Serological markers for prediction of response to anti-tumor necrosis factor treatment in Crohn's disease

peer reviewedOBJECTIVES: The use of monoclonal anti-tumor necrosis factor (TNF) antibodies (infliximab, Remicade) is a new therapeutic approach for severe refractory luminal or fistulizing, Crohn's disease (CD). However, up to 30% of patients do not respond to this treatment. So far, no parameters predictive of response to anti-TNT have been identified. Our aim was to determine whether serological markers ASCA (anti-Saccharomyces cerevisiae antibodies) or pANCA (perinuclear antineutrophil cytoplasmic antibodies) could identify Crohn's patients likely to benefit from anti-TNF therapy. METHODS: Serum samples of 279 CID patients were analyzed for ASCA and pANCA before anti-TNF therapy. A blinded physician determined clinical response at week 4 (refractory luminal CD) or week 10 (fistulizing CD) after the first infusion of infliximab (5 mg/kg). RESULTS: Overall, there was no relationship between ASCA or pANCA and response to therapy. However, lower response rates were observed for patients with refractory intestinal disease carrying the pANCA+/ASCA- combination, although this lacked significance (p = 0.067). CONCLUSIONS: In this cohort of infliximab-treated patients, neither ASCA nor pANCA could predict response to treatment. However, the combination pANCA+/ASCA- might warrant further investigation for its value in predicting nonresponse in patients with refractory luminal disease

Effects of school-based interventions on mental health stigmatization: a systematic review

Stigmatizing, or discriminatory, perspectives and behaviour, which target individuals on the basis of their mental health, are observed in even the youngest school children. We conducted a systematic review of the published and unpublished, scientific literature concerning the benefits and harms of school-based interventions, which were directed at students 18 years of age or younger to prevent or eliminate such stigmatization. Forty relevant studies were identified, yet only a qualitative synthesis was deemed appropriate. Five limitations within the evidence base constituted barriers to drawing conclusive inferences about the effectiveness and harms of school-based interventions: poor reporting quality, a dearth of randomized controlled trial evidence, poor methods quality for all research designs, considerable clinical heterogeneity, and inconsistent or null results. Nevertheless, certain suggestive evidence derived both from within and beyond our evidence base has allowed us to recommend the development, implementation and evaluation of a curriculum, which fosters the development of empathy and, in turn, an orientation toward social inclusion and inclusiveness. These effects may be achieved largely by bringing especially but not exclusively the youngest children into direct, structured contact with an infant, and likely only the oldest children and youth into direct contact with individuals experiencing mental health difficulties. The possible value of using educational activities, materials and contents to enhance hypothesized benefits accruing to direct contact also requires investigation. Overall, the curriculum might serve as primary prevention for some students and as secondary prevention for others

Roadmap on Machine learning in electronic structure

AbstractIn recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Rab protein evolution and the history of the eukaryotic endomembrane system

Spectacular increases in the quantity of sequence data genome have facilitated major advances in eukaryotic comparative genomics. By exploiting homology with classical model organisms, this makes possible predictions of pathways and cellular functions currently impossible to address in intractable organisms. Echoing realization that core metabolic processes were established very early following evolution of life on earth, it is now emerging that many eukaryotic cellular features, including the endomembrane system, are ancient and organized around near-universal principles. Rab proteins are key mediators of vesicle transport and specificity, and via the presence of multiple paralogues, alterations in interaction specificity and modification of pathways, contribute greatly to the evolution of complexity of membrane transport. Understanding system-level contributions of Rab proteins to evolutionary history provides insight into the multiple processes sculpting cellular transport pathways and the exciting challenges that we face in delving further into the origins of membrane trafficking specificity

Roadmap on Machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.</p

Roadmap on machine learning in electronic structure

In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century

Investigating the role of residential migration history on the relationship between attachment and sense of belonging: A SEM approach

With the rate of both domestic and international migration steadily increasing, the psychological impact of residential migration remains largely unexplored. Attachment, the emotional bond we establish with those close to us, and sense of belonging, the feeling of connectedness to a community, may be vulnerable to frequent migration. This study investigates the association between individuals' early attachment style, sense of belonging, and migration history. A large international sample (N = 465) aged between 18 and 50 years old (M = 21.85; SD = 4.48), completed a survey on early attachment primary attachment style questionnaire (PASQ), sense of belonging (SOBI), and migration. Results comparing non-movers (n = 240) to domestic movers (n = 52), international movers (n = 109), and domestic-international movers (n = 64), indicate important group differences related to early attachment and its relationship to one's sense of belonging. Moreover, insecure attachment was associated with increased migration early in life and, more in general, predictive of a negative sense of belonging later in life. Implications for both research and practice are discussed