Retraction Note: Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes.

The article "Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes", by E. Kalluçi, E. Noka, K. Bani, X. Dhamo, I. Alimehmeti, K. Dhuli, G. Madeo, C. Micheletti, G. Bonetti, C. Zuccato, E. Borghetti, G. Marceddu, M. Bertelli, published in Eur Rev Med Pharmacol Sci 2023; 27 (6 Suppl): 39-47-DOI: 10.26355/eurrev_202312_34688-PMID: 38112947 has been retracted by the Editor in Chief. Following concerns raised on PubPeer, the Editor in Chief has initiated an investigation to evaluate the validity of the results. Despite the authors' prompt responses to the identified issues, the Editor in Chief has decided to withdraw the article due to significant errors in the text and final statements, as well as undisclosed conflicts of interest. The Publisher apologizes if these concerns have not been detected during the review process. The authors have been informed about the retraction. This article has been retracted. The Publisher apologizes for any inconvenience this may cause. https://www.europeanreview.org/article/34688.

A comparative study of supervised and unsupervised machine learning algorithms applied to human microbiome.

Background: The human microbiome, consisting of diverse bacte-rial, fungal, protozoan and viral species, exerts a profound influence on various physiological processes and disease susceptibility. However, the complexity of microbiome data has presented significant challenges in the analysis and interpretation of these intricate datasets, leading to the development of specialized software that employs machine learning algorithms for these aims. Methods: In this paper, we analyze raw data taken from 16S rRNA gene sequencing from three studies, including stool samples from healthy control, patients with adenoma, and patients with colorectal cancer. Firstly, we use network-based methods to reduce dimensions of the dataset and consider only the most important features. In addition, we employ supervised machine learning algorithms to make prediction. Results: Results show that graph-based techniques reduces dimen-sion from 255 up to 78 features with modularity score 0.73 based on different centrality measures. On the other hand, projection methods (non-negative matrix factorization and principal component analysis) reduce dimensions to 7 features. Furthermore, we apply supervised machine learning algorithms on the most important features obtained from centrality measures and on the ones obtained from projection methods, founding that the evaluation metrics have approximately the same scores when applying the algorithms on the entire dataset, on 78 feature and on 7 features. Conclusions: This study demonstrates the efficacy of graph-based and projection methods in the interpretation for 16S rRNA gene sequencing data. Supervised machine learning on refined features from both approaches yields comparable predictive performance, emphasizing specific microbial features-bacteroides, prevotella, fusobacterium, lysinibacillus, blautia, sphingomonas, and faecalibacterium-as key in predicting patient conditions from raw data.

Correlation between COVID-19 and air pollution: the effects of PM2.5 and PM10 on COVID-19 outcomes.

OBJECTIVE: Given its effects on long-term illnesses, like heart problems and diabetes, air pollution may be among the reasons that led COVID-19 to get worse and kill a larger number of people. Experiments have shown that breathing in polluted air weakens the immune system, making it easier for viruses to enter the body and grow. Viruses may be able to survive in the air by interacting in complex ways with particles and gases. These interactions depend on the air's chemical makeup, the particles' electric charges, and environmental conditions like humidity, UV light, and temperature. Moreover, exposure to UV rays and air pollution may reduce the organism's production of antimicrobial molecules, thus supporting viral infections. More epidemiological studies are needed to determine what effects air pollution has on COVID-19. In this review, we will discuss how air pollutants such as PM2.5 and PM10 contribute to the transmission of COVID-19. MATERIALS AND METHODS: We have used nine target cities in the Tuscany region to verify this certainty, and in all these cases, the air pollution factors were found to be strongly correlated with COVID-19 cases. For each city, we applied a multivariate analysis and found an appropriate model that better fits the data. RESULTS: This review underlines that both short-term and long-term exposure to air pollution may be crucial exasperating factors for SARS-CoV-2 transmission and COVID-19 severity and lethality. The statistical analysis concludes that air pollution should be accounted for as a possible risk factor in future COVID-19 investigations, and it should be avoided as much as possible by the general population. CONCLUSIONS: Our research highlighted the correlation between COVID-19 and air pollution. Reducing air pollution exposure should be one of the first measures against COVID-19 spread.

The application of next generation matrix in the calculation of basic reproduction number for COVID-19.

Background: Infectious diseases are disorders caused by microorganisms such as bacteria, viruses, fungi, or parasites. Many organisms live in and on our bodies. They are normally harmless or even helpful. However under certain conditions, some organisms may cause disease. Infectious diseases are also called contagious diseases due to the fact that they can be passed from person to person. Some are transmitted by insects or other animals. COVID-19 is an infectious disease that has "pervaded" the whole world during the last three years. The World Health Organization (WHO) has declared COVID-19 a Public Health Emergency of International Concern. Methods: In this paper, we will study the outbreak of this pandemic in Albania based on some mathematical models, such as SIR, SIRD, and SEIRD. We will present a detailed analysis of these models and also demonstrate how they can be used to predict the spread of infectious diseases. More precisely, we will see the spread of COVID-19 in our country, Albania. Software such as MATLAB and RStudio will be used to do this. The data that we will use when working with these programs is taken from the Institute of Public Health, Tirana, Albania. Results: We've developed an application utilizing actual data to estimate SEIRD model parameters. It's able to compute the basic reproduction number and, more significantly, provides forecasts on the disease's progression. Conclusions: Our aim is to calculate the Basic Reproduction Number, using the Next Generation Matrix, and use it to see the future of the disease. This is the average number of new infections generated by an infected individual. A large value indicates that the infection is transmitted very quickly. We will try to calculate what the values of Basic Number Reproduction have been over different time periods.