Remodeling (re-engineering) of a tumor's stroma has been shown to improve the efficacy of anti-tumor therapies, without destroying the stroma. Even though it still remains unclear which stromal component/-s and what characteristics hinder the reach of nanoparticles deep into cancer cells, we hypothesis that mechanisms behind stroma's resistance to the penetration of nanoparticles rely heavily on extrinsic mechanical forces on stromal cells and cancer cells. Our hypothesis has been formulated on the basis of our previous study which has shown that changes in extracellular matrix (ECM) stiffness with tumor growth influence stresses exerted on fibroblasts and cancer cells, and that malignant cancer cells generate higher stresses on their stroma. This study attempts to establish a distinct identification of the components' remodeling on the distribution and magnitude of stress within a tumor tissue which ultimately will impact the resistance of stroma to treatment. In this study, our objective is to construct a three-dimensional in silico model of a pancreas tumor tissue consisting of cancer cells, stromal cells, and ECM to determine how stromal remodeling alters the stresses distribution and magnitude within the pancreas tumor tissue. Our results show that changes in mechanical properties of ECM significantly alter the magnitude and distribution of stresses within the pancreas tumor tissue. Our results revealed that these stresses are more sensitive to ECM properties as we see the stresses reaching to a maximum of 22,000 Pa for softer ECM with Young's modulus of 250 Pa. The stress distribution and magnitude within the pancreas tumor tissue does not show high sensitivity to the changes in mechanical properties of stromal cells surrounding stiffer cancer cells (PANC-1 with Young's modulus of 2400 Pa). However, softer cancer cells (MIA-PaCa-2 with (Young's modulus of 500 Pa) increase the stresses experienced by stiffer stromal cells and for stiffer ECM. By providing a unique platform to dissect and quantify the impact of individual stromal components on the stress distribution within a tumor tissue, this study serves as an important first step in understanding of which stromal components are vital for an efficient remodeling. This knowledge will be leveraged to overcome a tumor's resistance against the penetration of nanoparticles on a per-patient basis.
This review presents an in-depth examination of implantable antennas for various biomedical purposes. The development of implantable antennas, including their designs, materials, and operating principles, are introduced at the beginning of the discussion. An overview of the many kinds of implantable antennas utilized in implantable medical devices (IMDs) are presented in this study. The article then discusses the important factors to consider when developing implantable antennas for biomedical purposes, including implant placement, frequency range, and power needs. This investigation additionally examines the challenges and limitations encountered with implantable antennas, including the limited space available within the human body, the requirement for biocompatible materials, the impact of surrounding tissue on antenna performance, tissue attenuation, and signal interference. This review also emphasizes the most recent advances in implanted antenna technology, such as wireless power transmission, multiband operation, and miniaturization. Furthermore, it offers illustrations of several biomedical uses for implantable antennas, including pacemaker, capsule endoscopy, intracranial pressure monitoring, retinal prostheses, and bone implants. This paper concludes with a discussion of the future of implantable antennas and their possible use in bioelectronic medicine and novel medical implants. Overall, this survey offers a thorough analysis of implantable antennas in biomedical applications, emphasizing their importance in the development of implantable medical technology.
Objective: To analyze the impact of multiple protection model in the operating room on patients' physiological stress and risk events after coronary artery stent implantation (CASI).
Methods: During October 2021 to October 2022, 150 patients with coronary heart disease (CHD) were picked as the research subjects, all of whom underwent CASI. The clinical data were retrospectively analyzed, and the patients were divided into two groups according to different nursing methods, with 75 cases in each group. Patients in the intervention group received multiple protection model intervention in the operating room, and the patients in the control group adopted conventional care model. The patient satisfaction with nursing, postoperative recovery, psychological stress scores, physiological stress indicators, and adverse cardiac risk events were recorded.
Results: Patients in the intervention group had much higher percentage of the patient satisfaction with nursing than those in the control group (P < 0.05). The time to get out of bed and hospital stay was significantly shorter and the 6-min walking distance was markedly longer in the intervention group than the control (P < 0.05). The Hamilton Anxiety (HAMA) scale and Hamilton Depression (HAMD) scale score of patients in two groups were sharply decreased after the intervention (P < 0.05), which were strongly lower in the intervention group than the control (P < 0.001). After the intervention, the heart rate, cortisol and epinephrine of patients were all sensibly elevated in two groups (P < 0.05), which were all memorably lower in the intervention group than the control (P < 0.001). The incidence of adverse cardiac risk events in the intervention group was 5.33%, which was dramatically lower than 16.00% in the control group (P < 0.05).
Conclusion: The application of multiple protection model in the operating room on patients undergoing coronary stent implantation promoted postoperative recovery, reduced patients' psychological and physiological stress, maintained blood pressure and other vital signs, reduced the incidence of adverse cardiac risk events, and improved the patient satisfaction with nursing.
Background: Despite advances in total knee arthroplasty, many patients are still unsatisfied with the functional outcome. Multibody simulations enable a more efficient exploration of independent variables compared to experimental studies. However, to what extent numerical models can fully reproduce knee joint kinematics is still unclear. Hence, models must be validated with different test scenarios before being applied to biomechanical questions.
Methods: In our feasibility study, we analyzed a human knee specimen on a six degree of freedom joint simulator, applying a passive flexion and different laxity tests with sequential states of ligament resection while recording the joint kinematics. Simultaneously, we generated a subject-specific multibody model of the native tibiofemoral joint considering ligaments and contact between articulating cartilage surfaces.
