Iron, silicate, and light co-limitation of three Southern Ocean diatom species

The effect of combined iron, silicate, and light co-limitation was investigated in the three diatom species Actinocyclus sp. Ehrenberg, Chaetoceros dichaeta Ehrenberg, and Chaetoceros debilis Cleve, isolated from the Southern Ocean (SO). Growth of all species was co-limited by iron and silicate, reflected in a significant increase in the number of cell divisions compared to the control. Lowest relative Si uptake and drastic frustule malformation was found under iron and silicate co-limitation in C. dichaeta, while Si limitation in general caused cell elongation in both Chaetoceros species. Higher light intensities similar to SO surface conditions showed a negative impact on growth of C. dichaeta and Actinocyclus sp. and no effect on C. debilis. This is in contrast to the assumed light limitation of SO diatoms due to deep wind driven mixing. Our results suggest that growth and species composition of Southern Ocean diatoms is influenced by a sensitive interaction of the abiotic factors, iron, silicate, and light

Role of small Rhizaria and diatoms in the pelagic silica production of the Sourther Ocean

We examined biogenic silica production and elementary composition (biogenic Si, particulate organic carbon and particulate organic nitrogen) of Rhizaria and diatoms in the upper 200 m along a transect in the Southwest Pacific sector of the Southern Ocean during austral summer (January–February 2019). From incubations using the 32Si radioisotope, silicic acid uptake rates were measured at 15 stations distributed in the Polar Front Zone, the Southern Antarctic Circumpolar Current and the Ross Sea Gyre. Rhizaria cells are heavily silicified (up to 7.6 nmol Si cell−1), displaying higher biogenic Si content than similar size specimens found in other areas of the global ocean, suggesting a higher degree of silicification of these organisms in the silicic acid rich Southern Ocean. Despite their high biogenic Si and carbon content, the Si/C molar ratio (average of 0.05 ± 0.03) is quite low compared to that of diatoms and relatively constant regardless of the environmental conditions. The direct measurements of Rhizaria's biogenic Si production (0.8–36.8 μmol Si m−2 d−1) are of the same order of magnitude than previous indirect estimations, confirming the importance of the Southern Ocean for the global Rhizaria silica production. However, diatoms largely dominated the biogenic Si standing stock and production of the euphotic layer, with low rhizarians' abundances and biogenic Si production (no more than 1%). In this manuscript, we discuss the Antarctic paradox of Rhizaria, that is, the potential high accumulation rates of biogenic Si due to Rhizaria in siliceous sediments despite their low production rates in surface waters.Versión del editor3,38

Greater risk of incident asthma cases in adults with Allergic Rhinitis and Effect of Allergen Immunotherapy: A Retrospective Cohort Study

Asthma and rhinitis are often co-morbid conditions. As rhinitis often precedes asthma it is possible that effective treatment of allergic rhinitis may reduce asthma progression. The aim of our study is to investigate history of allergic rhinitis as a risk factor for asthma and the potential effect of allergen immunotherapy in attenuating the incidence of asthma. Hospital-referred non-asthmatic adults, aged 18–40 years between 1990 and 1991, were retrospectively followed up until January and April 2000. At the end of follow up, available subjects were clinically examined for asthma diagnosis and history of allergen specific immunotherapy, second-hand smoking and the presence of pets in the household. A total of 436 non-asthmatic adults (332 subjects with allergic rhinitis and 104 with no allergic rhinitis nor history of atopy) were available for final analyses. The highest OR (odds ratio) associated with a diagnosis of asthma at the end of follow-up was for the diagnosis of allergic rhinitis at baseline (OR, 7.8; 95%CI, 3.1–20.0 in the model containing the covariates of rhinitis diagnosis, sex, second-hand smoke exposure, presence of pets at home, family history of allergic disorders, sensitization to Parietaria judaica; grass pollen; house dust mites; Olea europea: orchard; perennial rye; and cat allergens). Female sex, sensitization to Parietaria judaica and the presence of pets in the home were also significantly predictive of new onset asthma in the same model. Treatment with allergen immunotherapy was significantly and inversely related to the development of new onset asthma (OR, 0.53; 95%CI, 0.32–0.86). In the present study we found that allergic rhinitis is an important independent risk factor for asthma. Moreover, treatment with allergen immunotherapy lowers the risk of the development of new asthma cases in adults with allergic rhinitis

