Tag: pubmed

J Environ Manage. 2022 Feb 16;309:114711. doi: 10.1016/j.jenvman.2022.114711. Online ahead of print.

ABSTRACT

Heavy metals (HMs) such as Lead (Pb) have played a vital role in increasing the sediments of the Australian bay’s ecosystem. Several meteorological parameters (i.e., minimum, maximum and average temperature (T_min, T_max and T_avg^oC), rainfall (R_n mm) and their interactions with the other batch HMs, are hypothesized to have high impact for the decision-making strategies to minimize the impacts of Pb. Three feature selection (FS) algorithms namely the Boruta method, genetic algorithm (GA) and extreme gradient boosting (XGBoost) were investigated to select the highly important predictors for Pb concentration in the coastal bay sediments of Australia. These FS algorithms were statistically evaluated using principal component analysis (PCA) Biplot along with the correlation metrics describing the statistical characteristics that exist in the input and output parameter space of the models. To ensure a high accuracy attained by the applied predictive artificial intelligence (AI) models i.e., XGBoost, support vector machine (SVM) and random forest (RF), an auto-hyper-parameter tuning process using a Grid-search approach was also implemented. Cu, Ni, Ce, and Fe were selected by all the three applied FS algorithms whereas the T_avg and R_n inputs remained the essential parameters identified by GA and Boruta. The order of the FS outcome was XGBoost > GA > Boruta based on the applied statistical examination and the PCA Biplot results and the order of applied AI predictive models was XGBoost-SVM > GA-SVM > Boruta-SVM, where the SVM model remained at the top performance among the other statistical metrics. Based on the Taylor diagram for model evaluation, the RF model was reflected only marginally different so overall, the proposed integrative AI model provided an evidence a robust and reliable predictive technique used for coastal sediment Pb prediction.

PMID:35182982 | DOI:10.1016/j.jenvman.2022.114711

J Environ Manage. 2022 Feb 16;309:114712. doi: 10.1016/j.jenvman.2022.114712. Online ahead of print.

ABSTRACT

Although the environmental significance of acid rock drainage (ARD) generated from mining wastes is well known, selecting the appropriate ARD management strategy can prove a complicated task. Chemical methods are favored for initial mine waste characterization but using these exclusively can overlook key factors, e.g., mineralogy, which controls the formation and elution of ARD. This paper first presents an ARD waste rock classification developed on Triple Characterization Criteria (TCC) which considers three input parameters: neutralizing potential ratio (NPR), net acid generation (NAG pH), and modal mineralogy weathering index (MMWI) values. Second, a new mixed-integer programming (MIP) model to guide waste dump construction with the dual aim of preventing ARD across the life-of-mine (LOM) and reducing waste rock re-handling, is introduced. Last, the spatial distribution of TCC in a planned waste dump is simulated via geo-statistical techniques to evaluate the MIP model. The proposed waste rock classification and dump planning model has been tested at an iron mine. The results of the MIP modeling and simulation of TCC showed the successful prevention of ARD by achieving large values of TCC (NPR ≥2, NAG pH ≥ 4.5, and MMWI ≥4.7) for dump cells, with the planned mine production maintained. The integrated TCC approach introduced in this study is intended to enable mine operators, at the start of the LOM, to effectively forecast ARD from future waste rock. Further, the MIP model will facilitate development of a mine schedule that optimizes the use of the waste materials based on TCC values. If used correctly, the TCC and MIP model have the potential to enable mine operators to reduce their environmental footprint across the entire LOM.

PMID:35182980 | DOI:10.1016/j.jenvman.2022.114712

J Environ Manage. 2022 Feb 16;309:114689. doi: 10.1016/j.jenvman.2022.114689. Online ahead of print.

ABSTRACT

Understanding the environmental and economic impacts of copper hydrometallurgy throughout the whole life cycle is necessary for sustainable development of the copper industry. In this study, the environmental impacts and economic costs throughout the two major copper hydrometallurgical routes in China, including heap leaching and heap-agitation leaching, are analyzed and compared using the life cycle assessment (LCA) and life cycle cost (LCC) technique. The life cycle inventory compiled from the annual statistics of the Muliashi Copper Mine, and the data regarding energy and materials process are based on the GaBi databases. The environmental impacts are quantified into 12 indicators. The results show that compared with heap leaching route, heap-agitation leaching route reduces 36.8% of abiotic depletion potential (ADP elements), but increases over half of cumulative energy demand (CED), marine aquatic ecotoxicity potential (MAETP) and human toxicity potential (HTP). Furthermore, the stage of electrowinning and agitation leaching contributes the largest environmental impact to heap leaching and heap-agitation leaching route, respectively. This is mainly due to huge consumption of electricity and sulfuric acid. The analysis of economic cost reveals that heap leaching route needs internal cost of $3225/t Cu and external cost of $426/t Cu. Compared with heap leaching route, heap-agitation leaching route increased the internal and external cost by 18.9% and 54.2%, respectively. But the economic return from heap-agitation leaching is double that from heap leaching. Together, these results indicate heap-agitation leaching has a larger environmental impact and higher economic benefit than heap leaching, which is helpful for the government to design ecological compensation policies in the balance between ecological environment and economic development.

