Haptic technology for micro-robotic cell injection training systems — a review

Currently, the micro-robotic cell injection procedure is performed manually by expert human bio-operators. In order to be proficient at the task, lengthy and expensive dedicated training is required. As such, effective specialized training systems for this procedure can prove highly beneficial. This paper presents a comprehensive review of haptic technology relevant to cell injection training and discusses the feasibility of developing such training systems, providing researchers with an inclusive resource enabling the application of the presented approaches, or extension and advancement of the work. A brief explanation of cell injection and the challenges associated with the procedure are first presented. Important skills, such as accuracy, trajectory, speed and applied force, which need to be mastered by the bio-operator in order to achieve successful injection, are then discussed. Then an overview of various types of haptic feedback, devices and approaches is presented. This is followed by discussion on the approaches to cell modeling. Discussion of the application of haptics to skills training across various fields and haptically-enabled virtual training systems evaluation are then presented. Finally, given the findings of the review, this paper concludes that a haptically-enabled virtual cell injection training system is feasible and recommendations are made to developers of such systems

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p<0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p<0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Haptic-based color representation framework

In this paper we propose a novel technique to associate color RGB dimensions into other dimensions that can be explored Haptically. These dimensions will be the force, vibration and geometric representation. The color association between the color information and the other modalities will be called the Haptic-Color Palette. This is of great importance for visually impaired and color blind persons to get access to media in which colors play a great role, such as artwork. The proposed color conversion technique is based on the human perception and interpretations of colors

Design of a virtual reality training system for micro-robotic cell injection

This paper discusses the design of a virtual reality (VR) training system for micro-robotic cell injection. A brief explanation of cell injection and the challenges associated with the procedure are first presented. This is followed by discussion of the skills required by the bio-operator to achieve successful injection, such as accuracy, trajectory and applied force. The design of the VR system which includes the visual display, input controllers, mapping strategies, haptic guidance and output data is then discussed. Initial evaluation of the VR system is presented including analysis and discussion based on conducted user evaluations. Finally, given the findings of the initial evaluation, this paper concludes that an effective haptically-enabled virtual cell injection training system is feasible, and recommendations for improvement and future work are given

Haptic virtual reality training environment for micro-robotic cell injection

Micro-robotic cell injection is typically performed manually by a trainedbio-operator, and success rates are often low. To enhance bio-operator performance during real-time cell injection, our earlier work introduced a haptically-enabled micro-robotic cell injection system. The system employed haptic virtual fixtures to provide haptic guidance according to articular performance metrics. This paper extends the work by replicating the system within a virtual reality (VR) environment for bio-operator training. Using the virtual environment, the bio-operator is able to control the virtual injection process in the same way they would with the physical haptic micro-robotic cell injection system, while benefiting from the enhanced visualisation capabilities offered by the 3D VR environment. The system is achieved using cost-effective components offering training at much lower cost than using the physical system

An Embedded System Using Convolutional Neural Network Model for Online and Real-Time ECG Signal Classification and Prediction

This paper presents an automatic ECG signal classification system that applied the Deep Learning (DL) model to classify four types of ECG signals. In the first part of our work, we present the model development. Four different classes of ECG signals from the PhysioNet open-source database were selected and used. This preliminary study used a Deep Learning (DL) technique namely Convolutional Neural Network (CNN) to classify and predict the ECG signals from four different classes: normal, sudden death, arrhythmia, and supraventricular arrhythmia. The classification and prediction process includes pulse extraction, image reshaping, training dataset, and testing process. In general, the training accuracy achieved up to 95% after 100 epochs. However, the prediction of each ECG single type shows a differentiation. Among the four classes, the results show that the predictions for sudden death ECG waveforms are the highest, i.e., 80 out of 80 samples are correct (100% accuracy). In contrast, the lowest is the prediction for normal sinus ECG waveforms, i.e., 74 out of 80 samples are correct (92.5% accuracy). This is due to the image features of normal sinus ECG waveforms being almost similar to the image features of supraventricular arrhythmia ECG waveforms. However, the model has been tuned to achieve an optimal prediction. In the second part, we presented the hardware implementation with the predictive model embedded in an NVIDIA Jetson Nanoprocessor for the online and real-time classification of ECG waveforms

