From the periphery to the brain: Lipocalin-2, a friend or foe?

Lipocalin-2 (LCN2) is an acute-phase protein that, by binding to iron-loaded siderophores, acts as a potent bacteriostatic agent in the iron-depletion strategy of the immune system to control pathogens. The recent identification of a mammalian siderophore also suggests a physiological role for LCN2 in iron homeostasis, specifically in iron delivery to cells via a transferrin-independent mechanism. LCN2 participates, as well, in a variety of cellular processes, including cell proliferation, cell differentiation and apoptosis, and has been mostly found up-regulated in various tissues and under inflammatory states, being its expression regulated by several inducers. In the central nervous system less is known about the processes involving LCN2, namely by which cells it is produced/secreted, and its impact on cell proliferation and death, or in neuronal plasticity and behaviour. Importantly, LCN2 recently emerged as a potential clinical biomarker in multiple sclerosis and in ageing-related cognitive decline. Still, there are conflicting views on the role of LCN2 in pathophysiological processes, with some studies pointing to its neurodeleterious effects, while others indicate neuroprotection. Herein, these various perspectives are reviewed and a comprehensive and cohesive view of the general function of LCN2, particularly in the brain, is provided.

Rational manipulation of mRNA folding free energy allows rheostat control of pneumolysin production by Streptococcus pneumoniae

Rational manipulation of mRNA folding free energy allows rheostat control of pneumolysin production by Streptococcus pneumoniae

Osteogenic differentiation of human mesenchymal stem cells in the absence of osteogenic supplements: a surface-roughness gradient study

The use of biomaterials to direct osteogenic differentiation of human mesenchymal stem cells (hMSCs) in the absence of osteogenic supplements is thought to be part of the next generation of orthopedic implants. We previously engineered surface-roughness gradients of average roughness (Ra) varying from the sub-micron to the micrometer range ( 0.5–4.7 lm), and mean distance between peaks (RSm) gradually varying from 214 lm to 33 lm. Here we have screened the ability of such surface-gradients of polycaprolactone to influence the expression of alkaline phosphatase (ALP), collagen type 1 (COL1) and mineralization by hMSCs cultured in dexamethasone (Dex)-deprived osteogenic induction medium (OIM) and in basal growth medium (BGM). Ra 1.53 lm/RSm 79 lm in Dex-deprived OI medium, and Ra 0.93 lm/RSm 135 lm in BGM consistently showed higher effectiveness at supporting the expression of the osteogenic markers ALP, COL1 and mineralization, compared to the tissue culture polystyrene (TCP) control in complete OIM. The superior effectiveness of specific surface-roughness revealed that this strategy may be used as a compelling alternative to soluble osteogenic inducers in orthopedic applications featuring the clinically relevant biodegradable polymer polycaprolactone.

Searching for therapeutic strategies in a mouse modelo of Machado-Joseph disease: targeting proteostases

Tese de Doutoramento em Ciências da Saúde

Predicting triage waiting time in maternity emergency care by means of data mining

Healthcare organizations often benefit from information technologies as well as embedded decision support systems, which improve the quality of services and help preventing complications and adverse events. In Centro Materno Infantil do Norte (CMIN), the maternal and perinatal care unit of Centro Hospitalar of Oporto (CHP), an intelligent pre-triage system is implemented, aiming to prioritize patients in need of gynaecology and obstetrics care in two classes: urgent and consultation. The system is designed to evade emergency problems such as incorrect triage outcomes and extensive triage waiting times. The current study intends to improve the triage system, and therefore, optimize the patient workflow through the emergency room, by predicting the triage waiting time comprised between the patient triage and their medical admission. For this purpose, data mining (DM) techniques are induced in selected information provided by the information technologies implemented in CMIN. The DM models achieved accuracy values of approximately 94% with a five range target distribution, which not only allow obtaining confident prediction models, but also identify the variables that stand as direct inducers to the triage waiting times.