Biomedical Engineering Letters最新文献

This study evaluated the biomechanical microenvironmental stimulating effect of pulsed electromagnetic field (PEMF) on the regeneration of crush-injured rat sciatic nerve, when combined with bone marrow mesenchymal stem cells (BMSCs) and recombinant human nerve growth factor (rhNGF-β), in the form of an adenoviral vector-mediated NGF. Sprague-Dawley rats were equally distributed into six groups; PBS, BMSC, NGF-Ad + BMSC, PEMF + PBS, PEMF + BMSC and PEMF + NGF-Ad + BMSC. The PBS group received PBS (volume: 10μL/rat), the BMSC group with BMSCs (1 × 10⁶ cell/10 μL/rat) and NGF-Ad group with the rhNGF-β Ad infected BMSCs (1 × 10⁶ cell/10 μL/rat) immediate after right sciatic nerve crush injury. The PEMF groups were exposed to PEMF of 1mT, 50 Hz, 1 h/day. The rats were observed for 3 weeks. PEMF alone did not showed the positive effect compared with negative control group. The groups transplanted with BMSCs showed higher axonal regeneration compared with the groups without transplantation of the cells whether BMSC was infected with NGF-Ad or not and whether the animals received PEMF. PEMF + NGF-Ad + BMSC group showed the significantly highest number of axons than the other groups. Functionally, all groups showed marked improvement at 3 weeks postoperatively although the difference was not statistically significant among the groups. PEMF showed the positive effect when combined with BMSC and NGF-ad in aspect of number of axons. Therefore, combining the microenvironment stimulation methods of PEMF and conventional methods such as transplantation of stem cells and growth factor could be considered for the regeneration methods in the nerve damage.

Skin damages are usual physical injuries and different studies have been done to improve wound healing. Hydrogel due to its properties like a moist environment and cooling wound site is a good option for wound treatment. In this study, we evaluated the consequence of using alginate/chitosan hydrogel contained various dosages of 4-Methylcatechol (0, 0.1, 1% (W/W)) on wound healing. After hydrogel fabrication, different tests like SEM, swelling, release, weight loss, and hemo- and cytocompatibility were done to characterize fabricated hydrogels. Finally, the rat model was used to assess Alginate/Chitosan hydrogel's therapeutic function containing 0.1 and 1% of 4-Methylcatechol. The pore size of hydrogel was between 24.5 ± 9 and 62.1 ± 11.63 µm and about 90% of hydrogel was lost after 14 days in the weight loss test. Blood compatibility and MTT assay showed that hydrogels were nontoxic and improved cell proliferation. In vivo test showed that Alginate/Chitosan/0.1%4-Methylcatechol improved wound healing and the results were significantly better than the gauze-treated wound. Our results showed dose depending effect of 4-Methylcatechol on wound healing. This study shows the treatment effect of 4-Methylcatechol on wound healing and the possibility of using it for treating skin injuries.

Various biometrics such as the face, irises, and fingerprints, which can be obtained in a relatively simple way in modern society, are used in personal authentication systems to identify individuals. These biometric data are extracted from an individual's physiological data and yield high performance in identifying an individual using unique data patterns. Biometric identification is also used in portable devices such as mobile devices because it is more secure than cryptographic token-based authentication methods. However, physiological data could include personal health information such as arrhythmia related patterns in electrocardiogram (ECG) signals. To protect sensitive health information from hackers, the biomarkers of certain diseases or disorders that exist in ECG signals need to be hidden. Additionally, to implement the inference models for both arrhythmia detection and personal authentication in a mobile device, a lightweight model such as a multi-task deep learning model should be considered. This study demonstrates a multi-task neural network model that simultaneously identifies an individual's ECG and arrhythmia patterns using a small network. Finally, the computational efficiency and model size of the single-task and multi-task models were compared based on the number of parameters. Although the multi-task model has 20,000 fewer parameters than the single-task model, they yielded similar performance, which demonstrates the efficient structure of the multi-task model.