Parametric Study and Electrocatalyst of Polymer Electrolyte Membrane (PEM) Electrolysis Performance

An investigation was conducted to determine the effects of operating parameters for various electrode types on hydrogen gas production through electrolysis, as well as to evaluate the efficiency of the polymer electrolyte membrane (PEM) electrolyzer. Deionized (DI) water was fed to a single-cell PEM electrolyzer with an active area of 36 cm2. Parameters such as power supply (50–500 mA/cm2), feed water flow rate (0.5–5 mL/min), water temperature (25−80 °C), and type of anode electrocatalyst (0.5 mg/cm2 PtC [60%], 1.5 mg/cm2 IrRuOx with 1.5 mg/cm2 PtB, 3.0 mg/cm2 IrRuOx, and 3.0 mg/cm2 PtB) were varied. The effects of these parameter changes were then analyzed in terms of the polarization curve, hydrogen flowrate, power consumption, voltaic efficiency, and energy efficiency. The best electrolysis performance was observed at a DI water feed flowrate of 2 mL/min and a cell temperature of 70 °C, using a membrane electrode assembly that has a 3.0 mg/cm2 IrRuOx catalyst at the anode side. This improved performance of the PEM electrolyzer is due to the reduction in activation as well as ohmic losses. Furthermore, the energy consumption was optimal when the current density was about 200 mA/cm2, with voltaic and energy efficiencies of 85% and 67.5%, respectively. This result indicates low electrical energy consumption, which can lower the operating cost and increase the performance of PEM electrolyzers. Therefore, the optimal operating parameters are crucial to ensure the ideal performance and durability of the PEM electrolyzer as well as lower its operating costs

Limited emergence of resistance to integrase strand transfer inhibitors (INSTIs) in ART-experienced participants failing dolutegravir-based antiretroviral therapy: a cross-sectional analysis of a Northeast Nigerian cohort.

BACKGROUND: Due to the high prevalence of resistance to NNRTI-based ART since 2018, consolidated recommendations from the WHO have indicated dolutegravir as the preferred drug of choice for HIV treatment globally. There is a paucity of resistance outcome data from HIV-1 non-B subtypes circulating across West Africa. AIMS: We characterized the mutational profiles of persons living with HIV from a cross-sectional cohort in North-East Nigeria failing a dolutegravir-based ART regimen. METHODS: WGS of plasma samples collected from 61 HIV-1-infected participants following virological failure of dolutegravir-based ART were sequenced using the Illumina platform. Sequencing was successfully completed for samples from 55 participants. Following quality control, 33 full genomes were analysed from participants with a median age of 40 years and median time on ART of 9 years. HIV-1 subtyping was performed using SNAPPy. RESULTS: Most participants had mutational profiles reflective of exposure to previous first- and second-line ART regimens comprised NRTIs and NNRTIs. More than half of participants had one or more drug resistance-associated mutations (DRMs) affecting susceptibility to NRTIs (17/33; 52%) and NNRTIs (24/33; 73%). Almost a quarter of participants (8/33; 24.4%) had one or more DRMs affecting tenofovir susceptibility. Only one participant, infected with HIV-1 subtype G, had evidence of DRMs affecting dolutegravir susceptibility-this was characterized by the T66A, G118R, E138K and R263K mutations. CONCLUSIONS: This study found a low prevalence of resistance to dolutegravir; the data are therefore supportive of the continual rollout of dolutegravir as the primary first-line regimen for ART-naive participants and the preferred switch to second-line ART across the region. However, population-level, longer-term data collection on dolutegravir outcomes are required to further guide implementation and policy action across the region