Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.

<p>Comparation of photocatalytic rate constants (k) of PCP with different photocatalyst conditions under 3 kinds of UV irradiation.</p

Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.

<p>Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under different pH values: (a) pH = 1; (b) pH = 4; (c) pH = 10; (d) pH = 13.</p

Schematic experimental system for photo degradation experiments.

<p>Schematic experimental system for photo degradation experiments.</p

Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.

<p>Comparation of phenol production under different photocatalyst conditions after 2 hours UV irradiation and its percentage of initial PCP.</p

Photocatalytic rate constants of PCP degradation with different TiO₂ loading, compared with the one of pure UV irradiation.

<p>Photocatalytic rate constants of PCP degradation with different TiO₂ loading, compared with the one of pure UV irradiation.</p

Percentage remained of PCBs after 2 hours photodegradation under UV light with TiO₂, graphene-TiO₂ and without catalyst: (a) congener 8; (b) congener 28; (c) congener 30; (d) congener 31; (e) congener 33; (f) congener 74.

<p>Percentage remained of PCBs after 2 hours photodegradation under UV light with TiO₂, graphene-TiO₂ and without catalyst: (a) congener 8; (b) congener 28; (c) congener 30; (d) congener 31; (e) congener 33; (f) congener 74.</p

Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under: (a) 9 W UV light; (b) 300 W UV light.

<p>Photocatalytic degradation of PCP with TiO₂, graphene-TiO₂ and without catalyst under: (a) 9 W UV light; (b) 300 W UV light.</p

Data_Sheet_1_Validation of GLIM criteria on malnutrition in older Chinese inpatients.pdf

ObjectiveMalnutrition is a nutritional disorder and common syndrome that has a high incidence and is easily ignored in hospitalized older patients. It can lead to multiple poor prognoses, such as frailty. Early identification and correct evaluation of possible malnutrition and frailty are essential to improve clinical outcomes in older patients. Therefore, our objective was to explore the applicability and effectiveness of the Global Leadership Initiative on Malnutrition (GLIM) criteria for identifying malnutrition in older patients.MethodsIn total, 223 participants aged ≥60 years were involved. Nutrition was evaluated using the Mini Nutritional Assessment-Full Form (MNA-FF) and GLIM criteria, which adopt a two-step procedure. The first step was to use three different methods for the screening of nutritional risk: the Nutrition Risk Screening 2002, the Mini Nutritional Assessment Short Form (MNA-SF), and the Malnutrition Universal Screening Tool. The second step was to link a combination of at least one phenotypical criterion and one etiological criterion to diagnose malnutrition. The Clinical Frailty Scale was used to assess frailty. Sensitivity, specificity, Youden index, kappa values, and positive and negative predictive values were used to evaluate the validity of the GLIM criteria. Logistic regression models were used to assess whether there was a correlation between malnutrition, as defined by the GLIM criteria, and frailty.ResultsWe found that 32.3–49.8% of our patient sample were at risk of malnutrition based on the GLIM diagnosis and using the three different screening tools; 19.3–27.8% of the patients were malnourished. GLIM criteria with MNA-SF as a diagnostic validation and MNA-FF as a reference showed high consistency (K = 0.629; p ConclusionsThe incidence of GLIM-defined malnutrition was 19.3–27.8% using different screening tools. The consistency between the GLIM criteria using the MNA-SF and the MNA methods was high. Malnutrition, as diagnosed by the GLIM criteria with MNA-SF, was significantly correlated with frailty. GLIM criteria with MNA-SF may be a more reliable malnutrition assessment process in older inpatients.</p

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed <i>t</i>‑SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue