Frontiers in Medical Technology最新文献

Aiming to address clinical requirements subsequent to SARS-CoV-2-related pulmonary disease, multiple research groups and industry groups carried out intensive studies to develop pandemic ventilators (PDVs). In vitro testing to critically evaluate the specific performance of the developed apparatuses is an essential requirement. This study presents a test protocol which promotes a test-oriented, iterative, and agile assessment and consecutive development of such PDVs. It allows for fast identification of specific characteristics of each PDV in the individual test features. The test protocol includes an evaluation of the accuracy of control systems and instruments at changing parameters, the oxygen dynamics, and the response to trigger signals. The test environment is a mechanical lung, which allows reproducing various lung mechanics and to simulate active breathing cycles. A total of three PDVs that are under development were iteratively tested, with a Hamilton T1 as a reference. Continuous testing of the PDVs under development enables quick identification of critical application aspects that deserve further improved. Based on the present test protocol, the ventilators demonstrate a promising performance justifying continued development.

Hypertension is one of the most prevalent chronic diseases that affects more than 20% of the adult population worldwide, but fortunately, most of their blood pressure can be effectively controlled via drug treatment. However, there still remains 5-30% of patients clinically who do not respond well to conventional medication, while the non-drug treatments currently existing are struggling with major drawbacks like irreversible nerve damage, huge side effects, and even non-effectiveness. In this study, based on the physiological regulation mechanism of blood pressure and state-of-the-art neuromodulation technique, we worked along with the vagus nerve stimulation scheme, developed, and explored whether and how a real-time neural recording and stimulation system could provide an insight into self-adaptive modulation in the blood pressure, in the hope to crack a crevice in the closed-loop treatment for resistant hypertension. Unlike traditional neuromodulation devices, additional signal recording and real-time wireless transmission functions are added to the same device to realize the features of a dynamic monitor and modulator. The system is tested both in vitro and in vivo, showing decent electrical performance of 8 kHz sampling rate and flexible stimulation outputs which sufficiently covers our needs in manipulating neural activities of interest. A relatively stable drop in the blood pressure resulting from stimulation was observed and specific patterns in the vagus nerve signals relating to blood pressure could also be primarily identified. This laid a solid foundation for further studies on the final realization of closed-loop automatic adjustment for resistive hypertension treatment.

Acute ischemic stroke is a consequence of disrupted blood flow to the brain, caused by thrombosis-the pathological formation of occlusive clots within blood vessels, which can embolize distally to downstream tissues and microvasculature. The highest priority of stroke treatment is the rapid removal of occlusive clots and restoration of tissue perfusion. Intravenous thrombolysis is the pharmacological standard-of-care for the dissolution of blood clots, wherein thrombolytic drugs are administered to restore vessel patency. While the introduction of recombinant tissue-plasminogen activator (rtPA) in 1996 demonstrated the benefit of acute thrombolysis for clot removal, this was countered by severe limitations in terms of patient eligibility, lytic efficacy, rethrombosis and safety implications. Development of safer and efficacious treatment strategies to improve clot lysis has not significantly progressed over many decades, due to the challenge of maintaining the necessary efficacy-safety balance for these therapies. As such, rtPA has remained the sole approved acute therapeutic for ischemic stroke for over 25 years. Attempts to improve thrombolysis with coadministration of adjunct antithrombotics has demonstrated benefit in coronary vessels, but remain contraindicated for stroke, given all currently approved antithrombotics adversely impact hemostasis, causing bleeding. This Perspective provides a brief history of stroke drug development, as well as an overview of several groups of emerging drugs which have the potential to improve thrombolytic strategies in the future. These include inhibitors of the platelet receptor glycoprotein VI and the signaling enzyme PI3-Kinase, novel anticoagulants derived from hematophagous creatures, and proteolysis-targeting chimeras.

