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Delayed cerebral ischemia (DCI) is a common and severe complication after subarachnoid hemorrhage (SAH). Logistic regression (LR) is the primary method to predict DCI, but it has low accuracy. This study assessed whether other machine learning (ML) models can predict DCI after SAH more accurately than conventional LR. PubMed, Embase, and Web of Science were systematically searched for studies directly comparing LR and other ML algorithms to forecast DCI in patients with SAH. Our main outcome was the accuracy measurement, represented by sensitivity, specificity, and area under the receiver operating characteristic. In the six studies included, comprising 1828 patients, about 28% (519) developed DCI. For LR models, the pooled sensitivity was 0.71 (95% confidence interval [CI] 0.57-0.84; p < 0.01) and the pooled specificity was 0.63 (95% CI 0.42-0.85; p < 0.01). For ML models, the pooled sensitivity was 0.74 (95% CI 0.61-0.86; p < 0.01) and the pooled specificity was 0.78 (95% CI 0.71-0.86; p = 0.02). Our results suggest that ML algorithms performed better than conventional LR at predicting DCI.Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42023441586; https://www.crd.york.ac.uk/prospero/display&#95;record.php?RecordID=441586., (© 2023. Springer Science+Business Media, LLC, part of Springer Nature and Neurocritical Care Society.)

In the age of big data, obtaining precise information about the research topic of interesting is extremely important. Keeping this in mind, this chapter focuses on providing a practical knowledge guide about computational tools and databases of transposable elements (TE) in plants. For that, we organize and present this text in three sections: (1) a discussion about tools and databases on this theme; (2) hands-on of how to use a few of them; (3) an exploratory data analysis on public TE data. Finally, we are going deep to present the main challenges and possible solutions to improve resources and tools.

This chapter provides two main contributions: (1) a description of computational tools and databases used to identify and analyze transposable elements (TEs) and circRNAs in plants; and (2) data analysis on public TE and circRNA data. Our goal is to highlight the primary information available in the literature on circular noncoding RNAs and transposable elements in plants. The exploratory analysis performed on publicly available circRNA and TEs data help discuss four sequence features. Finally, we investigate the association on circRNAs:TE in plants in the model organism Arabidopsis thaliana.

Peptidoglycan is a highly complex and essential macromolecule of bacterial outer cell wall; it is a heteropolymer made up of linear glycan strands cross-linked by peptides. Peptidoglycan has a particular composition which makes it a possible target for specific bacterial recognition. Aptamers are single-stranded DNA or RNA oligonucleotides that bind to target molecules with high affinity and specificity. Aptamers can be labeled with different radioisotopes and possess several properties that make them suitable for molecular imaging. The purpose of this study was to obtain aptamers for use as radiopharmaceutical in bacterial infection diagnosis. Two aptamers (Antibac1 and Antibac2) against peptidoglycan were selected through the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) methodology. The dissociation constant (Kd) for Antibac1 was 0.415 + 0.047 μM and for Antibac2 was 1.261 + 0.280 μM. These aptamers labeled with (32)P showed high affinity for Staphylococcus aureus cells. The binding to S. aureus and Escherichia coli in vitro were significantly higher than for Candida albicans and human fibroblasts, demonstrating their specificity for bacterial cells. These results point Antibac1 and Antibac2 as promising tools for bacterial infections identification.