Derivation of novel metabolic pathway score identifies alanine metabolism as a targetable influencer of TNF-alpha signaling.

Background: Better understanding of the interaction between metabolism and immune response will be key to understanding physiology and disease. Tumor Necrosis Factor-alpha (TNFα) has been studied widely. However, despite the extensive knowledge about TNFα, the cytokine appears to induce not only variable, but often contradictory, effects on inflammation and cell proliferation. Despite advancements in the metabolomics field, it is still difficult to analyze the types of multi-dose, multi-time point studies needed for elucidating the varied immunologic responses induced by TNFα. Results: We studied the dose and time course effects of TNFα on murine fibroblast cultures and further elucidated these connections using selective blockade of the TNF receptors (TNFR1 and TNFR2). To streamline analysis, we developed a method to collate the metabolic pathway output from MetaboAnalyst into a single value for the Index of pathway significance (IPS). Using this metric, we tested dose-, time-, and receptor-dependent effects of TNFα signaling on cell metabolism. Guided by these results, we then demonstrate that alanine supplementation enriched TNFR1-related responses in both cell and mouse models. Conclusions: Our results suggest that TNFα, particularly when signaling through TNFR1, may preferentially use alanine metabolism for energy. These results are limited in by cell type used and immune outputs measured. However, we anticipate that our novel method may assist other researchers in identifying metabolic targets that influence their disease or model of interest through simplifying the analysis of multi-condition experiments. Furthermore, our results endorse the consideration of follow up studies in immunometabolism to improve outcomes in TNF-mediated diseases.

Pitfalls in Interpretive Applications of Artificial Intelligence in Radiology.

Interpretive artificial intelligence (AI) tools are poised to change the future of radiology. However, certain pitfalls may pose particular challenges for optimal AI interpretative performance. These include anatomic variants, age-related changes, postoperative changes, medical devices, image artifacts, lack of integration of prior and concurrent imaging examinations and clinical information, as well as the satisfaction-of-search effect. Model training and development should account for such pitfalls, to minimize errors and optimize interpretation accuracy. More broadly, AI algorithms should be exposed to diverse and complex training data sets, to yield a holistic interpretation that considers all relevant information beyond the individual examination. Successful clinical deployment of AI tools will require that radiologist end-users recognize these pitfalls and other limitations of the available models. Furthermore, developers should incorporate explainable AI techniques (e.g., heat maps) into their tools, to improve radiologists' understanding of model outputs and to enable radiologists to provide feedback for guiding continuous learning and iterative refinement. In this article, we provide an overview of common pitfalls that radiologists may encounter when using interpretive AI products in daily practice. We present how such pitfalls lead to AI errors and offer potential strategies that AI developers may use for their mitigation.

Updates on the Applications of Spectral Computed Tomography for Musculoskeletal Imaging.

Spectral CT represents a novel imaging approach that can noninvasively visualize, quantify, and characterize many musculoskeletal pathologies. This modality has revolutionized the field of radiology by capturing CT attenuation data across multiple energy levels and offering superior tissue characterization while potentially minimizing radiation exposure compared to traditional enhanced CT scans. Despite MRI being the preferred imaging method for many musculoskeletal conditions, it is not viable for some patients. Moreover, this technique is time-consuming, costly, and has limited availability in many healthcare settings. Thus, spectral CT has a considerable role in improving the diagnosis, characterization, and treatment of gout, inflammatory arthropathies, degenerative disc disease, osteoporosis, occult fractures, malignancies, ligamentous injuries, and other bone-marrow pathologies. This comprehensive review will delve into the diverse capabilities of dual-energy CT, a subset of spectral CT, in addressing these musculoskeletal conditions and explore potential future avenues for its integration into clinical practice.

Artificial intelligence in the detection of non-biological materials.

Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Presently, there is a strong reliance on radiologists to accurately diagnose and characterize foreign bodies in a timely fashion, a task that can be readily augmented with AI tools. This article will first explore the most common clinical scenarios involving foreign bodies, such as retained surgical instruments, open and penetrating injuries, catheter and tube malposition, and foreign body ingestion and aspiration. By initially exploring the existing imaging techniques employed for diagnosing these conditions, the potential role of AI in detecting non-biological materials can be better elucidated. Yet, the heterogeneous nature of foreign bodies and limited data availability complicates the development of computer-aided detection models. Despite these challenges, integrating AI can potentially decrease radiologist workload, enhance diagnostic accuracy, and improve patient outcomes.

Examining allergy related diseases in Africa: A scoping review protocol.

During recent decades, allergy related diseases have emerged as a growing area of concern in developing regions of the world, including Africa. Worldwide prevalence of allergic diseases has grown to an estimated 262 million for asthma, 400 million for allergic rhinitis (or hay fever), 171 million with atopic dermatitis (or eczema), and over 200 million for food allergy. In Africa, considerable variability exists in the data surrounding prevalence at the continent-wide, regional, and study site levels. Furthermore, research conducted in many rural areas and underdeveloped countries in Africa remains limited, and presently, little has been done to characterize and map the extremely heterogeneous body of literature which confounds research efforts. This scoping review will seek to identify studies examining the prevalence, management strategies, outcomes, and associated risk factors for allergy related diseases in Africa. The Joanna Briggs Institute's scoping review methods will be followed, and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Review (PRISMA-ScR) was used for writing the protocol. Four databases (Embase, Global Health, PubMed, African Journals Online) will be searched for literature published from 2003 to 2023 in any language. Title and abstract screening and full-text screening will be completed by two independent reviewers using Covidence; conflicts resolved by a third reviewer. Data will be extracted using Covidence by two reviewers independently. To report the results, we will follow the PRISMA-ScR checklist and report descriptive statistics and a narrative summary.

The role of micro RNAs (miRNAs) in the regulation of Drosophila melanogaster's innate immunity.

MicroRNAs (miRNAs) are a class of small non-coding RNAs ~19-22 nt long which post-transcriptionally regulate gene expression. Their ability to exhibit dynamic expression patterns coupled with their wide variety of targets allows miRNAs to regulate many processes, including the innate immune response of Drosophila melanogaster. Recent studies have identified miRNAs in Drosophila which are differentially expressed during infection with different pathogens as well as miRNAs that may affect immune signalling when differentially expressed. This review provides an overview of miRNAswhich have been identified to play a role in the immune response of Drosophila through targeting of the Toll and IMD signalling pathways and other immune processes. It will also explore the role of miRNAs in fine-tuning the immune response in Drosophila and highlight current gaps in knowledge regarding the role of miRNAs in immunity and areas for further research.