• Asian Pacific Journal of Cancer Prevention
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• v.15 no.6
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• pp.2869-2873
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• 2014

Background: Human papillomavirus (HPV) integration within the E2 gene has been proposed as a critical event in cervical carcinogenesis. This study concerned whether HPV16 status and E2 gene intactness are predictive of radiation response in patients with cervical cancer. Materials and Methods: Biopsies of 44 patients with cervical cancer were collected before or after radiotherapy. The presence of HPV16 was assessed by polymerase chain reaction (PCR) using specific primers for the L1 region. E2 disruption was detected by amplifying the entire E2 gene. Results: HPV16 DNA was found in 54.5% of the clinical samples. Overall, 62.5% of the HPV16 positive tumors had integrated viral genome and 37.5% had episomal genome. There was a tendency of increase of HPV16 E2 negative tumors compared with HPV16 L1 ones in advanced stages (75% versus 20% in stage III respectively). Detection of E2 gene appeared influenced by the radiotherapy treatment, as the percentage of samples containing an intact HPV16 E2 was more frequent in pretreated patients compared to radiotherapy treated patients (66.6% versus 20%). The radiation therapy caused an eight-fold [OR= 8; CI=1.22-52.25; p=0.03] increase in the risk of HPV16 genome disruption. The integration status is influenced by the irradiation modalities, interestingly E2 disruption being found widely after radiotherapy treatment (75%) with a total fractioned dose of 50Gy. Conclusions: This study reveals that the status of the viral DNA may be used as a marker to optimize the radiation treatment.

• Journal of Microbiology and Biotechnology
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• v.28 no.8
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• pp.1247-1259
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• 2018

Raspberries are polyphenol-rich fruits with the potential to reduce the severity of the clinical signs associated with obesity, a phenomenon that may be related to changes in the gut microbiota. The aim of this study was to investigate the effect of raspberry supplementation on the fecal microbiota using an in vivo model of obesity. Obese diabetic db/db mice were used in this study and assigned to two experimental groups (with and without raspberry supplementation). Fecal samples were collected at the end of the supplementation period (8 weeks) and used for bacterial 16S rRNA gene profiling using a MiSeq instrument (Illumina). QIIME 1.8 was used to analyze the 16S data. Raspberry supplementation was associated with an increased abundance of Lachnospiraceae (p = 0.009), a very important group for gut health, and decreased abundances of Lactobacillus, Odoribacter, and the fiber degrader S24-7 family as well as unknown groups of Bacteroidales and Enterobacteriaceae (p < 0.05). These changes were enough to clearly differentiate bacterial communities accordingly to treatment, based on the analysis of UniFrac distance metrics. However, a predictive approach of functional profiles showed no difference between the treatment groups. Fecal metabolomic analysis provided critical information regarding the raspberry-supplemented group, whose relatively higher phytosterol concentrations may be relevant for the host health, considering the proven health benefits of these phytochemicals. Further studies are needed to investigate whether the observed differences in microbial communities (e.g., Lachnospiraceae) or metabolites relate to clinically significant differences that can prompt the use of raspberry extracts to help patients with obesity.

• Journal of Microbiology
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• v.33 no.3
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• pp.208-214
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• 1995

The effects of various 2, 4-D-degradative plasmids on the axenic growth patterns, the degradation phenotypes, and the competitiveness of different host bacteria were evaluated in liquid cultures; the organisms and plasmids used were Alcaligenes eutrophus JMP134/pJP4, Alcaligenes paradoxus/p2811, Pseudomonas pickettii/p712, pJP4, and p712 or p 2811 exhibited very different restriction fragment profiles in restriction endonuclease digests. These plasmids were transferred to the recipients (P. cepacia and Alcaligenes JMP228) at relatively high frequencies ranging from 8.9 $\times$ 10$^3$ to 1.6 $\times$ 10$^5$ per donar cell. In the axenic liquid cultures the fast-growing strains, such as P. pseudomallei/p745 and P. cepacia/pJP4, exhibited short lag periods, high specific growth rates, and high relative fitness coefficients, while the slow-growing strains, such as P. pickettii/p712 and A. paradoxus/p2811, had long lag periods, low specific growth rates, and low relative fitness coefficients. Depending on the type of plasmid containing the genes for the 2, 4-D pathway, some transconjugants exhibited intermediate grwoth patterns between the fast-growing strains and the slow-growing strains. The plasmid and plasmid-host interactions determined specific growth rate and lag time, respectively, which were shown to be principal determinants of competitiveness among the strains, but relative fitness coefficient derived from the axenic culture was not always predictive for the mixed culture condition.

