The effect of human settlement temperature and humidity on the growth rules of infected and recovered cases of COVID-19

This study investigated the impact of humidity and temperature on the spread of COVID-19 (SARS-CoV-2) by statistically comparing modelled pandemic dynamics (daily infection and recovery cases) with daily temperature and humidity of three climate zones (Mainland China, South America and Africa) from January to August 2020. We modelled the pandemic growth using a simple logistic function to derive information of the viral infection and describe the growth of infected and recovered cases. The results indicate that the infected and recovered cases of the first wave were controlled in China and managed in both South America and Africa. There is a negative correlation between both humidity (r = - 0.21; p = 0.27) and temperature (r = -0.22; p = 0.24) with spread of the virus. Though this study did not fully encompass socio-cultural factors, we recognise that local government responses, general health policies, population density and transportation could also affect the spread of the virus. The pandemic can be managed better in the second wave if stricter safety protocols are implemented. We urge various units to collaborate strongly and call on countries to adhere to stronger safety protocols in the second wave.

POS1028 PATIENT CHARACTERISTICS & CLINICAL FEATURES ASSOCIATE WITH HEALTH-RELATED QUALITY OF LIFE IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA) is a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis, & skin/nail psoriasis. Patients (pts) with PsA often experience reduced health-related quality of life (HRQoL) due to these features.

Objectives:

Using EuroQoL-5 dimension-5 level (EQ-5D-5L) questionnaire index & visual analog scale (EQ-VAS) scores, we assessed HRQoL in pts with PsA & its association with pt characteristics & clinical features of PsA, including fatigue.

Methods:

The Phase 3 DISCOVER-2 trial evaluated guselkumab (GUS), a human monoclonal antibody targeting the IL-23p19-subunit, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5, tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Pts were randomized 1:1:1 to GUS 100 mg every 4 weeks (Q4W); GUS 100 mg at Week 0 (W0), W4, then Q8W; or placebo (PBO). EQ-5D-5L index assesses mobility, self-care, usual activities, pain/discomfort, & anxiety/depression. EQ-VAS assesses pt health state. Spearman correlation testing was used to evaluate relationships between baseline (BL) pt characteristics & PsA clinical features & BL EQ-5D-5L index & EQ-VAS scores (Figure 1). Employing absolute observed scores at both W0 & W24, univariate linear regression was used to assess the association between EQ-5D-5L index & EQ-VAS scores & pt characteristics/PsA clinical features. Variables with p<0.20 in the univariate analysis were included in a multivariate analysis employing mixed-effect model for repeated measures (MMRM), controlling for all other variables; resulting p values <0.05 were considered statistically significant. Least-squares (LS) mean changes in EQ-5D-5L index & EQ-VAS were assessed at W24 using MMRM.

Results:

Among 738 pts, BL EQ-5D-5L index & EQ-VAS scores were moderately to strongly correlated (ie, ≥0.4) with BL pt-reported pain (0-10 VAS), physical function (Health Assessment Questionnaire-Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale), & 36-item Short Form Health Survey (SF-36) physical & mental component summary (PCS & MCS) scores & weakly correlated with other variables (Figure 1). Based on univariate analyses (p<0.20) & evaluation of collinearity between variables, attributes at W0 & W24 included in the multivariate models were age, sex, CRP, FACIT-F, pain, psoriasis area & severity index (PASI) score, TJC, SJC, enthesitis, & dactylitis. In the final model, CRP, FACIT-F, pain, PASI score, & the presence of dactylitis were significantly associated with EQ-5D-5L index & EQ-VAS scores. A higher TJC was significantly associated with a worse EQ-5D-5L index score. A higher SJC was significantly associated with a worse EQ-VAS score (Table 1). For reference, in the GUS Q4W (N=244), GUS Q8W (N=246), & PBO (N=244) groups, the LS mean changes from baseline at W24 were 0.12, 0.12, & 0.05, respectively, for EQ-5D-5L index & 18.1, 18.4, & 6.8, respectively, for EQ-VAS.

Conclusion:

Joint & skin symptoms, dactylitis, fatigue, pain, & elevated levels of CRP were significantly associated with reduced HRQoL (measured by EQ-5D-5L index & EQ-VAS) in bio-naïve pts with active PsA. Treatment of multiple PsA domains may help optimize HRQoL. Improvement across clinical domains1 & in HRQoL has been observed in GUS-treated pts with PsA.

References:

[1]Mease P, et al. Lancet 2020;395:1126-36.

Table 1.

Multivariate analysis of pt characteristics/clinical features & EQ-5D-5L index & EQ-VAS scores at W0 & W24

ParameterEQ-5D-5L IndexEQ-VASEstimatep valueEstimatep valueAge (y)-0.00010.690.060.12Female-0.0030.531.110.20CRP (mg/dL)-0.005<0.001-0.510.007FACIT-F (0-52)0.007<0.0010.57<0.001Pain (0-10)-0.02<0.001-3.47<0.001PASI (0-72)-0.0010.03-0.17<0.001SJC (0-66)-0.0010.21-0.170.02TJC (0-68)-0.0010.04-0.040.41Dactylitis (Y/N)0.010.021.740.49Enthesitis (Y/N)-0.0040.33-0.980.22

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Eli Lilly, Janssen, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: Abbvie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, May Shawi Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

POS0200 CLINICAL CHARACTERISTICS & OUTCOMES ASSOCIATE WITH WORK PRODUCTIVITY IN BIO-NAÏVE PATIENTS WITH ACTIVE PSORIATIC ARTHRITIS THROUGH WEEK 24 OF THE DISCOVER-2 STUDY

Background:

Psoriatic arthritis (PsA), a chronic inflammatory disease characterized by peripheral arthritis, axial inflammation, dactylitis, enthesitis & skin/nail psoriasis, causes impaired physical function, disability & loss of work productivity.