The rapid evolution of wearable technology in healthcare sectors has created the opportunity for people to measure their blood pressure (BP) using a smartwatch at any time during their daily activities. Several commercially-available wearable devices have recently been equipped with a BP monitoring feature. However, concerns about recalibration remain. Pulse transit time (PTT)-based estimation is required for initial calibration, followed by periodic recalibration. Recalibration using arm-cuff BP monitors is not practical during everyday activities. In this study, we investigated recalibration using PTT-based BP monitoring aided by a deep neural network (DNN) and validated the performance achieved with more practical wrist-cuff BP monitors. The PTT-based prediction produced a mean absolute error (MAE) of 4.746 ± 1.529 mmHg for systolic blood pressure (SBP) and 3.448 ± 0.608 mmHg for diastolic blood pressure (DBP) when tested with an arm-cuff monitor employing recalibration. Recalibration clearly improved the performance of both DNN and conventional linear regression approaches. We established that the periodic recalibration performed by a wrist-worn BP monitor could be as accurate as that obtained with an arm-worn monitor, confirming the suitability of wrist-worn devices for everyday use. This is the first study to establish the potential of wrist-cuff BP monitors as a means to calibrate BP monitoring devices that can reliably substitute for arm-cuff BP monitors. With the use of wrist-cuff BP monitoring devices, continuous BP estimation, as well as frequent calibrations to ensure accurate BP monitoring, are now feasible.

As the blood-brain barrier (BBB) hinders efficient drug delivery to the brain, drug delivery via the intranasal pathway, bypassing the BBB, has received considerable attention. However, intranasal administration still has anatomical and physiological limitations, necessitating further solutions to enhance effectiveness. In this study, we used transcranial magnetic stimulation (TMS) on fluorescent magnetic nanoparticles (MNPs) of different sizes (50, 100, and 300 nm) to facilitate MNP's transportation and delivery to the brain parenchyma. To validate this concept, anesthetized rats were intranasally injected with the MNPs, and TMS was applied to the center of the head. As the result, a two-fold increase in brain MNP delivery was achieved using TMS compared with passive intranasal administration. In addition, histological analysis that was performed to investigate the safety revealed no gross or microscopic damages to major organs caused by the nanoparticles. While future studies should establish the delivery conditions in humans, we expect an easy clinical translation in terms of device safety, similar to the use of conventional TMS. The strategy reported herein is the first critical step towards effective drug transportation to the brain.

Medical image alignment is an important tool for tracking patient conditions, but the quality of alignment is influenced by the effectiveness of low-dose Cone-beam CT (CBCT) imaging and patient characteristics. To address these two issues, we propose an unsupervised alignment method that incorporates a preprocessing super-resolution process. We constructed the model based on a private clinical dataset and validated the enhancement of the super-resolution on alignment using clinical and public data. Through all three experiments, we demonstrate that higher resolution data yields better results in the alignment process. To fully constrain similarity and structure, a new loss function is proposed; Pearson correlation coefficient combined with regional mutual information. In all test samples, the newly proposed loss function obtains higher results than the common loss function and improve alignment accuracy. Subsequent experiments verified that, combined with the newly proposed loss function, the super-resolution processed data boosts alignment, can reaching up to 9.58%. Moreover, this boost is not limited to a single model, but is effective in different alignment models. These experiments demonstrate that the unsupervised alignment method with super-resolution preprocessing proposed in this study effectively improved alignment and plays an important role in tracking different patient conditions over time.

Heating nanoparticles with a magnetic field could facilitate selective remote control of neural activity in deep tissue. However, current magnetothermal stimulation approaches are limited to single-channel stimulation. Here, we investigated various designs for multichannel magnetothermal stimulation based on an array of resonant coils that are driven by a single loop coil. Using a tuning capacitor that allows resonant coils to resonate at the operating frequency, each coil's ON and OFF resonance can be controlled, enabling us to select stimulation channels. We found that smaller inner diameters of resonant coils produce more localized magnetic fields while larger coils produce magnetic fields over a longer distance. The constructed multichannel resonant coil arrays can provide a high enough magnetic field intensity to raise the temperature of nanoparticles by 8 °C when we apply 35.2 W into the loop coil that is spaced 1 mm from the target neurons. This multichannel stimulation using a simple resonant circuit approach would be useful for clinical applications of magnetothermal neural stimulation.

