The transcription factor NF-κB regulates multiple aspects of innate and adaptive immune functions and serves as a pivotal mediator of inflammatory responses. NF-κB induces the expression of various pro-inflammatory genes, including those encoding cytokines and chemokines, and also participates in inflammasome regulation. In addition, NF-κB plays a critical role in regulating the survival, activation and differentiation of innate immune cells and inflammatory T cells. Consequently, deregulated NF-κB activation contributes to the pathogenic processes of various inflammatory diseases. In this review, we will discuss the activation and function of NF-κB in association with inflammatory diseases and highlight the development of therapeutic strategies based on NF-κB inhibition.

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NF-κB signaling in inflammation

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field.

https://biblio.ugent.be/publication/8555786/file/8555788.pdf

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

NLRP3 (NOD-, LRR- and pyrin domain-containing protein 3) is an intracellular sensor that detects a broad range of microbial motifs, endogenous danger signals and environmental irritants, resulting in the formation and activation of the NLRP3 inflammasome. Assembly of the NLRP3 inflammasome leads to caspase 1-dependent release of the pro-inflammatory cytokines IL-1β and IL-18, as well as to gasdermin D-mediated pyroptotic cell death. Recent studies have revealed new regulators of the NLRP3 inflammasome, including new interacting or regulatory proteins, metabolic pathways and a regulatory mitochondrial hub. In this Review, we present the molecular, cell biological and biochemical bases of NLRP3 activation and regulation and describe how this mechanistic understanding is leading to potential therapeutics that target the NLRP3 inflammasome.

The NLRP3 inflammasome: molecular activation and regulation to therapeutics

NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines

http://www.medicinabiomolecular.com.br/biblioteca/pdfs/Biomolecular/mb-0436.pdf

CD4 T cells: fates, functions, and faults

/pdf/comparing-treatments-for-resistant-kawasaki-disease-the-3utqjxfd.pdf

Comparing Treatments for Resistant Kawasaki Disease – The KIDCARE Study

/pdf/genetic-environmental-and-behavioral-correlates-of-lifetime-rzsi089a.pdf

Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.

Key Points Question Can risk for posttraumatic stress disorder (PTSD) be accurately predicted prior to military deployment? Findings This diagnostic/prognostic study of 4711 US Army soldiers developed machine learning models to predict postdeployment PTSD using self-reported predictors collected before deployment. An optimal model was selected based on performance in 2 cohorts and then validated in a temporally and geographically distinct cohort, where approximately one-third of participants with the highest predicted risk accounted for an estimated 62.4% of the PTSD cases. Meaning These findings suggest that PTSD risk may be accurately stratified prior to military deployment, which may facilitate the development of targeted intervention strategies.

/pdf/development-and-validation-of-a-machine-learning-prediction-248m8x4p.pdf

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment

Baseline Phenotype Differences in Black and White Patients With Chronic Thromboembolic Pulmonary Hypertension (CTEPH) Patients in the United States CTEPH Registry (US-CTEPH-R)

Many patients with mild traumatic brain injury (mTBI) are at risk for mental health problems such as posttraumatic stress disorder (PTSD). The objective of this study was to determine whether the polygenic risk for PTSD (or for related mental health disorders or traits including major depressive disorder [MDD] and neuroticism [NEU]) was associated with an increased likelihood of PTSD in the aftermath of mTBI. We used data from individuals of European ancestry with mTBI enrolled in TRACK-TBI (n = 714), a prospective longitudinal study of level 1 trauma center patients. One hundred and sixteen mTBI patients (16.3%) had probable PTSD (PCL-5 score ≥33) at 6 months post-injury. We used summary statistics from recent GWAS studies of PTSD, MDD, and NEU to generate polygenic risk scores (PRS) for individuals in our sample. A multivariable model that included age, sex, pre-injury history of mental disorder, and cause of injury explained 7% of the variance in the PTSD outcome; the addition of the PTSD-PRS (and five ancestral principal components) significantly increased the variance explained to 11%. The adjusted odds of PTSD in the uppermost PTSD-PRS quintile was nearly four times higher (aOR = 3.71, 95% CI 1.80-7.65) than in the lowest PTSD-PRS quintile. There was no evidence of a statistically significant interaction between PTSD-PRS and prior history of mental disorder, indicating that PTSD-PRS had similar predictive utility among those with and without pre-injury psychiatric illness. When added to the model, neither MDD-PRS nor NEU-PRS were significantly associated with the PTSD outcome. These findings show that the risk for PTSD in the context of mTBI is, in part, genetically influenced. They also raise the possibility that an individual's PRS could be clinically actionable if used-possibly with other non-genetic predictors-to signal the need for enhanced follow-up and early intervention; this precision medicine approach needs to be prospectively studied. 

/pdf/polygenic-risk-for-mental-disorders-as-predictors-of-vpwdkgbg.pdf

Feng He

Papers

Comparing Treatments for Resistant Kawasaki Disease – The KIDCARE Study

Genetic, environmental, and behavioral correlates of lifetime suicide attempt: Analysis of additive and interactive effects in two cohorts of US Army soldiers.

Development and Validation of a Machine Learning Prediction Model of Posttraumatic Stress Disorder After Military Deployment

Baseline Phenotype Differences in Black and White Patients With Chronic Thromboembolic Pulmonary Hypertension (CTEPH) Patients in the United States CTEPH Registry (US-CTEPH-R)

Polygenic risk for mental disorders as predictors of posttraumatic stress disorder after mild traumatic brain injury