Objectives:

Evaluate associations between PsA clinical characteristics & outcomes including fatigue & work productivity using Work Productivity & Activity Impairment Questionnaire: PsA (WPAI-PsA).

Methods:

The Phase 3 DISCOVER-2 trial assessed guselkumab (GUS), an anti-IL-23p19 subunit monoclonal antibody, in bio-naïve adults with active PsA (swollen joint count [SJC] ≥5 & tender joint count [TJC] ≥5, C-reactive protein [CRP] ≥0.6 mg/dL) despite standard therapies.1 Patients (Pts) were randomized 1:1:1 to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), productivity loss (absenteeism+presenteeism), & daily activity during the previous week. Spearman correlation testing evaluated relationships between pt demographics & disease characteristics of PsA & WPAI domain scores based on observed values at baseline. Univariate linear regression assessed associations between WPAI & these variables based on observed data at W0 & at W24. Variables with p<0.10 were included in a multivariate analysis employing a mixed-effects model for repeated measures, controlling for all other variables; resulting p-values <0.05 were considered statistically significant.

Results:

As reported elsewhere,2 least-squares mean % changes from baseline at W24 were -3.8/-19.5/-20.0/-20.5 for GUS Q4W, -3.1/-19.4/-19.7/-21.5 for GUS Q8W, & -3.5/-10.2/-10.9/-10.3 for PBO for absenteeism, presenteeism, absenteeism+presenteeism, & daily activity impairment, respectively. Among 738 pts, WPAI domain scores were moderately to strongly correlated (ie, ≥0.4) with pt-reported pain (0-10 visual analog scale), physical function (Health Assessment Questionnaire Disability Index [HAQ-DI]), fatigue (Functional Assessment of Chronic Illness Therapy-Fatigue [FACIT-F] scale) & 36-Item Short Form Health Survey (SF-36) Physical Component Summary (PCS) score, but weakly correlated with other variables (Figure 1). Based on univariate analyses & evaluation of collinearity between variables, attributes included in multivariate models were age, body mass index (BMI), gender, CRP, FACIT-F, pain, Psoriasis Area Severity Index (PASI), TJC, SJC, enthesitis & dactylitis. In final model, CRP, FACIT-F, & pain were statistically significantly associated with all WPAI domains (Table 1). Presence of enthesitis & higher PASI score were significantly associated with higher loss of work productivity & activity outside work.

Conclusion:

In PsA pts, extra-articular symptoms, fatigue, pain & elevated CRP were significantly associated with WPAI-assessed work & activity impairment. Treating all major clinical manifestations of PsA is needed to help pts improve work & activity impairment. GUS effectively treats all major clinical manifestations1 & improves work & activity impairment in PsA.2

References:

[1]Mease P. Lancet 2020;395:1126-36.

[2]Curtis J. ACR 2020; Poster 0332.

Table 1.

Multivariate analysis of clinical characteristics/outcomes & WPAI domains at W0 & W24

ParameterAbsenteeismaPresenteeismaProductivity LossaActivity ImpairmentbEstimatep-valueEstimatep-valueEstimatep-valueEstimatep-valueAge-0.050.42-0.27<0.001-0.28<0.001-0.060.17Female0.910.46-1.540.22-1.740.202.380.02CRP0.730.040.970.011.010.010.89<0.001FACIT-F-0.31<0.001-0.67<0.001-0.73<0.001-0.75<0.001Pain1.03<0.0014.15<0.0014.25<0.0014.02<0.001PASI0.060.360.160.020.140.050.150.003SJC0.080.48-0.050.61-0.050.660.030.75TJC-0.100.130.110.090.090.190.100.04Dactylitis (Y/N)-1.100.392.470.052.580.050.540.57Enthesitis (Y/N)1.520.202.380.042.990.012.400.01

aPts working at baseline

bAll pts in study

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Eli Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly, Janssen, and UCB, Dafna D Gladman Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer and UCB, Feifei Yang Shareholder of: Janssen, Employee of: Janssen, Steve Peterson Shareholder of: Janssen, Employee of: Janssen, Prasheen Agarwal Shareholder of: Janssen, Employee of: Janssen, Alexa Kollmeier Shareholder of: Janssen, Employee of: Janssen, Elizabeth C Hsia Shareholder of: Janssen, Employee of: Janssen, Chenglong Han Shareholder of: Janssen, Employee of: Janssen, May Shawi Shareholder of: Janssen, Employee of: Janssen, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Celgene, Eli Lilly, Janssen, Novartis, MSD, Pfizer, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, and Novartis, Philip J Mease Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, GlaxoSmithKline, Janssen, Novartis, Pfizer, SUN, and UCB, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis

POS1029 GUSELKUMAB-TREATED PATIENTS WITH PSORIATIC ARTHRITIS ACHIEVED CLINICALLY MEANINGFUL IMPROVEMENTS IN GENERAL HEALTH OUTCOMES MEASURED WITH PROMIS-29 THROUGH 52 WEEKS: RESULTS FROM THE PHASE 3 DISCOVER-1 TRIAL

Background:

In the DISCOVER-1 study, the interleukin-23 p19 subunit inhibitor guselkumab (GUS) demonstrated robust efficacy across joint and skin clinical manifestations of psoriatic arthritis (PsA).1 Patients (pts) with PsA also experience a broad range of symptoms that negatively impact health-related quality of life (eg, pain, fatigue, anxiety, depression, sleep disturbance, poor physical function).2

Objectives:

Assess the treatment effect of GUS on general health outcomes in pts with PsA in the DISCOVER-1 trial through Week (W) 52 using the Patient-Reported Outcomes Measurement Information System-29 (PROMIS-29) instrument.

