Activity

The crosswalk had modest prediction accuracy (MAE = 0.092, RMSE = 0.114, AIC = -2708 and BIC = -2595.6), which are comparable to prediction accuracies of other SF-6D crosswalks in the literature. About 24% and 52% of predictions fell within ± 5% and ± 10% of observed SF-6D, respectively. The observed mean disutility associated with acquiring clinically significant PCRA is 0.168 (standard deviation = 0.179).

This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors.

This study provides a crosswalk that converts MAX-PC scores to SF-6D utilities for economic evaluation of clinically significant PCRA treatment options for prostate cancer survivors.Profiling of hydrocarbon-contaminated soils for antibiotic resistance genes (ARGs) is becoming increasingly important due to emerging realities of their preponderance in hydrocarbon-inundated matrices. Thiazovivin ic50 In this study, the antibiotic resistome of an agricultural soil (1S) and agricultural soil contaminated with spent engine oil (AB1) were evaluated via functional annotation of the open reading frames (ORFs) of their metagenomes using the comprehensive antibiotic database (CARD) and KEGG KofamKOALA. CARD analysis of AB1 metagenome revealed the detection of 24 AMR (antimicrobial resistance) gene families, 66 ARGs, and the preponderance (69.7%) of ARGs responsible for antibiotic efflux in AB1 metagenome. CARD analysis of 1S metagenome revealed four AMR gene families and five ARGs. Functional annotation of the two metagenomes using KofamKOALA showed 171 ARGs in AB1 and 29 ARGs in 1S, respectively. Majority of the detected ARGs in AB1 (121; 70.8%) and 1S (16; 55.2%) using KofamKOALA are responsible for antibiotic efflux while ARGs for other resistance mechanisms were also detected. All the five major antibiotic efflux pump systems were detected in AB1 metagenome, though majority of the ARGs for antibiotic efflux belong to the RND (resistance-nodulation-cell division) and MFS (major facilitator superfamily) efflux systems. Significant differences observed in the ARGs recovered from 1S and AB1 metagenomes were statistically validated (P  less then  0.05). SEO contamination is believed to be responsible for ARGs increase in AB1 metagenome via mechanisms of cross-resistance especially with efflux pumps. The detection of these ARGs is of great public health concern in this era of multidrug resistant isolates resurgence.

Gestational diabetes mellitus (GDM) is associated with adverse perinatal outcomes. Accurate models for early prediction of GDM are lacking. This study aimed to explore an early risk prediction model to identify women at high risk of GDM through a risk scoring system.

This was a retrospective cohort study of 785 control pregnancies and 855 women with GDM. Maternal clinical characteristics and biochemical measures were extracted from the medical records. Logistic regression analysis was used to obtain coefficients of selected predictors for GDM in the training cohort. The discrimination and calibration of the risk scores were evaluated by the receiver-operating characteristic (ROC) curve and a Hosmer-Lemeshow test in the internal and external validation cohort, respectively.

In the training cohort (total = 1640), two risk scores were developed, one including predictors collected at the first antenatal care visit for early prediction of GDM, such as age, height, pre-pregnancy body mass index, educational background, family history of diabetes, menstrual history, history of cesarean delivery, GDM, polycystic ovary syndrome, hypertension, and fasting blood glucose (FBG), and the total risk score also including FBG and triglyceride values during 14-20 gestational weeks. Our total risk score yielded an area under the curve (AUC) of 0.845 (95% CI = 0.805-0.884). This performed better in an external validation cohort, with an AUC of 0.886 (95% CI = 0.856-0.916).

The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.

The GDM risk score, which incorporates several potential clinical features with routine biochemical measures of GDM, appears to be a sensitive and reliable screening tool for earlier detection of GDM risk.

Individuals with anorexia (AN) or bulimia nervosa (BN) often present with fear of loss of control in the context of eating. It is unclear whether this fear of loss of control, which has been associated with fear of failure and a sense of not being in charge of one’s own life in eating disorders, can be distinguished from self-perceived maintained control over food intake in AN. Further, anxious traits are elevated across eating disorders and could contribute to this fear of loss of control.

We recruited 113 adult women restricting type AN (n = 26), BN (n = 28), and healthy controls (CW, n = 59). Participants completed the Eating Expectancies Inventory (EEI), which assesses learned expectations on the effects of eating, including whether Eating Leads to Feeling out of Control, and the Trait Food Craving Questionnaire (FCQ-T), which measures food craving and the ability to withstand those cravings, including self-perceived Lack of Control Over Eating.

Eating Leads to Feeling out of Control was elevated in AN and BN compared to CW. Lack of Control Over Eating was similar between AN and CW but elevated in BN. Intolerance of uncertainty correlated with those measures in CW only.

Individuals with restricting-type AN experience feeling out of control when eating while maintaining self-perceived control over eating. The EEI’s eating leads to feeling out of control is associated with negative self-improvement expectations. Targeting self-improvement through more functional strategies could be an important aspect in psychotherapy in AN and reduce the perceived need to restrict food intake.

Level III, Evidence obtained from well-designed cohort or case-control analytic studies.

Level III, Evidence obtained from well-designed cohort or case-control analytic studies.