Background Serostudies are important resources when following pandemics and predicting their further spread, as well as determining the length of protection against reinfection and vaccine development. The aim of this study was to update data on the prevalence of seropositive individuals in Canton Sarajevo, Bosnia and Herzegovina (B&H) from September 2020 to May 2021. Methods Anti-SARS-CoV-2 antibodies were quantified using an electrochemiluminescence immunoassay. Results Compared to the period April–July 2020, when anti-SARS-CoV-2 antibodies were detected in 3.77% of samples, one year later (May 2021) the estimated percentage within the same population of the urban Canton Sarajevo was 29.9% (5,406/18,066). Of all anti-SARS-CoV-2 Ig-positive individuals, 53.27% were men, and 69.00% were of 50 years of age or younger. Also, the current update found the individuals 50 years of age or younger to be more frequently anti-SARS-CoV-2 Ig positive compared to older individuals. On the other hand, higher median anti-SARS-CoV-2 Ig levels were found in individuals > 50 years old than in younger individuals, as well as in men compared to women. Seropositivity gradually increased from September 2020 to May 2021, with the lowest frequency of positive cases (3.5%) observed in September 2020, and the highest frequency (77.7%) in January 2021. Conclusion Our results provided important seroprevalence data that could help in planning restrictive local public health measures to protect the population of Sarajevo Canton, especially considering that at the time of the study the vaccines were virtually inaccessible to the general population not belonging to any of the high-priority groups for vaccination.

Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.

Aim To develop an online biofilm calculation tool (Biofilm Classifier), which calculates the optical density cut off value and accordingly determines the biofilm forming categories for the tested isolates by standardized formulas, as well as to compare the results obtained by Biofilm Classifier to manual calculations and the use of predefined values. Methods The biofilm forming capacity of tested strains was evaluated using tissue culture plate method in 96 well plates, and optical density (OD) value of the formed biofilm was measured on an ELISA Microplate reader at 595 nm on a total of 551 bacterial isolates from clinical specimen. Results Comparative analysis indicated that the manual calculation was 100% in accordance with results obtained by the designed software as opposed to the results obtained by use of predefined values for biofilm categorization. When using predefined values compared to manual biofilm categorization for the determination of biofilm positive and biofilm negative strains the specificity was 100%, sensitivity 97.81%, positive predictive value 100%, negative predictive value 96.04% and accuracy 98.57%. Conclusion Considering obtained results, the use of the designed online calculator would simplify the interpretation of biofilm forming capacity of bacteria using tissue culture plate method.