Neutrophil Gelatinase-Associated Lipocalin induces the expression of heme oxygenase-1 and superoxide dismutase 1, 2

Lipocalin-2 (Lcn2, NGAL) is a member of the lipocalin super family with diverse function such as the induction of apoptosis, the suppression of bacterial growth, and modulation of inflammatory response. Much interest has recently been focused on the physiological/pathological role of the lipocalin-2 that is considered to be a novel protective factor against oxidative stress. However, its precise biological roles in this protection are not fully understood. In this report we intended to test the effect of lipocalin-2 on the expression of heme oxygenase (1, 2) and superoxide dismutase (1, 2) which are two strong antioxidants. NGAL was cloned to pcDNA3.1 plasmid by using genetic engineering method. The recombinant vector was transfected to CHO and HEK293T to establish stable cell expressing NGAL and the expression of HO-1, 2 and SOD1, 2 were compared with appropriate controls byRT-PCR and western blot. On the other hand, expression of NGAL was suppressed by siRNA transfection in order to study the effect of lipocalin-2 on mentioned genes/proteins. The results showed that the expression of HO-1 and SOD 1, 2 enzymes were higher in cells expressing recombinant lipocalin-2 compared with the control cells. Although the expression of HO-1 was lower in NGAL silencing cells, the expression of SOD1 and SOD2 were higher. Our data suggest that NGAL is a potent inducer of HO-1 and somewhat SOD1 and SOD2 and it appears that part of antioxidant property of NGAL could be attributed to the induction of HO-1and SOD 1, 2. © Cell Stress Society International 2009

Evaluation of an artificial intelligence-based medical device for diagnosis of autism spectrum disorder.

Autism spectrum disorder (ASD) can be reliably diagnosed at 18 months, yet significant diagnostic delays persist in the United States. This double-blinded, multi-site, prospective, active comparator cohort study tested the accuracy of an artificial intelligence-based Software as a Medical Device designed to aid primary care healthcare providers (HCPs) in diagnosing ASD. The Device combines behavioral features from three distinct inputs (a caregiver questionnaire, analysis of two short home videos, and an HCP questionnaire) in a gradient boosted decision tree machine learning algorithm to produce either an ASD positive, ASD negative, or indeterminate output. This study compared Device outputs to diagnostic agreement by two or more independent specialists in a cohort of 18-72-month-olds with developmental delay concerns (425 study completers, 36% female, 29% ASD prevalence). Device output PPV for all study completers was 80.8% (95% confidence intervals (CI), 70.3%-88.8%) and NPV was 98.3% (90.6%-100%). For the 31.8% of participants who received a determinate output (ASD positive or negative) Device sensitivity was 98.4% (91.6%-100%) and specificity was 78.9% (67.6%-87.7%). The Device's indeterminate output acts as a risk control measure when inputs are insufficiently granular to make a determinate recommendation with confidence. If this risk control measure were removed, the sensitivity for all study completers would fall to 51.6% (63/122) (95% CI 42.4%, 60.8%), and specificity would fall to 18.5% (56/303) (95% CI 14.3%, 23.3%). Among participants for whom the Device abstained from providing a result, specialists identified that 91% had one or more complex neurodevelopmental disorders. No significant differences in Device performance were found across participants' sex, race/ethnicity, income, or education level. For nearly a third of this primary care sample, the Device enabled timely diagnostic evaluation with a high degree of accuracy. The Device shows promise to significantly increase the number of children able to be diagnosed with ASD in a primary care setting, potentially facilitating earlier intervention and more efficient use of specialist resources