For the purpose of providing evidence for the treatment of osteoporosis and osteopenia, this study retrospectively identified succinylation‐modified sites and proteins in postmenopausal women, and bioinformatics analysis were performed.From January 2016 to June 2018, a total of 30 postmenopausal women aged from 55 to 70 years old were assigned to three groups: 10 cases with osteoporosis; 10 cases with osteopenia; and 10 cases with normal bone mass. Subsequently, the serum samples were collected from all cases for succinyl‐proteome. Measures comprised label‐free quantitative analysis, succinylation enrichment techniques, the liquid chromatograph–mass spectrometer/mass spectrometer (LC‐MS/MS) methods, and bioinformatics.A total of 113 succinylation sites on 35 proteins were identified based on quantitative information. The variation of the different multiple folds were more than 1.2 times as a significant increase for up‐regulated and less than 1/1.2 times as a significant decrease for down‐regulated. Among the quantified succinylation sites, 66 were up‐regulated and 11 down‐regulated in the Osteopenia/Normal comparison group, 24 were up‐regulated and 44 down‐regulated in the Osteoporosis/Osteopenia comparison group, 45 were up‐regulated and 32 down‐regulated in the Osteoporosis/Normal comparison group. Among the quantified succinylation proteins, 24 were up‐regulated and 7 down‐regulated in the Osteopenia/Normal comparison group, 15 were up‐regulated and 20 down‐regulated in the Osteoporosis/Osteopenia comparison group, 20 were up‐regulated and 17 down‐regulated in the Osteoporosis/Normal comparison group. The percentage of proteins differed in immune response, signaling pathway, proteolysis, lymphocyte, leukocyte, and cell activation. Four differentially expressed proteins  contained quantitative information; they were mediated with receptors, factors, mechanisms, that related to bone metabolism. Hemoglobin subunit alpha was screened for diagnosis of osteopenia.The succinyl‐proteome experimental data indicated that apolipoprotein A‐I, apolipoprotein A‐II, hemoglobin subunit alpha, and haptoglobin were valuable for diagnosis and treatment in postmenopausal women with osteoporosis and osteopenia. Osteoporosis, a bone metabolic disease of multiple causation, is characterized by bone loss. Bone mass continues decline after its peak during the postmenopausal period. The process of bone remodeling is another potential concern because bone mineral density(BMD), bone loss and mineral balance changes in older women, especially in those aged 75 and over, who have higher average annual bone losset al. analyzed the early stage of osteoporosis, identified protein expression differences for the first timeCurrently, proteomics technology has identified how proteomics interact with osteoporosis. Proteomics technology research includes highly dynamic presentation modes of protein functional molecules, physiological and pathological phenomena. In serum samples of osteoporosis patients, Liang et al. (2011) first discovered lysine succinylation, a new type of post‐translational modification of proteins. Subsequently, western blotting analysis was used to identify the lysine succinylated peptides derived from the proteins in vivoIn fact, there are still some problems have not yet been addressed. It is well accepted that post‐translational modification (PTM) is a dynamic and reversible protein chemical modification that be involved in almost all processes of cells and plays an important regulatory role. Proteomic succinylation modification, a notable type of modification, is thought to impact metabolism, epigenetics and immunity. Zhang Proteome analysis have widely applied to the macro level of cells, tissues or organisms in the presence of all proteins and their modes of activity2O was from Thermo Fisher Scientific . A BCA kit was purchased from Beyotime Biotechnology .Trypsin was purchased from Promega . Acetonitrile was from Fisher Chemical . Trifluoroacetic acid, iodoacetamide, dithiothreitol, urea, and formic acid were from Sigma‐Aldrich . HAccording to the diagnostic criteria recommended by the World Health Organization (WHO), the BMD of the lumbar spine was measured by dual‐energy X‐ray absorptiometry : (i) BMD (T score ≥ −1 SD) was considered normal; (ii) BMD (−1 < T score ≤ −2.5 SD) as osteopenia; and (iii) BMD (T score < −2.5 SD) as osteoporosis. A total of 30 postmenopausal women aged from 55 to 70 years old were divided into three groups: Osteoporosis group, 10 cases; Osteopenia group, 10 cases; Normal group, 10 cases. All samples were screened from January 2016 to June 2018 .The exclusion criteria were as follows: (i) patients who did not meet the diagnostic criteria of osteoporosis ; (ii) those who had taken estrogen, steroid hormones, calcitonin, parathyroid hormones, diphosphate, fluoride, vitamin D, anticonvulsants, diuretics, and other drugs that affect bone metabolism) in the latest 6 months; (iii) those with cardiovascular, cerebrovascular, and hematopoietic system and other serious primary diseases; (iv) those with severe liver and kidney dysfunction, and mental illness patients.According to screening proteomics from selected cases, without drug intervention, blood samples were taken from 30 cases once. Three comparison groups were designed to identify the succinylation sites and differentially expressed proteins, and to discover target proteins related to the diagnosis of osteoporosis and osteopenia.g/min, then washed three times on ice using a high intensity ultrasonic processor (Scientz) in lysis buffer . The remaining debris was removed by centrifugation at 12 000 g at 4°C for 10 min. As Osteoporosis/Normal, Osteopenia/Normal, and Osteoporosis/Osteopenia comparison groups, the supernatant was collected and the protein concentration was determined with the BCA kit according to the manufacturer's instructions.Serum was collected in the morning, centrifuged at 4°C for 10 min in 3000 4HCO3 to ensure that the urea concentration was less than 2 M. For the first digestion, trypsin was added at 1:50 trypsin‐to‐protein mass ratio overnight, and for the second digestion at 1:100 trypsin‐to‐protein mass ratio for 4 hWe added 8 M urea to the samples and 5 mM dithiothreitol to reduce the solution at 56°C for 30 min; then the samples were alkylated with 11 mM iodoacetamide for 15 min at room temperature in darkness. Finally, the samples were diluted by NHWe prepared high pH reverse‐phase high performance liquid chromatography for tryptic peptides fraction as previously described, using Thermo Betasil C18 column . The operation was as follows: the gradient of peptides fraction was acetonitrile , 60 components were separated over 60 min, and then the peptides were merged into three components dried by vacuum centrifuging for subsequent operations.2O was reused twice; then the bound peptides were eluted from the beads with 0.1% trifluoroacetic acid three times in total. Eventually, the elution was collected and vacuum‐driedThe peptides were dissolved in IP buffer solution , with the pre‐washed succinylated resin ; the clear supernatant was incubated at 4°C overnight and then gently shaken. The IP buffer solution was reused at least four times and HTryptic peptides consisted of solvent A (dissolved by 0.1% formic acid) and solvent B (dissolved by 0.1% formic acid in 98%). LC–MS/MS analysis was performed with the EASY‐nLC 1000 Ultra Performance Liquid Chromatography (UPLC) system. Liquid phase gradient setting: 0–38 min, 6%–26% B; 38–52 min, 26%–36% B; 52–57 min, 36%–80% B; 57–60 min, 80% B, current speed maintained at 350 nL/min.The peptides were separated by an ultra‐high‐performance liquid phase system, subjected by Nano‐spray ionization (NSI) ion source and then analyzed by Orbitrap Fusion  mass spectrometryhttp://www.maxquant.org/)For convenience of reference, incorporated MS/MS data were progressed using Maxquant . At the beginning of annotation, the system converted the protein ID to UniProt ID, then matched the GO ID with UniProt ID, and retrieved the corresponding information from the UniProt‐GOA database. If there was no protein information in the UniProt‐GOA database, based on protein sequences, the InterProScan soft would be used to predict the GO function. Shortly afterwards, the proteins were classified by Biological Process, Cellular Component and Molecular FunctionGene ontology (GO) annotation proteome was derived from the UniProt‐GOA database  in at least one cluster. Immediately after, the screened data matrix was transformed by the function x = −log10 , and then classified by Z‐transformation. The Z‐scores were analyzed by one‐way hierarchical clustering. Figures from the “gplots” R‐package “heatmap.2” function clearly demonstrated the cluster memberships.In terms of the multiple differentially expressed protein classification, cluster analysis offered the chance to authenticate the accuracy of potential connections and differences in specific functionsAn overview of the research process is presented in Fig. P > 0.05). A total of 113 succinylation sites on 35 proteins had quantitative information. The variation of the different multiple folds were more than 1.2 times as a significant increase for up‐regulated and less than 1/1.2 times as a significant decrease for down‐regulated. Among the quantified succinylation sites, 45 were up‐regulated and 32 down‐regulated in the Osteoporosis/Normal comparison group, 66 were up‐regulated and 11 down‐regulated in the Osteopenia/Normal comparison group, 24 were up‐regulated and 44 down‐regulated in the Osteoporosis/Osteopenia comparison group. Among the quantified succinylation proteins, 20 were up‐regulated and 17 down‐regulated in the Osteoporosis/Normal comparison group, 24 were up‐regulated and 7 down‐regulated in the Osteopenia/Normal comparison group, 15 were up‐regulated and 20 down‐regulated in the Osteoporosis/ Osteopenia comparison group .The average age of 30 cases was 63.28 ± 5.78 years, with no significant difference between groups . Table P‐value obtained by Fisher's exact test was transformed into a negative logarithm (−log10), the P‐value obtained by Fisher's exact test was obtained. The larger the converted value, the more significant the enrichment of this function type would be Fig. According to the annotations for all identified proteins containing modified sites and corresponding proteins with differentially modified sites, we analyzed the GO domain to detect whether there was a significant enrichment trend of differential expression in some functional types. For enrichment tests, the We visualized KEGG pathways from different comparison groups with significant enrichment of the corresponding proteins at