Early Indicators of the Impact of Using AI in Mammography Screening for Breast Cancer.

Background Retrospective studies have suggested that using artificial intelligence (AI) may decrease the workload of radiologists while preserving mammography screening performance. Purpose To compare workload and screening performance for two cohorts of women who underwent screening before and after AI system implementation. Materials and Methods This retrospective study included 50-69-year-old women who underwent biennial mammography screening in the Capital Region of Denmark. Before AI system implementation (October 1, 2020, to November 17, 2021), all screenings involved double reading. For screenings conducted after AI system implementation (November 18, 2021, to October 17, 2022), likely normal screenings (AI examination score ≤5 before May 3, 2022, or ≤7 on or after May 3, 2022) were single read by one of 19 senior full-time breast radiologists. The remaining screenings were read by two radiologists with AI-assisted decision support. Biopsy and surgical outcomes were retrieved between October 1, 2020, and April 15, 2023, ensuring at least 180 days of follow-up. Screening metrics were compared using the χ2 test. Reading workload reduction was measured as saved screening reads. Results In total, 60 751 and 58 246 women were screened before and after AI system implementation, respectively (median age, 58 years [IQR, 54-64 years] for both cohorts), with a median screening interval before AI of 845 days (IQR, 820-878 days) and with AI of 993 days (IQR, 968-1013 days; P < .001). After AI system implementation, the recall rate decreased by 20.5% (3.09% before AI [1875 of 60 751] vs 2.46% with AI [1430 of 58 246]; P < .001), the cancer detection rate increased (0.70% [423 of 60 751] vs 0.82% [480 of 58 246]; P = .01), the false-positive rate decreased (2.39% [1452 of 60 751] vs 1.63% [950 of 58 246]; P < .001), the positive predictive value increased (22.6% [423 of 1875] vs 33.6% [480 of 1430]; P < .001), the rate of small cancers (≤1 cm) increased (36.6% [127 of 347] vs 44.9% [164 of 365]; P = .02), the rate of node-negative cancers was unchanged (76.7% [253 of 330] vs 77.8% [273 of 351]; P = .73), and the rate of invasive cancers decreased (84.9% [359 of 423] vs 79.6% [382 of 480]; P = .04). The reading workload was reduced by 33.5% (38 977 of 116 492 reads). Conclusion In a population-based mammography screening program, using AI reduced the overall workload of breast radiologists while improving screening performance. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Lee and Friedewald in this issue.

Elucidating Structural Disorder in Ultra-Thin Bi-Rich Bismuth Oxyhalide Photocatalysts.

Advancing the field of photocatalysis requires the elucidation of structural properties that underpin the photocatalytic properties of promising materials. The focus of the present study is layered, Bi-rich bismuth oxyhalides, which are widely studied for photocatalytic applications yet poorly structurally understood, due to high levels of disorder, nano-sized domains, and the large number of structurally similar compounds. By connecting insights from multiple scattering techniques, utilizing electron-, X-ray- and neutron probes, the crystal phase of the synthesized materials is allocated as layered Bi24O31X10 (X = Cl, Br), albeit with significant deviation from the reported 3D crystalline model. The materials comprise anisotropic platelet-shaped crystalline domains, exhibiting significant in-plane ordering in two dimensions but disorder and an ultra-thin morphology in the layer stacking direction. Increased synthesis pH tailored larger, more ordered crystalline domains, leading to longer excited state lifetimes determined via femtosecond transient absorption spectroscopy (fs-TAS). Although this likely contributes to improved photocatalytic properties, assessed via the photooxidation of benzylamine, increasing the overall surface area facilitated the most significant improvement in photocatalytic performance. This study, therefore, enabled both phase allocation and a nuanced discussion of the structure-property relationship for complicated, ultra-thin photocatalysts.

Vesicle trafficking pathways in defence-related cell wall modifications: papillae and encasements.