PMID:35182981 | DOI:10.1016/j.jenvman.2022.114689

J Environ Radioact. 2022 Feb 16;246:106843. doi: 10.1016/j.jenvrad.2022.106843. Online ahead of print.

ABSTRACT

The major rivers in a region are usually vital sources of drinking water for local populations, and the concentration of radionuclides in the water is intimately tied to people’s health. The varying concentration limits set by the World Health Organization are appropriate as screening values for determining the pollution of water sources, but their capacities as regulatory or early warning limits are restricted. In daily management, the regulatory authority needs to manage water bodies by level based on the concentration of radionuclide to indicate the potential pollution risks. From 2017 to 2019, a statistical analysis and dosage evaluation were conducted on the water radioactivity level in the Chongqing section of the Yangtze River in this study. The Modified Nemerow Index method based on the dose conversion coefficients was applied for the grading evaluation of the water radioactivity level, allowing the grading effect discussed. The results showed that the concentration of radionuclides in the Chongqing section of the Yangtze River and its contribution to the annual effective dose of the human body were lower than the limits stated in the Guidelines for Drinking Water Quality (Fourth Edition). And the samples in the section were 52.94% in Grade Ⅰand 47.06% in Grade Ⅱ, meaning few potential radioactive pollution risks exist there. Compared with other methods. The Modified Nemerow Index method combines the Traditional Nemerow Index method with the dose conversion coefficient of nuclides making it more realistic for the early warning and control of radioactive pollution in water bodies, which is worth popularizing and implementing.

PMID:35182960 | DOI:10.1016/j.jenvrad.2022.106843

J Infect Public Health. 2022 Feb 12;15(3):349-359. doi: 10.1016/j.jiph.2022.02.004. Online ahead of print.

ABSTRACT

BACKGROUND: Noninvasive ventilation (NIV) is beneficial in exacerbations of chronic obstructive pulmonary disease (COPD), but its effectiveness in pneumonia-associated respiratory failure is still controversial. In the current meta-analysis, we aimed to investigate whether the use of NIV before intubation in pneumonia improves the mortality and intubation rates of respiratory failure as compared to no use of NIV in adults.

METHODS: We searched three databases from inception to December 2019. We included studies, in which pneumonia patients were randomized initially into either NIV-treated or non-NIV-treated groups. Five full-text publications, including 121 patients, reported eligible data for statistical analysis.

RESULTS: With NIV the overall hospital mortality rate seemed lower in patients with pneumonia-associated respiratory failure, but this was not significant [odds ratio (OR) = 0.39; 95% confidence interval (CI): 0.13-1.14; P = 0.085]. In the intensive care unit, the mortality was significantly lower when NIV was applied compared to no NIV treatment (OR = 0.22; 95% CI: 0.07-0.75; P = 0.015). NIV also decreased mortality compared to no NIV in patient groups, which did not exclude patients with COPD (OR = 0.25; 95% CI: 0.08-0.74; P = 0.013). The need for intubation was significantly reduced in NIV-treated patients (OR = 0.22; 95% CI: 0.09-0.53; P = 0.001), which effect was more prominent in pneumonia patient groups not excluding patients with pre-existing COPD (OR = 0.13; 95% CI: 0.03-0.46; P = 0.002).

CONCLUSION: NIV markedly decreases the death rate in the intensive care unit and reduces the need for intubation in patients with pneumonia-associated respiratory failure. The beneficial effects of NIV seem more pronounced in populations that include patients with COPD. Our findings suggest that NIV should be considered in the therapeutic guidelines of pneumonia, given that future clinical trials confirm the results of our meta-analysis.

AVAILABILITY OF DATA AND MATERIALS: All data and materials generated during the current study are available from the corresponding author on reasonable request.

PMID:35182933 | DOI:10.1016/j.jiph.2022.02.004

Comput Biol Med. 2022 Feb 12;143:105293. doi: 10.1016/j.compbiomed.2022.105293. Online ahead of print.

