Who perceived automation as a threat to their jobs in metro Atlanta: Results from the 2019 Metro Atlanta Speaks survey.

While ethnic minorities, less-educated or less-skilled workers, and low-income workers are, in general, deemed more vulnerable to automation, the literature has not adequately investigated whether or not these sociodemographic groups perceive automation as a threat to their jobs. Using the 2019 Metro Atlanta Speaks survey, we find that high-income residents and residents with a graduate or a professional degree did not perceive automation as a threat to their jobs, but relatively older residents, blacks or African Americans, and low-income residents perceived automation as a threat to their jobs. Although Hispanics or Latinos and less-educated residents are identified to be more vulnerable to automation, they did not perceive automation as a threat to their jobs. Hence, automation is most likely to make Hispanics or Latinos and less-educated residents unemployed in metro Atlanta as they do not perceive automation as a threat to their jobs despite being deemed more vulnerable to automation.

Development and validation of multivariable machine learning algorithms to predict risk of cancer in symptomatic patients referred urgently from primary care: a diagnostic accuracy study.

OBJECTIVES: To develop and validate tests to assess the risk of any cancer for patients referred to the NHS Urgent Suspected Cancer (2-week wait, 2WW) clinical pathways. SETTING: Primary and secondary care, one participating regional centre. PARTICIPANTS: Retrospective analysis of data from 371 799 consecutive 2WW referrals in the Leeds region from 2011 to 2019. The development cohort was composed of 224 669 consecutive patients with an urgent suspected cancer referral in Leeds between January 2011 and December 2016. The diagnostic algorithms developed were then externally validated on a similar consecutive sample of 147 130 patients (between January 2017 and December 2019). All such patients over the age of 18 with a minimum set of blood counts and biochemistry measurements available were included in the cohort. PRIMARY AND SECONDARY OUTCOME MEASURES: sensitivity, specificity, negative predictive value, positive predictive value, Receiver Operating Characteristic (ROC) curve Area Under Curve (AUC), calibration curves RESULTS: We present results for two clinical use-cases. In use-case 1, the algorithms identify 20% of patients who do not have cancer and may not need an urgent 2WW referral. In use-case 2, they identify 90% of cancer cases with a high probability of cancer that could be prioritised for review. CONCLUSIONS: Combining a panel of widely available blood markers produces effective blood tests for cancer for NHS 2WW patients. The tests are affordable, and can be deployed rapidly to any NHS pathology laboratory with no additional hardware requirements.

A simple breath test for tuberculosis using ion mobility: A pilot study.

Tuberculosis (TB) remains one of the world's major health burdens with 9.6 million new infections globally. Though considerable progress has been made in reduction of TB incidence and mortality, there is a continuous need for lower cost, simpler and more robust means of diagnosis. One method that may fulfil these requirements is in the area of breath analysis. In this study we analysed the breath of 21 patients with pulmonary or extra-pulmonary TB, recruited from a UK teaching hospital (University Hospital Coventry and Warwickshire) before or within 1 week of commencing treatment for TB. TB diagnosis was confirmed by reference tests (mycobacterial culture), histology or radiology. 19 controls were recruited to calculate specificity; these patients were all interferon-gamma release assay negative (T.SPOT(®).TB, Oxford Immunotec Ltd.). Whole breath samples were collected with subsequent chemical analysis undertaken by Ion Mobility Spectrometry. Our results produced a sensitivity of 81% and a specificity of 79% for all cases of TB (pulmonary and extra-pulmonary). Though lower than other studies analysing pulmonary TB alone, we believe that this technique shows promise, and a higher sensitivity could be achieved by further improving our sample capture methodology.

Non-invasive exhaled volatile organic biomarker analysis to detect inflammatory bowel disease (IBD).

INTRODUCTION: Early inflammatory bowel disease (IBD) diagnosis remains a clinical challenge. Volatile organic compounds (VOCs) have shown distinct patterns in Crohn's disease (CD) and ulcerative colitis (UC). VOC production, reflecting gut fermentome metabolites, is perturbed in IBD. VOC sampling is non-invasive, with various compounds identified from faecal, breath and urine samples. This study aimed to determine if FAIMS (field asymmetric ion mobility spectroscopy) analysis of exhaled VOCs could distinguish IBD from controls. METHODS: Seventy-six subjects were recruited, 54 established IBD (25 CD, 29 UC) and 22 healthy controls. End expiratory breath was captured using a Warwick device and analysed by FAIMS. Data were pre-processed using wavelet transformation, and classification performed in a 10-fold cross-validation. Feature selection was performed using Wilcoxon rank sum test, and sparse logistic regression gave class predictions, to calculate sensitivity and specificity. RESULTS: FAIMS breath VOC analysis showed clear separation of IBD from controls, sensitivity: 0.74 (0.65-0.82), specificity: 0.75 (0.53-0.90), AUROC: 0.82 (0.74-0.89), p-value 6.2×10(-7). IBD subgroup analysis distinguished UC from CD: sensitivity of 0.67 (0.54-0.79), specificity: 0.67 (0.54-0.79), AUROC: 0.70 (0.60-0.80), p-value 9.23×10(-4). CONCLUSION: This confirms the utility of exhaled VOC analysis to distinguish IBD from healthy controls, and UC from CD. It conforms to other studies using different technology, whilst affirming exhaled VOCs as biomarkers for diagnosing IBD.