Academic literature on the topic '658 : 658.14 /17 : 336.152'

ZusammenfassungDas Ziel der Arbeit war, Referenzwerte für Blutparameter bei gesunden Meerschweinchen verschiedener Rassen mit einer für die Praxis geeigneten Blutentnahmemethode zu erstellen. Zur Sammlung der Daten wurde Blut von 101 klinisch gesunden Meerschweinchen, im Alter von sechs Wochen bis fünfeinhalb Jahren, aus der V. saphena lateralis entnommen. Für die folgenden Parameter wurden Referenzbereiche bestimmt: Hämatokrit (0,39-0,55 l/l), Hämoglobinkonzentration (7,26-10,51 mmol/l), Erythrozytenzahl (4,51-6,36 × 1012/l), Erythrozytenindices (MCHC: 18,1-19,7 mmol/l; MCH: 1,50-1,70 fmol/l; MCV: 80,3-89,1 fl), Leukozytenzahl (2910-14420 × 106/l), Differenzialblutbild (Monozyten: 0-657 × 106/l [0-9%]; Lymphozyten: 1401- 10665 × 106/l [28-84%]; stabkernige neutrophile Granulozyten: 0-72 × 106/l [0-1%]; segmentkernige neutrophile Granulozyten: 889-5097 × 106/l [12-62%]; eosinophile Granulozyten: 0-1563 × 106/l [0-14%]; basophile Granulozyten: 0-106 × 106/l [0-2%]) und Thrombozytenzahl (273-745 × 109/l), die Enzymaktivitäten der Alaninaminotransferase (ALT: 0-61 IU/l), alkalischen Phosphatase (AP: 0-418 IU/l), Aspartataminotransferase (AST: 0-90 IU/l), Glutamatdehydrogenase (GLDH: 0-17 IU/l), γ-Glutamyltransferase (γ-GT: 0-13 IU/l), Laktatdehydrogenase (LDH: 0-515 IU/l), Kreatinkinase (CK: 0-2143 IU/l), α-Amylase (0-3159 IU/l), Lipase (0-152 IU/l) und Cholinesterase (CHE: 0-8052 IU/l), die Konzentration der Substrate Glukose (4,95-15,95 mmol/l), Fruktosamin (134-271 µmol/l), Gesamteiweiß (44,4-65,8 g/l) mit Auftrennung durch Elektrophorese (Albumin: 25,5- 41,1 g/l [53,0-69,7%], α1-Globulin: 1,0-3,6 g/l [2,0-6,3%]; α2-Globulin: 7,9-14,8 g/l [16,3-27,1%]; β-Globulin: 2,5-6,8 g/l [4,8-11,4%]; γ-Globulin: 1,7-7,8 g/l [3,3-13,4%]), Cholesterin (0,31-1,67 mmol/l), Triglyzeride (0,33-2,35 mmol/l), Serum- gallensäuren (0,0-84,5 µmol/l), Bilirubin (0-1,59 µmol/l), Harnstoff (3,34-10,33 mmol/l) und Kreatinin (0-77 µmol/l) sowie der Elektrolyte Kalzium (2,4-3,1 mmol/l), Phosphat (1,03-6,98 mmol/l), Magnesium (0,99-2,56 mmol/l), Natrium (130-150 mmol/l), Kalium (4,5-8,8 mmol/l), Chlorid (94-111 mmol/l) und Eisen (26-76 µmol/l). Alters- (≤ 4 Monate und > 4 Monate) und Geschlechtsabhängigkeiten (männlich/ weiblich) wurden ermittelt.

Abstract Background. Platelets (plt) prepared for transfusion contain multiple molecules that modulate immune function, mediate acute transfusion reactions, induce immune responses, and affect hemostasis. These cytokines/chemokines are secreted differentially from plt during storage (Transfusion2006;46:1184), and may be affected by processing, including pathogen inactivation. Aims. The INTERCEPT Blood System (IBS) for platelets utilizes amotosalen-HCl (S-59) with ultraviolet A (UVA) light to inactivate a broad spectrum of pathogens and leukocytes. This study was designed to evaluate the effects of photochemical treatment on in vitro release of immune modulation molecules after processing during 7 days (d) of storage. Methods. Platelet concentrates (n = 10) collected by aphaeresis (CPA) with process leuko-reduction (< 106) containing 8.15x1011 ± 0.8 platelets were suspended in 35% donor plasma and 65% platelet additive solution (Intersol, Baxter, France) and divided into two equal components. One served as an untreated control (C) and the other was prepared with 150 uM amotosalen and a 3 J/cm2 UVA photochemical treatment (PCT) and stored at 22°C with shaking for 7 days. Platelet concentration (106/uL), pH and levels of immune modulation factors were measured: CD62p(ng/mL), PDGF-AB(ng/mL), IL8(pg/mL), sCD40L(pg/mL), IL1β(pg/mL) and TNFα(pg/mL). The concentration of each factor was determined by specific enzyme linked immunosorbent assays in plt and supernatant (s) fractions isolated from stored PCT and C plt components. Mean values ± SD were calculated and compared by paired t-test. Results. Platelet content, pH and cytokine/chemokine content and release from CPA prepared with photochemical treatment were not statistically different (p > 0.05) from C during 7 d of storage (Table). From d1 to d7, the pH of PCT and C units decreased similarly, but remained within acceptable ranges. No detectable