Methods:

Pts with active PsA (≥3 swollen + ≥3 tender joints; C-reactive protein ≥0.3 mg/dL) and inadequate response to standard conventional therapies were randomized 1:1:1 to GUS 100 mg Q4W; GUS 100 mg at W0, W4, then Q8W; or placebo (PBO). PBO pts switched to GUS 100 mg Q4W at W24. PROMIS-29 contains 4 items for each of 7 domains (anxiety, depression, fatigue, pain interference, physical function, sleep disturbance, social participation) and 1 pain intensity item; 28 items are scored on a 5-point Likert-type scale, and pain intensity is rated from 0-10. The raw score of each domain is converted to a standardized T-score, with norms based on a general population mean score=50 and a standard deviation (SD)=10. Higher scores in anxiety, depression, fatigue, pain interference, and sleep disturbance indicate more severe symptoms; higher physical function and social participation scores indicate better health outcomes. Changes ≥5 points (1/2 SD of T-score) are considered clinically meaningful. Analyses were performed using both observed (mean scores/changes, effect sizes) and imputed (clinically meaningful response, whereby change from baseline was set to 0 at W24/52 for pts who had missing data or at W24 for pts who met treatment failure criteria prior to W24).

Results:

At baseline, mean PROMIS-29 T-scores for physical function, social participation, sleep disturbance, pain, and fatigue were worse in the 381 PsA pts enrolled in DISCOVER-1 than in the general US population. Across all 7 domains, observed mean PROMIS-29 T-scores showed improvements in GUS-treated pts from baseline to W24 and W52 (Figure 1). Observed mean changes from baseline to W24 and W52, with calculated effect size, are shown (Table 1). In all pts, including those with imputed data, significantly higher percentages of pts in both GUS treatment groups vs PBO had ≥5-point improvements in fatigue, pain interference, physical function, sleep disturbance, social participation, and pain intensity domains at W24 (all nominal p<0.05). Mean improvements in PROMIS-29 domains were maintained through W52.

Conclusion:

In pts with active PsA, PROMIS-29 results indicate that GUS treatment was associated with clinically meaningful reductions in fatigue and pain and improvement in physical function and social participation, which were maintained through 1 year.

References:

[1] Deodhar A et al. Lancet. 2020;395:1115-25.

[2] Orbai A et al. Ann Rheum Dis. 2017;76:673-80.

Table 1.

Mean Change and Effect Size of Change From Baseline in PROMIS-29 Domain Scores at W24 and W52 (Observed)

Mean Change From Baseline [Effect Size]GUS Q4WGUS Q8WPBOW0-24GUS Q4WW24-52W24W52W24W52W24W52Anxiety−3.1 [−0.3]−3.1 [−0.3]−3.7 [−0.4]−4.3 [−0.5]−1.5 [−0.2]−3.6 [−0.4]Depression−2.7 [−0.3]−3.0 [−0.4]−4.0 [−0.4]−4.0 [−0.4]−0.6 [−0.1]−2.5 [−0.3]Fatigue−4.8 [−0.5]−5.6 [−0.6]−4.8 [−0.5]−6.8 [−0.7]−2.1 [−0.2]−5.7 [−0.6]Pain interference−5.4 [−0.8]−6.2 [−1.0]−5.8 [−1.0]−7.0 [−1.1]−2.8 [−0.4]−6.3 [−1.0]Physical function5.0 [0.8]5.9 [0.9]4.1 [0.6]5.0 [0.7]1.7 [0.2]4.2 [0.6]Sleep disturbance−2.5 [−0.4]−3.9 [−0.6]−3.8 [−0.6]−4.4 [−0.6]−1.5 [−0.2]−3.3 [−0.5]Social participation4.2 [0.5]5.3 [0.7]5.3 [0.6]6.6 [0.8]1.7 [0.2]4.9 [0.6]Pain intensity*−2.3 [−1.2]−2.8 [−1.5]−2.1 [−1.1]−2.7 [−1.4]−0.7 [−0.4]−2.5 [−1.3]

*Raw score; all other domains reported as T-score.

