Frontiers in Medical Technology最新文献

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.

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.

Nanoparticles (NP) are being increasingly explored as vehicles for targeted drug delivery because they can overcome free therapeutic limitations by drug encapsulation, thereby increasing solubility and transport across cell membranes. However, a translational gap exists from animal to human studies resulting in only several NP having FDA approval. Because of this, researchers have begun to turn toward physiologically based pharmacokinetic (PBPK) models to guide in vivo NP experimentation. However, typical PBPK models use an empirically derived framework that cannot be universally applied to varying NP constructs and experimental settings. The purpose of this study was to develop a physics-based multiscale PBPK compartmental model for determining continuous NP biodistribution. We successfully developed two versions of a physics-based compartmental model, models A and B, and validated the models with experimental data. The more physiologically relevant model (model B) had an output that more closely resembled experimental data as determined by normalized root mean squared deviation (NRMSD) analysis. A branched model was developed to enable the model to account for varying NP sizes. With the help of the branched model, we were able to show that branching in vasculature causes enhanced uptake of NP in the organ tissue. The models were solved using two of the most popular computational platforms, MATLAB and Julia. Our experimentation with the two suggests the highly optimized ODE solver package DifferentialEquations.jl in Julia outperforms MATLAB when solving a stiff system of ordinary differential equations (ODEs). We experimented with solving our PBPK model with a neural network using Julia's Flux.jl package. We were able to demonstrate that a neural network can learn to solve a system of ODEs when the system can be made non-stiff via quasi-steady-state approximation (QSSA). Our model incorporates modules that account for varying NP surface chemistries, multiscale vascular hydrodynamic effects, and effects of the immune system to create a more comprehensive and modular model for predicting NP biodistribution in a variety of NP constructs.

Nose-to-brain delivery is a promising way to reach the central nervous system with therapeutic drugs. However, the location of the olfactory region at the top of the nasal cavity complexifies this route of administration. In this study, we used a 3D-printed replica of a nasal cavity (a so-called "nasal cast") to reproduce in vitro the deposition of a solid powder. We considered two different delivery devices: a unidirectional device generating a classical spray and a bidirectional device that relies on the user expiration. A new artificial mucus also coated the replica. Five parameters were varied to measure their influence on the powder deposition pattern in the olfactory region of the cast: the administration device, the instillation angle and side, the presence of a septum perforation, and the flow rate of possible concomitant inspiration. We found that the unidirectional powder device is more effective in targeting the olfactory zone than the bi-directional device. Also, aiming the spray nozzle directly at the olfactory area is more effective than targeting the center of the nasal valve. Moreover, the choice of the nostril and the presence of a perforation in the septum also significantly influence the olfactory deposition. On the contrary, the inspiratory flow has only a minor effect on the powder outcome. By selecting the more efficient administration device and parameters, 44% of the powder can reach the olfactory region of the nasal cast.

Stroke is one of the leading causes of disability worldwide. Early diagnosis and treatment of stroke are important for better clinical outcome. Rapid and accurate diagnosis of stroke subtypes is critical. This review discusses the advantages and disadvantages of the current diagnostic and assessment techniques used in clinical practice, particularly for diagnosing acute ischemic stroke. Alternative techniques for rapid detection of stroke utilizing blood based biomarkers and novel portable devices employing imaging methods such as volumetric impedance phase-shift spectroscopy, microwave tomography and Doppler ultrasound are also discussed. Current therapeutic approaches for treating acute ischemic stroke using thrombolytic drugs and endovascular thrombectomy are discussed, with a focus on devices and approaches recently developed to treat large cranial vessel occlusions.