Objective:To investigate the risk factors for refeeding syndrome (RFS) in patients with severe stroke.Methods:Patients with stroke admitted to the Neuro Intensive Care Unit, Nanfang Hospital, Southern Medical University and received enteral nutrition support >72 h from January 2013 to July 2019 were enrolled retrospectively. RFS was defined as a new onset of hypophosphatemia within 72 h after the start of nutritional support, that is, blood phosphorus <0.65 mmol/L and a decrease of >0.16 mmol/L from the baseline value. The independent risk factors for RFS were identified by multivariate logistic regression model. Results:A total of 209 patients with severe stroke were included, with a median age of 65 years (interquartile range [ IQR] 53 to 72 years), and 154 were males (73.7%); 136 patients had cerebral infarction (65.1%), 73 had intracerebral hemorrhage (34.9%). The baseline median National Institutes of Health Stroke Scale (NIHSS) score was 15 ( IQR, 11-20), the median Glasgow Coma Scale score was 9 ( IQR, 6-12), the median Acute Physiology and Chronic Health Score was 16 ( IQR, 11-20), the median Nutrition Risk in Critically Ill (NUTRIC) score was 3 ( IQR 2-5), and the median Sequential Organ Failure Assessment (SOFA) score was 4 ( IQR, 3-6); the baseline median serum phosphorus was 1.05 mmol/L ( IQR, 0.90-1.19 mmol/L). A total of 34 patients (16.3%) developed RFS. Multivariate logistic regression analysis showed that male (odds ratio 3.565, 95% confidence interval 1.150-11.053; P=0.028) and patients with higher SOFA score (odds ratio 1.246, 95% confidence interval 1.077-1.442; P=0.032) were more likely to develop RFS. Conclusions:RFS is not rare in patients with severe stroke. Males and patients with severe disease are more likely to develop RFS.

ObjectiveTo explore the role of saikosaponin D （SSD） targeting signal transducer and activator of transcription 3 （STAT3） in inducing apoptosis of bladder cancer cells by computer-aided drug design and experimental verification. MethodThe druggability and biotoxicity of SSD were explored by Bayesian classifier modeling. The information about SSD， the active ingredient of Bupleuri Radix， was searched against the Traditional Chinese Medicine Systematic Pharmacology Database and Analysis Platform （TCMSP）. The targets of SSD were predicted by PubChem， TCMSP， a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine （BATMAN-TCM）， Coremine， an Encyclopedia of Traditional Chinese Medicine （ETCM）， and SwissTargetPrediction. GeneCards， Therapeutic Target Database （TTD）， and Online Mendelian Inheritance in Man （OMIM） were employed to predict the potential therapeutic targets of bladder cancer. Then， the common targets shared by SSD and bladder cancer were selected for Gene Ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） pathway enrichment analyses. Molecular docking was adopted to explore the binding affinity and structural stability of SSD with target proteins. Cytoscape 3.9.1 was used to construct the STAT3-drug regulatory network and STAT3-apoptosis regulatory network. UM-UC-3 cells were treated with 0， 5， 10， 15 μmol·L^-1 SSD for 24 h. Then， flow cytometry was used to detect the apoptosis of bladder cancer cells， and Western blot was employed to determine the protein levels of B-cell lymphoma-2 （Bcl-2）， Bcl-2-associated X protein （Bax）， Bcl-2-associated death promoter （Bad）， STAT3， and phosphorylation （p）-STAT3. ResultBayesian classifier modeling and molecular docking showed that SSD had low biotoxicity and bound well to the target protein STAT3 to form a stable protein-ligand complex. There were 282 common targets between bladder cancer and SSD， among which STAT3 was the most central target. The GO enrichment analysis showed that the potential core therapeutic targets involved 3 036 biological processes， 82 cellular components， and 171 molecular functions. The KEGG enrichment analysis showed that the potential core targets were mainly related to the C-type lectin receptor signaling pathway， Toll-like receptor signaling pathway， and cell apoptosis pathway. The STAT3-drug regulatory network and STAT3-apoptosis regulatory network showed that 29 drugs interacted with STAT3， and 27 apoptosis-related genes had a strong correlation with STAT3. Flow cytometry showed that the apoptosis rate increased with the increase in SSD concentration （P<0.05）. Western blotting results showed that SSD down-regulated the protein levels of p-STAT3 and Bcl-2 and up-regulated the protein levels of Bax and Bad in a concentration-dependent manner （P<0.05）. ConclusionSSD has good druggability and low biotoxicity. It may promote the apoptosis of bladder cancer cells by targeting STAT3.