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Background: Pulmonary embolism (PE) is an important complication of Coronavirus disease 2019 (COVID-19). COVID-19 is associated with respiratory impairment and a pro-coagulative state, rendering PE more likely and difficult to recognize. Several decision algorithms relying on clinical features and D-dimer have been established. High prevalence of PE and elevated Ddimer in patients with COVID-19 might impair the performance of common decision algorithms. Here, we aimed to validate and compare five common decision algorithms implementing age adjusted Ddimer, the GENEVA, and Wells scores as well as the PEGeD- and YEARS-algorithms in patients hospitalized with COVID-19., Methods: In this single center study, we included patients who were admitted to our tertiary care hospital in the COVID-19 Registry of the LMU Munich. We retrospectively selected patients who received a computed tomography pulmonary angiogram (CTPA) or pulmonary ventilation/perfusion scintigraphy (V/Q) for suspected PE. The performances of five commonly used diagnostic algorithms (age-adjusted D-dimer, GENEVA score, PEGeD-algorithm, Wells score, and YEARS-algorithm) were compared., Results: We identified 413 patients with suspected PE who received a CTPA or V/Q confirming 62 PEs (15%). Among them, 358 patients with 48 PEs (13%) could be evaluated for performance of all algorithms. Patients with PE were older and their overall outcome was worse compared to patients without PE. Of the above five diagnostic algorithms, the PEGeD- and YEARS-algorithms performed best, reducing diagnostic imaging by 14% and 15% respectively with a sensitivity of 95.7% and 95.6%. The GENEVA score was able to reduce CTPA or V/Q by 32.2% but suffered from a low sensitivity (78.6%). Age-adjusted D-dimer and Wells score could not significantly reduce diagnostic imaging., Conclusion: The PEGeD- and YEARS-algorithms outperformed other tested decision algorithms and worked well in patients admitted with COVID-19. These findings need independent validation in a prospective study., (© 2023. The Author(s).)

Pancreatic ductal adenocarcinoma (PDAC) is an extremely challenging disease with a high mortality rate and a short overall survival time. The poor prognosis can be explained by aggressive tumor growth, late diagnosis, and therapy resistance. Consistent efforts have been made focusing on early tumor detection and novel drug development. Various strategies aim at increasing target specificity or local enrichment of chemotherapeutics as well as imaging agents in tumor tissue. Aptamers have the potential to provide early detection and permit anti-cancer therapy with significantly reduced side effects. These molecules are in-vitro selected single-stranded oligonucleotides that form stable three-dimensional structures. They are capable of binding to a variety of molecular targets with high affinity and specificity. Several properties such as high binding affinity, the in vitro chemical process of selection, a variety of chemical modifications of molecular platforms for diverse function, non-immunoreactivity, modification of bioavailability, and manipulation of pharmacokinetics make aptamers attractive targets compared to conventional cell-specific ligands. To explore the potential of aptamers for early diagnosis and targeted therapy of PDAC - as single agents and in combination with radiotherapy - we summarize the generation process of aptamers and their application as biosensors, biomarker detection tools, targeted imaging tracers, and drug-delivery carriers. We are furthermore discussing the current implementation aptamers in clinical trials, their limitations and possible future utilization.