Exploring novel indazole derivatives as ASK1 inhibitors: Design, synthesis, biological evaluation and docking studies.

Apoptosis signal regulated kinase 1 (ASK1, MAP3K5) is a member of the mitogen activated protein kinase (MAPK) signaling pathway, involved in cell survival, differentiation, stress response, and apoptosis. ASK1 kinase inhibition has become a promising strategy for the treatment of Non-alcoholic steatohepatitis (NASH) disease. A series of novel ASK1 inhibitors with indazole scaffolds were designed and synthesized, and their ASK1 kinase activities were evaluated. The System Structure Activity Relationship (SAR) study discovered a promising compound 33c, which has a strong inhibitory effect on ASK1. Noteworthy observations included a discernible reduction in lipid droplets within LO2 cells stained with Oil Red O, coupled with a decrease in LDL, CHO, and TG content within the NASH model cell group. Mechanistic inquiries revealed that compound 33c could inhibit the protein expression levels of the upregulated ASK1-p38/JNK signaling pathway in TNF-α treated HGC-27 cells and regulate apoptotic proteins. In summary, these findings suggest that compound 33c may be valuable for further research as a potential candidate compound against NASH.

Diagnostic value of bronchoalveolar lavage fluid metagenomic next-generation sequencing in pediatric pneumonia.

Objectives: The aim of this study was to evaluate the diagnostic value of bronchoalveolar lavage fluid (BALF) metagenomic next-generation sequencing (mNGS) versus conventional microbiological tests (CMTs) for pediatric pneumonia. Methods: This retrospective observational study enrolled 103 children who were diagnosed with pneumonia and hospitalized at Hubei Maternity and Child Health Care Hospital between 15 October 2020 and 15 February 2022. The pneumonia diagnosis was based on clinical manifestations, lung imaging, and microbiological tests. Pathogens in the lower respiratory tract were detected using CMTs and BALF mNGS (of DNA and RNA). The diagnostic performance of BALF mNGS was compared with that of CMTs. Results: In 96 patients, pathogens were identified by microbiological tests. The overall pathogen detection rate of mNGS was significantly higher than that of CMTs (91.3% vs. 59.2%, p = 0.000). The diagnostic performance of mNGS varied for different pathogens; however, its sensitivity and accuracy for diagnosing bacterial and viral infections were both higher than those of CMTs (p = 0.000). For the diagnosis of fungi, the sensitivity of mNGS (87.5%) was higher than that of CMTs (25%); however, its specificity and accuracy were lower than those of CMTs (p < 0.01). For the diagnosis of Mycoplasma pneumoniae, the specificity (98.8%) and accuracy (88.3%) of mNGS were high; however, its sensitivity (42.1%) was significantly lower than that of CMTs (100%) (p = 0.001). In 96 patients with definite pathogens, 52 cases (50.5%) were infected with a single pathogen, while 44 cases (42.7%) had polymicrobial infections. Virus-bacteria and virus-virus co-infections were the most common. Staphylococcus aureus, Haemophilus influenzae, rhinovirus, cytomegalovirus, parainfluenza virus, and fungi were more likely to be associated with polymicrobial infections. Conclusions: BALF mNGS improved the detection rate of pediatric pneumonia, especially in mixed infections. The diagnostic performance of BALF mNGS varies according to pathogen type. mNGS can be used to supplement CMTs. A combination of mNGS and CMTs may be the best diagnostic strategy.

Prediction of prostate cancer using hair trace element concentration and support vector machine method.

A change in the normal concentration of essential trace elements in the human body might lead to major health disturbances. In this study, hair samples were collected from 115 human subject, including 55 healthy people and 60 patients with prostate cancer. The concentrations of 20 trace elements (TEs) in these samples were measured by inductively coupled plasma-mass spectrometry. Asupport vector machine was used to investigate the relationship between TEs and prostate cancer. It is found that, among the 20 TEs, 10 (Mg P, K, Ca, Cr, Mn, Fe. Cu, Zn, and Se) are related to the risk of prostate cancer. These 10 TEs were used to build the prediction model for prostate cancer. The model obtained can satisfactorily distinguish the healthy samples from the cancer samples. Furthermore, the cross-validation by leaving-one method proved that the prediction ability of this model reaches as high as 95.8%. It is practical to predict the risk of prostate cancer using this model in the clinics.