Introduction: Anal examination and Videoanoscopy (VA) are rarely performed during colonoscopies. In recent years, there has been a considerable increase in sexually transmitted anal and rectal lesions and infections, but these conditions are not noticed or reported during routine colonoscopy.

Objective: The aim of this study is to raise awareness of fortuitous findings of lesions and Sexually Transmitted Infections (STI) in colonoscopy exams and demonstrate that anal examination and VA provide important information and should be routinely performed.

Methods: A descriptive retrospective study was carried out in 16132 patients screened by colonoscopy and VA, which were performed between 2006 and 2018. Among numerous other findings, the presence of anal condylomata and sexually transmitted dermatitis was observed. The percentages of each finding were calculated and subdivided into age groups every ten years, separately by sex and age groups.

Results: Of the 16,132 colonoscopies performed, 26 cases of condyloma (0.16%) and 50 cases of STI-suspected dermatitis (0.33%) were fortuitous.

Conclusion: Performing anal examination and videoanoscopy systematically in all routine colonoscopies allowed the identification of numerous anal conditions, including several fortuitous cases of STIs. The study proposes that anal examination and VA should be performed in all routine colonoscopies and, in suspected cases, complementary tests for STIs.

Background: Current demographic changes and improvement of quality of life of patients with chronic conditions have direct consequences on international traveling.

Aim: The aim of this study was to assess needs in pre-travel health care in a sample of travelers with some chronic condition compared to healthy travelers moving abroad.

Methods: A retrospective adult cohort study was performed including attendees of a Travel Medicine Clinic in a 2-year period of time traveling to tropical areas.

Results: Over the 2-year period, 10,108 subjects presented to the travel clinic for pre-travel health care, 51.3% of whom were females with a mean age of 40.6 years (± 12.2 SD), mainly European (85.6%), and traveling to sub-Saharian Africa (31%). One in five travelers had one or more documented chronic disease [21.3% (95%CI 20.50 - 22.10) ], statistically higher in males, older than 30 years of age, traveling to Middle East, as VFR or tourism purpose (p < 0.05). Main chronic conditions observed were cardiovascular diseases (10.9%, 95%CI 10.29 - 11.50) followed by endocrine-metabolic conditions (7.8%, 95%CI 7.32 - 8.37) and cancer (3.1%, 95%CI 2.77 - 3.44) statistically different by gender. While immunosuppressed conditions, independently of gender and travel destination, were present in 4.2% (95%CI 3.81 - 4.60) of travelers but higher in older than 30 years of age, traveling as VFR or organized tourism purpose (p < 0.05).

Conclusions: Findings from this large-scale study indicated a high amount of travelers with at least one chronic or immunosuppressed condition that should be taken in consideration into the pre-travel health advice in a current volatile, uncertain, complex and ambiguous scenario.

The clinical spectrum of COVID-19 ranges from asymptomatic disease to pneumonia and life-threatening complications, including acute respiratory distress syndrome, organ failure and death [1-3]. In this regard, the development of models that allow medical workers to quickly assess the likelihood of an unfavorable development of COVID-19 seems to be an extremely urgent task. The aim of this study is to develop a model for predicting the severity of the course of COVID-19. For training and validation of 19 machine learning models, 117 clinical and laboratory parameters were used for 10487 patients with coronavirus infection who were treated at City Hospital No 40 of St. Petersburg, Russia from 01.09.2020 to 15.10.2021. As a result, 2 best models were obtained, including 21 and 10 features with AUC = 0.91 ± 0.01 and 0.86 ± 0.01, respectively. This paper provides an extensive overview of the available models for predicting the severity of COVID-19 disease and proposes 2 developed models. A mobile application has been created for the convenience of accumulating new data and using the model.