20 research outputs found
Targeting neuroinflammation for therapeutic intervention in neurodegenerative pathologies: A role for the peptide analogue of thymulin (PAT)
Introduction: Inflammation has a vital task in protecting the organism, but when deregulated, it can have serious pathological consequences. The central nervous system (CNS) is capable of mounting immune and inflammatory responses, albeit different from that observed in the periphery. Neuroinflammation, however, can be a major contributor to neurodegenerative diseases and constitute a major challenge for medicine and basic research. Areas covered: Both innate and adaptive immune responses normally play an important role in homeostasis within the CNS. Microglia, astrocytes and neuronal cells express a wide array of toll-like receptors (TLR) that can be upregulated by infection, trauma, injuries and various exogenic or endogenic factors. Chronic hyper activation of brain immune cells can result in neurotoxic actions due to excessive production of several pro-inflammatory mediators. Several studies have recently described an important role for targeting receptors such as nicotinic receptors located on cells in the CNS or in other tissues for the control of inflammation. Expert opinion: Thymulin and its synthetic peptide analogue (PAT) appear to exert potent anti-inflammatory effects at the level of peripheral tissues as well as at the level of the brain. This effect involves, at least partially, the activation of cholinergic mechanisms. © 2012 Informa UK, Ltd.Al-Amin H, 2011, EXP NEUROL, V228, P30, DOI 10.1016-j.expneurol.2010.11.005; Allan SM, 2005, NAT REV IMMUNOL, V5, P629, DOI 10.1038-nri1664; Amor S, 2010, IMMUNOLOGY, V129, P154, DOI 10.1111-j.1365-2567.2009.03225.x; Arroyo DS, 2011, INT IMMUNOPHARMACOL, V11, P1415, DOI 10.1016-j.intimp.2011.05.006; BACH JF, 1983, CLIN IMMUNOL ALLERGY, V3, P133; BACH JF, 1977, NATURE, V266, P55, DOI 10.1038-266055a0; BANKS WA, 1991, J PHARMACOL EXP THER, V259, P988; Beers DR, 2008, P NATL ACAD SCI USA, V105, P15558, DOI 10.1073-pnas.0807419105; Bernardo A, 2000, EUR J NEUROSCI, V12, P2215, DOI 10.1046-j.1460-9568.2000.00110.x; Bernik TR, 2002, J EXP MED, V195, P781, DOI 10.1084-jem.20011714; Bertrand D, 2007, BIOCHEM PHARMACOL, V74, P1155, DOI 10.1016-j.bcp.2007.07.011; Besedovsky HO, 1996, ENDOCR REV, V17, P64, DOI 10.1210-er.17.1.64; Bjorkqvist M, 2009, NEURON, V64, P21, DOI 10.1016-j.neuron.2009.09.034; Blalock JE, 2002, J EXP MED, V195, pF25, DOI 10.1084-jem.20020602; Block ML, 2005, PROG NEUROBIOL, V76, P77, DOI 10.1016-j.pneurobio.2005.06.004; Boche D, 2006, NEUROBIOL DIS, V22, P638, DOI 10.1016-j.nbd.2006.01.004; Borovikova LV, 2000, NATURE, V405, P458; Brenneman DE, 1996, J CLIN INVEST, V97, P2299, DOI 10.1172-JCI118672; Butchi NB, 2011, AM J PATHOL, V179, P783, DOI 10.1016-j.ajpath.2011.04.011; Cardona AE, 2006, NAT NEUROSCI, V9, P917, DOI 10.1038-nn1715; Carson MJ, 2006, IMMUNOL REV, V213, P48, DOI 10.1111-j.1600-065X.2006.00441.x; COLTON CA, 1987, FEBS LETT, V223, P284, DOI 10.1016-0014-5793(87)80305-8; Conejero-Goldberg C, 2008, NEUROSCI BIOBEHAV R, V32, P693, DOI 10.1016-j.neubiorev.2007.10.007; Correa F, 2010, GLIA, V58, P135, DOI 