IL1β and TNFα were observed in PCT or C CPA. During platelet storage CD62p, PDGF-AB, IL8, and sCD40L increased similarly in supernatants of PCT and C units. The increase in supernatant levels correlated with a decrease of these cytokines in plt. Platelets in PCT and C retained measurable levels of CD62, IL8, sCD40L and PDGF-AB though 7 d. Levels of sCD40L demonstrated marked variation. Conclusions. Cytokines increased moderately in the supernatants of CPA and decreased in platelets during storage. After 7 d C and PCT platelets in CPA retained detectable levels of cytokines. PCT had no differential influence on release of immune modulation molecules in vitro over 7 d of storage. Day O O 5 5 7 7 Product C PCT C PCT C PCT pH 7.1 ±.1 7.1 ±.1 6.9 ±.1 6.8 ±.1 6.9 ±.1 6.8 ±.1 Plt ct 1.29 ±.3 1.30 ±.2 1.30 ±.3 1.19 ±.2 1.27 ±.2 1.18 ±.2 CD62p-s 89 ± 21 87 ± 17 110 ± 23 115 ± 27 117 ± 22 119 ± 25 CD62p-plt 149 ± 33 151 ± 33 141 ± 23 141 ± 25 139 ± 25 139 ± 26 PDGF-s 14.5 ± 3.5 13.6 ± 3.5 17.7 ± 2.4 15.8 ± 1.5 18.0 ± 2.1 17.5 ± 1.8 PDGF-plt 28.3 ± 3.5 30.3 ± 3.1 25.2 ± 3.6 24.9 ± 2.5 23.2 ± 4.0 23.3 ± 3.3 IL8-s 107 ± 17 108 ± 14 136 ± 42 116 ± 9 123 ± 20 120 ± 22 IL8-plt 135 ± 29 134 ± 28 110 ± 11 117 ± 12 103 ± 17 119 ± 32 CD40L-s 51 ± 86 66 ± 94 172 ± 157 237 ± 214 188 ± 198 201 ± 167 CD40L-plt 990 ± 805 1098 ± 747 485 ± 373 474 ± 331 346 ± 293 314 ± 282

AbstractOscarkempffite, ideally Ag10Pb4(Sb17Bi9)∑=26S48, is a new mineral species found in old material (1929–30) from the Colorada vein, Animas mine, Chocaya Province, Department of Potosi, Bolivia. It is associated with aramayoite, stannite, miargyrite, pyrargyrite and tetrahedrite. Oscarkempffite forms anhedral grains and grain aggregates up to 10 mm across. The mineral is opaque, greyish black with a metallic lustre; it is brittle without any discernible cleavage. In reflected light oscarkempffite is greyish white, pleochroism is distinct, white to dark grey. Internal reflections are absent. In crossed polars, anisotropism is distinct with rotation tints in shades of grey. The reflectance data (%, air) are: 39.9, 42.6 at 470 nm, 38.6, 41.7 at 546 nm, 38.1, 41.2 at 589 nm and 37.3, 40.6 at 650 nm. Mohs hardness is 3–3½, microhardness VHN50 exhibits a range 189–208, with a mean value 200 kg mm–2. The average results of four electron-microprobe analyses in a grain are: Cu 0.24(7), Ag 14.50(8), Pb 11.16(14), Sb 28.72(16), Bi 24.56(17), S 20.87(5), total 100.05(6) wt.%, corresponding to Cu0.24Ag9.92Pb4.00Sb17.36Bi8.64S47.84 (on the basis of Me + S = 88 apfu). The simplified formula, Ag10Pb4Sb17Bi9S48, is in accordance with the results of a crystal-structure determination. The density, 5.8 g cm–3, was calculated using the ideal formula. Oscarkempffite has an orthorhombic cell with a = 13.199(2), b = 19.332(3), c = 8.249(1) Å, V = 2116.3(5) Å3, space group Pnca and Z = 1. The strongest eight lines in the (calculated) powder-diffraction pattern are [d in Å(I)hkl]: 3.66(35)(122), 3.37(70)(132), 3.34(100)(250), 2.982(55)(312), 2.881(86)(322), 2.733(29)(332), 2.073(27)(004) and 2.062(31)(182). Comparison with gustavite, andorite and roshchinite confirms its independence as a mineral species.

Abstract Abstract 3125 Poster Board III-62 VTE is the most frequent complication of OS and it can be prevented through anticoagulant prophylaxis. Numerous studies have evaluated different agents for this purpose and there are new agents currently under development or recently approved for this indication. We conducted a systematic review of randomized controlled trials (RCT) evaluating administration of anticoagulants for VTE prophylaxis in OS and performed a MA of proportions to estimate the overall incidence of major VTE (proximal VTE, pulmonary embolism (PE), or death from PE), total VTE (proximal and distal VTE, PE or death from PE), symptomatic VTE and major bleeding episodes (as defined by the International Society on Thrombosis and Hemostasis). We included RCT comparing currently approved anticoagulants (head-to-head or placebo-controlled) for VTE prophylaxis in OS (hip and knee arthroplasty and hip fracture surgery) using systematic evaluation of VTE (ultrasound or venography, pulmonary angiography, CT pulmonary angiography, or ventilation