Ability and accuracy of the smartphone-based O`VIEW-M® sperm test: Useful tool in the era of Covid-19.

Male infertility affects up to 12% of men. Although manual testing using microscope examination and computer-assisted semen analysis are standard methods of measuring sperm count and motility, these methods are limited by being laboratory based. To investigate the usefulness of a novel semen analysis device using a smartphone camera. This prospective multicenter randomized parallel design trial enrolled 200 men aged ≥19 years of age between August and December 2018. Each subject was advised to use the Smart Sperm Test for OVIEW-M at home after 5 days of abstinence. The accuracy of the OVIEW-M test relative to the in-hospital test was determined. A questionnaire was administered to assess subject likelihood of using the OVIEW-M. Measurements using standard methods and the OVIEW-M showed similar sperm counts and similar motile sperm counts. Correlation analysis showed significant correlations between sperm count and sperm motility when measured by OVIEW-M tests (r = 0.893, p < 0.01) and standard microscope examination (r = 0.883, p < 0.01). Of the subjects who responded to questionnaires, 43% regarded the results of the OVIEW-M tests as reliable and 18% as unreliable. Semen analysis with the smartphone-based application and accessories yielded results not inferior to those of laboratory tests. Men who visit the hospital for evaluation of infertility can easily perform OVIEW-M semen tests at home.

Roseomonas wooponensis sp. nov., isolated from wetland freshwater.

A non-motile, cocobacilli-shaped and pink-pigmented bacterium, designated strain WW53T, was isolated from wetland freshwater (Woopo wetland, Republic of Korea). Cells were Gram-stain-negative, catalase- and oxidase-positive. The major fatty acids were C18 : 1ω7c/C18 : 1ω6c and C16 : 0.The predominant quinone and polyamine were ubiquinone 10 (Q-10) and spermidine, respectively. The DNA G+C content was 71 mol%. The major polar lipids were phosphatidylethanolamine, phosphatidylcholine and an unknown aminolipid. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain WW53T belongs to the family Acetobacteraceae, and is related to the genus Roseomonas. Strain WW53T was most closely related to Roseomonas stagni HS-69T (95.3 % 16S rRNA gene sequence similarity). Results of a polyphasic taxonomy study suggested that the isolate represents a novel species in the genus Roseomonas, for which the name Roseomonas wooponensis sp. nov. is proposed. The type strain is WW53T ( = KCTC 32534T = JCM 19527T).

Estimation methods with ordered exposure subject to measurement error and missingness in semi-ecological design.

BACKGROUND: In epidemiological studies, it is often not possible to measure accurately exposures of participants even if their response variable can be measured without error. When there are several groups of subjects, occupational epidemiologists employ group-based strategy (GBS) for exposure assessment to reduce bias due to measurement errors: individuals of a group/job within study sample are assigned commonly to the sample mean of exposure measurements from their group in evaluating the effect of exposure on the response. Therefore, exposure is estimated on an ecological level while health outcomes are ascertained for each subject. Such study design leads to negligible bias in risk estimates when group means are estimated from 'large' samples. However, in many cases, only a small number of observations are available to estimate the group means, and this causes bias in the observed exposure-disease association. Also, the analysis in a semi-ecological design may involve exposure data with the majority missing and the rest observed with measurement errors and complete response data collected with ascertainment. METHODS: In workplaces groups/jobs are naturally ordered and this could be incorporated in estimation procedure by constrained estimation methods together with the expectation and maximization (EM) algorithms for regression models having measurement error and missing values. Four methods were compared by a simulation study: naive complete-case analysis, GBS, the constrained GBS (CGBS), and the constrained expectation and maximization (CEM). We illustrated the methods in the analysis of decline in lung function due to exposures to carbon black. RESULTS: Naive and GBS approaches were shown to be inadequate when the number of exposure measurements is too small to accurately estimate group means. The CEM method appears to be best among them when within each exposure group at least a 'moderate' number of individuals have their exposures observed with error. However, compared with CEM, CGBS is easier to implement and has more desirable bias-reducing properties in the presence of substantial proportions of missing exposure data. CONCLUSION: The CGBS approach could be useful for estimating exposure-disease association in semi-ecological studies when the true group means are ordered and the number of measured exposures in each group is small. These findings have important implication for cost-effective design of semi-ecological studies because they enable investigators to more reliably estimate exposure-disease associations with smaller exposure measurement campaign than with the analytical methods that were historically employed.