Disclosure of Interests:

Ana-Maria Orbai Consultant of: Eli Lilly, Janssen, Novartis, Pfizer, UCB, Grant/research support from: Abbvie, Eli Lilly and Company, Celgene, Novartis, Janssen, Horizon, Laura C Coates Consultant of: AbbVie, Amgen, Biogen, Bristol Myers Squibb, Boehringer Ingelehim, Celgene, Domain, Eli Lilly, Gilead, Janssen, Medac, Novartis, Pfizer and UCB, Grant/research support from: AbbVie, Amgen, Celgene, Eli Lilly, Gilead, Novartis, Pfizer, Atul Deodhar Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Boehringer Ingelheim, Bristol Myers Squibb, Celgene, Eli Lilly, Galapagos, GSK, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: AbbVie, Eli Lilly, GSK, Novartis, Pfizer, and UCB, Philip Helliwell Consultant of: Galapagos, Janssen, and Novartis, Grant/research support from: Abbvie, Janssen, and Pfizer, Christopher T. Ritchlin Consultant of: AbbVie, Amgen, Gilead, Janssen, Lilly, Novartis, Pfizer, UCB Pharma, Grant/research support from: AbbVie, Amgen, UCB Pharma, Alexa Kollmeier Shareholder of: Johnson & Johnson, of which Janssen Research & Development, LLC is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, of which Janssen Research & Development, LLC is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC, Xie L Xu Shareholder of: Johnson & Johnson, of which Janssen Research & Development is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC, Shihong Sheng Shareholder of: Johnson & Johnson, of which Janssen Research & Development, LLC is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC, Yusang Jiang Employee of: Cytel, Inc., providing statistical support (funded by Janssen), Yan Liu Shareholder of: Johnson & Johnson, of which Janssen Research & Development, LLC is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, of which Janssen Research & Development, LLC is a wholly owned subsidiary, Employee of: Janssen Research & Development, LLC.

POS1026 GUSELKUMAB PROVIDES SUSTAINED IMPROVEMENTS IN WORK PRODUCTIVITY AND NON-WORK ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS THROUGH 1 YEAR OF A PHASE 3 TRIAL

Background:

DISCOVER-2 was a Phase 3 trial of the first-in-class anti-IL-23-specific mAb guselkumab (GUS) in patients (pts) with psoriatic arthritis (PsA). PsA impacts patients’ productivity at work and in daily activity.1

Objectives:

To evaluate the effect of GUS on work productivity and daily activity in DISCOVER-2 through 1 year using the Work Productivity and Activity Impairment Questionnaire: PsA (WPAI- PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every 4 weeks (Q4W); GUS 100 mg W0, W4, then Q8W; or placebo (PBO). At W24, PBO pts crossed over to GUS 100 mg Q4W. WPAI-PsA assesses PsA-related work time missed (absenteeism), impairment while working (presenteeism), impaired overall work productivity (absenteeism + presenteeism), and daily activity during the previous week. A shift analysis evaluated proportions of pts employed vs unemployed (regardless of desire to work) over time. Among pts working at baseline, least-squares (LS) mean changes from baseline in WPAI-PsA domains were determined using a mixed-effects model for repeated measures analysis, whereby mean changes in WPAI-PsA domains were calculated for each multiple imputation (MI) dataset using an analysis of covariance (ANCOVA); the reported LSmean is the average of all MI datasets. Also, among pts employed at baseline, indirect savings from improved overall work productivity were estimated using 2020 EU mean yearly wage estimate (all occupations).2

Results:

In pts working at baseline, significant improvement in work productivity and non-work activity vs PBO was observed at W24. Productivity gains seen with GUS at W24 continued to improve through 1 year (Table 1). Shift analysis showed relatively stable employment in pts employed at baseline (62% of shift analysis cohort) through 1 year of GUS (>91% continued to work when assessed at W16, W24, and W52 [data not shown]). For those unemployed at baseline (38% of cohort), the proportion of pts working increased by ~10% following 1 year of GUS (Figure 1). Potential yearly indirect savings from improved overall work productivity were: €7409 GUS Q4W and €7039 GUS Q8W vs €4075 PBO at W24 and were €8520 GUS Q4W, €9632 GUS Q8W, and €6668 PBO→GUS Q4W at W52.

Conclusion:

Improvement in work productivity and non-work activity was greater with GUS vs PBO among pts with active PsA through W52. Improvements demonstrated may result in reduction in PsA costs associated with work productivity.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–83.

[2]OECD (2020). Average wages (indicator). https://data.oecd.org/earnwage/average-wages.htm

Table 1.

Model-based estimates of LSmean changea (95% CI) from baseline in WPAI-PsA domains among pts working at baseline and with an observed change through W24 (N=474) and W52 (N=475)

Change from baselineGUS 100mg Q4WGUS 100mg Q8WPBO(W0-24)PBO → GUS 100 mg Q4W (W24-52)VisitW24W52W24W52W24W52Absenteeism, N145145147147162163LSmean-3.4 (-6.5,-0.3)-4.1 (-6.8,-1.5)-3.0 (-6.0,0.1)-4.0 (-6.6,-1.3)-3.0 (-6.0, 0.04)-3.0 (-5.5,-0.4)Diff vs. PBO-0.4 (-4.6,3.8)-0.01 (-4.2, 4.2)Presenteeism, N145145147147162163LSmean-20.1 (-23.7,-16.6)-22.4 (-26.3,-18.6)-19.6 (-23.2,-16.1)-25.7 (-29.5,-21.8)-10.5 (-13.9,-7.0)-18.5 (-22.2,-14.7)Diff vs PBO-9.7* (-14.4,-5.0)-9.2* (-13.9,-4.5)Work productivity, N145145147147162163LSmean-20.1 (-24.1,-16.1)-22.6 (-26.8,-18.3)-19.2 (-23.1,-15.2)-25.9 (-30.0,-21.7)-10.6 (-14.4,-6.8)-17.6 (-21.7,-13.6)Diff vs PBO-9.5* (-14.8,-4.2)-8.6* (-13.9,-3.3)Non-work Activity, N242242246246245245LSmean-20.5 (-23.3,-17.7)-25.7 (-28.6,-22.7)-21.2 (-23.9,-18.4)-25.4 (-28.4,-22.5)-9.9 (-12.6,-7.1)-22.3 (-25.3,-19.4)Diff vs PBO-10.6* (-14.4,-6.8)-11.3* (-15.1,-7.5)

CI=Confidence interval

a. LSmean for each MI dataset is calculated based on an ANCOVA model for the change from baseline at W24/W52. The combined LSmean, which is the average of the LSmean, taken over all the MI datasets, is presented.

