Training muscle activation patterns of the lower paretic extremity using directional exertion improves mobility in persons with hemiparesis: a pilot study

Background Controlled static exertion performed in the sagittal plane on a transducer attached to the foot requires coordinated moments of force of the lower extremity. Some exertions and plantarflexion recruit muscular activation patterns similar to synergies previously identified during gait. It is currently unknown if persons with hemiparesis following stroke demonstrate similar muscular patterns, and if force feedback training utilizing static exertion results in improved mobility in this population. Methods Electromyographic (EMG) activity of eight muscles of the lower limb were recorded using surface electrodes in healthy participants (n = 10) and in persons with hemiparesis (n = 8) during an exertion exercise (task) performed in eight directions in the sagittal plane of the foot and a plantarflexion exercise performed at 20 and 40% maximum voluntary effort (MVE). Muscle activation patterns identified during these exertion exercises were compared between groups and to synergies reported in the literature during healthy gait using cosine similarities (CS). Functional mobility was assessed in four participants with hemiparesis using GAITRite® and the Timed Up and Go (TUG) test at each session before, during and after static force feedback training. Tau statistics were used to evaluate the effect on mobility before and after training. Measures of MVE and the accuracy of directional exertion were compared before and after training using ANOVAs. Spearman Rho correlations were also calculated between changes in these parameters and changes in mobility before and after the training. Results Muscle activation patterns during directional exertion and plantarflexion were similar for both groups of participants (CS varying from 0.845 to 0.977). Muscular patterns for some of the directional and plantarflexion were also similar to synergies recruited during gait (CS varying from 0.847 to 0.951). Directional exertion training in hemiparetic subjects resulted in improvement in MVE (p < 0.040) and task performance accuracy (p < 0.001). Hemiparetic subjects also demonstrated significant improvements in gait velocity (p < 0.032) and in the TUG test (p < 0.022) following training. Improvements in certain directional efforts were correlated with changes in gait velocity (p = 0.001). Conclusion Static force feedback training following stroke improves strength and coordination of the lower extremity while recruiting synergies reported during gait and is associated with improved mobility.

Single-cell conventional pap smear image classification using pre-trained deep neural network architectures

Background Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy. Results Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.

3D Printed frames to enable reuse and improve the fit of N95 and KN95 respirators

Background In response to supply shortages caused by the COVID-19 pandemic, N95 filtering facepiece respirators (FFRs or “masks”), which are typically single-use devices in healthcare settings, are routinely being used for prolonged periods and in some cases decontaminated under “reuse” and “extended use” policies. However, the reusability of N95 masks is limited by degradation of fit. Possible substitutes, such as KN95 masks meeting Chinese standards, frequently fail fit testing even when new. The purpose of this study was to develop an inexpensive frame for damaged and poorly fitting masks using readily available materials and 3D printing. Results An iterative design process yielded a mask frame consisting of two 3D printed side pieces, malleable wire links that users press against their face, and cut lengths of elastic material that go around the head to hold the frame and mask in place. Volunteers (n = 45; average BMI = 25.4), underwent qualitative fit testing with and without mask frames wearing one or more of four different brands of FFRs conforming to US N95 or Chinese KN95 standards. Masks passed qualitative fit testing in the absence of a frame at rates varying from 48 to 94 % (depending on mask model). For individuals who underwent testing using respirators with broken or defective straps, 80–100 % (average 85 %) passed fit testing with mask frames. Among individuals who failed fit testing with a KN95, ~ 50 % passed testing by using a frame. Conclusions Our study suggests that mask frames can prolong the lifespan of N95 and KN95 masks by serving as a substitute for broken or defective bands without adversely affecting fit. Use of frames made it possible for ~ 73 % of the test population to achieve a good fit based on qualitative and quantitative testing criteria, approaching the 85–90 % success rate observed for intact N95 masks. Frames therefore represent a simple and inexpensive way of expanding access to PPE and extending their useful life. For clinicians and institutions interested in mask frames, designs and specifications are provided without restriction for use or modification. To ensure adequate performance in clinical settings, fit testing with user-specific masks and PanFab frames is required.

