Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks.

Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the availability of large data measurements, prediction algorithms based on neural networks are widely used in accurate power prediction. Firstly, the particularity of our work is represented by the size of the dataset consisting of 4 years of continuous real-time data measurements collected from the CETATEA photovoltaic power plant, a research site for renewable energies located in Cluj-Napoca, Romania. Secondly, the high granularity of the dataset with more than 4.2 million unified production and consumption power values recorded every 30 s guarantees the overall prediction accuracy of the system. Performance metrics used to evaluate the prediction accuracy are the mean bias error, the mean square error, the convergence time of the prediction system, the test performance, and the train mean performance. Test results indicate that the predicted unified electric power production and consumption closely resembles the unified electric power measured values.

Serum metabolomics can predict the outcome of first systematic transrectal prostate biopsy in patients with PSA <10 ng/ml.

AIM: To assess the predictive value of metabolomic analysis for the presence of prostate cancer (PCa) at first systematic biopsy. PATIENTS & METHODS: Ninety serum samples from patients with suspicion for PCa were included. Targeted and nontargeted metabolomic analysis was performed. RESULTS: Six metabolites were combined into a predictive score. A cutoff value of 0.528 for the metabolomic score showed a good accuracy for the prediction of PCa at biopsy (Area under the curve (AUC): 0.779; p < 0.001). These results were validated in a subgroup of patients, showing similar accuracy (p = 0.1). For patients with prostate specific antigen (PSA) less than 10 ng/ml, the score showed a Se 80.95%, Sp 64.52% for the detection of PCa at biopsy. CONCLUSION: Metabolomic analysis can predict the outcome of the first systematic biopsy.

Changes in Epidemiology and Antibiotic Prescription of Influenza: Before and after the Emergence of COVID-19.

BACKGROUND: The appearance of COVID-19 had a major impact on healthcare and the epidemiology of other diseases. Following the cessation of non-pharmacologic interventions destined to limit the spread of COVID-19, influenza reemerged. The aim of this study was to compare the pre-pandemic influenza seasons with the influenza seasons after the emergence of the COVID-19 pandemic, and to identify differences in terms of clinical characteristics, risk factors, complications, outcomes, and antiviral and antibiotic treatments. METHODS: We conducted a retrospective cohort study from the Teaching Hospital of Infectious Diseases database in Cluj-Napoca, Romania. We analyzed four pre-pandemic seasons and the seasons after the onset of COVID-19. We included adult patients hospitalized with confirmed influenza between October 2016 and August 2023. Variables such as age, sex, duration of hospitalization, severity, clinical manifestations, comorbidities, and Charlson comorbidity index were assessed. RESULTS: A total of 941 patients were included in the analysis. The percentage of severe influenza was similar in both groups, but mortality from influenza was significantly lower after 2022. Virtually all patients were prescribed antivirals; antibiotic prescriptions decreased in the post-COVID-19 influenza seasons. CONCLUSION: The present study suggests that influenza seasons after 2022 had lower mortality and attenuated clinical presentation.