二つの改良デシケータ装置のホルムアルデヒド除去効果の比較

The former desiccator system (Type I) and the new desiccator system (Type II) for the measurement of the dew condensation and the relative humidity were produced, and the formaldehyde (FA) removal rates were compared between the two desiccator systems. Those experiments were performed in the early morning in winter when the dew condensation tends to occur. The dew condensation was observed in the Type I. In the Type II, passage of FA gas through the hygroscopic bottle containing a desiccant resulted in the prevention of the dew condensation and maintenance of humidity at about 50%. The FA removal rates by coffee powders, black tea leaves, and green tea leaves were higher using the Type II than with the Type I. The FA removal rate using the Type II was higher for used-tea leaves or coffee powders than with unused ones. In particular with green tea leaves, the FA removal rates increased with the frequency and time of decoction. These results suggest the superiority of the Type II to the Type I in the measurement of the FA removal rates with various adsorbents

マクロファージからのリポポリサッカライド誘導NO産生に対するAsp-hemolysin関連合成ペプチドP-21の影響

To clarify the effect of Asp-hemolysin-related synthetic peptide (P-21) on lipopolysaccharide (LPS)-induced nitric oxide (NO) production in murine peritoneal macrophages (Mφ) was demonstrated this study. P-21 inhibited LPS (from Escherichia coli O111 : B4) -induced NO production of Mφ in a dose-dependent manner. P-21 slightly effected on NO production induced by LPS from Klebsiella pneumonias in Mφ. The inhibition ability of the P-21 was influenced by differences of LPS from various strains. These results suggest that P-21 has effects on the bioactivity of LPS, such as NO production in Mφ

数種の内分泌攪乱化学物質のマウス腹腔マクロファージ細胞機能に及ぼす影響

We investegated the effects of nine possible endocrine disrupting chemicals (EDCs) on the nitric oxide (NO) production and growth of mouse peritoneal macrophages. Genistein and coumestrol inhibited lipopolysaccharide-induced NO production in macrophages, whereas other EDCs had no effect. In WST-8 assay, the growth of mouse macrophages was induced by 17β-estradiol, bisphenol A, nonylphenol, diethyl phthalete, genistein and daidzein. In addition, the cell viability of daidzein-treated macrophages was 1.7-fold increased as compared with non-treated macrophages. These results suggest that EDCs affect cellular function in macrophages

ホルムアルデヒドの吸着剤による除去効果を測定するための改良デシケータの製作と測定

We developed a new desiccator system by improving the conventional desiccator method and measured the FA-removal effects of adsorbents using this system. The improved desiccator system consists of: 1) a pump, 2) a flow meter, 3) a FA gas generator, 4) five connected desiccators, and 5) an exhaust. A more stable concentration of FA in the desiccators was obtained when they were connected in series rather than in parallel. The FA removal rate for 3 days for red tea in used tea bags, green tea, oolong tea, or powdered coffee was more than 80%, and the removal rates for 1 day by activated carbon from palm nutshells and silica gel, as commercially available chemical agents, were 100% and 75%, respectively. This improved desiccator system allows direct collection of air in the desiccator, and may have wide applications

Differential effects of biologic versus bisphosphonate inhibition of wear debris-induced osteolysis assessed by longitudinal micro-CT.

Aseptic loosening of total joint replacements is caused by wear debris-induced osteoclastic bone resorption, for which bisphosphonates (BPs) and RANK antagonists have been developed. Although BPs are effective in preventing metabolic bone loss, they are less effective for inflammatory bone loss. Because this difference has been attributed to the antiapoptotic inflammatory signals that protect osteoclasts from BP-induced apoptosis, but not RANK antagonists, we tested the hypothesis that osteoprotegerin (OPG) is more effective in preventing wear debris-induced osteolysis than zoledronic acid (ZA) or alendronate (Aln) in the murine calvaria model using in vivo micro-CT and traditional histology. Although micro-CT proved to be incompatible with titanium (Ti) particles, we were able to demonstrate a 3.2-fold increase in osteolytic volume over 10 days induced by polyethylene (PE) particles versus sham controls (0.49 +/- 0.23 mm(3) versus 0.15 +/- 0.067 mm(3); p < 0.01). Although OPG and high-dose ZA completely inhibited this PE-induced osteolysis (p < 0.001), pharmacological doses of ZA and Aln were less effective but still reached statistical significance (p < 0.05). Traditional histomorphometry of the sagital suture area of calvaria from both Ti and PE-treated mice confirmed the remarkable suppression of resorption by OPG (p < 0.001) versus the lack of effect by physiological BPs. The differences in drug effects on osteolysis were largely explained by the significant difference in osteoclast numbers observed between OPG versus BPs in both Ti- and PE-treated calvaria; and linear regression analyses that demonstrated a highly significant correlation between osteolysis volume and sagittal suture area versus osteoclast numbers (p < 0.001)

Illuminating Clues of Cancer Buried in Prostate MR Image: Deep Learning and Expert Approaches.

Deep learning algorithms have achieved great success in cancer image classification. However, it is imperative to understand the differences between the deep learning and human approaches. Using an explainable model, we aimed to compare the deep learning-focused regions of magnetic resonance (MR) images with cancerous locations identified by radiologists and pathologists. First, 307 prostate MR images were classified using a well-established deep neural network without locational information of cancers. Subsequently, we assessed whether the deep learning-focused regions overlapped the radiologist-identified targets. Furthermore, pathologists provided histopathological diagnoses on 896 pathological images, and we compared the deep learning-focused regions with the genuine cancer locations through 3D reconstruction of pathological images. The area under the curve (AUC) for MR images classification was sufficiently high (AUC = 0.90, 95% confidence interval 0.87-0.94). Deep learning-focused regions overlapped radiologist-identified targets by 70.5% and pathologist-identified cancer locations by 72.1%. Lymphocyte aggregation and dilated prostatic ducts were observed in non-cancerous regions focused by deep learning. Deep learning algorithms can achieve highly accurate image classification without necessarily identifying radiological targets or cancer locations. Deep learning may find clues that can help a clinical diagnosis even if the cancer is not visible