Antiphospholipid syndrome; its implication in cardiovascular diseases: a review

Antiphospholipid syndrome (APLS) is a rare syndrome mainly characterized by several hyper-coagulable complications and therefore, implicated in the operated cardiac surgery patient. APLS comprises clinical features such as arterial or venous thromboses, valve disease, coronary artery disease, intracardiac thrombus formation, pulmonary hypertension and dilated cardiomyopathy. The most commonly affected valve is the mitral, followed by the aortic and tricuspid valve. For APLS diagnosis essential is the detection of so-called antiphospholipid antibodies (aPL) as anticardiolipin antibodies (aCL) or lupus anticoagulant (LA). Minor alterations in the anticoagulation, infection, and surgical stress may trigger widespread thrombosis. The incidence of thrombosis is highest during the following perioperative periods: preoperatively during the withdrawal of warfarin, postoperatively during the period of hypercoagulability despite warfarin or heparin therapy, or postoperatively before adequate anticoagulation achievement. Cardiac valvular pathology includes irregular thickening of the valve leaflets due to deposition of immune complexes that may lead to vegetations and valve dysfunction; a significant risk factor for stroke. Patients with APLS are at increased risk for thrombosis and adequate anticoagulation is of vital importance during cardiopulmonary bypass (CPB). A successful outcome requires multidisciplinary management in order to prevent thrombotic or bleeding complications and to manage perioperative anticoagulation. More work and reporting on anticoagulation management and adjuvant therapy in patients with APLS during extracorporeal circulation are necessary

Sparse Signal Recovery from Fixed Low-Rank Subspace via Compressive Measurement

This paper designs and evaluates a variant of CoSaMP algorithm, for recovering the sparse signal s from the compressive measurement  given a fixed low-rank subspace spanned by U. Instead of firstly recovering the full vector then separating the sparse part from the structured dense part, the proposed algorithm directly works on the compressive measurement to do the separation. We investigate the performance of the algorithm on both simulated data and video compressive sensing. The results show that for a fixed low-rank subspace and truly sparse signal the proposed algorithm could successfully recover the signal only from a few compressive sensing (CS) measurements, and it performs better than ordinary CoSaMP when the sparse signal is corrupted by additional Gaussian noise