Biomedical Physics & Engineering Express

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body's natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome.

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

The use of nanoparticles as biomaterials with applications in the biomedical field is growing every day. These nanomaterials can be used as contrast imaging agents, combination therapy agents, and targeted delivery systems in medicine and dentistry. Usually, nanoparticles are found as synthetic or natural organic materials, such as hydroxyapatite, polymers, and lipids. Besides that, they are could also be inorganic, for instance, metallic or metal-oxide-based particles. These inorganic nanoparticles could additionally present magnetic properties, such as superparamagnetic iron oxide nanoparticles. The use of nanoparticles as drug delivery agents has many advantages, for they help diminish toxicity effects in the body since the drug dose reduces significantly, increases drugs biocompatibility, and helps target drugs to specific organs. As targeted-delivery agents, one of the applications uses nanoparticles as drug delivery particles for bone-tissue to treat cancer, osteoporosis, bone diseases, and dental treatments such as periodontitis. Their application as drug delivery agents requires a good comprehension of the nanoparticle properties and composition, alongside their synthesis and drug attachment characteristics. Properties such as size, shape, core-shell designs, and magnetic characteristics can influence their behavior inside the human body and modify magnetic properties in the case of magnetic nanoparticles. Based on that, many different studies have modified the synthesis methods for these nanoparticles and developed composite systems for therapeutics delivery, adapting, and improving magnetic properties, shell-core designs, and particle size and nanosystems characteristics. This review presents the most recent studies that have been presented with different nanoparticle types and structures for bone and dental drug delivery.

In this article the Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox, a MATLAB/CUDA toolbox for fast and accurate 3D x-ray image reconstruction, is presented. One of the key features is the implementation of a wide variety of iterative algorithms as well as FDK, including a range of algorithms in the SART family, the Krylov subspace family and a range of methods using total variation regularization. Additionally, the toolbox has GPU-accelerated projection and back projection using the latest techniques and it has a modular design that facilitates the implementation of new algorithms. We present an overview of the structure and techniques used in the creation of the toolbox, together with two usage examples. The TIGRE Toolbox is released under an open source licence, encouraging people to contribute.

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

Radiomics-based prediction models have shown promise in predicting Radiation Pneumonitis (RP), a common adverse outcome of chest irradiation. Τhis study looks into more than just RP: it also investigates a bigger shift in the way radiomics-based models work. By integrating multi-modal radiomic data, which includes a wide range of variables collected from medical images including cutting-edge PET/CT imaging, we have developed predictive models that capture the intricate nature of illness progression. Radiomic features were extracted using PyRadiomics, encompassing intensity, texture, and shape measures. The high-dimensional dataset formed the basis for our predictive models, primarily Gradient Boosting Machines (GBM)—XGBoost, LightGBM, and CatBoost. Performance evaluation metrics, including Multi-Modal AUC-ROC, Sensitivity, Specificity, and F1-Score, underscore the superiority of the Deep Neural Network (DNN) model. The DNN achieved a remarkable Multi-Modal AUC-ROC of 0.90, indicating superior discriminatory power. Sensitivity and specificity values of 0.85 and 0.91, respectively, highlight its effectiveness in detecting positive occurrences while accurately identifying negatives. External validation datasets, comprising retrospective patient data and a heterogeneous patient population, validate the robustness and generalizability of our models. The focus of our study is the application of sophisticated model interpretability methods, namely SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), to improve the clarity and understanding of predictions. These methods allow clinicians to visualize the effects of features and provide localized explanations for every prediction, enhancing the comprehensibility of the model. This strengthens trust and collaboration between computational technologies and medical competence. The integration of data-driven analytics and medical domain expertise represents a significant shift in the profession, advancing us from analyzing pixel-level information to gaining valuable prognostic insights.

To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve's complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(μMS) is showing incredible promise. This neuromodulation technique uses microcoils(μcoils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday's law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidal μcoil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) following μMS of the vagus. The μcoils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000 μsec) at a frequency of 20 Hz. Under the influence of isoflurane, μMS of the left vagus at 1000 μsec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast, μMS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagal μMS suggesting the drop in MAP associated with vagus μMS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed upon μMS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy of μMS on the vagal nerve. Absence of HR modulation upon μMS could offer alternative treatment options using VNS with fewer heart-related side-effects.