Results: Our experimental data on the sequential states of ligament resection aligned well with the literature. The model-based knee joint kinematics during passive flexion showed good agreement with the experiment, with root-mean-square errors of less than 1.61 mm for translations and 2.1° for knee joint rotations. During laxity tests, the experiment measured up to 8 mm of anteroposterior laxity, while the numerical model allowed less than 3 mm.
Conclusion: Although the multibody model showed good agreement to the experimental kinematics during passive flexion, the validation showed that ligament parameters used in this feasibility study are too stiff to replicate experimental laxity tests correctly. Hence, more precise subject-specific ligament parameters have to be identified in the future through model optimization.
This study aims to develop a super-resolution (SR) algorithm tailored specifically for enhancing the image quality and resolution of early cervical cancer (CC) magnetic resonance imaging (MRI) images. The proposed method is subjected to both qualitative and quantitative analyses, thoroughly investigating its performance across various upscaling factors and assessing its impact on medical image segmentation tasks. The innovative SR algorithm employed for reconstructing early CC MRI images integrates complex architectures and deep convolutional kernels. Training is conducted on matched pairs of input images through a multi-input model. The research findings highlight the significant advantages of the proposed SR method on two distinct datasets at different upscaling factors. Specifically, at a 2× upscaling factor, the sagittal test set outperforms the state-of-the-art methods in the PSNR index evaluation, second only to the hybrid attention transformer, while the axial test set outperforms the state-of-the-art methods in both PSNR and SSIM index evaluation. At a 4× upscaling factor, both the sagittal test set and the axial test set achieve the best results in the evaluation of PNSR and SSIM indicators. This method not only effectively enhances image quality, but also exhibits superior performance in medical segmentation tasks, thereby providing a more reliable foundation for clinical diagnosis and image analysis.
Background: The structures around the navicular bones, which constitute the medial longitudinal arch, develop by 10 years of age. While navicular bone height is often emphasized in the assessment of flatfoot, three-dimensional (3D) evaluations, including those of structural parameters during inversion, have rarely been investigated. If the development of flatfoot during the growth process could be predicted, appropriate interventions could be implemented. Therefore, in this longitudinal cohort study, we developed a system, utilizing smartphones, to measure the 3D structure of the foot, performed a longitudinal analysis of changes in midfoot structures in 124 children aged 9-12 years, and identified factors influencing the height of the navicular bone. The foot skeletal structure was measured using a 3D system.
Results: Over 2 years, foot length and instep height increased during development, while navicular height decreased. The 25th percentile of the instep height ratio and navicular height ratio at ages 9-10 years did not exceed those at ages 11-12 years, with percentages of 17.9% and 71.6%, respectively, for boys, and 15.8% and 49.1%, respectively, for girls. As the quartiles of the second toe-heel-navicular angle (SHN angle) increased at ages 9-10 years, the axis of the bone distance (ABD) and SHN angles at ages 11-12 years also increased, resulting in a decrease in the navicular height ratio. A significant inverse correlation was found between changes in SHN angle and navicular height ratio. These findings indicate that the navicular bone rotation of the midfoot is a predictor of the descent of the navicular bone.
Conclusions: This study revealed that some children exhibit decreases in navicular bone height with growth. As a distinct feature, the inversion of the navicular bone promotes flattening of the midfoot. Thus, this study provides insights into changes in midfoot development in children and provides an effective evaluation index.
Background: Iron deficiency anemia (IDA) is a common health problem worldwide. The objective of this study was to noninvasively and quantitatively evaluate early changes in left ventricular systolic function in patients with IDA using the left ventricular press-strain loop (LV-PSL).
Methods: Sixty-two patients with IDA were selected and divided into two groups based on hemoglobin (Hb) concentration: Group B with Hb > 9 g/dL and group C with 6 g/dL < Hb < 9 g/dL. Thirty-three healthy individuals were used as the control (Group A). The global longitudinal strain (GLS), global work index (GWI), global constructive work (GCW), global waste work (GWW), global work efficiency (GWE) were derived using LV-PSL analysis. Receiver operating characteristic (ROC) curves were constructed for MW parameters to detect abnormal left ventricular systolic function in IDA patients.
Results: Compared to group A, GWI and GCW were reduced in group B (both P < 0.01). Compared with groups B and A, GLS, GWI, GCW and GWE, and E/A were all diminished, and GWW, LVEDV, LVESV, and E/mean e' were all increased in group C (all P < 0.01). GLS was positively correlated with GWI, GCW, and GWE (r = 0.679, 0.681, and 0.447, all P < 0.01), and negatively associated with GWW (r = - 0.411, all P < 0.01). For GWI, area under the ROC curve (AUROC) was 0.783. The optimal GWI threshold for detecting abnormal LV systolic function in IDA was1763 mmHg%, with sensitivity of 0.71 and specificity of 0.78.
Conclusions: LV-PSL allows noninvasive quantitative assessment of early impaired LV systolic function in IDA patients with preserved LV ejection fraction, and GWI has high sensitivity and specificity compared with other parameters.
Predicting curve progression during the initial visit is pivotal in the disease management of patients with adolescent idiopathic scoliosis (AIS)-identifying patients at high risk of progression is essential for timely and proactive interventions. Both radiological and clinical factors have been investigated as predictors of curve progression. With the evolution of machine learning technologies, the integration of multidimensional information now enables precise predictions of curve progression. This review focuses on the application of machine learning methods to predict AIS curve progression, analyzing 15 selected studies that utilize various machine learning models and the risk factors employed for predictions. Key findings indicate that machine learning models can provide higher precision in predictions compared to traditional methods, and their implementation could lead to more personalized patient management. However, due to the model interpretability and data complexity, more comprehensive and multi-center studies are needed to transition from research to clinical practice.
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