Distribution and ecological risk assessment of priority water pollutants in surface river sediments with emphasis on industrially affected areas.

Priority water pollutants comprising six plasticizers, 18 volatile organic compounds (VOCs), total petroleum hydrocarbon (TPH), 1,4-dioxane, epichlorohydrin, formaldehyde, acrylamide, and cyanides were determined in surface river sediments to assess their distribution patterns and ecological risks. Among these, di (2-ethylhexyl) phthalate (DEHP), toluene, TPH, and acrylamide were frequently found in sediments. The industrial sites had higher concentrations of ∑plasticizers (median 628 ng/g dry weight (dw)), ∑VOCs (median 3.35 ng/g dw), acrylamide (median 0.966 ng/g dw), and TPH (median 152 µg/g dw) in sediments than the mixed and non-industrial areas. The other pollutants did not show the significant differences in levels according to site types because of their relatively low detection frequencies. Volatile and soluble substances as well as hydrophobic pollutants were predominantly detected in surface sediments from industrial areas. Sediment contamination patterns were affected by the size and composition of the industrial zones around the sampling sites. The ecological risks determined using the sediment quality guidelines (DEHP, VOCs, and TPH) and the mean probable effect level quotients (DEHP) were mostly acceptable. However, the two most representative industrial regions (the largest industrial area and the first industrial city) showed risks of concern for DEHP and TPH.

Value of a deep learning-based algorithm for detecting Lung-RADS category 4 nodules on chest radiographs in a health checkup population: estimation of the sample size for a randomized controlled trial.

OBJECTIVE: To explore the value of a deep learning-based algorithm in detecting Lung CT Screening Reporting and Data System category 4 nodules on chest radiographs from an asymptomatic health checkup population. METHODS: Data from an annual retrospective cohort of individuals who underwent chest radiographs for health checkup purposes and chest CT scanning within 3 months were collected. Among 3073 individuals, 118 with category 4 nodules on CT were selected. A reader performance test was performed using those 118 radiographs and randomly selected 51 individuals without any nodules. Four radiologists independently evaluated the radiographs without and with the results of the algorithm; and sensitivities/specificities were compared. The sample size needed to confirm the difference in detection rates was calculated, i.e., the number of true-positive radiographs divided by the total number of radiographs. RESULTS: The sensitivity of the radiologists substantially increased aided by the algorithm (38.8% [183/472] to 45.1% [213/472]; p < .001) without significant change in specificity (94.1% [192/204] vs. 92.2% [188/204]; p = .22). Pooled radiologists detected more nodules with the algorithm (32.0% [156/488] vs. 38.9% [190/488]; p < .001), without alteration of false-positive rates (0.09 [62/676], both). Pooled detection rates for the annual cohort were 1.49% (183/12,292) and 1.73% (213/12,292) without and with the algorithm, respectively. A sample size of 41,776 in each arm would be required to demonstrate significant detection rate difference with < 5% type I error and > 80% power. CONCLUSION: Although readers substantially increased sensitivity in detecting nodules on chest radiographs from a health checkup population aided by the algorithm, detection rate difference was only 0.24%, requiring a sample size >80,000 for a randomized controlled trial. KEY POINTS: • Aided by a deep learning algorithm, pooled radiologists improved their sensitivity in detecting Lung-RADS category 4 nodules on chest radiographs from a health checkup population (38.8% [183/472] to 45.1% [213/472]; p < .001), without increasing false-positive rate. • The prevalence of the Lung-RADS category 4 nodules was 3.8% (118/3073) on the population, resulting in only 0.24% increase of the detection rate for the radiologists with assistance of the algorithm. • To confirm the significant detection rate increase by a randomized controlled trial, a sample size of 84,000 would be required.

Developing an Economic Case of Clinical Pharmacists' Interventions on Venous Thromboembolism Prophylaxis Through Service Evaluation.

