Developing a gene expression model for predicting ventilator-associated pneumonia in trauma patients: a pilot study

Definition, Epidemiology, and Pathogenesis of Severe Community-Acquired Pneumonia

The clinical presentation of community-acquired pneumonia (CAP) can vary widely among patients. While many individuals with mild symptoms can be managed as outpatients with excellent outcomes, there is a distinct subgroup of patients who present with severe CAP. In these cases, the mortality rate can reach approximately 25% within 30 days and even up to 50% within a year. It is crucial to focus attention on these patients who are at higher risk. Among the various definitions of severe CAP found in the literature, one commonly used criterion is the requirement for admission to intensive care unit. Notable epidemiological characteristics of these patients include the impact of acute cardiovascular diseases on clinical outcomes and the enduring, independent effect of pneumonia on long-term outcomes. Factors such as pathogen virulence, the presence of comorbidities, and the host response are important contributors to the pathogenesis of severe CAP. In these patients, the host response may be dysregulated and compartmentalized. Gaining a better understanding of the epidemiology and pathogenesis of severe CAP will provide a foundation for the development of new therapies for this condition. This manuscript aims to review the definition, epidemiology, and pathogenesis of severe CAP, shedding light on important aspects that can aid in the improvement of patient care and outcomes.

Leucine and branched-chain amino acid metabolism contribute to the growth of bone sarcomas by regulating AMPK and mTORC1 signaling

Osteosarcoma and chondrosarcoma are sarcomas of the bone and the cartilage that are primarily treated by surgical intervention combined with high toxicity chemotherapy. In search of alternative metabolic approaches to address the challenges in treating bone sarcomas, we assessed the growth dependence of these cancers on leucine, one of the branched-chain amino acids (BCAAs), and BCAA metabolism. Tumor biopsies from bone sarcoma patients revealed differential expression of BCAA metabolic enzymes. The cytosolic branched-chain aminotransferase (BCATc) that is commonly overexpressed in cancer cells, was down-regulated in chondrosarcoma (SW1353) in contrast with osteosarcoma (143B) cells that expressed both BCATc and its mitochondrial isoform BCATm. Treating SW1353 cells with gabapentin, a selective inhibitor of BCATc, further revealed that these cells failed to respond to gabapentin. Application of the structural analog of leucine, N-acetyl-leucine amide (NALA) to disrupt leucine uptake, indicated that all bone sarcoma cells used leucine to support their energy metabolism and biosynthetic demands. This was evident from the increased activity of the energy sensor AMP-activated protein kinase (AMPK), down-regulation of complex 1 of the mammalian target of rapamycin (mTORC1), and reduced cell viability in response to NALA. The observed changes were most profound in the 143B cells, which appeared highly dependent on cytosolic and mitochondrial BCAA metabolism. This study thus demonstrates that bone sarcomas rely on leucine and BCAA metabolism for energy and growth; however, the differential expression of BCAA enzymes and the presence of other carbon sources may dictate how efficiently these cancer cells take advantage of BCAA metabolism.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network analysis, to develop a diagnostic gene signature for the diagnosis of ventilator-associated pneumonia (VAP). MATERIAL AND METHODS GSE30385 from the Gene Expression Omnibus (GEO) database identified differentially expressed genes (DEGs) associated with patients with VAP. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment identified the molecular functions of the DEGs. The least absolute shrinkage and selection operator (LASSO) regression analysis algorithm was used to select key genes. Three modeling methods, including logistic regression analysis, random forest analysis, and fully-connected neural network analysis, also known as also known as the feed-forward multi-layer perceptron (MLP), were used to identify the diagnostic gene signature for patients with VAP. RESULTS Sixty-six DEGs were identified for patients who had VAP (VAP+) and who did not have VAP (VAP-). Ten essential or feature genes were identified. Upregulated genes included matrix metallopeptidase 8 (MMP8), arginase 1 (ARG1), haptoglobin (HP), interleukin 18 receptor 1 (IL18R1), and NLR family apoptosis inhibitory protein (NAIP). Down-regulated genes included complement factor D (CFD), pleckstrin homology-like domain family A member 2 (PHLDA2), plasminogen activator, urokinase (PLAU), laminin subunit beta 3 (LAMB3), and dual-specificity phosphatase 2 (DUSP2). Logistic regression, random forest, and MLP analysis showed receiver operating characteristic (ROC) curve area under the curve (AUC) values of 0.85, 0.86, and 0.87, respectively. CONCLUSIONS Logistic regression analysis, random forest analysis, and MLP analysis identified a ten-gene signature for the diagnosis of VAP.