Recently, we introduced a current limiter-based novel transcranial direct-current stimulation (tDCS) device that does not generate significant tDCS-induced electrical artifacts, thereby facilitating simultaneous electroencephalography (EEG) measurement during tDCS application. In this study, we investigated the neuromodulatory effect of the tDCS device using resting-state EEG data measured during tDCS application in terms of EEG power spectral densities (PSD) and brain network indices (clustering coefficient and path length). Resting-state EEG data were recorded from 10 healthy subjects during both eyes-open (EO) and eyes-closed (EC) states for each of five different conditions (baseline, sham, post-sham, tDCS, and post-tDCS). In the tDCS condition, tDCS was applied for 12 min with a current intensity of 1.5 mA, whereas tDCS was applied only for the first 30 s in the sham condition. EEG PSD and brain network indices were computed for the alpha frequency band most closely associated with resting-state EEG. Both alpha PSD and network indices were found to significantly increase during and after tDCS application compared to those of the baseline condition in the EO state, but not in the EC state owing to the ceiling effect. Our results demonstrate the neuromodulatory effect of the tDCS device that does not generate significant tDCS-induced electrical artifacts, thereby allowing simultaneous measurement of electrical brain activity. We expect our novel tDCS device to be practically useful in exploring the impact of tDCS on neuromodulation more precisely using ongoing EEG data simultaneously measured during tDCS application.

Cell entosis is a novel cell death process starting from cell-in-cell invasion. In general, cancer cells own higher incidence rate of cell entosis comparing to non-cancerous cells. Studies arguing whether cell entosis is a tumor suppressing process or a tumor accelerating process can deepen our understanding of tumor development. Cell elasticity is recognized as one of tumor malignant biomarkers. There have been some researchers studying cell elasticity in cell entosis. However, existing cell elasticity sensing technique (i.e. micropipette aspiration) can hardly be reliable neither high-throughput. In this work, we introduce an elasticity sensing method for quantifying both cell elasticity in cell-in-cell structures and single floating cells using a microfluidic cytometer. We not only argue our cell elasticity sensing method is reliable for already occurred entosis but also apply such method on predicting the "outer" cells in entosis of different cell types. The elasticity sensing method proposed in this manuscript is able to provide an effective and reliable way to further study deeper mechanism in cell entosis.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-023-00264-0.

An unpredictable dynamic surgical environment makes it necessary to measure morphological information of target tissue real-time for laparoscopic image-guided navigation. The stereo vision method for intraoperative tissue 3D reconstruction has the most potential for clinical development benefiting from its high reconstruction accuracy and laparoscopy compatibility. However, existing stereo vision methods have difficulty in achieving high reconstruction accuracy in real time. Also, intraoperative tissue reconstruction results often contain complex background and instrument information that prevents clinical development for image-guided systems. Taking laparoscopic partial nephrectomy (LPN) as the research object, this paper realizes a real-time dense reconstruction and extraction of the kidney tissue surface. The central symmetrical Census based semi-global block stereo matching algorithm is proposed to generate a dense disparity map. A GPU-based pixel-by-pixel connectivity segmentation mechanism is designed to segment the renal tissue area. An in-vitro porcine heart, in-vivo porcine kidney and offline clinical LPN data were performed to evaluate the accuracy and effectiveness of our approach. The algorithm achieved a reconstruction accuracy of ± 2 mm with a real-time update rate of 21 fps for an HD image size of 960 × 540, and 91.0% target tissue segmentation accuracy even with surgical instrument occlusions. Experimental results have demonstrated that the proposed method could accurately reconstruct and extract renal surface in real-time in LPN. The measurement results can be used directly for image-guided systems. Our method provides a new way to measure geometric information of target tissue intraoperatively in laparoscopy surgery.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-023-00263-1.