Nongenetic and genetic predictors of haemodynamic instability induced by propofol and opioids: A retrospective clinical study.

AIM: Propofol and opioids are commonly used in anaesthesia, but are highly susceptible to haemodynamic instability, thereby threatening the patient's surgical safety and prognosis. The purpose of this study was to investigate the predictors of haemodynamic instability and establish its predictive model. METHODS: A total of 150 Chinese patients undergoing thyroid or breast surgery participated in the study, with target-controlled infusion concentrations of propofol, opioids dosage, heart rate (HR), mean arterial pressure (MAP) and Narcotrend Index recorded at key points throughout the procedure. The Agena MassARRAY system was used to genotype candidate single nucleotide polymorphisms related to pharmacodynamics and pharmacokinetics of propofol and opioids. RESULTS: Among nongenetic factors, baseline HR (R = -.579, P < .001) and baseline MAP (R = -.725, P < .001) had a significant effect on the haemodynamic instability. Among genetic factors, the CT/CC genotype of GABRB1 rs4694846 (95% confidence interval [CI]: -11.309 to -3.155), AA/AG of OPRM1 rs1799971 (95%CI: 0.773 to 10.290), AA of CES2 rs8192925 (95%CI: 1.842 to 9.090) were associated with higher HR instability; the AA/GG genotype of NR1I2 rs6438550 (95%CI: 0.351 to 7.761), AA of BDNF rs2049046 (95%CI: -9.039 to -0.640) and GG of GABBR2 rs1167768 (95%CI: -10.146 to -1.740) were associated with higher MAP instability. The predictive models of HR and MAP fluctuations were developed, accounting for 45.0 and 59.2% of variations, respectively. CONCLUSION: We found that cardiovascular fundamentals and genetic variants of GABRB1, GABBR2, OPRM1, BDNF, CES2 and NR1I2 are associated with cardiovascular susceptibility, which can provide a reference for haemodynamic management in clinical anaesthesia.

A single nucleotide polymorphism-based formula to predict the risk of propofol TCI concentration being over 4 µg mL-1 at the time of loss of consciousness.

We aim to develop a formula based on single nucleotide polymorphisms (SNPs) to predict whether the propofol target-controlled infusion (TCI) concentration would be over 4 µg mL-1 at the time of loss of consciousness (LOC). We recruited 184 patients undergoing thyroid or breast surgeries with propofol anaesthesia. A total of 48 SNPs of CYP2B6, CYP2C9, UGT1A9, HNF4A, ABCB1, ABCC4, ABCG2, GABRA2, GABRA4, GABRB1, GABRB3, GABRG2, GABBR2, GAD1, SLC1A3, BDNF, and NRXN1, previously associated with propofol metabolic and pharmacology pathway, were genotyped. The formula was developed in the training cohort using the least absolute shrinkage and selection operator logistic regression model, and then validated in the testing cohort. The SNPs, GABBR2 rs1167768, GABBR2 rs1571927, NRXN1 rs601010, BDNF rs2049046, GABRA4 rs1512135, UGT1A9 rs11692021, GABBR2 rs2808536, HNF4A rs1884613, GABRB3 rs2017247, and CYP2B6 rs3181842 were selected to construct the SNP-based formula, which was used to calculate the risk score for over 4 µg mL-1 TCI concentration of propofol at the time of LOC. Patients in the high-risk group were more likely to require a propofol concentration higher than 4 µg mL-1 and presented a longer LOC latency. The SNP-based formula may significantly improve the safety and effectiveness of propofol-induced anaesthesia.

Shikonin Inhibits Non-Small-Cell Lung Cancer H1299 Cell Growth through Survivin Signaling Pathway.

Overexpressed survivin is associated with worse survival of several types of human tumors. In this study, the antitumor activity of shikonin in non-small-cell lung cancer (NSCLC) by regulating survivin pathway was investigated. Results showed that shikonin inhibited the NSCLC H1299 cell proliferation in a dose-dependent manner. Moreover, shikonin fits well with survivin by molecular docking. Shikonin also inhibited the mRNA expression and protein level of survivin in H1299 cells. Shikonin arrested H1299 cell cycle at the G0/G1 phase by regulating CDK/cyclin family members. In addition, shikonin regulated the expression of X-linked inhibitor of apoptosis- (XIAP-) mediated caspases 3 and 9, thus leading to the damage of mitochondrial membrane potential and induction of H1299 cell apoptosis. Overall, shikonin inhibited H1299 cell growth by inducing apoptosis and blocking the cell cycle. The underlying mechanism involves targeting survivin, which subsequently regulates the protein expression of XIAP/caspase 3/9, CDK2/4, and cyclin E/D1. Thus, shikonin, a survivin inhibitor, is a promising therapeutic strategy in NSCLC treatment.

The Effects of Gene Variations of GABRA2, GABRB1, GABRG2, GAD1 and SLC1A3 on Patients with Propofol During Anesthesia Induction.

