Langerhans cell histiocytosis in the maxillofacial area in adults : report of three cases

Langerhans cell histiocytosis (LCH) is a disease of unknown etiology, characterized by proliferation of pathological Langerhans cells within different organs. It mainly affects children, but adult cases also occur, with an incidence rate of one to two per million. The head and neck are affected in almost 90% of cases. Diagnosis is made by means of histopathological analysis, and imaging studies are necessary in order to determine extent of the disease. There are no controlled trials proposing an optimal treatment protocol for LCH. Prognosis in adults is generally good due to the slow evolution of the disease and its favourable response to treatment. In our report, we present three cases of LCH in patients aged 16, 24, and 28 years respectively, with primary manifestation in the maxillofacial area. A literature review was also conducted

Defects in memory B-cell and plasma cell subsets expressing different immunoglobulin-subclasses in patients with CVID and immunoglobulin subclass deficiencies

Background: Predominantly antibody deficiencies (PADs) are the most prevalent primary immunodeficiencies, but their B-cell defects and underlying genetic alterations remain largely unknown. Objective: We investigated patients with PADs for the distribution of 41 blood B-cell and plasma cell (PC) subsets, including subsets defined by expression of distinct immunoglobulin heavy chain subclasses. Methods: Blood samples from 139 patients with PADs, 61 patients with common variable immunodeficiency (CVID), 68 patients with selective IgA deficiency (IgAdef), 10 patients with IgG subclass deficiency with IgA deficiency, and 223 age matched control subjects were studied by using flow cytometry with EuroFlow immunoglobulin isotype staining. Patients were classified according to their B-cell and PC immune profile, and the obtained patient clusters were correlated with clinical manifestations of PADs. Results: Decreased counts of blood PCs, memory B cells (MB Cs), or both expressing distinct IgA and IgG subclasses were identified in all patients with PADs. In patients with IgAdef, B-cell defects were mainly restricted to surface membrane (sm)IgA(+) PCs and MBCs, with 2 clear subgroups showing strongly decreased numbers of smIgA(+) PCs with mild versus severe smIgA(+) MBC defects and higher frequencies of nonrespiratory tract infections, autoimmunity, and affected family members. Patients with IgG subclass deficiency with IgA deficiency and those with CVID showed defects in both smIgA(+) and smIgG(+) MBCs and PCs. Reduced numbers of switched PCs were systematically found in patients with CVID (absent in 98%), with 6 different defective MBC (and clinical) profiles: (1) profound decrease in MBC numbers; (2) defective CD27(+) MBCs with almost normal IgG(3)(+) MBCs; (3) absence of switched MBCs; and (4) presence of both unswitched and switched MBCs without and; (5) with IgG(2)(+) MBCs; and (6) with IgA(1)(+) MBCs. Conclusion: Distinct PAD defective B-cell patterns were identified that are associated with unique clinical profiles

Role of age and comorbidities in mortality of patients with infective endocarditis

Purpose: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. Methods: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015. Patients were stratified into three age groups:<65 years, 65 to 80 years, and = 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. Results: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 = 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients =80 years who underwent surgery were significantly lower compared with other age groups (14.3%, 65 years; 20.5%, 65-79 years; 31.3%, =80 years). In-hospital mortality was lower in the <65-year group (20.3%, <65 years;30.1%, 65-79 years;34.7%, =80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%, =80 years; p = 0.003).Independent predictors of mortality were age = 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI = 3 (HR:1.62; 95% CI:1.39–1.88), and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared, the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. Conclusion: There were no differences in the clinical presentation of IE between the groups. Age = 80 years, high comorbidity (measured by CCI), and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Diet quality index as a predictor of treatment efficacy in overweight and obese adolescents: The EVASYON study

Background & aim: A diet quality index (DQI) is a tool that provides an overall score of an individual''s dietary intake when assessing compliance with food-based dietary guidelines. A number of DQIs have emerged, albeit their associations with health-related outcomes are debated. The aim of the present study was to assess whether adherence to dietary intervention, and the overall quality of the diet, can predict body composition changes. Methods: To this purpose, overweight/obese adolescents (n = 117, aged: 13–16 years; 51 males, 66 females) were recruited into a multi-component (diet, physical activity and psychological support) family-based group treatment programme. We measured the adolescents’ compliance and body composition at baseline and after 2 months (intensive phase) and 13 months (extensive phase) of follow-up. Also, at baseline, after 6 months, and at the end of follow-up we calculated the DQI. Results: Global compliance with the dietary intervention was 37.4% during the intensive phase, and 14.3% during the extensive phase. Physical activity compliance was 94.1% at 2-months and 34.7% at 13months and psychological support compliance were growing over the intervention period (10.3% intensive phase and 45.3% during extensive phase). Adolescents complying with the meal frequency criteria at the end of the extensive phase had greater reductions in FMI z-scores than those did not complying (Cohen''s d = 0.53). A statistically significant association was observed with the diet quality index. DQI-A variation explained 98.1% of BMI z-score changes and 95.1% of FMI changes. Conclusions: We conclude that assessment of changes in diet quality could be a useful tool in predicting body composition changes in obese adolescents involved in a diet and physical activity intervention programme backed-up by psychological and family support