10.1002-glia.20907; D'Andrea MR, 2001, HISTOPATHOLOGY, V38, P120, DOI 10.1046-j.1365-2559.2001.01082.x; de Jonge WJ, 2007, BRIT J PHARMACOL, V151, P915, DOI 10.1038-sj.bjp.0707264; De Rosa MJ, 2005, J NEUROIMMUNOL, V160, P154, DOI 10.1016-j.jneuroim.2004.11.010; DICKSON DW, 1993, GLIA, V7, P75, DOI 10.1002-glia.440070113; Elkabes S, 1996, J NEUROSCI, V16, P2508; Ferrari CC, 2004, AM J PATHOL, V165, P1827, DOI 10.1016-S0002-9440(10)63438-4; Frank-Cannon TC, 2009, MOL NEURODEGENER, V4, DOI 10.1186-1750-1326-4-47; Galea I, 2007, TRENDS IMMUNOL, V28, P12, DOI 10.1016-j.it.2006.11.004; Gao BX, 2010, PAIN, V149, P33, DOI 10.1016-j.pain.2010.01.007; Gao HM, 2008, TRENDS IMMUNOL, V29, P357, DOI 10.1016-j.it.2008.05.002; Gill JK, 2011, P NATL ACAD SCI USA, V108, P5867, DOI 10.1073-pnas.1017975108; Giuliani F, 2003, J IMMUNOL, V171, P368; Glass CK, 2010, CELL, V140, P918, DOI 10.1016-j.cell.2010.02.016; Halliday G, 2000, CLIN EXP PHARMACOL P, V27, P1, DOI 10.1046-j.1440-1681.2000.03200.x; Hansen MK, 1998, J NEUROSCI, V18, P2247; Henriques-Coelho T, 2008, ENDOCRINOLOGY, V149, P4367, DOI 10.1210-en.2008-0018; Jo D, 2005, NAT MED, V11, P892, DOI 10.1038-nm1269; Kawai T, 2010, NAT IMMUNOL, V11, P373, DOI 10.1038-ni.1863; Kim YS, 2006, EXP MOL MED, V38, P333; Konat GW, 2006, J NEUROCHEM, V99, P1, DOI 10.1111-j.1471-4159.2006.04076.x; Konsman JP, 2008, EUR J NEUROSCI, V28, P2499, DOI 10.1111-j.1460-9568.2008.06549.x; LEE SC, 1993, J IMMUNOL, V150, P2659; Loram LC, 2010, BRAIN BEHAV IMMUN, V24, P959, DOI 10.1016-j.bbi.2010.03.008; Luheshi NM, 2009, BRIT J PHARMACOL, V157, P1318, DOI 10.1111-j.1476-5381.2009.00331.x; Menghetti L, 2005, BRAIN RES BRAIN RES, V48, P251; Moss DW, 2001, EUR J NEUROSCI, V13, P529, DOI 10.1046-j.1460-9568.2001.01418.x; Nathan C, 2010, CELL, V140, P871, DOI 10.1016-j.cell.2010.02.029; Newman TA, 2001, BRAIN, V124, P2203, DOI 10.1093-brain-124.11.2203; Nguyen MD, 2004, J NEUROSCI, V24, P1340, DOI 10.1523-JNEUROSCI.4786-03.2004; Nguyen MD, 2002, NAT REV NEUROSCI, V3, P216, DOI 10.1038-nrn752; Niehaus I., 2003, AD PD 6 INT C, P1; Nimmerjahn A, 2005, SCIENCE, V308, P1314, DOI 10.1126-science.1110647; Nordberg A, 2001, BIOL PSYCHIAT, V49, P200, DOI 10.1016-S0006-3223(00)01125-2; Olson JK, 2004, J IMMUNOL, V173, P3916; O'Neill LAJ, 2006, CURR OPIN IMMUNOL, V18, P3, DOI 10.1016-j.coi.2005.11.012; Peng H, 2010, J NEUROSCI RES, V88, P1041, DOI 10.1002-jnr.22269; PLEAU JM, 1979, IMMUNOL LETT, V1, P179, DOI 10.1016-0165-2478(79)90025-7; Popovich PG, 2008, NAT REV NEUROSCI, V9, P481, DOI 10.1038-nrn2398; Pul R, 2011, EXPERT OPIN BIOL TH, V11, P343, DOI 10.1517-14712598.2011.552884; Qin LY, 2007, GLIA, V55, P453, DOI 10.1002-glia.20467; Ramirez BG, 2005, J NEUROSCI, V25, P190; Reggiani PC, 2009, ANN NY ACAD SCI, V1153, P98, DOI 10.1111-j.1749-6632.2008.03964.x; Reyes TM, 1998, AM J PHYSIOL-REG I, V274, pR139; Rivest S, 2009, NAT REV IMMUNOL, V9, P429, DOI 10.1038-nri2565; Ron-Harel N, 2010, BRAIN BEHAV IMMUN, V25, P1036; Rosas-Ballina M, 2009, J INTERN MED, V265, P663, DOI 10.1111-j.1365-2796.2009.02098.x; Rosas-Ballina M, 2011, SCIENCE, V334, P98, DOI 10.1126-science.1209985; Rowbotham MC, 2009, PAIN, V146, P245, DOI 