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The Long non-coding RNA (lncRNA) expression profile data of ten samples including human Mesenchymal Stem Cell (MSC) adipogenic differentiation 0, 3, and 6 days from the GEO database, and then perform gene ID conversion, BLAST comparison, and annotation marking. Finally, group A (treatment group on day 3 of differentiation and control group on day 0 of differentiation) obtained a total of 1180 mRNA and 185 lncRNA; group B (treatment group on day 6 of differentiation and control group on day 0 of differentiation). A total of 1376 mRNA and 206 lncRNA were obtained. Finally, we processed the differential lncRNAs and mRNAs obtained in the two groups, and obtained 113 shared differential lncRNAs to further predict the targeted miRNA, a total of 815 lncRNA-miRNA pairs. The targeted mRNA was further predicted, and the grouped differential mRNAs were combined to obtain 64 differential mRNAs. In the end, we obtained 216 ceRNAs containing 26 lncRNAs, 27 miRNAs and 64 mRNAs. We found that the mRNAs in the ceRNA network were mainly enriched with 45 Gene Ontology (GO) terms, mainly including glucose homeostasis mechanism and insulin stimulation response. 69 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched. It mainly includes many pathways related to lipid metabolism such as Adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK), Rap1, cAMP, mitogen-activated protein kinase (MAPK), Ras, hypoxia inducible factor-1 (HIF-1), PI3K-Akt, insulin signaling and so on. In the end, we identified 216 ceRNA regulatory relationships related to obesity research. Our research provides a clearer direction for understanding the molecular mechanism of obesity, the screening and determination of drug targets biomarkers in the future.

Chlamydia pneumonia, a species of the family Chlamydiacea, is a leading cause of pneumonia. Failure to eradicate C. pneumoniae can lead to chronic infection, which is why it is also considered responsible for chronic inflammatory disorders such as asthma, arthritis, etc. There is an urgent need to tackle the major concerns arising due to persistent infections caused by C. pneumoniae as no FDA-approved drug is available against this chronic infection. In the present study, an approach named subtractive proteomics was employed to the core proteomes of five strains of C. pneumonia using various bioinformatic tools, servers, and software. However, 958 non-redundant proteins were predicted from the 4754 core proteins of the core proteome. BLASTp was used to analyze the non-redundant genes against the proteome of humans, and the number of potential genes was reduced to 681. Furthermore, based on subcellular localization prediction, 313 proteins with cytoplasmic localization were selected for metabolic pathway analysis. Upon subsequent analysis, only three cytoplasmic proteins, namely 30S ribosomal protein S4, 4-hydroxybenzoate decarboxylase subunit C, and oligopeptide binding protein, were identified, which have the potential to be novel drug target candidates. The Swiss Model server was used to predict the target proteins’ three-dimensional (3D) structure. The molecular docking technique was employed using MOE software for the virtual screening of a library of 15,000 phytochemicals against the interacting residues of the target proteins. Molecular docking experiments were also evaluated using molecular dynamics simulations and the widely used MM-GBSA and MM-PBSA binding free energy techniques. The findings revealed a promising candidate as a novel target against C. pneumonia infections.

Trends in routine laboratory tests, such as high white blood cell and low platelet counts, correlate with COVID-19-related intensive care unit (ICU) admissions. Other related biomarkers include elevated troponin, alanine aminotransferase, and aspartate transaminase levels (liver function tests). To this end, the aim of this study was to investigate the effect of changes in laboratory test parameters on ward-based and ICU COVID-19 patients. A total of 280 COVID-19 patients were included in the study and were divided based on admission status into ICU (37) or ward (243) patients. ICU admission correlated significantly with higher levels of several tested parameters, including lactate dehydrogenase, creatinine, D-dimer, creatine kinase, white blood cell count, and neutrophil count. In conclusion, routine laboratory tests offer an indication of which COVID-19 patients are most likely to be admitted to the ICU. These associations can assist healthcare providers in addressing the needs of patients who are at risk of COVID-19 complications.