Parameterization of physical properties of layered body structure into equivalent circuit model

Background This study presents a novel technique to develop an equivalent circuit model (ECM) for analyzing the responses of the layered body structure to transcutaneous electrical nerve stimulation (TENS) by parameterizing electrical and geometrical properties.Many classical ECMs are non-parametric because of the difficulty in projecting intrapersonal variability in the physical properties into ECM. However, not considering the intrapersonal variability hampers patient-specifically analyzing the body response to TENS and personal optimization of TENS parameter design. To overcome this limitation, we propose a tissue property-based (TPB) approach for the direct parameterization of the physical properties in the layered body structure and thus enable to quantify the effects of intrapersonal variability. Results The proposed method was first validated through in vitro phantom studies and then was applied in-vivo to analyze the TENS on the forearm. The TPB-ECM calculated the impedance network in the forearm and corresponding responses to TENS. In addition, the modelled impedance was in good agreement with well-known impedance properties that have been achieved empirically. Conclusions The TPB approach uses the parameterized circuit components compared to non-parametric conventional ECMs, thus overcoming the intrapersonal variability problem of the conventional ECMs. Therefore, the TPB-ECM has a potential for widely-applicable TENS analysis and could provide impactful guidance in the TENS parameter design.

Investigation of clinically acceptable agreement between two methods of automatic measurement of limb occlusion pressure: a randomised trial

Background Development of automatic, pneumatic tourniquet technology and use of personalised tourniquet pressures has improved the safety and accuracy of surgical tourniquet systems. Personalisation of tourniquet pressure requires accurate measurement of limb occlusion pressure (LOP), which can be measured automatically through two different methods. The ‘embedded LOP’ method measures LOP using a dual-purpose tourniquet cuff acting as both patient sensor and pneumatic effector. The ‘distal LOP’ method measures LOP using a distal sensor applied to the patient’s finger or toe of the operating limb, using photoplethysmography to detect volumetric changes in peripheral blood circulation. The distal LOP method has been used clinically for many years; the embedded LOP method was developed recently with several advantages over the distal LOP method. While both methods have clinically acceptable accuracy in comparison to LOP measured using the manual Doppler ultrasound method, these two automatic methods have not been directly compared. The purpose of this study is to investigate if the embedded and distal methods of LOP measurement have clinically acceptable agreement. The differences in pairs of LOP measurement in the upper and lower limbs of 81 healthy individuals were compared using modified Bland and Altman analysis. In surgery, it is common for cuff pressure to deviate from the pressure setpoint due to limb manipulation. Surgical tourniquet systems utilise a ± 15 mmHg pressure alarm window, whereby if the cuff pressure deviates from the pressure setpoint by > 15 mmHg, an audiovisual alarm is triggered. Therefore, if the difference (bias) ± SE, 95% CI of the bias and SD of differences ± SE in LOP measurement between the embedded and distal methods were all within ±15 mmHg, this would demonstrate that the two methods have clinically acceptable agreement. Results LOP measurement using the embedded LOP method was − 0.81 ± 0.75 mmHg (bias ± standard error) lower than the distal LOP method. The 95% confidence interval of the bias was − 2.29 to 0.66 mmHg. The standard deviation of the differences ± standard error was 10.35 ± 0.49 mmHg. These results show that the embedded and distal methods of LOP measurement demonstrate clinically acceptable agreement. Conclusions The findings of this study demonstrate clinically acceptable agreement between the embedded and distal methods of LOP measurement. The findings support the use of the embedded LOP method of automatic LOP measurement using dual-purpose tourniquet cuffs to enable accurate, effective and simple prescription of personalised tourniquet cuff pressures in a clinical setting.

Drug target ranking for glioblastoma multiforme

Background Glioblastoma Multiforme, an aggressive primary brain tumor, has a poor prognosis and no effective standard of care treatments. Most patients undergoing radiotherapy, along with Temozolomide chemotherapy, develop resistance to the drug, and recurrence of the tumor is a common issue after the treatment. We propose to model the pathways active in Glioblastoma using Boolean network techniques. The network captures the genetic interactions and possible mutations that are involved in the development of the brain tumor. The model is used to predict the theoretical efficacies of drugs for the treatment of cancer. Results We use the Boolean network to rank the critical intervention points in the pathway to predict an effective therapeutic strategy for Glioblastoma. Drug repurposing helps to identify non-cancer drugs that could be effective in cancer treatment. We predict the effectiveness of drug combinations of anti-cancer and non-cancer drugs for Glioblastoma. Conclusions Given the genetic profile of a GBM tumor, the Boolean model can predict the most effective targets for treatment. We also identified two-drug combinations that could be more effective in killing GBM cells than conventional chemotherapeutic agents. The non-cancer drug Aspirin could potentially increase the cytotoxicity of TMZ in GBM patients.