*p<0.05

Disclosure of Interests:

Jeffrey Curtis Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, and UCB, Iain McInnes Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Steve Peterson Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Feifei Yang Shareholder of: Johnson & Johnson, Employee of: Janssen Global Services, LLC, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Chenglong Han Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, William Tillett Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, and UCB, Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, and UCB, Philip J Mease Speakers bureau: Boehringer Ingelheim and GlaxoSmithKline, Grant/research support from: AbbVie, Amgen, Bristol Myers Squibb, Eli Lilly, Galapagos, Gilead, Janssen, Novartis, Pfizer, SUN, and UCB, Proton Rahman Speakers bureau: AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, and UCB, Consultant of: AbbVie, Amgen, Bristol Myers Squibb, Celgene, Eli Lilly, Janssen, Novartis, Pfizer, Roche, and UCB, Grant/research support from: Janssen and Novartis.

Untargeted Metabolomic Profiling of the Correlation Between Prognosis Differences and PD-1 Expression in Sepsis: A Preliminary Study

Objectives: The mortality rate of sepsis remains very high. Metabolomic techniques are playing increasingly important roles in diagnosis and treatment in critical care medicine. The purpose of our research was to use untargeted metabolomics to identify and analyze the common differential metabolites among patients with sepsis with differences in their 7-day prognosis and blood PD-1 expression and analyze their correlations with environmental factors.

Methods: Plasma samples from 18 patients with sepsis were analyzed by untargeted LC-MS metabolomics. Based on the 7-day prognoses of the sepsis patients or their levels of PD-1 expression on the surface of CD4+ T cells in the blood, we divided the patients into two groups. We used a combination of multidimensional and monodimensional methods for statistical analysis. At the same time, the Spearman correlation analysis method was used to analyze the correlation between the differential metabolites and inflammatory factors.

Results: In the positive and negative ionization modes, 16 and 8 differential metabolites were obtained between the 7-day death and survival groups, respectively; 5 and 8 differential metabolites were obtained between the high PD-1 and low PD-1 groups, respectively. We identified three common differential metabolites from the two groups, namely, PC (P-18:0/14:0), 2-ethyl-2-hydroxybutyric acid and glyceraldehyde. Then, we analyzed the correlations between environmental factors and the common differences in metabolites. Among the identified metabolites, 2-ethyl-2-hydroxybutyric acid was positively correlated with the levels of IL-2 and lactic acid (Lac) (P < 0.01 and P < 0.05, respectively).

Conclusions: These three metabolites were identified as common differential metabolites between the 7-day prognosis groups and the PD-1 expression level groups of sepsis patients. They may be involved in regulating the expression of PD-1 on the surface of CD4+ T cells through the action of related environmental factors such as IL-2 or Lac, which in turn affects the 7-day prognosis of sepsis patients.

Acellular bovine pericardium matrix in immediate breast reconstruction compared with conventional implant-based breast reconstruction

Background

Acellular Bovine Pericardium Matrix (ABPM) is a new material in implant-based breast reconstruction (IBBR). Few studies have reported on its outcome and complications worldwide and most studies were without a control group. Our aim was to compare its use in IBBR with the other two conventional implant-based reconstruction methods.

Methods

A retrospective review of patients undergoing IBBR from January to December 2018 was performed. Patients were assigned to the ABPM-assisted IBBR (group A), latissimus dorsi-assisted IBBR (group B) and two-stage IBBR (group C). Patients’ post-operative complications, cost-effectiveness and Quality of Life were compared.

Results

100 patients with 100 breasts were included in the study. No complications occurred in group C (n = 11). No significant differences were noted between group A (n = 44) and group B (n = 45) in terms of overall complications (9.1% vs 11.1%, p = 0.973). Group B had the longest operative duration (310.8 ± 62.3 min, p<0.001). The cost of hospitalization forthe three groups was $8051.3 ± 849.2, $7566.0 ± 1172.7 and $7896.5 ± 1762.2, respectively (p = 0.128). The postoperative Breast-Q scores  were similar across the three groups.

Conclusions

ABPM demonstrated acceptable complication rates, cost-effectiveness and quality of life outcomes when compared to LD-assisted IBBR and two-stage IBBR.