10.1016-j.pain.2009.06.013; Saade NE, 2003, NEUROSCIENCE, V119, P155, DOI 10.1016-S0306-4522(03)00072-1; Sabat R, 2010, CYTOKINE GROWTH F R, V21, P331, DOI 10.1016-j.cytogfr.2010.09.002; Safieh-Garabedian B, 2002, BRIT J PHARMACOL, V136, P947, DOI 10.1038-sj.bjp.0704793; Safieh-Garabedian B, 2004, CURR DRUG TARGETS CN, V3, P61; Safieh-Garabedian B, 2011, NEUROPHARMACOLOGY, V60, P496, DOI 10.1016-j.neuropharm.2010.11.004; SafiehGarabedian B, 1996, BRAIN RES, V717, P179, DOI 10.1016-0006-8993(95)01532-9; Safieh-Garabedian B, 2010, SOC NEUROSCI; Safieh-Garabedian B, 2000, NEUROPHARMACOLOGY, V39, P1653, DOI 10.1016-S0028-3908(99)00247-6; Safieh-Garabedian B, 2003, NEUROSCIENCE, V121, P865, DOI 10.1016-S0306-4522(03)00500-1; Safieh-Garabedian B, 2012, IASP M MIL; Saunders NR, 2008, TRENDS NEUROSCI, V31, P279, DOI 10.1016-j.tins.2008.03.003; Simard AR, 2006, NEURON, V49, P489, DOI 10.1016-j.neuron.2006.01.022; Streit WJ, 1999, PROG NEUROBIOL, V57, P563, DOI 10.1016-S0301-0082(98)00069-0; Streit WJ, 2002, GLIA, V40, P133, DOI 10.1002-glia.10154; Streit Wolfgang J., 2005, Current Alzheimer Research, V2, P187, DOI 10.2174-1567205053585765; Suzuki T, 2006, J NEUROSCI RES, V83, P1461, DOI 10.1002-jnr.20850; Szelenyi J, 2001, BRAIN RES BULL, V54, P329, DOI 10.1016-S0361-9230(01)00428-2; Unwin N, 2005, J MOL BIOL, V346, P967, DOI 10.1016-j.jmb.2004.12.031; Vaz AR, 2011, EXP NEUROL, V229, P381, DOI 10.1016-j.expneurol.2011.03.004; Waldburger JM, 2008, ARTHRITIS RHEUM, V58, P3439, DOI 10.1002-art.23987; Wang H, 2003, NATURE, V421, P384, DOI 10.1038-nature01339; Wolf SA, 2009, J IMMUNOL, V182, P3979, DOI 10.4049-jimmunol.0801218; Xin JP, 2011, BRAIN BEHAV IMMUN, V25, P820, DOI 10.1016-j.bbi.2010.08.004; Zhang R, 2008, BIOCHEM BIOPH RES CO, V372, P816, DOI 10.1016-j.bbrc.2008.05.128; Zhou ZG, 2009, J NEUROCHEM, V110, P1617, DOI 10.1111-j.1471-4159.2009.06263.x; Ziemssen T, 2002, BRAIN, V125, P2381, DOI 10.1093-brain-awf252; Zipp F, 2006, TRENDS NEUROSCI, V29, P518, DOI 10.1016-j.tins.2006.07.00623
Neurological Complications and Outcomes in Critically Ill Patients With COVID-19: Results From International Neurological Study Group From the COVID-19 Critical Care Consortium
Background: In this COVID-19 Critical Care Consortium (CCCC) sub-study, we qualified neurological complications associated with SARS-CoV2 infection. Methods: The CCCC is an international, multicenter study. Eligible patients were COVID-19 patients admitted to intensive care units (ICU) across 23 centers between 1/7/2020 to 6/23/2022. Incidence of neurological complications was estimated as number of events per hospital days and per admission using Poisson regression. Associations between neurological complications and risk factors were assessed using multivariable Poisson regression. Results: 713 patients were included. Median age = 56 years (interquartile range (IQR) = 45-65). Neurological complications reported in 61/480 patients (12.7%) with the majority being ischemic stroke (2.9%), intracranial hemorrhage (ICH) (2.8%), and seizures (2.6%). Multivariable analysis for neurological complications per admitted days showed comorbid neurological conditions (incidence rate ratio (IRR) = 6.35, 