A Path towards Timely VAP Diagnosis: Proof-of-Concept Study on Pyocyanin Sensing with Cu-Mg Doped Graphene Oxide

In response to the urgent requirement for rapid, precise, and cost-effective detection in intensive care units (ICUs) for ventilated patients, as well as the need to overcome the limitations of traditional detection methods, researchers have turned their attention towards advancing novel technologies. Among these, biosensors have emerged as a reliable platform for achieving accurate and early diagnoses. In this study, we explore the possibility of using Pyocyanin analysis for early detection of pathogens in ventilator-associated pneumonia (VAP) and lower respiratory tract infections in ventilated patients. To achieve this, we developed an electrochemical sensor utilizing a graphene oxide-copper oxide-doped MgO (GO - Cu - Mgo) (GCM) catalyst for Pyocyanin detection. Pyocyanin is a virulence factor in the phenazine group that is produced by Pseudomonas aeruginosa strains, leading to infections such as pneumonia, urinary tract infections, and cystic fibrosis. We additionally investigated the use of DNA aptamers for detecting Pyocyanin as a biomarker of Pseudomonas aeruginosa, a common causative agent of VAP. The results of this study indicated that electrochemical detection of Pyocyanin using a GCM catalyst shows promising potential for various applications, including clinical diagnostics and drug discovery.

Ambulatory extracorporeal membrane oxygenation simulator: The next frontier in clinical training

BACKGROUND

Current medical simulators for extracorporeal membrane oxygenation (ECMO) are expensive and rely on low-fidelity methodologies. This creates a challenge that demands a new approach to eliminate high costs and integrate with critical care environments, especially in light of the scarce resources and supplies available after the COVID-19 pandemic.

METHODS

To address this challenge, we examined the current state-of-the-art medical simulators and collaborated closely with Hamad Medical Corporation (HMC), the primary healthcare provider in Qatar, to establish criteria for advancing the cutting-edge ECMO simulation. This article presents a comprehensive ambulatory high-realism and cost-effective ECMO simulator.

RESULTS

Over the past 3 years, we have surveyed relevant literature, gathered data, and continuously developed a prototype of the system modules and the accompanying tablet application. By doing so, we have successfully addressed the issue of cost and fidelity in ECMO simulation, providing an effective tool for medical professionals to improve their understanding and treatment of patients requiring ECMO support.

CONCLUSIONS

This paper will focus on presenting an overall ambulatory ECMO simulator, detailing the various sub-systems and emphasizing the modular casing of the physical components and the simulated patient monitor.

Latest Developments in Adapting Deep Learning for Assessing TAVR Procedures and Outcomes

Aortic valve defects are among the most prevalent clinical conditions. A severely damaged or non-functioning aortic valve is commonly replaced with a bioprosthetic heart valve (BHV) via the transcatheter aortic valve replacement (TAVR) procedure. Accurate pre-operative planning is crucial for a successful TAVR outcome. Assessment of computational fluid dynamics (CFD), finite element analysis (FEA), and fluid-solid interaction (FSI) analysis offer a solution that has been increasingly utilized to evaluate BHV mechanics and dynamics. However, the high computational costs and the complex operation of computational modeling hinder its application. Recent advancements in the deep learning (DL) domain can offer a real-time surrogate that can render hemodynamic parameters in a few seconds, thus guiding clinicians to select the optimal treatment option. Herein, we provide a comprehensive review of classical computational modeling approaches, medical imaging, and DL approaches for planning and outcome assessment of TAVR. Particularly, we focus on DL approaches in previous studies, highlighting the utilized datasets, deployed DL models, and achieved results. We emphasize the critical challenges and recommend several future directions for innovative researchers to tackle. Finally, an end-to-end smart DL framework is outlined for real-time assessment and recommendation of the best BHV design for TAVR. Ultimately, deploying such a framework in future studies will support clinicians in minimizing risks during TAVR therapy planning and will help in improving patient care.

AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives

In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control of heating ventilation and air conditioning system systems. Therefore, many other tasks are left to the operator, e.g. evaluating buildings’ performance, detecting abnormal energy consumption, identifying the changes needed to improve efficiency, ensuring the security and privacy of end-users, etc. To that end, there has been a movement for developing artificial intelligence (AI) big data analytic tools as they offer various new and tailor-made solutions that are incredibly appropriate for practical buildings’ management. Typically, they can help the operator in (i) analyzing the tons of connected equipment data; and; (ii) making intelligent, efficient, and on-time decisions to improve the buildings’ performance. This paper presents a comprehensive systematic survey on using AI-big data analytics in BAMSs. It covers various AI-based tasks, e.g. load forecasting, water management, indoor environmental quality monitoring, occupancy detection, etc. The first part of this paper adopts a well-designed taxonomy to overview existing frameworks. A comprehensive review is conducted about different aspects, including the learning process, building environment, computing platforms, and application scenario. Moving on, a critical discussion is performed to identify current challenges. The second part aims at providing the reader with insights into the real-world application of AI-big data analytics. Thus, three case studies that demonstrate the use of AI-big data analytics in BAMSs are presented, focusing on energy anomaly detection in residential and office buildings and energy and performance optimization in sports facilities. Lastly, future directions and valuable recommendations are identified to improve the performance and reliability of BAMSs in intelligent buildings.

IoT-based Mock Oxygenator for Extracorporeal Membrane Oxygenation Simulator

BACKGROUND

Training is an essential aspect of providing high-quality treatment and ensuring patient safety in any medical practice. Because extracorporeal membrane oxygenation (ECMO) is a complicated operation with various elements, variables, and irregular situations, doctors must be experienced and knowledgeable about all conventional protocols and emergency procedures. The conventional simulation approach has a number of limitations. The approach is intrinsically costly since it relies on disposable medical equipment (i.e., oxygenators, heat exchangers, pumps) that must be replaced regularly due to the damage caused by the liquid used to simu- late blood. The oxygenator, which oxygenates the blood through a tailored membrane in ECMO, acts as a replacement for the patient's natural lung. For the context of simulation-based training (SBT) oxygenators are often expensive and cannot be recy- cled owing to contamination issues.

METHODS

Consequently, it is advised that the training process include a simu- lated version of oxygenators to optimize re-usability and decrease training expenses. Toward this goal, this article demonstrates a mock oxygenator for ECMO SBT, designed to precisely replicate the real machine structure and operation.

RESULTS

The initial model was reproduced using 3D modeling and printing. Addi- tionally, the mock oxygenator could mimic frequent events such as pump noise and clotting. Furthermore, the oxygenator is integrated with the modular ECMO simula- tor using cloud-based communication technology that goes in hand with the internet of things (IoT) technology to provide remote control via an instructor tablet applica- tion (App).

CONCLUSIONS

The final 3D modeled oxygenator body was tested and integrated with the other simulation modules at Hamad Medical Corporation (HMC) with several participants to evaluate the effectiveness of the training session.

Incorporating patient concerns into design requirements for IoMT-based systems: The fall detection case study

Internet of Medical Things (IoMT) systems are envisioned to provide high-quality healthcare services to patients in the comfort of their home, utilizing cutting-edge Internet of Things (IoT) technologies and medical sensors. Patient comfort and willingness to participate in such efforts is a prominent factor for their adoption. As IoT technology has provided solutions for all technical issues, patient concerns are those that seem to restrict their wider adoption. To enhance patient awareness of the system properties and enhance their willingness to adopt IoMT solutions, this paper presents a novel methodology to integrate patient concerns in the design requirements of such systems. It comprises a number of straightforward steps that an IoMT designer can follow, starting from identifying patient concerns, incorporating them in system design requirements as criticalities, proceeding to system implementation and testing, and finally, verifying that it fulfills the concerns of the patients. To showcase the effectiveness of the proposed methodology, the paper applies it in the design and implementation of a fall detection system for elderly patients remotely monitored in their homes.

Advanced Thermochromic Ink System for Medical Blood Simulation

Simulators for extracorporeal membrane oxygenation (ECMO) have problems of bulky devices and low-fidelity methodologies. Hence, ongoing efforts for optimizing modern solutions focus on minimizing expenses and blending training with the intensive care unit. This is particularly evident following the coronavirus pandemic, where economic resources have been extensively cut. In this paper, as a part of an ECMO simulator for training management, an advance thermochromic ink system for medical blood simulation is presented. The system was developed and enhanced as a prototype with successful and reversible transitions between dark and bright red blood color to simulate blood oxygenation and deoxygenation in ECMO training sessions.

