Toward a comprehensive life-cycle carcinogenic impact assessment: A statistical regression approach based on cancer burden

The current approach to life cycle carcinogenic impact assessment (LCCA) is hindered by its static and linear characteristics. This situation prevents the accurate prediction of the incidence, associated damage, and potential economic burden of cancer. This study explores a highly comprehensive pathway for LCCA assessment. It uses the impacts of Tracheal, bronchus, and lung (TBL) predicted by the LCCA of China's coal power industry through a screened statistical regression model as the research target. The latest global burden of disease estimates is utilized to quantify the health damage from TBL incidence, whereas an approach combining the actual cost of health and human capital is applied to further assess the economic burden of TBL. Findings indicate that the traditional and static LCCA method, which relies on animal toxicity data, can lead to underestimations in actual LCCA. The interaction among spatiotemporal meteorological factors, epidemiological cancer disease burden, and socioeconomic behaviors allows exhibits nonlinearity due to the changes in the combined toxicity of mixed key substances. Following the active implementation of ultralow emission and energy-saving transformations in China's coal power industry, the national percentage of TBL cancer incidence caused by pollutants from the coal power industry decreased from 25.2 % in 2004 to 11.5 % in 2020. Results indicate that the established dynamic LCCA model based on temporal and spatial climate, socioeconomic, and epidemiological cancer data can be feasibly employed for the accurate impact evaluation and mitigation of carcinogens in practical applications.

Low-cost PM2.5 sensors can help identify driving factors of poor air quality and benefit communities

Air quality is critical for public health. Residents rely chiefly on government agencies such as the Environmental Protection Agency (EPA) in the United States to establish standards for the measurement of harmful contaminants including ozone, sulfur dioxide, carbon monoxide, volatile organic chemicals (VOCs), and fine particulate matter at or below 2.5 μm. According to the California Air Resources Board [1], "short-term PM2.5 exposure (up to 24-h duration) has been associated with premature mortality, increased hospital admissions for heart or lung causes, acute and chronic bronchitis, asthma attacks, emergency room visits, respiratory symptoms, and restricted activity days". While public agency resources may provide guidance, it is often inadequate relative to the widespread need for effective local measurement and management of air quality risks. To that end, this paper explores the use of low-cost PM2.5 sensors for measuring air quality through micro-scale (local) analytical comparisons with reference grade monitors and identification of potential causal factors of elevated sensor readings. We find that a) there is high correlation between the PM2.5 measurements of low-cost sensors and reference grade monitors, assessed through calibration models, b) low-cost sensors are more prevalent and provide more frequent measurements, and c) low-cost sensor data enables exploratory and explanatory analytics to identify potential causes of elevated PM2.5 readings. This understanding should encourage community scientists to place more low-cost sensors in their neighborhoods, which can empower communities to demand policy changes that are necessary to reduce particle pollution, and provide a basis for subsequent research.

Is the Performance of a Periodontal Prediction Model for Identification of Diabetes affected by Participants' Characteristics?

OBJECTIVE

To evaluate whether the diagnostic accuracy of a novel periodontal prediction model (PPM) for identification of adults with diabetes varies according to participants' characteristics.

BASIC RESEARCH DESIGN

The study was carried out among 250 adults attending primary care clinics in Riyadh (Saudi Arabia). The study adopted a case-control approach, where diabetes status was first ascertained, and data collection carried out afterwards using questionnaires and periodontal examinations. Variations in the performance of the PPM by demographic (sex and age), socioeconomic (education) and behavioural factors (smoking status and last dental visit) were evaluated using receiver-operating characteristic (ROC) regression.

RESULTS

The PPM including 3 periodontal parameters (missing teeth, percentage of sites with pocket depth ≥6mm and mean pocket depth) had an area under the ROC curve (AUC) of 0.69 (95% Confidence Interval: 0.61-0.78), which dropped to 0.64 (95% CI: 0.53-0.75) after adjustment for covariates. Larger variations in performance were found by participants' sex, age and education, but not by smoking status or last dental visit. The PPM performed better among male (adjusted AUC: 0.76; 95% CI: 0.53 to 0.99), younger (0.67; 95% CI: 0.50 to 0.84) and less educated participants (0.76; 95% CI: 0.60, 0.92).

CONCLUSIONS

The diagnostic accuracy of a novel periodontal prediction model to identify individuals with diabetes varied according to participants' characteristics. This study highlights the importance of adjusting for covariates on studies of diagnostic accuracy.

