RESUMEN
Bloodstream infections (BSIs) are a major public health concern worldwide, requiring prompt and effective antibiotic therapy. Traditionally, intravenous (IV) antibiotics have been preferred for their rapid action and consistent absorption. However, interest is growing in transitioning to oral (PO) antibiotics when suitable, due to similar pharmacokinetics, improved patient outcomes, and reduced healthcare costs. This meta-analysis aims to evaluate the clinical effectiveness of switching from IV to PO antibiotics for both gram-negative and gram-positive BSIs. Scopus, Embase, and PubMed databases were comprehensively searched until March 2023. The review included randomized controlled trials and cohort studies comparing continued IV therapy with early transition from IV to PO antibiotics within the first week of admission. Inclusion criteria encompassed studies involving adult patients (≥18 years) and reporting specific outcomes such as treatment success, mortality, and hospital readmissions. Meta-analysis of 17 studies comprising 11,245 patients demonstrated higher treatment success rates overall (OR: 1.40, P=0.04), particularly in gram-negative infections (OR: 1.42, P=0.05). However, this effect was not statistically significant in the gram-positive subgroup (OR: 1.41, P=0.036). Oral switch significantly reduced all-cause mortality overall (OR: 0.35, P=0.003), especially in gram-negative infections (OR: 0.22, P=0.008), but not significantly in gram-positive infections (OR: 0.60, P=0.09). Both gram-negative and gram-positive infections benefited from shorter hospital stays (P<0.0001), despite significant heterogeneity. Hospital readmission rates did not significantly differ between IV and oral switch groups (P=0.53). Our meta-analysis suggests potential benefits of early transition from IV to PO antibiotics for BSIs, including improved treatment outcomes and shorter hospital stays without an increased risk of readmission. However, these findings are subject to selection bias, and further standardized randomized trials are essential to validate these results.
RESUMEN
This review offers a comprehensive review of the signals and the paramount role neuroinflammation plays in neurodegenerative diseases such as Alzheimer's, Parkinson's, Huntington's, and amyotrophic lateral sclerosis. The study explores the sophisticated interactions between microglial, astrocytic, and dendritic cells and how neuroinflammation affects long-term neuronal damage and dysfunction. There are specific pathways related to the mentioned inflammatory processes, including Janus kinases/signal transducer and activator of transcriptions, nuclear factor-κB, and mitogen-activated protein kinases pathways. Neuroinflammation is argued to be a double-edged sword, being not only a protective agent that prevents further neuron damage but also the causative factor in more cell injury development. This concept of contrasting inflammation with neuroprotection advocates for the use of therapeutic techniques that seek to modulate neuroinflammatory responses as part of the neurodegeneration treatment. The recent research findings are integrated with the established knowledge to help present a comprehensive image of neuroinflammation's impact on neurodegenerative diseases and its implications for future therapy.
RESUMEN
This narrative literature review undertakes a comprehensive examination of the burgeoning field, tracing the development of artificial intelligence (AI)-powered tools for depression and anxiety detection from the level of intricate algorithms to practical applications. Delivering essential mental health care services is now a significant public health priority. In recent years, AI has become a game-changer in the early identification and intervention of these pervasive mental health disorders. AI tools can potentially empower behavioral healthcare services by helping psychiatrists collect objective data on patients' progress and tasks. This study emphasizes the current understanding of AI, the different types of AI, its current use in multiple mental health disorders, advantages, disadvantages, and future potentials. As technology develops and the digitalization of the modern era increases, there will be a rise in the application of artificial intelligence in psychiatry; therefore, a comprehensive understanding will be needed. We searched PubMed, Google Scholar, and Science Direct using keywords for this. In a recent review of studies using electronic health records (EHR) with AI and machine learning techniques for diagnosing all clinical conditions, roughly 99 publications have been found. Out of these, 35 studies were identified for mental health disorders in all age groups, and among them, six studies utilized EHR data sources. By critically analyzing prominent scholarly works, we aim to illuminate the current state of this technology, exploring its successes, limitations, and future directions. In doing so, we hope to contribute to a nuanced understanding of AI's potential to revolutionize mental health diagnostics and pave the way for further research and development in this critically important domain.