Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the differences in hippocampal subfield volumes and structural covariance network properties among patients with mild cognitive impairment (MCI) exhibiting different cognitive outcomes and normal controls (NCs), and to further evaluate the predictive value of these imaging indicators for cognitive deterioration in MCI patients.Methods:A total of 43 NCs, 65 stable MCI (sMCI), and 26 progressive MCI (pMCI) patients enrolled in the Alzheimer′s Disease Neuroimaging Initiative (ADNI) database between December 2012 and May 2016 were included in this study. Baseline demographic information and T 1-weighted magnetic resonance imaging scans were collected. Hippocampal subfield volumes were extracted using freesurfer software, and structural covariance networks of hippocampal subfields were constructed. Multivariate analysis of covariance was used to compare hippocampal subfield volumes among the 3 groups. A general linear model was applied to examine group differences in hippocampal subfield structural covariance network properties. Least absolute shrinkage and selection operator (LASSO)-Logistic regression was employed to identify imaging predictors associated with conversion to Alzheimer′s disease (AD), based on which structural, network-based, and combined predictive models were constructed. Model discrimination was evaluated using the area under the curve (AUC); internal validation was performed using Bootstrap resampling; model calibration was assessed with the Hosmer-Lemeshow test; and clinical utility was evaluated through decision curve analysis. Results:Significant differences in hippocampal subfield volumes (mm3) were observed among the 3 groups (all P<0.05, Bonferroni-corrected). Specifically, left parasubiculum (65.58±13.30, 61.96±17.56, 49.56±11.82, F=9.900), right parasubiculum (65.92±15.21, 59.45±16.65, 47.69±15.48, F=11.612), left presubiculum (277.09±39.85, 258.15±44.86, 224.05±45.05, F=14.513), right presubiculum (262.85±40.43, 247.41±43.27, 209.97±46.11, F=14.500), left subiculum (399.66±32.19, 374.25±55.83, 306.12±51.62, F=32.923), right subiculum (417.93±48.92, 376.59±51.01, 316.82±70.22, F=28.764), left cornu ammonis 1 (CA1) (592.10±83.87, 561.96±94.72, 490.06±86.89, F=13.352), right CA1 (632.15±100.09, 601.24±88.88, 531.05±110.29, F=10.579), left CA3 (191.58±30.08, 180.47±34.66, 155.08±37.82, F=12.182), right CA3 (210.42±28.92, 203.84±34.80, 176.69±41.47, F=9.597), left CA4 (224.61±28.94, 210.49±35.04, 183.98±36.89, F=16.521), right CA4 (238.49±28.14, 227.43±30.65, 200.23±42.74, F=13.702), left granule cell-molecular layer-dentate gyrus (GC-ML-DG) (259.96±36.76, 239.42±41.17, 207.61±41.84, F=19.831), right GC-ML-DG (273.98±35.12, 258.79±36.82, 227.81±49.07, F=14.204), left molecular layer (505.62±66.16, 468.58±75.17, 402.68±75.47, F=22.293), right molecular layer (527.39±72.39, 493.14±70.39, 423.81±88.09, F=19.588), left hippocampal amygdala transition area (HATA) (54.91±9.99, 49.52±9.93, 43.27±9.59, F=13.571), right HATA (58.43±9.83, 54.55±10.80, 47.12±12.54, F=10.037), left fimbria (69.94±25.04, 56.63±23.74, 40.58±19.83, F=14.846), right fimbria (68.61±26.24, 53.95±23.16, 45.25±17.04, F=10.424), left hippocampal tail (488.37±83.44, 463.54±80.33, 393.83±77.73, F=13.570), and right hippocampal tail (519.78±80.22, 498.84±81.68, 419.75±93.29, F=14.339) all showed significant group differences. Significant group differences were also observed in small-worldness metric γ (0.51±0.10, 0.51±0.08, 0.62±0.14, F=9.317), small-worldness metric λ (0.39±0.02, 0.39±0.02, 0.43±0.04, F=9.925), global efficiency (0.19±0.01, 0.20±0.01, 0.18±0.01, F=3.189), local efficiency (0.26±0.02, 0.26±0.01, 0.27±0.01, F=3.068), clustering coefficient (0.23±0.01, 0.23±0.01, 0.24±0.02, F=4.274), and characteristic path length (0.73±0.06, 0.72±0.06, 0.76±0.07, F=4.477) of the hippocampal subfield structural covariance network (all P<0.05). Specifically, the pMCI group exhibited higher γ ( t=3.773, P<0.001), λ ( t=4.060, P<0.001), local efficiency ( t=2.445, P=0.047), and clustering coefficient ( t=2.849, P=0.015) than the NCs group, and higher γ ( t=4.074, P<0.001), λ ( t=4.068, P<0.001), and characteristic path length ( t=2.986, P=0.010) but lower global efficiency ( t=-2.444, P=0.047) than the sMCI group. The AUC of the structural, network, and combined models based on LASSO-Logistic regression was 0.837, 0.861, and 0.899, respectively. After internal validation, the corrected AUC was 0.835, 0.855, and 0.889, respectively. All models demonstrated good calibration ( P>0.05), and decision curve analysis indicated favorable clinical net benefit across models. Conclusions:Both sMCI and pMCI patients exhibit widespread hippocampal subfield atrophy and altered global properties of hippocampal subfield structural covariance networks compared to NCs. The models constructed based on hippocampal subfield volumes and structural covariance networks show strong potential for predicting cognitive decline in MCI patients.

Objective:To investigate the alterations of individual metabolic brain network properties in patients with mild cognitive impairment (MCI) and their correlations with cognitive function.Methods:One hundred and five participants from Alzheimer's Disease Neuroimaging Initiative (ADNI) database enrolled from March 2012 to February 2016 were chosen, including 61 MCI patients and 44 normal controls (NC). Cognitive assessments, including mini-mental state examination (MMSE), auditory verbal learning test (AVLT), trail making test (TMT), and semantic verbal fluency (SVF) score, were performed in both groups; differences of above scores and clinical data between the participants from the two groups were compared. T1-weighted imaging and fluorodeoxyglucose positron emission tomography (FDG-PET) images were collected in both groups; individual metabolic brain networks were constructed based on differences in effect sizes between brain regions and network properties were calculated. Spatial correlation analysis was used to compare the correlations of metabolic brain networks at the individual and group levels. General linear model was employed to compare the differences in network properties between the two groups. Partial correlation analysis was used to examine the correlations of differential network properties with cognitive function in MCI patients. A support vector machine (SVM) classification model was constructed based on individual metabolic brain network properties, and receiver operating characteristic (ROC) curve was used to explore the diagnostic value of this SVM classification model in MCI.Results:(1) Compared with the NC group, the MCI group had significantly lower MMSE and AVLT-immediate recall scores, and longer TMT-A completion time ( P<0.05). (2) Spatial correlation analysis revealed a positive correlation between individual metabolic brain networks and group-level metabolic brain networks in patients of the MCI group ( r=0.825, P<0.001). No significant differences in global network properties were noted between the two groups ( P>0.05). Compared with the NC group, the MCI group significantly decreased degree centrality in the left A8vl, right A39c, and right V5/MT+ regions, increased degree centrality in the left anterior cuneus, decreased nodal efficiency in the left A8vl, right V5/MT+, and right caudal hippocampus regions, increased nodal shortest path length and nodal clustering coefficient in the left A8vl region ( P<0.05). (3) The degree centrality at the A8vl of ventral part of the left middle frontal gyrus and nodal efficiency in right caudal hippocampus region were positively correlated with AVLT-immediate recall scores ( r=0.331, P=0.010; r=0.282, P=0.030), nodal efficiency in the left A8vl region was negatively correlated with TMT-A completion time ( r=-0.470, P<0.001), and nodal efficiency in the left A8vl region was positively correlated with SVF score ( r=0.263, P=0.044). (4) Area under the curve of SVM classification model in diagnosing MCI was 0.880 (95% CI: 0.813-0.945, P<0.001), with an accuracy rate of 0.790. Conclusions:Patients with MCI have alterations in individual metabolic brain network properties, among which the degree centrality and nodal efficiency of some nodes are closely related to cognitive function changes. Models constructed based on individual metabolic brain network properties can help to effectively diagnose MCI.