Bias in the estimation of exposure effects with individual- or group-based exposure assessment.

In this paper, we develop models of bias in estimates of exposure-disease associations for epidemiological studies that use group- and individual-based exposure assessments. In a study that uses a group-based exposure assessment, individuals are grouped according to shared attributes, such as job title or work area, and assigned an exposure score, usually the mean of some concentration measurements made on samples drawn from the group. We considered bias in the estimation of exposure effects in the context of both linear and logistic regression disease models, and the classical measurement error in the exposure model. To understand group-based exposure assessment, we introduced a quasi-Berkson error structure that can be justified with a moderate number of exposure measurements from each group. In the quasi-Berkson error structure, the true value is equal to the observed one plus error, and the error is not independent of the observed value. The bias in estimates with individual-based assessment depends on all variance components in the exposure model and is smaller when the between-group and between-subject variances are large. In group-based exposure assessment, group means can be assumed to be either fixed or random effects. Regardless of this assumption, the behavior of estimates is similar: the estimates of regression coefficients were less attenuated with a large sample size used to estimate group means, when between-subject variability was small and the spread between group means was large. However, if groups are considered to be random effects, bias is present, even with large number of measurements from each group. This does not occur when group effects are treated as fixed. We illustrate these models in analyses of the associations between exposure to magnetic fields and cancer mortality among electric utility workers and respiratory symptoms due to carbon black.

Bayesian method for improving logistic regression estimates under group-based exposure assessment with additive measurement errors.

The group-based exposure assessment has been widely used in occupational epidemiology. When the sample size used to estimate group means is "large", this leads to negligible attenuation in the estimation of odds ratio. However, the bias is proportional to the between-subject variability and is affected by the difference in true group means. We explore a Bayesian method, which adjusts in a natural way for the extra uncertainty in the outcome model associated with using the predicted values as exposures. We aim to improve the estimate obtained in naïve analysis by exploiting the properties of Berkson type error structure. We consider cases where differences in the proximity of group means and the between-subject variance are both large. The results of the simulations show that our Bayesian measurement error adjustment method that follows group-based exposure assessment improves estimates of odds ratios when the between-subject variance is large and group means are far apart.

Relative performance of different exposure modeling approaches for sulfur dioxide concentrations in the air in rural western Canada.

BACKGROUND: The main objective of this paper is to compare different methods for predicting the levels of SO2 air pollution in oil and gas producing area of rural western Canada. Month-long average air quality measurements were collected over a two-year period (2001-2002) at multiple locations, with some side-by-side measurements, and repeated time-series at selected locations. METHODS: We explored how accurately location-specific mean concentrations of SO2 can be predicted for 2002 at 666 locations with multiple measurements. Means of repeated measurements on the 666 locations in 2002 were used as the alloyed gold standard (AGS). First, we considered two approaches: one that uses one measurement from each location of interest; and the other that uses context data on proximity of monitoring sites to putative sources of emission in 2002. Second, we imagined that all of the previous year's (2001's) data were also available to exposure assessors: 9,464 measurements and their context (month, proximity to sources). Exposure prediction approaches we explored with the 2001 data included regression modeling using either mixed or fixed effects models. Third, we used Bayesian methods to combine single measurements from locations in 2002 (not used to calculate AGS) with different priors. RESULTS: The regression method that included both fixed and random effects for prediction (Best Linear Unbiased Predictor) had the best agreement with the AGS (Pearson correlation 0.77) and the smallest mean squared error (MSE: 0.03). The second best method in terms of correlation with AGS (0.74) and MSE (0.09) was the Bayesian method that uses normal mixture prior derived from predictions of the 2001 mixed effects applied in the 2002 context. CONCLUSION: It is likely that either collecting some measurements from the desired locations and time periods or predictions of a reasonable empirical mixed effects model perhaps is sufficient in most epidemiological applications. The method to be used in any specific investigation will depend on how much uncertainty can be tolerated in exposure assessment and how closely available data matches circumstances for which estimates/predictions are required.

Industrial sources influence air concentrations of hydrogen sulfide and sulfur dioxide in rural areas of western Canada.

