173102 research outputs found
Sort by
Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images.
No full textBACKGROUND AND OBJECTIVE: Heart failure (HF) is a multi-faceted and life-threatening syndrome that affects more than 64.3 million people worldwide. Current gold-standard screening technique, echocardiography, neglects cardiovascular information regulated by the circadian rhythm and does not incorporate knowledge from patient profiles. In this study, we propose a novel multi-parameter approach to assess heart failure using heart rate variability (HRV) and patient clinical information.
METHODS: In this approach, features from 24-hour HRV and clinical information were combined as a single polar image and fed to a 2D deep learning model to infer the HF condition. The edges of the polar image correspond to the timely variation of different features, each of which carries information on the function of the heart, and internal illustrates color-coded patient clinical information.
RESULTS: Under a leave-one-subject-out cross-validation scheme and using 7,575 polar images from a multi-center cohort (American and Greek) of 303 coronary artery disease patients (median age: 58 years [50-65], median body mass index (BMI): 27.28 kg/m2 [24.91-29.41]), the model yielded mean values for the area under the receiver operating characteristics curve (AUC), sensitivity, specificity, normalized Matthews correlation coefficient (NMCC), and accuracy of 0.883, 90.68%, 95.19%, 0.93, and 92.62%, respectively. Moreover, interpretation of the model showed proper attention to key hourly intervals and clinical information for each HF stage.
CONCLUSIONS: The proposed approach could be a powerful early HF screening tool and a supplemental circadian enhancement to echocardiography which sets the basis for next-generation personalized healthcare
Does Asset Location and Concentration Explain REIT IPO Valuation? A Comprehensive Analysis Across Europe, the UK, and the USA
No full textThis study endeavors to elucidate the influence of geographic and asset type concentrations on the valuation of Initial Public Offerings (IPOs) in the Real Estate Investment Trust (REIT) industry. It analyzes a dataset of 83 REIT IPOs in the US, UK, and EU from 2000 to 2022. The research aims to get a comprehensive understanding of the complex factors that influence the success of initial public offerings (IPOs) in the real estate investment trust (REIT) market. This will be accomplished through conducting an in-depth investigation that combines conventional and creative methods.
An important finding of the inquiry is the identification of a strong positive association between a company's market size and the first returns of its IPO. This suggests that the market has a tendency to favor larger organizations. In contrast, the analysis reveals an inverse correlation between the Price to Book (P/B) ratio before to the IPO quarter and initial returns. This indicates that firms with greater valuations compared to their book value just before going public generally have lower initial returns.
Contrary to commonly accepted beliefs, the research results suggest that the Herfindahl Index (HHI_GEO) for geographic concentration and the HHI_ASSET for asset type concentration do not have a substantial impact on IPO initial returns. This study suggests that investors might minimize the significance of geographical or asset type diversity in REIT IPOs, possibly because of the distinct characteristics and market dynamics associated with real estate investments. The research also examines the impact of strategic investment preferences, such as concentrated investment in certain property types, substantial investment in core property types, and a preference for investments inside the state where the headquarters is based. Nevertheless, these strategic orientations do not demonstrate a substantial influence on IPO initial returns, emphasizing that broader market dynamics and company-specific features have a more crucial effect in predicting IPO success than the strategy emphasis alone.
The incorporation of business-level and market-level control variables, such as the firm's age at IPO, total proceeds, and market circumstances, seeks to conduct a comprehensive assessment of the factors that impact IPO success. However, these characteristics do not exhibit a statistically significant link with IPO beginning returns, emphasizing the intricate interaction of factors influencing IPO results and the difficulties in identifying specific drivers of success
Rhizosheath drought responsiveness is variety‐specific and a key component of belowground plant adaptation
No full textBattery, hybrid electric vehicles—Trucks and buses: And I would drive 500 miles—With batteries?
No full textRecommender-based bone tumour classification with radiographs-a link to the past.
No full textOBJECTIVES: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis.
MATERIALS AND METHODS: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated.
RESULTS: Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%).
CONCLUSION: Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights.
CLINICAL RELEVANCE STATEMENT: The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities.
KEY POINTS: • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments
Minimally Invasive Breast Biopsy After Neoadjuvant Systemic Treatment to Identify Breast Cancer Patients with Residual Disease for Extended Neoadjuvant Treatment: A New Concept.
