Septicaemia with deep venous thrombosis and necrotising pneumonia caused by acute community-acquired methicillin-resistant Staphylococcus aureus in an infant with a three-year follow-up: a case report.

BACKGROUND: Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) is a common pathogen that usually causes bacteraemia, osteomyelitis, as well as skin and soft tissue infections. However, deep venous thrombosis (DVT) and necrotising pneumonia are rare in infants. CASE PRESENTATION: We report the case of a one-month-five-day-old girl who was hospitalised for DVT and necrotising pneumonia due to septicaemia associated with Staphylococcus aureus. She recovered after treatment with intravenous antibiotics and multiple anticoagulant therapy, but DVT persisted at the three-year follow-up. Collateral circulation around the DVT was well-formed. Post thrombotic syndrome was not observed. CONCLUSIONS: Staphylococcus aureus complicated by DVT and necrotising pneumonia is rare and can be successfully treated.

METTL3 promotes inflammation and cell apoptosis in a pediatric pneumonia model by regulating EZH2.

BACKGROUND: In recent times, the medical science has developed by leaps and bounds, however, the molecular mechanism of pediatric pneumonia is still unclear. Although prior researches have shown that methyltransferase-like 3 (METTL3) is up-regulated in a variety of inflammatory diseases, its role and mechanism has been rarely studied in pediatric pneumonia, and need to be defined elaborately. OBJECTIVE: In this study, the related molecular mechanism of METTL3 on inflammation and cell apoptosis in a pediatric pneumonia was investigated. MATERIALS AND METHODS: Quantitative real-time polymerase chain reaction (qPCR) and western blot assays were employed to examine the mRNA and protein expression level of METTL3 and EZH2 in peripheral blood monocytes from pediatric pneumonia patients or cell model (WI-38). Then, qPCR and ELISA assay were applied to verify the inflammatory response in LPS-treated WI-38 cell lines after knockdown of METTL3. Besides, MTT cell viability assays, flow cytometry, and western blot assays were applied to examine the cell viability and cell apoptosis of LPS-treated WI-38 cell after knockdown of METTL3. Further, the western blot assays were employed to examine the protein expression levels of p-JAK2, JAK2, p-STAT3, STAT3, and EZH2 in LPS-treated WI-38 cell after knockdown of METTL3. Finally, ELISA and western blot were applied to verify the inflammatory response and cell apoptosis of LPS-treated WI-38 cell after knockdown of METTL3 and overexpression of EZH2. RESULTS: In this study, the results showed that METTL3 and EZH2 were highly expressed in pediatric pneumonia patients and cell models (WI-38), respectively. Besides, downregulation of METTL3 inhibited LPS-induced inflammatory response and cell apoptosis. Then, the fact that METTL3 regulates the JAK2/STAT3 signaling pathway through EZH was proved. Furthermore, downregulation of METTL3 inhibits inflammation and apoptosis through EZH2. CONCLUSION: This study found that METTL3 promotes inflammation and cell apoptosis in a pediatric pneumonia model by regulating EZH2.

Multinomial machine learning identifies independent biomarkers by integrated metabolic analysis of acute coronary syndrome.

A multi-class classification model for acute coronary syndrome (ACS) remains to be constructed based on multi-fluid metabolomics. Major confounders may exert spurious effects on the relationship between metabolism and ACS. The study aims to identify an independent biomarker panel for the multiclassification of HC, UA, and AMI by integrating serum and urinary metabolomics. We performed a liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics study on 300 serum and urine samples from 44 patients with unstable angina (UA), 77 with acute myocardial infarction (AMI), and 29 healthy controls (HC). Multinomial machine learning approaches, including multinomial adaptive least absolute shrinkage and selection operator (LASSO) regression and random forest (RF), and assessment of the confounders were applied to integrate a multi-class classification biomarker panel for HC, UA and AMI. Different metabolic landscapes were portrayed during the transition from HC to UA and then to AMI. Glycerophospholipid metabolism and arginine biosynthesis were predominant during the progression from HC to UA and then to AMI. The multiclass metabolic diagnostic model (MDM) dependent on ACS, including 2-ketobutyric acid, LysoPC(18:2(9Z,12Z)), argininosuccinic acid, and cyclic GMP, demarcated HC, UA, and AMI, providing a C-index of 0.84 (HC vs. UA), 0.98 (HC vs. AMI), and 0.89 (UA vs. AMI). The diagnostic value of MDM largely derives from the contribution of 2-ketobutyric acid, and LysoPC(18:2(9Z,12Z)) in serum. Higher 2-ketobutyric acid and cyclic GMP levels were positively correlated with ACS risk and atherosclerosis plaque burden, while LysoPC(18:2(9Z,12Z)) and argininosuccinic acid showed the reverse relationship. An independent multiclass biomarker panel for HC, UA, and AMI was constructed using the multinomial machine learning methods based on serum and urinary metabolite signatures.