Anti-TIF-1γ Antibody Detection Using a Commercial Kit vs In-House Immunoblot: Usefulness in Clinical Practice

Càncer; Dermatomiositis; ImmunoassaigCáncer; Dermatomiositis; InmunoensayoCancer; Dermatomyositis; ImmunoassayObjectives: Anti-TIF-1γ autoantibody detection is important for cancer screening in patients with dermatomyositis. The gold standard for anti-TIF-1γ detection, immunoprecipitation, is only available from a few specialized laboratories worldwide, so commercial ELISA/immunoblot tests have emerged in recent years. To analyze their usefulness in diagnosing cancer-associated dermatomyositis, we compared Euroimmun Euroline profile with our previously validated in-house immunoblot assay with human recombinant TIF-1γ. Methods: We included 308 adult patients from Hospital de la Santa Creu I Sant Pau and Vall Hebrón Hospital (Barcelona, Spain) tested for anti-TIF-1γ autoantibodies using the Euroline profile and an in-house immunoblot assay. Results: A total of 27 anti-TIF-1γ were detected by the Euroline and 12 by the in-house assay. Fair agreement was observed between Euroline and the in-house immunoblot Cohen’s kappa 0.3163. Expected prevalence of anti-TIF-1γ autoantibodies was observed for the two methods for dermatomyositis and undifferentiated connective tissue diseases, but unexpectedly high prevalence of anti-TIF-1γ autoantibodies was detected by Euroline compared to the in-house immunoblot for other diseases (16.5% Euroline vs 0.8% in-house immunoblot, p<0.01). The in-house IB compared to Euroline more reliably detected cancer in patients with DM with anti-TIF-1γ antibodies (p=0.0014 vs p=0.0502 for in-house immunoblot vs Euroline). Conclusion: We recommend using a second validated method to confirm Euroline-detected anti-TIF-1γ antibodies when the dermatomyositis diagnosis is not definitive. Furthermore, in the context of definite DM diagnosis with negative anti-TIF-1γ antibodies by Euroline and no other myositis specific antibody, is also recommendable to confirm by a second validated metho

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Anti-HMGCR Specificity of HALIP: A Confirmatory Study

A distinctive new indirect immunofluorescence pattern in liver tissue has been associated with anti-HMGCR autoantibodies. It is known as HALIP (HMGCR Associated Liver Immunofluorescence Pattern). In this study, we furthered the original studies to demonstrate the association of anti-HMGCR antibodies with the HALIP. Human anti-HMGCR antibodies from patients’ sera were purified and incubated with rat triple tissue (kidney/stomach/liver). A characteristic HALIP was observed. Additionally, a colocalization assay of human anti-HMGCR antibodies with rabbit polyclonal anti-HMGCR antibodies showed colocalization of both immunofluorescence patterns. This study confirms that the HALIP is due to human anti-HMGCR antibodies

Anti-TIF-1γ Antibody Detection Using a Commercial Kit vs In-House Immunoblot : Usefulness in Clinical Practice

Anti-TIF-1γ autoantibody detection is important for cancer screening in patients with dermatomyositis. The gold standard for anti-TIF-1γ detection, immunoprecipitation, is only available from a few specialized laboratories worldwide, so commercial ELISA/immunoblot tests have emerged in recent years. To analyze their usefulness in diagnosing cancer-associated dermatomyositis, we compared Euroimmun Euroline profile with our previously validated in-house immunoblot assay with human recombinant TIF-1γ. We included 308 adult patients from Hospital de la Santa Creu I Sant Pau and Vall Hebrón Hospital (Barcelona, Spain) tested for anti-TIF-1γ autoantibodies using the Euroline profile and an in-house immunoblot assay. A total of 27 anti-TIF-1γ were detected by the Euroline and 12 by the in-house assay. Fair agreement was observed between Euroline and the in-house immunoblot Cohen's kappa 0.3163. Expected prevalence of anti-TIF-1γ autoantibodies was observed for the two methods for dermatomyositis and undifferentiated connective tissue diseases, but unexpectedly high prevalence of anti-TIF-1γ autoantibodies was detected by Euroline compared to the in-house immunoblot for other diseases (16.5% Euroline vs 0.8% in-house immunoblot, p<0.01). The in-house IB compared to Euroline more reliably detected cancer in patients with DM with anti-TIF-1γ antibodies (p=0.0014 vs p=0.0502 for in-house immunoblot vs Euroline). We recommend using a second validated method to confirm Euroline-detected anti-TIF-1γ antibodies when the dermatomyositis diagnosis is not definitive. Furthermore, in the context of definite DM diagnosis with negative anti-TIF-1γ antibodies by Euroline and no other myositis specific antibody, is also recommendable to confirm by a second validated method