differentially modified proteins. In Fig. Based on GO enrichment, the cluster analysis thermal map includes Biological Process (A), Cellular Component (B), and Molecular Function (C). Quantitative proteins of three comparison groups  cluster analysis were performed. The Biological Process Fig.  demonstrPseudomonas aeruginosa gave some targets to develop effective antibacterial agentsCurrently, compared with methylation and acetylation, lysine succinylation could induce more changes in protein properties, structure and functionet al. analyzed the succinate enhanced interleukin‐1 beta production during inflammationResults showed that the effective proteins covered the endocrine system, signal transduction, the sensory system, transcription, signal molecules and interaction, significantly enriched membrane transportion. Meanwhile, more approaches are needed to provide robust evidence. Tannahill Osteopenia, a preclinical state of osteoporosis, is characterized by slight loss of bone mass with an annual rate of 0.4%–0.6%. Approximately, this rate accelerates to 1.5%–2.5% in postmenopausal womenet al.Interestingly, in the Osteopenia/Normal comparison group, apolipoprotein A‐I was down‐regulated and apolipoprotein A‐II was up‐regulated, but the concentrations bucked the trend in the other two groups. Apolipoprotein A‐I deficiency increased adipocytes and reduced the production of osteoblasts, resulting in bone loss, bone mass lossvia suppression of nuclear factor‐kappa B pathway and mitogen‐activated protein kinase (MAPK) signaling pathway has been reported, the most noteworthy of the pathways lied in they were mediated by TLRFor the records, haptoglobin was identified in blood samples as a risk factor for postmenopausal osteoporosisApoptosis activity, a process of autonomous death in cells, was considered to be closely related to hormones. Lackness of estrogen in postmenopausal women would give rise to irregular apoptosis activity, ultimately leading to osteoporosis. Estrogen depletion resulted in osteocyte apoptosis, which stimulated osteoclasts, caused bone resorption and lossThere were also some deficiencies in this study. The number of participant proteins and sites were relatively low, besides, the expression levels required further verification. Although we had made use of Motif analysis, yet obtained no data. Establishing a larger samples of clinical trials for further study might credibly elucidate this area of research.In this study, discovery and identification from serum for differential sites, proteins and potential efficacies on bone remodeling provided invaluable evidence that succinyl‐proteome was related to occurrence of postmenopausal osteoporosis. Multiple factors and pathways seemed to be involved in the process, ranging from bone formation to resorption. These results demonstrated from functional analysis promoted apolipoprotein A‐I, apolipoprotein A‐II, hemoglobin subunit alpha, and haptoglobin mediated with TLR, TNF α, IL‐4, IL‐6 and IFN γ during apoptosis mechanism, could be potential avenues for prevention, treatment and monitoring in osteopenia. In the following studies, we will continue to verify the expression of these proteins by western blotting and ELISA. In addition, a larger sample instead of a small would be required to recognize the relationship among other differentially expressed proteins in postmenopausal women with osteoporosis and osteopenia.


There are conflicting researches on the relationship between muscular strength and depression, the most common mental illness. There is no study of relationship between muscular strength and depression using national data from young adults to seniors. For example, there has not been a study done explaining mediating pathways among the influences of handgrip strength on depression. Here, we conducted survey for the association between relative handgrip strength and depression and explain mediated pathways for quality of life.A cross-sectional study was administered to 4298 Korean adult subjects, aged 19–80 years, based on the 6th Korean National Health and Nutrition Examination Survey (KNHANES VI) of 2014. Handgrip strength reported as the average with each hand. The relative handgrip strength is defined as the handgrip strength divided by the body mass index (BMI). We performed analysis for all subjects and age groups . We analyzed the association using multivariate linear regression and logistic regression. We also conducted mediation analysis for quality of life, which was measured by the EuroQol Five-Dimension Questionnaire (EQ5D).P < 0.05). The odds ratios (OR) of depression symptoms were statistically significant for participants in the first and second quartile of handgrip strength compared to those with the highest quartile in entire sample, young adult, middle-aged, and elderly. There was about a 50% mediation effect of EQ5D in the relationship between handgrip strength and depression.After adjusting for covariates, handgrip strength was inversely associated with the PHQ-9 score . Our results suggest that increasing muscular strength may prevent depression in Korean adults.The online version of this article (10.1186/s12889-018-6030-4) contains supplementary material, which is available to authorized users. Depression is a common mental disorder, and about 350 million people suffer from depression all over the world . DepressIn a recent epidemiologic study, handgrip strength was a very simple and noninvasive measure of upper limb strength and an important marker of physical health . As a phFor older adults, epidemiologic studies have showed that low muscle strength is associated with mental health such as worse cognitive state and higher depressive symptoms , 7. LongTherefore, we conducted survey for the association between handgrip strength and depressive symptoms using the 6th Korean National Health and Nutrition Examination Survey (KNHANES VI). In addition, we analyzed the mediation effect of quality of life between handgrip strength and depression, because some studies have suggested that muscular strength, mediated by quality of life, influences depression .KNHANES has been implemented at the national level from the Korea Center for Disease Control (KCDC) since 1998 and conducts direct physical examinations, clinical and laboratory tests, personal interviews and related measurements , 14. Then = 1574), those data without handgrip strength for the right or left hand (n = 1130), those who did not have data for depressive symptoms (n = 546), and those who did not have data for body mass index (BMI) (n = 2). The final sample number was 4298 . Exclusion criteria were those who were under the age of 19 years (4. ExclusThe age range of the subjects was 19–80 years and the subjects were divided into three groups (young adult (19-39 years), middle aged (40–59 years), and elderly (60–80 years)).2).We divided participants into underweight (BMI < 18.5), normal weight (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), obese (30 ≤ BMI) group [A health questionnaire was used to obtain information on age (years), sex  and educational status . Subjects were divided into quartiles based on the household income: less than 680,600 won (KRW), 680,600-1,480,399 won (KRW), 1,480,400–2,499,999 won (KRW), and greater than 2,500,000 won (KRW). A skilled health technician calculated body mass index (BMI) by measuring body weight (kg) and height (cm) according to standardized procedures for all participants. (BMI = weight/ (height) I) group . The weiIn this study, HGS was measured by a handgrip strength dynamometer  and detailed measured of HGS could be found elsewhere . Measurehttps://www.phqscreeners.com/) [KNHANES VI collected the PHQ-9 to estimate the degree of depression in participants. The PHQ-9 was based on self-reported measures and is useful for screening major depressive disorder . The toors.com/) . The thrrs.com/) .EQ5D was a tool for measuring quality of life and consists of five descriptive sections , 21. MorContinuous and categorical variables were expressed as the mean ± standard deviation and n (%), respectively. Differences in general characteristics according to depression were compared using the Student’s t-test or Chi square test, as appropriate in entire sample and age group (young adult (19-39 years), middle aged (40–59 years), and elderly (60–80 years)).We analyzed the association between relative handgrip strength and depression (PHQ-9) using multivariate linear regression. We conducted model 1 (adjusted age and sex) and model 2 . Logistic regression analysis was performed to examine associations between quartile of relative handgrip strength with presence of depression in entire sample and age group (young adult (19-39 years), middle aged (40–59 years), and elderly (60–80 years)).https://knhanes.cdc.go.kr/knhanes/main.do) [We performed complex sample analyses using weight and further details of these weights can be found in the KNHANES VI (main.do) , 21. We main.do)  to assesWe performed sensitivity analyses dividing the total sample by sex.https://cran.r-project.org/) for Windows.We performed statistical analysis using STATA version 13  and found a significance level of 0.05 or less. Graph was performed using the R program version 3.3.3  were men. The average age was 44 years .The prevalence of depression in this study was 20.7% .Table P < 0.05) (Table p < 0.05) compared to the highest quartile  and model 2, relative handgrip strength was inversely associated with PHQ-9  . In particular, our results reveal that, compared to those with the highest quartile of relative handgrip strength, participants with the lowest quartiles were at 2.5–3.4 times the higher risk of depressive symptoms, respectively in all adults and age group . Results also revealed that quality of life has a mediation effect of approximately 50% in the relationships between handgrip strength and depression.Our prevalence of depression is consistent with previous studies (17.3–28.8%) . The KorOur results are consistent with previous studies. According to the cohort study, in Japanese adults aged 40–79 years, people with low grip strength had higher OR of depressive symptoms . Lino etIn some study, the risk of depression was higher in those who were obese and whose grip was reduced by 1SD over 4 years compare to those who were in normal weight and maintained constant handgrip . In our A recent study showed that muscle strength was significantly positively associated with quality of life in elderly people . Anotherr = − 0.016, p < 0.001, left hand: r = − 0.014, p < 0.001). Handgrip strength decreased with aging and appeared to be a predictor of development of geriatric disease, and lifelong management of handgrip indicates