Category: Statistics

Complement Ther Clin Pract. 2022 Feb 11;47:101548. doi: 10.1016/j.ctcp.2022.101548. Online ahead of print.

ABSTRACT

BACKGROUND: Disordered eating is prevalent among US Military Veterans who have a high incidence of obesity, diabetes, and mental illness. Mindfulness is an evidenced-based intervention for some mental health disorders, is well received by Veterans, and may be useful in treating disordered eating behavior in this population. The aim of this study was to assess and describe Veterans’ experience with MB-SAVOR, a novel mindfulness-based eating program, and determine if it improved their relationship with food and the body.

METHODS: In-depth qualitative interviews were conducted among 16 Veterans completing the program. Interviews were audio recorded, transcribed, and analyzed using constant comparative method, an iterative and inductive process. Rapid assessment process was used to understand their views on program structure. Inferential statistics were conducted to assess outcomes of pre-topost-intervention weight, BMI, and HbA1C, and influences of demographics.

RESULTS: Five themes were identified related to experience: Awareness of Eating Cues, Noticing Eating Behaviors and Patterns, Greater Enjoyment of Food, Dietary Improvements, and Mind Body Connection. Four themes were identified related to program structure: Reasons for Enrollment, Prior Experiences and Comparison with MB-SAVOR, Program Information, Impression, and Barriers, and Improvement Suggestions. Clinical outcomes were decreased weight (p = 0.007, d = 0.82), BMI (p = 0.004, d = 0.9), and HbA1C (p = 0.3) post-intervention.

CONCLUSIONS: These findings contribute to our understanding of the feasibility, safety, and efficacy of MB-SAVOR on improving Veterans’ relationship with food and the body. These data help us understand Veterans’ perspectives and motivations regarding treatment engagement for several diet related problems contributing to obesity and diabetes.

PMID:35183037 | DOI:10.1016/j.ctcp.2022.101548

Mar Pollut Bull. 2022 Feb 16;176:113434. doi: 10.1016/j.marpolbul.2022.113434. Online ahead of print.

ABSTRACT

Exposure risk is assessed based on modeling suitable habitat of large pelagic fish and oil spill scenarios originating at three wells located in the western GM’s deep waters. Since the fate of the oil depends on the oceanographic conditions present during the accident, as well as the magnitude and duration of the spill, which are not known a priori, the scenarios used are a statistical representation of the area in which oil spilled from the well could be found, given all possible outcomes. The ecological vulnerability assessment identified a subset of bony fish with low-medium vulnerability and elasmobranchs with medium-high vulnerability. The oiling probability and exposure risk of both bony fish and elasmobranchs hotspots vary by well analyzed. Thus, these results provide essential information for a risk management plan for the assessed species and others with economic or conservation importance distributed in the GM and worldwide.

PMID:35183025 | DOI:10.1016/j.marpolbul.2022.113434

J Surg Res. 2022 Feb 16;274:196-206. doi: 10.1016/j.jss.2021.12.051. Online ahead of print.

ABSTRACT

BACKGROUND: The relationship between sarcopenia and postoperative outcomes in patients with gastric cancer remains controversial. This study aimed to investigate the impact of sarcopenia on short-term outcomes after surgery for gastric cancer.

METHODS: Patients who underwent surgical treatment for gastric cancer were evaluated in this prospective observational study. Muscle strength, muscle mass, and physical performance were measured before surgery. Diagnosis of sarcopenia was based on the revised European Working Group on Sarcopenia criteria. Postoperative 30-day outcomes, including complications, reoperation, readmission, and operative mortality, were recorded.

RESULTS: Sarcopenia was observed in 31 out of 146 patients (21.2%). The overall complication incidence was 31.5%. The postoperative complication rate was higher in the sarcopenic patients compared to the nonsarcopenic patients (54.8% versus 25.2%, P = 0.003). There was no statistically significant difference in terms of surgical complication rates (25.8% versus 14.8%, P = 0.239), although the sarcopenic group had a significantly higher systemic complication rate (38.7% versus 13%, P = 0.003). No statistically significant difference was observed in terms of major complications (3.2% versus 5.2%, P = 1.000). Muscle strength, muscle mass, and physical performance were not identified as independent factors when tested alone at adjusted multivariable analysis. Sarcopenia (Odds ratio: 2.73, 95% CI 1.02-7.52, P = 0.047) and severe sarcopenia (Odds ratio: 4.44, 95% CI 1.57-13.34, P = 0.006) were identified as independent prognostic factors for postoperative complications.

