Category: Statistics

Vet Surg. 2021 Feb 24. doi: 10.1111/vsu.13594. Online ahead of print.

ABSTRACT

OBJECTIVE: To describe medium-term functional outcome after nail osteosynthesis in feline traumatology and report clinically relevant recommendations for I-Loc angle-stable interlocking nail use in cats.

STUDY DESIGN: Prospective clinical study.

SAMPLE POPULATION: Client-owned cats (n = 29).

METHODS: Consecutive cases with femoral, tibial, or humeral fractures were included. Outcome measures included fracture and surgical procedure description, limb alignment, nail size vs body weight (BW), percentage of nail medullary canal (MC) fill, time to limb function at clinical union (CU), and complications. Descriptive statistics were reported and compared with historical data.

RESULTS: Bone distribution was 53.3% femora, 30% tibiae, and 16.7% humeri. There were six epimetaphyseal and 24 diaphyseal fractures. Overall, 67% of fractures were comminuted. Open reduction and minimally invasive techniques were used in 73% and 27% of cases, respectively. Seventeen I-Loc 3 (cat mean BW 4.4 ± 2.2 kg) and 13 I-Loc 4 (cat mean BW 5.2 ± 1.2 kg) nails were placed with mean MC fill of ≤50%. Average time to CU was 7.2 weeks. At CU, lameness had resolved or was mild in every cat, and all cats ultimately regained full limb function. No major complications were encountered.

CONCLUSION: Because of improved CU times, excellent functional outcomes, and low complication rate, our results provide evidence that I-Loc nails are safe and effective for feline traumatology.

CLINICAL SIGNIFICANCE: The I-Loc may be advantageous for fixation of epimetaphyseal fractures. Because of feline bone specific dimensional constraints, I-Loc 3 is likely appropriate for all feline humeri and most tibiae, while I-Loc 4 is well sized for feline femora.

PMID:33625791 | DOI:10.1111/vsu.13594

J Magn Reson Imaging. 2021 Feb 24. doi: 10.1002/jmri.27542. Online ahead of print.

ABSTRACT

BACKGROUND: Clinical examination and lymphoscintigraphy are the current standard for investigating lymphatic function. Magnetic resonance imaging (MRI) facilitates three-dimensional (3D), nonionizing imaging of the lymphatic vasculature, including functional assessments of lymphatic flow, and may improve diagnosis and treatment planning in disease states such as lymphedema.

PURPOSE: To summarize the role of MRI as a noninvasive technique to assess lymphatic drainage and highlight areas in need of further study.

STUDY TYPE: Systematic review.

POPULATION: In October 2019, a systematic literature search (PubMed) was performed to identify articles on magnetic resonance lymphangiography (MRL).

FIELD STRENGTH/SEQUENCE: No field strength or sequence restrictions.

ASSESSMENT: Article quality assessment was conducted using a bespoke protocol, designed with heavy reliance on the National Institutes of Health quality assessment tool for case series studies and Downs and Blacks quality checklist for health care intervention studies.

STATISTICAL TESTS: The results of the original research articles are summarized.

RESULTS: From 612 identified articles, 43 articles were included and their protocols and results summarized. Field strength was 1.5 or 3.0 T in all studies, with 25/43 (58%) employing 3.0 T imaging. Most commonly, imaging of the peripheries, upper and lower limbs including the pelvis (32/43, 74%), and the trunk (10/43, 23%) is performed, including two studies covering both regions. Imaging protocols were heterogenous; however, T₂ -weighted and contrast-enhanced T₁ -weighted images are routinely acquired and demonstrate the lymphatic vasculature. Edema, vessel, quantity and morphology, and contrast uptake characteristics are commonly reported indicators of lymphatic dysfunction.

DATA CONCLUSION: MRL is uniquely placed to yield large field of view, qualitative and quantitative, 3D imaging of the lymphatic vasculature. Despite study heterogeneity, consensus is emerging regarding MRL protocol design. MRL has the potential to dramatically improve understanding of the lymphatics and detect disease, but further optimization, and research into the influence of study protocol differences, is required before this is fully realized.

LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

PMID:33625795 | DOI:10.1002/jmri.27542

J Magn Reson Imaging. 2021 Feb 24. doi: 10.1002/jmri.27568. Online ahead of print.

ABSTRACT

BACKGROUND: Longitudinal magnetic resonance imaging (MRI) studies have become increasingly important to assess the changes in brain morphology during normal aging and neurodegenerative disorders. However, the reliability of longitudinal morphometric changes has not been fully evaluated.

PURPOSE: To examine the reliability of longitudinal (2-year) changes in brain morphology determined by longitudinal voxel-based morphometry (VBM) in healthy elderly subjects, patients with mild cognitive impairment (MCI), and patients with Alzheimer’s disease (AD).

STUDY TYPE: Retrospective analysis.

SUBJECTS: Twenty-four healthy elderly subjects, 28 MCI patients, and 16 AD patients.

FIELD STRENGTH/SEQUENCE: A 1.5 T, magnetization-prepared rapid gradient-echo.

ASSESSMENT: Longitudinal (2-year) changes in gray matter volume determined by longitudinal VBM processing, and visual assessment of image quality.

STATISTICAL TESTS: Intraclass correlation coefficient (ICC) and Kruskal-Wallis test.

RESULTS: The ICC maps differed among the three groups. The mean ICC was 0.81 overall (0.86 for healthy elderly subjects, 0.75 for MCI patients, and 0.76 for AD patients). The reliability was good to excellent (ICC, 0.60-1.00) for 92% of voxels (99% for healthy elderly subjects, 83% for MCI patients, and 83% for AD patients). The image quality differed significantly among the three groups (P < 0.05).

DATA CONCLUSION: These results indicate that the reliability of longitudinal gray matter volume changes by VBM is good to excellent for most voxels. However, reliability may be affected by the disease, possibly due to differences in head motion during imaging. Evidence Level 3 Technical Efficacy Stage 1.

PMID:33625755 | DOI:10.1002/jmri.27568

Diabetes Obes Metab. 2021 Feb 24. doi: 10.1111/dom.14363. Online ahead of print.

ABSTRACT

AIMS: To explore early effects of dapagliflozin on myocardial function and metabolism in patients with type 2 diabetes without heart failure.

MATERIALS AND METHODS: Patients with type 2 diabetes on metformin treatment were randomized to double-blind 6-week placebo or dapagliflozin 10 mg daily treatment. Investigations included cardiac function and structure with myocardial resonance imaging (MRI); cardiac oxygen consumption, perfusion and efficiency with [¹¹ C]-acetate positron emission tomography (PET); and cardiac and hepatic fatty acid uptake with [¹⁸ F]-FTHA PET, analyzed by ANCOVA as least square means with 95% confidence intervals.

RESULTS: Evaluable patients (placebo: n = 24, dapagliflozin: n = 25; 53% males) had mean age 64.4 years, BMI 30.2 kg/m² , and HbA1c 6.7%. Body weight and HbA1c were significantly decreased by dapagliflozin vs placebo. Dapagliflozin had no effect on myocardial efficiency, but external left ventricular (LV) work -0.095 (-0.145, -0.043) J/g/min, and LV oxygen consumption were significantly reduced -0.30 (-0.49, -0.12) J/g/min by dapagliflozin, but changes were not statistically significant vs changes in placebo group. Change in left atrial maximal volume with dapagliflozin vs placebo was -3.19 (-6.32, -0.07) mL/m² , p = 0.056. Peak global radial strain decreased with dapagliflozin vs placebo, -3.92 (-7.57, -0.28) %, p = 0.035; peak global longitudinal and circumferential strains were unchanged. Hepatic fatty acid uptake was increased by dapagliflozin vs placebo, 0.024 (0.004, 0.044) μmol/g/min, p = 0.018, while cardiac uptake was unchanged.

CONCLUSIONS: This exploratory study indicates reduced heart work but limited effects on myocardial function, efficiency, and cardiac fatty acid uptake, while hepatic fatty acid uptake increased, after 6 weeks treatment with dapagliflozin.