Allergic rhinitis

Allergic rhinitis is a common disorder that is strongly linked to asthma and conjunctivitis. It is usually a long-standing condition that often goes undetected in the primary-care setting. The classic symptoms of the disorder are nasal congestion, nasal itch, rhinorrhea and sneezing. A thorough history, physical examination and allergen skin testing are important for establishing the diagnosis of allergic rhinitis. Second-generation oral antihistamines and intranasal corticosteroids are the mainstay of treatment. Allergen immunotherapy is an effective immune-modulating treatment that should be recommended if pharmacologic therapy for allergic rhinitis is not effective or is not tolerated. This article provides an overview of the pathophysiology, diagnosis, and appropriate management of this disorder

Describing the status of reproductive ageing simply and precisely: A reproductive ageing score based on three questions and validated with hormone levels

Equation 6. Quadratic logistic function approximating the function mu(B)(with age in years). Equation 1. Proportion of women who have regular menstruation for each number of reported menstruations in the last year(with period = number of periods per year, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months" and z = number of women answering "No, my periods have stopped", e.g. x(11) = number of women reporting regular menstruation among those who report 11 menstruations in the last 12 months). Equation 5. Biquadratic exponential function mu(A)depending of the number of periods. Equation 3. Age modification by smoking and oophorectomy. Equation 2. Proportion of women whose menstruations have already stopped, for each reported year of age(with age = age in years, x = number of women answering "Yes" to the question: "Do you have regular periods?", y = number of women answering "No, they have been irregular for a few months", z = number of women answering "No, my periods have stopped", e.g. x(40) = number of women reporting regular menstruations among those who are 40 years old). Equation 7. Final formula to calculate the reproductive ageing score (RAS)(with period being the number of periods per year and age as the age in years, modified according to smoking status and oophorectomy). Objective Most women live to experience menopause and will spend 4-8 years transitioning from fertile age to full menstrual stop. Biologically, reproductive ageing is a continuous process, but by convention, it is defined categorically as pre-, peri- and postmenopause;categories that are sometimes supported by measurements of sex hormones in blood samples. We aimed to develop and validate a new tool, a reproductive ageing score (RAS), that could give a simple and yet precise description of the status of reproductive ageing, without hormone measurements, to be used by health professionals and researchers. Methods Questionnaire data on age, menstrual regularity and menstrual frequency was provided by the large multicentre population-based RHINE cohort. A continuous reproductive ageing score was developed from these variables, using techniques of fuzzy mathematics, to generate a decimal number ranging from 0.00 (nonmenopausal) to 1.00 (postmenopausal). The RAS was then validated with sex hormone measurements (follicle stimulating hormone and 17 beta-estradiol) and interview-data provided by the large population-based ECRHS cohort, using receiver-operating characteristics (ROC). Results The RAS, developed from questionnaire data of the RHINE cohort, defined with high precision and accuracy the menopausal status as confirmed by interview and hormone data in the ECRHS cohort. The area under the ROC curve was 0.91 (95% Confidence interval (CI): 0.90-0.93) to distinguish nonmenopausal women from peri- and postmenopausal women, and 0.85 (95% CI: 0.83-0.88) to distinguish postmenopausal women from nonmenopausal and perimenopausal women. Conclusions: The RAS provides a useful and valid tool for describing the status of reproductive ageing accurately, on a continuous scale from 0.00 to 1.00, based on simple questions and without requiring blood sampling. The score allows for a more precise differentiation than the conventional categorisation in pre-, peri- and postmenopause. This is useful for epidemiological research and clinical trials. Equation 4. The reproductive ageing score as an aggregation function of mu(A)and mu(B)

Body silhouettes as a tool to reflect obesity in the past

Life course data on obesity may enrich the quality of epidemiologic studies analysing health consequences of obesity. However, achieving such data may require substantial resources. We investigated the use of body silhouettes in adults as a tool to reflect obesity in the past. We used large population-based samples to analyse to what extent self-reported body silhouettes correlated with the previously measured (9-23 years) body mass index (BMI) from both measured (European Community Respiratory Health Survey, N = 3 041) and self-reported (Respiratory Health In Northern Europe study, N = 3 410) height and weight. We calculated Spearman correlation between BMI and body silhouettes and ROC-curve analyses for identifying obesity (BMI ≥30) at ages 30 and 45 years. Spearman correlations between measured BMI age 30 (±2y) or 45 (±2y) and body silhouettes in women and men were between 0.62-0.66 and correlations for self-reported BMI were between 0.58-0.70. The area under the curve for identification of obesity at age 30 using body silhouettes vs previously measured BMI at age 30 (±2y) was 0.92 (95% CI 0.87, 0.97) and 0.85 (95% CI 0.75, 0.95) in women and men, respectively; for previously self-reported BMI, 0.92 (95% CI 0.88, 0.95) and 0.90 (95% CI 0.85, 0.96). Our study suggests that body silhouettes are a useful epidemiological tool, enabling retrospective differentiation of obesity and non-obesity in adult women and men