27 research outputs found

    DataSheet_1_Diagnostic value of cerebrospinal fluid human epididymis protein 4 for leptomeningeal metastasis in lung adenocarcinoma.doc

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    BackgroundThe diagnosis of lung adenocarcinoma (LUAD) leptomeningeal metastasis (LM) remains a clinical challenge. Human epididymis protein 4 (HE4) functions as a novel tumor biomarker for cancers. This study aimed to assess the diagnostic value of cerebrospinal fluid (CSF) HE4, and combined with CEACAM6, for LUAD LM.MethodsThe CSF HE4 protein level was measured in two independent cohorts by electrochemiluminescence. Test cohort included 58 LUAD LM patients, 22 LUAD patients without LM (Wiot-LM), and 68 normal controls. Validation cohort enrolled 50 LUAD LM patients and 40 normal controls, in parallel with Wiot-LM patients without brain metastases (19 Wiot-LM/BrM patients) or with BrM (26 BrM patients). The CSF level of CEA, CA125, CA153, CA199, CA724, NSE and ProGRP of these samples was measured by electrochemiluminescence, whereas the CSF CEACAM6 level was detected by enzyme-linked immunosorbent assay (ELISA). In addition, the serum level of these biomarkers was detected by same method as CSF.ResultsThe level of HE4 or CEACAM6 in CSF samples from LUAD LM patients was significantly higher than those from normal controls and Wiot-LM patients. The HE4 or CEACAM6 level in CSF was higher than that in serum of LM patient. The CSF HE4 or CEACAM6 level for distinguished LM from Wiot-LM showed good performance by receiver-operating characteristic analysis. The better discriminative power for LM was achieved when HE4 was combined with CEACAM6. In addition, the CSF HE4 and CEACAM6 level showed little or no difference between Wiot-LM/BrM and BrM patients, the BrM would not significantly influence the HE4 or CEACAM6 level in CSF. The diagnostic power of CSF CA125, CA153, CA199, CA724, NSE and ProGRP for LUAD LM were not ideal.ConclusionThe combination with HE4 and CEACAM6 has the promising application for the diagnosis of LUAD LM.</p

    Table_1_Diagnostic value of cerebrospinal fluid human epididymis protein 4 for leptomeningeal metastasis in lung adenocarcinoma.xlsx

    No full text
    BackgroundThe diagnosis of lung adenocarcinoma (LUAD) leptomeningeal metastasis (LM) remains a clinical challenge. Human epididymis protein 4 (HE4) functions as a novel tumor biomarker for cancers. This study aimed to assess the diagnostic value of cerebrospinal fluid (CSF) HE4, and combined with CEACAM6, for LUAD LM.MethodsThe CSF HE4 protein level was measured in two independent cohorts by electrochemiluminescence. Test cohort included 58 LUAD LM patients, 22 LUAD patients without LM (Wiot-LM), and 68 normal controls. Validation cohort enrolled 50 LUAD LM patients and 40 normal controls, in parallel with Wiot-LM patients without brain metastases (19 Wiot-LM/BrM patients) or with BrM (26 BrM patients). The CSF level of CEA, CA125, CA153, CA199, CA724, NSE and ProGRP of these samples was measured by electrochemiluminescence, whereas the CSF CEACAM6 level was detected by enzyme-linked immunosorbent assay (ELISA). In addition, the serum level of these biomarkers was detected by same method as CSF.ResultsThe level of HE4 or CEACAM6 in CSF samples from LUAD LM patients was significantly higher than those from normal controls and Wiot-LM patients. The HE4 or CEACAM6 level in CSF was higher than that in serum of LM patient. The CSF HE4 or CEACAM6 level for distinguished LM from Wiot-LM showed good performance by receiver-operating characteristic analysis. The better discriminative power for LM was achieved when HE4 was combined with CEACAM6. In addition, the CSF HE4 and CEACAM6 level showed little or no difference between Wiot-LM/BrM and BrM patients, the BrM would not significantly influence the HE4 or CEACAM6 level in CSF. The diagnostic power of CSF CA125, CA153, CA199, CA724, NSE and ProGRP for LUAD LM were not ideal.ConclusionThe combination with HE4 and CEACAM6 has the promising application for the diagnosis of LUAD LM.</p

    Schedule of enrolment, interventions, and assessments.

