A novel blood based triage test for colorectal cancer in primary care: a pilot study.

BACKGROUND: The majority of colorectal cancers (CRCs) are detected after symptomatic presentation to primary care. Given the shared symptoms of CRC and benign disorders, it is challenging to manage the risk of missed diagnosis. Colonoscopy resources cannot keep pace with increasing demand. There is a pressing need for access to simple triage tools in primary care to help prioritise patients for referral. AIM: To evaluate the performance of a novel spectroscopy-based CRC blood test in primary care. DESIGN & SETTING: Mixed-methods pilot study of test performance and GP focus group discussions in South Wales. METHOD: Patients on the urgent suspected cancer (USC) pathway were recruited for the Raman spectroscopy (RS) test coupled to machine learning classification ('Raman-CRC') to identify CRC within the referred population. Qualitative focus group work evaluated the acceptability of the test in primary care by thematic analysis of focus group theorising. RESULTS: A total of 532 patients aged ≥50 years referred on the USC pathway were recruited from 27 GP practices. Twenty-nine patients (5.0%) were diagnosed with CRC. Raman-CRC identified CRC with sensitivity 95.7%, specificity 69.3% with area under curve (AUC) of 0.80 compared with colonoscopy as the reference test (248 patients). Stage I and II cancers were detected with 78.6% sensitivity. Focus group themes underlined the convenience of a blood test for the patient and the test's value as a risk assessment tool in primary care. CONCLUSION: The findings support this novel, non-invasive, blood-based method to prioritise those patients most likely to have CRC. Raman-CRC may accelerate access to diagnosis with potential to improve cancer outcomes.

Optimised Pre-Processing of Raman Spectra for Colorectal Cancer Detection Using High-Performance Computing.

Spectral pre-processing is an essential step in data analysis for biomedical diagnostic applications of Raman spectroscopy, allowing the removal of undesirable spectral contributions that could mask biological information used for diagnosis. However, due to the specificity of pre-processing for a given sample type and the vast number of potential pre-processing combinations, optimisation of pre-processing via a manual "trial and error" format is often time intensive with no guarantee that the chosen method is optimal for the sample type. Here we present the use of high-performance computing (HPC) to trial over 2.4 million pre-processing permutations to demonstrate the optimisation on the pre-processing of human serum Raman spectra for colorectal cancer detection. The effect of varying pre-processing order, using extended multiplicative scatter correction, spectral smoothing, baseline correction, binning and normalization was considered. Permutations were assessed on their ability to detect patients with disease using a random forest (RF) algorithm trained with 102 patients (510 spectra) and independently tested with a set of 439 patients (1317 spectra) in a primary care patient cohort. Optimising via HPC enables improved performance in diagnostic abilities, with sensitivity increasing by 14.6%, specificity increasing by 6.9%, positive predictive value increasing by 3.4%, and negative predictive value increasing by 2.4% when compared to a standard pre-processing optimisation. Ultimate values of these metrics are very important for diagnostic adoption, and once diagnostics demonstrate good accuracy these types of optimisations can make a significant difference to roll-out of a test and demonstrating advantages over existing tests. We also provide tips/recommendations for pre-processing optimisation without the use of HPC. From the HPC permutations, recommendations for appropriate parameter constraints for conducting a more basic pre-processing optimisation are also detailed, thus helping model development for researchers not having access to HPC.

'calling-out' vs. 'calling-in' prejudice: Confrontation style affects inferred motive and expected outcomes.

Laypersons differentiate between two confrontations styles, varying in communication style, perceived motive, and typical context: call-outs (typically public, non-accommodating language, and self-promoting confronter) and call-ins (typically private, accommodating language, and education-focused confronter). Popular press espouses a general preference for call-ins relative to call-outs, but no empirical work has addressed perceptions of these confrontation styles. To investigate the presumed efficacy of these styles, we modelled communicative differences with Communication Accommodation Theory (CAT), which explores the strategies individuals use to modulate communicative differences with interaction partners. The present studies examined third-party perceptions of confronter motive and likely outcomes between call-in and call-out styles (Study 1) and between typical private and atypical public styles (Study 2) about an anti-Black comment. We examined responses of participants who imagined themselves taking part in public vs. private call-in and call-out confrontations (Study 3). Results showed that these styles are seen as similarly effective in terms of target compliance and internalization, but as operating through different mechanisms. Specifically, call-in styles facilitated positive inferences of motive, which fostered expectations of positive confrontation outcomes. Additionally, the effects of communication style superseded the impact of context. Results have implications for strategies allies can use to facilitate effective, educational confrontations of prejudice.

Model study of enhanced oil recovery by flooding with aqueous surfactant solution and comparison with theory.

With the aim of elucidating the details of enhanced oil recovery by surfactant solution flooding, we have determined the detailed behavior of model systems consisting of a packed column of calcium carbonate particles as the porous rock, n-decane as the trapped oil, and aqueous solutions of the anionic surfactant sodium bis(2-ethylhexyl) sulfosuccinate (AOT). The AOT concentration was varied from zero to above the critical aggregation concentration (cac). The salt content of the aqueous solutions was varied to give systems of widely different, post-cac oil-water interfacial tensions. The systems were characterized in detail by measuring the permeability behavior of the packed columns, the adsorption isotherms of AOT from the water to the oil-water interface and to the water-calcium carbonate interface, and oil-water-calcium carbonate contact angles. Measurements of the percent oil recovery by pumping surfactant solutions into calcium carbonate-packed columns initially filled with oil were analyzed in terms of the characterization results. We show that the measured contact angles as a function of AOT concentration are in reasonable agreement with those calculated from values of the surface energy of the calcium carbonate-air surface plus the measured adsorption isotherms. Surfactant adsorption onto the calcium carbonate-water interface causes depletion of its aqueous-phase concentration, and we derive equations which enable the concentration of nonadsorbed surfactant within the packed column to be estimated from measured parameters. The percent oil recovery as a function of the surfactant concentration is determined solely by the oil-water-calcium carbonate contact angle for nonadsorbed surfactant concentrations less than the cac. For surfactant concentrations greater than the cac, additional oil removal occurs by a combination of solubilization and emulsification plus oil mobilization due to the low oil-water interfacial tension and a pumping pressure increase.