Widespread issues in respirator availability and fit have been rendered acutely apparent by the COVID-19 pandemic. This study sought to determine whether personalized 3D printed respirators provide adequate filtration and function for healthcare workers through a Randomized Controlled Trial (RCT). Fifty healthcare workers recruited within NHS Lothian, Scotland, underwent 3D facial scanning or 3D photographic reconstruction to produce 3D printed personalized respirators. The primary outcome measure was quantitative fit-testing to FFP3 standard. Secondary measures included respirator comfort, wearing experience, and function instrument (R-COMFI) for tolerability, Modified Rhyme Test (MRT) for intelligibility, and viral decontamination on respirator material. Of the 50 participants, 44 passed the fit test with the customized respirator, not significantly different from the 38 with the control (p = 0.21). The customized respirator had significantly improved comfort over the control respirator in both simulated clinical conditions (p < 0.0001) and during longer wear (p < 0.0001). For speech intelligibility, both respirators performed equally. Standard NHS decontamination agents were able to eradicate 99.9% of viral infectivity from the 3D printed plastics tested. Personalized 3D printed respirators performed to the same level as control disposable FFP3 respirators, with clear communication and with increased comfort, wearing experience, and function. The materials used were easily decontaminated of viral infectivity and would be applicable for sustainable and reusable respirators.

Medical technologies present a huge potential in improving global health playing a key role toward achieving Sustainable Development Goal 3 by 2030. A number of clinicians, innovators, business entities and biomedical engineers among others have developed a number of innovative medical devices and technologies to address the healthcare challenges especially in Africa. Globally, medical devices clinical trials present the most acceptable method for determining the risks and benefits of medical device innovations with the aim of ascertaining their effectiveness and safety as compared with established medical practice. However, there are very few medical device clinical trials reported in Africa compared to other regions like USA, UK and Europe. Most of the medical device clinical trials reported in Africa are addressing challenges around HIV/AIDS, maternal health and NCDs. In this mini review, we report about some of the published medical device clinical trials in Africa PubMed and Google Scholar and their associated challenges.

Background: CPAP is the first line treatment of obstructive sleep apnea. Recently, the use of added filters has been debated following the field safety notice of Philips Respironics™ on potential health risks due to foam degradation used in their ventilators. However, the added resistance of filters has never been analyzed.

Objectives: The primary aim was to investigate the impact of four different filters on APAP mode performance with and without added unintentional air leaks (UIAL) with two simulated respiratory events. The secondary aim was to assess the pressure drop due to the increased filter resistance at different fixed CPAP pressure levels.

Method: This is a bench study. Performance tests were performed on a breathing simulator (ASL 5000™) with a DreamStation™ device. To assess the combined effect of UIAL, a controlled valve was added to the setup.

Results: Without UIAL, the algorithm was able to detect respiratory events and increase pressure level consequently. In the presence of UIAL, the device's response to simulated events was affected. In fixed CPAP mode, the median measured end-expiratory pressure was 6.2 to 10.0% (p < 0.001) below the set pressure with the additional filters. Additional UIAL severely impacted the delivered pressure with a median reduction up to 28.3% (p < 0.001) to the set pressure.

Conclusion: Despite a slight pressure drop, the APAP algorithm still performed with additional filters when UIAL were avoided. However, the combined effect of added filter resistance and UIAL severely impacted APAP performance and effectively delivered set pressure.

Extreme point-of-care refers to medical testing in unfavorable conditions characterized by a lack of primary resources or infrastructure. As witnessed in the recent past, considerable interest in developing devices and technologies exists for extreme point-of-care applications, for which the World Health Organization has introduced a set of encouraging and regulating guidelines. These are referred to as the ASSURED criteria, an acronym for Affordable (A), Sensitive (S), Specific (S), User friendly (U), Rapid and Robust (R), Equipment-free (E), and Delivered (D). However, the current extreme point of care devices may require an intermediate sample preparation step for performing complex biomedical analysis, including the diagnosis of rare-cell diseases and early-stage detection of sepsis. This article assesses the potential of carbon-electrode dielectrophoresis (CarbonDEP) for sample preparation competent in extreme point-of-care, following the ASSURED criteria. We first discuss the theory and utility of dielectrophoresis (DEP) and the advantages of using carbon microelectrodes for this purpose. We then critically review the literature relevant to the use of CarbonDEP for bioparticle manipulation under the scope of the ASSURED criteria. Lastly, we offer a perspective on the roadmap needed to strengthen the use of CarbonDEP in extreme point-of-care applications.

