ABSTRACT
Pulmonary embolism (PE) is common among hospitalized adults with SARS CoV-2 pneumonia. D-dimer (DD)>1 mug/mL has been found to be a severity risk factor. However, most of the studies are based on retrospective data and the real prevalence is unknown Objectives: To evaluate the prevalence of PE in patients with SARS CoV-2 pneumonia, regardless clinical suspicion. Demographic and laboratory data, comorbidities, and clinical outcomes were compared between patients with and without PE Methods: Single-center prospective study. All consecutive cases of SARS CoV-2 pneumonia with DD>1 mug/mL underwent computed tomography pulmonary angiography Results: 179 patients (64 (55-74 years), 65% male) were included. PE was diagnosed in 71 patients (39.7%), mostly with a peripheral location and low thrombotic load (Qanadli score 10%). We did not find disparity in PE prevalence between men and women, and between obese and not obese patients. There were no differences in the intensive care unit admission rate. Mortality rate was 8.5% in patients with PE vs. 3.7% in those without PE, but the differences were not significant. Patients with PE had more history of cardiovascular disease and required more fractional inspired oxygen. DD, platelet distribution width (PDW), neutrophil-lymphocyte ratio (NLR), DD-lactate dehydrogenase ratio (DD/LDH), and DD-ferritin ratio values were significantly higher among PE patients. ROC analysis showed that PDW and DD/LDH had the greatest area under the curve Conclusion(s): Patients with SARS CoV2 pneumonia and DD>1mug/mL presented a high prevalence of PE, regardless of clinical suspicion. PDW, NLR, DD/LDH and DD/Ferritin may help to identify patients with high risk of PE.
ABSTRACT
Background-aim: Tumor markers (TM) in body fluids have been studied for years and several authors have proposed different cut-off. An apparently more accurate strategy is the one proposed by Molina et al. considering that the ratio TM in fluid with regard to TM in serum >1.2 indicates local production in the pleura, however if the ratio is <1.2 the presence of TM in the fluid would be explained by serum extravasation. Despite enough evidence to manage this biomarkers in body fluids, the practice is not widely extended in the clinical setting yet. Methods: AFP, CA19.9, CA15.3, CEA, CA125, PSA and SCC were analyzed in Alinity i platform (Abbott diagnostics) HCG and NSE was performed in Cobas e411 (Roche diagnostics). Results: Here we describe the case of a 69-year-old patient attending the Emergency Room due to pain in both hemythoraxes. Also remarkable was a wasting syndrome (5 kg weight loss in the past month). In Emergency blood analysis: VSG 50, PT 75%, DD 765 ng/mL, ferritin 368 ng/mL and LDH 385 U/L were outsdanding. Thorax radiology showed a pleural effusion. The patient was diagnosed with COVID19 bronchitis.TC scan evidenced pleural solid metastasis, multiple bone lesions and hepatic M1. Serum TM: AFP, CA19.9, PSA, NSE, SCC and HCG were normal. CA125 2992,60 U/mL (<35), CA15.3 614,70 U/mL (<32), CEA 400.82 ng/mL (<5). Pleural fluid TM: CEA 284.32 ng/mL;CA15.3 2210.3 U/mL. TM ratio: CA15.3: 3.6 (>1.2) this result indicates local synthesis of CA15.3, therefore pleural metastasis;CEA: 0.7 (<1.2) indicates that the CEA found un the fluid was extravasated from serum. Pathological examination was only positive for CK7 and mixt CK. All other markers were negative. It was concluded to be an undifferentiated carcinoma, cytologically reminding of an adenocarcinoma. Due to TTF1 and napsine negativity lung neoplasm could not be discarded.The patient was diagnosed with undifferentiated lung cancer stage IV. Conclusions: This a good example of different molecular patterns reflecting tumor heterogeneity evidenced by protein expression by each lesion: Pleural metastases expressed high amounts of CA15.3, however not CEA. Hepatic metastases and probably main tumor in the lung expressed CEA and CA15.3. It is arguable whether CA15.3 was expressed at lower quantities from the main tumor or the dilution of the protein in the bloodstream results in lower concentrations in relation to the ones found in the pleura.
ABSTRACT
Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and reconstruction of static graph signals have played a central role in GSP. However, many real-world graph signals are inherently time-varying and the smoothness of the temporal differences of such graph signals may be used as a prior assumption. In the current work, we assume that the temporal differences of graph signals are smooth, and we introduce a novel algorithm based on the extension of a Sobolev smoothness function for the reconstruction of time-varying graph signals from discrete samples. We explore some theoretical aspects of the convergence rate of our Time-varying Graph signal Reconstruction via Sobolev Smoothness (GraphTRSS) algorithm by studying the condition number of the Hessian associated with our optimization problem. Our algorithm has the advantage of converging faster than other methods that are based on Laplacian operators without requiring expensive eigenvalue decomposition or matrix inversions. The proposed GraphTRSS is evaluated on several datasets including two COVID-19 datasets and it has outperformed many existing state-of-the-art methods for time-varying graph signal reconstruction. GraphTRSS has also shown excellent performance on two environmental datasets for the recovery of particulate matter and sea surface temperature signals. IEEE