perfusion scan). Heterogeneity of proportions was evaluated using a chi2 test and pooled estimates of proportions were obtained using either a fixed or a random effects model in which the weights were estimated as proposed by Laird and Mosteller. We retrieved 74 studies including180 research arms and enrolling 71,012 patients. The total number of events and evaluable patients, percentage of events and 95% CI, and number of study arms included are shown in the table. We found differences in the percentage of VTE and bleeding events associated with the use of different anticoagulants for VTE prophylaxis after OS. Due to the nature of the analysis no effect measure can be estimated. These estimates might help to design future studies. Major VTE Total VTE Symptomatic VTE Major Bleeding Cases / Evaluable Pts. (N) Percentage (95% CI) Study arms (N) Cases / Evaluable Pts. (N) Percentage (95% CI) Study arms (N) Cases / Evaluable Pts. (N) Percentage (95% CI) Study arms (N) Cases / Evaluable Pts. (N) Percentage (95% CI) Study arms (N) All patients LMWH 993/23692 5.96 (5.81, 6.11) 72 4068/22610 20.29 (20.04, 20.55) 80 193/19431 1.32 (1.27, 1.37) 35 476/28725 1.98 (1.93, 2.02) 70 UFH 234/2407 13.39 (12.86, 13.93) 14 596/2537 22.54 (22, 23.08) 17 11/339 3.24 (3.06, 3.43) 4 70/2849 2.75 (2.61, 2.89) 16 Warfarin 269/5677 6.28 (6.09, 6.46) 12 1317/4203 31.05 (30.44, 31.66) 12 71/4146 1.95 (1.83, 2.08) 6 96/6751 1.78 (1.69, 1.87) 12 Fonda 96/3673 3.81 (3.53, 4.09) 7 223/3477 6.82 (6.57, 7.07) 6 69/6398 1.06 (1.01, 1.1) 8 121/6576 1.63 (1.55, 1.71) 9 Riva 50/5025 2.02 (1.86, 2.19) 8 242/4595 13.05 (12.16, 13.94) 8 29/6252 0.46 (0.45, 0.48) 6 31/6643 0.63 (0.59, 0.68) 8 Dabi 149/4091 3.64 (3.59, 3.69) 6 834/4051 22.96 (21.91, 24.01) 6 26/3664 0.71 (0.67, 0.75) 4 67/5419 1.21 (1.17, 1.26) 6 Placebo 193/710 24.26 (23.17, 25.34) 10 379/816 49.35 (48.08, 50.62) 11 19/198 12.02 (10.32, 13.72) 3 12/753 1.59 (1.5, 1.68) 7 Total 1984/45275 129 7659/42289 140 418/40428 66 873/57716 128 Total Hip Arthroplasty LMWH 653/15978 6 (5.85, 6.16) 50 1817/14480 15.58 (15.35, 15.82) 55 81/11552 0.7 (0.69, 0.72) 19 306/18010 1.97 (1.92, 2.02) 45 UFH 187/1739 14.3 (13.64, 14.96) 11 354/1836 20.13 (19.46, 20.8) 13 11/246 4.47 (4.21, 4.73) 3 52/1451 3.2 (3.01, 3.39) 11 Warfarin 77/2758 4.28 (4.08, 4.48) 6 265/1273 20.82 (20.59, 21.04) 6 32/1833 1.75 (1.69, 1.81) 2 47/2856 2.23 (2.09, 2.37) 5 Fonda 28/1799 2.96 (2.58, 3.33) 3 85/1695 5.01 (4.91, 5.12) 2 15/2255 0.67 (0.63, 0.7) 2 69/2349 2.94 (2.87, 3.01) 3 Riva 25/2938 2.21 (1.95, 2.46) 5 73/2749 9.72 (8.92, 10.53) 5 10/3468 0.29 (0.27, 0.31) 3 14/3795 0.49 (0.44, 0.54) 5 Dabi 72/1803 3.99 (3.88, 4.11) 2 124/1766 7.02 (6.77, 7.27) 2 21/2293 0.92 (0.91, 0.93) 2 38/2309 1.65 (1.58, 1.72) 2 Placebo 105/414 26.01 (24.76, 27.27) 7 174/418 45.43 (43.74, 47.13) 7 4/147 2.72 (2.46, 2.98) 2 3/388 0.77 (0.69, 0.86) 5 Total 1147/27429 84 2892/24217 90 174/21794 33 529/31158 76 Total Knee Arthroplasty LMWH 277/6916 4.45 (4.34, 4.55) 25 2062/7326 30.72 (30.37, 31.07) 32 83/4902 1.69 (1.66, 1.73) 11 89/7808 1.14 (1.12, 1.16) 26 UFH 42/638 6.58 (6.39, 6.78) 3 226/638 35.42 (35.05, 35.79) 3 0/93 NE 1 3/318 0.94 (0.84, 1.05) 2 Warfarin 192/2919 8.1 (7.88, 8.32) 9 1052/2930 39.36 (38.69, 40.02) 9 39/2056 1.9 (1.84, 1.96) 3 28/3407 0.82 (0.79, 0.85) 8 Fonda 23/452 9.3 (7.93, 10.67) 2 45/361 12.47 (12.12, 12.81) 1 3/517 0.58 (0.51, 0.65) 1 12/601 2 (1.88, 2.11) 2 Riva 25/2087 1.2 (1.15, 1.24) 3 169/1846 18.55 (16.47, 20.63) 3 19/2784 0.68 (0.65, 0.71) 3 17/2848 0.6 (0.57, 0.63) 3 Dabi 77/2288 3.37 (3.32, 3.41) 4 710/2285 30.98 (30.42, 31.55) 4 5/1371 0.36 (0.32, 0.41) 2 29/3110 0.93 (0.89, 0.98) 4 Placebo 88/296 27.12 (24.54, 29.7) 4 205/398 55.19 (53.53, 56.84) 5 15/51 29.41 (28.16, 30.66) 1 9/365 2.47 (2.31, 2.62) 4 Total 724/15596 50 4469/15784 57 164/11774 22 187/18457 49 LMWH Low molecular weight heparin, UFH unfractionated heparin, Riva Rivaroxaban, Dabi Dabigatran etexilate Disclosures Lazo-Langner: Boehringer Ingelheim: Honoraria. Rodger:Bayer: Research Funding; Leo Pharma: Research Funding; Pfizer: Research Funding; Boehringer Ingelheim: Membership on an entity's Board of Directors or advisory committees; Biomerieux: Research Funding; GTC Therapeutics: Research Funding.

