Frontiers in Medical Technology
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Despite vaccines are the main strategy to control the ongoing global COVID-19 pandemic, their effectiveness could not be enough for individuals with immunosuppression. In these cases, as well as in patients with moderate/severe COVID-19, passive immunization with anti-SARS-CoV-2 immunoglobulins could be a therapeutic alternative. We used caprylic acid precipitation to prepare a pilot-scale batch of anti-SARS-CoV-2 intravenous immunoglobulins (IVIg) from plasma of donors immunized with the BNT162b2 (Pfizer-BioNTech) anti-COVID-19 vaccine (VP-IVIg) and compared their in vitro efficacy and safety with those of a similar formulation produced from plasma of COVID-19 convalescent donors (CP-IVIg). Both formulations showed immunological, physicochemical, biochemical, and microbiological characteristics that meet the specifications of IVIg formulations. Moreover, the concentration of anti-RBD and ACE2-RBD neutralizing antibodies was higher in VP-IVIg than in CP-IVIg. In concordance, plaque reduction neutralization tests showed inhibitory concentrations of 0.03–0.09 g/L in VP-IVIg and of 0.06–0.13 in CP-IVIg. Thus, VP-IVIg has in vitro efficacy and safety profiles that justify their evaluation as therapeutic alternative for clinical cases of COVID-19. Precipitation with caprylic acid could be a simple, feasible, and affordable alternative to produce formulations of anti-SARS-CoV-2 IVIg to be used therapeutically or prophylactically to confront the COVID-19 pandemic in middle and low-income countries.

Since the beginning of the COVID-19 pandemic, numerous studies have been conducted to identify interventions that could contribute to alleviating the burden it has caused. The Institut national d'excellence en santé et en services sociaux (INESSS) has played a key role in informing the government of Québec regarding the evaluation of specific pandemic-related interventions. This process took place in a context characterized by a sense of urgency to assess and recommend potential interventions that could save lives and reduce the effects of the disease on populations and healthcare systems, which increased the pressure on the regulatory agencies leading these evaluations. While some of the interventions examined were considered promising, results from COVID-19 studies often led to uncertainty regarding their efficacy or safety. Regulatory agencies evaluating the value of promising interventions thus face challenges in deciding whether these should be made available to the population, particularly when assessing their benefit-risk balance. To shed light on these challenges, we identified underlying ethical considerations that can influence such an assessment. A rapid literature review was conducted in February 2021, to identify the main challenges associated with the benefit-risk balance assessment of promising interventions. To reinforce our understanding of the underlying ethical considerations, we initiated a discussion among various social actors involved in critical thinking surrounding the evaluation of promising interventions, including ethicists, clinicians and researchers involved in clinical or public health practice, as well as patients and citizens. This discussion allowed us to create a space for exchange and mutual understanding among these various actors who contributed equally to the identification of ethical considerations. The knowledge and perspectives stemming from the scientific literature and those consulted were integrated in a common reflection on these ethical considerations. This allowed patients and citizens, directly affected by the evaluation of pandemic-related interventions and the resulting social choices, to contribute to the identification of the relevant ethical considerations. It also allowed for reflection on the responsibilities of the various actors involved in the development, evaluation, and distribution of promising interventions in a setting of urgency and uncertainty, such as that brought about by the COVID-19 pandemic.

Tongue examination is an important diagnostic method for judging pathological conditions in Kampo (traditional Japanese medicine), but it is not easy for beginners to learn the diagnostic technique. One reason is that there are few objective diagnostic criteria for tongue examination findings, and the educational method for tongue examination is not standardized in Japan, warranting the need for a tongue image database for e-learning systems that could dramatically improve the efficiency of education. Therefore, we constructed a database comprising tongue images whose findings were determined on the basis of votes given by five Kampo medicine specialists (KMSs) and confirmed the educational usefulness of the database for tongue diagnosis e-learning systems. The study was conducted in the following five steps: development of a tongue imaging collection system, collection of tongue images, evaluation and annotation of tongue images, development of a tongue diagnosis e-learning system, and verification of the educational usefulness of this system. Five KMSs evaluated the tongue images obtained from 125 participants in the following eight aspects: (i) tongue body size, (ii) tongue body color, (iii) tongue body dryness and wetness, (iv) tooth marks on the edge of the tongue, (v) cracks on the surface of the tongue, (vi) thickness of tongue coating, (vii) color of tongue coating, and (viii) dryness and wetness of tongue coating. Medical students (MSs) were given a tongue diagnosis test using an e-learning system after a lecture on tongue diagnosis. The cumulative and individual match rates (%) (individual match rates of 100% (5/5), 80% (4/5), and 60% (3/5) are shown in parentheses, respectively) were as follows: (i) tongue body size: 92.8 (26.4/26.4/40.0); (ii) tongue body color: 83.2 (10.4/20.8/52.0); (iii) tongue body dryness and wetness: 88.8 (13.6/34.4/40.8); (iv) tooth marks on the edge of the tongue: 88.8 (6.4/35.2/47.2); (v) cracks on the surface of the tongue: 96.8 (24.0/35.2/37.6); (vi) thickness of tongue coating: 84.8 (7.2/21.6/56.0); (vii) color of tongue coating: 88.0 (15.2/37.6/35.2); and (viii) dryness and wetness of tongue coating: 74.4 (4.8/19.2/50.4). The test showed that the tongue diagnosis ability of MSs who attended a lecture on tongue diagnosis was almost the same as that of KMSs. We successfully constructed a tongue image database standardized for training specialists on tongue diagnosis and confirmed the educational usefulness of the e-learning system using a database. This database will contribute to the standardization and popularization of Kampo education.