Superimposed gratings induce diverse response patterns of gamma oscillations in primary visual cortex

Stimulus-dependence of gamma oscillations (GAMMA, 30-90 Hz) has not been fully understood, but it is important for revealing neural mechanisms and functions of GAMMA. Here, we recorded spiking activity (MUA) and the local field potential (LFP), driven by a variety of plaids (generated by two superimposed gratings orthogonal to each other and with different contrast combinations), in the primary visual cortex of anesthetized cats. We found two distinct narrow-band GAMMAs in the LFPs and a variety of response patterns to plaids. Similar to MUA, most response patterns showed that the second grating suppressed GAMMAs driven by the first one. However, there is only a weak site-by-site correlation between cross-orientation interactions in GAMMAs and those in MUAs. We developed a normalization model that could unify the response patterns of both GAMMAs and MUAs. Interestingly, compared with MUAs, the GAMMAs demonstrated a wider range of model parameters and more diverse response patterns to plaids. Further analysis revealed that normalization parameters for high GAMMA, but not those for low GAMMA, were significantly correlated with the discrepancy of spatial frequency between stimulus and sites' preferences. Consistent with these findings, normalization parameters and diversity of high GAMMA exhibited a clear transition trend and region difference between area 17 to 18. Our results show that GAMMAs are also regulated in the form of normalization, but that the neural mechanisms for these normalizations might differ from those of spiking activity. Normalizations in different brain signals could be due to interactions of excitation and inhibitions at multiple stages in the visual system.

The Generation and Modulation of Distinct Gamma Oscillations with Local, Horizontal, and Feedback Connections in the Primary Visual Cortex: A Model Study on Large-Scale Networks

Gamma oscillation (GAMMA) in the local field potential (LFP) is a synchronized activity commonly found in many brain regions, and it has been thought as a functional signature of network connectivity in the brain, which plays important roles in information processing. Studies have shown that the response property of GAMMA is related to neural interaction through local recurrent connections (RC), feed-forward (FF), and feedback (FB) connections. However, the relationship between GAMMA and long-range horizontal connections (HC) in the brain remains unclear. Here, we aimed to understand this question in a large-scale network model for the primary visual cortex (V1). We created a computational model composed of multiple excitatory and inhibitory units with biologically plausible connectivity patterns for RC, FF, FB, and HC in V1; then, we quantitated GAMMA in network models at different strength levels of HC and other connection types. Surprisingly, we found that HC and FB, the two types of large-scale connections, play very different roles in generating and modulating GAMMA. While both FB and HC modulate a fast gamma oscillation (around 50-60 Hz) generated by FF and RC, HC generates a new GAMMA oscillating around 30 Hz, whose power and peak frequency can also be modulated by FB. Furthermore, response properties of the two GAMMAs in a network with both HC and FB are different in a way that is highly consistent with a recent experimental finding for distinct GAMMAs in macaque V1. The results suggest that distinct GAMMAs are signatures for neural connections in different spatial scales and they might be related to different functions for information integration. Our study, for the first time, pinpoints the underlying circuits for distinct GAMMAs in a mechanistic model for macaque V1, which might provide a new framework to study multiple gamma oscillations in other cortical regions.

Mechanisms of recurrent outbreak of COVID-19: a model-based study

Recurrent outbreaks of the coronavirus disease 2019 (COVID-19) have occurred in many countries around the world. We developed a twofold framework in this study, which is composed by one novel descriptive model to depict the recurrent global outbreaks of COVID-19 and one dynamic model to understand the intrinsic mechanisms of recurrent outbreaks. We used publicly available data of cumulative infected cases from 1 January 2020 to 2 January 2021 in 30 provinces in China and 43 other countries around the world for model validation and further analyses. These time series data could be well fitted by the new descriptive model. Through this quantitative approach, we discovered two main mechanisms that strongly correlate with the extent of the recurrent outbreak: the sudden increase in cases imported from overseas and the relaxation of local government epidemic prevention policies. The compartmental dynamical model (Susceptible, Exposed, Infectious, Dead and Recovered (SEIDR) Model) could reproduce the obvious recurrent outbreak of the epidemics and showed that both imported infected cases and the relaxation of government policies have a causal effect on the emergence of a new wave of outbreak, along with variations in the temperature index. Meanwhile, recurrent outbreaks affect consumer confidence and have a significant influence on GDP. These results support the necessity of policies such as travel bans, testing of people upon entry, and consistency of government prevention and control policies in avoiding future waves of epidemics and protecting economy.

Association of placenta-derived extracellular vesicles with pre-eclampsia and associated hypercoagulability: a clinical observational study

OBJECTIVE

Pre-eclampsia (PE) is a pregnancy-associated condition initiated by placental factors. We have demonstrated that placental extracellular vesicles (pcEVs) cause hypertension and proteinuria in pregnant and non-pregnant mice.

STUDY DESIGN

An observational study with both case-control and longitudinal designs.

SETTING

A single centre at the Department of Obstetrics and Gynaecology, Tianjin Medical University.

POPULATION

We collected blood samples and clinical information from 54 PE patients, 33 normally pregnant women at 30-36 gestational weeks and on postpartum days 1 and 4 for the cross-sectional study, and at 22-31, 32-35 and 36-40 weeks for the longitudinal study. Non-pregnant women were also recruited.

METHODS

Blood samples were analysed using flow cytometry, coagulation tests and ELISA.

MAIN OUTCOME MEASURES

The primary outcome was plasma pcEV and other extracellular vesicles (EVs), and their expressions of anionic phospholipids and von Willebrand factor (VWF). Secondary variables included coagulation, ADAMTS-13 and the anionic phospholipid-binding proteins.