a great potential for promoting healthy aging [The present study has important strength, including the large size nationally representative sample; our results evaluate the effect of muscular strength on depression. This is first report performed stratification analysis by age group  and found quality of life had a medication effect in relationship between relative handgrip strength and depression in adults. In addition, our results are especially important in terms of healthy aging. Similar to a previous study , there why aging . In pracThis study had several limitations. First, because of its cross-sectional study design, it is difficult to verify the causal relationship between handgrip strength and depression; a reverse relationship was not excluded. For example, some studies have found that depression and anxiety were associated with poor handgrip strength during a six-year follow-up . It is pOur study showed the association between muscular strength and depression. We found that the increased muscular strength may reduce the risk of becoming depressive symptom in all subjects and age groups , and our results suggest that quality of life mediates the relationship between handgrip strength and depression in adults. Future longitudinal studies focusing on proper management of lower handgrip strength in adults will help determine the clinical implications of these findings.Additional file 1:Table S1. Weight status of the participants for each sex, for each age group and according to depression. (DOCX 18 kb)Additional file 2:Table S2. The association between continuous handgrip strength value and depressive symptoms for sex using multiple linear regression. (DOCX 15 kb)


The computed tomography (CT) features of small solid lung cancers and their changing regularity as they grow have not been well studied. The purpose of this study was to analyze the CT features of solid lung cancerous nodules (SLCNs) with different sizes and their variations.Between February 2013 and April 2018, a consecutive cohort of 224 patients (225 nodules) with confirmed primary SLCNs was enrolled. The nodules were divided into four groups based on tumor diameter . CT features of nodules within each group were summarized and compared.p < 0.05 for all). The presence of halo sign in all groups was similar .Most nodules in different groups were located in upper lobes (groups A − D:50.8%–73.1%) and had a gap from the pleura (groups A − D:89.6%–100%). The main CT features of smaller (diameter ≤ 1 cm) and larger (diameter > 1 cm) nodules were significantly different. As nodule diameter increased, more lesions showed a regular shape, homogeneous density, clear but coarse tumor–lung interface, lobulation, spiculation, spinous protuberance, vascular convergence, pleural retraction, bronchial truncation, and beam-shaped opacity (The CT features vary among SLCNs with different sizes. Understanding their changing regularity is helpful for identifying smaller suspicious malignant nodules and early determining their nature in follow-up. Lung cancer is the most common type of tumor and the leading cause of cancer-related mortality worldwide , 2. PulmAmong the various CT features, spiculation, lobulation, vascular convergence, and pleural retraction have been associated with malignancy in lung cancer , 20–25; To date, no study has reported imaging features associated with the development of primary SLCNs. Therefore, the purpose of this study was to investigate and summarize CT manifestations of primary SLCNs and their differences based on the nodules size and to provide a reference for early and accurate identification of potentially malignant small nodules.The present study enrolled patients with pathologically confirmed peripheral lung cancer between February 2013 and April 2018. All patients underwent chest CT examination before surgery. Inclusion criteria were met by patients with (1) lesions comprising nodules with diameter ≤ 3 cm; (2) an interval of 1 month between chest CT scan and surgery, and (3) lesions not treated with antitumor therapy before CT examination. Patients were excluded if their CT images were of poor quality (5 cases) or if the lesions were metastatic lung cancers (14 cases). A total of 224 patients (225 lesions) were included in the study.All patients were examined using a 64-slice spiral CT scanner  with the following settings: tube voltage, 140kVp; tube current calculated according to individuals’ weight, height, and body mass index; rotation time, 0.5 s; pitch, 1.0; collimation, 0.6 mm; slice thickness and interval for axial images, 5 mm and 5 mm, respectively; and reorganization interval, 1 mm. Upon CT examination, patients were put in the supine position with both hands near the head. Image acquisition was performed from the level of the thoracic inlet to inferior to the costophrenic angle. Images were obtained with mediastinal window  and lung window  settings.All patients’ CT data were reviewed on a workstation  by two senior chest radiologists who were blinded to the pathological results of lesions. Interpretation discrepancy, if any, was resolved by consensus.The followings features of the lesions were evaluated on CT images: size ; distribution in different lobes ; location (clinging to the pleura or not); shape ; internal features ; density (homogenous or heterogeneous); margins ; and tumor–lung interface . In addition, peripheral lesion areas were also evaluated, including vascular convergence, pleural retraction, bronchial truncation, and beam–shaped opacity. Pleural effusions and lymph nodes in the hilum and mediastinum were further evaluated. Enlarged mediastinal and hilar lymph nodes were generally defined as those with diameter > 1 cm in short axis on chest CT scans.For investigating the differences in CT features of SLCNs with different sizes, especially for the smaller ones, nodules were divided into four groups based on tumor size: Group A: diameter ≤ 1.0 cm; Group B: 1.0 cm < diameter ≤ 1.5 cm; Group C: 1.5 cm < diameter ≤ 2.0 cm; and Group D: 2.0 cm < diameter ≤ 3.0 cm.t-tests were used to compare age among different groups. Chi-square test was used to compare the differences in patients’ clinical and pathological data, distribution and location of nodules, and various CT features among the groups. Tukey–Kramer test was used for multiple comparisons among the groups. A p-value less than 0.05 was considered statistically significant. All statistical analyses were performed using the statistical software SPSS .Clinical data and CT features of nodules were statistically analyzed for each group. Continuous variables were expressed as mean ± standard deviation, whereas categorical variables were expressed as absolute number and percentage. Independent sample p > 0.05).Among the 224 patients, 129 (57.6%) were male and 95 (42.4%) were female. Patients were aged 38–83 years, with an average age of 61.7 ± 9.5 years. A total of 127 (56.7%) cases had cough, expectoration, hemoptysis, chest pain, or chest tightness. The number of SLCNs in groups A, B, C, and D was 35, 60, 63, and 67, respectively. Table Nodule distribution and location are summarized in Table p < 0.05 for all) . There was also no significant difference in the incidence of calcification, vacuole, and cavity among the four groups.Nodule CT features are summarized in Table l) Figs. , 3 and 4Along with lesion distribution, location, and morphological features, the main CT characteristics indicating suspicious malignant nodules in group A are summarized in Table p < 0.001). The incidence of pleural effusion was extremely low in all four groups.As nodule size increased, the incidence of enlarged lymph nodes in the hilum or mediastinum also gradually increased  were usually different from those of the bigger ones (> 1 cm). As nodule size increased, the lesions acquired a more regular shape and the margins and surrounding features also gradually increased. It may be because a nodule becomes more regular with an increase in size, and the limitation of the surrounding structure becomes more obvious. Lobulation, spiculation, pleural retraction, and vascular convergence are considered common signs of malignancy in lung cancer , 20–25. Siegelman et al.  reportedThe growth of solid lung cancer is a gradual process. The tumor cells gradually accumulate, and the lesion size continuously increases. Theoretically, tumor density is more homogeneous on plain CT scan with an increase in the lesion size. In the present study, small nodules, particularly those with diameter < 1 cm, had a higher incidence of heterogeneous density. However, the incidence of internal calcification, vacuole sign, or cavity was low in each group, as previously reported , 35. TheBeam-shaped opacity is banded ground glass opacity, which is located on the side of the tumor close to the pleura in different directions. It is common in adenocarcinoma and highly significant in the diagnosis of lung cancer . This siHalo sign is a nonspecific sign around solid pulmonary nodules, and its border is usually clear in lung cancer lesions . In thisOverall, regarding the small pulmonary nodules, follow-up seems to be an effective way to discriminate their nature based on changes in CT features. A recent study has confirmed that quantitative image features (“radiomics”) can also help discriminate benign from malignant pulmonary nodules . AdditioThis study had several limitations. The evaluation of changing regularity of CT features for SLCNs was performed by comparing grouped nodules with different sizes rather than following only one group of lesions. Thus, results obtained here should be confirmed in clinical practice. Additionally, the pathological types of SLCNs varied but showed no significant differences among different groups. Therefore, present results seem to represent a general, rather than a specific tumor type. It should be noted that some tumor types within smaller samples may not conform to the general morphological development.Morphological changes in SLCNs exhibited some degree of regularity in their growth process. With diameter increasing, more nodules showed a regular shape, homogeneous density, clear but coarse tumor–lung interface, and margin or peripheral signs. Thus, larger nodules could be easily diagnosed due to the presence of more features. However, malignancy should be suspected for smaller nodules located in the upper lobe that are not clinging to the pleura and are showing irregular shape, heterogeneous density, unclear tumor–lung interface, or a well-defined halo sign. Understanding the changing regularity of SLCNs is helpful for identifying smaller suspicious malignant nodules and for early determination of their nature during follow-up.