• Journal of Microbiology and Biotechnology
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• v.34 no.4
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• pp.958-968
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• 2024

In recent years, there has been a growing recognition of the important role that long non-coding RNAs (lncRNAs) play in the immunological process of hepatocellular carcinoma (LIHC). An increasing number of studies have shown that certain lncRNAs hold great potential as viable options for diagnosis and treatment in clinical practice. The primary objective of our investigation was to devise an immune lncRNA profile to explore the significance of immune-associated lncRNAs in the accurate diagnosis and prognosis of LIHC. Gene expression profiles of LIHC samples obtained from TCGA database were screened for immune-related genes. The optimal immune-related lncRNA signature was built via correlational analysis, univariate and multivariate Cox analysis. Then, the Kaplan-Meier plot, ROC curve, clinical analysis, gene set enrichment analysis, and principal component analysis were performed to evaluate the capability of the immune lncRNA signature as a prognostic indicator. Six long non-coding RNAs were identified via correlation analysis and Cox regression analysis considering their interactions with immune genes. Subsequently, tumor samples were categorized into two distinct risk groups based on different clinical outcomes. Stratification analysis indicated that the prognostic ability of this signature acted as an independent factor. The Kaplan-Meier method was employed to conduct survival analysis, results showed a significant difference between the two risk groups. The predictive performance of this signature was validated by principal component analysis (PCA). Additionally, data obtained from gene set enrichment analysis (GSEA) revealed several potential biological processes in which these biomarkers may be involved. To summarize, this study demonstrated that this six-lncRNA signature could be identified as a potential factor that can independently predict the prognosis of LIHC patients.

• Journal of Microbiology
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• v.45 no.2
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• pp.98-104
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• 2007

Chlorophyll ${\alpha}$ concentration and cyanobacterial cell density are regularly employed as dual criteria for determinations of the alert level for cyanobacterial bloom. However, chlorophyll ${\alpha}$ is not confined only to the cyanobacteria, but is found universally in eukaryotic algae. Furthermore, the determination of cyanobacterial cell counts is notoriously difficult, and is unduly dependent on individual variation and trained skill. A cyanobacteria-specific parameter other than the cell count or chlorophyll ${\alpha}$ concentration is, accordingly, required in order to improve the present cyanobacterial bloom alert system. Phycocyanin has been shown to exhibit a strong correlation with a variety of bloom-related factors. This may allow for the current alert system criteria to be replaced by a three-stage alert system based on phycocyanin concentrations of 0.1, 30, and $700\;{\mu}g/L$. This would also be advantageous in that it would become far more simple to conduct measurements without the need for expensive equipment, thereby enabling the monitoring of entire lakes more precisely and frequently. Thus, an alert system with superior predictive ability based on highthroughput phycocyanin measurements appears feasible.

• Genomics & Informatics
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• v.11 no.4
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• pp.186-190
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• 2013

The advances in electronic medical records (EMRs) and bioinformatics (BI) represent two significant trends in healthcare. The widespread adoption of EMR systems and the completion of the Human Genome Project developed the technologies for data acquisition, analysis, and visualization in two different domains. The massive amount of data from both clinical and biology domains is expected to provide personalized, preventive, and predictive healthcare services in the near future. The integrated use of EMR and BI data needs to consider four key informatics areas: data modeling, analytics, standardization, and privacy. Bioclinical data warehouses integrating heterogeneous patient-related clinical or omics data should be considered. The representative standardization effort by the Clinical Bioinformatics Ontology (CBO) aims to provide uniquely identified concepts to include molecular pathology terminologies. Since individual genome data are easily used to predict current and future health status, different safeguards to ensure confidentiality should be considered. In this paper, we focused on the informatics aspects of integrating the EMR community and BI community by identifying opportunities, challenges, and approaches to provide the best possible care service for our patients and the population.