OBJECTIVE: Venous thromboembolism (VTE) has become a huge health problem as well as a financial burden for the National Health Service. The objective of this study was to characterize current practice of VTE prophylaxis (VTEP) and evaluate the economic impact of clinical pharmacists' interventions (CPIs) on VTEP. METHODS: A prospective service evaluation was conducted in a medical and surgical ward at a tertiary teaching hospital in London from 23 May to 08 June 2016. Appropriateness of risk assessment (RA) and VTEP and CPIs were categorized and assessed. Based on the results of the service evaluation, a pharmacoeconomic analysis was undertaken to estimate the cost savings by CPIs for inappropriate pharmacological VTEP. FINDINGS: A total of 203 cases were analyzed. The rates of appropriateness for RA on admission, RA at 24 h and pharmacological VTEP were 58.6%, 39.7%, and 75.4%, respectively. In the medical ward, there was a significant difference of appropriate RAs between on admission and at 24 h (70.3% vs. 23.8%, respectively). Whereas, the rate of appropriate pharmacological VTEP accounted for 75.4% and the rate of appropriate prophylaxis was significantly higher in the medical ward than surgical ward (80.5% vs. 68.2%, P = 0.045). Of 50 cases of inappropriate pharmacological prophylaxis, 39 cases (78.0%) were corrected by clinical pharmacists. These CPIs resulted in £1,286.23 cost savings during the study and it was estimated to be £517,522/annum. CONCLUSION: CPIs had significant positive clinical and economic impacts on VTEP. There is more scope for the improvement of RA at 24 h through CPIs.

Synergistic action modes of arabinan degradation by exo- and endo-arabinosyl hydrolases.

Two recombinant arabinosyl hydrolases, α-L-arabinofuranosidase from Geobacillus sp. KCTC 3012 (GAFase) and endo-(1,5)-α-L-arabinanase from Bacillus licheniformis DSM13 (BlABNase), were overexpressed in Escherichia coli, and their synergistic modes of action against sugar beet (branched) arabinan were investigated. Whereas GAFase hydrolyzed 35.9% of L-arabinose residues from sugar beet (branched) arabinan, endo-action of BlABNase released only 0.5% of L-arabinose owing to its extremely low accessibility towards branched arabinan. Interestingly, the simultaneous treatment of GAFase and BlABNase could liberate approximately 91.2% of L-arabinose from arabinan, which was significantly higher than any single exo-enzyme treatment (35.9%) or even stepwise exo- after endo-enzyme treatment (75.5%). Based on their unique modes of action, both exo- and endo-arabinosyl hydrolases can work in concert to catalyze the hydrolysis of arabinan to L-arabinose. At the early stage in arabinan degradation, exo-acting GAFase could remove the terminal arabinose branches to generate debranched arabinan, which could be successively hydrolyzed into arabinooligosaccharides via the endoaction of BlABNase. At the final stage, the simultaneous actions of exo- and endo-hydrolases could synergistically accelerate the L-arabinose production with high conversion yield.

Ultrasonographic assessment of breast density.

Ultrasonographic (US) assessment of breast density has the potential to provide a nonionizing method. This study was to prospectively evaluate intermodality and interobserver agreements for assessment of breast density between US and mammography. Institutional review board approval was obtained. Forty-one women (mean 52.1 years; range 25-72 years) with variable breast density consented to participate. Eight radiologists blinded to mammographic information performed breast US for all participants and assessed each breast density using four categories based on the proportion of the breast occupied by the fibroglandular tissue. All participants underwent full-field digital mammography and mammographic density was independently assessed by eight radiologists 2 weeks after US using the breast imaging reporting and data system (BI-RADS) 4-category system. Intermodality agreements between US and mammographic assessments and interobserver agreements among radiologists were assessed using kappa statistics (к) and intraclass correlation coefficients (ICCs). There was substantial intermodality agreement between the US and mammographic assessments of breast density (к = 0.65 and ICC = 0.80), and 68 % (222/328) of the assessments had exact agreement. When categories were dichotomized into fatty (categories 1 and 2) and dense (categories 3 and 4), 86 % (282/328) of the assessments had exact agreement (к = 0.71). The interobserver agreement for the US assessments of breast density was substantial (average к = 0.63, ICC = 0.82) and not significantly different from that for the mammographic assessments (average к = 0.74, ICC = 0.85) (P = 0.701). US and mammography demonstrated substantial intermodality and interobserver agreement for assessment of breast density.