Road crashes are a leading cause of death worldwide. In many countries, it is common to see spontaneous roadside memorials constructed in response to road fatalities. These memorials are controversial and are explicitly banned in many jurisdictions. Advocates argue that the presence of memorials improves safety by making other drivers aware of an especially dangerous road where others have died, whereas opponents argue that they are distracting and decrease safety by diverting drivers’ attention away from the road. However, there has been almost no research examining the effects of roadside memorials on road user behavior and safety. In this study, 40 drivers viewed videos of road scenes with and without memorials, to examine how the presence of roadside memorials influences drivers’ attentional allocation (indicated by eye movements to the roadside area) and safety-related behaviors (indicated by perceived risk ratings and preferred travel speeds for the road). The findings indicate memorials do capture visual attention, as participants were more likely to fixate on memorials compared with a comparison object placed on the roadside. However, fixations on the memorials, and to the roadside area in general, were relatively brief. The presence of memorials did not affect perceived risk and did not produce a clear systematic effect on preferred travel speed. Nearly all drivers in our study supported permitting roadside memorials, but a small number strongly opposed memorials on the belief they are distracting and/or distressing.Cognitive Research: Principles and Implications and received in-principle acceptance on 4 March 2018. The preregistered protocol is available here: 10.6084/m9.figshare.6181937.This study was preregistered with The online version of this article (10.1186/s41235-019-0184-1) contains supplementary material, which is available to authorized users. Many cultures have a tradition of establishing spontaneous roadside memorials following a fatal road crash. These memorials mark the place where someone died prematurely, which allows their loved ones to publicly mourn the death and may also serve to warn other drivers of dangerous road conditions. Government policies around roadside memorials are mixed, with many authorities believing they distract drivers. Despite this, there has been almost no scientific research examining whether roadside memorials are distracting or whether they influence safety-related behaviors. We conducted a study examining whether roadside memorials divert drivers’ attention away from the road  and whether it affects their judgements of how safe the road is and what speed they should adopt. The findings indicate that although memorials may capture drivers’ attention, they do not have a large impact on behavior and are unlikely to have a major impact on road safety (either positive or negative). Further, most drivers who participated believed roadside memorials should be permitted, with a minority strongly objecting to their presence. However, several participants suggested the design of memorials should be regulated to ensure they are safely located and not too eye-catching.Road crashes are a leading cause of death worldwide, with an estimated 1.25 million fatalities annually  and ontogenetic or modern threats, such as weapons . Furthermore, they signal danger (“someone died here”) and threat-related stimuli have been found to preferentially capture attention , and would fixate on the roadside for a longer duration when a memorial was present. Participants were asked to indicate what speed they would drive (preferred travel speed), and how safe or risky it would be to drive on the road (risk ratings). Based on previous research, participants were expected to give higher risk ratings to roads that feature roadside memorials. Logically, higher risk ratings should be associated with adopting lower travel speeds  aged 20–44 years  were recruited and offered a gift card (AUD$20/h) as compensation. All were fluent in English and had normal or corrected-to-normal visual acuity. Participants were required to hold a current valid open (unrestricted) driver’s license and drive at least once a week. To attain an open driver’s license in Queensland, drivers must accrue 100 h of supervised practice as a learner, and pass practical and theoretical tests, which grants them a provisional license to be held for 3 years. This then allows them to drive unaccompanied with some restrictions . The inclusion criteria imposed therefore ensured all participants were experienced, regular drivers.The experiment featured a repeated-measures design with a single factor .Visual stimuli were presented on a 22″ monitor with 1920 × 1080 resolution. Viewing distance was approximately 60 cm, but head position was not fixed, to allow naturalistic observation. Eye movements were tracked using a SensoMotoric Instruments (SMI) REDn eye-tracker, which tracks eye movements at 60 Hz. Stimulus presentation and data acquisition were controlled using SMI Experiment Center.Stimuli were 40 short video clips of daytime road scenes filmed from the driver’s perspective using a GoPro digital video camera. This comprised 10 videos of roads with roadside memorials and 30 videos of roads without roadside memorials. Among the 30 videos without memorials, 20 were matched clips , and 10 were “filler” clips. Filler clips were included to lower the relative prevalence of memorials in the stimulus set and were not included in the analyses.Clips were selected and classified by three individuals (both authors plus one independent research assistant) to ensure that each clip unambiguously fit in a single category , and to code key events . Location, posted speed limit, and date/time of recording were noted for each clip.Clips with memorials present were filmed by driving a passenger vehicle on real public roads with genuine roadside memorials present. Each clip was 23–44 s long, including at least 10 s of footage before the roadside memorial became visible  and at least 2 s after it was passed .Matched clips were selected such that for each clip with a memorial present, there were two matching clips without memorials filmed on the same road or a similar road  with nothing . To address this, we added a third condition, in which a traffic cone was placed on the left side of the road. A standard orange traffic cone was used because it is an object that is often found by the roadside, can also signify hazards and is a similar size to many of the cross memorials. Although the two target objects may differ in physical saliency, previous research has demonstrated that visual saliency does not influence or predict eye movements when viewing real-world scenes, despite being highly influential in basic visual search arrays . They were instructed to view each clip and to make three judgements: what travel speed they would adopt on the road (in kilometers/hour); what they believe is the posted speed limit (in kilometers/hour); and how risky or safe it would be to drive on this road . Participants were asked to verbally explain their reasons for these judgements. The explanations were recorded using a digital audio recorder and transcribed verbatim for analysis.Participants completed three practice trials before the experiment. During the practice block, eye movements were not tracked and participants had an opportunity to clarify instructions and task requirements as necessary. None of the practice clips contained memorials.The eye-tracker was calibrated at the beginning of the experimental block using a 9-point calibration grid and re-calibrated every eight trials to ensure accurate gaze tracking was maintained. Calibration accuracy was validated using a 4-point grid, and only accepted if the average error was <  0.5 °.Following the eye-tracking data collection, participants completed a brief questionnaire that asked them what they thought the study was about . Finally, they were asked a series of questions about roadside memorials, including how frequently they encounter them, whether they are aware of adjusting their behavior around roadside memorials , and whether they believe memorials should be permitted.dz = 0.53 with power of 0.95 in a pairwise within-subjects comparison. This would be considered a medium to large effect. It is likely that experimentally induced effects would be larger than any real-world changes in behavior, as real-world travel speeds are constrained and influenced by a multitude of interacting factors , whereas the current study involves manipulating a single factor. Further, as the results could have relevance to real-world policy, it is appropriate to consider effects that are large enough to produce a meaningful impact under complex real-world conditions.Power analysis using G*Power  (which were planned), which require averaging values within a condition, because there was large variability between trials, especially in speed data. Therefore, the analysis focused on individual trials, rather than condition averages. The models specified an exchangeable correlation matrix. A binary logistic generalized estimating equation (GEE) was used for binary variables , a negative binomial GEE was used for count variables  and a linear GEE was used for continuous variables. Statistical significance was assessed using an alpha level of .05. Descriptive statistics were reported for the post-experiment questionnaire.Statistical analyses were conducted in SPSS. Quantitative variables relating to eye movements and risk perception were analyzed using generalized estimating equations  were coded during this period, including the road, left roadside, right roadside (if visible within frame), and the horizon/sky. AOIs were defined within the video clips, so that they were applied consistently across all observers. The left roadside AOI was the focus of analysis, as this was the area where roadside memorials and traffic cones were placed. For the memorial and traffic cone clips, a custom “target object” AOI was added, which was valid only for the temporal period in which the memorial/cone was visible, to assess whether participants directly fixated on it. The SMI software uses dispersion-based algorithms to detect fixations because the system’s temporal resolution is 60 Hz. The default parameters were applied, which were minimum fixation duration of 80 ms with maximum dispersion of 2.00 °. Blinks, fixations outside of any AOI and periods where tracking was lost were treated as missing data.Three aspects of eye movements were compared between the memorial video clips and the matched non-memorial clips: probability of fixating roadside AOI ; number of fixations on roadside ; and