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To evaluate differences based on intrauterine device (IUD) frame geometry in force, and stress, and strain at the stem/arms junction during simulated IUD removal. We manufactured injection-molded frame models for three Nova-T IUDs (Mirena [model M]; Liletta [model L]; Kyleena [model K]) and a Tatum-T IUD (Paragard [model P]) at two-times scaling. We created a custom fixture to simulate the uterus and used a screw-driven machine to pull models at various displacement rates through the 10 cm fixture cavity to measure force and strain and calculate stress at the IUD stem/arms junction. We tested models at 30 mm/min and higher displacement rates for exploratory analyses. We used Mann-Whitney U test for statistical testing. We completed testing at 30 mm/min using five of each Nova-T model and nine model P samples. Resistance against the cavity walls created significantly more force on model P (11.83, interquartile range [IQR] 11.61–12.31) than any Nova-T model samples (p < 0.001). The smaller model K created slightly more median stress (MPa) than the larger model M (0.36 [IQR 0.33–0.38] and 0.79 [IQR 0.76–0.80], respectively, p = 0.008); model P samples generated significantly more median stress than other models (1.70 [IQR 1.67–1.77], p < 0.001). Strain plots demonstrated permanent deformation for some samples during IUD removal simulation. We tested 20 samples at various higher displacement rates up to 2500 mm/min, with stress notably increasing for model P samples with increasing rates. No fractures occurred. Force and stress at the stem/arms junction are higher with Tatum-T-shaped compared to Nova-T-shaped IUD models under the same testing conditions, and a higher speed of extraction causes more stress. Sharp corners create vulnerability under static and fatigue loading in structural components due to increased local stresses. Our findings suggest that IUDs with Tatum-T frames should be removed slowly to minimize the stress at the stem/arms junction. Future studies can provide more information if performed with commercially available products. [ABSTRACT FROM AUTHOR]

This study sought to assess the diagnostic accuracy of cardiac computed tomography (CT) and 18F-fluorodeoxyglucose (18F-FDG) with positron emission tomography/computed tomography (PET/CT) in defining the nature of cardiac masses. The diagnostic accuracy of cardiac CT and 18F-FDG PET/CT in identifying the nature of cardiac masses has been analyzed to date only in small samples. Of 223 patients with echocardiographically diagnosed cardiac masses, a cohort of 60 cases who underwent cardiac CT and 18F-FDG PET/CT was selected. All masses had histological confirmation, except for a minority of thrombotic formations. For each mass, 8 morphological CT signs, standardized uptake value (SUV max , SUV mean), metabolic tumor volume, and total lesion glycolysis in 18F-FDG PET were used as diagnostic markers. Irregular tumor margins, pericardial effusion, invasion, solid nature, mass diameter, CT contrast uptake, and pre-contrast characteristics were strongly associated with the malignant nature of masses. The coexistence of at least 5 CT signs perfectly identified malignant masses, whereas the detection of 3 or 4 CT signs did not accurately discriminate the masses' nature. The mean SUV max , SUV mean , metabolic tumor volume, and total lesion glycolysis values were significantly higher in malignant than in benign masses. The diagnostic accuracy of SUV, metabolic tumor volume, and total lesion glycolysis 18F-FDG PET/CT parameters was excellent in detecting malignant masses. Among patients with 3 or 4 pathological CT signs, the presence of at least 1 abnormal 18F-FDG PET/CT parameter significantly increased the identification of malignancies. Cardiac CT is a powerful tool to diagnose cardiac masses as the number of abnormal signs was found to correlate with the lesions' nature. Similarly, 18F-FDG PET/CT accurately identified malignant masses and contributed with additional valuable information in diagnostic uncertainties after cardiac CT. These imaging tools should be performed in specific clinical settings such as involvement of great vessels or for disease-staging purposes. [ABSTRACT FROM AUTHOR]

• Pecorello and Mantonico pomaces represent a useful source of bioactive compounds. • Calabrian white grape pomaces were firstly investigated for their vasoactive properties. • The effects of extracts were analyzed in in vitro experiments on rat aorta rings. • Pecorello appeared more efficacious than Mantonico as a vasorelaxant extract. The valorisation of agrochemical wastes has become a low cost and sustainable tendency for the development of functional food. In particular, these enriched products seem to be enjoyable tools to treat metabolic disorders. In this field, Calabrian autochthonous white grape pomaces (Mantonico and Pecorello cultivar) were firstly investigated for their vasoactive properties. Skins and seeds were extracted and characterized by NMR spectroscopy, revealig the presence of numerous vasoactive compounds. The effects of extracts were analyzed in in vitro experiments on rat aorta rings (with and without endothelium), contracted by phenylephrine or KCl. Seeds extracts showed, differently from skins, an appreciable endothelium-dependent, eNOS-mediated vasodilation in the range 1–100 µg/mL. From a food market point of view, all the extracts were enclosed into a pectin polymer matrix. In the same experimental conditions, the polymers demonstrated the persistance of vasodilator activity only for Pecorello seed extract, which presented the most rich chemical profile. [ABSTRACT FROM AUTHOR]