Objective:To summarize the best evidence for intermittent pneumatic compression devices in preventing venous thromboembolism for trauma patients.Methods:According to "6S" evidence model, computer evidence retrieval was carried out. Guidelines, clinical decisions, evidence summaries, expert consensuses, and systematic reviews regarding intermittent pneumatic compression in preventing of venous thromboembolism for trauma patients were considered. The retrieval time limit was from the establishment of the database to October 31, 2021. Two researchers independently appraised articles, and extracted data for eligible studies.Results:A total of 21 articles were enrolled, including 13 guidelines, 1 clinical decision, 1 evidence summary, 2 expert consensuses, 4 systematic reviews. Totally 27 items of best evidence were summarized from four aspects: pretherapeutic evaluation, contraindications and applicable conditions, therapeutic strategies, training and education.Conclusions:This study summarized the best evidence of using intermittent pneumatic compression devices to prevent venous thromboembolism for trauma patients, which can provide evidence-based practice bases for nurses to implement scientific and effective standardized management of mechanical thromboprophylaxis. It is necessary to select evidence according to clinical practice and patients ′ wishes, so as to improve the effectiveness of using IPC in preventing thrombosis.

Objective:To analyze the health fitness and its influencing factors among urban elderly residents in Lanzhou city.Methods:A multi-stage sampling method was used to survey urban elderly residents in Lanzhou city with self-designed questionnaire from July 17th to August 3rd, 2020. The questionnaire included general information, life habits and Healthy Fitness Measurement Scale Version 1.0 (HFMS V 1.0), a total of 1 124 questionnaires were distributed and 1 124 were collected, including 1 043 valid questionnaires (92.8%). The HFMS V 1.0 was scored with Likert 5-point scale, positive and negative scoring method. The health fitness status of the subjects was divided into low, medium and high levels according to the norms of HFMS V 1.0 for Chinese urban elderly residents. The influencing factors were analyzed by using the chi-square test and ordinal logistic regression.Results:The conversion score of HFMS V 1.0 for the urban elderly residents in Lanzhou city was 61.99±14.20, and the physical fitness score was the lowest (57.84±16.98); of the 1 043 subjects, 332 (31.83%), 360 (34.52%) and 351 (33.65%) subjects were classified with low, medium, and high health fitness levels, respectively. Chronic diseases and poor dietary habits were the risk factors for the health fitness of urban elderly residents in Lanzhou city (both P<0.05); more adequate sun exposure, physical exercise, sufficient sleep, high self-health concern and high frustration quotient were protective factors for health fitness in those subjects (all P<0.05). Conclusion:The overall level of health fitness in urban elderly residents in Lanzhou city is moderate, and chronic disease, dietary habits, sun exposure, physical exercise, length of sleep, self-health concern and frustration quotient are the main influencing factors.

Objective:To rapidly evaluate the level of healthcare resource demand for laboratory testing and prevention and control of corona virus disease 2019 (COVID-19) in different epidemic situation, and prepare for the capacity planning, stockpile distribution, and funding raising for infectious disease epidemic response.Methods:An susceptible, exposed, infectious, removed infectious disease dynamics model with confirmed asymptomatic infection cases and symptomatic hospitalized patients was introduced to simulate different COVID-19 epidemic situation and predict the numbers of hospitalized or isolated patients, and based on the current COVID-19 prevention and control measures in China, the demands of resources for laboratory testing and prevention and control of COVID-19 were evaluated.Results:When community or local transmission or outbreaks occur and total population nucleic acid testing is implemented, the need for human resources is 3.3-89.1 times higher than the reserved, and the current resources of medical personal protective equipment and instruments can meet the need. The surge in asymptomatic infections can also increase the human resource demand for laboratory testing and pose challenge to the prevention and control of the disease. When vaccine protection coverage reach ≥50%, appropriate adjustment of the prevention and control measures can reduce the need for laboratory and human resources.Conclusions:There is a great need in our country to reserve the human resources for laboratory testing and disease prevention and control for the response of the possible epidemic of COVID-19. Challenges to human resources resulted from total population nucleic acid testing and its necessity need to be considered. Conducting non-pharmaceutical interventions and encouraging more people to be vaccinated can mitigate the shock on healthcare resource demand in COVID-19 prevention and control.

