Early predictive value of scoring systems and routine laboratory tests in severity and prognosis of acute pancreatitis in pregnancy.

Background: Currently, no guidelines specifically recommend scoring systems and biomarkers for early evaluation of the severity and prognosis of acute pancreatitis in pregnancy (APIP). Objectives: This study aimed to explore the early predictive value of scoring systems and routine laboratory tests on APIP severity and maternofetal prognosis. Design: This study retrospectively analyzed 62 APIP cases in a 6-year period. Methods: The predictive value of scoring systems and routine laboratory tests that were collected 24 h and 48 h after admission, for APIP severity and fetal loss, were analyzed. Results: To detect severe acute pancreatitis (SAP), a 24-h Bedside Index for severity in acute pancreatitis (BISAP) achieved a higher area under the curve (AUC) value of 0.910 than the Acute Physiology and Chronic Health Evaluation II (AUC = 0.898) and Ranson score (AUC = 0.880). The combination of BISAP, glucose, neutrophil-to-lymphocyte ratio (NLR), hematocrit (Hct), and serum creatinine (Scr) provided an AUC value of 0.984, which had greater predictive power than BISAP (p = 0.015). 24-h BISAP and Hct were independent risk factors for predicting SAP of APIP. The cutoff values of Hct and blood urea nitrogen (BUN) to predict SAP were 35.60% and 3.75 mmol/l in the APIP. Furthermore, 24-h BISAP had the highest predictive power (AUC = 0.958) for fetal loss. Conclusion: BISAP is a convenient and reliable indicator for the early prediction of SAP and fetal loss in APIP. The combination of BISAP, glucose, NLR, Hct and Scr proved to be the optimal early markers for the prediction of SAP in APIP within 24 h after admission. In addition, Hct > 35.60% and BUN > 3.75 mmol/l may be suitable thresholds for predicting SAP in APIP.

Combining mass spectrometry and machine learning to discover bioactive peptides.

Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.

SNHG9 was upregulated in NSCLC and associated with DDP-resistance and poor prognosis of NSCLC patients.

Lung cancer, a leading cause of cancer-related mortalities worldwide and non-small cell lung cancer (NSCLC) is the main subtype of lung cancer. As a first-line chemotherapeutic drug used for NSCLC, acquired resistance retarded the clinical application of cisplatin (DDP). We herein reported long non-coding RNA SNHG9 was over-expressed in NSCLC tissues and cell lines compared with normal lung tissues and cell line; Increased SNHG9 was also observed in DDP resistant NSCLC tissues and cell lines compared with their DDP sensitive counterparts. Elevated expression of SNHG9 was associated with lower overall survival (OS) rate in NSCLC patients. Besides, silence of SNHG9 suppressed DDP resistance of NSCLC cells. Furthermore, CAPRIN1 was positively regulated by SNHG9 and mediated the promoting role of SNHG9 in DDP resistance of NSCLC cells. SNHG9 could be used as a potential target for DDP resistant NSCLC therapy.