Background: Diabetic foot ulcers (DFU) are a serious complication of diabetes that lead to significant morbidity and mortality. Recent studies reported that exosomes secreted by human adipose tissue-derived mesenchymal stem cells (ADSCs) might alleviate DFU development. However, the molecular mechanism of ADSCs-derived exosomes in DFU is far from being addressed. Methods: Human umbilical vein endothelial cells (HUVECs) were induced by high-glucose (HG), which were treated with exosomes derived from nuclear factor I/C (NFIC)-modified ADSCs. MicroRNA-204-3p (miR-204-3p), homeodomain-interacting protein kinase 2 (HIPK2), and NFIC were determined using real-time quantitative polymerase chain reaction. Cell proliferation, apoptosis, migration, and angiogenesis were assessed using cell counting kit-8, 5-ethynyl-2′-deoxyuridine (EdU), flow cytometry, wound healing, and tube formation assays. Binding between miR-204-3p and NFIC or HIPK2 was predicted using bioinformatics tools and validated using a dual-luciferase reporter assay. HIPK2, NFIC, CD81, and CD63 protein levels were measured using western blot. Exosomes were identified by a transmission electron microscope and nanoparticle tracking analysis. Results: miR-204-3p and NFIC were reduced, and HIPK2 was enhanced in DFU patients and HG-treated HUVECs. miR-204-3p overexpression might abolish HG-mediated HUVEC proliferation, apoptosis, migration, and angiogenesis in vitro. Furthermore, HIPK2 acted as a target of miR-204-3p. Meanwhile, NFIC was an upstream transcription factor that might bind to the miR-204-3p promoter and improve its expression. NFIC-exosome from ADSCs might regulate HG-triggered HUVEC injury through miR-204-3p-dependent inhibition of HIPK2. Conclusion: Exosomal NFIC silencing-loaded ADSC sheet modulates miR-204-3p/HIPK2 axis to suppress HG-induced HUVEC proliferation, migration, and angiogenesis, providing a stem cell-based treatment strategy for DFU.

Objective: This study investigated the feasibility of electrical impedance tomography (EIT) for monitoring the deterioration of ischemic lesion after the onset of stroke. Approach: Fifteen rats were randomly distributed into two groups: rats operated to establish a right middle cerebral artery occlusion (MCAO) (n = 10), and sham-operated rats (n = 5). Then, the operated rats were kept 2 h under anesthesia for EIT monitoring. Subsequently, descriptive statistical analysis was performed on whole-brain resistivity changes, and repeated-measures analysis of variance (ANOVA) on the average resistivity variation index. Additionally, pathological examinations were performed after 6 h of infarction. Main results: The results obtained showed that ischemic damage developed in the right corpus striatum of the rats with MCAO, whereas the brains of the sham group showed no anomalies. The descriptive statistical analysis revealed that the whole-brain resistivity changes after 30, 60, 90, and 120 min of infarction were 0.063 ± 0.038, 0.097 ± 0.046, 0.141 ± 0.062, and 0.204 ± 0.092 for the rats with MCAO and 0.029 ± 0.021, 0.002 ± 0.002, 0.017 ± 0.011, and −0.001 ± 0.011 for the sham-operated rats, respectively. The repeated-measures ANOVA revealed that the right MCAO model resulted in a significant impedance increase in the right hemisphere, which continued to increase over time after infarction. Significance: The overall study results indicate that EIT facilitates monitoring of local impedance variations caused by MCAO and may be a solution for real-time monitoring of intracranial pathological changes in ischemic stroke patients.

