Piezocone testing in Nordic soft clays: Comparison of high-quality databases

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Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study

Background: Response to immunotherapy in gastric cancer is associated with microsatellite instability (or mismatch repair deficiency) and Epstein-Barr virus (EBV) positivity. We therefore aimed to develop and validate deep learning-based classifiers to detect microsatellite instability and EBV status from routine histology slides. Methods: In this retrospective, multicentre study, we collected tissue samples from ten cohorts of patients with gastric cancer from seven countries (South Korea, Switzerland, Japan, Italy, Germany, the UK and the USA). We trained a deep learning-based classifier to detect microsatellite instability and EBV positivity from digitised, haematoxylin and eosin stained resection slides without annotating tumour containing regions. The performance of the classifier was assessed by within-cohort cross-validation in all ten cohorts and by external validation, for which we split the cohorts into a five-cohort training dataset and a five-cohort test dataset. We measured the area under the receiver operating curve (AUROC) for detection of microsatellite instability and EBV status. Microsatellite instability and EBV status were determined to be detectable if the lower bound of the 95% CI for the AUROC was above 0·5. Findings: Across the ten cohorts, our analysis included 2823 patients with known microsatellite instability status and 2685 patients with known EBV status. In the within-cohort cross-validation, the deep learning-based classifier could detect microsatellite instability status in nine of ten cohorts, with AUROCs ranging from 0·597 (95% CI 0·522–0·737) to 0·836 (0·795–0·880) and EBV status in five of eight cohorts, with AUROCs ranging from 0·819 (0·752–0·841) to 0·897 (0·513–0·966). Training a classifier on the pooled training dataset and testing it on the five remaining cohorts resulted in high classification performance with AUROCs ranging from 0·723 (95% CI 0·676–0·794) to 0·863 (0·747–0·969) for detection of microsatellite instability and from 0·672 (0·403–0·989) to 0·859 (0·823–0·919) for detection of EBV status. Interpretation: Classifiers became increasingly robust when trained on pooled cohorts. After prospective validation, this deep learning-based tissue classification system could be used as an inexpensive predictive biomarker for immunotherapy in gastric cancer. Funding: German Cancer Aid and German Federal Ministry of Health

The Fitness Cost of Antibiotic Resistance in Streptococcus pneumoniae: Insight from the Field

Laboratory studies have suggested that antibiotic resistance may result in decreased fitness in the bacteria that harbor it. Observational studies have supported this, but due to ethical and practical considerations, it is rare to have experimental control over antibiotic prescription rates.We analyze data from a 54-month longitudinal trial that monitored pneumococcal drug resistance during and after biannual mass distribution of azithromycin for the elimination of the blinding eye disease, trachoma. Prescription of azithromycin and antibiotics that can create cross-resistance to it is rare in this part of the world. As a result, we were able to follow trends in resistance with minimal influence from unmeasured antibiotic use. Using these data, we fit a probabilistic disease transmission model that included two resistant strains, corresponding to the two dominant modes of resistance to macrolide antibiotics. We estimated the relative fitness of these two strains to be 0.86 (95% CI 0.80 to 0.90), and 0.88 (95% CI 0.82 to 0.93), relative to antibiotic-sensitive strains. We then used these estimates to predict that, within 5 years of the last antibiotic treatment, there would be a 95% chance of elimination of macrolide resistance by intra-species competition alone.Although it is quite possible that the fitness cost of macrolide resistance is sufficient to ensure its eventual elimination in the absence of antibiotic selection, this process takes time, and prevention is likely the best policy in the fight against resistance

Association between multimorbidity and postoperative mortality in patients undergoing major surgery: a prospective study in 29 countries across Europe

