Pulmonologists-Level lung cancer detection based on standard blood test results and smoking status using an explainable machine learning approach

Lung cancer (LC) remains the primary cause of cancer-related mortality, largely due to late-stage diagnoses. Effective strategies for early detection are therefore of paramount importance. In recent years, machine learning (ML) has demonstrated considerable potential in healthcare by facilitating the detection of various diseases. In this retrospective development and validation study, we developed an ML model based on dynamic ensemble selection (DES) for LC detection. The model leverages standard blood sample analysis and smoking history data from a large population at risk in Denmark. The study includes all patients examined on suspicion of LC in the Region of Southern Denmark from 2009 to 2018. We validated and compared the predictions by the DES model with diagnoses provided by five pulmonologists. Among the 38,944 patients, 9,940 had complete data of which 2,505 (25\%) had LC. The DES model achieved an area under the roc curve of 0.77$\pm$0.01, sensitivity of 76.2\%$\pm$2.4\%, specificity of 63.8\%$\pm$2.3\%, positive predictive value of 41.6\%$\pm$1.2\%, and F\textsubscript{1}-score of 53.8\%$\pm$1.1\%. The DES model outperformed all five pulmonologists, achieving a sensitivity 9\% higher than their average. The model identified smoking status, age, total calcium levels, neutrophil count, and lactate dehydrogenase as the most important factors for the detection of LC. The results highlight the successful application of the ML approach in detecting LC, surpassing pulmonologists' performance. Incorporating clinical and laboratory data in future risk assessment models can improve decision-making and facilitate timely referrals.Comment: 9 pages, 4 figure

Endret lokalitetsstruktur i produksjonsområde 3 - vurdert virkning på spredning av lakselus, pankreassykdom og infektiøs lakseanemi

Havforskningsinstituttet (HI) og Veterinærinstituttet (VI) fikk en felles bestilling av Nærings- og fiskeridepartementet om å analysere effektene av ulike scenarioer for en ny lokalitetsstruktur i produksjonsområde 3 som kan gi mindre spredning av lakselus, ILA og PD mellom lokalitetene. Det er antatt at mindre smitte mellom lokalitetene vil gjøre det lettere å drive oppdrett av laks, med lave luseverdier, bedre fiskehelse og fiskevelferd, og dermed øke bærekraften. Ved hjelp av biofysisk modellering og nettverksanalyse av spredning av lakselus, PD og ILA mellom lokaliteter er det anslått at flytting av produksjon fra særlig smittespredende lokaliteter til mindre smittespredende lokaliteter, kan redusere den totale smitten mellom lokalitetene. Analysene viser at ved å fjerne tilfeldige lokaliteter hvor man flytter biomassen til de resterende lokalitetene vil redusere smittepresset, men ikke like effektivt som ved en strategisk flytting av biomasse fra de “verste” til de beste lokalitetene med tanke på smittespredning. I et av scenariene som er testet med Havforskningsinstituttet sine modeller er det indikert at smitten mellom lokalitetene kan reduseres med 46% for lakselus og 30% for virus, ved å redusere fra dagens 135 lokaliteter ned til 100 matfisklokaliteter – dette uten å redusere den totale produksjonen i produksjonsområdet. På tilsvarende måte indikerer Veterinærinstituttet sin lusemodell at en ved tilfeldig fjerning av halvparten av alle lokaliteter og refordeling av biomassen til andre lokaliteter i PO3 vil få rundt regnet ca. 20% færre voksne hunnlus, 20% færre andre mobile lus, og 20% færre behandlinger i hele området. Denne effekten blir større, dersom de lokalitetene som lukkes er strategisk valgt, dvs. at de er valgt på bakgrunn av hvor mye de bidrar til spredning av lus basert på HI sin nettverksmodell. Det synes dermed å være et stort potensial for å redusere smitten mellom lokaliteter ved å redusere antall lokaliteter i PO3 og samtidig opprettholde produksjonen. Imidlertid er det behov for mer omfattende analyser og utredning av ny lokalitetsstruktur, der en tar hensyn til en rekke andre faktorer som bl.a. om lokalitetene tåler økt biomasse med tanke på organisk belastning, selskapsstruktur, muligheter for sonevise utsett, samt påvirkning på vill laksefisk før en kan anbefale konkret ny lokalitetsstruktur i PO3.publishedVersio

Cross-species high-resolution transcriptome profiling suggests biomarkers and therapeutic targets for ulcerative colitis

Background: Ulcerative colitis (UC) is a disorder with unknown etiology, and animal models play an essential role in studying its molecular pathophysiology. Here, we aim to identify common conserved pathological UC-related gene expression signatures between humans and mice that can be used as treatment targets and/or biomarker candidates.Methods: To identify differentially regulated protein-coding genes and non-coding RNAs, we sequenced total RNA from the colon and blood of the most widely used dextran sodium sulfate Ulcerative colitis mouse. By combining this with public human Ulcerative colitis data, we investigated conserved gene expression signatures and pathways/biological processes through which these genes may contribute to disease development/progression.Results: Cross-species integration of human and mouse Ulcerative colitis data resulted in the identification of 1442 genes that were significantly differentially regulated in the same direction in the colon and 157 in blood. Of these, 51 genes showed consistent differential regulation in the colon and blood. Less known genes with importance in disease pathogenesis, including SPI1, FPR2, TYROBP, CKAP4, MCEMP1, ADGRG3, SLC11A1, and SELPLG, were identified through network centrality ranking and validated in independent human and mouse cohorts.Conclusion: The identified Ulcerative colitis conserved transcriptional signatures aid in the disease phenotyping and future treatment decisions, drug discovery, and clinical trial design

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)