ABSTRACT

As continuous glucose monitoring (CGM) sensors generate ever increasing amounts of CGM data, the need for methods to simplify the storage and analysis of this data becomes increasingly important. Lobo et al. developed a classifier of daily CGM profiles as an initial step in addressing this need. The classifier has several important applications including, but not limited to, data compression, data encryption, and indexing of databases. While the classifier has already successfully classified 99.0% of the 42,595 daily CGM profiles in a Test Set, this work presents an external validation using an external validation set (EVal Set) derived from 8 publicly available data sets. The Test Set and the EVal Set differ in terms of (but not limited to) demographics, data collection time periods, and data collection geographies. The classifier successfully classified 98.2% of the 137,030 daily CGM profiles in the EVal Set. Furthermore, each of the 483 distinct groups of classified daily CGM profiles from the EVal Set retains the same clinical characteristics as the corresponding group from the Test Set, as desired. Finally, the set of unclassified daily CGM profiles from the EVal Set retains the same statistical characteristics as the set of unclassified daily CGM profiles from the Test Set, as desired. These results establish the robustness and generalizability of the classifier: the performance of the classifier is unchanged despite the marked differences between the Test Set and the EVal Set.

PMID:35182951 | DOI:10.1016/j.compbiomed.2022.105293

Eur J Cancer. 2022 Feb 16;164:88-94. doi: 10.1016/j.ejca.2021.12.034. Online ahead of print.

ABSTRACT

BACKGROUND: Advances in biomedical artificial intelligence may introduce or perpetuate sex and gender discriminations. Convolutional neural networks (CNN) have proven a dermatologist-level performance in image classification tasks but have not been assessed for sex and gender biases that may affect training data and diagnostic performance. In this study, we investigated sex-related imbalances in training data and diagnostic performance of a market-approved CNN for skin cancer classification (Moleanalyzer Pro®, Fotofinder Systems GmbH, Bad Birnbach, Germany).

METHODS: We screened open-access dermoscopic image repositories widely used for CNN training for distribution of sex. Moreover, the sex-related diagnostic performance of the market-approved CNN was tested in 1549 dermoscopic images stratified by sex (female n = 773; male n = 776).

RESULTS: Most open-access repositories showed a marked under-representation of images originating from female (40%) versus male (60%) patients. Despite these imbalances and well-known sex-related differences in skin anatomy or skin-directed behaviour, the tested CNN achieved a comparable sensitivity of 87.0% [80.9%-91.3%] versus 87.1% [81.1%-91.4%], specificity of 98.7% [97.4%-99.3%] versus 96.9% [95.2%-98.0%] and ROC-AUC of 0.984 [0.975-0.993] versus 0.979 [0.969-0.988] in dermoscopic images of female versus male origin, respectively. In the sample at hand, sex-related differences in ROC-AUCs were not statistically significant in the per-image analysis nor in an additional per-individual analysis (p ≥ 0.59).

CONCLUSION: Design and training of artificial intelligence algorithms for medical applications should generally acknowledge sex and gender dimensions. Despite sex-related imbalances in open-access training data, the diagnostic performance of the tested CNN showed no sex-related bias in the classification of skin lesions.

PMID:35182926 | DOI:10.1016/j.ejca.2021.12.034

Spectrochim Acta A Mol Biomol Spectrosc. 2022 Feb 12;272:120998. doi: 10.1016/j.saa.2022.120998. Online ahead of print.

ABSTRACT

Simultaneous measurement of saxagliptin hydrochloride (SAG) and dapagliflozin propanediol monohydrate (DAG) in bulk powder, laboratory-prepared mixtures, and pharmaceutical dosage form were applied by utilizing three precise and sensitive spectrophotometric techniques which were developed and validated. The first method was the induced dual-wavelength approach (IDW), which relied primarily on the use of alternative equality factors (F) to abolish the effect of DAG when determining SAG and vice versa. The ratio difference method (RDM) was the second method, which used 25 μg/ml of DAG and 20 μg/ml of SAG as divisors to determine the amplitude difference on the ratio spectrum of SAG and DAG, respectively. SAG was determined at λ_max 221 nm after plateau subtraction followed by multiplication by the divisor of DAG 25 μg/ml using the third method, ratio subtraction coupled with extended ratio subtraction method (RSER). Subsequently, using an extension ratio subtraction of the spectra, DAG was determined at λ_max 225 nm was determined. The developed methods were effectively used to estimate SAG and DAG in laboratory-prepared mixtures and pharmaceutical dosage forms, with satisfactory recoveries. The methodologies were assessed for their environmental friendliness using the analytical eco-scale, analytical GREEnness calculator, and green analytical procedureindex (GAPI). These methodologies were validated following the International Conference on Harmonisation (ICH) requirements. A statistical comparison of the obtained findings to those of the published method revealed no significant differences in precision and accuracy. Because of their high precision and cost-effectiveness, the developed methods can be used in quality control laboratories to determine the binary mixture.

