ABSTRACT
Oligoasthenospermia is the primary cause of infertility. However, there are still enormous challenges in the screening of critical candidates and targets of oligoasthenospermia owing to its complex mechanism. In this study, stem cell factor (SCF), c-kit, and transient receptor potential vanilloid 1 (TRPV1) biosensors were successfully established and applied to studying apoptosis and autophagy mechanisms. Interestingly, the detection limit reached 2.787 × 10-15 g/L, and the quantitative limit reached 1.0 × 10-13 g/L. Furthermore, biosensors were used to investigate the interplay between autophagy and apoptosis. Schisandrin A is an excellent candidate to form a system with c-kit similar to SCF/c-kit with a detection constant (KD) of 5.701 × 10-11 mol/L, whereas it had no affinity for SCF. In addition, it also inhibited autophagy in oligoasthenospermia through antagonizing TRPV1 with a KD of up to 4.181 × 10-10 mol/L. In addition, in vivo and in vitro experiments were highly consistent with the biosensor. In summary, high-potency schisandrin A and two potential targets were identified, through which schisandrin A could reverse the apoptosis caused by excessive autophagy during oligoasthenospermia. Our study provides promising insights into the discovery of effective compounds and potential targets via a well-established in vitro-in vivo strategy.
ABSTRACT
Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.
ABSTRACT
Objective To analyze the clinically isolated bacterial distribution and drug resistance characteristics in the our hospital during 2016 to provide the pathogenic drug resistance monitoring data for rational bacterial drugs use in clinic.Methods The clinicaly submitted samples were performed the pathogenic bacterial isolation according to the routine method.The isolated pathogens were identified by the Vitek2-Compact system and the drug susceptibility test was performed by adopting the MIC and KB methods.The results were statistically analyzed by adopting the WHONET5.6 software.Results A total of 2 214 non-repeat strains of bacteria were isolated in 2016,including 1 614 strains of Gram-negative bacilli,accounting for 72.9％,600 strains of Gram-positive bacteria,accounting for 27.1 ％.The top five isolated bacteria were Klebsiella pneumoniae,Escherichia coli,Pseudomonas aeruginosa,Acinetobacter baumannii and Staphylococcus aureus.The detection rates of ESBLs producing Escherichia coli and Klebsiella pneumoniae were 51.8％ and 27.6％ respectively.The detection rates of methicillin-resistant Staphylococcus aureus (MRSA) was 26.5％.No vancomycin or linezolid resistant staphylococcal strains were found.Conclusion The main isolated pathogens in our hospital are dominated by Gram-negative bacteria.Hospital should strengthen reasonable and standardized use of antibacterial drugs to reduce the generation of drug resistant bacterial strains.