Evidence of West Nile virus (WNV) circulation in wild birds and WNV RNA negativity in mosquitoes of the Danube Delta Biosphere Reserve, Romania, 2016

West Nile virus (WNV) is a zoonotic flavivirus whose transmission cycle in nature includes wild birds as amplifying hosts and ornithophilic mosquito vectors. Bridge vectors can transmit WNV to mammal species potentially causing West Nile Fever. Wild bird migration is a mode of WNV introduction into new areas. The Danube Delta Biosphere Reserve (DDBR) is a major stopover of wild birds migrating between Europe and Africa. The aim of this study was to investigate the presence of WNV in the DDBR during the 2016 transmission season in wild birds and mosquitoes. Blood from 68 wild birds (nine different species) trapped at four different locations was analyzed by competitive ELISA and Virus Neutralization Test (VNT), revealing positive results in 8/68 (11.8%) of the wild birds by ELISA of which six samples (three from juvenile birds) were confirmed seropositive by VNT. Mosquitoes (n = 6523, 5 genera) were trapped with CDC Mini Light traps at two locations and in one location resting mosquitoes were caught. The presence of WNV RNA was tested in 134 pools by reverse transcription quantitative PCR (RT-qPCR). None of the pools was positive for WNV-specific RNA. Based on the obtained results, WNV was circulating in the DDBR during 2016

Species diversity, host preference and arbovirus detection of Culicoides (Diptera: Ceratopogonidae) in south-eastern Serbia

BackgroundCulicoides (Diptera: Ceratopogonidae) is a genus of small biting midges (also known as no-see ums) that currently includes 1368 described species. They are proven or suspected vectors for important pathogens affecting animals such as bluetongue virus (BTV) and Schmallenberg virus (SBV). Currently little information is available on the species of Culicoides present in Serbia. Thus, the aim of this study was to examine species diversity, host preference and the presence of BTV and SBV RNA in Culicoides from the Stara Planina Nature Park in south-eastern Serbia.ResultsIn total 19,887 individual Culicoides were collected during three nights of trapping at two farm sites and pooled into six groups (Obsoletus group, Pulicaris group, Others group and further each group according to the blood-feeding status to freshly engorged and non-engorged). Species identification was done on subsamples of 592 individual Culicoides specimens by morphological and molecular methods (MALDI-TOF mass spectrometry and PCR/sequencing). At least 22 Culicoides species were detected. Four animal species (cow, sheep, goat and common blackbird) as well as humans were identified as hosts of Culicoides biting midges. The screening of 8291 Culicoides specimens in 99 pools for the presence of BTV and SBV RNA by reverse-transcription quantitative PCR were negative.ConclusionsThe biodiversity of Culicoides species in the natural reserve Stara Planina was high with at least 22 species present. The presence of C. imicola Kieffer was not recorded in this area. Culicoides showed opportunistic feeding behaviour as determined by host preference. The absence of SBV and BTV viral RNA correlates with the absence of clinical disease in the field during the time of sampling. These data are the direct outcome of a training programme within the Institutional Partnership Project AMSAR: Arbovirus monitoring, research and surveillance-capacity building on mosquitoes and biting midges funded by the programme SCOPES of the Swiss National Science Foundation

Hybrid LSTM Model to Predict the Level of Air Pollution in Montenegro

Air pollution is a critical environmental concern that poses significant health risks and affects multiple aspects of human life. ML algorithms provide promising results for air pollution prediction. In the existing scientific literature, Long Short-Term Memory (LSTM) predictive models, as well as their combination with other statistical and machine learning approaches, have been utilized for air pollution prediction. However, these combined algorithms may not always provide suitable results due to the stochastic nature of the factors that influence air pollution, improper hyperparameter configurations, or inadequate datasets and data characterized by great variability and extreme dispersion. The focus of this paper is applying and comparing the performance of Support Vector Machine and hybrid LSTM regression models for air pollution prediction. To identify optimal hyperparameters for the LSTM model, a hybridization with the Genetic Algorithm is proposed. To mitigate the risk of overfitting, the bagging technique is employed on the best LSTM model. The proposed predicitive model aims to determine the Common Air Quality Index level for the next hour in Niksic, Montenegro. With the hybridization of the LSTM algorithm and by applying the bagging technique, our approach aims to significantly enhance the accuracy and reliability of hourly air pollution prediction. The major contribution of this paper is in the application of advanced machine learning analysis and the combination of the LSTM, Genetic Algorithm, and bagging techniques, which have not been previously employed in the analysis of air pollution in Montenegro. The proposed model will be made available to interested management structures, local governments, national entities, or other relevant institutions, empowering them to make effective pollution level predictions and take appropriate measures

A Fine Search Method for the Cubic-Phase Function-Based Estimator

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Microfluidic chip fabrication for application in low-cost DIY microPIV

The article presents the process of fabrication of the microfluidic chip to be used with the do-it-yourself (DIY) micro-PIV system previously made and compared to the classic PIV setup. This pilot study is an example of research being conducted in the Scientific fab lab (fabrication laboratory), founded at the Faculty of Mechanical Engineering, University of Belgrade. Fab labs and DIY principle are becoming more and more accepted by the scientific community and this article aims to contribute to such trend

Healthcare Service Quality from the Point of Healthcare Providers’ Perception at the Time of COVID-19

The pandemic of the Coronavirus 19 disease (COVID-19) has had significant impact on healthcare systems worldwide. The present study aims to investigate the service providers’ quality dimensions in public sector hospitals in the Republic of Serbia during the COVID-19 pandemic and to propose a sustainable model for healthcare improvement. The study was conducted from September 2021 to December 2021. A modified SERPERF quality measurement questionnaire was distributed to healthcare workers in nine secondary care public hospitals of the Serbian Autonomous Province of Vojvodina (APV). Six hundred one questionnaires were found to be complete in all aspects and compared to 528 questionnaires from the database of the Provincial Secretariat for Health Care obtained from healthcare workers before the COVID-19 outbreak. The present study suggests that supportive measures during the COVID-19 pandemic are effective and, from the providers’ perception, increase healthcare quality. Continual investment in healthcare would provide sustainable development of healthcare quality in the future, regardless of the pandemic conditions

Healthcare Service Quality from the Point of Healthcare Providers’ Perception at the Time of COVID-19

The pandemic of the Coronavirus 19 disease (COVID-19) has had significant impact on healthcare systems worldwide. The present study aims to investigate the service providers’ quality dimensions in public sector hospitals in the Republic of Serbia during the COVID-19 pandemic and to propose a sustainable model for healthcare improvement. The study was conducted from September 2021 to December 2021. A modified SERPERF quality measurement questionnaire was distributed to healthcare workers in nine secondary care public hospitals of the Serbian Autonomous Province of Vojvodina (APV). Six hundred one questionnaires were found to be complete in all aspects and compared to 528 questionnaires from the database of the Provincial Secretariat for Health Care obtained from healthcare workers before the COVID-19 outbreak. The present study suggests that supportive measures during the COVID-19 pandemic are effective and, from the providers’ perception, increase healthcare quality. Continual investment in healthcare would provide sustainable development of healthcare quality in the future, regardless of the pandemic conditions