely used synthesis methods like precipitation, co-precipitation, thermal decomposition, and green synthesis. It encompasses significant features of MNPs including surface chemistry and functionalization with various functional agents, such as polymer, bioactive molecules, inorganic metals and oxide and organic surfactant. Studies have found that coated MNPs are more biocompatible and protects it from quick elimination. It also highlights current researches for different applications of MNPs along with the commercially available magnetic products. It draws attentions to current challenges and future directions in the field of MNPs. All the current investigations summarized in the present review article has shown the potential of MNPs in personalized medicine with advancement in eco-friendly synthesis and surface functionalization ability.

In this article, the design of a resonator-based fat intra-body power transfer (Fat-IBPT) system has been presented for the application of implantable medical devices (IMDs). Herein, the low-loss properties of human fat tissue sandwiched between skin and muscle are considered to act as an effective waveguide for microwave power transmission. In the proposed implantable wireless power transfer (WPT) system, both the transmitting (Tx) and receiving (Rx) resonating elements have been placed in the fat layer of the human body model. The Fat-IBPT system is constructed to work in the industrial, scientific, and medical (ISM) frequency band of 2.40-2.48 GHz. The resonating structure is constructed by a planar circular spiral with a notch enclosed and excited by a loop antenna. During the numerical study, a three-layer human body tissue model (skin, fat, and muscle) is considered to optimize the resonator configuration by inserting in the fat layer. Also, to eliminate direct contact with the human fat tissue, a bio-compatible polydimethylsiloxane (PDMS) coating layer is considered all around the resonating structure. The wireless Fat-IBPT system is constructed by using two identical resonators, which act as Tx and Rx elements that have been placed in the fat tissue layer at different distances to show the power transmission. The concept of the proposed resonator-based Fat-IBPT system has been established by numerical studies. From the proposed Fat-IBPT system, maximum power transfer efficiency (PTE) of about 8.47 % has been achieved.

The global incidence of lung diseases, particularly lung cancer, is increasing at an alarming rate, underscoring the urgent need for early detection, robust monitoring, and timely intervention. This study presents design aspects of an artificial intelligence (AI)-integrated microwave-based diagnostic tool for the early detection of lung tumors. The proposed method assimilates the prowess of machine learning (ML) tools with microwave imaging (MWI). A microwave unit containing eight antennas in the form of a wearable belt is employed for data collection from the CST body models. The data, collected in the form of scattering parameters, are reconstructed as 2D images. Two different ML approaches have been investigated for tumor detection and prediction of the size of the detected tumor. The first approach employs XGBoost models on raw S-parameters and the second approach uses convolutional neural networks (CNN) on the reconstructed 2-D microwave images. It is found that the XGBoost-based classifier with S-parameters outperforms the CNN-based classifier on reconstructed microwave images for tumor detection. Whereas a CNN-based model on reconstructed microwave images performs much better than an XGBoost-based regression model designed on the raw S-parameters for tumor size prediction. The performances of both of these models are evaluated on other body models to examine their generalization capacity over unknown data. This work explores the feasibility of a low-cost portable AI-integrated microwave diagnostic device for lung tumor detection, which eliminates the risk of exposure to harmful ionizing radiations of X-ray and CT scans.

We have developed a new broadband microwave sensor for detecting and diagnosing subsurface medical conditions such as sarcopenia. The device is an open-ended coaxial concept that has been fabricated using 3D metal printing technologies. Exploiting the transmission mode, we have achieved very broadband performance while being able to sense to depths of 2.5-3.0 cm. Early comparisons with ultrasound measures in actual patients are very encouraging.