Background:Comorbidities in rheumatic diseases (RDs) have been associated with increased morbidity and mortality. Evidence on prevalence of comorbidities in antiphospholipid syndrome (APS) and its difference from high comorbidity burden RDs is limited.Objectives:To compare the prevalence of common comorbidities between APS [primary (PAPS) and Systemic lupus erythematosus (SLE)-APS] and Rheumatoid arthritis (RA) patients.Methods:326 APS patients from the Greek registry (237 women, mean age 48.7±13.4 years, 161 PAPS) were matched 1:2 for age and sex with 652 RA patients from Greek RA Registry. Prevalence of cardiovascular (CV) risk factors, stroke, coronary artery disease (CAD), osteoporosis, diabetes mellitus (DM), Chronic obstructive pulmonary disease (COPD), depression and neoplasms were compared between APS and RA using logistic regression analysis.Results:Regarding CV burden, hyperlipidemia and obesity (ΒMI≥30) were comparable while hypertension, smoking, CAD and stroke were more prevalent in APS compared to RA patients (Table 1). Osteoporosis and depression were more frequent in APS while DM, COPD and neoplasms were comparable between two groups. Comparison of APS subgroups to 1:2 matched RA patients revealed that smoking and stroke were more prevalent in PAPS and SLE-APS vs RA. Hypertension, CAD and osteoporosis were more prevalent only in SLE-APS vs. RA while DM was less prevalent in PAPS vs. RA patients.Table 1.Comparison of comorbidities between Antiphospholipid syndrome (APS) vs. matched Rheumatoid Arthritis (RA) patients and between primary APS (PAPS) or Systemic Lupus Erythematosus-APS (SLE-APS) vs matched RA patientsAPSRAOR*PAPSRAORSLE-APSRAORn (%)326652161322165330Hypertension97 (29.8)136 (21)1.61 (1.19-2.18)40 (25)75 (23.3)1.09 (0.70-1.69)57 (34.6)61 (18.5)2.33 (1.52-3.56)Smoking175 (53.7)264 (40.5)1.70 (1.30-2.22)87 (54)142 (44)1.49 (1.02-2.18)88 (53.3)122 (37)1.95 (1.33-2.85)Hyperlipidemia79 (24.2)135 (20.7)1.23 (0.89-1.68)40 (24.8)62 (19.3)1.39 (0.88-2.18)39 (23.6)73 (22)1.09 (0.70-1.70)Obesity48 (20.5)105 (19.5)1.06 (0.73-1.56)20 (17)51 (19)0.86 (0.49-1.52)28 (24)54 (19.7)1.28 (0.76-2.15)Stroke±66 (20.3)9 (1.4)13.8 (6.5-29.1)36 (22.4)4 (1.2)19.9 (6.6-59.9)30 (18.2)5 (1.5)7.8 (2.7-22.6)Coronary disease±16 (4.9)13 (2)3.14 (1.17-8.45)2 (1.2)7 (2.2)0.46 (0.04-4.77)14 (8.5)6 (1.8)10.9 (2.7-44.3)Osteoporosis×66 (20.3)92 (14)1.45 (1.01-2.06)19 (11.8)42 (13)0.96 (0.54-1.73)47 (28.5)50 (15)1.91 (1.20-3.05)Diabetes×18 (5.5)58 (9)0.58 (0.33-1.01)5 (3)29 (9)0.34 (0.13-0.89)13 (8)29 (9)0.88 (0.44-1.79)COPD≠11 (3.4)14 (2.2)1.26 (0.56-2.84)3 (1.9)6 (2)0.96 (0.23-4.0)8 (5)8 (2.4)1.28 (0.44-3.72)Depression#53 (16.3)66 (10)1.70 (1.15-2.53)23 (14)30 (9.3)1.69 (0.93-3.05)30 (18.2)36 (10.9)1.65 (0.96-2.84)Neoplasms˅14 (4.3)27 (4.1)1.05 (0.54-2.06)5 (3)12 (3.7)0.84 (0.28-2.52)9 (5.5)15 (4.6)1.31 (0.55-3.1)*OR: Odds ratio, crude or adjusted for: ± age, sex, smoking, hypertension, hyperlipidemia, BMI, corticosteroid (Cs) duration × Cs duration ≠ smoking, Cs duration #sex, disease duration, Cs duration ˅ age, disease durationConclusion:Comorbidity burden in APS (PAPS and SLE-APS) is comparable or even higher to that in RA, entailing a high level of diligence for CV risk prevention, awareness for depression and corticosteroid exposure minimization.Disclosure of Interests:Stylianos Panopoulos: None declared, Konstantinos Thomas: None declared, Georgios Georgiopoulos: None declared, Dimitrios Boumpas Grant/research support from: Unrestricted grant support from various pharmaceutical companies, Christina Katsiari: None declared, George Bertsias Grant/research support from: GSK, Consultant of: Novartis, Alexandros Drosos: None declared, Kyriaki Boki: None declared, Theodoros Dimitroulas: None declared, Alexandros Garyfallos Grant/research support from: MSD, Aenorasis SA, Speakers bureau: MSD, Novartis, gsk, Charalambos Papagoras: None declared, PELAGIA KATSIMPRI: None declared, Apostolos Tziortziotis: None declared, Christina Adamichou: None declared, Evripidis Kaltsonoudis: None declared, Evangelia Argyriou: None declared, GEORGIOS VOSVOTEKAS Grant/research support from: MSD, Janssen, Consultant of: MSD, Novartis, Roche, UCB pharma, Bristol-Myers Squibb, AbbVie, Speakers bureau: UCB pharma, Menarini, Bristol-Myers Squibb, MSD, Petros Sfikakis Grant/research support from: Grant/research support from Abvie, Novartis, MSD, Actelion, Amgen, Pfizer, Janssen Pharmaceutical, UCB, Dimitrios Vassilopoulos: None declared, Maria Tektonidou Grant/research support from: AbbVie, MSD, Novartis and Pfizer, Consultant of: AbbVie, MSD, Novartis and Pfizer

