“possibly significant”

There are a total of 21 comparisons, so in this section statistically "significant" is defined as p < 0.002, "marginally significant" as p < 0.005, and "possibly significant" as p < 0.05.

WILLIS Ion Transport in Hibernator Kidneys between the two was only possibly significant (P < 0.02).

Subgroup Analysis Subgroup analysis of the λ ex 436-nm dataset for location (fovea, perifovea, and near-periphery), age (young and aged eyes), and sex showed only one possibly significant difference in spectral peaks: between age groups in the near-periphery for peak S3 (643 nm for young eyes, 635 nm for aged eyes, P = 0.0351, t -test).

The only possibly significant differences were that the autism group was more likely to have Bacillus spp (21% vs. 2.6%, p = 0.05) and less likely to have Klebsiella oxytoca (1.7% vs. 12.8%, p = 0.04).

In univariate analyses, poor overall survival was associated with pretreatment neutrophilia (hazard ratio [HR] 5.58, 95% confidence interval [CI] 1.99–15.7, P = .001), pretreatment leukocytosis (HR 4.85, 95% CI 1.73–13.6, P = .003), grade 4 lymphopenia during radiotherapy (HR 3.28, 95% CI 1.14–9.44, P = .03), and possibly smoking status >10 pack-years (HR 2.88, 95% CI 1.01–8.18, P = .05), but only T status was possibly significant in multivariate analysis (HR 2.64, 95% CI 0.99–7.00, P = .05).

Variables found to be possibly significant predictors of OH ( p <0.05) in the univariate analysis were entered into a multivariate analysis.

As for risk factors for pancreatitis and complications, variables found to be possibly significant ( P < 0.05) by univariate analysis were chosen for entry into a multiple logistic regression.

As above, a possibly significant effect was defined as having a p< 0.05.

Possibly significant transcript reduction of GL2 ( p < 0.05) together with slight but not significant ( p > 0.05) reduction of the other three genes ( GL3 , EGL3 , and TTG2 ) could collectively involve in the trichome phenotypes in sk372 .

Each variable was analyzed using univariate analysis to identify possible significant predictors; those variables found to be possibly significant at the p < 0.10 level were then included in multivariate logistic regression analysis, which was reduced by successively removing the least significant variable from the model.

More than 80% were found to be significant markers with a p -value < 0.05; several were classified as possibly significant markers ( p -value < 0.1), while a few were removed due to background level.

On multivariable analysis using logistic regression and entering only variables with possibly significant association ( p < 0.1) on univariable analysis, the association remained for ethnicity ( p = 0.044), while HS was negatively associated with DM (OR 0.27, 95%CI 0.18–0.41, p < 0.001) and SM (OR 0.47, 95%CI 0.34–0.64, p < 0.001) and borderline with HD (OR 0.69, 95%CI 0.45–1.06, p = 0.088) (model 1) ( Table 2 ).

Only those traits that were possibly significant (P≤0.10) in univariate analysis were included in the multivariate model.

The univariate ANOVA models were conducted using the outcome variables (i.e., COVID-19 peritraumatic distress and loneliness) to identify possibly significant sociodemographic covariates, using a criterion of p ≤ 0.10.

As for risk factors for development in concomitant PDAC, variables found to be possibly significant ( P <0.15) by univariate analysis were chosen for entry into a multiple logistic regression.

A possibly significant difference between the groups above was refused because the applied paired sample T -test calculated significance as p = 0.191.

Only possibly significant variables ( P < 0.20) in the initial univariate-models were included in the multivariate model: company, father's occupational group, urbanisation level of the place of residence, self-assessed health, opinion about physical demands for a soldier, last degree achieved in school sports, belonging to a sports club and self-assessed physical fitness were included in the multivariate model as possible confounders.

Only possibly significant explanatory variables ( P < 0.20) in the initial age-adjusted models were included for the multivariate models: Higher age, company, smoking status (previous or current regular smoker), high alcohol intake, poor baseline medical condition (sports injury during last month, sum factor of earlier musc

Conceptually compatible and logical risk factors that were possibly significant variables ( P < 0.20) in the initial univariate models were included in the multivariate models.

Only possibly significant explanatory variables ( P < 0.20) in the initial univariate models were included for the multivariate conceptual models.

All genomic positions with LOD scores detected with composite interval mapping (CIM) methodology [ 19 ] that were superior to thresholds determined by permutation tests [ 20 , 21 ] were considered possibly significant QTLs (Additional file 1 : Table S5).

Considering the limitations of this study, further studies with a larger sample size are required to find possibly significant differences.

Challenges With Identifying More Unusual Antibody Diversification Mechanisms This contribution hopes to draw attention to a much larger repertoire of adaptive immune response mechanisms induced with Covid-19, and to highlight the possibly significant role of these largely un-assessed strategies.

Consequently, a possibly significant change in the initial linear dimensions of the product may cause the two-layer material to break down.

No differences in urinary phthalate concentrations between children with two allergic outcomes compared to children with one or no allergic outcomes were found (data not shown). Finally, we examined the association between urinary phthalate concentrations and ORs of allergic outcomes to study patterns or possibly significant associations in the study population.