LXR Deficiency Confers Increased Protection against Visceral Leishmania Infection in Mice

Leishmania spp. are protozoan single-cell parasites that are transmitted to humans by the bite of an infected sand fly, and can cause a wide spectrum of disease, ranging from self-healing skin lesions to potentially fatal systemic infections. Certain species of Leishmania that cause visceral (systemic) disease are a source of significant mortality worldwide. Here, we use a mouse model of visceral Leishmania infection to investigate the effect of a host gene called LXR. The LXRs have demonstrated important functions in both cholesterol regulation and inflammation. These processes, in turn, are closely related to lipid metabolism and the development of atherosclerosis. LXRs have also previously been shown to be involved in protection against other intracellular pathogens that infect macrophages, including certain bacteria. We demonstrate here that LXR is involved in susceptibility to Leishmania, as animals deficient in the LXR gene are much more resistant to infection with the parasite. We also demonstrate that macrophages lacking LXR kill parasites more readily, and make higher levels of nitric oxide (an antimicrobial mediator) and IL-1β (an inflammatory cytokine) in response to Leishmania infection. These results could have important implications in designing therapeutics against this deadly pathogen, as well as other intracellular microbial pathogens

Ruthenium oxide-carbon-based nanofiller-reinforced conducting polymer nanocomposites and their supercapacitor applications.

In this review article, we have presented for the first time the new applications of supercapacitor technologies and working principles of the family of RuO2-carbon-based nanofiller-reinforced conducting polymer nanocomposites. Our review focuses on pseudocapacitors and symmetric and asymmetric supercapacitors. Over the last years, the supercapacitors as a new technology in energy storage systems have attracted more and more attention. They have some unique characteristics such as fast charge/discharge capability, high energy and power densities, and long stability. However, the need for economic, compatible, and easy synthesis materials for supercapacitors have led to the development of RuO2-carbon-based nanofiller-reinforced conducting polymer nanocomposites with RuO2. Therefore, the aim of this manuscript was to review RuO2-carbon-based nanofiller-reinforced conducting polymer nanocomposites with RuO2 over the last 17 years

Whole genome identification of Mycobacterium tuberculosis vaccine candidates by comprehensive data mining and bioinformatic analyses

<p>Abstract</p> <p>Background</p> <p><it>Mycobacterium tuberculosis</it>, the causative agent of tuberculosis (TB), infects ~8 million annually culminating in ~2 million deaths. Moreover, about one third of the population is latently infected, 10% of which develop disease during lifetime. Current approved prophylactic TB vaccines (BCG and derivatives thereof) are of variable efficiency in adult protection against pulmonary TB (0%–80%), and directed essentially against early phase infection.</p> <p>Methods</p> <p>A genome-scale dataset was constructed by analyzing published data of: (1) global gene expression studies under conditions which simulate intra-macrophage stress, dormancy, persistence and/or reactivation; (2) cellular and humoral immunity, and vaccine potential. This information was compiled along with revised annotation/bioinformatic characterization of selected gene products and <it>in silico </it>mapping of T-cell epitopes. Protocols for scoring, ranking and prioritization of the antigens were developed and applied.</p> <p>Results</p> <p>Cross-matching of literature and <it>in silico</it>-derived data, in conjunction with the prioritization scheme and biological rationale, allowed for selection of 189 putative vaccine candidates from the entire genome. Within the 189 set, the relative distribution of antigens in 3 functional categories differs significantly from their distribution in the whole genome, with reduction in the Conserved hypothetical category (due to improved annotation) and enrichment in Lipid and in Virulence categories. Other prominent representatives in the 189 set are the PE/PPE proteins; iron sequestration, nitroreductases and proteases, all within the Intermediary metabolism and respiration category; ESX secretion systems, resuscitation promoting factors and lipoproteins, all within the Cell wall category. Application of a ranking scheme based on qualitative and quantitative scores, resulted in a list of 45 best-scoring antigens, of which: 74% belong to the dormancy/reactivation/resuscitation classes; 30% belong to the Cell wall category; 13% are classical vaccine candidates; 9% are categorized Conserved hypotheticals, all potentially very potent T-cell antigens.</p> <p>Conclusion</p> <p>The comprehensive literature and <it>in silico</it>-based analyses allowed for the selection of a repertoire of 189 vaccine candidates, out of the whole-genome 3989 ORF products. This repertoire, which was ranked to generate a list of 45 top-hits antigens, is a platform for selection of genes covering all stages of <it>M. tuberculosis </it>infection, to be incorporated in rBCG or subunit-based vaccines.</p

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Foreign Body Induced Neuralgia: A Diagnostic Challenge

Neuropathic pain is caused by neural injury or painful states associated with either peripheral or central nerve injury. One of the aetiologies of this type of pain is iatrogenic trauma. This case highlights the features of peripheral neuropathic pain caused by foreign body left in the mental foramen following a previous surgical procedure. The foreign body was detected on routine radiographic evaluation. Once the foreign body was removed by surgical intervention, the pain resolved. This stresses the importance of routine radiographic evaluation in proper diagnosis and treatment planning in the management of neuropathic pain. This paper also sheds light on the role of iatrogenic mechanical cause of peripheral neuropathic pain and warrants a tough degree of caution on the part of oral clinicians