Background:Four clinical phenotypes of IgG4-Related Disease (IgG4-RD) have been recently identified by Latent Class Analysis (LCA) - Pancreato/biliary (Group 1); Retroperitoneum/Aortitis (Group 2); Head-and-neck limited (Group 3); Mickulicz/Systemic (Group 4) - but the relevance of this classification for patient management remains unknown (1,2).Objectives:We aimed to assess whether clinical judgment can replicate LCA classification and to evaluate potential differences in epidemiological features, serological findings, and disease outcomes between disease phenotypes.Methods:The study included 179 patients. Four IgG4-RD experts were asked to classify a validation cohort of 40 patients according to published LCA derived phenotypes based on clinical judgment. Agreement between LCA and clinical clustering was calculated. To assess differences among disease phenotypes, the following variables were recorded on additional 139 patients: serum IgG4 and IgE; inflammatory markers; eosinophils; plasmablasts; IgG4-RD Responder Index (RI); history of atopy, diabetes, osteoporosis, relapses, and tumors; cumulative dose of glucocorticoids and use of rituximab.Results:Clinical judgment recapitulated LCA classification with strong agreement between IgG4-RD experts (κ= 0.841, p < 0.0005). Group 1 showed the highest levels of serum IgG4 and IgE. Group 2 and 4 had the lowest and highest IgG4-RD RI, respectively (Table 1). Increased cumulative doses of glucocorticoids and higher relapse rate were observed in Group 3 (Fig 1). A higher incidence of diabetes mellitus was observed in Group 1 and 4.Table 1Clinical and serological characteristics of patients cohort.Group 1(59 pts - 45%)Group 2(29 pts - 22%)Group 3(25 pts - 19%)Group 4(18 pts - 14%)P valueFemale n° (%)12 (20%)8 (28%)11 (44%)5 (28%)0.18Age67 (61-73)61 (56-70)52 (40-62)57 (51-62)<0.0001Serum IgG4 (mg/dL)331 (184-575)155 (49-258)150 (80-255)282 (166-460)0.0009IgG4-RD RI9 (6-9)6 (6-9)9 (6-12)9 (6-13)0.004Definite diagnosis n° (%)20 (34%)18 (62%)20 (80%)10 (55%)0.0008Probable diagnosis n° (%)1 (0.59%)0 (0%)1 (0.19%)1 (0.14%)0.6Possible diagnosis n° (%)38 (64%)10 (34%)4 (16%)7 (39)0.0003Emergency Department n° (%)37 (63%)14 (48%)7 (28%)10 (55%)0.03History of atopy n° (%)7 (12%)4 (14%)7 (28%)6 (23%)0.09ESR (mm/h)20 (8-39)40 (14-59)38 (14-54)12 (8-21)0.04CRP (mg/L)5 (2-8)10 (3-52)8 (3-28)3 (2-6)0.03Eosinophils (cell/mm3)200 (200-500)200 (100-275)300 (200-475)200 (100-500)0.3IgE (U/mL)283 (97-723)69 (28-264)120 (41-412)219 (54-657)0.02Plasmablast (cell/mL)1765 (627-4000)1890 (1020- 4000)2000 (370- 4780)2690 (140-5130)0.99Diagnostic delay (months)4 (2-9)7 (4-12)10 (3-18)11 (2-38)0.04Starting prednisone dose(mg, range)60 (37-70)50 (40-75)65 (40-90)40 (30-70)0.8Diabetes at disease onset10 (17%)1 (3%)2 (8%)2 (11%)0.09Figure 1.Relapse free survival of the four different IgG4-RD phenotypes.Conclusion:Clinical phenotypes of IgG4-RD reflect differences in epidemiological features and prognostic outcomes.References:[1]Bledsoe JR, Della-Torre E, Rovati L, Deshpande V. IgG4-related disease: review of the histopathologic features, differential diagnosis, and therapeutic approach. APMIS. 2018;126:459-476.[2]Wallace ZS, Zhang Y, Perugino CA, Naden R, Choi HK, Stone JH; ACR/EULAR IgG4-RD Classification Criteria Committee. Clinical phenotypes of IgG4-related disease: an analysis of two international cross-sectional cohorts. Ann Rheum Dis. 2019;78:406-412.Disclosure of Interests:Marco Lanzillotta: None declared, Emanuel Della Torre: None declared, Corrado Campochiaro Speakers bureau: Novartis, Pfizer, Roche, GSK, SOBI, Gaia Mancuso: None declared, Lorenzo Dagna Grant/research support from: The Unit of Immunology, Rheumatology, Allergy and Rare Diseases (UnIRAR) received unresctricted research/educational grants from Abbvie, Bristol-Myers Squibb, Celgene, Janssen, Merk Sharp & Dohme, Mundipharma Pharmaceuticals, Novartis, Pfizer, Roche, Sanofi-Genzyme, and SOBI., Consultant of: Prof Lorenzo Dagna received consultation honoraria from Abbvie, Amgen, Biogen, Bristol-Myers Squibb, Celltrion, Novartis, Pfizer, Roche, Sanofi-Genzyme, and SOBI.

Background:Abatacept (ABA) is a selective T-cell co-stimulatory modulator. After its approval, changes in therapeutic recommendations, the arrival of new drugs (e.g., biosimilars or Janus kinase inhibitors) and growing focus on comprehensive patient care may have changed prescription patterns, channeling ABA use towards specific patient subtypes. To date, studies analyzing these aspects in clinical practice settings are scarce.Objectives:We aimed to evaluate the epidemiological profile of ABA users and compare it to other DMARD groups included in the register.Methods:We performed an observational study based on the nationwide Spanish register BIOBADASER, which includes patients with rheumatic diseases receiving biologic disease-modifying antirheumatic drugs (bDMARDs) or targeted synthetic DMARDs (tsDMARDs) from 28 tertiary centers. For this analysis, all RA patients included from December 2015 to December 2020 were examined. Baseline features were analyzed descriptively grouping all b/tsDMARDs by mode of action. Clinical effectiveness was assessed through drug survival obtained by the Kaplan-Meier method. Patients were right-censored if data were not available if they were still on treatment at the time of data analysis. The safety profile was assessed by the adverse events (AE) and serious AE incidence rates (IR) expressed as events per 1000 patient-years.Results:There were 628 ABA-treated patients, 471 (75%) using the subcutaneous presentation. Only 142 (23%) were on first-line while 381 (61%) were on third or later-line therapy. ABA users were older and more likely to present certain comorbidities compared to the other b/tsDMARD groups. The biggest relative differences were seen for interstitial lung disease (ILD), chronic obstructive pulmonary disease (COPD), diabetes and ischemic heart