Multi-cohort study on cytokine and chemokine profiles in the progression of COVID-19.

Various substances in the blood plasma serve as prognostic indicators of the progression of COVID-19. Consequently, multi-omics studies, such as proteomic and metabolomics, are ongoing to identify accurate biomarkers. Cytokines and chemokines, which are crucial components of immune and inflammatory responses, play pivotal roles in the transition from mild to severe illness. To determine the relationship between plasma cytokines and the progression of COVID-19, we used four study cohorts to perform a systematic study of cytokine levels in patients with different disease stages. We observed differential cytokine expression between patients with persistent-mild disease and patients with mild-to-severe transformation. For instance, IL-4 and IL-17 levels significantly increased in patients with mild-to-severe transformation, indicating differences within the mild disease group. Subsequently, we analysed the changes in cytokine and chemokine expression in the plasma of patients undergoing two opposing processes: the transition from mild to severe illness and the transition from severe to mild illness. We identified several factors, such as reduced expression of IL-16 and IL-18 during the severe phase of the disease and up-regulated expression of IL-10, IP-10, and SCGF-ß during the same period, indicative of the deterioration or improvement of patients' conditions. These factors obtained from fine-tuned research cohorts could provide auxiliary indications for changes in the condition of COVID-19 patients.

S100A8/A9: An emerging player in sepsis and sepsis-induced organ injury.

Sepsis, the foremost contributor to mortality in intensive care unit patients, arises from an uncontrolled systemic response to invading infections, resulting in extensive harm across multiple organs and systems. Recently, S100A8/A9 has emerged as a promising biomarker for sepsis and sepsis-induced organ injury, and targeting S100A8/A9 appeared to ameliorate inflammation-induced tissue damage and improve adverse outcomes. S100A8/A9, a calcium-binding heterodimer mainly found in neutrophils and monocytes, serves as a causative molecule with pro-inflammatory and immunosuppressive properties, which are vital in the pathogenesis of sepsis. Therefore, improving our comprehension of how S100A8/A9 acts as a pathological player in the development of sepsis is imperative for advancing research on sepsis. Our review is the first-to the best of our knowledge-to discuss the biology of S100A8/A9 and its release mechanisms, summarize recent advances concerning the vital roles of S100A8/A9 in sepsis and the consequential organ damage, and underscore its potential as a promising diagnostic biomarker and therapeutic target for sepsis.

Integrative proteomic profiling of lung tissues and blood in acute respiratory distress syndrome.

Introduction: Acute respiratory distress syndrome and acute lung injury (ARDS/ALI) still lack a recognized diagnostic test and pharmacologic treatments that target the underlying pathology. Methods: To explore the sensitive non-invasive biomarkers associated with pathological changes in the lung of direct ARDS/ALI, we performed an integrative proteomic analysis of lung and blood samples from lipopolysaccharide (LPS)-induced ARDS mice and COVID-19-related ARDS patients. The common differentially expressed proteins (DEPs) were identified based on combined proteomic analysis of serum and lung samples in direct ARDS mice model. The clinical value of the common DEPs was validated in lung and plasma proteomics in cases of COVID-19-related ARDS. Results: We identified 368 DEPs in serum and 504 in lung samples from LPS-induced ARDS mice. Gene ontology (GO) classification and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that these DEPs in lung tissues were primarily enriched in pathways, including IL-17 and B cell receptor signaling pathways, and the response to stimuli. In contrast, DEPs in the serum were mostly involved in metabolic pathways and cellular processes. Through network analysis of protein-protein interactions (PPI), we identified diverse clusters of DEPs in the lung and serum samples. We further identified 50 commonly upregulated and 10 commonly downregulated DEPs in the lung and serum samples. Internal validation with a parallel-reacted monitor (PRM) and external validation in the Gene Expression Omnibus (GEO) datasets further showed these confirmed DEPs. We then validated these proteins in the proteomics of patients with ARDS and identified six proteins (HP, LTA4H, S100A9, SAA1, SAA2, and SERPINA3) with good clinical diagnostic and prognostic value. Discussion: These proteins can be viewed as sensitive and non-invasive biomarkers associated with lung pathological changes in the blood and could potentially serve as targets for the early detection and treatment of direct ARDS especially in hyperinflammatory subphenotype.

Role of released mitochondrial DNA in acute lung injury.

Acute lung injury(ALI)/acute respiratory distress syndrome(ARDS) is a form of acute-onset hypoxemic respiratory failure characterised by an acute, diffuse, inflammatory lung injury, and increased alveolar-capillary permeability, which is caused by a variety of pulmonary or nonpulmonary insults. Recently, aberrant mitochondria and mitochondrial DNA(mtDNA) level are associated with the development of ALI/ARDS, and plasma mtDNA level shows the potential to be a promising biomarker for clinical diagnosis and evaluation of lung injury severity. In mechanism, the mtDNA and its oxidised form, which are released from impaired mitochondria, play a crucial role in the inflammatory response and histopathological changes in the lung. In this review, we discuss mitochondrial outer membrane permeabilisation (MOMP), mitochondrial permeability transition pore(mPTP), extracellular vesicles (EVs), extracellular traps (ETs), and passive release as the principal mechanisms for the release of mitochondrial DNA into the cytoplasm and extracellular compartments respectively. Further, we explain how the released mtDNA and its oxidised form can induce inflammatory cytokine production and aggravate lung injury through the Toll-like receptor 9(TLR9) signalling, cytosolic cGAS-stimulator of interferon genes (STING) signalling (cGAS-STING) pathway, and inflammasomes activation. Additionally, we propose targeting mtDNA-mediated inflammatory pathways as a novel therapeutic approach for treating ALI/ARDS.

A scoring system developed from a nomogram to differentiate active pulmonary tuberculosis from inactive pulmonary tuberculosis.

Purpose: This study aimed to develop and validate a scoring system based on a nomogram of common clinical metrics to discriminate between active pulmonary tuberculosis (APTB) and inactive pulmonary tuberculosis (IPTB). Patients and methods: A total of 1096 patients with pulmonary tuberculosis (PTB) admitted to Wuhan Jinyintan Hospital between January 2017 and December 2019 were included in this study. Of these patients with PTB, 744 were included in the training cohort (70%; 458 patients with APTB, and 286 patients with IPTB), and 352 were included in the validation cohort (30%; 220 patients with APTB, and 132 patients with IPTB). Data from 744 patients from the training cohort were used to establish the diagnostic model. Routine blood examination indices and biochemical indicators were collected to construct a diagnostic model using the nomogram, which was then transformed into a scoring system. Furthermore, data from 352 patients from the validation cohort were used to validate the scoring system. Results: Six variables were selected to construct the prediction model. In the scoring system, the mean corpuscular volume, erythrocyte sedimentation rate, albumin level, adenosine deaminase level, monocyte-to-high-density lipoprotein ratio, and high-sensitivity C-reactive protein-to-lymphocyte ratio were 6, 4, 7, 5, 5, and 10, respectively. When the cut-off value was 15.5, the scoring system for recognizing APTB and IPTB exhibited excellent diagnostic performance. The area under the curve, specificity, and sensitivity of the training cohort were 0.919, 84.06%, and 86.36%, respectively, whereas those of the validation cohort were 0.900, 82.73, and 86.36%, respectively. Conclusion: This study successfully constructed a scoring system for distinguishing APTB from IPTB that performed well.