PMID:35182920 | DOI:10.1016/j.saa.2022.120998

Am J Emerg Med. 2022 Jan 23;54:228-231. doi: 10.1016/j.ajem.2022.01.015. Online ahead of print.

ABSTRACT

BACKGROUND: There is a paucity of data looking at resident error or contrasting errors and adverse events among residents and attendings. This type of data could be vital in developing and enhancing educational curricula OBJECTIVES: Using an integrated, readily accessible electronic error reporting system the objective of this study is to compare the frequency and types of error and adverse events attributed to emergency medicine residents with those attributed to emergency medicine attendings.

METHODS: Individual events were classified into errors and/or adverse events, and were attributed to one of three groups-residents only, attendings only, or both (if the event had both resident and attending involvement). Error and adverse events were also classified into five different categories of events-systems, documentation, diagnostic, procedural and treatment. The proportion of error events were compared between the residents only and the attendings only group using a one-sample test of proportions. Categorical variables were compared using Fisher’s exact test.

RESULTS: Of a total of 115 observed events over the 11-month data collection period, 96 (83.4%) were errors. A majority of these errors, 40 (41.7%), were attributed to both residents and attendings, 20 (20.8%) were attributed to residents only, and 36 (37.5%) were attributed to attendings only. Of the 19 adverse events, 14 (73.7%) were attributed to both residents and attendings, and 5 (26.3%) adverse events were attributed to attendings only. No adverse events were attributed solely to residents (Table 1). Excluding events attributed to both residents and attendings, there was a significant difference between the proportion of errors attributed to attendings only (64.3%, CI: 50.6, 76.0), and residents only (35.7%, CI: 24.0, 49.0), p = 0.03. (Table 2). There was no significant difference between the residents only and the attendings only group in the distribution of errors and adverse events (Fisher’s exact, p = 0.162). (Table 2). There was no statistically significant difference between the two groups in errors that did not result in adverse events and the rate of errors proceeding to adverse events (Fisher’s exact, p = 0.15). (Table 3). There was no statistically significant difference between the two groups in the distribution of the types of errors and adverse events (Fisher’s exact, p = 0.09). Treatment related errors were the most common error types, for both the attending and the resident groups.

CONCLUSIONS: Resident error, somewhat expectedly, is most commonly related to treatment interventions, and rarely is due to an individual resident mistake. Resident error instead seems to reflect concomitant error on the part of the attending. Error, in general as well as adverse events, are more likely to be attributed to an attending alone rather than to a resident.

PMID:35182916 | DOI:10.1016/j.ajem.2022.01.015

Gen Hosp Psychiatry. 2022 Feb 4;75:61-67. doi: 10.1016/j.genhosppsych.2022.01.010. Online ahead of print.

ABSTRACT

OBJECTIVE: Schizophrenia has been associated with patients’ poor quality of life, disability, and hospitalization. As of today, evidence that highlights the association between schizophrenia and coronavirus disease (Covid-19) outcomes remains unclear. This study sought to analyze whether patients with pre-existing schizophrenia are at higher risk for Covid-19 mortality.

METHODS: Using specific keywords, we comprehensively searched PubMed, Scopus, OVID, and Cochrane Library sources until November 15th, 2021. All published studies on schizophrenia and Covid-19 were collected. We used Review Manager 5.4 and Comprehensive Meta-Analysis 3 software to conduct statistical analysis.

RESULTS: There were 10 studies with 263,207 Covid-19 patients included in the analysis. Evaluation of the data gathered yielded an association between schizophrenia and increased mortality from Covid-19 (RR 2.22; 95%CI: 1.54-3.20, p < 0.00001, I² = 82% random-effect model). The increased risk of developing mortality from Covid-19 in patients with schizophrenia was significantly influenced by older age (p = 0.0004) and smoking (p = 0.0048).

CONCLUSIONS: This study proposes that patients with pre-existing schizophrenia are at risk of developing higher Covid-19 mortality. Patients with schizophrenia need special attention and should be prioritized to receive the SARS-CoV-2 vaccine. Registration details: CRD42021293997.

PMID:35182908 | DOI:10.1016/j.genhosppsych.2022.01.010