disease. (Table 1)Table 1.This 12-month interim analysis includes 496 patients (336 with axSpA, 98 with RA and 62 with PsA)NABAIL-6CD20JAKiTNFi6287861816691787Mean age, years (SD)64.1 (12.0)50.7 (12.7)63.2 (12.3)59.6 (12.3)60.7 (13.1)Female sex, n (%)482 (77)652 (83)135 (75)537 (80)1418 (79)Median disease duration (p25-p75)10.1 (5.1-16.5)9.4 (4.6-15.9)13.3 (8.3-20.6)10.4 (5.0-17.2)7.4 (3.2-13.7)ACPA, n (%)352 (72)425 (71)113 (79)407 (70)875 (70)RF, n (%)380 (77)454 (75)124 (86)411 (70)915 (72)Current smokers, n (%)98 (16)137 (17)338 (19)90 (20)259 (18)ILD, n (%)64 (13)21 (3)25 (2)8 (2)19 (2)COPD, n (%)38 (6)23(3)52 (3)14 (3)43 (3)Chronic Kidney Disease, n (%)18 (3)13 (2)19 (1)13 (3)18 (1)Diabetes73 (12)66 (8)13 (7)46 (10)100 (7)Ischemic Heart Disease, n (%)36 (6)23 (3)012 (3)30 (2)Hypertension, n (%)197 (31)198 (25)416 (23)131 (30)338 (23)Heart Failure, n (%)19 (4)12 (2)10 (1)6 (2)5 (1)Osteoporosis, n (%)133 (21)141 (18)26 (14)72 (16)215 (15)IL-6: Tocilizumab and Sarilumab; CD20: Rituximab and biosimilars; JAKi: Janus kinase inhibitors (Tofacitinib and Baricitinib); TNFi: TNF inhibitors and biosimilars; ACPA: anti-citrullinated peptide antibodies; RF: rheumatoid factor; ILD: interstitial lung disease; COPD: chronic obstructive pulmonary disease.Overall, 63% of patients remained on ABA at 1 year, 48% at 2 and 31% at 5 years after drug initiation. The corresponding proportions were 79%, 65% and 52% for bionaïve and 59%, 43% and 30% for those in third or later-line therapy. From 394 total discontinuations, loss of efficacy in 225 (57%) and AE in 98 (25%) were the main reasons. This trend was consistent among all therapy lines.The total IR of AE was 886.5 (837.3-938.5) and 156.4 (136.5-179.2) for SAE. Infections were the most frequent AE overall, IR 44.4 (34.4-57.3), and the highest IR was seen among bionaïve patients (69.6 (44.9-107.9)).Conclusion:ABA-treated RA patients in Spain are older and have more comorbidities (vs other b/tsDMARDs), especially ILD, COPD, ischemic heart disease and diabetes and receive ABA as third or later-line therapy. Although these features are associated with worse response to treatment and a higher risk of infection, ABA presents a good drug survival and infectious AE are not the main cause of discontinuation.Acknowledgements:On behalf of the BIOBADASER Working groupDisclosure of Interests:Sebastián C Rodriguez-García Speakers bureau: Sanofi, MSD, UCB-Pharma, Bristol-Myers-Squibb, Novartis, Janssen, Consultant of: Bristol-Myers-Squibb, Galápagos, Carlos Sánchez-Piedra: None declared, Raul Castellanos-Moreira Speakers bureau: Roche, Sanofi, MSD, UCB-Pharma, Bristol-Myers-Squibb, Novartis, Lilly, and Pfizer., Dolores Ruiz-Montesinos: None declared, Manuel Pombo: None declared, Fernando Sánchez-Alonso: None declared, Juan J. Gómez-Reino Consultant of: Pfizer, Grant/research support from: Abbvie, Lilly, MSD, Pfizer, Roche, and UCB.

5039 Background: Plasma ctDNA is a promising minimally invasive biomarker in mCRPC. Pre-treatment high levels of ctDNA reflect poor prognosis (Romanel et al, Sci Transl Med 2015; Annala et al, Cancer Discov 2018). However, the role of plasma ctDNA in prostate tumour monitoring is largely unexplored. We aimed to determine if monitoring tumour response by quantifying ctDNA levels in plasma could enable early assessment of therapy efficacy for mCRPC. Methods: Between January 2011 and June 2016, 132 sequential plasma samples from 54 mCRPC patients (pts) (30 pre- and 24 post-chemotherapy) treated with abiraterone (abi) were collected. Targeted next-generation sequencing was performed on the PGM Ion Torrent using a 316 or 318 Chip to account for 1000X expected coverage per target. We estimated the global tumour content for each sequential plasma sample from study patients by using the approach developed in (Carreira et al, Sci Trasl Med 2014; Romanel et al, Sci Transl Med 2015 ), which extends the CLONET framework (Prandi et al, Genome Biol 2014). Prostate Cancer Working Group -3 (PCWG3) criteria were used to assess clinical, biochemical (PSA) and radiographic (RAD) progression disease (PD). We considered ctDNA PD any increase of ctDNA from baseline value. Results: In our cohort of 54 pts (median age: 75 years, range 70-78), we observed 17 (31.5%) PD, 14 (25.9%) stable disease, and 23 (42.6%) partial/complete response after the first 3 months (mo) abi therapy. The odds ratio (OR) for PD having any increase in ctDNA and a PSA decline < 50% at ~3-mo therapy was 10.83, 95% CI 2.55-45.95, P = 0.001, and 3.27, 95% CI 0.89-12.3, P = 0.074, respectively. In addition, we assessed all 3 types of median PD time from starting abi treatment, suggesting the ability of ctDNA variation to predict overall PD [RAD PD = 6.8 mo, PSA PD = 4.4 mo, and ctDNA PD = 3.0 mo, P = 0.008). An increase of ctDNA levels during the first 3-mo abi treatment was significantly associated with a long-term androgen deprivation therapy (ADT) before plasma sample collection (previous ADT > 24 mo vs 12 > previous ADT ≤23 mo vs < 12 mo: P = 0.036). Conclusions: In mCRPC, an early change in ctDNA fraction may be considered as a predictive biomarker playing a key role in individualized disease monitoring. Prospective evaluation of treatment decisions based on ctDNA is now required.

