Investigating a Detection Method for Viruses and Pathogens Using a Dual-Microcantilever Sensor.

Piezoresistive microcantilever sensors for the detection of viruses, pathogens, and trace chemical gasses, with appropriate measurement and signal processing methods, can be a powerful instrument with high speed and sensitivity, with in situ and real-time capabilities. This paper discusses a novel method for mass sensing on the order of a few femtograms, using a dual-microcantilever piezoresistive sensor with a vibrating common base. The two microcantilevers have controllably shifted natural frequencies with only one of them being active. Two active piezoresistors are located on the surfaces of each of the two flexures, which are specifically connected in a Wheatstone bridge with two more equivalent passive resistors located on the sensor base. A dedicated experimental system measures the voltages of the two half-bridges and, after determining their amplitude-frequency responses, finds the modulus of their differences. The modified amplitude-frequency response possesses a cusp point which is a function of the natural frequencies of the microcantilevers. The signal processing theory is derived, and experiments are carried out on the temperature variation in the natural frequency of the active microcantilever. Theoretical and experimental data of the temperature-frequency influence and equivalent mass with the same impact are obtained. The results confirm the sensor's applicability for the detection of ultra-small objects, including early diagnosis and prediction in microbiology, for example, for the presence of SARS-CoV-2 virus, other viruses, and pathogens. The versatile nature of the method makes it applicable to other fields such as medicine, chemistry, and ecology.

Cardiovascular Manifestations of Multisystem Inflammatory Syndrome in Children: A Single-Center Bulgarian Study.

Background and objectives: Multisystem inflammatory syndrome in children (MIS-C) poses challenges to the healthcare system, especially with frequent heart involvement. The current retrospective observational study aims to summarize the type and degree of cardiovascular involvement in children with MISC and to find possible associations between laboratory, inflammatory, and imaging abnormalities and the predominant clinical phenotype using a cluster analysis. Material and methods: We present a retrospective observational single-center study including 51 children meeting the MIS-C criteria. Results: Fifty-three percent of subjects presented with at least one sign of cardiovascular involvement (i.e., arterial hypotension, heart failure, pericardial effusion, myocardial dysfunction, pericarditis without effusion, myocarditis, coronaritis, palpitations, and ECG abnormalities). Acute pericarditis was found in 30/41 of the children (73%) assessed using imaging: 14/30 (46.7%) with small pericardial effusion and 16/30 (53.3%) without pericardial effusion. The levels of CRP were significantly elevated in the children with pericarditis (21.6 ± 13 mg/dL vs. 13.9 ± 11 mg/dL, p = 0.035), and the serum levels of IL-6 were higher in the children with small pericardial effusion compared to those without (191 ± 53 ng/L vs. 88 ± 27 ng/L, p = 0.041). Pericarditis with detectable pericardial effusion was significantly more frequent in the female vs. male subjects, 72% vs. 30% (p = 0.007). The hierarchical clustering analysis showed two clusters: Cluster 1 includes the children without cardiovascular symptoms, and Cluster 2 generalizes the MIS-C children with mild and severe cardiovascular involvement, combining pericarditis, myocarditis, heart failure, and low blood pressure. Also, subjects from Cluster 2 displayed significantly elevated levels of fibrinogen (5.7 ± 0.3 vs. 4.6 ± 0.3, p = 0.03) and IL-6 (158 ± 36 ng/mL vs. 66 ± 22 ng/mL, p = 0.032), inflammatory markers suggestive of a cytokine storm. Conclusions: Our results confirm that children with oligosymptomatic MIS-C or those suspected of long COVID-19 should be screened for possible cardiological involvement.

Role of children in the Bulgarian COVID-19 epidemic: A mathematical model study.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic affects all aspects of our lives, including children. With the advancement of the pandemic, children under five years old are at increased risk of hospitalization relative to other age groups. This makes it paramount that we develop tools to address the two critical aspects of preserving children's health - new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-COVID among this age group. AIM: To demonstrate the role of children in the COVID-19 spread in Bulgaria and to test the hypothesis that there are no secondary transmissions in schools and from children to adults. METHODS: Our modeling and data show with high probability that in Bulgaria, with our current measures, vaccination strategy and contact structure, the pandemic is driven by the children and their contacts in school. RESULTS: This makes it paramount that we develop tools to address the two critical aspects of preserving children's health - new treatment protocols and new predictive models. For those purposes, we need to understand better the effects of COVID-19 on children, and we need to be able to predict the number of affected children as a proportion of the number of infected children. This is why our research focuses on clinical and epidemiological pictures of children with heart damage post-COVID, as a part of the general picture of post-Covid among this age group. CONCLUSION: Our modeling rejects that hypothesis, and the epidemiological data supports that. We used epidemiological data to support the validity of our modeling. The first summer wave in 2020 from the listed here school proms endorse the idea of transmissions from students to teachers.

Linear Interval Approximation of Sensor Characteristics with Inflection Points.

The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic.