Depth-wise progression of osteoarthritis in human articular cartilage: investigation of composition, structure and biomechanics

SummaryObjectiveOsteoarthritis (OA) is characterized by the changes in structure and composition of articular cartilage. However, it is not fully known, what is the depth-wise change in two major components of the cartilage solid matrix, i.e., collagen and proteoglycans (PGs), during OA progression. Further, it is unknown how the depth-wise changes affect local tissue strains during compression. Our aim was to address these issues.MethodsData from the previous microscopic and biochemical measurements of the collagen content, distribution and orientation, PG content and distribution, water content and histological grade of normal and degenerated human patellar articular cartilage (n=73) were reanalyzed in a depth-wise manner. Using this information, a composition-based finite element (FE) model was used to estimate tissue function solely based on its composition and structure.ResultsThe orientation angle of collagen fibrils in the superficial zone of cartilage was significantly less parallel to the surface (P<0.05) in samples with early degeneration than in healthy samples. Similarly, PG content was reduced in the superficial zone in early OA (P<0.05). However, collagen content decreased significantly only at the advanced stage of OA (P<0.05). The composition-based FE model showed that under a constant stress, local tissue strains increased as OA progressed.ConclusionFor the first time, depth-wise point-by-point statistical comparisons of structure and composition of human articular cartilage were conducted. The present results indicated that early OA is primarily characterized by the changes in collagen orientation and PG content in the superficial zone, while collagen content does not change until OA has progressed to its late stage. Our simulation results suggest that impact loads in OA joint could create a risk for tissue failure and cell death

Minor Changes in the Hemagglutinin of Influenza A(H1N1)2009 Virus Alter Its Antigenic Properties

BACKGROUND: The influenza A(H1N1)2009 virus has been the dominant type of influenza A virus in Finland during the 2009-2010 and 2010-2011 epidemic seasons. We analyzed the antigenic characteristics of several influenza A(H1N1)2009 viruses isolated during the two influenza seasons by analyzing the amino acid sequences of the hemagglutinin (HA), modeling the amino acid changes in the HA structure and measuring antibody responses induced by natural infection or influenza vaccination. METHODS/RESULTS: Based on the HA sequences of influenza A(H1N1)2009 viruses we selected 13 different strains for antigenic characterization. The analysis included the vaccine virus, A/California/07/2009 and multiple California-like isolates from 2009-2010 and 2010-2011 epidemic seasons. These viruses had two to five amino acid changes in their HA1 molecule. The mutation(s) were located in antigenic sites Sa, Ca1, Ca2 and Cb region. Analysis of the antibody levels by hemagglutination inhibition test (HI) indicated that vaccinated individuals and people who had experienced a natural influenza A(H1N1)2009 virus infection showed good immune responses against the vaccine virus and most of the wild-type viruses. However, one to two amino acid changes in the antigenic site Sa dramatically affected the ability of antibodies to recognize these viruses. In contrast, the tested viruses were indistinguishable in regard to antibody recognition by the sera from elderly individuals who had been exposed to the Spanish influenza or its descendant viruses during the early 20(th) century. CONCLUSIONS: According to our results, one to two amino acid changes (N125D and/or N156K) in the major antigenic sites of the hemagglutinin of influenza A(H1N1)2009 virus may lead to significant reduction in the ability of patient and vaccine sera to recognize A(H1N1)2009 viruses

6.-luokkalaisten lasten kaverisuhteet koulussa sekä koulukaveruuden tärkeimmät tekijät