Objective To study the inhibitory effect of aspirin on proliferation of human hepatocellular carcinoma HepG2 cell line and its possible mechanismt. Methods MTT assay and plate cloning experiments was used to detect proliferation of human hepatoma HepG2 cells. Effects of aspirin on autophagosomes in HepG2 cells were detected by acridine orange fluorescence staining. The expression of adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) protein in human hepatocellular carcinoma HepG2 cells was detected by Western blot. Results 10 mmol/L concentration of aspirin could inhibit the proliferation of HepG2 cells, but increase the number of autophagosomes of HepG2 cells, increase AMPK expression, decrease mTOR expression. After combination treatemnt with 40 μmol/L autophagy inhibitor chloroquine (CQ), CQ could enhance the inhibitory effect of 10 mmol/L aspirin on proliferation of human hepatoma HepG2 cells. Conclusion Combination treatment with autophagy inhibitor CQ attenuates 10 mmol/L aspirin-induced autophagy thus enhance its anti-HepG2 effect.

Objective To investigate the predictive value of diffusion-weighted (DW) magnetic resonance imaging (MRI) for invasiveness of hilar cholangiocarcinoma (HC).Methods The retrospective casecontrol study was conducted.The clinicopathological data of 65 HC patients who were admitted to the Sun Yat-sen Memorial Hospital from January 2012 to November 2017 were collected.Patients received DW MRI before treatment,and 2 senior imaging doctors analyzed imaging data and measured the apparent diffusion coefficient (ADC) for the primary lesions of HC.Observation indicators:(1) MRI situations of HC;(2) relationship between ADC and clinicopathological factors;(3) receiver operator characteristic (ROC) curve analysis;(4) treatment and follow-up situations.According to patients' conditions,treatment plans were done within 2 weeks after MRI and patients underwent radical resection of HC.Follow-up using telephone interview was performed to detect tumor recurrence up to December 2017.Measurement data with normal distribution were represented as (x)±s,and comparisons between group and among group were respectively analyzed using the t test and one-way ANOVA.Spearman's rank correlation was performed to analyze the relationship between ADC and clinicopathological factors.ROC curves assessed the diagnostic efficiency of ADC.Results (1) MRI situations of HC:MRI and magnetic resonanced cholangio-pancreatography (MRCP) in 65 patients showed varying degrees of soft rattan-like dilations of intrahepatic bile ducts and truncation signs of bile tracts in hepatic port.Of 65 patients,tumors in 23,7 and 35 patients were respectively pedunculated type,polypoid type and infiltrating type.The pedunculated-type lesions of 23 patients presented as low signal on T1WI and slightly high signal on T2WI;after enhanced scans of MRI,pedunculated-type lesions of 7 patients demonstrated moderate homogenous enhancement in 3 patients,ring-like enhancement with internal liquefaction necrosis in 10 patients and moderate heterogeneous enhancement in 10 patients,respectively.The polypoid-type lesions presented as low signal on T1WI and high signal on T2WI,and moderate homogenous enhancement by enhanced scans of MRI.There were varying degrees of bile duct wall thickness and irregular nodules in the infiltrating-type lesions of 35 patients,showing moderate enhancement by enhanced scans of MRI.All the lesions of 65 patients using DW MRI demonstrated restricted diffusion,showing a clear boundary between lesions and normal surrounding bile ducts or liver tissues;heterogeneous enhancement lesions by MRI scans presented as heterogeneously high signal on DWI and heterogeneously low signal on ADC map,and necrotic area of lesions showed low signal on DWI;homogenous enhancement by MRI scans presented as homogenously high signal on DWI and homogenously low signal on ADC map.