CLINICAL TRIAL REGISTRATION: NCT03387683. This article is protected by copyright. All rights reserved.

PMID:33625777 | DOI:10.1111/dom.14363

Mol Divers. 2021 Feb 24. doi: 10.1007/s11030-021-10181-y. Online ahead of print.

ABSTRACT

Histone deacetylases (HDACs) were highlighted as a novel category of anticancer targets. Several HDACs inhibitors were approved for therapeutic use in cancer treatment. Comparatively, receptor-dependent 4D-QSAR, LQTA-QSAR, is a new approach which generates conformational ensemble profiles of compounds by molecular dynamics simulations at binding site of enzyme. This work describes a receptor-dependent 4D-QSAR studies on hydroxamate-based HDACs inhibitors. The 4D-QSAR model was generated by multiple linear regression method of QSARINS. Leave-N-out cross-validation (LNO) and Y-randomization were performed to analysis of the independent test set and to verify the robustness of the model. Best 4D-QSAR model showed the following statistics: R² = 0.8117, Q²_LOO = 0.6881, Q²_LNO = 0.6830, R²_Pred = 0.884. The results may be used for further virtual screening and design for novel HDACs inhibitors. The receptor dependent 4D-QSAR model was developed for the hydroxamate derivatives as HDAC inhibitors by making use of molecular dynamics simulation to obtain conformational ensemble profile for each compound. The multiple linear regression method was used to generate 4D-QSAR model with the suitable predictive ability and the excellent statistical parameters.

PMID:33625673 | DOI:10.1007/s11030-021-10181-y

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12792-2. Online ahead of print.

ABSTRACT

Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.

PMID:33625698 | DOI:10.1007/s11356-021-12792-2

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12894-x. Online ahead of print.

ABSTRACT

The last mile connectivity through public transport is a challenging task in India. However, according to the Society of Indian Automotive Manufacturers (SIAM) statistics, three-wheelers sales increased by 10.27% in the year financial year of 2018-2019 compared to the previous financial year. In this growth, it is recorded that the passenger carrier three-wheeler sales increased by 10.62% and goods carrier three-wheeler grew by 8.75% in the year 2019 compared to the previous year. The existing design consideration in the three-wheeler development shows poor performance in the real-world scenario because the three-wheeler’s open drive compartment creates more aerodynamic drag to the vehicle. This increases the amount of energy consumption to achieve the same amount of range (km/L). Three-wheeler’s extra energy consumption will directly increase the amount of exhaust emission in internal combustion engines and electrical energy consumption in electric vehicles. The present paper attempts in designing a solar-powered electrical auto-rickshaw for rural transportation. The paper aims to obtain an optimal solar module placement angle and analyzes the solar-powered electrical auto-rickshaw performance by incorporating the National Advisory Committee for Aeronautics (NACA) aerodynamic design principles. The optimal solar module placement angle is identified by analyzing the various configurations like front alone tilt at 16°-degree, rear alone tilt at the 5°-degree and combined front at 16°-degree, and rear at 5°-degree to reduce the aerodynamic drag effect. The paper also aims to identify the effect of the optimal angle on vehicle speed, and solar power generation to enhance the performance and energy efficiency for achieving environmentally sustainable transportation.

PMID:33625701 | DOI:10.1007/s11356-021-12894-x

Environ Sci Pollut Res Int. 2021 Feb 24. doi: 10.1007/s11356-021-12661-y. Online ahead of print.

ABSTRACT

This study analyses the long-run effects of economic growth, energy consumption and financial development on carbon dioxide (CO₂) emissions in Turkey using annual time series data for the period 1965-2018. This research investigates the relationship between the variables using a RALS-EG (residual augmented least squares-Engle and Granger) cointegration test procedure developed by Lee et al. Stud Nonlinear Dyn Econ 19:397-413, (2015). In addition, this study uses a bootstrap causality analysis developed by Hacker and Hatemi-J J Econ Stud 39:144-160, (2012) to specify the causal relationship between the series. RALS cointegration test results show a long-run relationship between CO₂ emissions and economic growth, energy consumption and financial development. According to a dynamic ordinary least squares estimation, economic growth has a negative and statistically significant effect on CO₂ emissions, whereas energy consumption and financial development have positive and statistically significant effects on CO₂ emissions in the long run. In particular, energy consumption is the most effective parameter of environmental pollution in Turkey. However, the causality test results indicate a unidirectional causal relationship from financial development to CO₂ emissions, economic growth and energy consumption. Increasing the investment in renewable energy sources will be an effective policy tool to improve the environmental quality in Turkey.