Tiivistelmä. Tutkimuksessa tarkastellaan sattumanvaraisesti valittujen 6.-luokkalaisten lasten koulukaverisuhteita. Kaverisuhteet ovat kaikille lapsille tärkeitä tuottaen ryhmään kuulumisen tunnetta. Vertaissuhteet ovat koulumaailmassa merkityksellisiä ja vaikuttavat lapsen kokonaisvaltaiseen kasvuun ja kehitykseen. Koulu on lapsuuden keskeinen ympäristö ja kaverisuhteiden luominen on yksi tärkeimmistä saavutuksista koulussa. Tutkimuksen tavoite oli selvittää 6.-luokkalaisten lasten luokan sisäisiä kaverivalintoja sekä oppilaiden perusteluita valinnoille. Aineistonkeruu tehtiin kahteen erikokoiseen kouluun. Tutkimusluokat edustivat isoa ja pientä koulua. Kyseiseen ratkaisuun päädyttiin kahden koulun tulosten vertailun saamiseksi. Menetelmäksi valittiin sosiometriikka ja aineisto kerättiin kyselylomakkeella. Sosiometriikkalla tarkoitetaan sosiaalisten suhteiden mittaamista. Sosiometrisillä menetelmillä tutkitaan ryhmän sisällä tapahtuvia sosiaalisia valintoja, kuten kaverisuhteita. Tutkimuksen aineistona toimi yhteensä 32 oppilaan kyselylomakkeiden vastaukset. Tutkimuksessa keskeisiä teemoja olivat sukupuolen sekä ulkoisten ja sisäisten tekijöiden merkitys koulukaveruudelle. Oppilaiden sosiaaliset roolit luokassa näkyivät ulkopuolisuuden ja sisäpuolisuuden erilaisiana esiintyvyyksinä. Tarkastelun kohteena olivat myös erot ja yhtäläisyydet koulujen ryhmädynamiikkassa sekä oppilaiden vastauksissa. Oppilaita pyydettiin muun muassa valitsemaan 18 vaihtoehdosta hänelle tärkeimmät tekijät kaverissa. Vaihtoehdot kyselylomakkeeseen muodostettiin Salmivallin (1998) ominaispiirteitä käsittelevän teorian pohjalta. Lisäksi teoreettisenä lähtökohtana tutkimuksessa toimivat Sheelyn ja Gutsteinin (2002) tutkimus osaamisalueista sekä Ojasen (2006) sosiaalista käyttäytymistä käsittelevä teoria. Verrattaessa ison ja pienen koulun oppilaiden valintoja, painottui pienessä koulussa enemmän kiltteyden merkitys ja isossa koulussa se, että kaverin kanssa ollaan usein samaa mieltä. Tutkimuksen molempien koulujen 6.-luokkalaisille oppilaille tärkeimmäksi tekijäksi koulukaverissa ilmeni hauskuus. Lisäksi pinnalle nousivat riitelemättömyys, avuliaisuus sekä ystävällisyys, jotka tutkimustuloksissa yhdistettiin yhteistyötaidoiksi. Tutkimustulokset ovat sidoksissa kyseisen kulttuurin, yhteiskunnan, luokan sekä sen oppilaiden yksilöllisiin kokemuksiin. Tutkimus on ajankohtainen, sillä mukaan tutkimukseen otettiin kulutusyhteiskunnan, jonka kulutustuotteet lisäävät eriarvoisuutta kouluissa, mahdollinen vaikuttavuus koulukaveruuteen

The role of individual and social variables in task performance.

This paper reports on a data-based study in which we explored - as part of a larger-scale British-Hungarian research project - the effects of a number of affective and social variables on foreign language (L2) learners’ engagement in oral argumentative tasks. The assumption underlying the investigation was that students’ verbal behaviour in oral task situations is partly determined by a number of non-linguistic and non-cognitive factors whose examination may constitute a potentially fruitful extension of existing task-based research paradigms. The independent variables in the study included various aspects of L2 motivation and several factors characterizing the learner groups the participating students were members of (such as group cohesiveness and intermember relations), as well as the learners’ L2 proficiency and ‘willingness to communicate’ in their L1. The dependent variables involved objective measures of the students’ language output in two oral argumentative tasks (one in the learners’ L1, the other in their L2): the quantity of speech and the number of turns produced by the speakers. The results provide insights into the interrelationship of the multiple variables determining the learners’ task engagement, and suggest a multi-level construct whereby some independent variables only come into force when certain conditions have been met

