This study was aimed to establish the quality standard of Pyrethri Tatsienenis Flos. The medical material was identified by the microscopy and the thin layer chromatography ( TLC ) methods . The moisture , total ash , acid-insoluble ash and alcohol-soluble extract were determined according to procedures recorded in the Chi-nese Pharmacopoeia (2010 edition). The content of luteolin was determined by the HPLC method. The results showed a strong characteristic microscopic of Pyrethri Tatsienenis Flos , and its TLC identification had a good resolution with clear spots . The mass fractions of luteolin was 0 . 036%~0 . 104% ( average of 0 . 078%) , moisture was 9 . 32%~15 . 82% ( average of 13 . 11%) , total ash was 6 . 65%~8 . 29% ( average of 7 . 45%) , acid-insoluble ash was 0 . 23%~0 . 59% ( average of 0 . 42%) , and the extraction was 21 . 42%~30 . 15% ( average of 24 . 86%) . It was concluded that this established standard was simple to operate with good stability and reproducibility , which can be used for quality evaluation of Pyrethri Tatsienenis Flos .

Objective To compare the safety of octogenarian patients receiving non-cardiac surgery within 1 week versus within 1-3 weeks after coronary computed tomographic angiography(CTA).Methods Octogenarian patients who underwent non-cardiac surgery after coronary CTA in Fujian Medical University Union Hospital,were retrospectively analyzed.All patients were divided into two groups:those received surgery within 1 week after coronary CTA as group 1 (n =73),those within 1-3 week after coronary CTA as group 2,(n =35).The baseline clinical characteristics,the changes in pre-and postoperative serum creatinine levels (Scr)and estimated glomerular filtration rate(eGFR),and the incidence of acute kidney injury(AKI)were compared between two groups.The revised cardiac risk index(RCRI)score was evaluated for each octogenarian inpatient,and the RCRI sum score for different types of non-cardiac surgery were calculated.Finally,the RCRI sum score of the preoperative risk factors were compared between different types of non-cardiac surgery so as to assess their specifically safety.Results In 108 patients who performed coronary CTA,only one patients developed palpitation and three had injection site pain.All patients receiving different types of non-cardiac operation had low revised cardiac risk index(RCRI ≤ 2).Death was not found.The serum levels of Scr and eGFR were similar between two groups before coronary CTA and after operation(all P＞0.05).Conclusions Octogenarian patients with low preoperative cardiac risk index(RCRI ≤ 2) are safe for performing non-cardiac surgery within 1 week after coronary CTA.

Solute carriers (SLCs) constitute the largest superfamily of membrane transporter proteins. These transporters, present in various SLC families, play a vital role in energy metabolism by facilitating the transport of diverse substances, including glucose, fatty acids, amino acids, nucleotides, and ions. They actively participate in the regulation of glucose metabolism at various steps, such as glucose uptake (e.g., SLC2A4/GLUT4), glucose reabsorption (e.g., SLC5A2/SGLT2), thermogenesis (e.g., SLC25A7/UCP-1), and ATP production (e.g., SLC25A4/ANT1 and SLC25A5/ANT2). The activities of these transporters contribute to the pathogenesis of type 2 diabetes mellitus (T2DM). Notably, SLC5A2 has emerged as a valid drug target for T2DM due to its role in renal glucose reabsorption, leading to groundbreaking advancements in diabetes drug discovery. Alongside SLC5A2, multiple families of SLC transporters involved in the regulation of glucose homeostasis hold potential applications for T2DM therapy. SLCs also impact drug metabolism of diabetic medicines through gene polymorphisms, such as rosiglitazone (SLCO1B1/OATP1B1) and metformin (SLC22A1-3/OCT1-3 and SLC47A1, 2/MATE1, 2). By consolidating insights into the biological activities and clinical relevance of SLC transporters in T2DM, this review offers a comprehensive update on their roles in controlling glucose metabolism as potential drug targets.

Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.