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    AI-AntiDelirium: the Artificial Intelligence Assisted Prevention and Management for Delirium, PADIS guideline: the Pain, Agitation, Delirium, Immobility, and Sleep (PADIS) Guidelines.</p

    SPIRIT 2013 checklist.

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    BackgroundDelirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.MethodsThe proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle.DiscussionIf this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.Clinical trial registrationChiCTR1900023711 (Chinese Clinical Trial Registry).</div

    Flowchart of the study.

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    BackgroundDelirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.MethodsThe proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle.DiscussionIf this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.Clinical trial registrationChiCTR1900023711 (Chinese Clinical Trial Registry).</div

    Interventions targeting risk factors.

    No full text
    BackgroundDelirium is a common complication among intensive care unit (ICU) patients that is linked to negative clinical outcomes. However, adherence to the Clinical Practice Guidelines for the Prevention and Management of Pain, Agitation/Sedation, Delirium, Immobility, and Sleep Disruption in Adult Patients in the ICU (PADIS guidelines), which recommend the use of the ABCDEF bundle, is sub-optimal in routine clinical care. To address this issue, AI-AntiDelirium, a nurse-led artificial intelligence-assisted prevention and management tool for delirium, was developed by our research team. Our pilot study yielded positive findings regarding the use of AI-AntiDelirium in preventing patient ICU delirium and improving activities of daily living and increasing intervention adherence by health care staff.MethodsThe proposed large-scale pragmatic, open-label, parallel-group, cluster randomized controlled study will assess the impact of AI-AntiDelirium on the incidence of ICU delirium and delirium-related outcomes. Six ICUs in two tertiary hospitals in China will be randomized in a 1:1 ratio to an AI-AntiDelirium or a PADIS guidelines group. A target sample size of 1,452 ICU patients aged 50 years and older treated in the ICU for at least 24 hours will be included. The primary outcome evaluated will be the incidence of ICU delirium and the secondary outcomes will be the duration of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, patient activities of daily living, and ICU nurse adherence to the ABCDEF bundle.DiscussionIf this large-scale trial provides evidence of the effectiveness of AI-AntiDelirium, an artificial intelligence-assisted system tool, in decreasing the incidence of ICU delirium, length of ICU and hospital stay, ICU and in-hospital mortality rates, patient cognitive function, and patient activities of daily living while increasing ICU nurse adherence to the ABCDEF bundle, it will have a profound impact on the management of ICU delirium in both research and clinical practice.Clinical trial registrationChiCTR1900023711 (Chinese Clinical Trial Registry).</div

    Supplementary data from Metal-free C<sub>60</sub>/CNTs/g-C<sub>3</sub>N<sub>4</sub> ternary heterostructures: synthesis and enhanced visible-light-driven photocatalytic performance

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    A metal-free C<sub>60</sub>/CNTs/g-C<sub>3</sub>N<sub>4</sub> nanoheterostructure with excellent visible-light photocatalysis for rhodamine B (Rh B) degradation has been reported. Via a convenient low-temperature solution-phase method, g-C<sub>3</sub>N<sub>4</sub> nanosheets can serve as substrate for dispersion of C<sub>60</sub>/CNTs. The loading of C<sub>60</sub>/CNTs onto g-C<sub>3</sub>N<sub>4</sub> nanosheets surfaces significantly enhanced visible-light-driven photocatalytic activity of g-C<sub>3</sub>N<sub>4</sub> catalyst, for oxidation of organic pollutant (Rh B, 100%). Excellent photocatalytic properties of C<sub>60</sub>/CNTs/g-C<sub>3</sub>N<sub>4</sub> can be predominantly attributed to the intimate interfacial contact among constructing compounds, increased specific surface area and enhanced light adsorption efficiency resulted from C<sub>60</sub>/CNTs carbon materials. Particularly, the synergistic heterostructure interaction remarkably hinders the electrons–holes pairs recombination, giving rise to significantly enhanced photocatalytic performance of C<sub>60</sub>/CNTs/g-C<sub>3</sub>N<sub>4</sub> in comparison with other counterparts
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