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Background Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address. Main body We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high ( R  > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups. Conclusions Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Background To increase the chances of finding efficacious anticancer drugs, improve development times and reduce costs, it is of interest to rank test compounds based on their potential for human use as early as possible in the drug development process. In this paper, we present a method for ranking radiosensitizers using preclinical data. Methods We used data from three xenograft mice studies to calibrate a model that accounts for radiation treatment combined with radiosensitizers. A nonlinear mixed effects approach was utilized where between-subject variability and inter-study variability were considered. Using the calibrated model, we ranked three different Ataxia telangiectasia-mutated inhibitors in terms of anticancer activity. The ranking was based on the Tumor Static Exposure (TSE) concept and primarily illustrated through TSE-curves. Results The model described data well and the predicted number of eradicated tumors was in good agreement with experimental data. The efficacy of the radiosensitizers was evaluated for the median individual and the 95% population percentile. Simulations predicted that a total dose of 220 Gy (5 radiation sessions a week for 6 weeks) was required for 95% of tumors to be eradicated when radiation was given alone. When radiation was combined with doses that achieved at least 8 μ g / mL of each radiosensitizer in mouse blood, it was predicted that the radiation dose could be decreased to 50, 65, and 100 Gy, respectively, while maintaining 95% eradication. Conclusions A simulation-based method for calculating TSE-curves was developed, which provides more accurate predictions of tumor eradication than earlier, analytically derived, TSE-curves. The tool we present can potentially be used for radiosensitizer selection before proceeding to subsequent phases of the drug discovery and development process.

The aim of the present study was to optimize the conventional method of sperm freezing in liquid nitrogen (LN2) vapour for successful cryopreservation of Wallachian ram sperm, the genetic resources of the Czech Republic. Sperm in straws were frozen using the conventional freezing method via a static exposure of sperm doses to LN2 vapour, or by four different modified freezing methods. Under modified freezing, straws were frozen by a discontinuous, time-dependent decremental change in the distance between the straws and the surface of LN2. The viability of sperm was evaluated by flow cytometry after sperm equilibration, and immediately after thawing. Besides the observed inter-sire and daily variation, the obtained results suggest the methodological weakness of the conventional freezing method via the static exposure of sperm doses to LN2 vapour. With the use of the optimized freezing procedure, all parameters of thawed sperm were significantly (P < 0.05) improved in comparison with the conventional method: percentage of thawed sperm viability increased up to 48.3%, percentage of sperm with plasma membrane damage after thawing decreased to 6.58%, percentage of sperm with acrosome damage decreased to 24.4%, and percentage of sperm with deteriorated mitochondrial activity decreased to 6.28%. In conclusion, our results suggest that an optimized freezing procedure should be routinely used instead of the conventional method to cryopreserve Wallachian ram sperm.

A central question in drug discovery is how to select drug candidates from a large number of available compounds. This analysis presents a model-based approach for comparing and ranking combinations of radiation and radiosensitizers. The approach is quantitative and based on the previously-derived Tumor Static Exposure (TSE) concept. Combinations of radiation and radiosensitizers are evaluated based on their ability to induce tumor regression relative to toxicity and other potential costs. The approach is presented in the form of a case study where the objective is to find the most promising candidate out of three radiosensitizing agents. Data from a xenograft study is described using a nonlinear mixed-effects modeling approach and a previously-published tumor model for radiation and radiosensitizing agents. First, the most promising candidate is chosen under the assumption that all compounds are equally toxic. The impact of toxicity in compound selection is then illustrated by assuming that one compound is more toxic than the others, leading to a different choice of candidate.

A suspended growth photobioreactor was utilized to treat pharmaceutical wastewater by a wild strain purple non-sulfur photosynthetic bacterium isolated from the soil. The strain was named Z08 and identified as Rhodobacter-sphaeroides by 16SrDN. The photobioreactor was illuminated externally with two (40 W) fluorescent compact light sources on both sides. Its operation pH and temperature were between 6.8 - 7.0 and 20 - 30 ºC, respectively. Optimum growth of the isolate was obtained after enrichment of the pharmaceutical wastewater with 0.5 % ammonium sulfate and 0.1 % yeast extract under microaerobic optimum light (6000 lx) condition at 5d retention. Using these optimum conditions, the maximum dry cell weight and chemical oxygen demand percentage removal were 880 mg/L and 80 %. Chemical analysis of the culture after treatment of the enriched and non-enriched wastewater showed the crude protein content of the biomass to be 54.6 % and 38.0 %, respectively. This study proved that photosynthetic bacteria could transform complex wastewater that contains recalcitrant organic compounds with a resultant recovery of useful products.