Filamentous pathogens that cause plant diseases such as powdery mildew, rust, anthracnose, and late blight continue to represent an enormous challenge for farmers worldwide. Interestingly, these pathogens, although phylogenetically distant, initiate pathogenesis in a very similar way by penetrating the cell wall and establishing a feeding structure inside the plant host cell. To prevent pathogen ingress, the host cell responds by forming defence structures known as papillae and encasements that are thought to mediate pre- and post-invasive immunity, respectively. This form of defence is evolutionarily conserved in land plants and is highly effective and durable against a broad selection of non-adapted filamentous pathogens. As most pathogens have evolved strategies to overcome the defences of only a limited range of host plants, the papilla/encasement response could hold the potential to become an optimal transfer of resistance from one plant species to another. In this review I lay out current knowledge of the involvement of membrane trafficking that forms these important defence structures and highlight some of the questions that still need to be resolved.

Breast density and risk of breast cancer.

Early studies reported a 4- to 6-fold risk of breast cancer between women with extremely dense and fatty breasts. As most early studies were case-control studies, we took advantage of a population-based screening program to study density and breast cancer incidence in a cohort design. In the Capital Region, Denmark, women aged 50 to 69 are invited to screening biennially. Women screened November 2012 to December 2017 were included, and classified by BI-RADS density code, version 4, at first screen after recruitment. Women were followed up for incident breast cancer, including ductal carcinoma in situ (DCIS), to 2020 in nationwide pathology data. Rate ratios (RRs) and 95% confidence intervals (CI) were compared across density groups using Poisson-regression. We included 189 609 women; 1 067 282 person-years; and 4110 incident breast cancers/DCIS. Thirty-three percent of women had BI-RADS density code 1; 38% code 2; 24% code 3; 4.7% code 4; and missing 0.3%. Using women with BI-RADS density code 1 as baseline; women with code 2 had RR 1.69 (95% CI 1.56-1.84); women with code 3, RR 2.06 (95% CI 1.89-2.25); and women with code 4, RR 2.37 (95% CI 1.05-2.74). Results differed between observations accumulated during screening and above screening age. Our results indicated less difference in breast cancer risk across level of breast density than normally stated. Translated into absolute risk of breast cancer after age 50, we found a 6.2% risk for the one-third of women with lowest density, and 14.7% for the 5% of women with highest density.

Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture.

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.

Barley endosomal MONENSIN SENSITIVITY1 is a target of the powdery mildew effector CSEP0162 and plays a role in plant immunity.

Encasements formed around haustoria and biotrophic hyphae as well as hypersensitive reaction (HR) cell death are essential plant immune responses to filamentous pathogens. In this study we examine the components that may contribute to the absence of these responses in susceptible barley attacked by the powdery mildew fungus. We find that the effector CSEP0162 from this pathogen targets plant MONENSIN SENSITIVITY1 (MON1), which is important for the fusion of multivesicular bodies to their target membranes. Overexpression of CSEP0162 and silencing of barley MON1 both inhibit encasement formation. We find that the Arabidopsis ecotype No-0 has resistance to powdery mildew, and that this is partially dependent on MON1. Surprisingly, we find the MON1-dependent resistance in No-0 not only includes an encasement response, but also an effective HR. Similarly, silencing of MON1 in barley also blocks Mla3-mediated HR-based powdery mildew resistance. Our results indicate that MON1 is a vital plant immunity component, and we speculate that the barley powdery mildew fungus introduces the effector CSEP0162 to target MON1 and hence reduce encasement formation and HR.

Coordinated Activation of ARF1 GTPases by ARF-GEF GNOM Dimers Is Essential for Vesicle Trafficking in Arabidopsis.

