Exploration of potential biomarkers and therapeutic targets for trauma-related acute kidney injury.

PURPOSE: Acute kidney injury (AKI) is one of the most common functional injuries observed in trauma patients. However, certain trauma medications may exacerbate renal injury. Therefore, the early detection of trauma-related AKI holds paramount importance in improving trauma prognosis. METHODS: Qualified datasets were selected from public databases, and common differentially expressed genes related to trauma-induced AKI and hub genes were identified through enrichment analysis and the establishment of protein-protein interaction (PPI) networks. Additionally, the specificity of these hub genes was investigated using the sepsis dataset and conducted a comprehensive literature review to assess their plausibility. The raw data from both datasets were downloaded using R software (version 4.2.1) and processed with the "affy" package19 for correction and normalization. RESULTS: Our analysis revealed 585 upregulated and 629 downregulated differentially expressed genes in the AKI dataset, along with 586 upregulated and 948 downregulated differentially expressed genes in the trauma dataset. Concurrently, the establishment of the PPI network and subsequent topological analysis highlighted key hub genes, including CD44, CD163, TIMP metallopeptidase inhibitor 1, cytochrome b-245 beta chain, versican, membrane spanning 4-domains A4A, mitogen-activated protein kinase 14, and early growth response 1. Notably, their receiver operating characteristic curves displayed areas exceeding 75%, indicating good diagnostic performance. Moreover, our findings postulated a unique molecular mechanism underlying trauma-related AKI. CONCLUSION: This study presents an alternative strategy for the early diagnosis and treatment of trauma-related AKI, based on the identification of potential biomarkers and therapeutic targets. Additionally, this study provides theoretical references for elucidating the mechanisms of trauma-related AKI.

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study.

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

Clinical analysis of 215 consecutive cases with fever of unknown origin: A cohort study.

Fever of unknown origin (FUO) is a frequently observed phenomenon in clinical practice. The present study was aimed to investigate potential causes of FUO, thereby improving clinical diagnosis of this disorder.In this retrospective study, clinical data were collected from 215 patients who were diagnosed with FUO between January 2009 and December 2010, and an 18 to 36 months follow-up visit was also performed for these patients.Among these FUO cases, the most common causes of the disease were infectious diseases (IDs) (42.3%), followed by connective tissue diseases (CTDs) (32.1%), miscellaneous (Mi) (10.7%) and neoplasm (N) (6.5%), while the causes for the other 18 cases (8.4%) were still unknown. The most common types of ID, CTD, and N were tuberculosis (16/91, 17.6%), adult onset Still disease (AOSD) (37/69, 53.6%) and non-Hodgkin lymphoma (6/14, 42.9%), respectively.IDs still represent the most common causes of FUO. Regularly intermittent fever with urinary infections and irregularly intermittent fever with infective endocarditis may be regarded as some signs in clinical diagnosis of FUO.

Identification of potential key genes associated with severe pneumonia using mRNA-seq.

This study aimed to identify the potential key genes associated with severe pneumonia using mRNA-seq. Nine peripheral blood samples from patients with severe pneumonia alone (SP group, n=3) and severe pneumonia accompanied with chronic obstructive pulmonary disease (COPD; CSP group, n=3), as well as volunteers without pneumonia (control group, n=3) underwent mRNA-seq. Based on the sequencing data, differentially expressed genes (DEGs) were identified by Limma package. Following the pathway enrichment analysis of DEGs, the genes that were differentially expressed in the SP and CSP groups were selected for pathway enrichment analysis and coexpression analysis. In addition, potential genes related to pneumonia were identified based on the information in the Comparative Toxicogenomics Database. In total, 645 and 528 DEGs were identified in the SP and CSP groups, respectively, compared with the normal controls. Among these DEGs, 88 upregulated genes and 80 downregulated genes were common between the two groups. The functions of the common DEGs were similar to those of the DEGs in the SP group. In the coexpression network, the commonly downregulated genes (including ND1, ND3, ND4L, and ND6) and the commonly upregulated genes (including TSPY6P and CDY10P) exhibited a higher degree. In addition, 131 DEGs (including ND1, ND3, ND6, MIR449A and TAS2R43) were predicted to be potential pneumonia-related genes. In conclusion, the present study demonstrated that the common DEGs may be associated with the progression of severe pneumonia.