A steady increase in sleep problems has been observed along with the development of society. Overnight exposure to a static magnetic field has been found to improve sleep quality; however, such studies were mainly based on subjective evaluation. Thus, the presented data cannot be used to infer sleep architecture in detail. In this study, the subjects slept on a magneto-static mattress for four nights, and self-reported scales and electroencephalogram (EEG) were used to determine the effect of static magnetic field exposure (SMFE) on sleep. Machine learning operators, i.e., decision tree and supporting vector machine, were trained and optimized with the open access sleep EEG dataset to automatically discriminate the individual sleep stages, determined experimentally. SMEF was found to decrease light sleep duration (N2%) by 3.51%, and sleep onset latency (SOL) by 15.83%, while it increased deep sleep duration (N3%) by 8.43%, compared with the sham SMFE group. Further, the overall sleep efficiency (SE) was also enhanced by SMFE. It is the first study, to the best of our knowledge, where the change in sleep architecture was explored by SMFE. Our findings will be useful in developing a non-invasive sleep-facilitating instrument.

Analyzing exposure‐response (E‐R) relationships for time‐to‐event (TTE) endpoints presents challenges due to the inherent time‐dependent nature of the data. Some authors address these difficulties by using a fixed timepoint approach, where exposure is assessed at a predetermined time rather than dynamically over time. (e.g., initial exposure or last exposure). The aim of the current work is to compare the use of time‐static and time‐varying metrics to assess the E‐R relationship through simulations. PK exposures were simulated from a one‐compartment model and TTE data from a parametric proportional hazard model, involving the weekly average PK concentration as a time‐varying covariate. Several scenarios were considered to handle the type of dosing (fixed or adaptive), the accumulation of the drug (low or strong), the type of event (efficacy, safety or independent), and the timing of the event onset (early or late). Wald tests on the exposure effect parameter were performed to assess the significance of the E‐R relationship. For each simulation scenario, the type‐I error and the power of the Wald tests were reported, revealing that no time‐static metric consistently produced reliable results across all conditions. In order to ensure adequate statistical properties, we recommend using time‐varying exposure, which shows good performance across all scenarios.

Toxicity of micro or nanoplastics (MP/NP) in aquatic life is well-documented, however, information about the consequences of exposure to these particles in terrestrial species is scarce. This study was used to evaluate the uptake and/or toxicity of polystyrene MP/NP in human gastric cells, comparing doses, particle sizes (50, 100, 200, 500, 1000 or 5000 nm) and surface functionalization (aminated, carboxylated or non-functionalized). In general, the uptake of 50 nm particles was significantly higher than 1000 nm particles. Among the 50 nm particles, the aminated particles were more avidly taken up by the cells and were cytotoxic at a lower concentration (≥ 7.5 μg/mL) compared to same sized carboxylated or non-functionalized particles (≥ 50 μg/mL). High toxicity of 50 nm aminated particles corresponded well with significantly high rates of apoptosis-necrosis induced by these particles in 4 h (29.2% of total cells) compared to all other particles (≤ 16.8%). The trend of apoptosis-necrosis induction by aminated particles in 4 h was 50 > 5000 > 1000 > 500 > 200 > 100 nm. The 50 nm carboxylated or non-functionalized particles also induced higher levels of apoptosis-necrosis in the cells compared to 100, 1000 and 5000 nm particles with same surface functionalization but longer exposure (24 h) to 50 nm carboxylated or non-functionalized particles significantly (p<0.0001) increased apoptosis-necrosis in the cells. The study demonstrated that the toxicity of MP/NP to gastric cells was dependent on particle size, dose surface functionalization and exposure period.

Background Air pollution exposure assessment during pregnancy often relies on static models based solely on residential addresses. These models may underestimate true exposure and distort assessments of environmental inequalities by neglecting daily mobility. This study aimed to quantify exposure misclassification due to mobility and to assess how it varies across socioeconomic groups in Strasbourg, France. Methods We analyzed data from 497 pregnant women enrolled in the MOBIFEM cohort. Exposure to nitrogen dioxide (NO₂), particulate matter ≤ 10 μm (PM₁₀), and ≤ 2.5 μm (PM₂.₅), was modeled using six scenarios: one static (residential address only) and five dynamic scenarios incorporating frequented locations and travel itineraries. A local socioeconomic deprivation index was used to stratify the analyses. High-resolution hourly pollutant concentrations were estimated using the ADMS-Urban dispersion model. Results Dynamic models incorporating mobility data consistently yielded significantly higher mean exposure estimates than the static residential model. The degree of underestimation was greatest among women living in the least deprived neighborhoods (Tertile 1). For instance, the largest discrepancies in NO₂ exposure between static and dynamic models were observed in this group (T-value = -4.72, p  < 0.001). Conclusion Relying solely on a residential address leads to substantial underestimation of personal air pollution exposure during pregnancy and may distort the characterization of social inequalities in exposure. In our study area, neglecting mobility disproportionately affected the least deprived women, likely due to longer commuting distances. These findings underscore the importance of incorporating individual mobility data into exposure assessment to improve the accuracy of environmental health risk analyses and inform public health policies.