Characteristics of the Chiba environmental challenge chamber.

BACKGROUND: An environmental challenge chamber (ECC), which we refer to as the α-chamber, was built at Chiba University in 2008. The aim of this study was to validate the functionality of the ECC. METHODS: The stability of the pollen distribution and concentration in the ECC and symptoms of patients with Japanese cedar pollinosis induced by cedar pollen exposure were examined. Carryover effects of symptoms induced by different exposure protocols and correlations between symptoms induced in the ECC and those in the natural cedar pollen season were also determined. All the studies using the α-chamber were conducted out of the cedar pollen season. RESULTS: The severity of symptoms in the chamber reached a peak about 2 hours after the start of pollen exposure and plateaued thereafter. After subjects left the chamber, the symptoms persisted for several days. There was no significant difference between the severity of symptoms at exposure levels of 8000 and 12000 grains/m3. The symptoms were significantly increased by exposure for 3 consecutive days; however, there were no carryover effects in a study performed with a two-week interval. The total nasal symptom score (TNSS) in the natural pollen season showed a weak correlation with the mean TNSS on the day of exposure and the following 3 days. Symptoms in the ECC also had weak correlations with those in the early natural pollen season. CONCLUSIONS: The ECC under well-controlled conditions is suitable for clinical studies and might accelerate development of treatment for seasonal allergic rhinitis. A complete evaluation requires inclusion of the persistent reaction after subjects leave the ECC.

Side-by-side comparison of automatic pollen counters for use in pollen information systems.

BACKGROUND: Recent effort to build an unmanned pollen monitoring network in Japan has led to new developments in automatic pollen counters. In-the-field performance tests of these automatic counters have not been reported. OBJECTIVE: To characterize recently developed automatic pollen counters, with a view of using their data in pollen information systems. METHODS: We performed side-by-side comparisons between 2 recently developed automatic pollen counters and 2 reference samplers at 2 sites during the 2005 pollen season. RESULTS: Both automatic counters were found to have similar overall performance in terms of their correlations with the reference samplers. The linear correlation coefficient between the hourly values of the counters and one of the reference samplers was larger than 0.8 at both sites for both counters. Although these results are encouraging, our analysis also points to weaknesses of the investigated automatic counters in the areas of pollen discrimination, minimum measurable concentration, and calibration. Both counters were found to be affected by large concentrations of particulate matter, although the conditions and extent to which the particulate matter disrupted the measurements differ for the 2 sensors. The effect of particulate matter is particularly noticeable at the start and end of the pollen season, that is, when pollen concentration is low relative to particulate matter concentration. Further, it was found that one of the automatic counters could not differentiate snow particles from pollen grains. CONCLUSIONS: The tested automatic pollen counters had good overall performances, but weaknesses in the areas of pollen discrimination, minimum measurable concentration, and calibration still have to be addressed for these counters to find widespread use in the allergy community.

Application of revised version of neural independent component analysis to classification problems of confiscated methamphetamine.

Recently, profiling the chemical substances in illegally distributed drugs has been needed in order to reveal the drug channels. However, this kind of profiling is often difficult because such drugs contain various kinds of impurities and the quantity of these impurities changes. Due to these circumstances, several methods, including a slightly revised ICA (Independent Component Analysis) by a Hebbian learning artificial neural network, were applied for profiling illegally distributed methamphetamine. Eventually, better classification results with the ICA than with other methods were obtained. These results show that ICA could make it easier to profile illegally distributed methamphetamine.