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Objectives: This study aimed to investigate and provide insight into the prevalence and patterns of off-label drug use in the pediatric population from the perspective of community pharmacists, addressing the existing data gap in a developing setting. Methods: A questionnaire-based cross-sectional study was conducted on Albanian community pharmacists in June 2021. The online administered survey explored the participants' demographic details, perceptions, and experiences with off-label prescriptions in pediatric patients. The statistical analysis conducted on the survey data comprised the construction of frequency tables and the application of the chi-square test for independence. Key findings: Three hundred and thirty-six community pharmacists nationwide completed the survey, out of which 186 (55.3%) were practiced in Tirana, the capital of Albania. Over 80% of surveyed pharmacists (n = 275) had encountered off-label drug prescriptions in pediatric patients, yet only 40% of participants reported dispensing medicines for off-label use. Community pharmacists reported that general pediatricians tended to prescribe off-label medications more frequently than pediatric subspecialists or general practitioners. It was found that off-label prescriptions were more frequently observed among children aged between 2 and 11 years. Antibiotics were the most reported medicines for off-label use in this study mentioned in almost all off-label categories. Conclusions: Prescribing medicines for unapproved uses for the treatment of pediatric patients is present in community settings in Albania. This indicates the need for further data collection and analysis to understand off-label practices in our country's pediatric population comprehensively. [ABSTRACT FROM AUTHOR]

Many studies have recently focused on the importance of the effective integration of Information and Communication Technology (ICT) tools in science education and the need for science teachers to receive adequate training and support to use them effectively. This paper aims to explore the Albanian science teachers' perceptions and use of ICTs in teaching processes. The study provides an interpretative analysis of the opinions of science teachers teaching in the middle and high schools of several Albanian regions expressed in an online survey. Overall, the results suggest that the use of ICT by science teachers in Albania is limited by a lack of proper infrastructure, limited access to technology, and training of teachers on integrating technology. However, there are indications that the adoption of ICT in science education may increase in the future, particularly after the boost from the COVID-19 pandemic emergency. [ABSTRACT FROM AUTHOR]

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis. [ABSTRACT FROM AUTHOR]

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. [ABSTRACT FROM AUTHOR]

The invasive blue crab Callinectes sapidus has been largely distributed along the Adriatic coast of Albania during the recent years. The aim of the present study is to provide data on the distribution, assessment of the abundance, the structure and biometric characteristics of the 'blue crab population in Viluni Lagoon. The data presented in this paper has been collected during 2014 - 2015. Besides direct observation and samples, collection in the studied area, questionnaires have also been distributed to the local fishermen with the purpose of gathering information about the presence of the blue crab, assessment of its state and its possible impact on the other populations in the Viluni Lagoon, as this species seems to be already established in this lagoon. [ABSTRACT FROM AUTHOR]

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers. Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings. Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets. Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome. Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy. Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.