A survey of monthly average concentrations of sulfur dioxide (SO2) and hydrogen sulfide (H2S) at rural locations in western Canada (provinces of Alberta, British Columbia, and Saskatchewan) was conducted in 2001-2002, as part of an epidemiological study of the effects of oil and gas industry emissions on the health of cattle. Repeated measurements were obtained at some months and locations. We aimed to develop statistical models of the effect of oil and gas infrastructure on air concentrations. The regulatory authorities supplied the information on location of the different oil and gas facilities during the study period and, for Alberta, provided data on H2S content of wells and flaring volumes. Linear mixed effects models were used to relate observed concentrations to proximity and type of oil and gas infrastructure. Low concentrations were recorded; the monthly geometric mean was 0.1-0.2 ppb for H2S, and 0.3-1.3 ppb for SO2. Substantial variability between repeated measurements was observed. The precision of the measurement method was 0.005 ppb for both contaminants. There were seasonal trends in the concentrations, but the spatial variability was greater. This was explained, in part, by proximity to oil/gas/bitumen wells and (for SO2) gas plants. Wells within 2 km of monitoring stations had the greatest impact on measured concentrations. For H2S, 8% of between-location variability was explained by proximity to industrial sources of emissions; for SO2 this proportion was 18%. In Alberta, proximity to sour gas wells and flares was associated with elevated H2S concentrations; however, the estimate of the effect of sour gas wells in the immediate vicinity of monitoring stations was unstable. Our study was unable to control for all possible sources of the contaminants. However, the results suggest that oil and gas extraction activities contribute to air pollution in rural areas of western Canada.

Reduced protease activity in transformed rice cell suspension cultures expressing a proteinase inhibitor.

In this study, we synthesized a synthetic serine proteinase inhibitor II gene (sPI-II) that harbored the chymotrypsin and trypsin inhibitor domains of the PI-II gene from Nicotiana alata. In an effort to reduce protease activity in a rice cell suspension culture, we first synthesized sPI-II using overlap PCR and then introduced the gene into a rice calli (Oryza sativa L. cv. Dongin) by particle bombardment-mediated transformation. The sPI-II gene was under the control of a rice alpha-amylase 3D promoter induced by sugar starvation. To verify the integration and expression of the sPI-II gene in the transformed rice cells, we employed genomic DNA PCR amplification and Northern blot analysis, respectively. The relative protease activity of the transformed cell suspension culture was reduced to approximately 23% when compared to the non-transformed culture. This indicates that a transformed suspension culture system expressing a proteinase inhibitor, may be a useful tool to protect against recombinant protein losses resulting from extracellular proteases.

Attenuation in risk estimates in logistic and Cox proportional-hazards models due to group-based exposure assessment strategy.

In occupational epidemiology, it is often possible to obtain repeated measurements of exposure from a sample of subjects (workers) who belong to exposure groups associated with different levels of exposure. Average exposures from a sample of workers can be assigned to all members of that group including those who are not sampled, leading to a group-based exposure assessment. We discuss how this group-based exposure assessment leads to approximate Berkson error model when the number of subjects with exposure measurements in each group is large, and how the error variance approximates the between-worker variability. Under the normality assumption of exposures and with moderately large number of workers in each group, there is attenuation in the estimate of the association parameter, the magnitude of which depends on the sizes of the between-worker variability and the true association parameter. Approximate equations for attenuation have been derived in logistic and Cox proportional-hazards models. These equations show that the attenuation in Cox proportional-hazards models is generally more severe than in logistic regression. Furthermore, when the between-worker variability is large, our simulation study found that the approximation by equation is poor for the Cox proportional-hazards model. If the number of subjects is small, the approximation does not hold for either model.

Memory enhancing and neuroprotective effects of selected ginsenosides.

