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Background: Coronavirus disease 2019 (COVID-19) has spread rapidly worldwide with global financial and health care systems consequences. It is already well recognized that immunization against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a precondition for blocking mutations and prevent the emergence of variants. The aim of the study was to investigate the possible relationship between COVID-19 vaccines and the commonly used disease-related blood biomarkers. Methods: Adult patients with confirmed SARS-CoV-2 infection who were hospitalized from November 8, 2021, to December 31, 2021, were included. The retrospective study was conducted in Patras University Hospital, Greece. Two groups of patients were assessed, the ones who were previously vaccinated against SARS-CoV-2 (group A, n = 21), and those who were not (group B, n = 55). After analysis of peripheral blood, we calculated on admission day for each patient the total white blood cell (WBC), absolute lymphocytes count (ALC), absolute monocyte count, D-dimers, C-reactive protein (CRP) plasma levels, lactate dehydrogenase (LDH), ferritin, high-sensitive troponin, as well as the arterial oxygen partial pressure/fractional inspired oxygen (PO2/FiO2) ratio. Results: The median age of all patients was 65.3 ± 15.2 years old; 68.4% were men and 31.6% were women. Comorbidities were present in 51 patients (67.1%). Hypertension and diabetes were observed as the most common comorbidities (33.3%). About 72.4% of the patients were unvaccinated or have received the first dose of vaccine, and 27.6% were completely vaccinated. No statistical difference was found in the total WBC count and ALC between the two groups (group A vs. group B: 8,168.95 ± 7,584.4 vs. 8,521.9 ± 6,571.3, P = 0.848 and 3,052.1 ± 7,230.7 vs. 1,279.6 ± 1,218.6, P = 0.087). Monocytes count in both groups did not show statistical difference: group A vs. group B: 672.6 ± 384.7 vs. 637.9 ± 477.8 (P = 0.754). Similarly, no difference for D-dimers (1,348.5 ± 1,397.6 vs. 1,850.9 ± 3,877.5, P = 0.575), ferritin (1,082.8 ± 1,399.5 vs. 1,327.4 ± 1,307.8, P = 0.508), high-sensitive troponin (113.6 ± 318.1 vs. 157.5 ± 48.8, P = 0.252), and CRP (6.92 ± 4.9 vs. 7.4 ± 5.9, P = 0.732). For LDH plasma levels, the statistical difference was significant (274.2 ± 85.6 vs. 387.5 ± 223.4, P = 0.003), as well as for the PO2/FiO2 ratio (355.6 ± 129.7 vs. 260.5 ± 123.3, P = 0,006). Conclusions: In a mixed population hospitalized for COVID-19, only LDH plasma levels and the PaO2/FiO2 on admission day showed statistically significant difference between vaccinated and unvaccinated patients. Although unvaccinated patients are more likely to develop severe illness, they did not express significantly higher values of commonly used plasma biomarkers such as ferritin, CRP, and D-dimers which are related to disease severity.
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Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target's radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.
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The continuous monitoring of chronic diseases serves as one of the cornerstones in the efforts to improve the quality of life of patients and maintain the healthcare services provided to them. This study aims to provide an in-depth understanding of the perspectives of healthcare professionals on using sensor-based networks (SBN) used for remote and continuous monitoring of patients with chronic illness in Kosovo, a developing country. A qualitative research method was used to interview 26 healthcare professionals. The study results demonstrate the positive attitudes of participants to using SBN, and considers their concerns on the impact of these platforms on the patient's life, the number of visits in the medical centre, data privacy concerning interactions between patients and their medical personnel and the costs of the platform. Further to that, the study makes an important contribution to knowledge by identifying the challenges and drawbacks of these platforms and provides recommendations for system designers.