Anti­aging effects of melatonin on the myocardial mitochondria of rats and associated mechanisms.

The present study investigated the anti-aging effects of melatonin on the myocardial mitochondria of D-galactose-aged rats and associated mechanisms. A total of 30 male Sprague­Dawley (SD) rats were randomly divided into three equal groups: An accelerated aging group that received 125 mg/kg/day D­galactose; a melatonin­treated group of D­galactose­aged rats that received 10 mg/kg/day melatonin; and a control group receiving normal saline. ATP, ADP and AMP levels in the left ventricular myocardium of rats were determined by high performance liquid chromatography and the total adenylic acid number (TAN) was subsequently calculated. Bax, Bcl­2, and cytochrome c (cyt­c) protein expression levels in myocardial mitochondria and cytoplasm were quantified by western blot analysis. In the melatonin­treated group, ATP levels were significantly higher when compared with the untreated control group and the accelerated­ageing group (0.068 vs. 0.052 and 0.058; P=0.002 and P=0.045, respectively), and TAN was significantly increased in the melatonin­treated group when compared with controls (P=0.011). In addition, cyt­c levels in the cytoplasm, but not in the mitochondria, were significantly higher in the accelerated­aging group compared with the control and melatonin­treated groups (P=0.001 and P=0.002, respectively). Bcl­2 and Bax ratios were significantly higher in the control and melatonin­treated groups when compared with the accelerated­aging group (P=0.004 and P=0.032, respectively). These results suggest that melatonin exhibits a protective effect on mitochondrial function in a rat model of accelerated aging.

Identification of miRNA biomarkers of pneumonia using RNA-sequencing and bioinformatics analysis.

Pneumonia is a lower respiratory tract infection that causes dramatic mortality worldwide. The present study aimed to investigate the pathogenesis of pneumonia and identify microRNA (miRNA) biomarkers as candidates for targeted therapy. RNA from the peripheral blood plasma of participants with pneumonia (severe, n=9; non-severe, n=9) and controls (n=9) was isolated and paired-end sequencing was performed on an Illumina HiSeq4000 system. Following the processing of raw reads, the sequences were aligned against the Genome Reference Consortium human genome assembly 38 reference genome using Bowtie2 software. Reads per kilobase of transcript per million mapped read values were obtained and the limma software package was used to identify differentially expressed miRNAs (DE-miRs). Then, DE-miR targets were predicted and subjected to enrichment analysis. In addition, a protein-protein interaction (PPI) network of the predicted targets was constructed. This analysis identified 11 key DE-miRs in pneumonia samples, including 6 upregulated miRNAs (including hsa-miR-34a and hsa-miR-455) and 5 downregulated miRNAs (including hsa-let-7f-1). All DE-miRs kept their upregulation/downregulation pattern in the control, non-severe pneumonia and severe pneumonia samples. Predicted target genes of DE-miRs in the subjects with non-severe pneumonia vs. the control and the subjects with severe pneumonia vs. the non-severe pneumonia group were markedly enriched in the adherens junction and Wnt signaling pathways. KALRN, Ras homolog family member A (RHOA), ß-catenin (CTNNB1), RNA polymerase II subunit K (POLR2K) and amyloid precursor protein (APP) were determined to encode crucial proteins in the PPI network constructed. KALRN was predicted to be a target of hsa-mir-200b, while RHOA, CTNNB1, POLR2K and APP were predicted targets of hsa-let-7f-1. The results of the present study demonstrated that hsa-let-7f-1 may serve a role in the development of cancer and the Notch signaling pathway. Conversely, hsa-miR-455 may be an inhibitor of pneumonia pathogenesis. Furthermore, hsa-miR-200b might promote pneumonia via targeting KALRN.