Reading comprehension abilities: a study with psychology students

This research meant to explore the relation among reading comprehension and academic performance in specific contents of the Psychology course. The sample was composed of by 115 freshman students of the private university in São Paulo. The instruments used were two texts prepared in accordance to Cloze's technique and a questionnaire focusing the most used characterization types in higher education assessment. The results showed clearly a correlation, statistically significant, among reading comprehension, academic performance and learning assessment in Psychology course, specially when the grade results of the student individual production in assessment situation.Este estudo objetivou analisar a relação entre a compreensão em leitura e o rendimento acadêmico em disciplinas específicas do curso de Psicologia. Participaram 115 universitários ingressantes de uma universidade do interior paulista. Foram aplicados dois textos preparados segundo a técnica de Cloze e um questionário com questões fechadas, visando identificar os tipos de avaliação de aprendizagem utilizados pelos professores. Os resultados obtidos evidenciaram a existência de índices de correlação positiva entre os escores somados dos testes de Cloze e as notas obtidas em cada disciplina cursada. Pode-se concluir que a compreensão em leitura relaciona-se com o desempenho acadêmico, especialmente quando a nota é resultante de uma produção individual do aluno na situação de avaliação.294

OP0291 TOFACITINIB FOR THE TREATMENT OF POLYARTICULAR COURSE JUVENILE IDIOPATHIC ARTHRITIS: RESULTS OF A PHASE 3, RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED WITHDRAWAL STUDY

