Актуальность и основные аспекты антикризисного менеджмента предприятием

Целью данной статьи является выявление значимости и основных аспектов антикризисного менеджмента

Volumetric breast density affects performance of digital screening mammography

PURPOSE: To determine to what extent automatically measured volumetric mammographic density influences screening performance when using digital mammography (DM). METHODS: We collected a consecutive series of 111,898 DM examinations (2003-2011) from one screening unit of the Dutch biennial screening program (age 50-75 years). Volumetric mammographic density was automatically assessed using Volpara. We determined screening performance measures for four density categories comparable to the American College of Radiology (ACR) breast density categories. RESULTS: Of all the examinations, 21.6% were categorized as density category 1 ('almost entirely fatty') and 41.5, 28.9, and 8.0% as category 2-4 ('extremely dense'), respectively. We identified 667 screen-detected and 234 interval cancers. Interval cancer rates were 0.7, 1.9, 2.9, and 4.4‰ and false positive rates were 11.2, 15.1, 18.2, and 23.8‰ for categories 1-4, respectively (both p-trend < 0.001). The screening sensitivity, calculated as the proportion of screen-detected among the total of screen-detected and interval tumors, was lower in higher density categories: 85.7, 77.6, 69.5, and 61.0% for categories 1-4, respectively (p-trend < 0.001). CONCLUSIONS: Volumetric mammographic density, automatically measured on digital mammograms, impacts screening performance measures along the same patterns as established with ACR breast density categories. Since measuring breast density fully automatically has much higher reproducibility than visual assessment, this automatic method could help with implementing density-based supplemental screening

Computer-Aided Diagnosis in Multiparametric Magnetic Resonance Imaging Screening of Women With Extremely Dense Breasts to Reduce False-Positive Diagnoses

OBJECTIVES: To reduce the number of false-positive diagnoses in the screening of women with extremely dense breasts using magnetic resonance imaging (MRI), we aimed to predict which BI-RADS 3 and BI-RADS 4 lesions are benign. For this purpose, we use computer-aided diagnosis (CAD) based on multiparametric assessment. MATERIALS AND METHODS: Consecutive data were used from the first screening round of the DENSE (Dense Tissue and Early Breast Neoplasm Screening) trial. In this trial, asymptomatic women with a negative screening mammography and extremely dense breasts were screened using multiparametric MRI. In total, 4783 women, aged 50 to 75 years, enrolled and were screened in 8 participating hospitals between December 2011 and January 2016. In total, 525 lesions in 454 women were given a BI-RADS 3 (n = 202), 4 (n = 304), or 5 score (n = 19). Of these lesions, 444 were benign and 81 were malignant on histologic examination.The MRI protocol consisted of 5 different MRI sequences: T1-weighted imaging without fat suppression, diffusion-weighted imaging, T1-weighted contrast-enhanced images at high spatial resolution, T1-weighted contrast-enhanced images at high temporal resolution, and T2-weighted imaging. A machine-learning method was developed to predict, without deterioration of sensitivity, which of the BI-RADS 3- and BI-RADS 4-scored lesions are actually benign and could be prevented from being recalled. BI-RADS 5 lesions were only used for training, because the gain in preventing false-positive diagnoses is expected to be low in this group. The CAD consists of 2 stages: feature extraction and lesion classification. Two groups of features were extracted: the first based on all multiparametric sequences, the second based only on sequences that are typically used in abbreviated MRI protocols. In the first group, 49 features were used as candidate predictors: 46 were automatically calculated from the MRI scans, supplemented with 3 clinical features (age, body mass index, and BI-RADS score). In the second group, 36 image features and the same 3 clinical features were used. Each group was considered separately in a machine-learning model to differentiate between benign and malignant lesions. We developed a Ridge regression model using 10-fold cross validation. Performance of the models was analyzed using an accuracy measure curve and receiver-operating characteristic analysis. RESULTS: Of the total number of BI-RADS 3 and BI-RADS 4 lesions referred to additional MRI or biopsy, 425/487 (87.3%) were false-positive. The full multiparametric model classified 176 (41.5%) and the abbreviated-protocol model classified 111 (26.2%) of the 425 false-positive BI-RADS 3- and BI-RADS 4-scored lesions as benign without missing a malignant lesion.If the full multiparametric CAD had been used to aid in referral, recall for biopsy or repeat MRI could have been reduced from 425/487 (87.3%) to 311/487 (63.9%) lesions. For the abbreviated protocol, it could have been 376/487 (77.2%). CONCLUSIONS: Dedicated multiparametric CAD of breast MRI for BI-RADS 3 and 4 lesions in screening of women with extremely dense breasts has the potential to reduce false-positive diagnoses and consequently to reduce the number of biopsies without missing cancers