3D-printed microplate inserts for long term high-resolution imaging of live brain organoids

Background Organoids are a reliable model used in the study of human brain development and under pathological conditions. However, current methods for brain organoid culture generate tissues that range from 0.5 to 2 mm of size, which need to be constantly agitated to allow proper oxygenation. The culture conditions are, therefore, not suitable for whole-brain organoid live imaging, required to study developmental processes and disease progression within physiologically relevant time frames (i.e. days, weeks, months). Results Here we designed 3D-printed microplate inserts adaptable to standard 24 multi-well plates, which allow the growth of multiple organoids in pre-defined and fixed XYZ coordinates. This innovation facilitates high-resolution imaging of whole-cerebral organoids, allowing precise assessment of organoid growth and morphology, as well as cell tracking within the organoids, over long periods. We applied this technology to track neocortex development through neuronal progenitors in brain organoids, as well as the movement of patient-derived glioblastoma stem cells within healthy brain organoids. Conclusions This new bioengineering platform constitutes a significant advance that permits long term detailed analysis of whole-brain organoids using multimodal inverted fluorescence microscopy.

A carbon nanotubes based in situ multifunctional power assist system for restoring failed heart function

Background End-stage heart failure is a major risk of mortality. The conductive super-aligned carbon nanotubes sheets (SA-CNTs) has been applied to restore the structure and function of injured myocardium through tissue engineering, and developed as efficient cardiac pacing electrodes. However, the interfacial interaction between SA-CNTs and the surface cells is unclear, and it remains challenge to restore the diminished contraction for a seriously damaged heart. Results A concept of a multifunctional power assist system (MPS) capable of multipoint pacing and contraction assisting is proposed. This device is designed to work with the host heart and does not contact blood, thus avoiding long-term anticoagulation required in current therapies. Pacing electrode constructed by SA-­CNTs promotes the epithelial-mesenchymal transition and directs the migration of pro-regenerative epicardial cells. Meanwhile, the power assist unit reveals an excellent frequency response to alternating voltage, with natural heart mimicked systolic/diastolic amplitudes. Moreover, this system exhibits an excellent pacing when attached to the surface of a rabbit heart, and presents nice biocompatibility in both in vitro and in vivo evaluation. Conclusions This MPS provides a promising non-blood contact strategy to restore in situ the normal blood-pumping function of a failed heart.

Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method

Background Liver cancer is the sixth most common cancer worldwide. It is mostly diagnosed with a computed tomography scan. Nowadays deep learning methods have been used for the segmentation of the liver and its tumor from the computed tomography (CT) scan images. This research mainly focused on segmenting liver and tumor from the abdominal CT scan images using a deep learning method and minimizing the effort and time used for a liver cancer diagnosis. The algorithm is based on the original UNet architecture. But, here in this paper, the numbers of filters on each convolutional block were reduced and new batch normalization and a dropout layer were added after each convolutional block of the contracting path. Results Using this algorithm a dice score of 0.96, 0.74, and 0.63 were obtained for liver segmentation, segmentation of tumors from the liver, and the segmentation of tumor from abdominal CT scan images respectively. The segmentation results of liver and tumor from the liver showed an improvement of 0.01 and 0.11 respectively from other works. Conclusion This work proposed a liver and a tumor segmentation method using a UNet architecture as a baseline. Modification regarding the number of filters and network layers were done on the original UNet model to reduce the network complexity and improve segmentation performance. A new class balancing method is also introduced to minimize the class imbalance problem. Through these, the algorithm attained better segmentation results and showed good improvement. However, it faced difficulty in segmenting small and irregular tumors.

Approaches in cooling of resistive coil-based low-field Magnetic Resonance Imaging (MRI) systems for application in low resource settings

Magnetic Resonance Imaging (MRI), a non-invasive method for the diagnosis of diverse health conditions has experienced growing popularity over other imaging modalities like ultrasound and Computer Tomography. Initially, proof-of-concept and earlier MRI systems were based on resistive and permanent magnet technology. However, superconducting magnets have long held monopoly of the market for MRI systems with their high-field (HF) strength capability, although they present high construction, installation, and siting requirements. Such stringent prerequisites restrict their availability and use in low-middle income countries. Resistive coil-based magnet, albeit low-field (LF) in capacity, represent a plausible boost for the availability and use of MRI systems in resource constrained settings. These systems are characterized by low costs coupled with substantial image quality for diagnosis of some conditions such as hydrocephalus common is such regions. However, the nature of resistive coils causes them to heat up during operation, thus necessitating a dedicated cooling system to improve image quality and enhance system longevity. This paper explores a range of cooling methods as have been applied to resistive magnets, citing their pros and cons and areas for improvement.