Abstract Background: chronic lymphocytic leukemia (CLL) is characterized by progressive immunodeficiency with high prevalence of infections, autoimmune phenomena and secondary malignancies. The immune deregulation may be due to the disease itself or it may be a consequence of the treatment performed. Despite the use of highly effective chemo-immunotherapy, CLL remains incurable nowadays, even if the availability of new drugs is improving life expectancy. Aims: to evaluate the incidence of second cancers in CLL patients, and to investigate their relationship with disease features and therapy lines. Methods: 514 CLL patients diagnosed and followed from 1983 until 2014 at our Institution were retrospectively evaluated. Secondary cancers were categorized according to the originating organ or tissue; skin cancers were divided into melanoma and non-melanoma. History of neoplasia preceding CLL diagnosis was also registered. Results: clinical, hematological and biological characteristics at CLL diagnosis are listed in Table 1. During the follow up 88 patients (17%) developed secondary cancers, with a mean time from diagnosis to secondary neoplasia of 9 years. Considering tumor site, we observed 9 hematological malignancies, 9 lung, 5 breast, 19 uro-genital tract (5 kidney, 10 prostate, 4 bladder, 2 uterus, and 2 ovarian), 15 gastro-enteric tract (12 colon, 2 gastric and 1 tongue), 4 pancreas, 3 melanoma and 15 skin cancers other than melanoma. No significant differences were observed according to age, gender, Rai/Binet stage and hematologic parameters in patients with or without secondary tumors (Table 1). Considering prognostic features, no association was found with 13q deletion, chromosome 12 trisomy, VHIG mutational status, or with ZAP-70 and CD-38 positivity. On the contrary, the development of second cancers was associated with the presence of chromosome 17p (8% with secondary neoplasia versus 6% without, p=0.05) and 11q deletions (13% versus 9%, p=0.08). Medical history was positive for malignancies in 70 patients (13%): 2 hematological malignancies, 3 airways (2 lower and 1 upper), 3 breast, 6 uro-genital tract (3 bladder, 3 prostate, 2 uterus and 1 ovarian), 3 gastro-enteric tract, 3 skin cancers other than melanoma, and 3 melanoma. As regards treatment, 46/88 (52.3%) and 219/426 (51.4%) patients with or without secondary cancers, underwent at least one therapy line. Eighty-six patients were treated with fludarabine containing regimens, of whom 11 developed a secondary cancer; 180 patients received chlorambucil and 34 developed a secondary tumor. Among 65 patients who underwent alemtuzumab treatment, 10 were later diagnosed with a second cancer. During the follow up, 121 patients died, 18 with secondary malignancy. Of note, 41 patients died from CLL progression, 2 from thrombotic events, 11 from infections and 8 from secondary malignancy. Conclusions: secondary malignancies are not infrequent in patients with CLL and their occurrence is not clearly related to biologic markers or to the treatment performed. A careful clinical follow up, encompassing sex and age adjusted tumors screening, is advisable for an early diagnosis and appropriate treatment of secondary malignancies in CLL. Table 1. clinical and laboratory features of 514 CLL patients with or without secondary malignancies. Data are expressed as median (range) or absolute number (%). Secondary malignancies Yes (N=88) No (N=426) Age years 64 (39-80) 63 (31-90) Gender M/F 57/31 250/176 Follow-up years 12 (0-30) 12 (1-25) Rai/Binet<C/III> C/III 85 (97) 3 (3) 401 (94) 25 (6) WBC x10e3/mmc 18.74 (3.6-138) 17.3 (3-384) ALC x10e3/mmc 12.63 (1.8-91.8) 11.9 (1.4-381) Hb g/dL 14 (9-17) 14 (8-18) PLT x10e3/mmc 198 (77-608) 184 (58-472) LDH U/L 325 (144-838) 334 (142-795) Beta2microglobulin mg/L 2 (1-23) 2 (0-10) FISH*Del11qDel13qDel17p+12 6 (13) 19 (40) 4 (8) 6 (13) 23 (9) 101 (40) 15 (6) 38 (15) VHIG unmutated** 15 (42) 80 (39) ZAP-70 positive*** 14 (39) 73 (36) CD-38 positive**** 15 (42) 76 (37) *Tested in 48 and 252 patients respectively. **Tested in 36 and 205 patients respectively. ***Tested in 33 and 182 patients respectively. ****Tested in 56 and 291 patients respectively. Disclosures No relevant conflicts of interest to declare.

Modeling using structural equations, is a second generation statistical data analysis technique, it has been positioned as the methodological options most used by researchers in various fields of science. The best known method is the covariance-based approach, but it presents some limitations for its application in certain cases. Another alternative method is based on the variance structure, through the analysis of partial least squares, which is an appropriate option when the research involves the use of latent variables (for example, composite indicators) prepared by the researcher, and where it is necessary to explain and predict complex models. This article presents a brief summary of the structural equation modeling technique, with an example on the relationship of constructs, sustainability and competitiveness in iron mining, and is intended to be a brief guide for future researchers in the engineering sciences. Keywords: Competitiveness, Structural equations, Iron mining, Sustainability. References [1]J. Hair, G. Hult, C. Ringle and M. Sarstedt. A Primer on Partial Least Square Structural Equation Modeling (PLS-SEM). California: United States. Sage, 2017. [2]H. Wold. Model Construction and Evaluation when Theoretical Knowledge Is Scarce: An Example of the Use of Partial Least Squares. Genève. Faculté des Sciences Économiques et Sociales, Université de Genève. 1979. [3]J. Henseler, G. Hubona & P. Ray. “Using PLS path modeling new technology research: updated guidelines”. Industrial Management & Data Systems, 116(1), 2-20. 2016. [4]G. Cepeda and Roldán J. “Aplicando en la Práctica la Técnica PLS en la Administración de Empresas”. Congreso de la ACEDE, Murcia, España, 2004. [5]D. Garson. Partial Least Squares. Regresión and Structural Equation Models. USA. Statistical Associates Publishing: 2016. [6]D. Barclay, C. Higgins & R. Thompson. “The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computer Adoption and Use as an Illustration”. Technology Studies. Special Issue on Research Methodology. (2:2), pp. 285-309. 1995. [7]J. Medina, N. Pedraza & M. Guerrero. “Modelado de Ecuaciones Estructurales. Un Enfoque de Partial Least Square Aplicado en las Ciencias Sociales y Administrativas”. XIV Congreso Internacional de la Academia de Ciencias Administrativas A.C. (ACACIA). EGADE – ITESM. Monterrey, México, 2010. [8]J. Medina & J. Chaparro. “The Impact of the Human Element in the Information Systems Quality for Decision Making and User Satisfaction”. Journal of Computer Information Systems. (48:2), pp. 44-52. 