total fixation on roadside . The left roadside AOI analysis focused on 1314 ms of each clip, comprising the last 1280 ms when the memorial/traffic cone was visible, plus an additional 17-ms buffer before/after this period to account for the eye-tracker recording in 16.7-ms frames. This duration was selected because all memorials/cones were visible for at least 1280 ms. For control clips, the time period was matched to the period used for the memorial clips. The object AOI analysis focused on the full period that the object  was visible; because this varied between trials, fixation duration was converted into a percentage of the visible time. Three other quantitative variables were compared between the memorial and non-memorial clips, specifically, posted speed limit, self-nominated travel speed and risk rating.Verbal comments were coded to assess which aspects of the road participants considered when making their speed and risk ratings; specifically, whether they commented on the roadside memorial, if present. This involved coding concepts related to memorials  and accidents . These were used to assist in interpretation of the statistical analyses; for instance, if the presence of memorials affected eye movements but not risk ratings, this could be because participants are not considering the presence of memorials as a relevant factor when judging the safety of the road.Participants had held their license an average of 13.6 years  and reported driving an average of 302 km (SD = 255) or 8.8 h (SD = 6.4) per week. Most participants had not received any fines (80%) or been involved in any crashes (77.5%) in the preceding 12 months. Six participants reported having been involved in a property-damage-only crash as a driver within the past 12 months, with three others having been involved in a property-damage-crash as a passenger. No participants had experienced a crash that resulted in injury of any parties within the past 12 months.n = 9, 23%) or 2 (n = 9) of the 10 memorials, with the largest number of memorials noted being 6. In total, there were 61 instances  in which participants commented on the memorials. The comments varied: some simply mentioned the cross , some noted that it signified a crash site , whereas others simply mentioned that someone had died without mentioning the memorial itself . Among participants who mentioned memorials, only half (n = 13) explicitly mentioned that they factored a memorial into one of their risk ratings . One participant repeatedly reported finding the sight of memorials quite distressing:“Someone died back there. I hate those signs on the side of the road, I find that very traumatising and now I’m going to think about it for the whole rest of the drive down the highway, that someone died there. That’s distracting to me.” (Clip 7)“Look there’s another one of those crosses on the side of the road. Are they, I just don’t get it. I think there should be a law that stops people having crosses on the side of the road, it’s dangerous.” (Clip 4)In contrast, only 8 (20%) participants commented on the traffic cones. All 8 commented on only 1–2 of the 10 cones presented, meaning that participants commented on < 3% of traffic cones, and only 1 person suggested the cone might signify increased risk .Most participants noticed at least one of the memorials: 26 participants (65%) commented on at least one memorial while viewing the clips. Two additional participants mentioned afterwards that they had seen the memorials, even though they did not comment on them while viewing the clips. However, most only explicitly commented on 1 (2(2) = 18.92, p < .001. Participants were significantly less likely to fixate the left roadside area in the control condition compared with the memorial condition, χ2(1) = 17.49, p < .001, odds ratio (OR) = 0.67, 95% confidence interval (CI) for OR = 0.56, 0.81. There was no significant difference between the memorial and traffic cone conditions, χ2(1) = 0.61, p = .436, OR = 1.10, 95% CI for OR = 0.87, 1.39.Participants fixated the left roadside AOI during 57% of all trials (when considering only the 1314-ms interest period). There was a significant main effect of experimental condition on fixation probability, χ2(2) = 63.82, p < .001. Compared with the memorials-present condition, participants made significantly fewer fixations in the control condition, χ2(1) = 63.81, p < .001, B = −  0.5, SE = 0.07, but not the traffic cone condition, χ2(1) = 2.51, p = .113, B = −  0.1, SE = 0.06.Participants made 0–6 fixations on the left roadside area during the 1314-ms interest period. Number of fixations significantly differed between experimental conditions, χ2(2) = 25.61, p < .001, as shown in Fig. 2(1) = 25.38, p < .001, B = −  0.30, SE = 0.06, but not the traffic cone condition, χ2(1) = 3.04, p = .081, B = −  0.10, SE = 0.05, compared with the memorial condition.Dwell time was calculated as the total of all fixations on the left roadside during the 1314-ms interest period. There was a main effect of experimental condition, χ2(1) = 12.74, p < .001, OR = 0.49, 95% CI for OR 0.33, 0.73, and made significantly fewer fixations on the traffic cones, χ2(1) = 10.33, p = .001, B = −  0.7, SE = 0.2. However, participants spent significantly less time fixating on memorials  compared with traffic cones , χ2(1) = 17.04, p < .001, B = 0.09, SE = 0.02, see Fig. Target fixations were defined as fixations directly on the area of the memorial or traffic cone. Control trials were therefore not included in this analysis. Data were taken from the entire period that the target was visible , so dwell time was calculated as a percentage of visible time, rather than absolute time in milliseconds. Participants were significantly less likely to fixate the traffic cones  compared with the memorials , χInterestingly, the trials on which participants fixated the memorial were not necessarily trials during which they mentioned the memorial. Participants both fixated and mentioned the memorial in 8% of trials in which a memorial was presented, but in 10% of trials they fixated the memorial without mentioning it and on 7% they mentioned it despite not directly fixating on it. Participants fixated on and mentioned the traffic cone in 1% of trials, fixated on it without mentioning it in 8.75% of trials, and mentioned it without fixating on it in 1.75% of trials. Because participants were not explicitly asked about whether they noticed these objects in specific trials, the fact that 8–10% fixated on but did not comment on these objects does not necessarily mean that they “looked but failed to see”; rather, they may not have considered these objects relevant to their interpretation of the road scene.Because speed questions were answered verbally, participants could give answers that were not specific integers. The following rules were applied for the actual speed limit question: “X or less” was coded as X ; “less than X” was coded as the next valid speed limit under X ; and if multiple options were suggested , the higher speed was used unless they explicitly indicated that the lower speed was more likely.2(2) = 24.35, p < .001. Pairwise comparisons revealed that there was no significant difference between the memorial and control clips, χ2(1) = 0.72, p = .397, but there was a significant difference when comparing the memorial and traffic cone clips, χ2(1) = 23.92, p < .001. Participants were less likely to underestimate and more likely to overestimate the speed limit in the traffic cone clips  = 5.62, p = .060, suggesting this effect was an artifact of road features and not attributable to the presence or absence of memorials.Answers were then matched against the actual speed limit, to assess whether participants’ estimates were accurate, overestimated, or underestimated. This analysis revealed that participants only correctly identified the posted speed limit in 52% of trials overall and were more likely to underestimate the speed limit (44%) than overestimate it (4%). Ordinal logistic GEE indicated that estimate category  varied significantly with experimental condition, χFor the question on what speed they would likely drive, responses were dealt with in a similar manner as for actual speed. If they indicated they would drive at the speed limit, the value they gave for actual speed limit was used. If they specified a range, the midpoint was used . If they said they would drive “just over/under” a given speed, this was coded as ±3 km/h from the stated speed .2(2) = 3.59, p = .166. A limitation of this analysis is that the posted speed limits for the roads depicted in the clips varied from 60 km/h to 110 km/h, meaning there was greater variation within conditions than between, and behavioral patterns may differ for high-speed versus moderate-speed roads. When road speed limit was added to the model as a factor, there was a significant effect of experimental condition, χ2(2) = 8.09, p = .017, but also a significant effect of road-speed zone, χ2(5) = 3854.77, p < .0011, and a significant interaction between experimental condition and speed zone, χ2(9) = 37.14, p < .001  = 28.99, p < .001. Participants’ self-nominated travel speeds were significant slower in the memorial condition  compared with the traffic cone condition , χ2(1) = 11.38, p = .001, B = 1.2, SE = 0.04. This effect is very small but was statistically significant due to range restriction . There was no significant difference between the memorial and control conditions, χ2(1) = 2.02, p = .155, B = −  0.7, SE = 0.5.For videos filmed on 110 km/h roads, there was a significant main effect of condition, χ2(2) = 8.85, p = .012, but the direction of the effect was reversed. Participants’ self-nominated travel speeds were significantly faster for clips containing memorials  compared with those containing traffic cones , χ2(1) = 8.82, p = .003, B = − 1.8, SE = 0.6. There was no significant difference between the memorial and control conditions, χ2(1) = 2.86, p = .091, B = − 1.3, SE = 0.8.For the clips filmed on 60 km/h roads, there was also a significant main effect of condition, χ2(2) = 0.38, p = .535), 80 km/h (χ2(2) = 3.49, p = .175) or 100 km/h (χ2(2) = 3.64, p = .162). For the 90 km/h clips, there was a significant main effect of condition, χ2(2) = 6.28, p = .043. However, pairwise comparisons were not statistically significant when comparing the memorials condition to either the control condition, χ2(1) = 1.75, p = .186, B = − 2.3, SE = 1.7, or the traffic cone condition, χ2(1) = 2.66, p = .103, B = 2.8, SE = 1.7. In other words, the significant main effect reflected differences between the control and traffic cone conditions.The analyses did not reveal significant main effects of