In vitro and in vivo antimicrobial effects of domiphen combined with itraconazole against Aspergillus fumigatus.

Aspergillus fumigatus, a prevalent saprophytic fungus in the atmosphere, is known to rapidly induce severe invasive aspergillosis (IA) upon inhalation of its conidia by humans or animals. The mortality rate associated with IA exceeds 50%. The misuse of antifungal agents has contributed to the emergence of numerous highly pathogenic drug-resistant strains of A. fumigatus. Our study found that the combination of domiphen and itraconazole had sound synergistic antimicrobial effects against wild-type and itraconazole-resistant A. fumigatus in vivo and in vitro through MIC, FIC, plate inoculation, growth curve experiments, and Galleria mellonella infection model. Drug cytotoxicity and pharmacological tests for acute toxicity assays demonstrated that both itraconazole and domiphen showed minimal cytotoxicity and good biocompatibility. The transcriptome sequencing experiment demonstrated that domiphen exerted a suppressive effect on the expression of various genes, including those involved in drug efflux, redox regulation, and cellular membrane and cell wall remodeling. The present investigation explores the synergistic antimicrobial mechanisms of domiphen and itraconazole, encompassing three key aspects: (i) domiphen inhibited the efflux of itraconazole by reducing the expression of drug efflux-related genes, (ii) the combination has good ability to disrupt the cell membrane and cell wall, (iii) the combination also can remove biofilm more effectively. In summary, the utilization of domiphen as a synergist of itraconazole exhibited disruptive effects on the biofilm, cell wall, and cell membrane of A. fumigatus. This subsequently led to a modified distribution of itraconazole within the fungal organism, ultimately resulting in enhanced antifungal efficacy. The results of this study may provide a new therapeutic strategy for the treatment of IA caused by drug-resistant A. fumigatus.

[The Trace Methane Sensor Based on TDLAS-WMS].

Methane is a colorless, odorless, flammable and explosive gas, which not only is the cause to induce significant security risk in coal mining operation, but also one of the important greenhouse gases, so the monitoring of methane is extremely critical. A trace methane gas sensor is designed and developed using the combination of tunable diode laser absorption spectroscopy (TDLAS) and wavelength modulation spectroscopy (WMS) detection technology, which is based on the methane R(3) absorption branch in 2v3 second harmonic band. Through tuning parameters -0.591 cm(-1) x K(-1), using the method that change the working temperature of distributed feedback (DFB) laser to obtain the best absorption wavelength of methane at 1.654 µm. When the mid-wavelength of DFB laser is selected, the appropriate emitting intension can be obtained via adjusting the amplitude of inject current of DFB laser. Meanwhile, combining the frequency modulation technology to move the bandwidth of detection signal from low frequency to high frequency to reduce the 1/f noise. With aspect to the optical structure, utilizing herriott cell with 76 m effective optical path to guarantee the detection of trace methane is successful. Utilizing the proposed trace methane sensor to extract the second harmonic signal of detected methane in the range of 50 to 5 000 µmol x mol(-1), and adopting minimum mean square error criterion to fit the relationship between methane concentration and signal noise ratio, harmonic peak signal and methane concentration, respectively. In addition, the minimum detection limit is 1.4 µmol x mol(-1). The experiment results show the symmetry of harmonic waveform is good, no intensity modulation, and the factor of intensity-modulated impacts on harmonic detection is eliminated.

Inferring gene regulatory networks by singular value decomposition and gravitation field algorithm.

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.