2.57-15.7) were an independent risk factor for ischemic stroke. Extracorporeal membrane oxygenation (IRR = 5.32, 1.52-18.6), low-middle income countries (LMIC) vs high income countries (HIC) (IRR = 4.70, 1.62-13.7), and age >55 (IRR = 3.66, 1.23-10.9) were independent risk factors for ICH. Co-morbid neurological conditions (IRR = 3.43, 1.11-10.6), LMIC vs HIC (IRR = 8.69, 2.15-35.2), July-December 2020 vs January-June 2020 (IRR = 0.17, 0.04-0.69) and age >55 (IRR = 4.05, 1.15-14.3) were independent risk factors for seizure.ConclusionsDecision-making should incorporate salient risk factors to inform management of SARS-CoV2 infection and avoid neurological complications
Risk factors analysis according to regional distribution of white matter hyperintensities in a stroke cohort
Objectives: The spectrum of distribution of white matter hyperintensities (WMH) may reflect different functional, histopathological, and etiological features. We examined the relationships between cerebrovascular risk factors (CVRF) and different patterns of WMH in MRI using a qualitative visual scale in ischemic stroke (IS) patients. Methods: We assembled clinical data and imaging findings from patients of two independent cohorts with recent IS. MRI scans were evaluated using a modified visual scale from Fazekas, Wahlund, and Van Swieten. WMH distributions were analyzed separately in periventricular (PV-WMH) and deep (D-WMH) white matter, basal ganglia (BG-WMH), and brainstem (B-WMH). Presence of confluence of PV-WMH and D-WMH and anterior-versus-posterior WMH predominance were also evaluated. Statistical analysis was performed with SPSS software. Results: We included 618 patients, with a mean age of 72 years (standard deviation [SD] 11 years). The most frequent WMH pattern was D-WMH (73%). In a multivariable analysis, hypertension was associated with PV-WMH (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.29-2.50, p = 0.001) and BG-WMH (OR 2.13, 95% CI 1.19-3.83, p = 0.012). Diabetes mellitus was significantly related to PV-WMH (OR 1.69, 95% CI 1.24-2.30, p = 0.001), D-WMH (OR 1.46, 95% CI 1.07-1.49, p = 0.017), and confluence patterns of D-WMH and PV-WMH (OR 1.62, 95% CI 1.07-2.47, p = 0.024). Hyperlipidemia was found to be independently related to brainstem distribution (OR 1.70, 95% CI 1.08-2.69, p = 0.022). Conclusions: Different CVRF profiles were significantly related to specific WMH spatial distribution patterns in a large IS cohort
Association of Dexamethasone with Shunt Requirement, Early Disability, and Medical Complications in Aneurysmal Subarachnoid Hemorrhage
Early short course of neuromuscular blocking agents in patients with COVID-19 ARDS:a propensity score analysis
Background: The role of neuromuscular blocking agents (NMBAs) in coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) is not fully elucidated. Therefore, we aimed to investigate in COVID-19 patients with moderate-to-severe ARDS the impact of early use of NMBAs on 90-day mortality, through propensity score (PS) matching analysis. Methods: We analyzed a convenience sample of patients with COVID-19 and moderate-to-severe ARDS, admitted to 244 intensive care units within the