Background A number of biomarkers have the potential of differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract, however, a standardised panel for that purpose does not exist yet. We aimed to identify the smallest panel that is most sensitive and specific at differentiating between primary lung tumours and secondary lung tumours from the gastrointestinal tract. Methods A total of 170 samples were collected, including 140 primary and 30 non-primary lung tumours and staining for CK7, Napsin-A, TTF1, CK20, CDX2, and SATB2 was performed via tissue microarray. The data was then analysed using univariate regression models and a combination of multivariate regression models and Receiver Operating Characteristic (ROC) curves. Results Univariate regression models confirmed the 6 biomarkers’ ability to independently predict the primary outcome (p < 0.001). Multivariate models of 2-biomarker combinations identified 11 combinations with statistically significant odds ratios (ORs) (p < 0.05), of which TTF1/CDX2 had the highest area under the curve (AUC) (0.983, 0.960–1.000 95% CI). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 75.7, 100, 100, and 37.5% respectively. Multivariate models of 3-biomarker combinations identified 4 combinations with statistically significant ORs (p < 0.05), of which CK7/CK20/SATB2 had the highest AUC (0.965, 0.930–1.000 95% CI). The sensitivity, specificity, PPV, and NPV were 85.1, 100, 100, and 41.7% respectively. Multivariate models of 4-biomarker combinations did not identify any combinations with statistically significant ORs (p < 0.05). Conclusions The analysis identified the combination of CK7/CK20/SATB2 to be the smallest panel with the highest sensitivity (85.1%) and specificity (100%) for predicting tumour origin with an ROC AUC of 0.965 (p < 0.001; SE: 0.018, 0.930–1.000 95% CI).

Background and Objectives: Atrial fibrillation (AF) is the most common sustained arrhythmia worldwide. The relationship between AF and iron deficiency is poorly understood. Materials and Methods: We conducted an observational study investigating the prevalence of iron deficiency in those with AF. Iron deficiency was defined by the American College of Cardiology (ACC) criteria for iron deficiency in heart failure. Results: Of 134 eligible subjects, 81 (60.4%) met the ACC definition of iron deficiency in heart failure. Those who were iron deficient were more likely to be female (OR 1.876, p = 0.005), have a history of diabetes mellitus (OR 3.085, p = 0.001) a history of stroke (OR 3.147, p = 0.016), and have higher CHA2DS2-VASc (p ≤ 0.0001) and Charlson Comorbidity Index scores (CCI) (p = 0.007). Conclusions: The prevalence of iron deficiency in those with AF appears high and warrants evaluation in a prospective study.

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