The aim of the study is to evaluate the effects of the single and mixed decoction of Thallus laminariae (kelp) and Glycyrrhiza glabra (licorice) on the metabolism and their difference. The mixed decoction of kelp and licorice and the single decoction were made and intragastrically administered to the SD rats. The effect on system metabolism, the toxicity of liver and kidney were assessed by GC-MS profiling of the endogenous molecules in serum, routine biochemical assays and histographic inspection of tissues from SD rats, separately. The mixed decoction of kelp and licorice induced more obvious pathological abnormalities in SD rats than a single decoction of kelp, while the extracts of licorice did not show any pathological change. Neither the mixed, nor the single decoction showed abnormal histopathology. After intragastric administration of extracts for 5 days, the mixed decoction induced a decrease of ALT (no significant change in the groups of single decoction) and an increase of BUN (so did the single decoction of kelp). Metabolomic profile of the molecules in serum revealed that the metabolic patterns were all obviously affected for the three groups, i.e., the mixed and single decoction of kelp and licorice. The rats given with the single decoction of kelp showed a similar pattern to that of the mixed decoction, indicating that the kelp primarily contributed the perturbation of metabolism for the mixed decoction. All three groups induced a decrease of branched chain amino acids, TCA cycle intermediates and glycolysis intermediates (e.g., pyruvic acid and lactic acid) and an increase of 3-hydroxybutyric acid. Kelp decoction showed stronger potential in reducing TCA cycle intermediates and glycolysis intermediates than the other two groups, while the levels of branched chain amino acids were the lowest after licorice extracts were given. These results suggested that the effect of the mixed decoction on metabolism was closely associated with both kelp and licorice. The continuous administration of single decoction of kelp and the mixed decoction of licorice and kelp resulted in pathological abnormalities in kidney of SD rats. The mixed decoction of kelp and licorice distinctly perturbed sera molecules and hence system metabolism, which showed associated with those of kelp and licorice. Although the metabolic effect was associated with both kelp and licorice, the results suggested kelp contributed to it primarily.

AIM: To evaluate the effect of remimazolam on early postoperative cognitive function in elderly patients with hip fracture based on a randomized controlled trial. METHODS: A total of 106 elderly patients, aged 65-90 years, ASA grade Ⅱ or III, who underwent hip fracture surgery under combined spinal-epidural anesthesia in the Sixth Affiliated Hospital of Wenzhou Medical University from December 2022 to June 2023 and met the inclusion criteria, were selected and randomized into remimazolam group (group R) and propofol group (group P) according to the random number table, with 53 cases in each group. Patients in group P received a slow intravenous injection of propofol at a dose of 0.3-0.5 mg / kg (injection time of 1min), followed by a pump infusion at 0.5-3 mg · kg

A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest.
Drug Delivery Systems ; Proteins ; Transcriptome