Notes

Strong transfer mastering centered classification design regarding covid-19 making use of chest muscles CT-scans.
Cerebrovascular disease (stroke) is one of the ten leading causes of death in children and adolescents. Multiple etiologies, from arteriopathies to prothrombic states, can cause stroke in youth. In adult stroke, hypertension has been shown to be the single most important modifiable risk factor. Although hypertension has not been strongly identified as a risk factor in childhood stroke to date, there is preliminary evidence that suggests that hypertension may also be associated with stroke in children. In this review, we summarize the literature that may link hypertension to stroke in the young. We have identified a series of barriers and limitations in the fields of pediatric hypertension and pediatric neurology that might explain why hypertension has been overlooked in childhood stroke. We suggest that hypertension may be a relevant risk factor that, alone or in combination with other multiple factors, contributes to the development of stroke in children. Currently, there are no consensus guidelines for the management of post-stroke hypertension in children. Thus, we recommend that blood pressure be assessed carefully in every child presenting with acute stroke in order to better understand the effects of hypertension in the development and the outcome of childhood stroke. We suggest a treatment algorithm to help practitioners manage hypertension after a stroke.In the above mentioned publication, part of Fig. 1b was distorted (48 h after TSSM Infestation). The original article has been corrected and the proper version of Fig. 1 is also published here.OBJECTIVES To evaluate the ability of high-frequency (29 MHz) transrectal micro-ultrasound (microUS) as a second-look examination after biparametric MRI (bp-MRI) and to reidentify focal lesions seen on diagnostic MRI and to detect new ones METHODS A total of 118 consecutive men (mean age, 66 ± 13 [SD] years; range, 49-93 years) with a mean prostate-specific antigen level of 11 ± 19 (SD) ng/mL (range, 2-200 ng/mL) and at least one focal lesion (MRI+) with a score > 2 on bp-MRI were included. Of these, 79/118 (66.9%) were biopsy-naïve and 102/118 (86.5%) had non-suspicious rectal examination. All patients had MRI-directed microUS-guided biopsy using a 29-MHz transducer. All lesions visible on micro-ultrasound (microUS+) were targeted without image fusion, which was only used for MRI+/microUS- lesions. Significant prostate cancer (sPCa) was defined by a Gleason score ≥ 7 or a maximum cancer core length > 3 mm. RESULTS A total of 144 focal prostatic lesions were analyzed, including 114 (114/144, 79.2%) MRI+/microUS+ lesions, 13 MRI+/microUS- lesions (13/144, 9%), and 17 MRI-/microUS+ lesions (17/144, 11.8%). Significant PCa was detected in 70 MRI+/microUS+ lesions (70/114, 61.4%), in no MRI+/microUS- lesion (0/13, 0%), and in 4 MRI-/microUS+ lesions (4/17, 23.5%). The sensitivity and specificity of microUS on a per-patient and a per-lesion basis were 100% (95% CI, 84.9-100%) and 22.8% (95% CI, 12.5-35.8%) and 100% (95% CI, 85.1-100%) and 22.6% (95% CI, 12.3-36.2%), respectively. CONCLUSION MicroUS, as a second-look examination, may show promise to localize targets detected on bp-MRI. KEY POINTS • Used as a second-look examination, microUS-guided biopsies have a 100% detection rate of sCa originating in the PZ or lower third of the TZ, without microUS-MRI image fusion. Infigratinib • MicroUS results may provide additional information about lesions visible on MRI. • MicroUS may provide the ability to detect small PZ lesions undetected by bp-MRI.OBJECTIVES To develop a deep learning-based method for automated classification of renal cell carcinoma (RCC) from benign solid renal masses using contrast-enhanced computed tomography (CECT) images. METHODS This institutional review board-approved retrospective study evaluated CECT in 315 patients with 77 benign (57 oncocytomas, and 20 fat-poor angiomyolipoma) and 238 malignant (RCC 123 clear cell, 69 papillary, and 46 chromophobe subtypes) tumors identified consecutively between 2015 and 2017. We employed a decision fusion-based model to aggregate slice level predictions determined by convolutional neural network (CNN) via a majority voting system to evaluate renal masses on CECT. The CNN-based model was trained using 7023 slices with renal masses manually extracted from CECT images of 155 patients, cropped automatically around kidneys, and augmented artificially. We also examined the fully automated approach for renal mass evaluation on CECT. Moreover, a 3D CNN was trained and tested using the same dataset images. • Employing 3D CNN-based methodology yielded slightly lower accuracy for renal mass classification compared with the semi- automated 2D CNN-based algorithm (79.24%).OBJECTIVE To compare the diagnostic performance of models based on a combination of contrast-enhanced (CE) magnetic resonance imaging (MRI) with diffusion-weighted imaging (DWI) or time-intensity curves (TIC) in diagnosing malignancies of breast lesions. METHODS A double-blind retrospective study was conducted in 328 patients (254 for training and the following 74 for validation) who underwent dynamic contrast-enhanced MRI (DCE-MRI) of the breast with pathological results. Two score models, the DWI model (apparent diffusion coefficient (ADC) + morphology + enhanced information) and the TIC model (TIC + morphology + enhanced information), were established with binary logistic regression for mass and non-mass enhancements (NMEs) in the training set. The sensitivity, specificity, and area under the curve (AUC) were compared between the two models (DWI model vs. TIC model); p  0.05). CONCLUSIONS Combined with CE MRI, the DWI model was superior or equal to the TIC model in differentiating benign and malignant breast lesions. KEY POINTS • Diffusion magnetic resonance imaging played an important role in the diagnosis of breast neoplasms. • On the basis of contrast-enhanced MRI, the DWI model had significantly higher diagnostic ability than the TIC model in distinguishing benign and malignant masses. • It would be reasonable to replace the time-consuming TIC with DWI for less scan time and similar diagnostic efficiency.Incidental findings are an inevitable part of radiology reporting.