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Abstract Clostridioides difficile causes a wide range of intestinal diseases through the action of two main cytotoxins, TcdA and TcdB. Ingested spores germinate in the intestine establishing a population of cells that produce toxins and spores. The pathogenicity locus, PaLoc, comprises several genes, including those coding for TcdA/B, for the holin-like TcdE protein, and for TcdR, an auto-regulatory RNA polymerase sigma factor essential for tcdA/B and tcdE expression. Here we show that tcdR, tcdA, tcdB and tcdE are expressed in a fraction of the sporulating cells, in either the whole sporangium or in the forespore. The whole sporangium pattern is due to protracted expression initiated in vegetative cells by σD, which primes the TcdR auto-regulatory loop. In contrast, the forespore-specific regulatory proteins σG and SpoVT control TcdR production and tcdA/tcdB and tcdE expression in this cell. We detected TcdA at the spore surface, and we show that wild type and ΔtcdA or ΔtcdB spores but not ΔtcdR or ΔtcdA/ΔtcdB spores are cytopathic against HT29 and Vero cells, indicating that spores may serve as toxin-delivery vehicles. Since the addition of TcdA and TcdB enhance binding of spores to epithelial cells, this effect may occur independently of toxin production by vegetative cells.

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings.

Este artigo tem por objetivo identificar, na literatura, formações continuadas – que abordem metodologias ativas – para docentes das licenciaturas na modalidade de Educação a Distância. Cuida-se de uma pesquisa exploratório-descritiva com levantamento bibliográfico e análise qualitativa dos resultados, tendo por referência autores como Abrucio, Masetto, Moriconi e Valente. Devido às dificuldades encontradas, revisaram-se estudos similares à temática. Verificou-se que as formações costumam ocorrer pontualmente, sem uma sistematização dos dados que permita atestar práticas replicáveis e pontos de melhoria. Assim, para futuras pesquisas, espera-se das instituições e dos profissionais da Educação a Distância uma reflexão de como pretendem garantir qualidade ao processo de ensino-aprendizagem. Palavras-chave: Ensino Superior. Educação a Distância. Formação continuada. Metodologias ativas.

Delirium presents a significant clinical challenge, primarily due to its profound impact on patient outcomes and the limitations of the current diagnostic methods, which are largely subjective. During the COVID-19 pandemic, this challenge was intensified as the frequency of delirium assessments decreased in Intensive Care Units (ICUs), even as the prevalence of delirium among critically ill patients increased. The present study evaluated how the serum molecular fingerprint, as acquired by Fourier-Transform InfraRed (FTIR) spectroscopy, can enable the development of predictive models for delirium. A preliminary univariate analysis of serum FTIR spectra indicated significantly different bands between 26 ICU patients with delirium and 26 patients without, all of whom were admitted with COVID-19. However, these bands resulted in a poorly performing Naïve-Bayes predictive model. Considering the use of a Fast-Correlation-Based Filter for feature selection, it was possible to define a new set of spectral bands with a wider coverage of molecular functional groups. These bands ensured an excellent Naïve-Bayes predictive model, with an AUC, a sensitivity, and a specificity all exceeding 0.92. These spectral bands, acquired through a minimally invasive analysis and obtained rapidly, economically, and in a high-throughput mode, therefore offer significant potential for managing delirium in critically ill patients.

Robust data normalization and analysis are pivotal in biomedical research to ensure that observed differences in populations are directly attributable to the target variable, rather than disparities between control and study groups. ArsHive addresses this challenge using advanced algorithms to normalize populations (e.g., control and study groups) and perform statistical evaluations between demographic, clinical, and other variables within biomedical datasets, resulting in more balanced and unbiased analyses. The tool’s functionality extends to comprehensive data reporting, which elucidates the effects of data processing, while maintaining dataset integrity. Additionally, ArsHive is complemented by A.D.A. (Autonomous Digital Assistant), which employs OpenAI’s GPT-4 model to assist researchers with inquiries, enhancing the decision-making process. In this proof-of-concept study, we tested ArsHive on three different datasets derived from proprietary data, demonstrating its effectiveness in managing complex clinical and therapeutic information and highlighting its versatility for diverse research fields.