The effects of ginsenosides Rg3(R), Rg3(S) and Rg5/Rk1 (a mixture of Rg5 and Rk1, 1:1, w/w), which are components isolated from processed Panax ginseng C.A. Meyer (Araliaceae), on memory dysfunction were examined in mice using a passive avoidance test. The ginsenosides Rg3(R), Rg3(S) or Rg5/Rk1, when orally administered for 4 days, significantly ameliorated the memory impairment induced by the single oral administration of ethanol. The memory impairment induced by the intraperitoneal injection of scopolamine was also significantly recovered by ginsenosides Rg3(S) and Rg5/Rk1. Among the three ginsenosides tested in this study, Rg5/Rk1 enhanced the memory function of mice most effectively in both the ethanoland scopolamine-induced amnesia models. Moreover, the latency period of the Rg5/Rk1-treated mice was 1.2 times longer than that of the control (no amnesia) group in both models, implying that Rg5/Rk1 may also exert beneficial effects in the normal brain. We also evaluated the effects of these ginsenosides on the excitotoxic and oxidative stress-induced neuronal cell damage in primary cultured rat cortical cells. The excitotoxicity induced by glutamate or N-methyl-D-aspartate (NMDA) was dramatically inhibited by the three ginsenosides. Rg3(S) and Rg5/Rk1 exhibited a more potent inhibition of excitotoxicity than did Rg3(R). In contrast, these ginsenosides were all ineffective against the H2O2- or xanthine/xanthine oxidase-induced oxidative neuronal damage. Taken together, these results indicate that ginsenosides Rg3(S) and Rg5/Rk1 significantly reversed the memory dysfunction induced by ethanol or scopolamine, and their neuroprotective actions against excitotoxicity may be attributed to their memory enhancing effects.

Neuroprotective and antioxidant effects of the ethyl acetate fraction prepared from Tussilago farfara L.

The flower buds of Tussilago farfara L. (Compositae) have been traditionally used in Oriental medicine for the treatment of bronchitis and asthma. The extract of T. farfara was reported to exhibit antiinflammatory actions by inhibiting arachidonic acid metabolism and nitric oxide (NO) production in lipopolysaccharide-activated macrophages. In the present study, we investigated the effects of the ethyl acetate (EA) fraction on various types of neuronal cell damage induced in primary cultured rat cortical cells. Its antioxidant activities were also evaluated by cell-free bioassays. We found that the EA fraction potently inhibited the neuronal damage induced by arachidonic acid. We also found that it significantly attenuated the neuronal damage induced by spermine NONOate, a stable NO generator. In addition, it inhibited the A(beta(25-35))-induced neurotoxicity and glutamate- or N-methyl-D-aspartic acid-induced excitotoxicity. It was found that the oxidative neuronal damage induced by H2O2, xanthine/xanthine oxidase, or Fe(2+)/ascorbic acid was also inhibited by the EA fraction. Furthermore, it was shown to inhibit lipid peroxidation initiated by Fe(2+)/ascorbic acid in rat brain homogenates, and scavenge DPPH radicals. This is the first demonstration of neuroprotective and antioxidant effects of T. farfara. Although complex mechanisms may be involved in the neuroprotective actions, T. farfara may be useful for the management of neurodegenerative disorders associated with inflammation, A(beta), excitotoxicity, and/or oxidative stress.

Expression of heteropolymeric ferritin improves iron storage in Saccharomyces cerevisiae.

Saccharomyces cerevisiae was engineered to express different amount of heavy (H)- and light (L)-chain subunits of human ferritin by using a low-copy integrative vector (YIp) and a high-copy episomal vector (YEp). In addition to pep4::HIS3 allele, the expression host strain was bred to have the selection markers leu2(-) and ura3(-) for YIplac128 and YEp352, respectively. The heterologous expression of phytase was used to determine the expression capability of the host strain. Expression in the new host strain (2805-a7) was as high as that in the parental strain (2805), which expresses high levels of several foreign genes. Following transformation, Northern and Western blot analyses demonstrated the expression of H- and L-chain genes. The recombinant yeast was more iron tolerant, in that transformed cells formed colonies on plates containing more than 25 mM ferric citrate, whereas none of the recipient strain cells did. Prussian blue staining indicated that the expressed isoferritins were assembled in vivo into a complex that bound iron. The expressed subunits showed a clear preference for the formation of heteropolymers over homopolymers. The molar ratio of H to L chains was estimated to be 1:6.8. The gel-purified heteropolymer took up iron faster than the L homopolymer, and it took up more iron than the H homopolymer did. The iron concentrations in transformants expressing the heteropolymer, L homopolymer, and H homopolymer were 1,004, 760, and 500 micro g per g (dry weight) of recombinant yeast cells, respectively. The results indicate that heterologously expressed H and L subunits coassemble into a heteropolymer in vivo and that the iron-carrying capacity of yeast is further enhanced by the expression of heteropolymeric isoferritin.