RESULTS

Plasma pcEVs progressively increased from pregnant women during non-menstrual period (NW) to PE patients (interquartile range [IQR] for NW: 206/microlitre [116-255], normal pregnancy [NP]: 1108/microlitre [789-1969] and PE: 8487/microlitre [4991-16 752]) and predicted PE. EVs from endothelial cells, platelets and erythrocytes accounted for <10% of pcEVs. VWF became hyper-adhesive in PE patients and contributed to the pregnancy-associated hypercoagulability.

CONCLUSION

Placental, platelet- and endothelial cell-derived EVs were significantly elevated in PE patients, but only pcEVs predicted PE. These EVs played a causal role in the pregnancy-induced hypercoagulability.

TWEETABLE ABSTRACT

Placenta-derived extracellular vesicles predict pre-eclampsia and the associated hypercoagulability.

[Aldo-keto reductase family 1 B10 participates in the regulation of hepatoma cell cycle through p27/p-Rb signaling pathway]

Objective: Aldo-keto reductase family 1 member B10 (AKR1B10) pathogenesis, early diagnosis and prognosis are closely related with hepatoma. Therefore, this study explores the effect and mechanism of AKR1B10 on cell cycle in hepatoma cells. Methods: HepG2 cells were infected with lentivirus LV-AKR1B10-shRNA or treated with epalrestat, an AKR1B10 inhibitor. The expression level of AKR1B10 was detected by Western blot assay and real-time fluorescence quantitative PCR (RT-qPCR). Decreased AKR1B10 activity was detected by reduced coenzyme II (NADPH) absorbance at 340 nm. The low expression of AKR1B10 and the effect of different concentrations of epalrestat on cell proliferation and cell cycle were detected by CCK-8 method and flow cytometry. The protein expression levels of p-rb, cyclin D1, E1, p27 in HepG2 cells were detected by Western blot. The mean of the two samples was tested using independent sample t-test. Results: AKR1B10 expression level in hepatoma cells was significantly increased compared to normal liver cells, and the relative expression level of AKR1B10 protein in HepG2 cells was 6.71 ± 1.11 (P = 0.012). Epalrestat was significantly inhibited with the enzymatic activity of AKR1B10 in a dose-dependent manner. AKR1B10 gene in HepG2 cells was effectively silenced. HepG2 cells treated with different concentrations of epalrestat (AKR1B10 inhibitor) for 24, 48 and 72 h had inhibited cell proliferation, promoted G0/G1 cell cycle arrest, reduced the expression of p-Rb, cyclin D1, and cyclin E1 and increased the expression of cyclin dependent kinase inhibitor p27 expression. Conclusion: AKR1B10 inhibitory expression and activity can promote G0/G1 cell cycle arrest in HepG2 cells through the p27 / p-Rb pathway.

[Efficacy analysis of the radiotherapy and chemotherapy in patients with stage Ⅳ esophageal squamous carcinoma: a multicenter retrospective study of Jing-Jin-Ji Esophageal and Esophagogastric Cancer Radiotherapy Oncology Group (3JECROG R-01F)]

Objective: To evaluate the survival and prognostic factors of radiotherapy in patient with Ⅳ stage esophageal squamous carcinoma treated with radiation or chemoradiation. Methods: The medical records of 608 patients with stage Ⅳ esophageal squamous cell carcinoma who met the inclusion criteria in 10 medical centers in China from 2002 to 2016 were retrospectively analyzed. The overall survival and prognostic factors of all patients at 1, 3 and 5 years were analyzed. Results: The 1-, 3-, 5- year overall survival (OS) rates was 66.7%, 29.5% and 24.3% in stage ⅣA patients, and 58.8%, 29.0% and 23.5% in stage ⅣB patients. There was no statistical difference between the two groups (P=0.255). Univariate analysis demonstrated that the length of lesion, treatment plan, planned tumor target volume (PGTV) dose, subsequent chemotherapy, and degrees of anemia, radiation esophagitis, radiation pneumonia were related to the prognoses of patients with Ⅳ stage esophageal carcinomas after radiotherapy and chemotherapy (P<0.05). Multivariate analysis demonstrated that PGTV dose (OR=0.693, P=0.004), radiation esophagitis (OR=0.867, P=0.038), and radiation pneumonia (OR=1.181, P=0.004) were independent prognostic factors for OS. Conclusions: For patients with stage Ⅳ esophageal squamous cell carcinoma, chemoradiotherapy followed by sequential chemotherapy is recommended, which can extend the total survival and improve the prognosis of the patients. PGTV dose more than 60 Gy has better efficacy.

AB0756 GUSELKUMAB IMPROVED WORK PRODUCTIVITY AND DAILY ACTIVITY IN PATIENTS WITH PSORIATIC ARTHRITIS: RESULTS FROM A PHASE 3 TRIAL

Background:

DISCOVER 2 (DISC 2) is a Phase 3 trial of anti-IL-23-specific mAb guselkumab (GUS) in psoriatic arthritis (PsA) pts, who experience impaired physical function, resulting in disability, work productivity loss, and economic consequences.1

Objectives:

To evaluate the effect of GUS on impaired work productivity and daily activity in DISC 2 using the Work Productivity and Activity Impairment Questionnaire: Psoriatic Arthritis (WPAI-PsA).

