The long-term prognostic value of serum 25(OH)D, albumin, and LL-37 levels in acute respiratory diseases among older adults

Abstract Background: Older adults are more susceptible to respiratory tract infection than healthy working age adults. The increased susceptibility of older adults is thought to be interlinked with vitamin D status, nourishment, and immunological state in general. Data are scarce whether these parameters could serve as prognostic markers. Aim: To study whether serum 25(OH)D, albumin, and LL-37 level could give prognostic value of long-term survival in the older adults with multimorbidity and acute respiratory infection. Methods: Consecutive episodes of hospital care of patients 65 years and older with respiratory symptoms were prospectively studied as a cohort. Standard clinical questionnaire was filled by the study physician. Laboratory markers included serum levels of 25(OH)D, albumin and LL-37, C-reactive protein (CRP), white blood cell count (WBC) and polymerase chain reaction diagnostics for 14 respiratory viruses. Pneumonia was confirmed by chest radiographs. Respiratory illness severity, death at ward, length of hospital stays, and 5-year survival were used as outcomes. Results: In total, 289 older adult patients with mean age of 83 years were included in the study. Serum 25(OH)D deficiency (< 50 nmol/liter) was present in 59% and hypoalbuminemia (< 3.5 g/dL) in 55% of the study patients. Low serum albumin level was associated to one, two- and five-year mortality after hospital stay (all P <.05). In addition, it was associated with pneumonia, dyspnea, over 13-night long stay at ward and death at ward (all P < .05). No associations were seen between serum 25(OH)D and LL-37 levels and disease severity, short-term clinical outcome, or long-term survival. Associations between serum 25(OH)D, albumin, and LL-37 levels and respiratory virus presence were not seen. Conclusions: Serum albumin level on admission seems to give valuable information about the patients’ general health and recovery potential in treating older adults with respiratory symptoms. Serum 25(OH)D and LL-37 had no associations with disease severity or long- and short-term prognosis among older adults hospitalized with respiratory symptoms

Associative Networks and Cell Assemblies

Since the time of McCulloch and Pitts’ Theory (1943) there have been many attempts to model the flow of activity in neural networks. It is possible to simulate neural networks (of rather small size) on a computer, relying on quite reasonable — more or less simplified — assumptions on the dynamic behavior of single neurons. One problem is the arbitrariness of the design of the network (i.e. the connectivity matrix). Here many investigations have studied random connectivity (e.g. Anninos et al. 1970, Griffith 1971, Amari 1974, Dammasch and Wagner 1984) or connectivity that itself changes subject to certain rules (for an overview see Palm 1982)

Trypanosoma cruzi infection down-modulates the Immunoproteasome Biosynthesis and the MHC class I cell surface expression in HeLa cellsurface expression in HeLa cells

Generally, Trypanosoma cruzi infection in human is persistent and tends to chronicity, suggesting that the parasite evade the immune surveillance by down regulating the intracellular antigen processing routes. Within the MHC class I pathway, the majority of antigenic peptides are generated by the proteasome. However, upon IFN-c stimulation, the catalytic constitutive subunits of the proteasome are replaced by the subunits b1i/LMP2, b2i/MECL-1 and b5i/LMP7 to form the immunoproteasome. In this scenario, we analyzed whether the expression and activity of the constitutive and the immunoproteasome as well as the expression of other components of the MHC class I pathway are altered during the infection of HeLa cells with T. cruzi. By RT-PCR and two-dimensional gel electrophoresis analysis, we showed that the expression and composition of the constitutive proteasome is not affected by the parasite. In contrast, the biosynthesis of the b1i, b2i, b5i immunosubunits, PA28b, TAP1 and the MHC class I molecule as well as the proteasomal proteolytic activities were down-regulated in infected-IFN-c-treated cell cultures. Taken together, our results provide evidence that the protozoan T. cruzi specifically modulates its infection through an unknown posttranscriptional mechanism that inhibits the expression of the MHC class I pathway components

Advances in the use of terminal restriction fragment length polymorphism (T-RFLP) analysis of 16S rRNA genes to characterize microbial communities

Terminal restriction fragment length polymorphism (T-RFLP) analysis is a popular high-throughput fingerprinting technique used to monitor changes in the structure and composition of microbial communities. This approach is widely used because it offers a compromise between the information gained and labor intensity. In this review, we discuss the progress made in T-RFLP analysis of 16S rRNA genes and functional genes over the last 10 years and evaluate the performance of this technique when used in conjunction with different statistical methods. Web-based tools designed to perform virtual polymerase chain reaction and restriction enzyme digests greatly facilitate the choice of primers and restriction enzymes for T-RFLP analysis. Significant improvements have also been made in the statistical analysis of T-RFLP profiles such as the introduction of objective procedures to distinguish between signal and noise, the alignment of T-RFLP peaks between profiles, and the use of multivariate statistical methods to detect changes in the structure and composition of microbial communities due to spatial and temporal variation or treatment effects. The progress made in T-RFLP analysis of 16S rRNA and genes allows researchers to make methodological and statistical choices appropriate for the hypotheses of their studies.Ursel M. E. Schütte, Zaid Abdo, Stephen J. Bent, Conrad Shyu, Christopher J. Williams, Jacob D. Pierson, Larry J. Forne

When Artificial Intelligence and Computational Neuroscience Meet

International audienceComputational Intelligence and Artificial Intelligence are both aiming at building machines and softwares capable of intelligent behavior. They are consequently prone to interactions, even if the latter is not necessarily interested in understanding how cognition emerges from the brain substrate. In this chapter, we enumerate, describe and discuss the most important fields of interactions. Some are methodological and are concerned with information representation, processing and learning. At the functional level, the focus is set on major cognitive functions like perception, navigation, decision making and language. Among the salient characteristics of the critical contributions of Computational Neuroscience to the development of intelligent systems, its systemic view of the cerebral functioning is particularly precious to model highly multimodal cognitive functions like decision makin