CONCLUSIONS: Sarcopenia was associated with postoperative complications after gastrectomy for gastric cancer. Severe sarcopenia may serve as a more robust prognostic indicator. The variation in the complication rates between sarcopenic and nonsarcopenic patients was mainly due to difference in systemic complications rather than surgical complications.

PMID:35183030 | DOI:10.1016/j.jss.2021.12.051

EBioMedicine. 2022 Feb 16;77:103883. doi: 10.1016/j.ebiom.2022.103883. Online ahead of print.

ABSTRACT

BACKGROUND: Systemic Lupus Erythematosus (SLE) is a complex and heterogeneous autoimmune disease mediated by quantities of autoantibodies in which anti-double-stranded DNA (anti-dsDNA) antibodies are important. Besides, glycosylation is one of the most commonly post-translational modifications of antibodies. The association of anti-dsDNA antibodies glycosylation and SLE disease activity is still unknown.

METHODS: We enrolled 101 consecutive treatment-naïve SLE patients with positive anti-dsDNA antibodies from the Department of Rheumatology and Immunology at Ruijin Hospital, Shanghai, between 2017 and 2019. Serum samples were used in this study. We analysed the glycosylation of anti-dsDNA IgG and total IgG subclasses according to systemic lupus erythematosus disease activity index (SLEDAI) scores. Statistical analysis and machine learning were performed to assess the correlation between glycosylation of anti-dsDNA IgG and total IgG with disease activity.

FINDINGS: Serum samples from 86 patients could be detected with anti-dsDNA IgG glycopeptide and subclass of IgG glycoform. Cluster analysis showed that glycosylation of anti-dsDNA IgG and total IgG subclasses were different in SLE patients. Fucosylation, galactosylation, and sialylation levels of anti-dsDNA IgG1 were increased with SLEDAI scores (all p<0.05). The results of machine learning showed that all the glycoforms of anti-dsDNA IgG1 had better performance with lower standardised square error (SSE) than that of total IgG1, with anti-dsDNA IgG1 fucosylation level having the lowest SSE (0.009).

INTERPRETATION: Our study indicated that glycosylation of anti-dsDNA IgG was different from that of total IgG and fucosylation of anti-dsDNA IgG1 correlated best with SLE disease activity.

FUNDING: This work is supported by the National Key Research and Development Program of China (2018YFC0910303), National Natural Science Foundation of China (81801592, 82101876), Clinical Research Plan of SHDC (SHDC2020CR4011), Ruijin Hospital Youth Incubation Project (KY2021607) and Shanghai Pujiang Young Rheumatologists Training Program (SPROG202006).

PMID:35182998 | DOI:10.1016/j.ebiom.2022.103883

Leg Med (Tokyo). 2022 Feb 12;56:102044. doi: 10.1016/j.legalmed.2022.102044. Online ahead of print.

ABSTRACT

Allele frequency distributions and statistical forensic parameters of 19 autosomal STR loci in a sample of 535 unrelated healthy Hui individuals from Yunnan province were estimated. A total of 236 alleles at these loci were identified and their corresponding allele frequencies ranged from 0.000935 to 0.527103. Penta E is the most informative in Hui population, whereas TPOX showed the lowest. All of the STR loci reached the Hardy-Weinberg equilibrium after Bonferroni correction. The combined discrimination power and probability of excluding paternity of the 19 STR loci were 0.999 999 999 999 999 999 999 984 47 16 and 0.999 999 988, respectively. Furthermore, the genetic relationship between the Yunnan Hui population and other 8 different Hui groups or 25 previously investigated groups residing in other areas of China were also estimated based on pairwise genetic distance. These results suggest that the 19 STR loci are highly polymorphic, which is suitable for forensic personal identification and paternity testing.

PMID:35182995 | DOI:10.1016/j.legalmed.2022.102044

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

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