The COVID-19 pandemic has posed an unprecedented threat to the global public health system, primarily infecting the airway epithelial cells in the respiratory tract. Chest X-ray (CXR) is widely available, faster, and less expensive therefore it is preferred to monitor the lungs for COVID-19 diagnosis over other techniques such as molecular test, antigen test, antibody test, and chest computed tomography (CT). As the pandemic continues to reveal the limitations of our current ecosystems, researchers are coming together to share their knowledge and experience in order to develop new systems to tackle it. In this work, an end-to-end IoT infrastructure is designed and built to diagnose patients remotely in the case of a pandemic, limiting COVID-19 dissemination while also improving measurement science. The proposed framework comprises six steps. In the last step, a model is designed to interpret CXR images and intelligently measure the severity of COVID-19 lung infections using a novel deep neural network (DNN). The proposed DNN employs multi-scale sampling filters to extract reliable and noise-invariant features from a variety of image patches. Experiments are conducted on five publicly available databases, including COVIDx, COVID-19 Radiography, COVID-XRay-5K, COVID-19-CXR, and COVIDchestxray, with classification accuracies of 96.01%, 99.62%, 99.22%, 98.83%, and 100%, and testing times of 0.541, 0.692, 1.28, 0.461, and 0.202 s, respectively. The obtained results show that the proposed model surpasses fourteen baseline techniques. As a result, the newly developed model could be utilized to evaluate treatment efficacy, particularly in remote locations.
Five new anionic aqueous dioxidovanadium(V) complexes, [{VO2L1,2}A(H2O)n]α (1-5), with the aroylhydrazone ligands pyridine-4-carboxylic acid (3-ethoxy-2-hydroxybenzylidene)hydrazide (H2L1) and furan-2-carboxylic acid (3-ethoxy-2-hydroxybenzylidene)hydrazide (H2L2) incorporating different alkali metals (A = Na+, K+, Cs+) as countercation were synthesized and characterized by various physicochemical techniques. The solution-phase stabilities of 1-5 were determined by time-dependent NMR and UV-vis, and also the octanol/water partition coefficients were obtained by spectroscopic techniques. X-ray crystallography of 2-4 confirmed the presence of vanadium(V) centers coordinated by two cis-oxido-O atoms and the O, N, and O atoms of a dianionic tridentate ligand. To evaluate the biological behavior, all complexes were screened for their DNA/protein binding propensity through spectroscopic experiments. Finally, a cytotoxicity study of 1-5 was performed against colon (HT-29), breast (MCF-7), and cervical (HeLa) cancer cell lines and a noncancerous NIH-3T3 cell line. The cytotoxicity was cell-selective, being more active against HT-29 than against other cells. In addition, the role of hydrophobicity in the cytotoxicity was explained in that an optimal hydrophobicity is essential for high cytotoxicity. Moreover, the results of wound-healing assays indicated antimigration in case of HT-29 cells. Remarkably, 1 with an IC50 value of 5.42 ± 0.15 μM showed greater activity in comparison to cisplatin against the HT-29 cell line.