No full textBACKGROUND: Breast cancer patients with residual disease after neoadjuvant systemic treatment (NAST) have a worse prognosis compared with those achieving a pathologic complete response (pCR). Earlier identification of these patients might allow timely, extended neoadjuvant treatment strategies. We explored the feasibility of a vacuum-assisted biopsy (VAB) after NAST to identify patients with residual disease (ypT+ or ypN+) prior to surgery.
METHODS: We used data from a multicenter trial, collected at 21 study sites (NCT02948764). The trial included women with cT1-3, cN0/+ breast cancer undergoing routine post-neoadjuvant imaging (ultrasound, MRI, mammography) and VAB prior to surgery. We compared the findings of VAB and routine imaging with the histopathologic evaluation of the surgical specimen.
RESULTS: Of 398 patients, 34 patients with missing ypN status and 127 patients with luminal tumors were excluded. Among the remaining 237 patients, tumor cells in the VAB indicated a surgical non-pCR in all patients (73/73, positive predictive value [PPV] 100%), whereas PPV of routine imaging after NAST was 56.0% (75/134). Sensitivity of the VAB was 72.3% (73/101), and 74.3% for sensitivity of imaging (75/101).
CONCLUSION: Residual cancer found in a VAB specimen after NAST always corresponds to non-pCR. Residual cancer assumed on routine imaging after NAST corresponds to actual residual cancer in about half of patients. Response assessment by VAB is not safe for the exclusion of residual cancer. Response assessment by biopsies after NAST may allow studying the new concept of extended neoadjuvant treatment for patients with residual disease in future trials
Comparison of assessment of programmed death-ligand 1 (PD-L1) status in triple-negative breast cancer biopsies and surgical specimens.
No full textAIMS: Programmed death-ligand 1 (PD-L1) status in triple-negative breast cancer (TNBC) is important for immune checkpoint inhibitor therapies but may vary between different immunohistochemical assays, scorings and the type of specimen used for analysis.
METHODS: We compared the analytical concordance of three clinically relevant PD-L1 assays (VENTANA SP142, VENTANA SP263 and DAKO 22C3 pharmDx) assessing immune cell score (IC), tumour proportion score and combined positive score (CPS) in preoperative biopsies and resection specimens of primary TNBC. PD-L1 expression was scored on virtual whole slide images and compared with expression data from corresponding surgical specimens.
RESULTS: The mean PD-L1 positivity in TNBC biopsies defined as IC ≥1% and CPS ≥1 ranged between 11% and 61% with the lowest positivity for SP142 and highest for SP263. The corresponding surgical specimens showed overall higher positivity rates (53%-75%). When comparing biopsies with surgical specimens, the agreement for PD-L1 positivity with SP263 and 22C3 at IC score ≥1% and CPS ≥1 was fair (kappa 0.47-0.52) and poor for SP142 (kappa 0.15-0.19). Using CPS ≥10 cut-off, the agreement for SP263 was excellent (kappa 0.751) but poor for 22C3 (kappa 0.261). Spearman correlation coefficients ranged between 0.489 and 0.75 indicating a generally moderate to strong correlation between biopsies and surgical specimens for all assays and scores.
CONCLUSIONS: We demonstrate high accordance between biopsies and surgical specimens for SP263 and 22C3 scoring but less for SP142. Generally, biopsies are suitable for PD-L1 testing in TNBC but the appropriate assay, scoring and cut-off must be considered
New Work in Rural Regions
No full textDiese Arbeit untersucht Standorteigenschaften, Nutzerpräferenzen und Synergiepotentiale von Orten Neuer Arbeit – Coworking Spaces – mit umgebenden Nutzungen. Weitere Forschungsgegenstände sind Perspektiven, Politiken und Programme von Ministerien auf bzw. für Coworking Spaces. Neue Formen der Arbeit in Coworking Spaces bieten für Kommunen in ländlichen Räumen die Chance Menschen zurückzugewinnen, somit Steuereinnahmen zu steigern und Lebendigkeit im öffentlichen Räumen auszubauen. Dies kann auch dem Donut-Effekt entgegenwirken.This thesis examines location characteristics, user preferences and synergy potentials of places of new work - coworking spaces - with surrounding uses. Further research topics are perspectives, policies and programs of ministries on or for coworking spaces. New forms of work in coworking spaces offer municipalities in rural areas the opportunity to win people back, thus increasing tax revenues and expanding vitality in public spaces. This can also counteract the donut effect