Predicting Functional Responses of Progenitor Cell Exosome Potential with Computational Modeling

Congenital heart disease can lead to severe right ventricular heart failure (RVHF). We have shown that aggregated c‐kit⁺ progenitor cells (CPCs) can improve RVHF repair, likely due to exosome‐mediated effects. Here, we demonstrate that miRNA content from monolayer (2D) and aggregated (3D) CPC exosomes can be related to in vitro angiogenesis and antifibrosis responses using partial least squares regression (PLSR). PLSR reduced the dimensionality of the data set to the top 40 miRNAs with the highest weighted coefficients for the in vitro biological responses. Target pathway analysis of these top 40 miRNAs demonstrated significant fit to cardiac angiogenesis and fibrosis pathways. Although the model was trained on in vitro data, we demonstrate that the model can predict angiogenesis and fibrosis responses to exosome treatment in vivo with a strong correlation with published in vivo responses. These studies demonstrate that PLSR modeling of exosome miRNA content has the potential to inform preclinical trials and predict new promising CPC therapies. stem cells translational medicine2019;8:1212–1221

Reference equation for the incremental shuttle walk test in Japanese adults

BACKGROUND

The incremental shuttle walk test (ISWT) is widely used in clinical and research settings. However, there are no reference equations to predict the walk distance achieved in the ISWT (ISWD) for healthy Japanese adults. We aimed to establish a reference equation for the ISWD prediction in Japanese adults.

METHODS

The sample comprised 590 healthy Japanese subjects (237 male). All subjects performed the ISWT twice, and their anthropometric and demographic data were collected, including gender, age, height, weight, and body mass index (BMI).

RESULTS

Subjects walked 640 [490-793] m in the ISWT. The ISWD correlated (p < 0.001 for all) with age (r = - 0.51), gender (r = 0.56), weight (r = 0.39), and height (r = 0.62), but not with BMI (r = - 0.01, p = 0.74). The stepwise multiple regression model showed that age, gender, and height were independent contributors to the ISWT in healthy subjects, explaining 50% of the variability. The reference equation for the ISWD was: ISWD(m) = - 4.894 - 4.107 × Age (years) + 131.115 × Gender + 4.895 × Height (cm), where male gender = 1.

CONCLUSION

We have established a reference equation for the ISWD prediction in Japanese adults. The prediction accuracy was high (R² = 50%), and a reference equation was established using anthropometric and demographic variables that can be easily assessed in clinical settings. The reference equation developed in this study will be useful for evaluating the magnitude of exercise intolerance in Japanese adults.

Relationship between the curve of Spee and craniofacial variables: A regression analysis

INTRODUCTION

The aim of this regression analysis was to identify the determining factors, which impact the curve of Spee during its genesis, its therapeutic reconstruction, and its stability, within a continuously evolving craniofacial morphology throughout life.

MATERIAL AND METHODS

We selected a total of 107 patients, according to the inclusion criteria. A morphological and functional clinical examination was performed for each patient: plaster models, tracing of the curve of Spee, crowding, Angle's classification, overjet and overbite were thus recorded. Then, we made a cephalometric analysis based on the standardized lateral cephalograms. In the sagittal dimension, we measured the values of angles ANB, SNA, SNB, SND, I/i; and the following distances: AoBo, I/NA, i/NB, SE and SL. In the vertical dimension, we measured the values of angles FMA, GoGn/SN, the occlusal plane, and the following distances: SAr, ArD, Ar/Con, Con/Gn, GoPo, HFP, HFA and IF. The statistical analysis was performed using the SPSS software with a significance level of 0.05.

RESULTS

Our sample including 107 subjects was composed of 77 female patients (71.3%) and 30 male patients (27.8%) 7 hypodivergent patients (6.5%), 56 hyperdivergent patients (52.3%) and 44 normodivergent patients (41.1%). Patients' mean age was 19.35±5.95 years. The hypodivergent patients presented more pronounced curves of Spee compared to the normodivergent and the hyperdivergent populations; patients in skeletal Class I presented less pronounced curves of Spee compared to patients in skeletal Class II and Class III. These differences were non significant (P>0.05). The curve of Spee was positively and moderately correlated with Angle's classification, overjet, overbite, sellion-articulare distance, and breathing type (P<0.05). We found no correlation between age, gender and the other parameters included in the study with the curve of Spee (P>0.05). Seventy five percent (75%) of the hyperdivergent patients with an oral breathing presented an overbite of 3mm, which is quite excessive given the characteristics often admitted for this typology; this parameter could explain the overbite observed in the hyperdivergent population included in this study. For the multivariate analysis, the overbite and the sellion-articulare distance remained independently related to the curve of Spee according to the breathing type, Angle's classification, and overjet. This regression model explains 21.4% of the changes in the curve of Spee.

Prognostic Index Model for Progression-Free Survival in Chemotherapy-Naïve Metastatic Castration-Resistant Prostate Cancer Treated With Abiraterone Acetate Plus Prednisone

BACKGROUND

Radiographic progression-free survival (rPFS) is associated with overall survival (OS) in chemotherapy-naïve metastatic castration-resistant prostate cancer (mCRPC) patients. Using readily assessable baseline clinical and laboratory parameters, we developed a prognostic index model for rPFS in chemotherapy-naïve mCRPC patients without visceral disease who were treated with abiraterone acetate plus prednisone.

