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Silica nanoparticles (SiNPs) have been widely used in industry, electronics, and pharmaceutical industries. In addition, it is also widely used in medicine, tumor treatment and diagnosis, as well as other biomedical and biotechnology fields. The opportunities for people to contact SiNPs through iatrogenic, occupational, and environmental exposures are gradually increasing. The damage and biological effects of SiNPs on the nervous system have attracted widespread attention in the field of toxicology. Central nerve cells are rich in mitochondria. It is suggested that the effects of SiNPs on mitochondrial damage of nerve cells may involve the maintenance of neuronal membrane potential, the synthesis and operation of neurotransmitters, and the transmission of nerve pulses, and so on. We established an experimental model of SH-SY5Y cells to detect the cell survival rate, apoptosis, changes of reactive oxygen species and mitochondrial membrane potential, and the expression of mitochondrial function-related enzymes and proteins, so as to reveal the possible mechanism of SiNPs on neuronal mitochondrial damage. It was found that SiNPs could cause oxidative damage to cells and mitochondria, destroy some normal functions of mitochondria, and induce apoptosis in SH-SY5Y cells. The voltage-dependent anion channel 1(VDAC1) protein inhibitor DIDS could effectively reduce intracellular oxidative stress, such as the reduction of ROS content, and could also usefully restore some functional proteins of mitochondria to normal levels. The inhibition of VDAC1 protein may play an important role in the oxidative damage and dysfunction of neuronal mitochondria induced by SiNPs. [ABSTRACT FROM AUTHOR]

Acid stress is one of the most common adverse conditions during fermentation of fruit wines, and the acid tolerance of yeasts is, therefore, critical for fruit wine production. However, the biological mechanism underlying the acquired tolerance of yeasts against acid stress is poorly understood. We have previously obtained an evolved Saccharomyces cerevisiae strain ET008-c54 with increased tolerance against acid stress, and potentially, it serves as a promising yeast strain for greengage wine fermentation. In the current study, we further revealed the alterations responsible for the adaptation of ET008-c54 to low pH by whole-genome re-sequencing, transcriptomic, and metabolic analyses. Results confirmed the outstanding fermenting performance of ET008-c54 at low pH as compared with the parental ET008. More specifically, the growth rate of ET008-c54 at low pH was increased by 6.24 times and the fermentation time was shortened by 70%. Differences were also observed in the physiology of the strains through ergosterol, H+-ATPase activity, and aroma determinations. By integrating both RNA-seq and whole-genome re-sequencing data, we demonstrated some metabolic pathways in ET008-c54, namely ergosterol synthesis and ferrous iron uptake, in which several acid-responsive genes were involved being upregulated. Also, upregulation of the pathways responsible for aroma compound formation, including fatty acid ethyl ester synthesis and aromatic amino acid biosynthesis, was identified. Thus, the enhanced fermentation ability of ET008-c54 at low pH should be, at least partly, contributed by the altered gene expressions associated with the aforementioned pathways. By elucidating the biological mechanism of yeasts against acid stress, this current study allows better-defined targets for future studies of genetic improvement of wine yeasts and enhancement of the fermentation processes. Key points: • Metabolic analysis confirmed the excellent fermentation performance of ET008-c54. • Acid tolerance genes for ergosterol synthesis and ferrous iron uptake were upregulated. • Aroma genes for fatty acid ethyl ester and aromatic amino acid synthesis were upregulated. [ABSTRACT FROM AUTHOR]

The idea of constructing the biological neural system model as realistic as possible can not only provide a new artificial neural network (ANN), but also offer an effective object to study biological neural systems. As a very meaningful attempt about the idea, a bionic model of olfactory neural system, KIII model, is introduced in this paper. There are the unique characteristics of KIII model different from those of general ANNs. The KIII model realistically simulates the structure of the real olfactory neural system and the process of odor molecules gradually transformed by the core components of the olfactory system including olfactory receptor, olfactory bulb and olfactory cortex. The neuron model of the KIII model is constructed and optimized based on neurophysiological experimental data and accurately reflects the response of olfactory neurons to odor stimulation. In particular, the noise introduced to KIII model can further improve the performance of the model. In addition, the KIII model is analyzed based on the idea of deep learning. The qualitative analysis shows that there are obvious similarities between the KIII model and the deep learning model. Furthermore, with the epileptic electroencephalograph (EEG) recognition task, two groups of experiments are designed to comprehensively analyze the performance of the KIII model. In the first group of experiments, a typical pattern recognition experiment with feature extraction stage is shown. The features of epileptic EEG were extracted based on Empirical Mode Decomposition (EMD), and the KIII model was used as a classifier. The experimental results show that the KIII model only needs a small number of iterations to memorize different modes and has a high recognition rate, over 91%. In the second group of experiments, a direct recognition experiment without feature extraction stage is shown. The original epileptic EEG signals as KIII model inputs directly were recognized. The experimental results show that there is still an excellent performance in the KIII model, over 96%, and the recognition result is similar to the characteristics of the deep learning model. The theoretical analysis and experimental results prove that KIII model with the idea of deep learning is an excellent bionic model of olfactory neural system and gets a good balance between high bionics and good performance, which is a good reference for related research. [ABSTRACT FROM AUTHOR]