Membrane trafficking maintains the organization of the eukaryotic cell and delivers cargo proteins to their subcellular destinations, such as sites of action or degradation. The formation of membrane vesicles requires the activation of the ADP-ribosylation factor ARF GTPase by the SEC7 domain of ARF guanine-nucleotide exchange factors (ARF-GEFs), resulting in the recruitment of coat proteins by GTP-bound ARFs. In vitro exchange assays were done with monomeric proteins, although ARF-GEFs form dimers in vivo. This feature is conserved across eukaryotes, although its biological significance is unknown. Here, we demonstrate the proximity of ARF1•GTPs in vivo by fluorescence resonance energy transfer-fluorescence lifetime imaging microscopy, mediated through coordinated activation by dimers of Arabidopsis (Arabidopsis thaliana) ARF-GEF GNOM, which is involved in polar recycling of the auxin transporter PIN-FORMED1. Mutational disruption of ARF1 spacing interfered with ARF1-dependent trafficking but not with coat protein recruitment. A mutation impairing the interaction of one of the two SEC7 domains of the GNOM ARF-GEF dimer with its ARF1 substrate reduced the efficiency of coordinated ARF1 activation. Our results suggest a model of coordinated activation-dependent membrane insertion of ARF1•GTP molecules required for coated membrane vesicle formation. Considering the evolutionary conservation of ARFs and ARF-GEFs, this initial regulatory step of membrane trafficking might well occur in eukaryotes in general.

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.

OBJECTIVES: To validate an AI system for standalone breast cancer detection on an entire screening population in comparison to first-reading breast radiologists. MATERIALS AND METHODS: All mammography screenings performed between August 4, 2014, and August 15, 2018, in the Region of Southern Denmark with follow-up within 24 months were eligible. Screenings were assessed as normal or abnormal by breast radiologists through double reading with arbitration. For an AI decision of normal or abnormal, two AI-score cut-off points were applied by matching at mean sensitivity (AIsens) and specificity (AIspec) of first readers. Accuracy measures were sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and recall rate (RR). RESULTS: The sample included 249,402 screenings (149,495 women) and 2033 breast cancers (72.6% screen-detected cancers, 27.4% interval cancers). AIsens had lower specificity (97.5% vs 97.7%; p < 0.0001) and PPV (17.5% vs 18.7%; p = 0.01) and a higher RR (3.0% vs 2.8%; p < 0.0001) than first readers. AIspec was comparable to first readers in terms of all accuracy measures. Both AIsens and AIspec detected significantly fewer screen-detected cancers (1166 (AIsens), 1156 (AIspec) vs 1252; p < 0.0001) but found more interval cancers compared to first readers (126 (AIsens), 117 (AIspec) vs 39; p < 0.0001) with varying types of cancers detected across multiple subgroups. CONCLUSION: Standalone AI can detect breast cancer at an accuracy level equivalent to the standard of first readers when the AI threshold point was matched at first reader specificity. However, AI and first readers detected a different composition of cancers. CLINICAL RELEVANCE STATEMENT: Replacing first readers with AI with an appropriate cut-off score could be feasible. AI-detected cancers not detected by radiologists suggest a potential increase in the number of cancers detected if AI is implemented to support double reading within screening, although the clinicopathological characteristics of detected cancers would not change significantly. KEY POINTS: • Standalone AI cancer detection was compared to first readers in a double-read mammography screening population. • Standalone AI matched at first reader specificity showed no statistically significant difference in overall accuracy but detected different cancers. • With an appropriate threshold, AI-integrated screening can increase the number of detected cancers with similar clinicopathological characteristics.

An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload.

