Suicide notes are essential for investigating the psychological state of suicide victims and establishing suicide prevention programs. Since only a few studies have attempted to identify the causes of suicidal behavior through suicide notes, it would be worth examining suicide notes. Quantitative research on suicide has offered a limited understanding of suicide. Results showed that the suicide victims had used the suicide note as a tool for their last communication. Further, in addition to neutral contents such as directions for funeral, the note often contained information about precipitating events that caused the suicidal ideation. Writing a suicide note seemed to help the victims consider concrete plans for suicide. This study proved that qualitative research on suicide notes would be helpful for researchers to understand suicide victims in depth, which cannot be achieved by quantitative methods alone. Based on these results, several suggestions for suicide prevention programs were discussed.
Qualitative Research ; Suicidal Ideation ; Suicide* ; Writing

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.

Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area). For the 12 drugs we tested, the predictive performance in terms of Pearson correlation coefficient exceeded 0.6 and the highest one was 17-AAG for which Pearson correlation coefficient was 0.811 for IC50 and 0.81 for AUC. We identify common predictor genes for IC50 and AUC, with which the performance was similar to those with genes separately found for IC50 and AUC, but with much smaller number of predictor genes. By using only common predictor genes, the highest performance was AZD6244 (0.8016 for IC50, 0.7945 for AUC) with 321 predictor genes.

No abstract available.
Chromosome Aberrations*

TLR6 forms a heterodimer with TLR2 and TLR4. While proinflammatory roles of TLR2 and TLR4 are well documented, the role of TLR6 in inflammation is poorly understood. In order to understand mechanisms of action of TLR6 in inflammatory responses, we investigated the effects of FSL-1, the TLR6 ligand, on expression of chemokine CCL2 and cytokine IL-1beta and determined cellular factors involved in FSL-1-mediated expression of CCL2 and IL-1beta in mononuclear cells. Exposure of human monocytic leukemia THP-1 cells to FSL-1 resulted not only in enhanced secretion of CCL2 and IL-1beta, but also profound induction of their gene transcripts. Expression of CCL2 was abrogated by treatment with OxPAPC, a TLR-2/4 inhibitor, while treatment with OxPAPC resulted in partially inhibited expression of IL-1beta. Treatment with FSL-1 resulted in enhanced phosphorylation of Akt and mitogen-activated protein kinases and activation of protein kinase C. Treatment with pharmacological inhibitors, including SB202190, SP6001250, U0126, Akt inhibitor IV, LY294002, GF109203X, and RO318220 resulted in significantly attenuated FSL-1-mediated upregulation of CCL2 and IL-1beta. Our results indicate that activation of TLR6 will trigger inflammatory responses by upregulating expression of CCL2 and IL-1beta via TLR-2/4, protein kinase C, PI3K-Akt, and mitogen-activated protein kinases.
Butadienes ; Chemokine CCL2 ; Chromones ; Humans ; Imidazoles ; Indoles ; Inflammation ; Leukemia ; Macrophages ; Maleimides ; Mitogen-Activated Protein Kinases ; Morpholines ; Nitriles ; Phosphatidylcholines ; Phosphorylation ; Protein Kinase C ; Pyridines ; Toll-Like Receptor 6 ; Toll-Like Receptors ; Up-Regulation