Acid tolerance and aroma profile are crucial factors for wine production in Saccharomyces cerevisiae. However, most wine yeasts to date fail to endure low-pH environments, therefore resulting in problems such as lengthened fermentation and poor flavor during acidic fruit wine production. In the present study, we established a multiple-step screening strategy, which was composed of atmospheric and room temperature plasma (ARTP), high-throughput screening (HTS), and laboratory adaptive evolution (ALE), to screen yeast strains for potential wine-producing with enhanced performances during low pH conditions. Importantly, we obtained the S. cerevisiae strain from the mutant library, ET008-c54, which displayed exhibited excellent performances in survival rate, fermentation time, aroma profile, and genetic stability. More specifically, the survival rate of ET008-c54 at low pH was increased by 10-fold, the fermentation time of greengage plum wine was shortened by about 70%, and the content of main aroma compounds was significantly increased by 52%. Collectively, we demonstrate the practical application of the screening platform designed for discovering mutant strains in winemaking technology. Key Points • A novel multi-step method was developed for screening wine-producing yeast. • Acid tolerance and aroma profiles of yeast were improved by the proposed method. • ET008-c54 showed excellent fermenting performance and stability at low pH. [ABSTRACT FROM AUTHOR]

Genistein, a major isoflavone constituent in soybeans, has been reported to exhibit multiple anti-tumor effects, such as inducing cell cycle arrest, triggering apoptosis, and inactivating critical signaling pathways in a few human cancer cells. Here, we investigated the anti-tumor effects of genistein on the small-cell lung cancer (SCLC) cell line H446 and the underlying molecular mechanisms. H446 cells were treated with various concentrations of genistein, and experiments including CCK-8 assay, colony formation assay, flow cytometry analysis, wound healing assay, real-time polymerase chain reaction (PCR), western blot analysis, and plasmid transfection were used to investigate the influence of genistein on cell proliferation, migration ability, apoptosis, cell cycle progression, as well as the mRNA and protein alterations of FoxM1 pathway molecules. We found that genistein significantly inhibited the proliferation and migration ability of H446 cell, accompanied by apoptosis and G2/M phase cell cycle arrest. In addition, genistein enhanced the anti-proliferative effect of cisplatin on H446 cells. Importantly, genistein led to attenuation of the FoxM1 protein and down-regulated a series of FoxM1 target genes regulating cell cycle and apoptosis including Cdc25B, cyclin B1, and survivin. In addition, up-regulation of FoxM1 by cDNA transfection prior to genistein treatment could reduce genistein-induced H446 proliferation inhibition. Thus, for the first time, we demonstrated that genistein exerted multiple anti-tumor effects in H446 SCLC cell line at least partly mediated by the down-regulation of FoxM1. FoxM1 has the potential as a novel therapeutic agent in SCLC and is worthy of further study. [ABSTRACT FROM AUTHOR]

The patient-derived tumor xenograft (PDTX) model has become the most realistic model for preclinical studies. PDTX models of gastric cancer using surgical tissues are reported occasionally; however, the PDTX models using gastroscopic biopsies, which are best for evaluating new drugs, are unreported. In our study, a total of 185 fresh gastroscopic biopsies of gastric cancer were subcutaneously transplanted into NOD/SCID (Nonobese Diabetic/Severe Combined Immunodeficiency) mice. Sixty-three PDTX models were successfully established (34.1%, 63/185) and passaged to maintain tumors in vivo, and the mean latency period of xenografts was 65.86 ± 32.84 days (11-160 days). Biopsies of prior chemotherapy had a higher transplantation rate (52.1%, 37/71) than biopsies after chemotherapy (21.9%, 25/114; P = 0.000). No differences were found between the latency period of xenografts and characteristics of patients. The pathological and molecular features of PDTX as well as chemosensitivity were highly consistent with those of primary tumors of patients. The genetic characteristics were stable during passaging of PDTX models. In summary PDTX models using gastroscopic biopsies in gastric cancer were demonstrated for the first time, and the biological characteristics of the PDTX models were highly consistent with patients, which provided the best preclinical study platform for gastric cancer. [ABSTRACT FROM AUTHOR]