Background:Tofacitinib is an oral JAK inhibitor that is being investigated for JIA.Objectives:To assess tofacitinib efficacy and safety in JIA patients (pts).Methods:This was a Phase 3, randomised, double-blind (DB), placebo (PBO)-controlled withdrawal study in pts aged 2−<18 years with polyarticular course JIA (pcJIA), PsA or ERA (NCT02592434). In the 18-week open-label Part 1, pts received weight-based tofacitinib doses (5 mg BID or lower). Pts with ≥JIA ACR30 response at Week (W)18 were randomised 1:1 in the DB Part 2 (W18−44) to continue tofacitinib or switch to PBO. Primary endpoint: disease flare rate by W44. Key secondary endpoints: JIA ACR50/30/70 response rates; change from Part 2 baseline (Δ) in CHAQ-DI at W44. Other efficacy endpoints: time to disease flare in Part 2; JADAS27-CRP in Parts 1 and 2. PsA/ERA pts were excluded from these efficacy analyses. Safety was evaluated in all pts up to W44.Results:225 enrolled pts with pcJIA (n=184), PsA (n=20) or ERA (n=21) received tofacitinib in Part 1. At W18, 173/225 (76.9%) pts entered Part 2 (pcJIA n=142, PsA n=15, ERA n=16). In pcJIA pts, disease flare rate in Part 2 was significantly lower with tofacitinib vs PBO by W44 (p=0.0031; Fig 1a). JIA ACR50/30/70 response rates (Fig 1b) and ΔCHAQ-DI (Fig 1c) at W44, and time to disease flare in Part 2 (Fig 2a), were improved with tofacitinib vs PBO. Tofacitinib reduced JADAS27-CRP in Part 1; this effect was sustained in Part 2 (Fig 2b). Overall, safety was similar with tofacitinib or PBO (Table): 77.3% and 74.1% had adverse events (AEs); 1.1% and 2.4% had serious AEs. In Part 1, 2 pts had herpes zoster (non-serious) and 3 pts had serious infections (SIs). In Part 2, SIs occurred in 1 tofacitinib pt and 1 PBO pt. No pts died.Conclusion:In pcJIA pts, tofacitinib vs PBO resulted in significantly fewer disease flares, and improved time to flare, disease activity and physical functioning. Tofacitinib safety was consistent with that in RA pts.Table.Safety in all ptsPart 1Part 2TofacitinibaN=225TofacitinibaN=88PBO N=85Pts with events, n (%)AEs153 (68.0)68 (77.3)63 (74.1)SAEs7 (3.1)1 (1.1)2 (2.4)Permanent discontinuations due to AEs26 (11.6)16 (18.2)29 (34.1)AEs of special interest Death000 Gastrointestinal perforationb000 Hepatic eventb3 (1.3)00 Herpes zoster (non-serious and serious)2 (0.9)c00 Interstitial lung diseaseb000 Major adverse cardiovascular eventsb000 Malignancy (including non-melanoma skin cancer)b000 Macrophage activation syndromeb000 Opportunistic infectionb000 SI3 (1.3)1 (1.1)d1 (1.2) Thrombotic event (deep vein thrombosis, pulmonary embolismbor arterial thromboembolism)000 Tuberculosisb000a5 mg BID or equivalent weight-based lower dose in pts <40 kgbAdjudicated eventscBoth non-seriousdOne SAE of pilonidal cyst repair was coded to surgical procedures instead of infections, and was inadvertently not identified as an SI. Following adjudication, the SAE did not meet opportunistic infection criteria; it is also included in the table as an SIAE, adverse event; BID, twice daily; PBO, placebo; pts, patients; SAE, serious AE; SI, serious infectionAcknowledgments:Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.Disclosure of Interests:Nicolino Ruperto Grant/research support from: Bristol-Myers Squibb, Eli Lily, F Hoffmann-La Roche, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sobi (paid to institution), Consultant of: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Speakers bureau: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Olga Synoverska Speakers bureau: Sanofi, Tracy Ting: None declared, Carlos Abud-Mendoza Speakers bureau: Eli Lilly, Pfizer Inc, Alberto Spindler Speakers bureau: Eli Lilly, Yulia Vyzhga Grant/research support from: Pfizer Inc, Katherine Marzan Grant/research support from: Novartis, Vladimir Keltsev: None declared, Irit Tirosh: None declared, Lisa Imundo: None declared, Rita Jerath: None declared, Daniel Kingsbury: None declared, Betül Sözeri: None declared, Sheetal Vora: None declared, Sampath Prahalad Grant/research support from: Novartis, Elena Zholobova Grant/research support from: Novartis and Pfizer Inc, Speakers bureau: AbbVie, Novartis, Pfizer Inc and Roche, Yonatan Butbul Aviel: None declared, Vyacheslav Chasnyk: None declared, Melissa Lerman Grant/research support from: Amgen, Kabita Nanda Grant/research support from: Abbott, AbbVie, Amgen and Roche, Heinrike Schmeling Grant/research support from: Janssen, Pfizer Inc, Roche and USB Bioscience, Heather Tory: None declared, Yosef Uziel Speakers bureau: Pfizer Inc, Diego O Viola Grant/research support from: Bristol-Myers Squibb, GSK, Janssen and Pfizer Inc, Speakers bureau: AbbVie and Bristol-Myers Squibb, Holly Posner Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Keith Kanik Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ann Wouters Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Richard Zhang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Irina Lazariciu Consultant of: Pfizer Inc, Employee of: IQVIA, Ming-Ann Hsu Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ricardo Suehiro Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Alberto Martini Consultant of: AbbVie, Eli Lily, EMD Serono, Janssen, Novartis, Pfizer, UCB, Daniel J Lovell Consultant of: Abbott (consulting and PI), AbbVie (PI), Amgen (consultant and DSMC Chairperson), AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb (PI), Celgene, Forest Research (DSMB Chairman), GlaxoSmithKline, Hoffman-La Roche, Janssen (co-PI), Novartis (consultant and PI), Pfizer (consultant and PI), Roche (PI), Takeda, UBC (consultant and PI), Wyeth, Employee of: Cincinnati Children's Hospital Medical Center, Speakers bureau: Wyeth, Hermine Brunner Consultant of: Hoffman-La Roche, Novartis, Pfizer, Sanofi Aventis, Merck Serono, AbbVie, Amgen, Alter, AstraZeneca, Baxalta Biosimilars, Biogen Idec, Boehringer, Bristol-Myers Squibb, Celgene, EMD Serono, Janssen, MedImmune, Novartis, Pfizer, and UCB Biosciences, Speakers bureau: GSK, Roche, and Novarti

Paraoxonase 1 (PON1) Polymorphisms, Haplotypes and Activity in Predicting CAD Risk in North-West Indian Punjabis