Background Preclinical models that mimic pathological and molecular features of solitary fibrous tumour (SFT) represent an important tool to select effective regimes and novel compounds to be tested in the clinic. This study was aimed at developing two preclinical models of SFT, assessing their predictive value in the clinic and selecting potential novel effective treatments. Material and methods Two dedifferentiated-SFT (D-SFT) models obtained from patients' biopsies were grown in immunodeficient mice. The antitumour activity on these models of doxorubicin, dacarbazine (DTIC), ifosfamide (monotherapy or combination), trabectedin and eribulin was tested. Twelve SFT patients were treated with doxorubicin and DTIC. Response by RECIST, progression-free survival and overall survival were retrospectively evaluated, distinguishing malignant-SFT (M-SFT) and D-SFT. Results Two D-SFT patient-derived xenografts (PDXs) that represent the first available preclinical in vivo models of SFT were developed and characterised. Doxorubicin/DTIC, DTIC/ifosfamide, doxorubicin/ifosfamide combinations consistently induced better antitumour activity than the single-agents. Particularly, doxorubicin/DTIC combination caused a max tumour volume inhibition >80% in both models. Doxorubicin/DTIC combo showed activity also in the case-series. Best RECIST responses were: 6 responses (M-SFT = 2 of 7, D-SFT = 4 of 5), 1 stable disease, 5 progressions, with a 6-month median progression-free survival (M-SFT = 6, D-SFT = 10 months). The PDXs were very sensitive to trabectedin and eribulin. Conclusion Doxorubicin plus DTIC combination was effective in our two D-SFT mice models and appeared to be active also in the clinic, especially in high-grade D-SFT patients. Among additional drugs tested in the PDXs, trabectedin and eribulin were highly effective, providing a rational to test these drugs in D-SFT patients. [ABSTRACT FROM AUTHOR]

Soft tissue sarcomas (STS) are rare and heterogeneous tumours. A correct definition of STS is imperative from the very beginning of disease management, to guide the diagnostic and imaging work-up, and help to establish the prognosis on which the therapeutic strategy will be based. Over the last few years, many efforts have been made to identify characteristics that could predict disease behaviour and to enrich the therapeutic armamentarium against the advanced disease, that is still characterised by poor prognosis. Surgery remains the milestone of treatment for localised STS, whereas many uncertainties regarding the role of adjuvant and neoadjuvant treatment persist. Some controlled evidence is available, but often conflicting and insufficient to make chemotherapy (CT) a standard practice and, currently, a common and shared strategy does not exist. The biggest question concerns the prospective identification of the subgroup of patients who would benefit the most from (neo)adjuvant therapies. In light of the growing understanding of different biologies and sensitivities of the various sarcoma subtypes, the value of histology in the selection of peri-operative treatments is one of the most intriguing topics under discussion. In this perspective, a new generation of neoadjuvant trials have been planned and are currently ongoing. The aim of this review was to rekindle interest in the long-standing topic of (neo)adjuvant CT in localised STS, providing an update on its role in sarcomas' management and highlighting future directions and consequential factors needed to further improve outcomes in this disease. [ABSTRACT FROM AUTHOR]

• Features extracted by optimal WPT can characterize the variation pattern of PM 2.5. • Multi-scale analysis helps to improve the generalization ability of deep learning. • Multi-dimensional LSTM can handle hourly PM 2.5 concentration prediction well. • The time costs of WPT can be negligible compared with the deep learning reasoning. Air pollution is one of the most serious environmental problems faced by human beings, and it is also a hot topic in the development of sustainable cities. Accurate PM 2.5 prediction plays an important supporting role in urban governance and planning, and government decision-making. Hence, air quality sensing and prediction systems based on artificial intelligence take more and more place in the governance towards sustainable cities. In this paper, we propose a wavelet-packet transform (WPT) driven deep learning model to predict the hourly PM 2.5 concentration and verify its effectiveness when applied to Qingdao, China. The wavelet packet is first applied to decompose the meteorological data into sub-time series with different frequencies at different resolutions (STSs-DFDR). Then a multi-dimensional LSTM considering both spatial and temporal information is developed to extract key features from STSs-DFDR to accomplish PM 2.5 prediction. As far as we know, this is the first attempt to simultaneously predict PM 2.5 concentrations in different regions with a single model. Moreover, we find that the multi-scale analysis of time series is of great help to improve the cross-regional generalization of deep learning models. Finally, experimental results show that the proposed method achieves state-of-the-art PM 2.5 prediction performance by comparing it with various methods. [ABSTRACT FROM AUTHOR]

This paper presents the design of a compact Radar for real-time detection of targets in smart mobility applications. The Radar integrates Fabry–Perot resonating antennas, X-band and configurable continuous wave transceiver, high-speed analog-digital-converter and low-power/low-cost FPGA for the baseband signal processing. The latter includes region-of-interest selection, 2D Fast Fourier Transform for range-Doppler map extraction, peak detection and alarm decision logic. The transmitted power can be configured from few mW to 1.8 W. This allows for a trade-off between the maximum detection range, from few hundreds of meters up to 1.54 km, and the Radar power consumption, from 2.56 W to 11.66 W. The measured speed is up to 40 m/s. The speed and distance resolutions are 0.3 m/s and 37.5 cm, respectively. The configurable Radar features increased robustness vs. laser-scanners, video cameras, or induction loops detection techniques, and stands for its better trade-off in terms of covered range, size, and low-power consumption vs. state-of-the-art surveillance Radars. [ABSTRACT FROM AUTHOR]