COVID-19 Critical Care Consortium, from February 1, 2020, through October 31, 2021. Patients undergoing at least 2 days and up to 3 consecutive days of NMBAs (NMBA treatment), within 48 h from commencement of IMV were compared with subjects who did not receive NMBAs or only upon commencement of IMV (control). The primary objective in the PS-matched cohort was comparison between groups in 90-day in-hospital mortality, assessed through Cox proportional hazard modeling. Secondary objectives were comparisons in the numbers of ventilator-free days (VFD) between day 1 and day 28 and between day 1 and 90 through competing risk regression. Results: Data from 1953 patients were included. After propensity score matching, 210 cases from each group were well matched. In the PS-matched cohort, mean (± SD) age was 60.3 ± 13.2 years and 296 (70.5%) were male and the most common comorbidities were hypertension (56.9%), obesity (41.1%), and diabetes (30.0%). The unadjusted hazard ratio (HR) for death at 90 days in the NMBA treatment vs control group was 1.12 (95% CI 0.79, 1.59, p = 0.534). After adjustment for smoking habit and critical therapeutic covariates, the HR was 1.07 (95% CI 0.72, 1.61, p = 0.729). At 28 days, VFD were 16 (IQR 0–25) and 25 (IQR 7–26) in the NMBA treatment and control groups, respectively (sub-hazard ratio 0.82, 95% CI 0.67, 1.00, p = 0.055). At 90 days, VFD were 77 (IQR 0–87) and 87 (IQR 0–88) (sub-hazard ratio 0.86 (95% CI 0.69, 1.07; p = 0.177). Conclusions: In patients with COVID-19 and moderate-to-severe ARDS, short course of NMBA treatment, applied early, did not significantly improve 90-day mortality and VFD. In the absence of definitive data from clinical trials, NMBAs should be indicated cautiously in this setting
Correction: Epidemiology and outcomes of early-onset AKI in COVID-19-related ARDS in comparison with non-COVID-19-related ARDS: insights from two prospective global cohort studies (Critical Care, (2023), 27, 1, (3), 10.1186/s13054-022-04294-5)
Following publication of the original article [1], the authors identified that the collaborating authors part of the collaborating author group CCCC Consortium was missing. The collaborating author group is available and included as Additional file 1 in this article
Early short course of neuromuscular blocking agents in patients with COVID-19 ARDS: a propensity score analysis
Background: The role of neuromuscular blocking agents (NMBAs) in coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) is not fully elucidated. Therefore, we aimed to investigate in COVID-19 patients with moderate-to-severe ARDS the impact of early use of NMBAs on 90-day mortality, through propensity score (PS) matching analysis. Methods: We analyzed a convenience sample of patients with COVID-19 and moderate-to-severe ARDS, admitted to 244 intensive care units within the COVID-19 Critical Care Consortium, from February 1, 2020, through October 31, 2021. Patients undergoing at least 2 days and up to 3 consecutive days of NMBAs (NMBA treatment), within 48 h from commencement of IMV were compared with subjects who did not receive NMBAs or only upon commencement of IMV (control). The primary