• Food Science and Biotechnology
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• v.27 no.6
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• pp.1865-1869
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• 2018

This study examined the effects of alfalfa seed germination on growth of Salmonella enterica. We investigated the population changes of S. enterica during early sprout development. We found that the population density of S. enterica, which was inoculated on alfalfa seeds was increased during sprout development under all experimental temperatures, whereas a significant reduction was observed when S. enterica was inoculated on fully germinated sprouts. To establish a model for predicting S. enterica growth during alfalfa sprout development, the kinetic growth data under isothermal conditions were collected and evaluated based on Baranyi model as a primary model for growth data. To elucidate the influence of temperature on S. enterica growth rates, three secondary models were compared and found that the Arrhenius-type model was more suitable than others. We believe that our model can be utilized to predict S. enterica behavior in alfalfa sprout and to conduct microbial risk assessments.

• Food Science of Animal Resources
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• v.41 no.2
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• pp.288-299
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• 2021

Biofilm formation of Geobacillus thermodenitrificans, Geobacillus thermoglucosidans and Anoxybacillus flavithermus in milk on stainless steel were monitored at 55℃, 60℃, and 65℃ for various incubation times. Although species of Geobacillus showed a rapid response and produced biofilm within 4 h on stainless steel, a delay (lag time) was observed for Anoxybacillus. A hyperbolic equation and a hyperbolic equation with lag could be used to describe the biofilm formation of Geobacillus and Anoxybacillus, respectively. The highest biofilm formation amount was obtained at 60℃ for both Geobacillus and Anoxybacillus. However, the biofilm formation rates indicated that the lowest rates of formation were obtained at 60℃ for Geobacillus. Moreover, biofilm formation rates of G. thermodenitrificans (1.2-1.6 Log10CFU/mL∙h) were higher than G. thermoglucosidans (0.4-0.7 Log10CFU/mL∙h). Although A. flavithermus had the highest formation rate values (2.7-3.6 Log10CFU/mL∙h), this was attained after the lag period (4 or 5 h). This study revealed that modeling could be used to describe the biofilm formation of thermophilic bacilli in milk.

• Journal of Microbiology and Biotechnology
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• v.15 no.6
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• pp.1323-1329
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• 2005

A mathematical model was developed for predicting the growth kinetics of Listeria monocytogenes in tryptic soy broth (TSB) as a function of combined effects of temperature, pH, and NaCl. The TSB containing four different concentrations of NaCl (2, 4, 5, and $10\%$) was initially adjusted to six different pH levels (pH 5, 6, 7, 8, 9, and 10) and incubated at 4, 10, 25, or 37$^{circ}C$. In all experimental variables, the primary growth curves were well fitted ($r^{2}$=0.982 to 0.998) to a Gompertz equation to obtain the lag time (LT) and specific growth rate (SGR). Surface response models were identified as appropriate secondary models for LT and SGR on the basis of coefficient determination ($r^{2}$=0.907 for LT, 0.964 for SGR), mean square error (MSE=3.389 for LT, 0.018 for SGR), bias factor ($B_{1}$B,=0.706 for LT, 0.836 for SGR), and accuracy factor ($A_{f}$=1.567 for LT, 1.213 for SGR). Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on both LT and SGR for L. monocytogenes in TSB.

• Journal of Microbiology and Biotechnology
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• v.28 no.4
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• pp.645-651
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• 2018

The carcinogenicity of chemicals in the environment is a major concern. Recently, numerous studies have attempted to develop methods for predicting carcinogenicity, including rodent and cell-based approaches. However, rodent carcinogenicity tests for evaluating the carcinogenic potential of a chemical to humans are time-consuming and costly. This study focused on the development of an alternative method for predicting carcinogenicity using quantitative PCR (qPCR) and colon cancer stem cells. A toxicogenomic method, mRNA profiling, is useful for predicting carcinogenicity. Using microarray analysis, we optimized 16 predictive gene sets from five carcinogens (azoxymethane, 3,2'-dimethyl-4-aminobiphenyl, N-ethyl-n-nitrosourea, metronidazole, 4-(n-methyl-n-nitrosamino)-1-(3-pyridyl)-1-butanone) used to treat colon cancer stem cell samples. The 16 genes were evaluated by qPCR using 23 positive and negative carcinogens in colon cancer stem cells. Among them, six genes could differentiate between positive and negative carcinogens with a p-value of ${\leq}0.05$. Our qPCR-based prediction system for colon carcinogenesis using colon cancer stem cells is cost- and time-efficient. Thus, this qPCR-based prediction system is an alternative to in vivo carcinogenicity screening assays.