A Modular Approach for a Patient Unit for Extracorporeal Membrane Oxygenation Simulator

Despite many advancements in extracorporeal membrane oxygenation (ECMO), the procedure is still correlated with a high risk of patient complications. Simulation-based training provides the opportunity for ECMO staff to practice on real-life scenarios without exposing ECMO patients to medical errors while practicing. At Hamad Medical Corporation (HMC) in Qatar, there is a critical need of expert ECMO staff. Thus, a modular ECMO simulator is being developed to enhance the training process in a cost-effective manner. This ECMO simulator gives the instructor the ability to control the simulation modules and run common simulation scenarios through a tablet application. The core modules of the simulation system are placed in the patient unit. The unit is designed modularly such that more modules can be added throughout the simulation sessions to increase the realism of the simulation sessions. The new approach is to enclose the patient unit in a trolley, which is custom-designed and made to include all the components in a modular fashion. Each module is enclosed in a separate box and then mounted to the main blood simulation loop box using screws, quick connect/disconnect liquid fittings, and electrical plugs. This method allows fast upgrade and maintenance for each module separately as well as upgrading modules easily without modifying the trolley’s design. The prototype patient unit has been developed for portability, maintenance, and extensibility. After implementation and testing, the prototype has proven to successfully simulate the main visual and audio cues of the real emergency scenarios, while keeping costs to a minimum.

A novel gateway-based solution for remote elderly monitoring

Internet of Things (IoT) technologies have been applied to various fields such as manufacturing, automobile industry and healthcare. IoT-based healthcare has a significant impact on real-time remote monitoring of patients' health and consequently improving treatments and reducing healthcare costs. In fact, IoT has made healthcare more reliable, efficient, and accessible. Two major drawbacks which IoT suffers from can be expressed as: first, thelimited battery capacityof thesensorsis quickly depleted due to the continuous stream of data; second, the dependence of the system on the cloud for computations and processing causes latency in data transmission which is not accepted in real-time monitoring applications. This research is conducted to develop a real-time, secure, and energy-efficient platform which provides a solution for reducing computation load on the cloud and diminishing data transmission delay. In the proposed platform, the sensors utilize a state-of-the-art power saving technique known as Compressive Sensing (CS). CS allows sensors to retrieve the sensed data using fewer measurements by sending a compressed signal. In this framework, the signal reconstruction and processing are computed locally on a Heterogeneous Multicore Platform (HMP) device to decrease the dependency on the cloud. In addition, a framework has been implemented to control the system, set different parameters, display the data as well as send live notifications to medical experts through the cloud in order to alert them of any eventual hazardous event or abnormality and allow quick interventions. Finally, a case study of the system is presented demonstrating the acquisition and monitoring of the data for a given subject in real-time. The obtained results reveal that the proposed solution reduces 15.4% of energy consumption in sensors, that makes this prototype a good candidate for IoT employment in healthcare.

Lattice-Boltzmann interactive blood flow simulation pipeline

PURPOSE

Cerebral aneurysms are one of the prevalent cerebrovascular disorders in adults worldwide and caused by a weakness in the brain artery. The most impressive treatment for a brain aneurysm is interventional radiology treatment, which is extremely dependent on the skill level of the radiologist. Hence, accurate detection and effective therapy for cerebral aneurysms still remain important clinical challenges. In this work, we have introduced a pipeline for cerebral blood flow simulation and real-time visualization incorporating all aspects from medical image acquisition to real-time visualization and steering.

METHODS

We have developed and employed an improved version of HemeLB as the main computational core of the pipeline. HemeLB is a massive parallel lattice-Boltzmann fluid solver optimized for sparse and complex geometries. The visualization component of this pipeline is based on the ray marching method implemented on CUDA capable GPU cores.

RESULTS

The proposed visualization engine is evaluated comprehensively and the reported results demonstrate that it achieves significantly higher scalability and sites updates per second, indicating higher update rate of geometry sites' values, in comparison with the original HemeLB. This proposed engine is more than two times faster and capable of 3D visualization of the results by processing more than 30 frames per second.

CONCLUSION

A reliable modeling and visualizing environment for measuring and displaying blood flow patterns in vivo, which can provide insight into the hemodynamic characteristics of cerebral aneurysms, is presented in this work. This pipeline increases the speed of visualization and maximizes the performance of the processing units to do the tasks by breaking them into smaller tasks and working with GPU to render the images. Hence, the proposed pipeline can be applied as part of clinical routines to provide the clinicians with the real-time cerebral blood flow-related information.

