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BACKGROUND: Fecal Immunochemical Test (FIT) is widely used in population-based screening for colorectal cancer (CRC). This had led to major challenges regarding colonoscopy capacity. Methods to maintain high sensitivity without compromising the colonoscopy capacity are needed. This study investigates an algorithm that combines FIT result, blood-based biomarkers associated with CRC, and individual demographics, to triage subjects sent for colonoscopy among a FIT positive (FIT+) screening population and thereby reduce the colonoscopy burden.MATERIALS AND METHODS: From the Danish National Colorectal Cancer Screening Program, 4048 FIT+ (≥100 ng/mL Hemoglobin) subjects were included and analyzed for a panel of 9 cancer-associated biomarkers using the ARCHITECT i2000. Two algorithms were developed: 1) a predefined algorithm based on clinically available biomarkers: FIT, age, CEA, hsCRP and Ferritin; and 2) an exploratory algorithm adding additional biomarkers: TIMP-1, Pepsinogen-2, HE4, CyFra21-1, Galectin-3, B2M and sex to the predefined algorithm. The diagnostic performances for discriminating subjects with or without CRC in the 2 models were benchmarked against the FIT alone using logistic regression modeling.RESULTS: The discrimination of CRC showed an area under the curve (AUC) of 73.7 (70.5-76.9) for the predefined model, 75.3 (72.1-78.4) for the exploratory model, and 68.9 (65.5-72.2) for FIT alone. Both models performed significantly better (P < .001) than the FIT model. The models were benchmarked vs. FIT at cutoffs of 100, 200, 300, 400, and 500 ng/mL Hemoglobin using corresponding numbers of true positives and false positives. All performance metrics were improved at all cutoffs.CONCLUSION: A screening algorithm including a combination of FIT result, blood-based biomarkers and demographics outperforms FIT in discriminating subjects with or without CRC in a screening population with FIT results above 100 ng/mL Hemoglobin.

There is a need for representative small volume devices that reflect monoclonal antibody (mAb) aggregation during freezing and thawing (FT) in large containers. We characterised two novel devices that aim to mimic the stress in rectangular 2 L bottles. The first scale-down device (SDD) consists of a 125 mL bottle surrounded by a 3D printed cover that manipulates heat exchange. The second device, a micro scale-down device (mSDD), adapts cooling and heating of 10 mL vials to extend stress time. MAb aggregation upon repeated FT was evaluated considering formation of higher molecular weight species, subvisible particles, and the increase in hydrodynamic radius, polydispersity index, and optical density at 350 nm. Three different mAb solutions were processed. Both an unshielded 125 mL bottle and the SDD can be used to predict aggregation during FT in 2 L bottles. In specific cases the unshielded 125 mL bottle underestimates whereas the SDD slightly overestimates soluble aggregate formation. The mSDD increases aggregation compared to 10 mL vials but is less representative than the SDD. Ultimately, both SDDs enable characterisation of protein sensitivity to large-scale FT with two orders of magnitude less volume and are superior to simply using smaller bottles.

Forecasting wind power generation up to a few hours ahead is of the utmost importance for the efficient operation of power systems and for participation in electricity markets. Recent statistical learning approaches exploit spatiotemporal dependence patterns among neighbouring sites, but their requirement of sharing confidential data with third parties may limit their use in practice. This explains the recent interest in distributed, privacy preserving algorithms for high-dimensional statistical learning, e.g. with auto-regressive models. The few approaches that have been proposed are based on batch learning. However, these approaches are potentially computationally expensive and do not allow for the accommodation of nonstationary characteristics of stochastic processes like wind power generation. This paper closes the gap between online and distributed optimisation by presenting two novel approaches that recursively update model parameters while limiting information exchange between wind farm operators and other potential data providers. A simulation study compared the convergence and tracking ability of both approaches. In addition, a case study using a large dataset from 311 wind farms in Denmark confirmed that online distributed approaches generally outperform existing batch approaches while preserving privacy such that agents do not have to actively share their private data.

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.

Implementation of population screening for colorectal cancer by direct colonoscopy or follow-up colonoscopy after a positive fecal blood test has challenged the overall capacity of bowel examinations. Certain countries are facing serious colonoscopy capacity constraints, which have led to waiting lists and long-time latency of follow-up examinations. Various options for improvement are considered, including increased cut-off values of the fecal blood tests. Results from major clinical studies of blood-based, cancer-associated biomarkers have led to focus, however, on a triage concept for improved selection to colonoscopy. The triage test may include subject age, concentration of hemoglobin in a feces test and a combination of certain blood-based cancer associated biomarkers. Recent results have indicated that triage may reduce the requirements for colonoscopy by around 30%. Such results may be advantageous for the capacity, the heath budgets and in particular, the subjects, who do not need an unnecessary, unpleasant and risk-associated bowel examination.

The time between the moment of referral for the diagnostic workup for thyroid nodules and the outcome can be worrisome for patients. In general, patients experience high levels of anxiety during the evaluation of a lesion suspicious for cancer. Therefore, the implementation of same-day fine-needle aspiration cytology (FNAC) diagnosis is becoming standard-of-care for many solid tumors. Our aim was to assess the feasibility of same-day FNAC diagnosis for thyroid nodules and to assess patient anxiety during the diagnostic process.For feasibility of same-day FNAC diagnosis, we assessed the proportion of patients receiving a diagnosis at the end of the visit. Accuracy was measured by comparing histology with the FNAC result. Patient anxiety was measured by the State Trait Anxiety Inventory at 6 moments during the diagnostic workup.Of the 131 included patients, 112 (86%) were female, and the mean age was 53 years. All patients, except those with a nondiagnostic FNAC result (n = 26; 20%), had a diagnosis at the end of the day. There were only two discordant results. Anxiety levels at the beginning of the day were high throughout the group, State Trait Anxiety Inventory (STAI) score 43.1 (SD 2.0) and decreased significantly more in patients with a benign FNAC result (STAI score 30.2), compared to patients with a malignant or indeterminate result (STAI score 39.6).Distress of patients with a thyroid nodule undergoing same-day FNAC diagnostics was high. Same-day FNAC diagnosis is feasible and accurate for the evaluation of thyroid nodules. Therefore, same-day FNAC diagnosis seems a safer, more patient-friendly approach to diagnose thyroid nodules.