Automated rating of background parenchymal enhancement in MRI of extremely dense breasts without compromising the association with breast cancer in the DENSE trial

Objectives: Background parenchymal enhancement (BPE) on dynamic contrast-enhanced MRI (DCE-MRI) as rated by radiologists is subject to inter- and intrareader variability. We aim to automate BPE category from DCE-MRI. Methods: This study represents a secondary analysis of the Dense Tissue and Early Breast Neoplasm Screening trial. 4553 women with extremely dense breasts who received supplemental breast MRI screening in eight hospitals were included. Minimal, mild, moderate and marked BPE rated by radiologists were used as reference. Fifteen quantitative MRI features of the fibroglandular tissue were extracted to predict BPE using Random Forest, Naïve Bayes, and KNN classifiers. Majority voting was used to combine the predictions. Internal-external validation was used for training and validation. The inverse-variance weighted mean accuracy was used to express mean performance across the eight hospitals. Cox regression was used to verify non inferiority of the association between automated rating and breast cancer occurrence compared to the association for manual rating. Results: The accuracy of majority voting ranged between 0.56 and 0.84 across the eight hospitals. The weighted mean prediction accuracy for the four BPE categories was 0.76. The hazard ratio (HR) of BPE for breast cancer occurrence was comparable between automated rating and manual rating (HR = 2.12 versus HR = 1.97, P = 0.65 for mild/moderate/marked BPE relative to minimal BPE). Conclusion: It is feasible to rate BPE automatically in DCE-MRI of women with extremely dense breasts without compromising the underlying association between BPE and breast cancer occurrence. The accuracy for minimal BPE is superior to that for other BPE categories

Indocyanine green versus technetium-99m with blue dye for sentinel lymph node detection in early-stage cervical cancer: A systematic review and meta-analysis

BACKGROUND: The fluorescent dye indocyanine green (ICG) has emerged as a promising tracer for intraoperative detection of sentinel lymph nodes (SLNs) in early-stage cervical cancer. Although researchers suggest the SLN detection of ICG is equal to the more conventional combined approach of a radiotracer and blue dye, no consensus has been reached. AIMS: We aimed to assess the differences in overall and bilateral SLN detection rates with ICG versus the combined approach, the radiotracer technetium-99m (99m Tc) with blue dye. METHODS AND RESULTS: We searched MEDLINE, Embase, and the Cochrane Library from inception to January 1, 2020 and included studies reporting on a comparison of SLN detection with ICG versus 99m Tc with blue dye in early-stage cervical cancer. The overall and bilateral detection rates were pooled with random-effects meta-analyses. From 118 studies retrieved seven studies (one cross-sectional; six retrospective cohorts) were included, encompassing 589 patients. No significant differences were found in the pooled overall SLN detection rate of ICG versus 99m Tc with blue dye. Meta-analyses of all studies showed ICG to result in a higher bilateral SLN detection rate than 99m Tc with blue dye; 90.3% (95%CI, 79.8-100.0%) with ICG versus 73.5% (95%CI, 66.4-80.6%) with 99mTc with blue dye. This resulted in a significant and clinically relevant risk difference of 16.6% (95%CI, 5.3-28.0%). With sensitivity analysis, the risk difference of the bilateral detection rate maintained in favor of ICG but was no longer significant (13.2%, 95%CI -0.8-27.3%). CONCLUSION: ICG appears to provide higher bilateral SLN detection rates compared to 99m Tc with blue dye in patients with early-stage cervical cancer. However, in adherence with the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) guidelines, the quality of evidence is too low to provide strong recommendations and directly omit the combined approach of 99m Tc with blue dye