Towards next generation cannulation simulators

Background: Cannulation, in extracorporeal membrane oxygenation (ECMO), is the act of inserting a cannula through the body¹. For femoral veins, femoral arteries, and the jugular vein, the cannula stops at the inferior vena cava (IVC) beside the hepatic vein and at the beginning of the distal aorta, and the superior vena cava at the right atrium, respectively. Cannulation is considered a critical operation and requires intensive training. Simulation-based training (SBT) is the gold standard, allowing for training in risk-free, versatile, and realistic environments². A research collaboration was established between Hamad Medical Corporation and Qatar University College of Engineering to support the development of the ECMO training programme. Initially an ECMO machine simulator was developed with thermochromic ink to simulate blood and modules that simulate common emergencies practitioners may face during ECMO runs³. This cannulation simulator is now being designed to close the gap in the market in relation to cost and fidelity^4,5. Methods: The cannulation simulator is composed of several modules. Firstly, a 3D-printed femoral pad mold was constructed to facilitate the production of cannulation pads (Figure 1(a), (c)). Secondly, cannulation pads were designed so they are anatomically correct and ultrasound compatible. For the arteries, the superficial artery was added at the access point to simulate possible incorrect routes for the cannula. Furthermore, the orientation of the veins and arteries were set to further resemble the human anatomy, where the arteries are situated above the veins (Figure 1(a), (b)). In addition to the implementation of a closed loop linking the jugular to the femoral, cannulation access points with a pump connected to a tank between them to regulate the flow. The blood flow in the arteries was enhanced with a pump to simulate a pulsatile flow while the flow in the veins is laminar as seen in the single loop implementation (Figure 1(h)). The connection of the pump to the embedded system is shown in Figure 1(g). The junctional point in the IVC was designed in the venous loop to allow for two cannulas to pass and an alternative path simulating the renal vein was added. A force sensing resistor (FSR) was connected to detect and measure incorrect entry of the guide-wire as this, in real-time scenarios, could cause internal bleeding to the patient (Figure 1(g)). Lastly, the Y-connector showing the renal vein entry is shown in Figure 1(d) and (e). Results: Tests were done on the system namely on the FSR to recalibrate it in the presence of liquid. Tests on the pulsatile flow were conducted to optimize for realism in terms of pressure. Since both jugular and femoral cannulation access points are included, the simulator can be used for training for all ECMO modes including veno-arterial and veno-venous. After testing, the main limitations of the current prototype include the flexibility of the tubes, limits on FSR measurements, and the rigidity of the available 3D printing material. Conclusion: After implementing the stated features, the anticipated outcome is a realistic and cost-efficient ECMO cannulation simulator.

A wireless oxygen saturation and heart rate monitoring and alarming system based on the Qatar Early Warning Scoring system

Background: Peripheral oxygen saturation (SpO2) and heart rate (HR) are important indicators for various medical conditions such as cardiopulmonary disorders and respiratory diseases. The main objectives of this study is to design and implement a portable embedded medical system. This system wirelessly obtains SpO2 and HR data from a patient as well as his/her coordination, and sends a short messaging service (SMS) alarm to the emergency control room to contact the patient and confirm his/her health status or dispatch an ambulance in case of his/her measurements are outside the normal range based on the Qatar Early Warning Scoring (QEWS) system.

Methods: The system mainly consists of a Bluetooth finger pulse oximeter, a Bluetooth-enabled microcontroller, a global positioning system (GPS) and a General Packet Radio Service (GPRS) module. It is divided into three main stages. In the first stage, the readings of SpO2 and HR are obtained from the patient in real time. During the second stage, the readings obtained are sent over Bluetooth to the signal acquisition and processing unit. The received data is processed and a decision is made whether a SMS alarm should be sent or not. The final stage is concerned with sending the alarming SMS to the emergency control room over the GPRS network based on the QEWS system.

Results: The system was implemented and successfully tested as a stand-alone unit by avoiding the use of a PC or a smartphone for data processing. The transmitted SMS alarm includes the SpO2 and HR readings, the QEWS score and the GPS coordinates.

Conclusions: The designed system is wireless, portable, and user-friendly. This system possibly promotes quality of care for the patient living outside hospital and could improve response time from an ambulance service point of view by determining the exact location of the patient.