Adhesives that involve adhesion to the skin have been of great technological importance in medical or pharmaceutical fields, including recently emerging wearable sensors and electronics. The objective of this work was to evaluate the performances of silicone-based adhesives with skin, using a peel adhesion test. Specifically, we explored the effect of adhesive cleansing, which is an inevitable daily event for patients' comfort in long-term applications. Firstly, three medical grade silicone gels, Silbione® RT 4717, Silbione® RT 4642, and Silpuran® 2130, were used to fabricate adhesive pads. Their peel strength values were subsequently measured and compared, among which Silbione® RT gel 4717 possessed the highest peel strength. Therefore, it was selected as the raw material to fabricate the pads with a thickness range of 640–740 μm. Secondly, the peel adhesion of Silbione® RT 4717 adhesive pad was further compared with a series of commercial products that employ various medical-grade adhesives. The peel strength results indicated that our custom-made adhesive pad had an adequately strong adhesion for clinical use. Thirdly, in order to observe and predict the long-term performance of the adhesives, an aging test was performed in an ambient environment, revealing that Silbione® RT 4717 adhesive remained highly sticky for 5 days. Lastly, adequate cleansing protocols were established by monitoring the changes in peel strength after washing and wiping events. The reusability analysis showed that Silbione® 4717 adhesive pad was reusable in a one-week period for the washing method and 3 days for the wiping method.

In this study, the performance of a new iterative reconstruction algorithm, the pre-clinical AcurosXB iCBCT algorithm, has been characterized on Varian Halcyon linear accelerators with respect to the potential of radiotherapy dose calculations on CBCT images. The study utilized various phantom setups to verify the accuracy of the pre-clinical algorithm under different scatter conditions and compared dose calculations performed on CBCT images reconstructed with the pre-clinical algorithm to those performed on typical planning CT images. The results indicated that despite showing improvements compared to the existing iCBCT protocol, certain restrictions should be introduced when the pre-clinical AcurosXB iCBCT algorithm was used for dose calculations. Changes in the scatter condition exhibited a larger effect on CBCTs than on planning CTs. Therefore, users should be careful in offsetting the patient and positioning the patient's arms if the resultant images will be used for dose calculations. In addition, protocols with different kV settings should be approached with caution, where 100 kV protocols should only be used to scan the head and neck area, while the rest of the body should be scanned with the 125 kV and 140 kV protocols. When the patient is set up properly and the appropriate energy is selected for the anatomical area, the uncertainty of using the novel AcurosXB iCBCT algorithm for treatment planning dose calculation is within ±2.0%.

In this study, a combined subject-specific numerical and experimental investigation was conducted to explore the plantar pressure of an individual. The research utilized finite element (FE) and musculoskeletal modelling based on computed tomography (CT) images of an ankle-foot complex and three-dimensional gait measurements. Muscle forces were estimated using an individualized multi-body musculoskeletal model in five gait phases. The results of the FE model and gait measurements for the same subject revealed the highest stress concentration of 0.48 MPa in the forefoot, which aligns with previously-reported clinical observations. Additionally, the study found that the encapsulated soft tissue FE model with hyper-elastic properties exhibited higher stresses compared to the model with linear-elastic properties, with maximum ratios of 1.16 and 1.88 MPa in the contact pressure and von-Mises stress, respectively. Furthermore, the numerical simulation demonstrated that the use of an individualized insole caused a reduction of 8.3% in the maximum contact plantar pressure and 14.7% in the maximum von-Mises stress in the encapsulated soft tissue. Overall, the developed model in this investigation holds potential for facilitating further studies on foot pathologies and the improvement of rehabilitation techniques in clinical settings.

Epilepsy, a chronic non-communicable disease is characterized by repeated unprovoked seizures, which are transient episodes of abnormal electrical activity in the brain. While Electroencephalography (EEG) is considered as the gold standard for diagnosis in current clinical practice, manual inspection of EEG is time consuming and biased. This paper presents a novel hybrid 1D CNN-Bi LSTM feature fusion model for automatically detecting seizures. The proposed model leverages spatial features extracted by one dimensional convolutional neural network and temporal features extracted by bi directional long short-term memory network. Ictal and inter ictal data is first acquired from the long multichannel EEG record. The acquired data is segmented and labelled using small fixed windows. Signal features are then extracted from the segments concurrently by the parallel combination of CNN and Bi-LSTM. The spatial and temporal features thus captured are then fused to enhance classification accuracy of model. The approach is validated using benchmark CHB-MIT dataset and 5-fold cross validation which resulted in an average accuracy of 95.90%, with precision 94.78%, F1 score 95.95%. Notably model achieved average sensitivity of 97.18% with false positivity rate at 0.05/hr. The significantly lower false positivity and false negativity rates indicate that the proposed model is a promising tool for detecting seizures in epilepsy patients. The employed parallel path network benefits from memory function of Bi-LSTM and strong feature extraction capabilities of CNN. Moreover, eliminating the need for any domain transformation or additional preprocessing steps, model effectively reduces complexity and enhances efficiency, making it suitable for use by clinicians during the epilepsy diagnostic process.

