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1.
Surv Ophthalmol ; 67(4): 883-891, 2022.
Article in English | MEDLINE | ID: mdl-34843745

ABSTRACT

Age-related macular degeneration (AMD) is a leading cause of severe, irreversible vision impairment in developed countries, and its prevalence is rising all over the world, increasing sharply with age. AMD represents an acquired degeneration of the retina that causes significant central visual impairment through a combination of noneovascular and neovascular derangement. The main risk factors for the development of advanced AMD are increasing age, genetic factors, and cigarette smoking; however, the exact pathophysiology of AMD is yet relatively poorly understood. In recent years, the gut microbiota has been intensively studied and linked to several pathologic processes, including ocular diseases. In this sense, the aim of this review is to gather published evidence about the relationship between gut microbiota and AMD.


Subject(s)
Gastrointestinal Microbiome , Macular Degeneration , Humans , Macular Degeneration/etiology , Retina , Risk Factors
2.
Resuscitation ; 133: 88-94, 2018 12.
Article in English | MEDLINE | ID: mdl-30321624

ABSTRACT

AIM: To assess the feasibility of an integrated program of extracorporeal cardiopulmonary resuscitation (ECPR) and uncontrolled donation after circulatory determination of death (uDCDD) in refractory cardiac arrest (rCA). METHODS: Single center, prospective, observational study of selected patients with in-hospital (IHCA) and out-of-hospital (OHCA) rCA occurring in an urban area of ∼1.5 million inhabitants, between October-2016 and May-2018. 65 year old or younger patients without significant bleeding or comorbidities with witnessed nonasystolic cardiac arrests were triaged to ECPR if they had a reversible cause and high quality CPR lasting < 60 min. Otherwise they were considered for uDCDD after a ten minute no touch period using normothermic regional perfusion. RESULTS: 58 patients were included, of which 41 (71%) were OHCA and 18 (31%) had ECPR initiated. Median age was 52 (IQR 45-56) years. Cannulation was successful in 49/58 (84%) cases. Compared to ECPR, patients referred for uDCDD were more frequently OHCA (90 vs. 28%), had bystander CPR (28 vs. 83%) and prolonged low-flow period (40 (35-50) vs. 60 (49-78) min). Survival to hospital discharge with full neurological recovery (cerebral performance category 1) occurred in 6/18 (33%) ECPR patients. uDCDD resulted in transplantation of 44 kidneys. CONCLUSIONS: An integrated program for rCA consisting of a formal pathway to uDCDD referral in ECPR ineligible patients is feasible. ECPR-referred patients had a reasonable survival with full neurologic recovery. Successful kidney transplantation was achieved with uDCDD.


Subject(s)
Cardiopulmonary Resuscitation/mortality , Extracorporeal Membrane Oxygenation/methods , Kidney Transplantation/statistics & numerical data , Out-of-Hospital Cardiac Arrest/mortality , Tissue and Organ Harvesting/methods , Feasibility Studies , Female , Humans , Male , Middle Aged , Organ Preservation/methods , Portugal/epidemiology , Prospective Studies , Tissue Donors/statistics & numerical data
3.
Article in English | MEDLINE | ID: mdl-26356858

ABSTRACT

Network motif algorithms have been a topic of research mainly after the 2002-seminal paper from Milo et al. [1], which provided motifs as a way to uncover the basic building blocks of most networks. Motifs have been mainly applied in Bioinformatics, regarding gene regulation networks. Motif detection is based on induced subgraph counting. This paper proposes an algorithm to count subgraphs of size k + 2 based on the set of induced subgraphs of size k. The general technique was applied to detect 3, 4 and 5-sized motifs in directed graphs. Such algorithms have time complexity O(a(G)m), O(m(2)) and O(nm(2)), respectively, where a(G) is the arboricity of G(V, E). The computational experiments in public data sets show that the proposed technique was one order of magnitude faster than Kavosh and FANMOD. When compared to NetMODE, acc-Motif had a slightly improved performance.


Subject(s)
Algorithms , Computational Biology/methods , Gene Regulatory Networks
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