Comparison of Zaire ebolavirus realtime RT-PCRs targeting the nucleoprotein gene

In last five years, the Africa has faced two outbreaks of Zaire ebolavirus. These outbreaks have been the largest so far, and latest outbreak is still ongoing and affecting the Democratic Republic of the Congo. We tested in parallel three different Zaire ebolavirus (EBOV) realtime RT-PCRs targeting the nucleoprotein gene (EBOV NP-RT-qPCRs) described by Trombley et al. (2010); Huang et al. (2012) and Weidmann et al. (2004). These assays are used regularly in diagnostic laboratories. The limit of detection (LOD), intra-assay repeatability using different matrixes, sensitivity and specificity were determined. In addition, the primers and probes were aligned with the sequences available in ongoing and past outbreaks in order to check the mismatches. The specificity of all three EBOV NP-RT-qPCRs were excellent (100 %), and LODs were under or 10 copies per PCR reaction. Intra-assay repeatability was good in all assays, however the Ct-values were bit higher using the EDTA-blood based matrix. All of the primers and probes in EBOV NP-RT-qPCR assays have one or more mismatches in the probes and primers when the 2267 Zaire EBOV NP sequences, including strains Ituri from DRC outbreak (year 2018), was aligned. The EBOV strain of Bikoro (year 2018) circulating in DRC was 100 % match in Trombley and Weidmann assay, but had one mismatch in Huang assay.Peer reviewe

Composition of the pericellular matrix modulates the deformation behaviour of chondrocytes in articular cartilage under static loading

The aim was to assess the role of the composition changes in the pericellular matrix (PCM) for the chondrocyte deformation. For that, a three-dimensional finite element model with depth-dependent collagen density, fluid fraction, fixed charge density and collagen architecture, including parallel planes representing the split-lines, was created to model the extracellular matrix (ECM). The PCM was constructed similarly as the ECM, but the collagen fibrils were oriented parallel to the chondrocyte surfaces. The chondrocytes were modelled as poroelastic with swelling properties. Deformation behaviour of the cells was studied under 15% static compression. Due to the depth-dependent structure and composition of cartilage, axial cell strains were highly depth-dependent. An increase in the collagen content and fluid fraction in the PCMs increased the lateral cell strains, while an increase in the fixed charge density induced an inverse behaviour. Axial cell strains were only slightly affected by the changes in PCM composition. We conclude that the PCM composition plays a significant role in the deformation behaviour of chondrocytes, possibly modulating cartilage development, adaptation and degeneration. The development of cartilage repair materials could benefit from this information

Influenza virus NS1 protein binds cellular DNA to block transcription of antiviral genes

Influenza NS1 protein is an important virulence factor that is capable of binding double-stranded (ds) RNA and inhibiting dsRNA-mediated host innate immune responses. Here we show that NS1 can also bind cellular dsDNA. This interaction prevents loading of transcriptional machinery to the DNA, thereby attenuating IAV-mediated expression of antiviral genes. Thus, we identified a previously undescribed strategy, by which RNA virus inhibits cellular transcription to escape antiviral response and secure its replication. (C) 2016 Elsevier B.V. All rights reserved.Peer reviewe

Learning with multiple pairwise kernels for drug bioactivity prediction

Motivation: Many inference problems in bioinformatics, including drug bioactivity prediction, can be formulated as pairwise learning problems, in which one is interested in making predictions for pairs of objects, e.g. drugs and their targets. Kernel-based approaches have emerged as powerful tools for solving problems of that kind, and especially multiple kernel learning (MKL) offers promising benefits as it enables integrating various types of complex biomedical information sources in the form of kernels, along with learning their importance for the prediction task. However, the immense size of pairwise kernel spaces remains a major bottleneck, making the existing MKL algorithms computationally infeasible even for small number of input pairs.Results: We introduce pairwiseMKL, the first method for time- and memory-efficient learning with multiple pairwise kernels. pairwiseMKL first determines the mixture weights of the input pairwise kernels, and then learns the pairwise prediction function. Both steps are performed efficiently without explicit computation of the massive pairwise matrices, therefore making the method applicable to solving large pairwise learning problems. We demonstrate the performance of pairwiseMKL in two related tasks of quantitative drug bioactivity prediction using up to 167 995 bioactivity measurements and 3120 pairwise kernels: (i) prediction of anticancer efficacy of drug compounds across a large panel of cancer cell lines; and (ii) prediction of target profiles of anticancer compounds across their kinome-wide target spaces. We show that pairwiseMKL provides accurate predictions using sparse solutions in terms of selected kernels, and therefore it automatically identifies also data sources relevant for the prediction problem