79 research outputs found

    Developing composite indicators for ecological water quality assessment based on network interactions and expert judgment

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    Increasingly, composite indicators and multi-criteria approaches are applied in environmental assessment and decision-making, including the EU Water Framework Directive. For example, integrated evaluation of aquatic ecosystem conditions and functioning usually involves a group of criteria, such as biological organisms and communities, physicochemical and hydromorphological variables, which are measured individually and combined by a weighted linear function into an overall 'score’. We argue that the network interactions of evaluation components are useful information for expert judgments, which have not been sufficiently considered in existing multi-criteria combination strategies in environmental assessment and management. Built upon the Analytic Network Process and demonstrated with the Chishui River Basin in China, this paper introduces a network-based expert judgment approach to construct ecological water quality indicators, and to determine and adjust their variable weight settings with information of interaction networks. This approach has potential to construct composite indicators for a broad environmental context.</p

    Early-onset Alzheimer’s disease with depression as the first symptom: a case report with literature review

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    BackgroundAlzheimer’s disease is a common neurodegenerative disease, and patients with early-onset Alzheimer’s disease (onset age &lt; 65 years) often have atypical symptoms, which are easily misdiagnosed and missed. Multimodality neuroimaging has become an important diagnostic and follow-up method for AD with its non-invasive and quantitative advantages.Case presentationWe report a case of a 59-year-old female with a diagnosis of depression at the age of 50 after a 46-year-old onset and a 9-year follow-up observation, who developed cognitive dysfunction manifested by memory loss and disorientation at the age of 53, and eventually developed dementia. Combined with neuropsychological scales (MMSE and MOCA scores decreased year by year and finally reached the dementia criteria) and the application of multimodal imaging. MRI showed that the hippocampus atrophied year by year and the cerebral cortex was extensively atrophied. 18F-FDG PET image showed hypometabolism in right parietal lobes, bilateral frontal lobes, bilateral joint parieto-temporal areas, and bilateral posterior cingulate glucose metabolism. The 18F-AV45 PET image showed the diagnosis of early-onset Alzheimer’s disease was confirmed by the presence of Aβ deposits in the cerebral cortex.ConclusionEarly-onset Alzheimer’s disease, which starts with depression, often has atypical symptoms and is prone to misdiagnosis. The combination of neuropsychological scales and neuroimaging examinations are good screening tools that can better assist in the early diagnosis of Alzheimer’s disease.Graphical Abstrac

    WenTong HuoXue Cream Can Inhibit the Reduction of the Pain-Related Molecule PLC- β

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    WenTong HuoXue Cream (WTHX-Cream) has been shown to effectively alleviate clinical symptoms of diabetic peripheral neuropathy (DPN). This study investigated the gene and protein expression of the pain-related molecule PLC-β3 in the dorsal root ganglion (DRG) of DPN rats. 88 specific pathogen-free male Wistar rats were randomly divided into placebo (10 rats) and DPN model (78 rats) groups, and the 78 model rats were used to establish the DPN model by intraperitoneal injection of streptozotocin and were then fed a high-fat diet for 8 weeks. These rats were randomly divided into the model group, the high-, medium-, and low-dose WTHX-Cream + metformin groups, the metformin group, the capsaicin cream group, and the capsaicin cream + metformin group. After 4 weeks of continuous drug administration, the blood glucose, body weight, behavioral indexes, and sciatic nerve conduction velocity were measured. The pathological structure of the DRG and the sciatic nerve were observed. PLC-β3 mRNA and protein levels in the DRG of rats were measured. Compared with the model group, the high-dose WTHX-Cream group showed increased sciatic nerve conduction velocity, improved sciatic nerve morphological changes, and increased expression of PLC-β3 mRNA and protein in the DRG. This study showed that WTHX-Cream improves hyperalgesia symptoms of DPN by inhibiting the reduction of PLC-β3 mRNA and protein expression in the diabetic DRG of DPN rats

    Effects of phonon interference through long range interatomic bonds on thermal interface conductance

