Targeting Toll-like receptor-driven systemic inflammation by engineering an innate structural fold into drugs

Petruk, Ganna; Puthia, Manoj; Samsudin, Firdaus; Petrlova, Jitka; Olm, Franziska; Mittendorfer, Margareta; Hyllén, Snejana; Edström, Dag; Strömdahl, Ann-Charlotte; Diehl, Carl; Ekström, Simon; Walse, Björn; Kjellström, Sven; Bond, Peter J.; Lindstedt, Sandra; Schmidtchen, Artur

doi:10.1038/s41467-023-41702-y

Cited by 4 publications

(2 citation statements)

References 70 publications

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“…Soluble CD14 molecules capture LPS and enhance its elimination in the liver through HDL binding. Thrombin-derived C peptides may inhibit the binding of LPS to CD14, as recently described [ 41 ].…”

Section: Discussionmentioning

confidence: 95%

TLRs1-10 Protein Expression in Circulating Human White Blood Cells during Bacterial and COVID-19 Infections

Chomel,

Vogt,

Demiselle

et al. 2024

J Innate Immun

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Introduction: Toll-like receptors play crucial roles in the sepsis-induced systemic inflammatory response. Septic shock mortality correlates with overexpression of neutrophilic TLR2 and TLR9, while the role of TLR4 overexpression remains a debate. In addition, TLRs are involved in the pathogenesis of viral infections such as COVID-19, where the single-stranded RNA of SARS-CoV-2 is recognized by TLR7 and TLR8, and the spike protein activates TLR4. Methods: In this study, we conducted a comprehensive analysis of TLRs 1–10 expressions in white blood cells from 71 patients with bacterial and viral infections. Patients were divided into 4 groups based on disease type and severity (sepsis, septic shock, moderate, and severe COVID-19) and compared to 7 healthy volunteers. Results: We observed a significant reduction in the expression of TLR4 and its co-receptor CD14 in septic shock neutrophils compared to the control group (p < 0.001). Severe COVID-19 patients exhibited a significant increase in TLR3 and TLR7 levels in neutrophils compared to controls (p < 0.05). Septic shock patients also showed a similar increase in TLR7 in neutrophils along with elevated intermediate monocytes (CD14+CD16+) compared to the control group (p < 0.005 and p < 0.001, respectively). However, TLR expression remained unchanged in lymphocytes. Conclusion: This study provides further insights into the mechanisms of TLR activation in various infectious conditions. Additional analysis is needed to assess their correlation with patient outcome and to evaluate the impact of TLR-pathway modulation during septic shock and severe COVID-19.

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Section: Discussionmentioning

confidence: 95%

TLRs1-10 Protein Expression in Circulating Human White Blood Cells during Bacterial and COVID-19 Infections

Chomel,

Vogt,

Demiselle

et al. 2024

J Innate Immun

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“…Machine learning and deep learning breakthroughs in recent years have revolutionized the area of medical data analysis 15,16,17 . These strategies have shown great potential in a variety of healthcare applications, including medical picture analysis, disease diagnosis, drug development, and personalized medical care recommendations 18,19,20 .…”

Section: Introductionmentioning

confidence: 99%

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Akter,

Pias

et al. 2024

Preprint

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In the healthcare industry, many artificial intelligence (AI) models have attempted to overcome bias from class imbalances while also maintaining high results. Firstly, when utilizing a large number of unbalanced samples, current AI models and related research have failed to balance specificity and sensitivity – a problem that can undermine the reliability of medical research. Secondly, no reliable method for obtaining detailed interpretability has been put forth when addressing large numbers of input features. The present research addresses these two critical research gaps with a proposed lightweight Artificial Neural Network (ANN) model. Using 43 input features from the 2021 Behavioral Risk Factor Surveillance System (BRFSS) dataset, the proposed model outperforms prior models in producing balanced outcomes from markedly unbalanced large survey data. The efficacy of this proposed ANN model is attributed to its simplified design, which reduces processing demands, and its resilience in identifying the probability of myocardial infarction (MI). This is demonstrated by its 80% specificity and 77% sensitivity, and is substantiated by a Receiver Operating Characteristic Area Under the Curve (AUC) of 0.87. The outcomes across the scopes of each specified data domain were also separately represented, thus demonstrating the proposed model’s robust sensitivity. The interpretability of the model, as measured by Shapley values, reveals substantial correlations between myocardial infarction (MI) and its risk factors, including long-term medical conditions, socio-demographic factors, personal health habits, economic and social status, healthcare availability and affordability, as well as impairment statuses, providing valuable insights for improved cardiovascular risk assessment and personalized healthcare strategies.

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Strategies to turn adversity into profits: Potential roles of tetrabromobisphenol a and tetrachlorobisphenol a in promoting carbon sequestration and lipid accumulation in microalgae

Chen,

Yang,

Yan

et al. 2024

Chemical Engineering Journal

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Targeting Toll-like receptor-driven systemic inflammation by engineering an innate structural fold into drugs

Cited by 4 publications

References 70 publications

TLRs1-10 Protein Expression in Circulating Human White Blood Cells during Bacterial and COVID-19 Infections

TLRs1-10 Protein Expression in Circulating Human White Blood Cells during Bacterial and COVID-19 Infections

Identification of Myocardial Infarction (MI) Probability from Imbalanced Medical Survey Data: An Artificial Neural Network (ANN) with Explainable AI (XAI) Insights

Strategies to turn adversity into profits: Potential roles of tetrabromobisphenol a and tetrachlorobisphenol a in promoting carbon sequestration and lipid accumulation in microalgae

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