This paper presents a study on the ultra wideband (UWB) radio channel characteristics between a capsule endoscope and a directive on-body antenna in different parts of the small intestine. The study is conducted using a finite integration technique (FIT) based electromagnetic simulation software CST Studio Suite and four of its anatomical voxel models. The capsule endoscope model is set inside different areas of the small intestine of the voxel models. A recently published directive on-body antenna designed for in-body communications is used in the evaluations. The obtained frequency and time domain channel characteristics are compared with previously published results with another directive on-body antenna designed for capsule endoscopy communications. Power flow presentations are used to understand differences obtained with two on-body antennas. Different rotation angles of the capsule are also considered in this study. It is found that channel characteristics vary remarkably depending on the antenna location in the small intestine and location of the on-body antenna. Thus, the on-body antennas should be located carefully to ensure coverage over the whole intestine area. Path loss does not only depend on the distance between a capsule and the on-body antenna but also on the tissues between the capsule and on-body antennas. Obviously, the antenna patterns have clear impact on the received signal’s strength. Furthermore, orientation of the capsule affects also strongly impact when linearly polarized antennas are used.

This paper presents comprehensive study on fat as propagation medium in abdominal implant communication system at low ultrawideband (UWB) frequency range 3.75-4.25 GHz. The main aim is to investigate how signal propagates through visceral and subcutaneous fat layers and how that information can be exploited in implant communication systems. The study is conducted using different methods: electromagnetic simulations, power flow analysis, propagation path calculations and radio channel measurements with animal meat pieces. Simulations are conducted using layer models and anatomical voxel models having different sizes. Results of channel simulations are verified with propagation path calculations. Power flow analysis on cross-cuts of the voxel models is conducted to investigate how the signal propagates inside the tissues. Furthermore, measurements using different animal meat pieces are performed to evaluate the impact of fat constitution on channel characteristics. It is found that similar tendency on fat propagation is seen in the evaluations with different methods. It is also observed that channel attenuation depends not only on the types and thicknesses of the tissues between transmitter and receiver antennas, but also how the tissues, especially fat, is located between the antennas. Channel attenuation difference between different voxels is maximum 14 dB in the studied antenna locations. Furthermore, propagation channel is evaluated with measurements using pork meat having different fat and muscle constitutions. It is found that antenna location respect to fat layers has clear impact on the channel strength although the fat tissue is not directly above the in-body antenna. The difference is noted to be 3-15 dB especially on the side peaks of channel impulse response. The knowledge on fat as a propagation medium is crucial when designing medical monitoring or implant communication systems. Location of antennas/sensor nodes for the monitoring devices can be established so that propagation through fat layer can be exploited.

The paper presents a novel antenna operating at the lower UWB band (3.75–4.25 GHz), defined originally in IEEE 802.15.6 standard for Body Area Networks (BAN) applications. The proposed antenna is designed for biomedical application, wireless capsule endoscopy localization. In other words, the concerned application is dedicated to track a capsule, by means of an external device, swallowed by the patient to provide captured images of the Small Intestine (SI), essential part of the GastroIntestinal (GI) tract, and transfer them in real-time to the external device. In this context, antenna with and without cavity-backed structures, are presented and compared with the requirements for a receiving antenna in terms of directivity and bandwidth coverage in question. It was revealed that the cavity approach improved the antenna gain up to 8 dBi, at the 4 GHz center frequency, compared to 6 dBi without the cavity presence. Simulations were carried out using CST Microwave Studio, and the results were validated by measurements in proximity to human body. The antenna safety issue was assessed with CST SAR (Specific Absorption Rate) calculation, in compliance with IEEE/IEC 62704-1 standard. Results showed a maximum SAR of 0.112 W/kg and 0.005 W/kg at 4 mm and 30 mm antenna-skin distance, in the range of the SAR limit guidelines defined by safety standards. The cavity-backed antenna ability to penetrate the human tissues, to reach the small intestine layer was studied by means of CST voxel model and compared to a multi-layer model emulating the dielectric properties of the human tissues at 4 GHz. This analysis was conducted using power flow results and completed by the power field probes at the several tissue interfaces.

A recent reflector-backed antenna model is proposed in this paper for wireless capsule endoscopy localization. The antenna is designed to operate at the lowest 802.15.6 mandatory UWB (ultrawideband) channel, i.e., 4 GHz center frequency with 500 MHz bandwidth. The antenna achieves a good directivity and radiates well over the frequency band of interest. The proposed antenna was constructed within three successive steps. Initially, a planar omnidirectional antenna was designed of 3.15 dBi gain at 4 GHz. Since the antenna aims to operate as a receiving antenna, good directivity is preferred. Thus, an air-filled cavity was included backing the planar antenna to bolster the directivity toward the radiating element. The cavity-backed antenna has a measured gain of 6.4 dBi. The antenna was evaluated next to the homogenous and multilayer models. Then, the antenna design was optimized, by reducing its size, to a reflector-backed antenna structure reaching a maximum gain of 5.3 dBi, which is still promising for the regarded application. The body effect on the antenna matching was evaluated by means of multilayer and voxel models simulating the human body. This was followed by on-body measurements involving real subject. The depth of in-body propagation, from skin to small intestine, was studied using the multilayer and voxel models. Simulations were run using the CST Microwave Studio tool. While prototyping, free-space and on-body measurements took place at University of Oulu, Finland.

Chemotherapy resistance reduces treatment success in endometrial cancer and limits patient survival. Many predictive models depend on genomic or imaging data that are expensive and difficult to use in everyday care. This work introduces a machine learning framework that predicts chemotherapy resistance using only data collected before treatment. Two feature sets were evaluated. The first used four core variables: age, tumor stage, grade, and site of resection or biopsy. The second added race, and ethnicity. This study used data from the TCGA-UCEC cohort and applied Random Forest, XGBoost, logistic regression, and support vector machines. Disease progression was used as the target outcome to reflect resistance more accurately than treatment response. Models were tested with stratified validation and cross-dataset evaluation on CPTAC-UCEC cohort. The best models achieved an accuracy of 0.98, precision of 0.98, recall of 1.00, and F1-score of 0.998. Results showed that the four-feature model performed nearly the same as the six-feature model, supporting the value of simple inputs. SHAP analysis explained the influence of each variable on model output. An interactive decision-support module allows clinicians to input patient data and receive real-time risk estimates. This approach helps clinicians/pathologists/oncologists predict resistance early, improve treatment planning, and avoid unnecessary toxicity. The framework provides a clear, interpretable path toward personalized chemotherapy selection using routine clinical datas. Furthermore, a proof-of-concept study based on an extension analysis of transcriptomic data (RNA-seq) derived from TCGA-UCEC was performed to assess if the addition of only using molecular biomarkers could increase the prediction of resistance. Even with careful feature selection and rigorous cross validation, the RNA-based models had poor and unstable performance given the high dimensionality, class imbalance, as well as limited sample size. These observations underline that in realistic data situation simple and interpretable clinical models might well offer more robust and deployable predictions as high-dimensional molecular strategies.