Low-Copy Number Polymorphism in DEFA1/DEFA3 Is Associated with Susceptibility to Hospital-Acquired Infections in Critically Ill Patients

DEFA1/DEFA3, genes encoding human neutrophil peptides (HNP) 1-3, display wide-ranging copy number variations (CNVs) and is functionally associated with innate immunity and infections. To identify potential associations between DEFA1/DEFA3 CNV and hospital-acquired infections (HAIs), we enrolled 106 patients with HAIs and 109 controls in the intensive care unit (ICU) and examined their DEFA1/DEFA3 CNVs. DEFA1/DEFA3 copy number ranged from 2 to 16 per diploid genome in all 215 critically ill patients, with a median of 7 copies. In HAIs, DEFA1/DEFA3 CNV varied from 2 to 12 with a median of 6, which was significantly lower than that in controls (2 to 16 with a median of 8, p = 0.017). Patients with lower DEFA1/DEFA3 copy number (CNV < 7) were far more common in HAIs than in controls (52.8% in HAIs versus 35.8% in controls; p = 0.014; OR, 2.010; 95% CI, 1.164-3.472). The area under the receiver operating characteristic (AUROC) of DEFA1/DEFA3 CNV combined with clinical characteristics to predict the incidence of HAIs was 0.763 (95% CI 0.700-0.827), showing strong predictive ability. Therefore, lower DEFA1/DEFA3 copy number contributes to higher susceptibility to HAIs in critically ill patients, and DEFA1/DEFA3 CNV is a significant hereditary factor for predicting HAIs.

Investigating PIK3R3 and ATp2A1 Genes Expressions in Ventilator-Associated Pneumonia Patients Admitted to the Intensive Care Unit of Masih Daneshvari Hospital in 2016

BACKGROUND

Infectious diseases such as ventilator- associated pneumonia (VAP) are one of the serious problems in intensive care units (ICU) of hospitals. To date, there has been no appropriate clinical and diagnostic marker for early detection of this disease. In this study, expression of PIK₃R₃ and ATp₂A₁ genes in patients with VAP were assessed to be used as biomarkers to identify and confirm the disease.

METHODS

This study was conducted by using peripheral blood samples of 60 individuals, including 30 patients with VAP and 30 healthy volunteers. First, the peripheral blood samples were taken and then RNA was extracted and converted into cDNA. Finally, the assessment of genes was performed by Real-time PCR.

RESULTS

In peripheral blood samples, 46.6% and 30% were positive for PIK₃R₃ expression in patients and healthy groups, respectively. The ATp₂A₁ expression in patients and healthy controls were found 40% and 23.3%, respectively. Comparing the ΔCT obtained for the PIK₃R₃ and ATp₂A₁ genes showed statistically significant differences between the two groups of patients and healthy subjects (p=0.042, p=0.036).

CONCLUSION

ATp₂A₁ and PIK₃R₃ may be used as biomarkers for early detection of VAP disease. However, further studies are required.

Gene expression profile analysis of ventilator-associated pneumonia

Based on the gene expression profile of patients with ventilator-associated pneumonia (VAP) and patients not affected by the disease, the present study aimed to enhance the current understanding of VAP development using bioinformatics methods. The expression profile GSE30385 was downloaded from the Gene Expression Omnibus database. The Linear Models for Microarray Data package in R language was used to screen and identify differentially expressed genes (DEGs), which were grouped as up- and down-regulated genes. The up- and downregulated genes were functionally enriched using the Database for Annotation, Visualization and Integrated Discovery system and then annotated according to TRANSFAC, Tumor Suppressor Gene and Tumor Associated Gene databases. Subsequently, the protein-protein interaction (PPI) network was constructed, followed by module analysis using CFinder software. A total of 69 DEGs, including 33 up- and 36 downregulated genes were screened out in patients with VAP. Upregulated genes were mainly enriched in functions and pathways associated with the immune response (including the genes ELANE and LTF) and the mitogen-activated protein kinase (MAPK) signaling pathway (including MAPK14). The PPI network comprised 64 PPI pairs and 44 nodes. The top two modules were enriched in different pathways, including the MAPK signaling pathway. Genes including ELANE, LTF and MAPK14 may have important roles in the development of VAP via altering the immune response and the MAPK signaling pathway.

ADAM-family metalloproteinases in lung inflammation: potential therapeutic targets

Acute and chronic lung inflammation is driven and controlled by several endogenous mediators that undergo proteolytic conversion from surface-expressed proteins to soluble variants by a disintegrin and metalloproteinase (ADAM)-family members. TNF and epidermal growth factor receptor ligands are just some of the many substrates by which these proteases regulate inflammatory or regenerative processes in the lung. ADAM10 and ADAM17 are the most prominent members of this protease family. They are constitutively expressed in most lung cells and, as recent research has shown, are the pivotal shedding enzymes mediating acute lung inflammation in a cell-specific manner. ADAM17 promotes endothelial and epithelial permeability, transendothelial leukocyte migration, and inflammatory mediator production by smooth muscle and epithelial cells. ADAM10 is critical for leukocyte migration and alveolar leukocyte recruitment. ADAM10 also promotes allergic asthma by driving B cell responses. Additionally, ADAM10 acts as a receptor for Staphylococcus aureus (S. aureus) α-toxin and is crucial for bacterial virulence. ADAM8, ADAM9, ADAM15, and ADAM33 are upregulated during acute or chronic lung inflammation, and recent functional or genetic analyses have linked them to disease development. Pharmacological inhibitors that allow us to locally or systemically target and differentiate ADAM-family members in the lung suppress acute and asthmatic inflammatory responses and S. aureus virulence. These promising results encourage further research to develop therapeutic strategies based on selected ADAMs. These studies need also to address the role of the ADAMs in repair and regeneration in the lung to identify further therapeutic opportunities and possible side effects.

A translational approach to ventilator associated pneumonia

The management of Ventilator Associated Pneumonia (VAP) presents many difficulties because of the heterogeneity of the disease; the way the immunocompromised host and the aggressive ICU environment interact is only partially discovered, the available biomarkers for diagnosis are not sufficient to ensure prompt differentiation between sick patients and patients at risk, the microbiological cultures require invasive techniques and time consuming methods. A translational medicine and bio-informatics approach can enable the identification of the main players of pathology, which may represent novel therapeutic targets or biomarker candidates. Analysis of proteome i.e. allows to individuate proteins that act as biomarkers, for patient-centered research strategies. Similarly, the genomic approach has proved useful to individuate those patients who are prone to develop VAP, and, in the future, we could be able to immunomodulate their responses to save them from nosocomial infections.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Empirical Antibiotic Therapy for Ventilator-Associated Pneumonia

Ventilator-associated pneumonia (VAP) is the most common infectious complication in the intensive care unit. It can increase duration of mechanical ventilation, length of stay, costs, and mortality. Improvements in the administration of empirical antibiotic therapy have potential to reduce the complications of VAP. This review will discuss the current data addressing empirical antibiotic therapy and the effect on mortality in patients with VAP. It will also address factors that could improve the administration of empirical antibiotics and directions for future research.