PMID:33625708 | DOI:10.1007/s11356-021-12661-y

Dig Dis Sci. 2021 Feb 24. doi: 10.1007/s10620-021-06872-z. Online ahead of print.

ABSTRACT

BACKGROUND: Famotidine was reported to potentially provide benefits to Coronavirus Disease 2019 (COVID-19) patients. However, it remains controversial whether it is effective in treating COVID-19.

AIMS: This study aimed to explore whether famotidine use is associated with reduced risk of the severity, death, and intubation for COVID-19 patients.

METHODS: This study was registered on International Prospective Register of Systematic Reviews (PROSPERO) (ID: CRD42020213536). A comprehensive search was performed to identify relevant studies up to October 2020. I-squared statistic and Q-test were utilized to assess the heterogeneity. Pooled risk ratios (RR) and 95% confidence intervals (CI) were calculated through the random effects or fixed effects model according to the heterogeneity. Subgroup analyses, sensitivity analysis, and publication bias assessment were also conducted.

RESULTS: Five studies including 36,635 subjects were included. We found that famotidine use was associated with a statistically non-significant reduced risk of progression to severe disease, death, and intubation for Coronavirus Disease 2019 (COVID-19) patients (pooled RR was 0.82, 95% CI = 0.52-1.30, P = 0.40).

CONCLUSION: Famotidine has no significant protective effect in reducing the risk of developing serious illness, death, and intubation for COVID-19 patients. More original studies are needed to further clarify whether it is associated with reduced risk of the severity, death, and intubation for COVID-19 patients.

PMID:33625613 | DOI:10.1007/s10620-021-06872-z

Int J Comput Assist Radiol Surg. 2021 Feb 24. doi: 10.1007/s11548-021-02321-4. Online ahead of print.

ABSTRACT

PURPOSE: Dental radiography represents 13% of all radiological diagnostic imaging. Eliminating the need for manual classification of digital intraoral radiographs could be especially impactful in terms of time savings and metadata quality. However, automating the task can be challenging due to the limited variation and possible overlap of the depicted anatomy. This study attempted to use neural networks to automate the classification of anatomical regions in intraoral radiographs among 22 unique anatomical classes.

METHODS: Thirty-six literature-based neural network models were systematically developed and trained with full supervision and three different data augmentation strategies. Only libre software and limited computational resources were utilized. The training and validation datasets consisted of 15,254 intraoral periapical and bite-wing radiographs, previously obtained for diagnostic purposes. All models were then comparatively evaluated on a separate dataset as regards their classification performance. Top-1 accuracy, area-under-the-curve and F1-score were used as performance metrics. Pairwise comparisons were performed among all models with Mc Nemar’s test.

RESULTS: Cochran’s Q test indicated a statistically significant difference in classification performance across all models (p < 0.001). Post hoc analysis showed that while most models performed adequately on the task, advanced architectures used in deep learning such as VGG16, MobilenetV2 and InceptionResnetV2 were more robust to image distortions than those in the baseline group (MLPs, 3-block convolutional models). Advanced models exhibited classification accuracy ranging from 81 to 89%, F1-score between 0.71 and 0.86 and AUC of 0.86 to 0.94.

CONCLUSIONS: According to our findings, automated classification of anatomical classes in digital intraoral radiographs is feasible with an expected top-1 classification accuracy of almost 90%, even for images with significant distortions or overlapping anatomy. Model architecture, data augmentation strategies, the use of pooling and normalization layers as well as model capacity were identified as the factors most contributing to classification performance.

PMID:33625664 | DOI:10.1007/s11548-021-02321-4