2008. [9]D. Leidner, S. Carlsson, J. Elam & M. Corrales. “Mexican and Swedish Managers’ Perceptions of the Impact of EIS on Organizational Intelligence, Decisión Making, and Structure”. Decision Science. (30:3), pp. 633-658. 1999.[10]W. Chin. “The partial least squares approach for structural equation modeling”. Chapter Ten, pp. 295-336 in Modern methods for business research. Edited by Macoulides, G. A., New Jersey: Lawrence Erlbaum Associates, 1998. [11]M. Höck & C. Ringle M. “Strategic networks in the software industry: An empirical analysis of the value continuum”. IFSAM VIIIth World Congress, Berlin 2006. [12]J. Henseler, Ch. Ringle & M. Sarstedt. Handbook of partial least squares: Concepts, methods and applications in marketing and related fields. Berlin: Springer, 2012. [13]S. Daskalakis & J. Mantas. “Evaluating the impact of a service-oriented framework for healthcare interoperability”. Studies in Health Technology and Informatics. pp. 285-290. 2008. [14]C. Fornell & D. Larcker: “Evaluating Structural Equation Models with Unobservable Variables and Measurement Error”, Journal of Marketing Research, vol. 18, pp. 39-50. Februay 1981. [15]C. Fornell. A Second Generation of Multivariate Analysis: An Overview. Vol. 1. New York, U.S.A. Praeger Publishers: 1982. [16]R. Falk and N. Miller. A Primer for Soft Modeling. Ohio: The University of Akron. 1992. [17]M. Martínez. Aplicación de la técnica PLS-SEM en la gestión del conocimiento: un enfoque técnico práctico. Revista Iberoamericana para Investigación y el Desarrollo Educativo. Vol. 8, Núm. 16. 2018. [18]S. Geisser. “A predictive approach to the random effects model”. Biometrika, Vol. 61(1), pp. 101-107. 1974. [19]J. Cohen. Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum, 1988. [20]GRI (2013). G4 Sustainability Reporting Guidelines. Global Reporting Initiative. Available: www.globalreporting.org

Article 4(11) (Definition of consent) (see too recitals 32 and 33 on consent for scientific research purposes); Article 6(1)(a) (Lawfulness of processing—consent) (see also recitals 40 and 42–43); Article 8 (Conditions applicable to child’s consent in relation to information society services); Article 9(2) (Processing of special categories of personal data—consent) (see too recital 50); Article 13(2)(c) (Information to be provided—withdrawal of consent); Article 14(2)(d) (Information to be provided—withdrawal of consent); Article 17(1)(b) (Right to erasure—withdrawal of consent) (see also recital 65); Article 18(2) (Right to restriction of processing); Article 20(1)(a) (Right to data portability) (see also recital 68); Article 22(2)(c) (Automated decisions and profiling) (see also recital 71); Article 49(1)(a) (Derogations for specific situations) (see also recital 111); Article 83 (General conditions for imposing administrative fines) (see also recitals 155 and 171).

Article 5 (Principles relating to processing of personal data) (see also recitals 33, 39 and 50); Article 6(1)(a) (Lawfulness of processing on basis of consent) (see too recital 40); Article 7 (Conditions for consent) (see also recital 42); Article 8 (Conditions applicable to child’s consent in relation to information society services) (see too recital 38); Article 9(2)(a) (Processing of special categories of personal data on basis of consent) (see too recital 51); Article 13 (Information to be provided where personal data are collected from the data subject) (see too recitals 60–62); Article 14 (Information to be provided where personal data have not been obtained from the data subject); Article 17 (Right to erasure) (see too recital 65); Article 20 (Right to data portability) (see too recital 68); Article 22 (Automated individual decision-making, including profiling) (see too recital 71); Article 49(1)(a) (Transfer of personal data to third country or international organisation on basis of consent) (see too recitals 111–112).

Obesity represents a major global public health problem. In the past few decades the prevalence of obesity has increased worldwide. In 2016, an estimated 1.9 billion adults were overweight; of these more than 650 million were obese. There is an urgent need for potential solutions and deeper understanding of the risk factors responsible for obesity. A bibliometric analysis study was designed to provide a comprehensive overview of top 100 most cited studies on obesity indexed in Web of Science database. The online search was conducted on June 6, 2021 using the keywords “Obesity” OR “Obese” OR “Overweight” in title filed with no limitations on document types or languages. The top 100 cited studies were selected in descending order based on number of citations. The obtained data were imported in to Microsoft Excel 2019 to extract the basic information such as title, authors name, journal name, year of publication and total citations. In addition, the data were also imported in to HistCite™ for further citation analysis, and VOSviewer software for windows to plot the data for network visualization mapping. The initial search retrieved a total of 167,553 documents on obesity. Of the total retrieved documents, only top 100 most cited studies on obesity were included for further analysis. These studies were published from 1982 to 2017 in English language. Most of the studies were published as an article (n = 84). The highly cited study on obesity was “Establishing a standard definition for child overweight and obesity worldwide: international survey” published in BMJ-British Medical Journal (Impact Factor 39.890, Incites Journal Citation Reports, 2021) in 2000 cited 10,543 times. The average number of citations per study was 2,947.22 (ranging from 1,566 to 10,543 citations). Two studies had more than 10,000 citations. A total of 2,272 authors from 111 countries were involved. The most prolific author was Flegal KM authored 14 studies with 53,558 citations. The highly active country in obesity research was United States of America. The included studies were published in 33 journals. The most attractive journal was JAMA-Journal of the American Medical Association (Impact Factor 56.272) published 17 studies and cited globally 51,853 times. The most frequently used keywords were obesity (n = 87) and overweight (n = 22). The countries with highest total link strength was United States of America (n = 155), followed by England (n = 140), and Scotland (n = 130). Our results show that most number of highly cited studies were published in developed countries. The findings of this study can serve as a standard benchmark for researchers to provide the quality bibliographic references and insights into the future research trends and scientific cooperation in obesity research.