Effectiveness of an mHealth intervention combining a smartphone app and smart band on body composition in an overweight and obese population: Randomized controlled trial (EVIDENT 3 study)

Background: Mobile health (mHealth) is currently among the supporting elements that may contribute to an improvement in health markers by helping people adopt healthier lifestyles. mHealth interventions have been widely reported to achieve greater weight loss than other approaches, but their effect on body composition remains unclear. Objective: This study aimed to assess the short-term (3 months) effectiveness of a mobile app and a smart band for losing weight and changing body composition in sedentary Spanish adults who are overweight or obese. Methods: A randomized controlled, multicenter clinical trial was conducted involving the participation of 440 subjects from primary care centers, with 231 subjects in the intervention group (IG; counselling with smartphone app and smart band) and 209 in the control group (CG; counselling only). Both groups were counselled about healthy diet and physical activity. For the 3-month intervention period, the IG was trained to use a smartphone app that involved self-monitoring and tailored feedback, as well as a smart band that recorded daily physical activity (Mi Band 2, Xiaomi). Body composition was measured using the InBody 230 bioimpedance device (InBody Co., Ltd), and physical activity was measured using the International Physical Activity Questionnaire. Results: The mHealth intervention produced a greater loss of body weight (–1.97 kg, 95% CI –2.39 to –1.54) relative to standard counselling at 3 months (–1.13 kg, 95% CI –1.56 to –0.69). Comparing groups, the IG achieved a weight loss of 0.84 kg more than the CG at 3 months. The IG showed a decrease in body fat mass (BFM; –1.84 kg, 95% CI –2.48 to –1.20), percentage of body fat (PBF; –1.22%, 95% CI –1.82% to 0.62%), and BMI (–0.77 kg/m2, 95% CI –0.96 to 0.57). No significant changes were observed in any of these parameters in men; among women, there was a significant decrease in BMI in the IG compared with the CG. When subjects were grouped according to baseline BMI, the overweight group experienced a change in BFM of –1.18 kg (95% CI –2.30 to –0.06) and BMI of –0.47 kg/m2 (95% CI –0.80 to –0.13), whereas the obese group only experienced a change in BMI of –0.53 kg/m2 (95% CI –0.86 to –0.19). When the data were analyzed according to physical activity, the moderate-vigorous physical activity group showed significant changes in BFM of –1.03 kg (95% CI –1.74 to –0.33), PBF of –0.76% (95% CI –1.32% to –0.20%), and BMI of –0.5 kg/m2 (95% CI –0.83 to –0.19). Conclusions: The results from this multicenter, randomized controlled clinical trial study show that compared with standard counselling alone, adding a self-reported app and a smart band obtained beneficial results in terms of weight loss and a reduction in BFM and PBF in female subjects with a BMI less than 30 kg/m2 and a moderate-vigorous physical activity level. Nevertheless, further studies are needed to ensure that this profile benefits more than others from this intervention and to investigate modifications of this intervention to achieve a global effect

Defects in memory B-cell and plasma cell subsets expressing different immunoglobulin-subclasses in patients with CVID and immunoglobulin subclass deficiencies