METHODS

Data from the abiraterone acetate plus prednisone arm of COU-AA-302 were used. rPFS was defined based on modified Prostate Cancer Working Group 2 criteria. Baseline variables were assessed for association with rPFS through univariate Cox modeling. The lower (LLN) and upper (ULN) limits of laboratory normal were used to dichotomize most laboratory parameters; baseline median was used to dichotomize prostate-specific antigen (PSA). Prognostic factors for rPFS were identified by multivariate Cox modeling. Model accuracy was estimated by the C-index.

RESULTS

Presence of lymph node metastasis (hazard ratio [HR] = 1.76, P < .0001), lactate dehydrogenase > ULN (234 IU/L) (HR = 1.71, P = .0001), ≥ 10 bone metastases (HR = 1.71, P = .0015), hemoglobin ≤ LLN (12.7 g/dL) (HR = 1.47, P = .0030) and PSA > 39.5 ng/mL (HR = 1.42, P = .0078) were associated with poor outcome. Patients were categorized into 3 prognostic groups (good, n = 230; intermediate, n = 152; poor, n = 164) based on number of risk factors. Median rPFS was calculated (27.6, 16.6, and 8.3 months for good, intermediate, and poor, respectively). The C-index was 0.83 (95% confidence interval = 0.73-0.91).

CONCLUSIONS

The prognostic index model for rPFS reveals differential outcomes based on factors readily available in clinical practice. If validated, this model can be integrated into clinical practice and design of risk-stratified trials.

Estimation of anthropogenic heat emissions in urban Taiwan and their spatial patterns

High energy consumption in the urban environment impacts the urban surface energy budget and causes the emission of anthropogenic heat fluxes (AHFs) into the atmosphere. AHFs vary over time and space. Thus, a reliable estimation of AHF is needed for mesoscale meteorological modeling. This study used a statistical regression method to estimate the annual mean gridded AHF with high spatial (1-km) resolution. Compared with current methods for AHF estimation, the statistical regression method is straightforward and can be easily incorporated with meteorological modeling. AHF of the highly populated urban areas in Taiwan were estimated using data from the anthropogenic pollutant emission inventory of CO and NOx for year 2010. Over 40% of the total AHF values in Taiwan main island fell within the range of 10-40 Wm(-2). When the study domain was confined to urban land, the percentage contributions from AHF values were increased, with over 68% of the total AHF values within the range of 10-40 Wm(-2). AHF values > 40 Wm(-2) were more abundant in the Southern region, followed by the Central and Northern regions. An assessment of the heat emissions by the large scale urban consumption of energy (LUCY) model revealed that the mean AHFs are reasonably close to those produced while the maximum AHFs are underestimated. The results obtained evidence the impact of spatial distribution of land use types, particularly population densities, main highways and industries on AHF generation in Taiwan.

Regression-Based Norms for a Bi-factor Model for Scoring the Brief Test of Adult Cognition by Telephone (BTACT)

The current study developed regression-based normative adjustments for a bi-factor model of the The Brief Test of Adult Cognition by Telephone (BTACT). Archival data from the Midlife Development in the United States-II Cognitive Project were used to develop eight separate linear regression models that predicted bi-factor BTACT scores, accounting for age, education, gender, and occupation-alone and in various combinations. All regression models provided statistically significant fit to the data. A three-predictor regression model fit best and accounted for 32.8% of the variance in the global bi-factor BTACT score. The fit of the regression models was not improved by gender. Eight different regression models are presented to allow the user flexibility in applying demographic corrections to the bi-factor BTACT scores. Occupation corrections, while not widely used, may provide useful demographic adjustments for adult populations or for those individuals who have attained an occupational status not commensurate with expected educational attainment.

Evaluation of the long-term variability of seawater salinity and temperature in response to natural and anthropogenic stressors in the Arabian Gulf

Evaluating the long-term variability of the seawater salinity and temperature due to climate change is a limiting economical and operational factor in planning the design of new and expansion of existing desalination plants. This need is amplified in the Arabian Gulf due to the natural arid climate and anthropological stresses related to energy exploration and ongoing major developments. The lack of data in this region further adds additional dimension to the problem. The present work represents a systematic innovative approach to evaluate the anticipated long-term changes in the seawater salinity and temperature under the stresses of projected climate change and massive industrial effluents using statistical correlation and hydrodynamic simulation. The proposed approach employs the direct relation between the net freshwater losses (evaporation) entrenched with the investigated stressors and the mean sea salinity and sea temperature variation of an inverse estuary to formulate the statistical correlation and the hydrodynamic simulation conditions.