Objective. In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e. bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.

Approach. DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson's correlation and Czekanowski's index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon's signed rank test and the Kolmogorov-Smirnov two sample test returned p ≤ 0.05 for both tests.

Main results. Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with all p ≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with all p < 10⁻⁶. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.

Significance. Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system's DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B-US) images created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection. Approach. We presented an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection. Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers. Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications.

A novel fiber optic biosensor was purposed for a new approach to monitor amyloid beta protein fragment 1–42 (Aβ42) for Alzheimer's Disease (AD) early detection. The sensor was fabricated by etching a part of fiber from single mode fiber loop in pure hydrofluoric acid solution and utilized as a Local Optical Refractometer (LOR) to monitor the change Aβ42 concentration in Artificial Cerebrospinal Fluid (ACSF). The Fiber Loop Ringdown Spectroscopy (FLRDS) technique is an ultra-sensitive measurement technique with low-cost, high sensitivity, real-time measurement, continuous measurement and portability features that was utilized with a fiber optic sensor for the first time for the detection of a biological signature in an ACSF environment. Here, the measurement is based on the total optical loss detection when specially fabricated sensor heads were immersed into ACSF solutions with and without different concentrations of Aβ42 biomarkers since the bulk refractive index change was performed. Baseline stability and the reference ring down times of the sensor head were measured in the air as 0.87% and 441.6 μs ± 3.9 μs, respectively. Afterward, the total optical loss of the system was measured when the sensor head was immersed in deionized water, ACSF solution, and ACSF solutions with Aβ42 in different concentrations. The lowest Aβ42 concentration of 2 ppm was detected by LOR. Results showed that LOR fabricated by single-mode fibers for FLRDS system design are promising candidates to be utilized as fiber optic biosensors after sensor head modification and have a high potential for early detection applications of not only AD but possibly also several fatal diseases such as diabetes and cancer.

To localize the unusual cardiac activities non-invasively, one has to build a prior forward model that relates the heart, torso, and detectors. This model has to be constructed to mathematically relate the geometrical and functional activities of the heart. Several methods are available to model the prior sources in the forward problem, which results in the lead field matrix generation. In the conventional technique, the lead field assumed the fixed prior sources, and the source vector orientations were presumed to be parallel to the detector plane with the unit strength in all directions. However, the anomalies cannot always be expected to occur in the same location and orientation, leading to misinterpretation and misdiagnosis. To overcome this, the work proposes a new forward model constructed using the VCG signals of the same subject. Furthermore, three transformation methods were used to extract VCG in constructing the time-varying lead fields to steer to the orientation of the source rather than just reconstructing its activities in the inverse problem. In addition, the unit VCG loop of the acute ischemia patient was extracted to observe the changes compared to the normal subject. The abnormality condition was achieved by delaying the depolarization time by 15ms. The results involving the unit vectors of VCG demonstrated the anisotropic nature of cardiac source orientations, providing information about the heart's electrical activity.

Guided tissue/bone regeneration (GTR/GBR) is a widely used technique in dentistry to facilitate the regeneration of damaged bone and tissue, which involves guiding materials that eventually degrade, allowing newly created tissue to take its place. This comprehensive review the evolution of biomaterials for guided bone regeneration that showcases a progressive shift from non-resorbable to highly biocompatible and bioactive materials, allowing for more effective and predictable bone regeneration. The evolution of biomaterials for guided bone regeneration GTR/GBR has marked a significant progression in regenerative dentistry and maxillofacial surgery. Biomaterials used in GBR have evolved over time to enhance biocompatibility, bioactivity, and efficacy in promoting bone growth and integration. This review also probes into several promising fabrication techniques like electrospinning and latest 3D printing fabrication techniques, which have shown potential in enhancing tissue and bone regeneration processes. Further, the challenges and future direction of GTR/GBR are explored and discussed.

