Diagnosis of acute aortic syndromes with ultrasound and D-dimer: the PROFUNDUS study

Background: In patients complaining common symptoms such as chest/abdominal/back pain or syncope, acute aortic syndromes (AAS) are rare underlying causes. AAS diagnosis requires urgent advanced aortic imaging (AAI), mostly computed tomography angiography. However, patient selection for AAI poses conflicting risks of misdiagnosis and overtesting. Objectives: We assessed the safety and efficiency of a diagnostic protocol integrating clinical data with point-of-care ultrasound (POCUS) and d-dimer (single/age-adjusted cutoff), to select patients for AAI. Methods: This prospective study involved 12 Emergency Departments from 5 countries. POCUS findings were integrated with a guideline-compliant clinical score, to define the integrated pre-test probability (iPTP) of AAS. If iPTP was high, urgent AAI was requested. If iPTP was low and d-dimer was negative, AAS was ruled out. Patients were followed for 30 days, to adjudicate outcomes. Results: Within 1979 enrolled patients, 176 (9 %) had an AAS. POCUS led to net reclassification improvement of 20 % (24 %/-4 % for events/non-events, P < 0.001) over clinical score alone. Median time to AAS diagnosis was 60 min if POCUS was positive vs 118 if negative (P = 0.042). Within 941 patients satisfying rule-out criteria, the 30-day incidence of AAS was 0 % (95 % CI, 0-0.41 %); without POCUS, 2 AAS were potentially missed. Protocol rule-out efficiency was 48 % (95 % CI, 46-50 %) and AAI was averted in 41 % of patients. Using age-adjusted d-dimer, rule-out efficiency was 54 % (difference 6 %, 95 % CI, 4-9 %, vs standard cutoff). Conclusions: The integrated algorithm allowed rapid triage of high-probability patients, while providing safe and efficient rule-out of AAS. Age-adjusted d-dimer maximized efficiency. Clinical trial registration: Clinicaltrials.gov, NCT04430400

Scenarioer som grunnlag for innovasjon

-With permission from publisher, FagbokforlagetArtikkelen belyser hvordan bedrifter kan omgjøre fremtidsbilder til strategiske beslutninger for innovasjon. I første del av artikkelen drøftes teori og begreper som omhandler koblingen mellom scenario­analyse og innovasjon. Det vises blant annet til hvilke bidrag strategisk fremsyn kan gi i innovasjonsarbeid, herunder tre roller som strategisk fremsyn kan spille for å styrke en virksomhets innovasjonskapasitet. I andre del av artikkelen bygger vi på en studie av tre norske selskaper som samarbeider om ut­­­­­­vik­­­­­­lin­­­­­­gen av fremtidsbilder for å innovere. Studien viser at fremtidsbilder har potensial til å fremme innovasjon ved å identifisere og derigjennom gjøre det mulig å møte sentrale utviklingstrekk i bedriftens omgivelser. Fremtidsbilder kan gi beslutningstakere ny forståelse, og dermed åpne for innovasjonsmuligheter. Studien viser også viktigheten av at formålet med fremtidsbildene er klargjort før arbeidet starter. Casen har for øvrig lært oss at det er nødvendig å utvikle veikart som angir utviklingsbanene frem mot fremtidsbildene, slik at innovasjonstiltak kan planlegges og gjennomføres til riktig tid

Komani [Dalmace] (Albanie) : chronique de fouille 2009

Nallbani Etleva, Bitri Eduart, Bregu B., Haxhiraj E., Lela Surja, Meschini Marco, Naipi Pëllumb, Përzhita G., Tomas E. Komani [Dalmace] (Albanie) : chronique de fouille 2009. In: Mélanges de l'École française de Rome. Moyen-Age, tome 121, n°2. 2009. pp. 453-461

Komani [Dalmace] (Albanie) : chronique de fouille 2009

Nallbani Etleva, Bitri Eduart, Bregu B., Haxhiraj E., Lela Surja, Meschini Marco, Naipi Pëllumb, Përzhita G., Tomas E. Komani [Dalmace] (Albanie) : chronique de fouille 2009. In: Mélanges de l'École française de Rome. Moyen-Age, tome 121, n°2. 2009. pp. 453-461

Inter-laboratory automation of the in vitro micronucleus assay using imaging flow cytometry and deep learning

AbstractThe in vitro micronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However, it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25–5.0 μg/mL) and/or carbendazim (0.8–1.6 μg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent concentration regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.</jats:p

Inter-laboratory automation of the<i>in vitro</i>micronucleus assay using imaging flow cytometry and deep learning

ABSTRACTThein vitromicronucleus assay is a globally significant method for DNA damage quantification used for regulatory compound safety testing in addition to inter-individual monitoring of environmental, lifestyle and occupational factors. However it relies on time-consuming and user-subjective manual scoring. Here we show that imaging flow cytometry and deep learning image classification represents a capable platform for automated, inter-laboratory operation. Images were captured for the cytokinesis-block micronucleus (CBMN) assay across three laboratories using methyl methanesulphonate (1.25 – 5.0 µg/mL) and/or carbendazim (0.8 – 1.6 µg/mL) exposures to TK6 cells. Human-scored image sets were assembled and used to train and test the classification abilities of the “DeepFlow” neural network in both intra- and inter-laboratory contexts. Harnessing image diversity across laboratories yielded a network able to score unseen data from an entirely new laboratory without any user configuration. Image classification accuracies of 98%, 95%, 82% and 85% were achieved for ‘mononucleates’, ‘binucleates’, ‘mononucleates with MN’ and ‘binucleates with MN’, respectively. Successful classifications of ‘trinucleates’ (90%) and ‘tetranucleates’ (88%) in addition to ‘other or unscorable’ phenotypes (96%) were also achieved. Attempts to classify extremely rare, tri- and tetranucleated cells with micronuclei into their own categories were less successful (≤ 57%). Benchmark dose analyses of human or automatically scored micronucleus frequency data yielded quantitation of the same equipotent dose regardless of scoring method. We conclude that this automated approach offers significant potential to broaden the practical utility of the CBMN method across industry, research and clinical domains. We share our strategy using openly-accessible frameworks.</jats:p