Human serum paraoxonase-1 (PON1) prevents oxidation of low density lipoprotein cholesterol (LDL-C) and hydrolyzes the oxidized form, therefore preventing the development of atherosclerosis. The polymorphisms of PON1 gene are known to affect the PON1 activity and thereby coronary artery disease (CAD) risk. As studies are lacking in North-West Indian Punjabi's, a distinct ethnic group with high incidence of CAD, we determined PON1 activity, genotypes and haplotypes in this population and correlated them with the risk of CAD.350 angiographically proven (≥ 70% stenosis) CAD patients and 300 healthy controls were investigated. PON1 activity was determined towards paraoxon (Paraoxonase; PONase) and phenylacetate (Arylesterase; AREase) substrates. In addition, genotyping was carried out by using multiplex PCR, allele specific oligonucleotide -PCR and PCR-RFLP methods and haplotyping was determined by PHASE software. The serum PONase and AREase activities were significantly lower in CAD patients as compared to the controls. All studied polymorphisms except L55M had significant effect on PONase activity. However AREase activity was not affected by them. In a logistic regression model, after adjustment for the conventional risk factors for CAD, QR (OR: 2.73 (1.57-4.72)) and RR (OR, 16.24 (6.41-41.14)) genotypes of Q192R polymorphism and GG (OR: 2.07 (1.02-4.21)) genotype of -162A/G polymorphism had significantly higher CAD risk. Haplotypes L-T-G-Q-C (OR: 3.25 (1.72-6.16)) and L-T-G-R-G (OR: 2.82 (1.01-7.80)) were also significantly associated with CAD.In conclusion this study shows that CAD patients had lower PONase and AREase activities as compared to the controls. The coding Q192R polymorphism, promoter -162A/G polymorphism and L-T-G-Q-C and L-T-G-R-G haplotypes are all independently associated with CAD

Virus Adaptation by Manipulation of Host's Gene Expression

Viruses adapt to their hosts by evading defense mechanisms and taking over cellular metabolism for their own benefit. Alterations in cell metabolism as well as side-effects of antiviral responses contribute to symptoms development and virulence. Sometimes, a virus may spill over from its usual host species into a novel one, where usually will fail to successfully infect and further transmit to new host. However, in some cases, the virus transmits and persists after fixing beneficial mutations that allow for a better exploitation of the new host. This situation would represent a case for a new emerging virus. Here we report results from an evolution experiment in which a plant virus was allowed to infect and evolve on a naïve host. After 17 serial passages, the viral genome has accumulated only five changes, three of which were non-synonymous. An amino acid substitution in the viral VPg protein was responsible for the appearance of symptoms, whereas one substitution in the viral P3 protein the epistatically contributed to exacerbate severity. DNA microarray analyses show that the evolved and ancestral viruses affect the global patterns of host gene expression in radically different ways. A major difference is that genes involved in stress and pathogen response are not activated upon infection with the evolved virus, suggesting that selection has favored viral strategies to escape from host defenses

Interpretable machine learning models for classifying low back pain status using functional physiological variables.

PURPOSE:To evaluate the predictive performance of statistical models which distinguishes different low back pain (LBP) sub-types and healthy controls, using as input predictors the time-varying signals of electromyographic and kinematic variables, collected during low-load lifting. METHODS:Motion capture with electromyography (EMG) assessment was performed on 49 participants [healthy control (con) = 16, remission LBP (rmLBP) = 16, current LBP (LBP) = 17], whilst performing a low-load lifting task, to extract a total of 40 predictors (kinematic and electromyographic variables). Three statistical models were developed using functional data boosting (FDboost), for binary classification of LBP statuses (model 1: con vs. LBP; model 2: con vs. rmLBP; model 3: rmLBP vs. LBP). After removing collinear predictors (i.e. a correlation of > 0.7 with other predictors) and inclusion of the covariate sex, 31 predictors were included for fitting model 1, 31 predictors for model 2, and 32 predictors for model 3. RESULTS:Seven EMG predictors were selected in model 1 (area under the receiver operator curve [AUC] of 90.4%), nine predictors in model 2 (AUC of 91.2%), and seven predictors in model 3 (AUC of 96.7%). The most influential predictor was the biceps femoris muscle (peak [Formula: see text]  = 0.047) in model 1, the deltoid muscle (peak [Formula: see text] =  0.052) in model 2, and the iliocostalis muscle (peak [Formula: see text] =  0.16) in model 3. CONCLUSION:The ability to transform time-varying physiological differences into clinical differences could be used in future prospective prognostic research to identify the dominant movement impairments that drive the increased risk. These slides can be retrieved under Electronic Supplementary Material