Background: Predominantly antibody deficiencies (PADs) are the most prevalent primary immunodeficiencies, but their B-cell defects and underlying genetic alterations remain largely unknown. Objective: We investigated patients with PADs for the distribution of 41 blood B-cell and plasma cell (PC) subsets, including subsets defined by expression of distinct immunoglobulin heavy chain subclasses. Methods: Blood samples from 139 patients with PADs, 61 patients with common variable immunodeficiency (CVID), 68 patients with selective IgA deficiency (IgAdef), 10 patients with IgG subclass deficiency with IgA deficiency, and 223 agematched control subjects were studied by using flow cytometry with EuroFlow immunoglobulin isotype staining. Patients were classified according to their B-cell and PC immune profile, and the obtained patient clusters were correlated with clinical manifestations of PADs. Results: Decreased counts of blood PCs, memory B cells (MBCs), or both expressing distinct IgA and IgG subclasses were identified in all patients with PADs. In patients with IgAdef, B-cell defects were mainly restricted to surface membrane (sm)IgA1 PCs and MBCs, with 2 clear subgroups showing strongly decreased numbers of smIgA1 PCs with mild versus severe smIgA1 MBC defects and higher frequencies of nonrespiratory tract infections, autoimmunity, and affected family members. Patients with IgG subclass deficiency with IgA deficiency and those with CVID showed defects in both smIgA1 and smIgG1 MBCs and PCs. Reduced numbers of switched PCs were systematically found in patients with CVID (absent in 98%), with 6 different defective MBC (and clinical) profiles: (1) profound decrease in MBC numbers; (2) defective CD271 MBCs with almost normal IgG3 1 MBCs; (3) absence of switched MBCs; and (4) presence of both unswitched and switched MBCs without and; (5) with IgG2 1 MBCs; and (6) with IgA

Expression of MALT1 oncogene in hematopoietic stem/progenitor cells recapitulates the pathogenesis of human lymphoma in mice

Chromosomal translocations involving the MALT1 gene are hallmarks of mucosa-associated lymphoid tissue (MALT) lymphoma. To date, targeting these translocations to mouse B cells has failed to reproduce human disease. Here, we induced MALT1 expression in mouse Sca1(+)Lin(-) hematopoietic stem/progenitor cells, which showed NF-κB activation and early lymphoid priming, being selectively skewed toward B-cell differentiation. These cells accumulated in extranodal tissues and gave rise to clonal tumors recapitulating the principal clinical, biological, and molecular genetic features of MALT lymphoma. Deletion of p53 gene accelerated tumor onset and induced transformation of MALT lymphoma to activated B-cell diffuse large-cell lymphoma (ABC-DLBCL). Treatment of MALT1-induced lymphomas with a specific inhibitor of MALT1 proteolytic activity decreased cell viability, indicating that endogenous Malt1 signaling was required for tumor cell survival. Our study shows that human-like lymphomas can be modeled in mice by targeting MALT1 expression to hematopoietic stem/progenitor cells, demonstrating the oncogenic role of MALT1 in lymphomagenesis. Furthermore, this work establishes a molecular link between MALT lymphoma and ABC-DLBCL, and provides mouse models to test MALT1 inhibitors. Finally, our results suggest that hematopoietic stem/progenitor cells may be involved in the pathogenesis of human mature B-cell lymphomas

Gaia Early Data Release 3: The celestial reference frame (Gaia-CRF3)

Context. Gaia-CRF3 is the celestial reference frame for positions and proper motions in the third release of data from the Gaia mission, Gaia DR3 (and for the early third release, Gaia EDR3, which contains identical astrometric results). The reference frame is defined by the positions and proper motions at epoch 2016.0 for a specific set of extragalactic sources in the (E)DR3 catalogue. Aims. We describe the construction of Gaia-CRF3 and its properties in terms of the distributions in magnitude, colour, and astrometric quality. Methods. Compact extragalactic sources in Gaia DR3 were identified by positional cross-matching with 17 external catalogues of quasi-stellar objects (QSO) and active galactic nuclei (AGN), followed by astrometric filtering designed to remove stellar contaminants. Selecting a clean sample was favoured over including a higher number of extragalactic sources. For the final sample, the random and systematic errors in the proper motions are analysed, as well as the radio-optical offsets in position for sources in the third realisation of the International Celestial Reference Frame (ICRF3). Results. Gaia-CRF3 comprises about 1.6 million QSO-like sources, of which 1.2 million have five-parameter astrometric solutions in Gaia DR3 and 0.4 million have six-parameter solutions. The sources span the magnitude range G = 13-21 with a peak density at 20.6 mag, at which the typical positional uncertainty is about 1 mas. The proper motions show systematic errors on the level of 12 μas yr-1 on angular scales greater than 15 deg. For the 3142 optical counterparts of ICRF3 sources in the S/X frequency bands, the median offset from the radio positions is about 0.5 mas, but it exceeds 4 mas in either coordinate for 127 sources. We outline the future of Gaia-CRF in the next Gaia data releases. Appendices give further details on the external catalogues used, how to extract information about the Gaia-CRF3 sources, potential (Galactic) confusion sources, and the estimation of the spin and orientation of an astrometric solution

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362