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    We investigate the role of two-path destructive phonon interference induced by interatomic bonds beyond the nearest neighbor in the thermal conductance of a silicon-germanium-like metasurface. Controlled by the ratio between the second and first nearest-neighbor harmonic force constants, the thermal conductance across a germanium atomic plane in the silicon lattice exhibits a trend switch induced by the destructive interference of the nearest-neighbor phonon path with a direct path bypassing the defect atoms. We show that bypassing of the heavy isotope impurity is crucial to the realization of the local minimum in the thermal conductance. We highlight the effect of the second phonon path on the distinct behaviors of the dependence of the thermal conductance on the impurity mass ratio. All our conclusions are confirmed both by Green’s Function calculations for the equivalent quasi-1D lattice models and by molecular dynamics simulations

    Causal effect of PM1 on morbidity of cause-specific respiratory diseases based on a negative control exposure

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    Background: Extensive studies have linked PM2.5 and PM10 with respiratory diseases (RD). However, few is known about causal association between PM1 and morbidity of RD. We aimed to assess the causal effects of PM1 on cause-specific RD. Methods: Hospital admission data were obtained for RD during 2014 and 2019 in Beijing, China. Negative control exposure and extreme gradient boosting with SHapley Additive exPlanation was used to explore the causality and contribution between PM1 and RD. Stratified analysis by gender, age, and season was conducted. Results: A total of 1,183,591 admissions for RD were recorded. Per interquartile range (28 μg/m3) uptick in concentration of PM1 corresponded to a 3.08% [95% confidence interval (CI): 1.66%–4.52%] increment in morbidity of total RD. And that was 4.47% (95% CI: 2.46%–6.52%) and 0.15% (95% CI: 1.44%-1.78%), for COPD and asthma, respectively. Significantly positive causal associations were observed for PM1 with total RD and COPD. Females and the elderly had higher effects on total RD, COPD, and asthma only in the warm months (Z = 3.03, P = 0.002; Z = 4.01, P \u3c 0.001; Z = 3.92, P \u3c 0.001; Z = 2.11, P = 0.035; Z = 2.44, P = 0.015). Contribution of PM1 ranked first, second and second for total RD, COPD, and asthma among air pollutants. Conclusion: PM1 was causally associated with increased morbidity of total RD and COPD, but not causally associated with asthma. Females and the elderly were more vulnerable to PM1-associated effects on RD

    MRI radiomics-based interpretable model and nomogram for preoperative prediction of Ki-67 expression status in primary central nervous system lymphoma

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    ObjectivesTo investigate the value of interpretable machine learning model and nomogram based on clinical factors, MRI imaging features, and radiomic features to predict Ki-67 expression in primary central nervous system lymphomas (PCNSL).Materials and methodsMRI images and clinical information of 92 PCNSL patients were retrospectively collected, which were divided into 53 cases in the training set and 39 cases in the external validation set according to different medical centers. A 3D brain tumor segmentation model was trained based on nnU-NetV2, and two prediction models, interpretable Random Forest (RF) incorporating the SHapley Additive exPlanations (SHAP) method and nomogram based on multivariate logistic regression, were proposed for the task of Ki-67 expression status prediction.ResultsThe mean dice Similarity Coefficient (DSC) score of the 3D segmentation model on the validation set was 0.85. On the Ki-67 expression prediction task, the AUC of the interpretable RF model on the validation set was 0.84 (95% CI:0.81, 0.86; p &lt; 0.001), which was a 3% improvement compared to the AUC of the nomogram. The Delong test showed that the z statistic for the difference between the two models was 1.901, corresponding to a p value of 0.057. In addition, SHAP analysis showed that the Rad-Score made a significant contribution to the model decision.ConclusionIn this study, we developed a 3D brain tumor segmentation model and used an interpretable machine learning model and nomogram for preoperative prediction of Ki-67 expression status in PCNSL patients, which improved the prediction of this medical task.Clinical relevance statementKi-67 represents the degree of active cell proliferation and is an important prognostic parameter associated with clinical outcomes. Non-invasive and accurate prediction of Ki-67 expression level preoperatively plays an important role in targeting treatment selection and patient stratification management for PCNSL thereby improving prognosis
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