Bone Mineral Density in Patients with Ankylosing Spondylitis: Incidence and Correlation with Demographic and Clinical Variables

Objective: To evaluate bone mineral density (BMD) in patients with ankylosing spondylitis (AS) and determine its correlation with the demographic and clinical characteristics of AS. Patients and Methods: Demographic, clinical and osteodensitometric data were evaluated in a cross-sectional study that included 136 patients with AS. Spine and hip BMD were measured by means of dual energy X-ray absorptiometry (DXA). Using the modified Schober’s test we assessed spine mobility. We examined the sacroiliac, anteroposterior and lateral dorso-lumbar spine radiographs in order to grade sacroiliitis and assess syndesmophytes. Disease activity was evaluated using C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR). Demographic data and BMD measurements were compared with those of 167 age- and sex-matched healthy controls. Results: Patients with AS had a significantly lower BMD at the spine, femoral neck, trochanter and total hip as compared to age-matched controls (all p<0.01). According to the WHO classification, osteoporosis was present in 20.6% of the AS patients at the lumbar spine and in 14.6% at the femoral neck. There were no significant differences in BMD when comparing men and women with AS, except for trochanter BMD that was lower in female patients. No correlations were found between disease activity markers (ESR, CRP) and BMD. Femoral neck BMD was correlated with disease duration, Schober’s test and sacroiliitis grade. Conclusion: Patients with AS have a lower spine and hip BMD as compared to age- and sex-matched controls. Bone loss at the femoral neck is associated with disease duration and more severe AS

Shaping AAM Educational Methods: A Comparison of Traditional and Compressed Class Schedules in UAS Classes.

Advanced Air Mobility (AAM) is a paradigm shift in the versatility of the aviation industry. The obvious benefits of this platform will revolutionize a variety of aspects of the industry. One of the aspects in which the Federal Aviation Administration (FAA) has recommended research is the effectiveness of the traditional educational methods. Spurred by the COVID-19 pandemic, online teaching methodologies have become widely accepted in aviation. In addition, the format of traditional, instructor-led courses have also changed. Compressed schedules (accelerated courses) have become an additional option in some programs. To better understand if there are statistically significant differences in testing results among these three methods (traditional semester, compressed semester, or online format), a comparison study of the small Unmanned Aircraft Systems (sUAS) Remote Pilot Course was conducted. The study examined Federal Aviation Administration (FAA) Unmanned Aircraft – General (UAG) test results of two traditional face-to-face sUAS Remote Pilot Certificate classes, two sUAS Remote Pilot Certificate asynchronous online classes, and two compressed scheduled sUAS Remote Pilot Certificate classes. While there were subtle differences in the assessments and grading criteria in all three courses, the content remained the same. All students were required to take and pass the FAA UAG initial knowledge test to successfully pass the course. Preliminary results from this comparison study will be presented

Application of Plasticity Theory to Reinforced Concrete Deep Beams

yesThis paper reviews the application of the plasticity theory to reinforced concrete deep beams. Both the truss analogy and mechanism approach were employed to predict the capacity of reinforced concrete deep beams. In addition, most current codes of practice, for example Eurocode 1992 and ACI 318-05, recommend the strut-and-tie model for designing reinforced concrete deep beams. Compared with methods based on empirical or semi-empirical equations, the strut-and-tie model and mechanism analyses are more rational, adequately accurate and sufficiently simple for estimating the load capacity of reinforced concrete deep beams. However, there is a problem of selecting the effectiveness factor of concrete as reflected in the wide range of values reported in the literature for deep beams

Neural network modelling of RC deep beam shear strength

YesA 9 x 18 x 1 feed-forward neural network (NN) model trained using a resilient back-propagation algorithm and early stopping technique is constructed to predict the shear strength of deep reinforced concrete beams. The input layer covering geometrical and material properties of deep beams has nine neurons, and the corresponding output is the shear strength. Training, validation and testing of the developed neural network have been achieved using a comprehensive database compiled from 362 simple and 71 continuous deep beam specimens. The shear strength predictions of deep beams obtained from the developed NN are in better agreement with test results than those determined from strut-and-tie models. The mean and standard deviation of the ratio between predicted capacities using the NN and measured shear capacities are 1.028 and 0.154, respectively, for simple deep beams, and 1.0 and 0.122, respectively, for continuous deep beams. In addition, the trends ascertained from parametric study using the developed NN have a consistent agreement with those observed in other experimental and analytical investigations

INPUT SELECTION BY EPR-MOGA

The growing availability of field data, from information and communication technologies (ICTs) in "smart'' urban infrastructures, allows data modeling to understand complex phenomena and to support management decisions. Among the analyzed phenomena, those related to storm water quality modeling have recently been gaining interest in the scientific literature. Nonetheless, the large amount of available data poses the problem of selecting relevant variables to describe a phenomenon and enable robust data modeling. This paper presents a procedure for the selection of relevant input variables using the multi-objective evolutionary polynomial regression (EPR-MOGA) paradigm. The procedure is based on scrutinizing the explanatory variables that appear inside the set of EPR-MOGA symbolic model expressions of increasing complexity and goodness of fit to target output. The strategy also enables the selection to be validated by engineering judgement. In such context, the multiple case study extension of EPR-MOGA, called MCS-EPR-MOGA, is adopted. The application of the proposed procedure to modeling storm water quality parameters in two French catchments shows that it was able to significantly reduce the number of explanatory variables for successive analyses. Finally, the EPR-MOGA models obtained after the input selection are compared with those obtained by using the same technique without benefitting from input selection and with those obtained in previous works where other data-modeling techniques were used on the same data. The comparison highlights the effectiveness of both EPR-MOGA and the input selection procedure

Therapeutic hypothermia in adult patients receiving extracorporeal life support: early results of a randomized controlled study

Cardiac arrest with cerebral ischaemia frequently leads to severe neurological impairment. Extracorporeal life support (ECLS) has emerged as a valuable adjunct in resuscitation of cardiac arrest. Despite ECLS, the incidence of permanent neurological injury remains high. We hypothesize that patients receiving ECLS for cardiac arrest treated with therapeutic hypothermia at 34 °C have lower neurological complication rates compared to standard ECLS therapy at normothermia. Early results of this randomized study suggest that therapeutic hypothermia is safe in adult patients receiving ECLS, with similar complication rates as ECLS without hypothermia. Further studies are warranted to measure the efficacy of this therapy

Tuftsin Promotes an Anti-Inflammatory Switch and Attenuates Symptoms in Experimental Autoimmune Encephalomyelitis

Multiple sclerosis (MS) is a demyelinating autoimmune disease mediated by infiltration of T cells into the central nervous system after compromise of the blood-brain barrier. We have previously shown that administration of tuftsin, a macrophage/microglial activator, dramatically improves the clinical course of experimental autoimmune encephalomyelitis (EAE), a well-established animal model for MS. Tuftsin administration correlates with upregulation of the immunosuppressive Helper-2 Tcell (Th2) cytokine transcription factor GATA-3. We now show that tuftsin-mediated microglial activation results in shifting microglia to an anti-inflammatory phenotype. Moreover, the T cell phenotype is shifted towards immunoprotection after exposure to tuftsin-treated activated microglia; specifically, downregulation of pro-inflammatory Th1 responses is triggered in conjunction with upregulation of Th2-specific responses and expansion of immunosuppressive regulatory T cells (Tregs). Finally, tuftsin-shifted T cells, delivered into animals via adoptive transfer, reverse the pathology observed in mice with established EAE. Taken together, our findings demonstrate that tuftsin decreases the proinflammatory environment of EAE and may represent a therapeutic opportunity for treatment of MS