Background Developments in artificial intelligence (AI) systems to assist radiologists in reading mammograms could improve breast cancer screening efficiency. Purpose To investigate whether an AI system could detect normal, moderate-risk, and suspicious mammograms in a screening sample to safely reduce radiologist workload and evaluate across Breast Imaging Reporting and Data System (BI-RADS) densities. Materials and Methods This retrospective simulation study analyzed mammographic examination data consecutively collected from January 2014 to December 2015 in the Danish Capital Region breast cancer screening program. All mammograms were scored from 0 to 10, representing the risk of malignancy, using an AI tool. During simulation, normal mammograms (score < 5) would be excluded from radiologist reading and suspicious mammograms (score > recall threshold [RT]) would be recalled. Two radiologists read the remaining mammograms. The RT was fitted using another independent cohort (same institution) by matching to the radiologist sensitivity. This protocol was further applied to each BI-RADS density. Screening outcomes were measured using the sensitivity, specificity, workload, and false-positive rate. The AI-based screening was tested for noninferiority sensitivity compared with radiologist screening using the Farrington-Manning test. Specificities were compared using the McNemar test. Results The study sample comprised 114 421 screenings for breast cancer in 114 421 women, resulting in 791 screen-detected, 327 interval, and 1473 long-term cancers and 2107 false-positive screenings. The mean age of the women was 59 years ± 6 (SD). The AI-based screening sensitivity was 69.7% (779 of 1118; 95% CI: 66.9, 72.4) and was noninferior (P = .02) to the radiologist screening sensitivity of 70.8% (791 of 1118; 95% CI: 68.0, 73.5). The AI-based screening specificity was 98.6% (111 725 of 113 303; 95% CI: 98.5, 98.7), which was higher (P < .001) than the radiologist specificity of 98.1% (111 196 of 113 303; 95% CI: 98.1, 98.2). The radiologist workload was reduced by 62.6% (71 585 of 114 421), and 25.1% (529 of 2107) of false-positive screenings were avoided. Screening results were consistent across BI-RADS densities, although not significantly so for sensitivity. Conclusion Artificial intelligence (AI)-based screening could detect normal, moderate-risk, and suspicious mammograms in a breast cancer screening program, which may reduce the radiologist workload. AI-based screening performed consistently across breast densities. © RSNA, 2022 Online supplemental material is available for this article.

Robust parametric modeling of Alzheimer's disease progression.

Quantitative characterization of disease progression using longitudinal data can provide long-term predictions for the pathological stages of individuals. This work studies the robust modeling of Alzheimer's disease progression using parametric methods. The proposed method linearly maps the individual's age to a disease progression score (DPS) and jointly fits constrained generalized logistic functions to the longitudinal dynamics of biomarkers as functions of the DPS using M-estimation. Robustness of the estimates is quantified using bootstrapping via Monte Carlo resampling, and the estimated inflection points of the fitted functions are used to temporally order the modeled biomarkers in the disease course. Kernel density estimation is applied to the obtained DPSs for clinical status classification using a Bayesian classifier. Different M-estimators and logistic functions, including a novel type proposed in this study, called modified Stannard, are evaluated on the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for robust modeling of volumetric magnetic resonance imaging (MRI) and positron emission tomography (PET) biomarkers, cerebrospinal fluid (CSF) measurements, as well as cognitive tests. The results show that the modified Stannard function fitted using the logistic loss achieves the best modeling performance with an average normalized mean absolute error (NMAE) of 0.991 across all biomarkers and bootstraps. Applied to the ADNI test set, this model achieves a multiclass area under the ROC curve (AUC) of 0.934 in clinical status classification. The obtained results for the proposed model outperform almost all state-of-the-art results in predicting biomarker values and classifying clinical status. Finally, the experiments show that the proposed model, trained using abundant ADNI data, generalizes well to data from the National Alzheimer's Coordinating Center (NACC) with an average NMAE of 1.182 and a multiclass AUC of 0.929.

VPS9a Activates the Rab5 GTPase ARA7 to Confer Distinct Pre- and Postinvasive Plant Innate Immunity.

Plant innate immunity can effectively prevent the proliferation of filamentous pathogens. Papilla formation at the site of attack is essential for preinvasive immunity; in postinvasive immunity, the encasement of pathogen structures inside host cells can hamper disease. Whereas papillae are highly dependent on transcytosis of premade material, little is known about encasement formation. Here, we show that endosome-associated VPS9a, the conserved guanine-nucleotide exchange factor activating Rab5 GTPases, is required for both pre- and postinvasive immunity against a nonadapted powdery mildew fungus (Blumeria graminis f. sp hordei) in Arabidopsis thaliana Surprisingly, VPS9a acts in addition to two previously well-described innate immunity components and thus represents an additional step in the regulation of how plants resist pathogens. We found VPS9a to be important for delivering membrane material to the encasement and VPS9a also plays a predominant role in postinvasive immunity. GTP-bound Rab5 GTPases accumulate in the encasement, but not the papillae, suggesting that two independent pathways form these defense structures. VPS9a also mediates defense to an adapted powdery mildew fungus, thus regulating a durable type of defense that works in both host and nonhost resistance. We propose that VPS9a plays a conserved role in organizing cellular endomembrane trafficking, required for delivery of defense components in response to powdery mildew fungi.

Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial.

OBJECTIVE. The purpose of this study is to establish whether texture analysis and densitometry are complementary quantitative measures of chronic obstructive pulmonary disease (COPD) in a lung cancer screening setting. MATERIALS AND METHODS. This was a retrospective study of data collected prospectively (in 2004-2010) in the Danish Lung Cancer Screening Trial. The texture score, relative area of emphysema, and percentile density were computed for 1915 baseline low-dose lung CT scans and were evaluated, both individually and in combination, for associations with lung function (i.e., forced expiratory volume in 1 second as a percentage of predicted normal [FEV1% predicted]), diagnosis of mild to severe COPD, and prediction of a rapid decline in lung function. Multivariate linear regression models with lung function as the outcome were compared using the likelihood ratio test or the Vuong test, and AUC values for diagnostic and prognostic capabilities were compared using the DeLong test. RESULTS. Texture showed a significantly stronger association with lung function (p < 0.001 vs densitometric measures), a significantly higher diagnostic AUC value (for COPD, 0.696; for Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1, 0.648; for GOLD grade 2, 0.768; and for GOLD grade 3, 0.944; p < 0.001 vs densitometric measures), and a higher but not significantly different association with lung function decline. In addition, only texture could predict a rapid decline in lung function (AUC value, 0.538; p < 0.05 vs random guessing). The combination of texture and both densitometric measures strengthened the association with lung function and decline in lung function (p < 0.001 and p < 0.05, respectively, vs texture) but did not improve diagnostic or prognostic performance. CONCLUSION. The present study highlights texture as a promising quantitative CT measure of COPD to use alongside, or even instead of, densitometric measures. Moreover, texture may allow early detection of COPD in subjects who undergo lung cancer screening.

Sensitivity of screening mammography by density and texture: a cohort study from a population-based screening program in Denmark.

BACKGROUND: Screening mammography works better in fatty than in dense breast tissue. Computerized assessment of parenchymal texture is a non-subjective method to obtain a refined description of breast tissue, potentially valuable in addition to breast density scoring for the identification of women in need of supplementary imaging. We studied the sensitivity of screening mammography by a combination of radiologist-assessed Breast Imaging Reporting and Data System (BI-RADS) density score and computer-assessed parenchymal texture marker, mammography texture resemblance (MTR), in a population-based screening program. METHODS: Breast density was coded according to the fourth edition of the BI-RADS density code, and MTR marker was divided into quartiles from 1 to 4. Screening data were followed up for the identification of screen-detected and interval cancers. We calculated sensitivity and specificity with 95% confidence intervals (CI) by BI-RADS density score, MTR marker, and combination hereof. RESULTS: Density and texture were strongly correlated, but the combination led to the identification of subgroups with different sensitivity. Sensitivity was high, about 80%, in women with BI-RADS density score 1 and MTR markers 1 or 2. Sensitivity was low, 67%, in women with BI-RADS density score 2 and MTR marker 4. For women with BI-RADS density scores 3 and 4, the already low sensitivity was further decreased for women with MTR marker 4. Specificity was 97-99% in all subgroups. CONCLUSION: Our study showed that women with low density constituted a heterogenous group. Classifying women for extra imaging based on density only might be a too crude approach. Screening sensitivity was systematically high in women with fatty and homogenous breast tissue.

Change in mammographic density across birth cohorts of Dutch breast cancer screening participants.

