Objective: To investigate if markers of exposure to foodborne and orofecal microbes versus airborne viruses are associated with atopy and respiratory allergies. Design: Retrospective case-control study. Participants: 240 atopic cases and 240 non-atopic controls from a population sample of 1659 participants, all Italian male cadets aged 17-24. Setting: Air force school in Caserta, Italy. Main outcome measures: Serology for Toxoplasma gondii, Helicobacter pylori, hepatitis A virus, measles, mumps, rubella, chickenpox, cytomegalovirus, and herpes simplex virus type 1; skin sensitisation and IgE antibodies to relevant airborne allergens; total IgE concentration; and diagnosis of allergic asthma or rhinitis. Results: Compared with controls there was a lower prevalence of T gondii (26% v 18%, P=0.027), hepatitis A virus (30% v 16%, P=0.004), and H pylori (18%v 15%, P=0.325) in atopic participants. Adjusted odds ratios of atopy decreased with a gradient of exposure to H pylori, T gondii, and hepatitis A virus (none, odds ratio 1; one, 0.70; two or three, 0.37; P for trend=0.000045) but not with cumulative exposure to the other viruses. Conversely, total IgE concentration was not independently associated with any infection. Allergic asthma was rare (1/245, 0.4%) and allergic rhinitis infrequent (16/245, 7%) among the participants (245/1659) exposed to at least two orofecal and foodborne infections (H pylori, T gondii, hepatitis A virus). Conclusion: Respiratory allergy is less frequent in people heavily exposed to orofecal and foodborne microbes. Hygiene and a westernised, semisterile diet may facilitate atopy by influencing the overall pattern of commensals and pathogens that stimulate the gut associated lymphoid tissue thus contributing to the epidemic of allergic asthma and rhinitis in developed countries.

Obesity and colorectal cancer: Role of adipokines in tumor initiation and progression

Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accuracy of 86%. Furthermore, the model was capable of stratifying the testing set into two groups of patients with low- or high-risk of progression with a hazard ratio (HR) of 10.9 (p < 0.0001). Validation in multicenter prospective studies and appropriate management of privacy issues in relation to digital electronic health records (EHR) data are presently needed. Nonetheless, we may conclude that the implementation of ML algorithms and RO models into EHR data might help to achieve prognostic information, and has the potential to revolutionize the practice of personalized medicine.

Background: KRAS and BRAF mutations are well-established predictive and prognostic factors in metastatic colorectal cancer; however, their impact in the adjuvant setting has not yet been established. Methods: We performed a meta-analysis of adjuvant phase III trials in patients with stage II and III colon cancer with available data on the impact of KRAS or BRAF mutations on both disease-free survival (DFS) and overall survival (OS). Trials were subgrouped based on whether adjustment for microsatellite instability (MSI) was performed and the subgroup effect was analyzed through a meta-regression. To increase the precision of the estimates, a joint DFS–OS (so-called “multivariate”) meta-analysis was performed. All statistical tests were 2-sided. Results: Nine trials were selected (QUASAR 2, PETACC-8, N0147, CALGB-89803, NSABP-C07, NSABP-C08, PETACC-3, QUASAR, MOSAIC) including a total of 10 893 patients. In the primary meta-analysis, KRAS mutation was associated with poor DFS (pooled hazard ratio [HR] = 1.36, 95% confidence interval [CI] = 1.15 to 1.61, P < .001) and OS (pooled HR = 1.27, 95% CI = 1.03 to 1.55, P = .03) and BRAF mutation was also associated with poor DFS (pooled HR = 1.33, 95% CI = 1.00 to 1.78, P = .05) and OS (pooled HR = 1.49, 95% CI = 1.31 to 1.70, P < .001). The effect of the mutations on outcome was enhanced in the MSI-adjusted subgroup for both the KRAS mutation (pooled HR for DFS = 1.43, 95% CI = 1.15 to 1.79, P = .001; and pooled HR for OS = 1.33, 95% CI = 1.03 to 1.71, P = .03) and the BRAF mutation (pooled HR for DFS = 1.59, 95% CI = 1.22 to 2.07, P = .001; and pooled HR for OS = 1.67, 95% CI = 1.37 to 2.04, P < .001). The interaction between BRAF and MSI adjustment was statistically significant for DFS (Pinteraction = .02). This interaction was even more pronounced in the DFS–OS multivariate meta-analysis. Conclusions: Both KRAS and BRAF mutations were statistically significantly associated with both DFS and OS, with the mutation effect being enhanced by MSI adjustment. Effective adjuvant treatment for microsatellite-stable BRAF or KRAS-mutated colon cancer represents an unmet clinical need, and exploring the use of recently available BRAF and KRAS inhibitors in this setting would be highly desirable.