• Detection of incidental findings may be beneficial, but may result in unnecessary further investigation, and potential harm to patients. • Incidental findings should be reported in context, with an attempt to offer guidance as to their significance, and the need to investigate further.OBJECTIVES To build models based on conventional logistic regression (LR) and machine learning (ML) algorithms combining clinical, morphological, and hemodynamic information to predict individual rupture status of unruptured intracranial aneurysms (UIAs), afterwards tested in internal and external validation datasets. METHODS Patients with intracranial aneurysms diagnosed by computed tomography angiography and confirmed by invasive cerebral angiograph or clipping surgery were included. The prediction models were developed based on clinical, aneurysm morphological, and hemodynamic parameters by conventional LR and ML methods. RESULTS The training, internal validation, and external validation cohorts were composed of 807 patients, 200 patients, and 108 patients, respectively. The area under curves (AUCs) of conventional LR models 1 (clinical), 2 (clinical and aneurysm morphological), and 3 (clinical, aneurysm morphological and hemodynamic characteristics) were 0.608, 0.765, and 0.886, respectively (all p  0.05/6), while the AUC of MLP was lower than that of conventional LR (p = 0.0055). CONCLUSION Hemodynamic parameters play an important role in the prediction performance of the models. ML methods cannot outperform conventional LR in prediction models for rupture status of UIAs integrating clinical, aneurysm morphological, and hemodynamic parameters. KEY POINTS • The addition of hemodynamic parameters can improve prediction performance for rupture status of unruptured intracranial aneurysms. • Machine learning algorithms cannot outperform conventional logistic regression in prediction models for rupture status integrating clinical, aneurysm morphological, and hemodynamic parameters. • Models integrating clinical, aneurysm morphological, and hemodynamic parameters may help choose the optimal management.OBJECTIVES To investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. METHODS We retrospectively collected 2088 abnormal (nodule/mass) and 352 normal chest radiographs from two institutions. Three thoracic radiologists drew 2758 abnormalities regions. A total of 1736 abnormal chest radiographs were used for training and tuning convolutional neural networks (CNNs). The remaining 352 abnormal and 352 normal chest radiographs were used as a test set. Two CNNs (Mask R-CNN and RetinaNet) were selected to validate the effects of the squared different matrix size of chest radiograph (256, 448, 896, 1344, and 1792). For comparison, figure of merit (FOM) of jackknife free-response receiver operating curve and sensitivity were obtained. RESULTS In Mask R-CNN, matrix size 896 and 1344 achieved significantly higher FOM (0.869 and 0.856, respectively) for detecting abnormalities than 256, 448, and 1792 (0.667-0.820) (p  less then  0.05). enhance CAD performance without additional training data.We present an analysis of the impact of circulation weather types (CT) on a factor that might influence biological systems and the human condition, the electric state of the atmosphere. We present results on the influence of CT to the magnitude, the direction (positive or negative), the fluctuation magnitude, and the short-term peaks of the atmospheric electric field (potential gradient, PG), using data from a station in Greece. CTs with high vorticity centers over Greece are associated with high positive and negative excursions of the PG, higher PG variability, and rain events. CTs with thinner 850-500 hPa layer are associated with higher daily mean values of fair-weather PG. We also examine the influence of CT on the frequency and amplitude of the naturally occurring extremely low-frequency electric field fluctuations known as Schumann resonances (SR) using data from a station in Hungary. The first and second mode SR frequencies are increased during CTs associated with higher 500 hPa geopotential heights and higher 850-500 hPa layer thickness. This hints to a lower-upper atmosphere coupling. So, CTs not only influence the general temperature and humidity conditions to which the biosphere is exposed, but they also affect its exposure to atmospheric electric fields.Malnutrition in an obese world was the fitting title of the 13th Federation of European Nutrition Societies (FENS) conference held in October 2019. Many individuals do not eat a healthy, well-balanced diet, and this is now understood to be a major driver of increased disease risk and illness. Moreover, both our current eating patterns and the food system as a whole are environmentally unsustainable, threatening the planetary systems we depend on for survival. As we attempt to feed a growing global population, food systems will increasingly be confronted with their environmental impacts, with the added challenge of climate change-induced threats to food production. As we move into the third decade of the twenty-first century, these challenges demand that the nutrition research community reconsider its scope, concepts, methods, and societal role. At a pre-meeting workshop held at the FENS conference, over 70 researchers active in the field explored ways to advance the discipline's capacity to address cross-cutting issues of personal, public and planetary health.
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