Call for biotechnological approach to degrade plastic in the era of COVID-19 pandemic

Plastic pollution is a global issue and has become a major concern since Coronavirus disease (COVID)-19. In developing nations, landfilling and illegal waste disposal are typical ways to dispose of COVID-19-infected material. These technologies worsen plastic pollution and other human and animal health problems. Plastic degrades in light and heat, generating hazardous primary and secondary micro-plastic. Certain bacteria can degrade artificial polymers using genes, enzymes, and metabolic pathways. Microorganisms including bacteria degrade petrochemical plastics slowly. High molecular weight, strong chemical bonds, and excessive hydrophobicity reduce plastic biodegradation. There is not enough study on genes, enzymes, and bacteria-plastic interactions. Synthetic biology, metabolic engineering, and bioinformatics methods have been created to biodegrade synthetic polymers. This review will focus on how microorganisms' degrading capacity can be increased using recent biotechnological techniques.

Comprehensive comparison of three different workstations for accurate planning of endovascular stent implantation in patients with thoracic aortic aneurysms

Purpose

To assess the diagnostic precision of three different workstations for measuring thoracic aortic aneurysms (TAAs) in vivo and ex vivo using either pre-interventional computed tomography angiography scans (CTA) or a specifically designed phantom model.

Methods

This retrospective study included 23 patients with confirmed TAA on routinely performed CTAs. In addition to phantom tube diameters, one experienced blinded radiologist evaluated the dimensions of TAAs on three different workstations in two separate rounds. Precision was assessed by calculating measurement errors. In addition, correlation analysis was performed using Pearson correlation.

Results

Measurements acquired at the Siemens workstation deviated by 3.54% (range, 2.78–4.03%; p = 0.14) from the true size, those at General Electric by 4.05% (range, 1.46–7.09%; p < 0.0001), and at TeraRecon by 4.86% (range, 3.22–6.45%; p < 0.0001). Accordingly, Siemens provided the most precise workstation at simultaneously most fluctuating values (scattering of 4.46%). TeraRecon had the smallest fluctuation (scattering of 2.83%), but the largest deviation from the true size of the phantom. The workstation from General Electric showed a scattering of 2.94%. The highest overall correlation between the 1st and 2nd rounds was observed with measurements from Siemens (r = 0.898), followed by TeraRecon (r = 0.799), and General Electric (r = 0.703). Repetition of measurements reduced processing times by 40% when using General Electric, by 20% with Siemens, and by 18% with TeraRecon.

Conclusions

In conclusion, all three workstations facilitated precise assessment of dimensions in the majority of cases at simultaneously high reproducibility, ensuring accurate pre-interventional planning of thoracic endovascular aortic repair.

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Pre-interventional planning of TEVAR in patients with TAAs using CTA is feasible.

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All three workstations facilitated accurate measurements in vivo and ex vivo.

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Repetition of measurements resulted in faster processing due to learning effects.