High mammographic density is a well-known risk factor for breast cancer. This study aimed to search for a possible birth cohort effect on mammographic density, which might contribute to explain the increasing breast cancer incidence. We separately analyzed left and right breast density of Dutch women from a 13-year period (2003-2016) in the breast cancer screening programme. First, we analyzed age-specific changes in average percent dense volume (PDV) across birth cohorts. A linear regression analysis (PDV vs. year of birth) indicated a small but statistically significant increase in women of: 1) age 50 and born from 1952 to 1966 (left, slope = 0.04, p = 0.003; right, slope = 0.09, p < 0.0001); 2) age 55 and born from 1948 to 1961 (right, slope = 0.04, p = 0.01); and 3) age 70 and born from 1933 to 1946 (right, slope = 0.05, p = 0.002). A decrease of total breast volume seemed to explain the increase in PDV. Second, we compared proportion of women with dense breast in women born in 1946-1953 and 1959-1966, and observed a statistical significant increase of proportion of highly dense breast in later born women, in the 51 to 55 age-groups for the left breast (around a 20% increase in each age-group), and in the 50 to 56 age-groups for the right breast (increase ranging from 27% to 48%). The study indicated a slight increase in mammography density across birth cohorts, most pronounced for women in their early 50s, and more marked for the right than for the left breast.

The combined effect of mammographic texture and density on breast cancer risk: a cohort study.

BACKGROUND: Texture patterns have been shown to improve breast cancer risk segregation in addition to area-based mammographic density. The additional value of texture pattern scores on top of volumetric mammographic density measures in a large screening cohort has never been studied. METHODS: Volumetric mammographic density and texture pattern scores were assessed automatically for the first available digital mammography (DM) screening examination of 51,400 women (50-75 years of age) participating in the Dutch biennial breast cancer screening program between 2003 and 2011. The texture assessment method was developed in a previous study and validated in the current study. Breast cancer information was obtained from the screening registration system and through linkage with the Netherlands Cancer Registry. All screen-detected breast cancers diagnosed at the first available digital screening examination were excluded. During a median follow-up period of 4.2 (interquartile range (IQR) 2.0-6.2) years, 301 women were diagnosed with breast cancer. The associations between texture pattern scores, volumetric breast density measures and breast cancer risk were determined using Cox proportional hazard analyses. Discriminatory performance was assessed using c-indices. RESULTS: The median age of the women at the time of the first available digital mammography examination was 56 years (IQR 51-63). Texture pattern scores were positively associated with breast cancer risk (hazard ratio (HR) 3.16 (95% CI 2.16-4.62) (p value for trend <0.001), for quartile (Q) 4 compared to Q1). The c-index of texture was 0.61 (95% CI 0.57-0.64). Dense volume and percentage dense volume showed positive associations with breast cancer risk (HR 1.85 (95% CI 1.32-2.59) (p value for trend <0.001) and HR 2.17 (95% CI 1.51-3.12) (p value for trend <0.001), respectively, for Q4 compared to Q1). When adding texture measures to models with dense volume or percentage dense volume, c-indices increased from 0.56 (95% CI 0.53-0.59) to 0.62 (95% CI 0.58-0.65) (p < 0.001) and from 0.58 (95% CI 0.54-0.61) to 0.60 (95% CI 0.57-0.63) (p = 0.054), respectively. CONCLUSIONS: Deep-learning-based texture pattern scores, measured automatically on digital mammograms, are associated with breast cancer risk, independently of volumetric mammographic density, and augment the capacity to discriminate between future breast cancer and non-breast cancer cases.

Subclinical depressive symptoms during late midlife and structural brain alterations: A longitudinal study of Danish men born in 1953.

We explored whether depressive symptoms measured three times during midlife were associated with structural brain alterations quantified using magnetic resonance imaging measurements of volume, cortical thickness, and intensity texture. In 192 men born in 1953 with depressive symptoms measured at age 51, 56, and 59 years, magnetic resonance imaging was performed at age 59. All data processing was performed using the Freesurfer software package except for the texture-scores that were computed using in-house software. Structural brain alterations and associations between depressive symptoms and brain structure outcomes were tested using Pearson's correlation, t test, and linear regression. Depressive symptoms at age 51 showed clear inverse correlations with total gray matter, pallidum, and hippocampal volume with the strongest estimate for hippocampal volume (r = -.22, p < .01). After exclusion of men (n = 3) with scores in the range of clinical depression the inverse correlation between depressive symptoms and hippocampal volume became insignificant (r = -13, p = .08). Depressive symptoms at age 59 correlated positively with hippocampal and amygdala texture-potential early markers of atrophy. Inverse relations with total gray matter and pallidum volumes lost significance when the analysis was adjusted for intracranial volume. In men, depressive symptoms at age 51 were associated with a reduced volume of the hippocampus at age 59 independent of later symptoms. Amygdala and hippocampal textures might be the early markers for brain alterations associated with depression in midlife.