Introduction: The illness course of bipolar disorder (BD) is highly heterogeneous with substantial variation between individuals with the same BD subtype and within individuals over time. This heterogeneity is not well-delineated and hampers the development of more targeted treatment. Furthermore, although lifestyle-related behaviors are believed to play a role in the illness course, such mechanisms are poorly understood. To address some of these knowledge gaps, we aimed to develop an app for collection of multi-dimensional longitudinal data on BD-relevant symptoms and lifestyle-related behaviors.

Methods: An app named MinDag was developed at the Norwegian Center for Mental Disorders Research in Oslo, Norway. The app was designed to tap into selected areas: mood, sleep, functioning/activities (social, occupational, physical exercise, leisure), substance use, emotional reactivity, and psychotic experiences. Ethical, security and usability issues were highly prioritized throughout the development and for the final app solution. We conducted beta- and pilot testing to eliminate technical problems and enhance usability and acceptability.

Results: The final version of MinDag comprises six modules; three which are presented for the user once daily (the Sleep module in the morning and the Mood and Functoning/Activities modules in the evening) and three which are presented once weekly (Substance Use, Emotional Reactivity, and Psychotic Experiences modules). In general, MinDag was well received in both in the beta-testing and the pilot study, and the participants provided valuable feedback that was taken into account in the final development. MinDag is now in use as part of the research protocol at the NORMENT center and in a specialized treatment unit for BD at Oslo University Hospital in Norway.

Discussion: We believe that MinDag will generate unique longitudinal data well suited for capturing the heterogeneity of BD and clarifying important unresolved issues such as how life-style related behavior may influence BD symptoms. Also, the experiences and knowledge derived from the development of MinDag may contribute to improving the security, acceptability, and benefit of digital tools in mental health.

Deep neural networks (DNNs) have started to find their role in the modern healthcare system. DNNs are being developed for diagnosis, prognosis, treatment planning, and outcome prediction for various diseases. With the increasing number of applications of DNNs in modern healthcare, their trustworthiness and reliability are becoming increasingly important. An essential aspect of trustworthiness is detecting the performance degradation and failure of deployed DNNs in medical settings. The softmax output values produced by DNNs are not a calibrated measure of model confidence. Softmax probability numbers are generally higher than the actual model confidence. The model confidence-accuracy gap further increases for wrong predictions and noisy inputs. We employ recently proposed Bayesian deep neural networks (BDNNs) to learn uncertainty in the model parameters. These models simultaneously output the predictions and a measure of confidence in the predictions. By testing these models under various noisy conditions, we show that the (learned) predictive confidence is well calibrated. We use these reliable confidence values for monitoring performance degradation and failure detection in DNNs. We propose two different failure detection methods. In the first method, we define a fixed threshold value based on the behavior of the predictive confidence with changing signal-to-noise ratio (SNR) of the test dataset. The second method learns the threshold value with a neural network. The proposed failure detection mechanisms seamlessly abstain from making decisions when the confidence of the BDNN is below the defined threshold and hold the decision for manual review. Resultantly, the accuracy of the models improves on the unseen test samples. We tested our proposed approach on three medical imaging datasets: PathMNIST, DermaMNIST, and OrganAMNIST, under different levels and types of noise. An increase in the noise of the test images increases the number of abstained samples. BDNNs are inherently robust and show more than 10% accuracy improvement with the proposed failure detection methods. The increased number of abstained samples or an abrupt increase in the predictive variance indicates model performance degradation or possible failure. Our work has the potential to improve the trustworthiness of DNNs and enhance user confidence in the model predictions.