The paper aims to explore the current state of understanding surrounding in silico oral modelling. This involves exploring methodologies, technologies and approaches pertaining to the modelling of the whole oral cavity; both internally and externally visible structures that may be relevant or appropriate to oral actions. Such a model could be referred to as a 'complete model' which includes consideration of a full set of facial features (i.e. not only mouth) as well as synergistic stimuli such as audio and facial thermal data. 3D modelling technologies capable of accurately and efficiently capturing a complete representation of the mouth for an individual have broad applications in the study of oral actions, due to their cost-effectiveness and time efficiency. This review delves into the field of clinical phonetics to classify oral actions pertaining to both speech and non-speech movements, identifying how the various vocal organs play a role in the articulatory and masticatory process. Vitaly, it provides a summation of 12 articulatory recording methods, forming a tool to be used by researchers in identifying which method of recording is appropriate for their work. After addressing the cost and resource-intensive limitations of existing methods, a new system of modelling is proposed that leverages external to internal correlation modelling techniques to create a more efficient models of the oral cavity. The vision is that the outcomes will be applicable to a broad spectrum of oral functions related to physiology, health and wellbeing, including speech, oral processing of foods as well as dental health. The applications may span from speech correction, designing foods for the aging population, whilst in the dental field we would be able to gain information about patient's oral actions that would become part of creating a personalised dental treatment plan.

Positron emission tomography (PET) is a powerful medical imaging modality used in nuclear medicine to diagnose and monitor various clinical diseases in patients. It is more sensitive and produces a highly quantitative mapping of the three-dimensional biodistribution of positron-emitting radiotracers inside the human body. The underlying technology is constantly evolving, and recent advances in detection instrumentation and PET scanner design have significantly improved the medical diagnosis capabilities of this imaging modality, making it more efficient and opening the way to broader, innovative, and promising clinical applications. Some significant achievements related to detection instrumentation include introducing new scintillators and photodetectors as well as developing innovative detector designs and coupling configurations. Other advances in scanner design include moving towards a cylindrical geometry, 3D acquisition mode, and the trend towards a wider axial field of view and a shorter diameter. Further research on PET camera instrumentation and design will be required to advance this technology by improving its performance and extending its clinical applications while optimising radiation dose, image acquisition time, and manufacturing cost. This article comprehensively reviews the various parameters of detection instrumentation and PET system design. Firstly, an overview of the historical innovation of the PET system has been presented, focusing on instrumental technology. Secondly, we have characterised the main performance parameters of current clinical PET and detailed recent instrumental innovations and trends that affect these performances and clinical practice. Finally, prospects for this medical imaging modality are presented and discussed. This overview of the PET system's instrumental parameters enables us to draw solid conclusions on achieving the best possible performance for the different needs of different clinical applications.

Intrapartum fetal hypoxia is related to long-term morbidity and mortality of the fetus and the mother. Fetal surveillance is extremely important to minimize the adverse outcomes arising from fetal hypoxia during labour. Several methods have been used in current clinical practice to monitor fetal well-being. For instance, biophysical technologies including cardiotocography, ST-analysis adjunct to cardiotocography, and Doppler ultrasound are used for intrapartum fetal monitoring. However, these technologies result in a high false-positive rate and increased obstetric interventions during labour. Alternatively, biochemical-based technologies including fetal scalp blood sampling and fetal pulse oximetry are used to identify metabolic acidosis and oxygen deprivation resulting from fetal hypoxia. These technologies neither improve clinical outcomes nor reduce unnecessary interventions during labour. Also, there is a need to link the physiological changes during fetal hypoxia to fetal monitoring technologies. The objective of this article is to assess the clinical background of fetal hypoxia and to review existing monitoring technologies for the detection and monitoring of fetal hypoxia. A comprehensive review has been made to predict fetal hypoxia using computational and machine-learning algorithms. The detection of more specific biomarkers or new sensing technologies is also reviewed which may help in the enhancement of the reliability of continuous fetal monitoring and may result in the accurate detection of intrapartum fetal hypoxia.

In recent years, nanoparticles (NPs) have been extensively developed as drug carriers to overcome the limitations of cancer therapeutics. However, there are several biological barriers to nanomedicines, which include the lack of stability in circulation, limited target specificity, low penetration into tumors and insufficient cellular uptake, restricting the active targeting toward tumors of nanomedicines. To address these challenges, a variety of promising strategies were developed recently, as they can be designed to improve NP accumulation and penetration in tumor tissues, circulation stability, tumor targeting, and intracellular uptake. In this Review, we summarized nanomaterials developed in recent three years that could be utilized to improve drug delivery for cancer treatments.

The role of bacterial cell division on the damage of adherent bacteria to titanium (Ti) nano-pillar cicada wing like surface was analyzed. Therefore nano-pillar Ti thin films were fabricated by glancing angle sputter deposition (GLAD) on silicon substrates. Gram-negative E. coli bacteria were allowed to adhere and to proliferate on these nanostructured samples for 3 h at 37 °C either under optimal cell growth conditions (brain heart infusion medium, BHI) or limited growth conditions (RPMI1640 medium). The bacteria adhered to the samples in both media. Compared to BHI medium the growth of E. coli in RPMI1640 medium was significantly inhibited. Concomitantly, the ratio of dead/living adherent bacteria on the nano-pillar surface was significantly decreased after the incubation period in RPMI1640. In addition, when the bacterial proliferation was biochemically halted using DL-serine-hydroxamate a comparable decrease in the ratio of dead/living adherent bacteria was also obtained in BHI medium. These results indicate that cell growth of adherent E. coli which is accompanied by cell elongations of the rod structure is involved in the damage induced by the titanium nano-pillar surface.