condition for clips featuring roads with speed limits of 70 km/h (χp < .001) correlations between risk ratings and both the posted speed limit, r (1159) = − .18, 95% CI − .23, − .12, and participants’ likely driving speed, r (1159) = − .10, 95% CI − .17, − .05. To account for the repeated measurements , the correlations were calculated using the rmcorr package in R  = 3.83, p = .148. When considering only trials in which the participant explicitly mentioned the memorial (and the matched comparison clips), the main effect of experimental condition was still not statistically significant, χ2(2) = 4.32, p = .115. Although this analysis is underpowered as it is based on a total of 183 trials , as shown in Fig. When considering the full sample of all trials, there was no significant effect of experimental condition on risk ratings, χWhen asked to describe what they thought the study was about, after having participated, no participants mentioned roadside memorials. All mentioned other relevant concepts, such as visual attention, hazard perception, risk perception and risk awareness, speed limits and eye movements.decrease headway, which was explicitly defined as “following distance”. The latter result seems counterintuitive, and probably reflects participants not fully understanding or thinking about the concept .All participants indicated that they had noticed a roadside memorial while driving, and encountered them at least once a month, with over two thirds indicating that they encountered them at least once a week . Figure When asked to describe what they thought about when driving past memorials, the most common themes were wondering how the accident occurred (43%), especially if the road did not seem obviously hazardous; the need to take care because it is a dangerous area or driving itself is inherently dangerous (35%); the fact that someone died (33%); feelings of sadness (33%) and thoughts about the loved ones of the deceased (25%). Several participants also made comments about speed and/or the need to slow down (18%), with a smaller proportion commenting on characteristics of the memorial itself (13%), such as flowers or “ghost bikes” or wondering who it was that died (13%).Nearly all participants (95%) indicated that roadside memorials should be permitted. The two participants who thought they should be prohibited both suggested they could be distracting. Interestingly, the participant who repeatedly commented negatively about memorials while viewing the clips later indicated in the post-experiment questionnaire that they should be permitted, because they help remind others how dangerous driving can be. Several participants who thought memorials should be allowed also noted that they could potentially distract drivers (11%), for example, if the memorials were large/salient or if drivers were especially sensitive, but they either believed that the benefits outweighed this potential risk or “it’s up to individual drivers to ignore them”. Overwhelmingly, the most common theme in the comments by those who endorsed roadside memorials was that they were an effective safety message (76%) to signal danger and remind drivers to take care: “They are a reminder for other road users that that section of road can be dangerous enough for a fatal accident, even if it appears safe.” One person declared they were “essential!” for this reason, while another noted “if … the roadsides become too full of the memorials well that speaks for itself”. Other recurring themes were that memorials were helpful to family and friends of the deceased (29%) and that they did not pose a danger or distraction (16%). A few of these participants specifically mentioned that memorials were no more distracting than billboards. However, several people mentioned the need to regulate or limit the scope of memorials (21%); for example, ensuring they were safely located, small and not too eye-catching. One person suggested there should be a time limit, particularly so that drivers  do not have to be repeatedly reminded of the fatality.This study was designed to systematically explore the impact of roadside memorials on drivers’ attention and risk perception. In general, the findings suggest that roadside memorials capture drivers’ attention at least some of the time, but do not necessarily have a large or meaningful impact on safety-related behaviors - despite the fact that many drivers believe that memorials can improve safety.Analysis of eye tracking data provides evidence that roadside memorials can capture attention. Participants were more likely to look at the left roadside area and spent longer fixating on it when memorials were present, compared with the memorial-absent control condition. This effect seems to be partly because there was a roadside object to look at, because there was no significant difference in glance behavior to the left roadside when comparing the memorials condition with the traffic-cone condition. Nevertheless, participants were significantly more likely to fixate directly on the memorials themselves compared with traffic cones, suggesting that memorials are slightly more likely to capture attention than some other roadside objects. Although these effects were statistically significant, they were small in magnitude and the total fixation time on memorials averaged approximately 400 ms. Such short glances away from the forward roadway are common and are not considered unsafe  and most likely an artifact of the stimuli being filmed on different sections of the road with subtly varying features, despite our efforts to match the stimuli as closely as feasible. This highlights a fundamental limitation of conducting experimental research with naturalistic stimuli; researchers must consider the potential for hidden confounders. In the current study, having two different control conditions helped interpret significant differences, but also made it more difficult to precisely match the stimuli. It is notable that although there were several significant effects, in all cases only one of the two comparison conditions differed significantly from the memorial condition. This suggests that the effects are either not solely attributable to the presence of memorials or are not sufficiently large to be meaningful.Overall, our data do not provide any evidence that roadside memorials meaningfully improve road safety: their presence did not impact risk perception and had little to no impact on drivers’ speed choices. The memorials used in our current study did not induce any effects that would have a clearly negative impact on safety either, but notably a small number of participants expressed strong negative reactions to or opinions on memorials. Many drivers reported emotional reactions when passing memorials, ranging from brief sadness  to fairly intense discomfort . Despite this, most of the memorials shown did not evoke any measurable change in behavior at all - specifically, 75% of the memorials shown were neither fixated on nor commented on by participants. When the memorials were noticed, participants made at most a few short glances toward the memorial. This implies that memorials of the type shown - white crosses - are not unduly distracting for drivers in general, even though a small proportion of drivers find them distressing or inappropriate and think they should not be allowed.One issue worth noting is that the stimuli were filmed on local roads, many of which are distinctively recognizable and likely familiar to participants; specifically, the Bruce Highway, Sunshine Motorway and Kawana Way. In other research that explicitly assessed location familiarity, we have found that 86–89% of participants recognize these specific roads .Additional file 1:Supplementary information: details of video clip stimuli. (PDF 654 kb)


Lycium barbarum (goji berry) is of interest, as it has been shown to increase postprandial oxygen consumption. Although this suggests that energy expenditure was also increased, energy expenditure and substrate oxidation can only be assessed accurately when both oxygen consumption and carbon dioxide production are measured. We therefore investigated the effects of a single dose of Lycium barbarum fruit on postprandial energy expenditure and substrate oxidation in a randomized, double-blind crossover trial. In addition, markers of lipid and glucose metabolism were measured.  Increasing energy expenditure is an effective strategy for the prevention of obesity. In this respect, Lycium barbarum fruit or a control meal matched for caloric content and macronutrient composition. Energy expenditure and the respiratory quotient were determined using indirect calorimetry before and up to 4 hours after meal intake. Blood was sampled before and after meal intake at regular intervals for analyses of plasma glucose, serum triacylglycerol, and free fatty acid concentrations.  Seventeen healthy, overweight men received in a random order a meal containing 25 grams of dried Lycium barbarum and control meal, but no differences were found between the meals (p=0.217). Postprandial changes in respiratory quotient (p=0.719) and concentrations of glucose (p=0.663), triacylglycerol (p=0.391), and free fatty acids (p=0.287) were also not affected by Lycium barbarum intake.  