(2) Relationship between ADC and clinicopathological factors:ADC was respectively (1.382±0.165)× 10-3 mm2/s,(1.343±0.138)× 10-3 mm2/s,(1.291-±0.226)×10-3 mm2/s,(1.111±0.243)×10-3 mm2/s in stage Ⅰ,Ⅱ,Ⅲ and Ⅳ (TNM staging) and (1.441± 0.355) × 10-3 mm2/s,(1.226 ± 0.177) × 10-3 mm2/s,(1.061 ± 0.228) × 10-3 mm2/s in highdifferentiated,moderate-differentiated and low-differentiated tumors (pathological grading) and (1.403±0.176)× 10-3 mm2/s,(1.121±0.238)× 10-3 mm2/s in Ki-67 score ≤ 10％ and ＞ 10％ and (1.115±0.241)× 10-3 mm2/s,(1.347±0.174)× 10-3 mm2/s in HC patients with and without lymph node metastasis,with statistically significant differences in the above indicators (F =4.158,9.866,t =11.607,13.464,P＜0.05).Results of Spearman's rank correlation analysis showed that ADC had a negative correlation with TNM staging,pathological grading and Ki-67 score (r=-0.532,-0.522,-0.409,P＜0.05).(3) ROC curve analysis:using 1.225×10-3 mm2/s as a critical value of ADC,the sensitivity and specificity of ADC in the diagnosis of stage Ⅰ-Ⅱ HC and stage Ⅲ-Ⅳ HC were 70.5％ and 81.0％,and area under ROC curve was 0.705 (95％CI:0.62-0.84,P＜0.05).Using 1.100×10-3 mm2/s as a critical value of ADC,the sensitivity and specificity of ADC in the diagnosis of lowdifferentiated HC and moderate-and high-differentiated HC were 88.2％ and 64.3％,and area under ROC curve was 0.814 [95％ confidence interval (CI):0.69-0.90,P＜0.05].Using 1.243×10-3 mm2/s as a critical value of ADC,the sensitivity and specificity of ADC in the diagnosis of Ki-67 score ≤ 10％ and ＞ 10％ were 66.7％ and 75.0％,and area under ROC curve was 0.783 (95％CI:0.62-0.90,P＜0.05).Using 1.222×10-3 mm2/s as a critical value of ADC,the sensitivity and specificity of ADC in the diagnosis of lymph node metastasis were 91.3％ and 71.4％,and area under ROC curve was 0.873 (95％CI:0.76-0.94,P＜0.05).(4) Treatment and followup situations:65 patients underwent successful radical resection of HC.Thirty-three patients were followed up for 1-24 months.Of 33 patients,5 had tumor recurrence within 6 months postoperatively,including 4 with ADC ＜ 1.100× 10-3 mm2/s,13 had tumor recurrence after 6 months postoperatively,and 15 didn't have tumor recurrence or metastasis,including 1 with ADC ＜ 1.100× 10-3 mm2/s.Conclusions There are different ADC in differentTNM staging,pathological grading,Ki-67 score and with or without lymph node metastasis of HC.ADC of DWMRI can be used as a preoperative imaging predictor for invasiveness of HC.

BACKGROUND:SenseWear Pro Armband (SWA) is a monitoring device of physical activity energy consumption which consists of many kinds of motion sensors, and it has been widely used in the study of physical activity energy consumption in recent years. OBJECTIVE:To summarize the SWA accuracy, software version, time of wear as wel as its application in different populations and at different physical activity levels, for the purpose of providing some practical reference in the future usage of SWA. METHODS:A computer-based search of CNKI (ful-text database), PubMed and Web of Science databases was done for relevant articles published before January 2015 using the keywords of “physical activity, sensewear pro armband, energy expenditure, review” in Chinese and English, respectively. Articles published in core journals or SCI database were preferred. RESULTS AND CONCLUSION:Double labeled water method and indirect measurement method serve as standard criteria, and the relevant study shows that SWA has higher accuracy and can effectively evaluate the daily physical activity energy consumption. But under the special circumstances, there are some differences, such as different people and different exercise intensities. Meanwhile, to determine the appropriate wearing time and software version for data statistics are also one of the factors influencing the accuracy of the final result. SWA can accurately predict physical activity energy consumption, but for different populations and exercise intensities, it is necessary to improve the further calculation formula of SWA.