objective in the PS-matched cohort was comparison between groups in 90-day in-hospital mortality, assessed through Cox proportional hazard modeling. Secondary objectives were comparisons in the numbers of ventilator-free days (VFD) between day 1 and day 28 and between day 1 and 90 through competing risk regression. Results: Data from 1953 patients were included. After propensity score matching, 210 cases from each group were well matched. In the PS-matched cohort, mean (± SD) age was 60.3 ± 13.2 years and 296 (70.5%) were male and the most common comorbidities were hypertension (56.9%), obesity (41.1%), and diabetes (30.0%). The unadjusted hazard ratio (HR) for death at 90 days in the NMBA treatment vs control group was 1.12 (95% CI 0.79, 1.59, p = 0.534). After adjustment for smoking habit and critical therapeutic covariates, the HR was 1.07 (95% CI 0.72, 1.61, p = 0.729). At 28 days, VFD were 16 (IQR 0–25) and 25 (IQR 7–26) in the NMBA treatment and control groups, respectively (sub-hazard ratio 0.82, 95% CI 0.67, 1.00, p = 0.055). At 90 days, VFD were 77 (IQR 0–87) and 87 (IQR 0–88) (sub-hazard ratio 0.86 (95% CI 0.69, 1.07; p = 0.177). Conclusions: In patients with COVID-19 and moderate-to-severe ARDS, short course of NMBA treatment, applied early, did not significantly improve 90-day mortality and VFD. In the absence of definitive data from clinical trials, NMBAs should be indicated cautiously in this setting
Stroke in critically ill patients with respiratory failure due to COVID-19: Disparities between low-middle and high-income countries
Purpose: We aimed to compare the incidence of stroke in low-and middle-income countries (LMICs) versus high-income countries (HICs) in critically ill patients with COVID-19 and its impact on in-hospital mortality. Methods: International observational study conducted in 43 countries. Stroke and mortality incidence rates and rate ratios (IRR) were calculated per admitted days using Poisson regression. Inverse probability weighting (IPW) was used to address the HICs vs. LMICs imbalance for confounders. Results: 23,738 patients [20,511(86.4 %) HICs vs. 3,227(13.6 %) LMICs] were included. The incidence stroke/1000 admitted-days was 35.7 (95 %CI = 28.4–44.9) LMICs and 17.6 (95 %CI = 15.8–19.7) HICs; ischemic 9.47 (95 %CI = 6.57–13.7) LMICs, 1.97 (95 %CI = 1.53, 2.55) HICs; hemorrhagic, 7.18 (95 %CI = 4.73–10.9) LMICs, and 2.52 (95 %CI = 2.00–3.16) HICs; unspecified stroke type 11.6 (95 %CI = 7.75–17.3) LMICs, 8.99 (95 %CI = 7.70–10.5) HICs. In regression with IPW, LMICs vs. HICs had IRR = 1.78 (95 %CI = 1.31–2.42, p < 0.001). Patients from LMICs were more likely to die than those from HICs [43.6% vs 29.2 %; Relative Risk (RR) = 2.59 (95 %CI = 2.29–2.93), p < 0.001)]. Patients with stroke were more likely to die than those without stroke [RR = 1.43 (95 %CI = 1.19–1.72), p < 0.001)]. Conclusions: Stroke incidence was low in HICs and LMICs although the stroke risk was higher in LMICs. Both LMIC status and stroke increased the risk of death. Improving early diagnosis of stroke and redistribution of healthcare resources should be a priority. Trial registration: ACTRN12620000421932 registered on 30/03/2020
