Background: Strengthening community-based healthcare is a valuable strategy to reduce health inequalities and improve the integration of migrants and refugees into local communities in the European Union. However, little is known about how to effectively develop and run community-based healthcare models for migrants and refugees. Aiming at identifying the most-promising best practices, we performed a scoping review of the international academic literature into effective community-based healthcare models and interventions for migrants and refugees as part of the Mig-HealthCare project. Methods: A systematic search in PubMed, EMBASE, and Scopus databases was conducted in March 2018 following the PRISMA methodology. Data extraction from eligible publications included information on general study characteristics, a brief description of the intervention/model, and reported outcomes in terms of effectiveness and challenges. Subsequently, we critically assessed the available evidence per type of healthcare service according to specific criteria to establish a shortlist of the most promising best practices. Results: In total, 118 academic publications were critically reviewed and categorized in the thematic areas of mental health (n = 53), general health services (n = 36), noncommunicable diseases (n = 13), primary healthcare (n = 9), and women’s maternal and child health (n = 7). Conclusion: A set of 15 of the most-promising best practices and tools in community-based healthcare for migrants and refugees were identified that include several intervention approaches per thematic category. The elements of good communication, the linguistic barriers and the cultural differences, played crucial roles in the effective application of the interventions. The close collaboration of the various stakeholders, the local communities, the migrant/refugee communities, and the partnerships is a key element in the successful implementation of primary healthcare provision.

In recent decades, studies have underscored nuclear proteins and signaling pathways in prostate cancer (PCa) development. Key biomarkers like Enhancer of zeste homolog 2 (EZH2) and Forkhead box M1 (FOXM1) are expressed in both healthy and malignant prostate cells. This study aimed to demonstrate the relationship between pathological characteristics, survival, recurrence, and tissue expression of EZH2 and FOXM1 in high-risk PCa patients. PCa tissues were used in a retrospective analysis that spanned from September 2009 to August 2019. Inclusion criteria comprised pathological tumor stage (pT) 3 patients with positive surgical margins or tumor proximity to inked margins within 5 mm. After case selection, tissue slides were stained for EZH2 and FOXM1 antibodies, and Allred scores were calculated. Patients or relatives of deceased patients were contacted for signed agreements and disease follow-ups. The pT3b, ductal carcinoma component, and moderate EZH2 expression were associated with relapse (odds ratio [OR] 6.21, 95% confidence interval [CI] 1.41-27.27, p=0.016; OR 7.29, 95% CI 1.03-51.43, p=0.046; OR 5.96, 95% CI 1.09-32.48, p=0.039; respectively). The unilateral and bilateral seminal vesicle invasion increased the likelihood of recurrence by 9.98 times and 5.36 times, and the risk of death by around 9.78 times and 10.79 times, respectively. The pT3b was linked to higher death likelihood (OR 7.16, 95% CI 1.38-37.23, p=0.019), while moderate EZH2 expression did not show statistical significance (OR 4.54, 95% CI 0.87-23.60, p=0.072, marginally). Pathological regional lymph node stage (pN) 1 had significantly higher probability of mortality than pN unknown (3.9% vs. 27%, p<0.001). PCa in the neck and apex of the prostate gland increased death risk tenfold. Sufficient immunoexpression of EZH2, ductal carcinoma component, and neoplastic proliferation in the seminal vesicles, apex and neck of the prostate gland correlates with elevated risks of recurrence and mortality. Clinicians should use these criteria for appropriate patient referrals, and a multicenter trial could provide accurate classifications.