Rare host variants in ciliary expressed genes contribute to COVID-19 severity in Bulgarian patients.

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), a pneumonia with extremely heterogeneous clinical presentation, ranging from asymptomatic to severely ill patients. Previous studies have reported links between the presence of host genetic variants and the outcome of the COVID-19 infection. In our study, we used whole exome sequencing in a cohort of 444 SARS-CoV-2 patients, admitted to hospital in the period October-2020-April-2022, to search for associations between rare pathogenic/potentially pathogenic variants and COVID-19 progression. We used gene prioritization-based analysis in genes that have been reported by host genetic studies. Although we did not identify correlation between the presence of rare pathogenic variants and COVID-19 outcome, in critically ill patients we detected known mutations in a number of genes associated with severe disease related to cardiovascular disease, primary ciliary dyskinesia, cystic fibrosis, DNA damage repair response, coagulation, primary immune disorder, hemoglobin subunit ß, and others. Additionally, we report 93 novel pathogenic variants found in severely infected patients who required intubation or died. A network analysis showed main component, consisting of 13 highly interconnected genes related to epithelial cilium. In conclusion, we have detected rare pathogenic host variants that may have influenced the COVID-19 outcome in Bulgarian patients.

Surface-Enhanced Raman Spectroscopy of Ammonium Nitrate Using Al Structures, Fabricated by Laser Processing of AlN Ceramic.

This work presents results on laser-induced surface structuring of AlN ceramic and its application in Surface-Enhanced Raman Spectroscopy (SERS). The laser processing is performed by nanosecond pulses in air and vacuum. Depending on the processing conditions, different surface morphology can be obtained. The ablation process is realized by ceramic decomposition as the formation of an aluminium layer is detected. The efficiency of the fabricated structures as active substrates in SERS is estimated by the ability of the detection of ammonium nitrate (NH4NO3). It is conducted for Raman spectrometer systems that operate at wavelengths of 514 and 785 nm where the most common commercial systems work. The obtained structures contribute to enhancement of the Raman signal at both wavelengths, as the efficiency is higher for excitation at 514 nm. The limit of detection (LOD) of ammonium nitrate is estimated to be below the maximum allowed value in drinking water. The analysis of the obtained results was based on the calculations of the near field enhancement at different conditions based on Finite Difference Time Domain simulation and the extinction spectra calculations based on Generalized Mie scattering theory. The structures considered in these simulations were taken from the SEM images of the real samples. The oxidation issue of the ablated surface was studied by X-ray photoelectron spectroscopy. The presented results indicated that laser structuring of AlN ceramics is a way for fabrication of Al structures with specific near-field properties that can be used for the detection of substances with high social impact.

Unlocking the Value: Quantifying the Return on Investment of Hospital Artificial Intelligence.

PURPOSE: A comprehensive return on investment (ROI) calculator was developed to evaluate the monetary and nonmonetary benefits of an artificial intelligence (AI)-powered radiology diagnostic imaging platform to inform decision makers interested in adopting AI. METHODS: A calculator was constructed to calculate comparative costs, estimated revenues, and quantify the clinical value of using an AI platform compared with no use of AI in radiology workflows of a US hospital over a 5-year time horizon. Parameters were determined on the basis of expert interviews and a literature review. Scenario and deterministic sensitivity analyses were conducted to evaluate calculator drivers. RESULTS: In the calculator, the introduction of an AI platform into the hospital radiology workflow resulted in labor time reductions and delivery of an ROI of 451% over a 5-year period. The ROI was increased to 791% when radiologist time savings were considered. Time savings for radiologists included more than 15 8-hour working days of waiting time, 78 days in triage time, 10 days in reading time, and 41 days in reporting time. Using the platform also provided revenue benefits for the hospital in bringing in patients for clinically beneficial follow-up scans, hospitalizations, and treatment procedures. Results were sensitive to the time horizon, health center setting, and number of scans performed. Among those, the most influential outcome was the number of additional necessary treatments performed because of AI identification of patients. CONCLUSIONS: The authors demonstrate a substantial 5-year ROI of implementing an AI platform in a stroke management-accredited hospital. The ROI calculator may be useful for decision makers evaluating AI-powered radiology platforms.