Cherenkov radiation is the primary source of unwanted light in a scintillator dosimetry system. In this work we compare two techniques for temporally separating Cherenkov radiation from a slow scintillator signal. These techniques are applicable to a pulsed radiation beam. We found that by analysing the rising edge of the light pulse to identify the fast Cherenkov light only removed 74% of the Cherenkov light. By integrating the tail of the signal where only scintillation light is present a more accurate result is achieved. The average of the results of the two methods provides up to a 90% improvement in the accuracy of the relative dose when compared to ionisation chamber, in certain measurements. This work demonstrates an alternative methodology for the removal of Cherenkov light using signal analysis, while preserving all the scintillation light signal and minimising the bulk of the experimental equipment.

Materials, such as biopolymers, can be applied to produce scaffolds as mechanical support for cell growth in regenerative medicine. Two examples are polycaprolactone (PCL) and poly (lactic-co-glycolic acid) (PLGA), both used in this study to evaluate the behavior of umbilical cord-derived mesenchymal stem cells. The scaffolds were produced by the 3D printing technique using PCL as a polymer covered with PLGA fibers obtained by electrospinning. The cells were seeded in three concentrations: 8.5 × 10³; 25.5 × 10³ and 51.0 × 10³ on the two surfaces of the scaffolds. With scanning electron microscopy (SEM), it was observed that the electrospun fibers were integrated into the 3D printed matrices. Confocal laser scanning microscopy and SEM confirmed the presence of attached cells and the lactate dehydrogenase release test showed the scaffolds were not cytotoxic. The cells were able to differentiate into osteogenic and chondrogenic lineages on the scaffolds. Mechanical test showed that the cells seeded on the 3D printed PCL matrices coated with PLGA electrospun nanofibers (3D + ES + SC) did not show significant difference in tensile modulus than the pure PCL matrix (3D) or PCL matrices coated with PLGA electrospun nanofibers (3D + ES). The combination of the two polymers facilitated the production of a support with greater mechanical stability due to the presence of the 3D printed PCL matrices fabricated by melted filaments and greater cell adhesion due to the PLGA fibers. The scaffolds are suitable for use in cell therapy and also for tissue regeneration purposes.

Objective. Ultrasound-assisted orthopaedic navigation held promise due to its non-ionizing feature, portability, low cost, and real-time performance. To facilitate the applications, it was critical to have accurate and real-time bone surface segmentation. Nevertheless, the imaging artifacts and low signal-to-noise ratios in the tomographical B-mode ultrasound (B-US) images created substantial challenges in bone surface detection. In this study, we presented an end-to-end lightweight US bone segmentation network (UBS-Net) for bone surface detection. Approach. We presented an end-to-end lightweight UBS-Net for bone surface detection, using the U-Net structure as the base framework and a level set loss function for improved sensitivity to bone surface detectability. A dual attention (DA) mechanism was introduced at the end of the encoder, which considered both position and channel information to obtain the correlation between the position and channel dimensions of the feature map, where axial attention (AA) replaced the traditional self-attention (SA) mechanism in the position attention module for better computational efficiency. The position attention and channel attention (CA) were combined with a two-class fusion module for the DA map. The decoding module finally completed the bone surface detection. Main Results. As a result, a frame rate of 21 frames per second (fps) in detection were achieved. It outperformed the state-of-the-art method with higher segmentation accuracy (Dice similarity coefficient: 88.76% versus 87.22%) when applied the retrospective ultrasound (US) data from 11 volunteers. Significance. The proposed UBS-Net for bone surface detection in ultrasound achieved outstanding accuracy and real-time performance. The new method out-performed the state-of-the-art methods. It had potential in US-guided orthopaedic surgery applications.

To localize the unusual cardiac activities non-invasively, one has to build a prior forward model that relates the heart, torso, and detectors. This model has to be constructed to mathematically relate the geometrical and functional activities of the heart. Several methods are available to model the prior sources in the forward problem, which results in the lead field matrix generation. In the conventional technique, the lead field assumed the fixed prior sources, and the source vector orientations were presumed to be parallel to the detector plane with the unit strength in all directions. However, the anomalies cannot always be expected to occur in the same location and orientation, leading to misinterpretation and misdiagnosis. To overcome this, the work proposes a new forward model constructed using the VCG signals of the same subject. Furthermore, three transformation methods were used to extract VCG in constructing the time-varying lead fields to steer to the orientation of the source rather than just reconstructing its activities in the inverse problem. In addition, the unit VCG loop of the acute ischemia patient was extracted to observe the changes compared to the normal subject. The abnormality condition was achieved by delaying the depolarization time by 15ms. The results involving the unit vectors of VCG demonstrated the anisotropic nature of cardiac source orientations, providing information about the heart's electrical activity.