Screening mammography: benefit of double reading by breast density.

PURPOSE: The currently recommended double reading of all screening mammography examinations is an economic burden for screening programs. The sensitivity of screening is higher for women with low breast density than for women with high density. One may therefore ask whether single reading could replace double reading at least for women with low density. We addressed this question using data from a screening program where the radiologists coded their readings independently. METHODS: Data include all screening mammography examinations in the Capital Region of Denmark from 1 November 2012 to 31 December 2013. Outcome of screening was assessed by linkage to the Danish Pathology Register. We calculated sensitivity, specificity, number of interval cancers, and false positive-tests per 1000 screened women by both single reader and consensus BI-RADS density code. RESULTS: In total 54,808 women were included. The overall sensitivity of double reading was 72%, specificity was 97.6%, 3 women per 1000 screened experienced an interval cancer, and 24 a false-positive test. Across all BI-RADS density codes, single reading consistently decreased sensitivity as compared with consensus reading. The same was true for specificity, apart from results across BI-RADS density codes set by reader 2. CONCLUSIONS: Single reading decreased sensitivity as compared with double reading across all BI-RADS density codes. This included results based on consensus BI-RADS density codes. This means that replacement of double with single reading would have negative consequences for the screened women, even if density could be assessed automatically calibrated to the usual consensus level.

Plant exosomes: using an unconventional exit to prevent pathogen entry?

The ability to ward off filamentous pathogens, such as powdery mildew fungi, is one of the best studied examples of membrane trafficking-dependent disease resistance in plants. Here, papilla formation at the site of attack is essential for the pre-invasive immunity, whereas the encasement can hamper disease post-invasively. Exosomes containing antifungal peptides and small RNAs are thought to play a vital role in forming papillae and encasements that block fungal growth. While exosomes are well described in mammals, and have been shown to play important roles in cell-cell communication regulating development and disease, their function is not well-known in plants. In this review, we focus on some of the recent discoveries on plant exosomes and try to link this information with our current understanding of how plants use this form of unconventional secretion to acquire this durable and effective form of resistance.

The plant membrane surrounding powdery mildew haustoria shares properties with the endoplasmic reticulum membrane.

Many filamentous plant pathogens place specialized feeding structures, called haustoria, inside living host cells. As haustoria grow, they are believed to manipulate plant cells to generate a specialized, still enigmatic extrahaustorial membrane (EHM) around them. Here, we focused on revealing properties of the EHM. With the help of membrane-specific dyes and transient expression of membrane-associated proteins fused to fluorescent tags, we studied the nature of the EHM generated by barley leaf epidermal cells around powdery mildew haustoria. Observations suggesting that endoplasmic reticulum (ER) membrane-specific dyes labelled the EHM led us to find that Sar1 and RabD2a GTPases bind this membrane. These proteins are usually associated with the ER and the ER/cis-Golgi membrane, respectively. In contrast, transmembrane and luminal ER and Golgi markers failed to label the EHM, suggesting that it is not a continuum of the ER. Furthermore, GDP-locked Sar1 and a nucleotide-free RabD2a, which block ER to Golgi exit, did not hamper haustorium formation. These results indicated that the EHM shares features with the plant ER membrane, but that the EHM membrane is not dependent on conventional secretion. This raises the prospect that an unconventional secretory pathway from the ER may provide this membrane's material. Understanding these processes will assist future approaches to providing resistance by preventing EHM generation.