Methods:

Bio-naïve adults with active PsA despite nonbiologic DMARDs &/or NSAIDs received subcutaneous GUS 100 mg every (q) 4 weeks (W); GUS 100 mg W0, W4, q8W; or placebo (PBO). WPAI-PsA assesses, due to PsA over the previous week, work time missed (absenteeism), impairment while working (presenteeism), and impaired overall work productivity (absenteeism + presenteeism) and daily activity. Percentage change from baseline was analyzed for WPAI-PsA domains using mixed-effect model repeated measure (MMRM). Indirect savings from improved overall work productivity were estimated with 2018 US mean yearly wage estimate (all occupations).2

Results:

At Week 24, impaired overall work productivity and daily activity were improved 20-22% in GUS-treated and 10-11% in PBO-treated pts (Table). Potential yearly indirect savings from improved overall work productivity was $10,242 with GUS q8W and $10,404 with GUS q4W vs $5,648 with PBO; $4,594 and $4,756 difference, respectively.

Conclusion:

Improvement in overall work productivity and daily activity was greater with GUS versus PBO among pts with moderate-to-severe PsA, resulting in potential annual incremental economic gains.

References:

[1]Tillett W et al. Rheumatol (Oxford). 2012;51:275–283.

[2]US Bureau of Labor Statistics. May 2018 National Occupational Employment and Wage Estimates United States.https://www.bls.gov/oes/current/oes_nat.htm#00-000

Table.

Model-based estimates of mean change from baseline in WPAI-PsA domains

% change from baselinePBOGUS 100 mg q8WGUS 100 mg q4WW16W24W16W24W16W24Work time missed (absenteeism), n155152141145145143LSMean-4.6 (-7.2,-1.9)-3.5 (-6.4,-0.6)-3.5 (-6.2,-0.7)-3.1 (-6.1,-0.1)-4.7 (-7.4,-2.0)-3.8 (-6.8,-0.8)LSMean diff1.1 (-2.6,-4.8)*0.4 (-3.7,4.5)*-0.2 (-3.9,3.5)*-0.3 (-4.4,3.8)*Impairment while working (presenteeism), n131130125129133130LSMean-10.3 (-13.9,-6.7)-10.2 (-13.7,-6.7)-16.1 (-19.7,-12.4)-19.4 (-22.9,-15.9)-15.1 (-18.7,-11.5)-19.5 (-23.0,-16.0)LSMean diff-5.8 (-10.8,-0.8)†-9.2 (-14.0,-4.4)‡-4.8 (-9.7,0.1)*-9.3 (-14.1,-4.5)‡Overall work productivity impairment (absenteeism + presenteeism), n131130125129133130LSMean-11.2 (-15.0,-7.5)-10.9 (-14.6,-7.1)-15.9 (-19.7,-12.2)-19.7 (-23.4,-16.0)-15.8 (-19.5,-12.1)-20.0 (-23.7,-16.3)LSMean diff-4.7 (-9.9,0.5)*-8.8 (-14.0,-3.7)‡-4.6 (-9.7,0.5)*-9.2 (-14.3,-4.0)‡Daily activity impairment, n244244247246243245LSMean-10.6 (-13.3,-7.9)-10.3 (-13.1,-7.6)-17.1 (-19.8,-14.4)-21.5 (-24.2,-18.7)-17.0 (-19.7,-14.3)-20.5 (-23.2,-17.7)LSMean diff-6.5 (-10.2,-2.8)‡-11.1 (-15.0,-7.4)‡-6.5 (-10.2,-2.7)‡-10.2 (-14.0,-6.4)‡

Data are % (95% CI)

*p>0.05, †p<0.05,‡p<0.001

LSmeans, p values based on MMRM

LSmean diffs, p values vs PBO

Acknowledgments:

None

Disclosure of Interests:

Jeffrey Curtis Grant/research support from: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Consultant of: AbbVie, Amgen, Bristol-Myers Squibb, Corrona, Janssen, Lilly, Myriad, Pfizer, Regeneron, Roche, UCB, Iain McInnes Grant/research support from: Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Janssen, and UCB, Consultant of: AbbVie, Bristol-Myers Squibb, Celgene, Eli Lilly and Company, Gilead, Janssen, Novartis, Pfizer, and UCB, Proton Rahman Grant/research support from: Janssen and Novartis, Consultant of: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, and Pfizer., Speakers bureau: Abbott, AbbVie, Amgen, BMS, Celgene, Lilly, Janssen, Novartis, Pfizer, William Tillett Grant/research support from: AbbVie, Celgene, Eli Lilly, Janssen, Novartis, Pfizer Inc, UCB, Consultant of: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, MSD, Pfizer Inc, UCB, Speakers bureau: AbbVie, Amgen, Celgene, Lilly, Janssen, Novartis, Pfizer Inc, UCB, Philip J Mease Grant/research support from: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – grant/research support, Consultant of: Abbott, Amgen, Biogen Idec, BMS, Celgene Corporation, Eli Lilly, Novartis, Pfizer, Sun Pharmaceutical, UCB – consultant, Speakers bureau: Abbott, Amgen, Biogen Idec, BMS, Eli Lilly, Genentech, Janssen, Pfizer, UCB – speakers bureau, Alexa Kollmeier Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Elizabeth C Hsia Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Bei Zhou Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Prasheen Agarwal Shareholder of: Johnson & Johnson, Employee of: Janssen Research & Development, LLC, Steve Peterson Employee of: Janssen Research & Development, LLC, Chenglong Han Employee of: Janssen Research & Development, LLC