PURPOSE: Propofol is one of the most commonly used intravenous sedatives in general anesthesia, while the individual variations of propofol are apparent. The objective of this study was to investigate the influence of genetic variations in GABAergic neurons and glutamatergic neurons on time to loss of consciousness (LOC) and the incidence of hypotension during anesthesia induction. PATIENTS AND METHODS: A total of 140 Chinese patients undergoing thyroid surgery or breast surgery were recruited. Genotyping of candidate genes was carried out using the Agena Bioscience MassARRAY system. Anesthesia induction was initiated with a propofol target plasma concentration (Cp) of 4.0 µg mL-1. The LOC latency, systolic blood pressure, diastolic blood pressure, mean arterial pressure were documented. RESULTS: We found that GABRA2 rs35496835, GABRB1 rs1372496, GABRG2 rs11135176, GABRG2 rs209358, GAD1 rs3791878, SLC1A3 rs1049522 and gender were significant determinants of the patient's LOC latency following propofol administration. GABRA2 rs11503014 was highly correlated with blood pressure reduction during anesthesia induction. Multiple linear regression analysis revealed that GABRB1 rs1372496, GABRG2 rs11135176, and SLC1A3 rs1049522 accounted for 35.3% variations in LOC latency following propofol administration. CONCLUSION: Our findings indicate that genetic variants of GABRA2, GABRB1, GABRG2, GAD1 and SLC1A3 may have influence on propofol susceptibility, which would be an important guidance towards building clinical models that can precisely predict the efficacy of propofol with various populations before surgery.

The prevalence of fimA genotypes of Porphyromonas gingivalis in patients with chronic periodontitis: A meta-analysis.

FimA is an important virulence factor of Porphyromonas gingivalis (P. gingivalis). According to its DNA sequence, the fimA genotype of P. gingivalis can be divided into six categories (I, Ib, Ⅱ, III, IV, V). The fimA gene may be a key factor in the diversity of virulence found in P. gingivalis. Moreover, the role fimA plays in the pathogenesis of P. gingivalis is closely associated with periodontitis, making it an important factor of study for disease prevention and treatment. In this study, the prevalence of fimA genotypes of P. gingivalis in patients with periodontal diseases was evaluated by meta-analysis. The Embase and PubMed databases were searched for articles from 1999 to 2019 using the following search terms: Porphyromonas gingivalis or P. gingivalis; periodontitis or chronic periodontal disease; fimA or fimA genotype. The reference lists of relevant published articles were searched manually. A total of 17 studies were included in this report. A statistical software package (Stata, version 11.0/mp, StataCorp) was utilized to calculate and analyze the P. gingivalis fimA genotypes for each combined incidence estimate. The pooled rates of fimA Ⅰ, fimA Ib, fimA Ⅱ, fimA Ⅲ, fimA Ⅳ and fimA Ⅴ genotypes of P. gingivalis were 8.4% (95% CI: 5.7-11.1), 11.7% (95% CI: 7.4-16), 42.9% (95% CI: 34.2-51.7), 6.5% (95% CI: 5.1-7.9), 17.8% (95% CI: 9.0-26.5), and 3.2% (95% CI: 1.6-4.9), respectively. This study showed that the fimA Ⅱ and fimA Ⅳ genotypes of P. gingivalis are highly present in patients with periodontal disease. Therefore, these two genotypes may be related to the pathogenesis and progress of periodontal disease, one of the main risk factors of periodontitis.

Identification of the Phytoplasma Associated with Wheat Blue Dwarf Disease in China.

Wheat blue dwarf disease (WBD) was first reported in China in the 1960s. It has caused severe losses on several occasions in winter wheat (Triticum aestivum) in northwestern China, and the nature of the pathogenic agent has been unknown. Here we have shown that WBD was caused by a 16SrI-C phytoplasma transmitted by Psammotettix striatus. This finding was based on molecular diagnostics, insect transmission trials, and host-range determination. Portions of the 16S rRNA and ribosomal protein (rp) genes, rpsS (rps19), rplV (rpl22), and rpsC (rps3), were amplified from DNA samples of WBD-infected wheat seedlings by polymerase chain reaction (PCR) utilizing phytoplasma specific primer pairs. The nucleotide sequences of these amplicons showed high identity to these genes from phytoplasma strains in the aster yellows group (16SrI). Pairwise nucleotide sequence identities of WBD 16S rDNA compared to representative genes of 16SrI group strains ranged from 98.9 to 99.9%, whereas compared to 17 other phytoplasma groups (16SrII to 16SrXVIII), sequence identity ranged from 88.6 to 96.0%. Similarly, the sequence identities of rps19, rpl22, and rps3 between WBD and 16SrI group strains varied from 96.6 to 99.7%, but only 60.3 to 65% between WBD and other phytoplasma groups. Phylogenetic analyses were carried out on sequences from 16S rRNA and ribosomal protein genes (rps19, rpl22, and rps3), respectively, and both results indicated that WBD phytoplasma was a member of the 16SrI group and most closely related to subgroup 16SrI-C. WBD-infected P. striatus were present in wheat fields with WBD, and phytoplasma infection was verified by PCR detection followed by DNA sequencing. Insect transmission trials confirmed that P. striatus transmitted the WBD phytoplasmal agent from infected wheat to healthy wheat seedlings and seven other different plant species in the greenhouse. A survey of various weed species near WBD-infected wheat fields found 10 plant species in seven families to be positive for the presence of WBD phytoplasma.