Objective: Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters. This limited robustness hinders the generalizable validity of radiomics-assisted models. Our aim is to investigate a possible harmonization strategy based on matching image quality to improve feature robustness.&#xD;Approach: We acquired CT scans of a phantom with two scanners across different dose levels and percentages of Iterative Reconstruction algorithms. The detectability index was used as a comprehensive task-based image quality metric. A statistical analysis&#xD;based on the Intraclass Correlation Coefficient was performed to determine if matching image quality/appearance could enhance the robustness of radiomics features extracted from the phantom images. Additionally, an Artificial Neural Network was trained on these features to automatically classify the scanner used for image acquisition.&#xD;Main results: We found that the ICC of the features across protocols providing a similar detectability index improves with respect to the ICC of the features across protocols providing a different detectability index. This improvement was particularly noticeable in features relevant for distinguishing between scanners. &#xD;Significance: This preliminary study demonstrates that a harmonization based on image quality/appearance matching could improve radiomics features robustness and heterogeneous protocols can be used to obtain a similar image appearance in terms of the detectability index. Thus protocols with a lower dose level could be selected to reduce the amount of radiation dose delivered to the patient and simultaneously obtain a more robust quantitative analysis.

We have previously reported the design of a portable ¹⁰⁹Cd x-ray fluorescence (XRF) system to measure iron levels in the skin of patients with either iron overload disease, such as thalassemia, or iron deficiency disease, such as anemia. In phantom studies, the system was found to have a detection limit of 1.35 μg Fe per g of tissue for a dose of 1.1 mSv. However, the system must provide accurate as well as precise measurements of iron levels in the skin in order to be suitable for human studies. The accuracy of the system has been explored using several methods. First, the iron concentrations of ten pigskin samples were assessed using both the portable XRF system and ICP-MS, and the results were compared. Overall, it was found that XRF and ICP-MS reported average values for iron in skin that were comparable to within uncertainties. The mean difference between the two methodologies was not significant, 2.5 ± 4.6 μg Fe per g. On this basis, the system could be considered accurate. However, ICP-MS measurements reported a wider range of values than XRF, with two individual samples having ICP-MS results that were significantly elevated (p < 0.05) compared to XRF. Synchrotron μXRF maps of iron levels in pigskin were acquired on the BioXAS beam line of the Canadian Light Source. The μXRF maps indicated two important features in the distribution of iron in pigskin. First, there were small areas of high iron concentration in the pigskin samples, that were predominantly located in the dermis and hypodermis at depths greater than 0.5 mm. Monte Carlo modelling using the EGS 5 code determined that if these iron 'hot spots' were located towards the back of the skin at depths greater than 0.5 mm, they would not be observed by XRF, but would be measured by ICP-MS. These results support a hypothesis that iron levels in the two samples that reported significantly elevated ICP-MS results compared to XRF may have had small blood vessels at the back of the skin. Second, the synchrotron μXRF maps also showed a narrow (approximately 100μm thick) layer of elevated iron at the surface of the skin. Monte Carlo models determined that, as expected, the XRF system was most sensitive to these skin layers. However, the simulations found that the XRF system, when calibrated against homogenous water-based phantoms, was found to accurately measure average iron levels in the skin of normal pigs despite the greater sensitivity to the surface layer. The Monte Carlo results further indicated that with highly elevated skin surface iron levels, the XRF system would not provide a good estimate of average skin iron levels. The XRF estimate could, with correction factors, provide a good estimate of the iron levels in the surface layers of skin. There is limited data on iron distribution in skin, especially under conditions of disease. If iron levels are elevated at the skin surface by diseases including thalassemia and hemochromatosis, this XRF device may prove to be an accurate clinical tool. However, further data are required on skin iron distributions in healthy and iron overload disease before this system can be verified to provide accurate measurements.