Природный и антропогенный факторы формирования и развития культурного ландшафта Форосского парка

Цель данной статьи: на примере небольшой территории Южного берега Крыма – парка в пгт. Форос и прилегающей к нему местности – показать роль и место культурного ландшафта в формировании человеком исторического геокультурного пространства

Quaking promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression

A hallmark of inflammatory diseases is the excessive recruitment and influx of monocytes to sites of tissue damage and their ensuing differentiation into macrophages. Numerous stimuli are known to induce transcriptional changes associated with macrophage phenotype, but posttranscriptional control of human macrophage differentiation is less well understood. Here we show that expression levels of the RNA-binding protein Quaking (QKI) are low in monocytes and early human atherosclerotic lesions, but are abundant in macrophages of advanced plaques. Depletion of QKI protein impairs monocyte adhesion, migration, differentiation into macrophages and foam cell formation in vitro and in vivo. RNA-seq and microarray analysis of human monocyte and macrophage transcriptomes, including those of a unique QKI haploinsufficient patient, reveal striking changes in QKI-dependent messenger RNA levels and splicing of RNA transcripts. The biological importance of these transcripts and requirement for QKI during differentiation illustrates a central role for QKI in posttranscriptionally guiding macrophage identity and function.No sponso

Drought, mutualism breakdown, and landscape-scale degradation of seagrass beds

In many marine ecosystems, biodiversity critically depends on foundation species such as corals and seagrasses that engage in mutualistic interactions [1-3]. Concerns grow that environmental disruption of marine mutualisms exacerbates ecosystem degradation, with breakdown of the obligate coral mutualism ("coral bleaching") being an iconic example [2, 4, 5]. However, as these mutualisms are mostly facultative rather than obligate, it remains unclear whether mutualism breakdown is a common risk in marine ecosystems, and thus a potential accelerator of ecosystem degradation. Here, we provide evidence that drought triggered landscape-scale seagrass degradation and show the consequent failure of a facultative mutualistic feedback between seagrass and sulfide-consuming lucinid bivalves that in turn appeared to exacerbate the observed collapse. Local climate and remote sensing analyses revealed seagrass collapse after a summer with intense low-tide drought stress. Potential analysis-a novel approach to detect feedback-mediated state shifts-revealed two attractors (healthy and degraded states) during the collapse, suggesting that the drought disrupted internal feedbacks to cause abrupt, patch-wise degradation. Field measurements comparing degraded patches that were healthy before the collapse with patches that remained healthy demonstrated that bivalves declined dramatically in degrading patches with associated high sediment sulfide concentrations, confirming the breakdown of the mutualistic seagrass-lucinid feedback. Our findings indicate that drought triggered mutualism breakdown, resulting in toxic sulfide concentrations that aggravated seagrass degradation. We conclude that external disturbances can cause sudden breakdown of facultative marine mutualistic feedbacks. As this may amplify ecosystem degradation, we suggest including mutualisms in marine conservation and restoration approaches

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Co-display of diverse spike proteins on nanoparticles broadens sarbecovirus neutralizing antibody responses

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants poses continuous challenges in combating the virus. Here, we describe vaccination strategies to broaden SARS-CoV-2 and sarbecovirus immunity by combining spike proteins based on different viruses or viral strains displayed on two-component protein nanoparticles. First, we combined spike proteins based on ancestral and Beta SARS-CoV-2 strains to broaden SARS-CoV-2 immune responses. Inclusion of Beta spike improved neutralizing antibody responses against SARS-CoV-2 Beta, Gamma, and Omicron BA.1 and BA.4/5. A third vaccination with ancestral SARS-CoV-2 spike also improved cross-neutralizing antibody responses against SARS-CoV-2 variants, in particular against the Omicron sublineages. Second, we combined SARS-CoV and SARS-CoV-2 spike proteins to broaden sarbecovirus immune responses. Adding SARS-CoV spike to a SARS-CoV-2 spike vaccine improved neutralizing responses against SARS-CoV and SARS-like bat sarbecoviruses SHC014 and WIV1. These results should inform the development of broadly active SARS-CoV-2 and pan-sarbecovirus vaccines and highlight the versatility of two-component nanoparticles for displaying diverse antigens