Electrical impedance tomography (EIT) is a noninvasive, radiation-free, and low-cost imaging modality for monitoring the conductivity distribution inside a patient. Nowadays, time-difference EIT (tdEIT) is used extensively as it has fast imaging speed and can reflect the dynamic changes of diseases, which make it attractive for a number of medical applications. Moreover, modeling errors are compensated to some extent by subtraction of voltage measurements collected before and after the change. However, tissue conductivity varies with frequency and tdEIT does not efficiently exploit multi-frequency information as it only uses measurements associated with a single frequency. This paper proposes a tdEIT algorithm that imposes spectral constraints on the framework of the linear least squares problem. Simulation and phantom experiments are conducted to compare the proposed spectral constraints algorithm (SC) with the damped least squares algorithm (DLS), which is a stable tdEIT algorithm used in clinical practice. The condition number and rank of the matrices needing inverses are analyzed, and image quality is evaluated using four indexes. The possibility of multi-tissue imaging and the influence of spectral errors are also explored. Significant performance improvement is achieved by combining multi-frequency and time-difference information. The simulation results show that, in one-step iteration, both algorithms have the same condition number and rank, but SC effectively reduces image noise by 20.25% compared to DLS. In addition, deformation error and position error are reduced by 8.37% and 7.86%, respectively. In two-step iteration, the rank of SC is greatly increased, which suggests that more information is employed in image reconstruction. Image noise is further reduced by an average of 32.58%, and deformation error and position error are also reduced by 20.20% and 31.36%, respectively. The phantom results also indicate that SC has stronger noise suppression and target identification abilities, and this advantage is more obvious with iteration. The results of multi-tissue imaging show that SC has the unique advantage of automatically extracting a single tissue to image. SC enables tdEIT to utilize multi-frequency information in cases where the spectral constraints are known and then provides higher quality images for applications.

The objective of the study was to develop an optimized method for dynamic electrical impedance tomography (EIT) to image large area conductive perturbation (LACP), a new type of imaging target that we found during the monitoring and evaluation of the mannitol dehydration treatment of patients with brain edema based on brain EIT. Previously, we reconstructed LACP images with the commonly used NOSER algorithm with polar driven pattern. However, conductivity changes near the center of the LACP were blurred or remained undetected, and blocky artifacts appeared in the reconstructed images, making the interpretation of the results difficult. To solve this problem, we, for the first time, propose an optimized algorithm for imaging LACP. This algorithm comprises a modified sensitivity matrix to compensate for the blurred conductivity changes near the center. It also uses the Markov random field constraint to reduce blocky artifacts. To verify the performance of the proposed method, we conducted experiments based on head models and human subjects. Specific metrics, including shape error (SE) and image fluctuation (IF) artifacts, were also proposed to evaluate the image quality of the LACP. The experimental results demonstrated that, compared with the NOSER algorithm, the proposed method could respectively reduce SE and IF by 51.7% and 47.5%. Therefore, it can optimize the imaging of LACP and provide references for other applications at present or in the future which involve imaging LACP.

Intracranial hypertension can potentially cause ischemia, herniation, and even death. Mannitol dehydration treatment is a common method for reducing increased intracranial pressure (ICP). However, the efficacy varies among patients and improper use of mannitol may cause severe side effects. This study aims to find ways to individually evaluate and provide guidance for mannitol dehydration treatments based on dynamic brain electrical impedance tomography (EIT). First, clustering methods were applied to analyze the relationship between EIT data and patients’ different responses to mannitol. With the clustering results as well as empirical experiences, doctors could establish a rule for classifying the efficacy of mannitol dehydration treatments. Then, to rapidly categorize following EIT datasets, an automatic classifier was developed using the multiclass support vector machines (SVMs). Additionally, to enhance the accuracy of the classifier, a novel priori and principle components (PPCs) based index was introduced as the input data for training the classifier. The experimental results demonstrated that different clustering methods yielded similar results and the optimal number of clusters was three, which corresponded to three types of efficacy of mannitol. For the automatic classifier, its classification accuracy improved to 93.3% with the PPCs index. As such, it can be concluded that brain EIT promises to be an effective tool for individually evaluating the efficacy of the dehydration treatments and make guidance for rational use of mannitol.

Defects on the feather leaf are mainly scar, wormhole and stain. Accurate segmentation of these defects on feather leaf will improve largely the quality of the feather. In this study, this paper analyses brightness distribution characteristic of feather image, proposes a denoising method based on the improved mean shift . The outstanding of differences between feather leaf and feather defect can be gotten by this proposed method. Experimental results with both better value of evaluation index and improved visual quality indicated the successful implementation of the proposed method in the denoising of feather images. Moreover, the algorithm can accurately segment feather image and effectively suppress the undesired background texture information to segment feather image with unclear color information.