Dalymoreno6279

Continuous monitoring of the intracranial pressure (ICP) is essential in neurocritical care. There are a variety of ICP monitoring systems currently available, with the intraventricular fluid filled catheter transducer currently representing the "gold standard". As the placement of catheters is associated with the attendant risk of infection, hematoma formation, and seizures, there is a need for a reliable, non-invasive alternative. In the present study we suggest a unique theoretical framework based on differential geometry invariants of cranial micro-motions with the potential for continuous non-invasive ICP monitoring in conservative traumatic brain injury (TBI) treatment. As a proof of this concept, we have developed a pillow with embedded mechanical sensors and collected an extensive dataset (> 550 h on 24 TBI coma patients) of cranial micro-motions and the reference intraparenchymal ICP. From the multidimensional pulsatile curve we calculated the first Cartan curvature and constructed a "fingerprint" image (Cartan map) associated with the cerebrospinal fluid (CSF) dynamics. The Cartan map features maxima bands corresponding to a pressure wave reflection corresponding to a detectable skull tremble. We give evidence for a statistically significant and patient-independent correlation between skull micro-motions and ICP time derivative. Our unique differential geometry-based method yields a broader and global perspective on intracranial CSF dynamics compared to rather local catheter-based measurement and has the potential for wider applications.Conformal transformation optics is employed to enhance an H-plane horn's directivity by designing a graded-index all-dielectric lens. The transformation is applied so that the phase error at the aperture is gradually eliminated inside the lens, leading to a low-profile high-gain lens antenna. The physical space shape is modified such that singular index values are avoided, and the optical path inside the lens is rescaled to eliminate superluminal regions. A prototype of the lens is fabricated using three-dimensional printing. The measurement results show that the realized gain of an H-plane horn antenna can be improved by 1.5-2.4 dB compared to a reference H-plane horn.Dupilumab is a dual inhibitor of interleukin-4 and interleukin-13 and is mainly used to treat moderate-to-severe atopic dermatitis. Post-marketing safety data related to dupilumab have been accumulated, and it has been found that ocular surface diseases are closely associated with dupilumab treatment. The aim of this study was to detect dupilumab-related signals and to determine the safety characteristics of dupilumab with respect to eye disorders using real-world big data. Data on dupilumab use until December 29, 2019 were collected. The data were mined by calculating three indices proportional reporting ratios, reporting odds ratios, and information components. The detected signals were classified using the primary system organ class in MedDRA terminology. Among 21,161,249 reports for all drugs, 20,548 reports were recorded for dupilumab. Lenvatinib A total of 246 signals in the preferred terms were detected for dupilumab. Among the 246 positive signals obtained, 61 signals were related to eye disorders, which accounted for the largest percentage (24.8%), and 38 signals were anatomically related to the ocular surface. Dupilumab may cause extensive eye disorders; however, the underlying mechanisms and risk factors remain unclear. Our findings may facilitate broad safety screening of dupilumab-related eye disorders using real-world big data.In developing countries, breast cancer is diagnosed at a much younger age. In this study we investigate the dichotomies between older and young breast cancer patients in our region. The study involved two cohorts; older patients (≥ 65 years, n = 553) and younger ones (≤ 40 years, n = 417). Statistical models were used to investigate the associations between age groups, clinical characteristics and treatment outcomes. Compared to younger patients, older patients were more likely to present with advanced-stage disease (20.6% vs. 15.1%, p = .028). However, among those with non-metastatic disease, younger patients tended to have more aggressive pathological features, including positive axillary lymph nodes (73.2% vs. 55.6%, p  less then  .001), T-3/4 (28.2% vs. 13.8%, p  less then  .001) and HER2-positive disease (29.3% vs. 16.3%, p  less then  .001). The 5-year overall survival (OS) rate was significantly better for the younger (72.1%) compared to the older (67.6%), p = .035. However, no significant difference was observed in disease-free survival (DFS) between the two groups.In conclusion, younger patients with breast cancer present with worse clinical and pathological features, albeit a better OS rate. The difference in DFS between the two groups was not insignificant, suggesting that older women were more likely to die from non-cancer related causes.Diabetic retinopathy (DR) is one of the leading causes of vision loss across the world. Yet despite its wide prevalence, the majority of affected people lack access to the specialized ophthalmologists and equipment required for monitoring their condition. This can lead to delays in the start of treatment, thereby lowering their chances for a successful outcome. Machine learning systems that automatically detect the disease in eye fundus images have been proposed as a means of facilitating access to retinopathy severity estimates for patients in remote regions or even for complementing the human expert's diagnosis. Here we propose a machine learning system for the detection of referable diabetic retinopathy in fundus images, which is based on the paradigm of multiple-instance learning. Our method extracts local information independently from multiple rectangular image patches and combines it efficiently through an attention mechanism that focuses on the abnormal regions of the eye (i.e. those that contain DR-induced lesions), thus resulting in a final image representation that is suitable for classification. Furthermore, by leveraging the attention mechanism our algorithm can seamlessly produce informative heatmaps that highlight the regions where the lesions are located. We evaluate our approach on the publicly available Kaggle, Messidor-2 and IDRiD retinal image datasets, in which it exhibits near state-of-the-art classification performance (AUC of 0.961 in Kaggle and 0.976 in Messidor-2), while also producing valid lesion heatmaps (AUPRC of 0.869 in the 81 images of IDRiD that contain pixel-level lesion annotations). Our results suggest that the proposed approach provides an efficient and interpretable solution against the problem of automated diabetic retinopathy grading.