Energy expenditure significantly increased after the Lycium barbarum does not affect postprandial energy expenditure, substrate oxidation, and markers for lipid and glucose metabolism in healthy, overweight men. A single dose of  One of the main risk factors for the onset of cardiovascular diseases (CVDs) and type II diabetes mellitus is obesity, which develops when energy intake exceeds energy expenditure . Thus, iLycium barbarum  originate from Asia and have recently gained popularity in European countries [Lycium barbarum polysaccharides (LBP), carotenoids (mainly zeaxanthin), and vitamins [Lycium barbarum has been consumed in Asia as part of traditional medicine for decades due to its potential beneficial effects on, among others, the development of CVDs and type II diabetes mellitus. It has also been suggested that the goji berry stimulates metabolism. Indeed, animal studies have indicated that expression of genes related to energy metabolism, such as UCP-1 and PGC1α, was elevated after Lycium barbarum intake [Lycium barbarum on energy metabolism has been investigated in one human trial. In that study, it was found that a single dose of Lycium barbarum increased postprandial oxygen consumption in healthy men and women [Lycium barbarum fruit as part of a meal on postprandial energy expenditure and substrate oxidation in healthy, overweight men. In addition, effects on markers for postprandial lipid and glucose metabolism were studied, since postprandial hyperglycemia and hypertriglyceridemia are established risk factors for CVD [Berries from the plant ountries . Major cvitamins . Lycium m intake . Unfortund women . Althoug for CVD , 9. Over2, nonsmoking, no use of anticoagulants or medications known to affect lipid or glucose metabolism, no conditions that might interfere with study outcomes, stable body weight (≤3 kg weight loss or gain in the past 3 months), no participation in another biomedical study during the past month, and no abuse of drugs or alcohol. During a screening visit, fasting blood samples were taken for analysis of serum lipids and plasma glucose. In addition, height and weight were measured. Eighteen subjects were enrolled. These subjects had fasting serum triacylglycerol concentrations below 2.2 mmol/L and no elevated fasting serum cholesterol (<8.0 mmol/L) or plasma glucose (<7.0 mmol/L) concentrations. All subjects signed informed consent before the screening visit. This study was approved by the medical ethical committee of Maastricht University Medical Centre+ (MUMC+) and registered at clinicaltrials.gov as NCT02779985.Apparently healthy overweight men were recruited using advertisement in local newspapers, online advertisements, and posters in university buildings and the hospital. Furthermore, subjects that had already participated in earlier studies from our department were approached. Men were invited for a screening visit if they met the following criteria: aged between 18 and 65 years, BMI between 25 and 30 kg/mA randomized, double-blind, crossover study with two treatments was carried out. For this, subjects visited the university during two occasions separated by a washout period of at least 7 days. On the day preceding each test day, subjects were asked to abstain from alcohol consumption, exercise, and caffeine consumption (from 12.00 PM onwards) and to consume a standardized meal in the evening. Subjects were instructed to select a ready-made meal with a fixed macronutrient composition  from a list and to consume the same meal the evening before both test days to eliminate potential effects of the previous meal .After an overnight fast (from 08.00 PM), subjects came to the university by public transport or by car to limit physical activities in the morning as much as possible. Before the start of the measurements, subjects rested for 15 minutes in supine position, and an intravenous catheter was placed for blood withdrawal. Next, indirect calorimetry was performed for 30 minutes and a blood sample was drawn (T0). Subjects were then asked to consume one of the two test meals in three equal parts divided over 10 minutes. After meal intake, indirect calorimetry measurements were continued for another 140 minutes. Next, the hood was removed, and after a 20-minute break, subjects were allowed to stroll around; measurements were continued for another 40 minutes (T160–T200). The last hour of the test days consisted of another 20-minute break and 40 minutes of indirect calorimetry measurements (T220–T260). Blood samples were taken 15 min (T15), 30 min (T30), 45 min (T45), 60 min (T60), 90 min (T90), 120 min (T120), 180 min (T180), and 240 min (T240) after meal intake. A validated food frequency questionnaire was used to assess habitual food intake over the past month. Calorie content and macronutrient composition  or a control meal matched for macronutrient composition and energy content  and macronutrient composition . Meals contained over 40 grams of fat to trigger a postprandial triacylglycerol response [At the two test days, subjects received a meal containing 25 grams of dried  content . The amoresponse . The tes2) and carbon dioxide production (VCO2) were measured during fasting and postprandial conditions using a ventilated hood system . Calibration of the indirect calorimeter was performed automatically every 30 min with span gas (18% O2 and 0.8% CO2) and nitrogen gas (100%). Validation of the system was performed regularly during the study with a methanol combustion test. VO2, VCO2, and respiratory quotient (RQ) were averaged over 20 minutes at baseline (T0) and 10–30 min (T20), 30–50 min (T40), 50–70 min (T60), 70–90 min (T80), 90–110 min (T100), 110–130 min (T120), 170–190 min (T180), and 230–250 min (T240) after meal intake. Energy expenditure was calculated from VO2 and VCO2 data using the formula of Weir [Oxygen consumption  were placed on ice immediately after blood withdrawal. Tubes were centrifuged within 30 minutes at 1300 × g for 15 minutes at 4°C. Serum separator tubes  were allowed to clot for 30–60 minutes at room temperature after withdrawal and centrifuged at 21°C for 15 minutes at 1300 × g. All plasma and serum samples were directly frozen in liquid nitrogen and stored at −80°C until analysis.At all time points, NaF plasma was used for analysis of glucose  and serum for analysis of free fatty acids . Serum triacylglycerol concentrations corrected for free glycerol  were measured at T0, T30, T60, T120, T180, and T240.It was calculated that a sample size of 18 subjects was needed to detect a difference of 0.18 kJ/min with a power of 80% and a within-subject variation of 0.25 kJ/min .T-test. Postprandial changes from baseline were analyzed using linear mixed models with meal and time as fixed factors and meal ∗ time as an interaction term. The interaction term was not significant in any of the models and therefore removed from all models. If the factor time was significant, time points were compared to baseline using post hoc tests with Bonferroni correction. The incremental area under the curve (iAUC), defined as the area above baseline values, was calculated with the trapezoidal rule [p values < 0.05 were considered statistically significant. Statistical analyses were performed with SPSS 21.0 for Mac .All data are presented as mean ± SD. Differences between test days in fasting values were compared using a paired-sample dal rule  for the 2. Inspection of the diaries did not reveal any protocol deviations that may have affected the results. Baseline characteristics of the 17 subjects who completed the study are presented in Eighteen subjects were included and completed the study . After meal intake, energy expenditure increased significantly . The iAUC over 4 hours was also not significantly different between the two meals  and after meal intakes .VOp=0.845, data not shown) but increased 40 minutes after meal intake and returned to baseline after 240 minutes  and after meal intake .Fat and carbohydrate oxidation . Glucose concentrations increased significantly after meal intake and returned to baseline after 45 minutes . Sixty minutes after meal intake, triacylglycerol concentrations were increased and remained elevated throughout the test days  were examined, which are thought to be the main bioactive component of goji berries [Lycium barbarum on postprandial oxygen consumption were examined. Compared to placebo, only the highest dose of 120 ml goji berry juice significantly increased oxygen consumption 1 and 4 hours after meal intake. One of the limitations of that trial, as also discussed by the authors, was the small sample size. Additionally, only oxygen consumption was measured. To assess energy expenditure and substrate oxidation accurately, both oxygen consumption and carbon dioxide production need to be measured, as we did. Nonetheless, as already mentioned, no effects were found, also not on oxygen consumption. We have no obvious explanation for these discrepancies in results. Differences in subject characteristics may have played a role. Although in both trials healthy overweight subjects participated, participants in the trial of Amagase and Nance were younger (34.5 ± 7 years), while both men and women were included.In this study, we found no effects of a single dose of 25 g dried activity . In two activity , 16, the berries . However berries , effectsLycium barbarum fruit used in the current study fits within the range of 15–30 grams of the dried fruit frequently used in traditional Asian medicine [Lycium barbarum provided in both trials was processed differently, it is unlikely that this explains the difference in results. Different preparations of Lycium barbarum, such as powders or juices [The single dose of 25 grams of dried r juices , 18 and r juices , suggestLycium barbarum was provided with a meal, but caloric intake as well as macronutrient composition of the meals was different between the two studies. In the study of Amagase and Nance, the caloric content of the highest dose of goji berry juice combined with the meal was 260 kcal less than our meal. The meal, excluding the goji berry juice, provided 55% of the energy from carbohydrates, whereas our meal provided 55% of the energy from fat. However, whether differences in meal composition can explain the discrepancies in results warrants further study.In both trials, Lycium barbarum intake on measures of energy expenditure. However, some studies have measured changes in body weight, which could serve as a proxy for energy balance. In mice, body weight was reduced after long-term Lycium barbarum intake [Lycium barbarum intake on body weight [Lycium barbarum or placebo for 14 days to 3 months [No long-term studies have been performed investigating the effects of m intake , 20. In y weight . In the 3 months , 22–24. Lycium barbarum may reduce cardiovascular disease risk by improving postprandial lipid and glucose metabolism. Only one other study has addressed this question. This was, however, an intervention trial with intake of Lycium barbarum over three months instead of a single dose. Nevertheless, postprandial glucose concentrations were lowered in this trial [Lycium barbarum might not be sufficient to induce changes in glucose concentrations, whereas repeated intake does. In the study of Cai et al. [Lycium barbarum consumption were found on postprandial triacylglycerol concentrations, which is in line with the results from our single dose study.Another question is whether is trial . In ratsis trial . Furtheris trial . In our i et al. , no effeLycium barbarum does not influence postprandial energy expenditure and substrate oxidation in healthy, overweight men. Furthermore, Lycium barbarum intake did not affect postprandial plasma glucose, serum-free fatty acids, and triacylglycerol concentrations.In summary, our study indicates that a single dose of


Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1–5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.Eligible cases (3) than the normal breast (− 0.39% and − 2.74 cm3) for a difference of 0.13%  and 0.63 cm3 (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (− 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54  for VPD and 0.56  for DV.Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (− 0.26% and − 2.10 cmThere is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer. Mammographic breast density (MBD) has been shown to be a major risk factor for breast cancer –3 and inAutomated volumetric density measures for full-field digital mammography (FFDM) have the potential to replace or at least complement the radiologist’s BI-RADS density assessment as they provide objective, reproducible density estimates , 13. AutBreast cancer usually develops in one breast and has the same radiographic X-ray attenuation as normal dense fibroglandular tissue , 16–20. The participating studies included two retrospective case-control studies nested within large breast screening facilities at Mayo Clinic Rochester (MCR) and in the Bay Area that participate in the San Francisco Mammography Registry (SFMR) . Passiven = 1322) and did not have breast implants or a prior breast cancer diagnosis (DCIS or invasive).Since 2006, women were included in the underlying mammography cohort if they had a FFDM at one of four facilities . Women wn = 648) and did not have breast implants or a prior breast cancer diagnosis (DCIS or invasive).Women from the tri-state region of Minnesota, Iowa, and Wisconsin who had FFDM at MCR between April 2008 and December 2015 serve as the underlying mammography cohort [Because our goal was to assess the density change between breasts over time, we required a bilateral screening or diagnostic FFDM within 2 months prior to the cancer diagnosis and at least one additional FFDM 1–5 years prior to diagnosis to be included as cases. Patient age, clinical risk factors, and tumor characteristics were obtained from clinical questionnaires or abstracted from medical records and tumor registries. Due to missing data on menopause, we defined menopausal status as age 55 or older .Women without prior breast cancer, and with bilateral FFDM over the same time period, were matched to each case on age (within 5 years), race (as close as feasible), date of mammogram (within 1 year), FFDM machine (exact), and facility (exact).Volpara™  is a fulBaseline characteristics were summarized using number and percentage for categorical variables and median and quartiles for continuous variables. Using the Volpara™ calculations for each breast, the change in DV and VPD between mammograms was calculated for each breast separately. To assess the difference between breasts among cases, we subtracted the change in DV and VPD in the normal contralateral breast from the change in the breast with cancer . The difference  was examined by tumor size, clinical BI-RADS density (grouped into dense (heterogeneously or extremely dense) vs. non-dense (fatty or scattered density)), and menopausal status (defined as age ≥ 55 years) at the time of the earliest mammogram. The above analysis was done in a similar fashion in the controls by defining the “ipsilateral” and “contralateral” sides based on the sides of the matched case. By analyzing the difference in the change between the two breasts within each woman, we control for systemic variables that should affect both breasts equally, such as aging, hormone therapy, and weight change. Statistical significance of the change was evaluated using the Wilcoxon signed-rank test.p ≤ 0.05.A secondary analysis was performed to simulate clinical practice, where the cancerous breast is not known at the time of the mammogram review. In this analysis, the change in VPD and DV between mammograms was again defined for left and right breasts separately. However, the absolute difference in the change between breasts was calculated for cases and controls without knowledge of the cancerous breast; change was defined as the absolute value of the maximum difference between the two breasts. The correlation of change between the two breasts was examined using Lin’s concordance correlation coefficients, which summarizes the agreement along the line of identity. The cumulative distribution of the absolute difference in VPD and DV was examined in cases and controls. Logistic regression was used to evaluate the ability of the absolute difference to discriminate breast cancer case status from controls, with and without adjustment for age, BMI, change in BMI, and time between mammograms. The difference in the area under the ROC curve (AUC) between the baseline model  and the model including the absolute difference was calculated and summarized with corresponding 95% confidence interval. The analysis was done using SAS version 9.4 , and statistical significance was set at A total 1160 of the 1970 potential cancer cases met eligibility criteria. Reasons for exclusion included 795 without an available bilateral mammogram within 2 months of diagnosis and 15 without a prior bilateral mammogram within 5 years of diagnosis. Distributions were not markedly different when analyzed by study cohort . For DV, the ipsilateral breast decreased 2.10 cm3 and the contralateral breast decreased 2.74 cm3 for a difference of 0.63 cm3 (p = 0.002).The median (IQR) time between the oldest prior mammogram within 5 years and the mammogram closest to the diagnosis was 3.0 years IQR 1.9, 3.9). Over this period, both VPD and DV decreased on average among both cases and controls Table . Among c.9, 3.9. n = 540) had a difference of 0.11% (p < 0.001) and 0.63 cm3 (p = 0.002) between the breast with cancer and the normal breast for VPD and DV, respectively. Women with dense breasts (n = 480) had a difference of 0.21% (p = 0.11) for VPD and 0.61 cm3 (p = 0.38) for DV (Table p = 0.23) and DV difference of 0.02 cm3 (p = 0.76) between the cancerous and non-cancerous breasts. For women with cancers 2.1–5 cm, the difference increased to 0.40% (p < 0.001) for VPD and 4.15 cm3 (p < 0.001) for DV. For women with cancers > 5 cm, the difference was even greater with 1.18% for VPD (p < 0.001) and 12.97 cm3 (p < 0.001) for DV. Similar results were found for post-menopausal women (age ≥ 55) when analyzed as a sub-group  assessed at the earliest mammogram, tumor size and menopausal status, the VPD and DV of the cancerous ipsilateral breast decreased to a lesser degree than the contralateral breast without cancer in all subsets; but not all results reached statistical significance. Women with non-dense breasts ( p = 0.00 between N = 2360), selecting the sides corresponding to the matched case. Similar to breast cancer cases, mammograms spanned a median of 3.1 years (IQR 2.0. 4.1), and density decreased on average over this time period (Table p = 0.92) for VPD and 0.05 cm3 (p = 0.77) for DV (Table p = 0.93) and 0.09 cm3 (p = 0.89), respectively) and for those with dense breasts (difference − 0.00% (p = 0.97) and − 0.02 cm3 (p = 0.77), respectively). Similar findings were seen among the subset of post-menopausal women (age ≥ 55)  in VPD and DV were 0.54  and 0.56 , respectively. Importantly, the asymmetry measures only slightly improved discrimination over the baseline model  for VPD and 0.03  for DV.In order to simulate clinical practice, where the laterality of malignancy is not known at the time of mammogram interpretation, we compared the absolute difference in density change between breasts among cases and controls. The cumulative distribution of this difference for cases and controls is shown in Fig. Commercially available automated density assessment methods provide volumetric density calculations for each breast separately in addition to assigning a single overall density category per woman, similar to clinical BI-RADS. We evaluated the potential of Volpara™-automated density measures (VPD and DV) calculated for each breast separately to identify clinically relevant differential change between breasts in women with breast cancer. Comparing the automated measures at the time of diagnosis to those from prior mammograms within 5 years, we found that VPD and DV decreased on average in both breasts, but to a lesser degree in the breast with cancer. Density measures also decreased over time in the controls, but this decrease was symmetric with no significant difference between breasts.Prior studies report that mammographic feature asymmetry between breasts, using complex computerized feature analysis, predicts individual near-term breast cancer risk –23, 28 oThe attenuated decrease in density in the breast with cancer, compared to the normal breast, found in this study is consistent with our hypothesis that the presence of breast cancer can result in an asymmetric density increase in that breast. However, this finding alone does not appear to be a good discriminator between cases and controls using the automated density method in this study. Using the absolute difference in DV and VPD between the two breasts, irrespective of knowing which breast has cancer, the improvement in the discrimination of cases from controls was minimal . This automated density measure may incrementally improve discrimination, but our results suggest that it cannot be used in a stand-alone fashion in clinical practice to determine whether further examination is indicated.Our findings in controls provide important information for future studies. Many published studies evaluating breast density and cancer risk use the non-cancerous breast to avoid possible bias in the density measurement , 31, 32.The strengths of this study include the large number of cases and controls from two breast imaging practices using FFDM. In addition, by comparing right and left breasts, we controlled for variation in density due to systemic factors that affect both breasts such as weight change, hormones, and aging. However, by restricting our study to participants with a bilateral mammogram near diagnosis, we eliminated multiple women and potentially reduced the generalizability of our findings. The relatively small number of cases with tumors greater than 2 cm at diagnosis limited our ability to perform subset analyses. We also evaluated FFDM raw images rather than tomosynthesis, which is rapidly replacing FFDM in clinical practice. Although studies have shown a high correlation between FFDM and tomosynthesis density measures assessed using Volpara™ , 34, it The results of this study showed a relative increase of DV and VPD over time in the breast with cancer compared to the contralateral normal breast, as well as compared to a set of healthy controls. This automated asymmetry density measure may incrementally improve discrimination of cancer risk, but our results suggest that it cannot be used in a stand-alone fashion in clinical practice to determine whether further examination is indicated.Additional file 1. Supplementary tables.