Background. Modern radiation therapy technologies aim to enhance radiation dose precision to the tumor and utilize hypofractionated treatment regimens. Verifying the dose distributions associated with these advanced radiation therapy treatments remains an active research area due to the complexity of delivery systems and the lack of suitable three-dimensional dosimetry tools. Gel dosimeters are a potential tool for measuring these complex dose distributions. A prototype tabletop solid-tank fan-beam optical CT scanner for readout of gel dosimeters was recently developed. This scanner does not have a straight raypath from source to detector, thus images cannot be reconstructed using filtered backprojection (FBP) and iterative techniques are required. Purpose. To compare a subset of the top performing algorithms in terms of image quality and quantitatively determine the optimal algorithm while accounting for refraction within the optical CT system. The following algorithms were compared: Landweber, superiorized Landweber with the fast gradient projection perturbation routine (S-LAND-FGP), the fast iterative shrinkage/thresholding algorithm with total variation penalty term (FISTA-TV), a monotone version of FISTA-TV (MFISTA-TV), superiorized conjugate gradient with the nonascending perturbation routine (S-CG-NA), superiorized conjugate gradient with the fast gradient projection perturbation routine (S-CG-FGP), superiorized conjugate gradient with with two iterations of CG performed on the current iterate and the nonascending perturbation routine (S-CG-2-NA). Methods. A ray tracing simulator was developed to track the path of light rays as they traverse the different mediums of the optical CT scanner. Two clinical phantoms and several synthetic phantoms were produced and used to evaluate the reconstruction techniques under known conditions. Reconstructed images were analyzed in terms of spatial resolution, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal non-uniformity (SNU), mean relative difference (MRD) and reconstruction time. We developed an image quality based method to find the optimal stopping iteration window for each algorithm. Imaging data from the prototype optical CT scanner was reconstructed and analysed to determine the optimal algorithm for this application. Results. The optimal algorithms found through the quantitative scoring metric were FISTA-TV and S-CG-2-NA. MFISTA-TV was found to behave almost identically to FISTA-TV however MFISTA-TV was unable to resolve some of the synthetic phantoms. S-CG-NA showed extreme fluctuations in the SNR and CNR values. S-CG-FGP had large fluctuations in the SNR and CNR values and the algorithm has less noise reduction than FISTA-TV and worse spatial resolution than S-CG-2-NA. S-LAND-FGP had many of the same characteristics as FISTA-TV; high noise reduction and stability from over iterating. However, S-LAND-FGP has worse SNR, CNR and SNU values as well as longer reconstruction time. S-CG-2-NA has superior spatial resolution to all algorithms while still maintaining good noise reduction and is uniquely stable from over iterating. Conclusions. Both optimal algorithms (FISTA-TV and S-CG-2-NA) are stable from over iterating and have excellent edge detection with ESF MTF 50% values of 1.266 mm⁻¹ and 0.992 mm⁻¹. FISTA-TV had the greatest noise reduction with SNR, CNR and SNU values of 424, 434 and 0.91 × 10⁻⁴, respectively. However, low spatial resolution makes FISTA-TV only viable for large field dosimetry. S-CG-2-NA has better spatial resolution than FISTA-TV with PSF and LSF MTF 50% values of 1.581 mm⁻¹ and 0.738 mm⁻¹, but less noise reduction. S-CG-2-NA still maintains good SNR, CNR, and SNU values of 168, 158 and 1.13 × 10⁻⁴, respectively. Thus, S-CG-2-NA is a well rounded reconstruction algorithm that would be the preferable choice for small field dosimetry.

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society's (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

To treat diseases associated with vagal nerve control of peripheral organs, it is necessary to selectively activate efferent and afferent fibers in the vagus. As a result of the nerve's complex anatomy, fiber-specific activation proves challenging. Spatially selective neuromodulation using micromagnetic stimulation(μMS) is showing incredible promise. This neuromodulation technique uses microcoils(μcoils) to generate magnetic fields by powering them with a time-varying current. Following the principles of Faraday's law of induction, a highly directional electric field is induced in the nerve from the magnetic field. In this study on rodent cervical vagus, a solenoidal μcoil was oriented at an angle to left and right branches of the nerve. The aim of this study was to measure changes in the mean arterial pressure (MAP) and heart rate (HR) following μMS of the vagus. The μcoils were powered by a single-cycle sinusoidal current varying in pulse widths(PW = 100, 500, and 1000 μsec) at a frequency of 20 Hz. Under the influence of isoflurane, μMS of the left vagus at 1000 μsec PW led to an average drop in MAP of 16.75 mmHg(n = 7). In contrast, μMS of the right vagus under isoflurane resulted in an average drop of 11.93 mmHg in the MAP(n = 7). Surprisingly, there were no changes in HR to either right or left vagal μMS suggesting the drop in MAP associated with vagus μMS was the result of stimulation of afferent, but not efferent fibers. In urethane anesthetized rats, no changes in either MAP or HR were observed upon μMS of the right or left vagus(n = 3). These findings suggest the choice of anesthesia plays a key role in determining the efficacy of μMS on the vagal nerve. Absence of HR modulation upon μMS could offer alternative treatment options using VNS with fewer heart-related side-effects.