BackgroundMultimorbidity poses a global challenge to healthcare delivery. This study aimed to describe the prevalence of multimorbidity, common disease combinations and outcomes in a contemporary cohort of patients undergoing major abdominal surgery.MethodsThis was a pre-planned analysis of a prospective, multicentre, international study investigating cardiovascular complications after major abdominal surgery conducted in 446 hospitals in 29 countries across Europe. The primary outcome was 30-day postoperative mortality. The secondary outcome measure was the incidence of complications within 30 days of surgery.ResultsOf 24,227 patients, 7006 (28.9%) had one long-term condition and 10,486 (43.9%) had multimorbidity (two or more long-term health conditions). The most common conditions were primary cancer (39.6%); hypertension (37.9%); chronic kidney disease (17.4%); and diabetes (15.4%). Patients with multimorbidity had a higher incidence of frailty compared with patients <= 1 long-term health condition. Mortality was higher in patients with one long-term health condition (adjusted odds ratio 1.93 (95%CI 1.16-3.23)) and multimorbidity (adjusted odds ratio 2.22 (95%CI 1.35-3.64)). Frailty and ASA physical status 3-5 mediated an estimated 31.7% of the 30-day mortality in patients with one long-term health condition (adjusted odds ratio 1.30 (95%CI 1.12-1.51)) and an estimated 36.9% of the 30-day mortality in patients with multimorbidity (adjusted odds ratio 1.61 (95%CI 1.36-1.91)). There was no improvement in 30-day mortality in patients with multimorbidity who received pre-operative medical assessment.ConclusionsMultimorbidity is common and outcomes are poor among surgical patients across Europe. Addressing multimorbidity in elective and emergency patients requires innovative strategies to account for frailty and disease control. The development of such strategies, that integrate care targeting whole surgical pathways to strengthen current systems, is urgently needed for multimorbid patients. Interventional trials are warranted to determine the effectiveness of targeted management for surgical patients with multimorbidity

Small strain stiffness within logarithmic contractancy model for structured anisotropic clay

Stiffness of soils in the small strain region is high and it decays nonlinearly with increasing shear strains or with mobilization of shear stresses. However, the commonly used critical state based constitutive models use a simple elastic formulation at small strains that falls short in the prediction of the small strain nonlinearity and anisotropy. This paper proposes a simple way for rendering the existing constitutive models with the capability to capture the small strain behaviour of soils. This is illustrated by proposing a new model for structured anisotropic clay extending an existing model that uses the framework of logarithmic contractancy called ESCLAY1S. The proposed model is implemented into a Finite Element program as a user-defined soil model. The model predictions are compared with experimental data for various clays. Furthermore, the effect of nonlinearity is investigated for an excavation in soft clay

Spatial Dispersion of Interconnection Clusters in the European Internet

Abstract This paper studies the effects of geographical distance on the interconnection agreements between providers participating at Internet Exchange Points (IXPs) in Europe. We assess separately two main issues: the extent of existing clustering, as well as the role of proximity in bilateral interconnection decisions. Our results show the dominance of spatial random patterns in the interconnection agreement distributions. On the other hand, we find that proximity positively affects the probability of establishing interconnection for all the IXPs studied but one. Interesting, the latter is the only one showing an initially clustered morphology. This indicates a prevalence of centripetal forces—local spillovers and mutual knowledge—over geographical differentiation for any pre-existing interconnection pattern morphology except the clustered ones.Internet, peering, clustering, agglomeration, networks, IXP, C21, C25, D85, L86, R12, Z13,

New technologies in breast cancer sentinel lymph node biopsy; from the current gold standard to artificial intelligence

Sentinel lymph node biopsy is an important diagnostic procedure performed in early breast cancer patients with clinically negative axillary lymph nodes. Detection and examination of sentinel lymph nodes determine further therapy decisions, therefore a choice of optimal technique minimising the risk of false-negative results is of great importance. Currently, the gold standard is the dual technique comprising the blue dye and radiotracer, however, this method creates a logistical problem for many medical units. The intrinsic constraints of the existing methods led to the development of a very wide range of alternatives with varying clinical efficiency and feasibility. While each method presents with its own advantages and disadvantages, many techniques have improved enough to become a non-inferior alternative in the sentinel lymph node biopsy. Along with the improvement of the existing technologies, there are evolving trends such as multimodality of the techniques maximising the diagnostic outcome or an emerging use of artificial intelligence (AI) improving the workflow of the procedure. This literature review aims to give an overview of the current status of the standard techniques and emerging cutting-edge technologies in the sentinel lymph node biopsy