The COVID-19 pandemic and lockdown measures in the United Kingdom resulted in significant challenges and created opportunities for innovation to keep patients at the heart of HTA. The introduction of the Coronavirus Act 2020 and the associated public health guidance meant that NICE's conventional HTA methods were no longer feasible. NICE introduced rapid, innovative updates to patient and public involvement (PPI), decision-making meetings, and consultations to harness the expertise of patients and the public to ensure guidance addressed the expected concerns and identified barriers which could impact access. This article describes the PPI support for NICE's rapid shift to virtual meetings and virtual engagement. We utilize the authors' experience and patient and public contributor feedback to understand the experience of participating in a virtual setting and identify four themes: accessibility; inclusivity; transparency; and intrapersonal relationships and committee dynamics. The article also considers how patient representatives participated in, and facilitated, the development of guidance for a hypothetical technology to keep patients and the public at the heart of expedited and novel HTA processes to identify and understand the expected patient concerns and potential barriers for when a technology would be introduced.

Nucleoside analogs are very effective antiviral agents with currently over 25 compounds approved for the therapy of viral infections. Still, their successful use against RNA viruses is very recent, despite RNA viruses comprising some of the most damaging human pathogens (e.g., Coronaviruses, Influenza viruses, or Flaviviridae such as dengue, Zika and hepatitis C viruses). The breakthrough came in 2013–2014, when the nucleoside analog Sofosbuvir became one of the cornerstones of current curative treatments for hepatitis C virus (HCV). An analog designed on the same principles, Remdesivir, has been the first approved compound against SARS-CoV-2, the coronavirus that causes the current COVID-19 pandemic. Both of these nucleoside analogs target the RNA-dependent RNA polymerase (RdRp) (NS5B for HCV, nsp12 for SARS-CoV-2). RdRps of RNA viruses display a peculiar elaboration of the classical polymerase architecture that leads to their active site being caged. Thus, triphosphate nucleosides and their analogs must access this active site in several steps along a narrow and dynamic tunnel. This makes straightforward computational approaches such as docking unsuitable for getting atomic-level details of this process. Here we give an account of ribose-modified nucleoside analogs as inhibitors of viral RdRps and of why taking into account the dynamics of these polymerases is necessary to understand nucleotide selection by RdRps. As a case study we use a computational protocol we recently described to examine the approach of the NTP tunnel of HCV NS5B by cellular metabolites of Sofosbuvir. We find major differences with natural nucleotides even at this early stage of nucleotide entry.

Deep learning has become an active research topic in the field of medical image analysis. In particular, for the automatic segmentation of stomatological images, great advances have been made in segmentation performance. In this paper, we systematically reviewed the recent literature on segmentation methods for stomatological images based on deep learning, and their clinical applications. We categorized them into different tasks and analyze their advantages and disadvantages. The main categories that we explored were the data sources, backbone network, and task formulation. We categorized data sources into panoramic radiography, dental X-rays, cone-beam computed tomography, multi-slice spiral computed tomography, and methods based on intraoral scan images. For the backbone network, we distinguished methods based on convolutional neural networks from those based on transformers. We divided task formulations into semantic segmentation tasks and instance segmentation tasks. Toward the end of the paper, we discussed the challenges and provide several directions for further research on the automatic segmentation of stomatological images.

Magnetocardiography is a modern method of registration of the magnetic component of electromagnetic field, generated by heart activity. Magnetocardiography results are a useful source for the diagnosis of various heart diseases and states, but their usage is still undervalued in the cardiology community. In this study, a two-stage classification by correlation analysis using a k-Nearest Neighbor (k-NN) algorithm is applied for the binary classification of myocardium current density distribution maps (CDDMs). Fourteen groups of CDDMs from patients with different heart states, healthy volunteers, sportsmen, patients with negative T-peak, patients with myocardial damage, male and female patients with microvascular disease, patients with ischemic heart disease, and patients with left ventricular hypertrophy, divided into five and three different groups depending on the degree of pathology, were compared. Selection of best metric, used in classifier and number of neighbors, was performed to define the classifier with best performance for each pair of heart states. Accuracy, specificity, sensitivity, and precision values dependent on the number of neighbors are obtained for each class. The proposed method allows to obtain a value of average accuracy equal to 96%, 70% sensitivity, 98% specificity, and 70% precision.

The assessment of cardiovascular hemodynamics with computational techniques is establishing its fundamental contribution within the world of modern clinics. Great research interest was focused on the aortic vessel. The study of aortic flow, pressure, and stresses is at the basis of the understanding of complex pathologies such as aneurysms. Nevertheless, the computational approaches are still affected by sources of errors and uncertainties. These phenomena occur at different levels of the computational analysis, and they also strongly depend on the type of approach adopted. With the current study, the effect of error sources was characterized for an aortic case. In particular, the geometry of a patient-specific aorta structure was segmented at different phases of a cardiac cycle to be adopted in a computational analysis. Different levels of surface smoothing were imposed to define their influence on the numerical results. After this, three different simulation methods were imposed on the same geometry: a rigid wall computational fluid dynamics (CFD), a moving-wall CFD based on radial basis functions (RBF) CFD, and a fluid-structure interaction (FSI) simulation. The differences of the implemented methods were defined in terms of wall shear stress (WSS) analysis. In particular, for all the cases reported, the systolic WSS and the time-averaged WSS (TAWSS) were defined.