This article describes the development of a system for in vivo measurements of lead body burden in mice using ¹⁰⁹Cd K x-ray fluorescence (XRF). This K XRF system could facilitate early-stage studies on interventions that ameliorate or reverse organ tissue damage from lead poisoning by reducing animal numbers through a cross-sectional study approach. A novel mouse phantom was developed based on a mouse atlas and 3D-printed using PLA plastic with plaster of Paris 'bone' inserts. PLA plastic was found to be a good surrogate for soft tissue in XRF measurements and the phantoms were found to be good models of mice. As expected, lead detection limits varied with mouse size, mouse orientation, and mouse position with respect to the source and detector. The work suggests that detection limits of 10 to 20 μg Pb per g bone mineral may be possible for a 2 to 3 hour XRF measurement in a single animal, an adequate limit for some pre-clinical studies. The ¹⁰⁹Cd K XRF mouse measurement system was also modeled using the Monte Carlo code MCNP. The combination of experiment and modeling found that contrary to expectation, accurate measurements of lead levels in mice required calibration using mouse-specific calibration standards due to the coherent scatter peak normalization failing when small animals are measured. MCNP modeling determined that this was because the coherent scatter signal from soft tissue, which until now has been assumed negligible, becomes significant when compared to the coherent scatter signal in bone in small animals. This may have implications for some human measurements. This work suggests that ¹⁰⁹Cd K x-ray fluorescence measurements of lead body burden are precise enough to make the system feasible for small animals if appropriately calibrated. Further work to validate the technology's measurement accuracy and performance in vivo will be required.

Objective&#xD;We present a novel concept to treat ophthalmic tumors which combines brachytherapy and low-energy x-ray therapy. Brachytherapy with ¹⁰⁶Ru applicators applicators is inadequate for intraocular tumors with a height of 7 mm and more. This results from a steep dose gradient, and it is unfeasible to deliver the required dose at the tumor apex without exceeding the maximum tolerable sclera dose of usually 1000 to 1500 Gy. Other modalities, such as irradiation with charged particles, may be individually contraindicated. A dose boost at the apex provided by an x-ray therapy unit, performed simultaneously with the brachytherapy, results in a more homogeneous dose profile than brachytherapy alone. This avoids damage to organs at risk. The applicator may also serve as a beam stop for x-rays passing through the target volume, which reduces healthy tissue dosage. This study aims to investigate the suitability of the applicator to serve as a beam stop for the x-rays. &#xD;&#xD;Approach&#xD;A phantom with three detector types comprising a soft x-ray ionization chamber, radiochromic films, and a self-made scintillation detector was constructed to perform dosimetry. Measurements were performed using a conventional x-ray unit for superficial therapy to investigate the uncertainties of the phantom and the ability of the applicator to absorb x-rays. The manufacturer provided a dummy plaque to obtain x-ray dose profiles without noise from ¹⁰⁶Ru decays.&#xD;&#xD;Results&#xD;The phantom is generally feasible to obtain dose profiles with three different detector types. The interaction of x-rays with the silver of the applicator leads to an increased dose rate in front of the applicator. The dose rate of the x-rays is reduced by up to 90 % behind a ¹⁰⁶Ru applicator. Therefore, a ¹⁰⁶Ru applicator can be used as a beam stop in combined x-ray and brachytherapy treatment.

In this study, a combined subject-specific numerical and experimental investigation was conducted to explore the plantar pressure of an individual. The research utilized finite element (FE) and musculoskeletal modelling based on computed tomography (CT) images of an ankle-foot complex and three-dimensional gait measurements. Muscle forces were estimated using an individualized multi-body musculoskeletal model in five gait phases. The results of the FE model and gait measurements for the same subject revealed the highest stress concentration of 0.48 MPa in the forefoot, which aligns with previously-reported clinical observations. Additionally, the study found that the encapsulated soft tissue FE model with hyper-elastic properties exhibited higher stresses compared to the model with linear-elastic properties, with maximum ratios of 1.16 and 1.88 MPa in the contact pressure and von-Mises stress, respectively. Furthermore, the numerical simulation demonstrated that the use of an individualized insole caused a reduction of 8.3% in the maximum contact plantar pressure and 14.7% in the maximum von-Mises stress in the encapsulated soft tissue. Overall, the developed model in this investigation holds potential for facilitating further studies on foot pathologies and the improvement of rehabilitation techniques in clinical settings.