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Background: Overweight is increasing in transition countries, while iron deficiency remains common. In industrialized countries, greater adiposity increases risk of iron deficiency. Higher hepcidin levels in obesity may reduce dietary iron absorption. Therefore, we investigated the association between body mass index (BMI) and iron absorption, iron status and the response to iron fortification in populations from three transition countries (Thailand, Morocco and India). Methods: In Thai women (n=92), we examined the relationship between BMI and iron absorption from a reference meal containing 4 mg of isotopically labeled fortification iron. We analyzed data from baseline (n=1688) and intervention (n=727) studies in children in Morocco and India to look for associations between BMI Z-scores and baseline hemoglobin, serum ferritin and transferrin receptor, whole blood zinc protoporphyrin and body iron stores, and changes in these measures after provision of iron. Results: In the Thai women, 20% were iron deficient and 22% were overweight. Independent of iron status, a higher BMI Z-score was associated with decreased iron absorption (P=0.030). In the Indian and Moroccan children, 42% were iron deficient and 6.3% were overweight. A higher BMI Z-score predicted poorer iron status at baseline (P<0.001) and less improvement in iron status during the interventions (P<0.001). Conclusions: Adiposity in young women predicts lower iron absorption, and pediatric adiposity predicts iron deficiency and a reduced response to iron fortification. These data suggest the current surge in overweight in transition countries may impair efforts to control iron deficiency in these target groups. Interactions of the 'double burden' of malnutrition during the nutrition transition may have adverse consequences.

The methodology adopted in teaching and evaluating learning EFL seems to lack various strands of communication. Neither does it allow for that partnership between the two aspects of discourse analysis: the linguistic and the functional (illocutionary), nor does its scope for the development of students' capacity of use seem tenable. What is more, it does not sufficiently allow them to attend to contextual elements associated with given types of discourse. A model is suggested for the introduction of an interactive component likely to enable students to attend actively to the learning input when being taught and evaluated. This, of course, follows an interactive discourse analysis-based model of teaching. It is argued that a doze of such an interactional discourse 'medicine' will do students much better in the course of building up their discourse competence and hence their communicative competence

Formerly, language was looked at in isolation and the focus was on acquiring propositional and accuracy rules in the target language without considering the context in which it is embedded. A formal approach to language is not enough for FL learners to become members of the target discourse community. What is needed is a functional view of language that allows the learners to communicate socially. A discourse-based approach to language teaching is adopted which concentrates on meaning and communication. Its aim is to unlock the supra-sentential relationships. This approach aims at developing learners' discourse competence, a component accounting for describing the inter-sentential relationships that exist within a text. The present paper endeavors to develop learners' discourse competence through reading.

Despite the ever-increasing amount of work on developing and testing FL learners' communicative competence, there remain, nevertheless, some constraints enshrouding assessing types of competence coming under this general concept. This is accounted for by the fact that the very nature of the concept does not lend itself as easily as it may appear to those modes of measurement that are applicable to formal aspects of language. Indeed, the current study attempts at presenting a testing model growing from the idea that contextual features, at the base of discourse competence do not allow for the same assessing rationale and procedures as those found with tests of formal accuracy. In this paper our concern will be to present a brief account of some test features and problems involved in evaluating discourse competence.

To assess the effectiveness of Triclosan coated suture in reducing surgical site infections (SSIs) rate after caesarian delivery (CD). Three hundred eighty patients were randomly assigned to closure with polyglactin non coated suture VICRYL, or with polyglactin coated suture VICRYL Plus after caesarian section. The primary outcome was the rate of SSIs within 30 days after surgery and secondary outcomes were the rate of wound healing complications. SSI rate was 2.5% in Triclosan group compared to 8.1% with non-coated suture. Use of Triclosan coated suture (TCS) was associated with 69% reduction in SSI rate (p = 0.037; ORa:0.294; 95% CI:0.094-0.921). The use of Triclosan coated suture was associated with statistically lower risk of wound oedema (2.5% vs 10%), (p = 0.019; OR:0.595), dehiscence (3.8% vs 10.6%), (p = 0.023; OR:0.316) and hematoma (p = 0.035; OR:0.423). Our results confirm the effectiveness of Triclosan coated suture in reducing SSI rate and wound healing disturbances. Registered at ClinicalTrials.gov / ID (NCT05330650).

Machine learning based on neural networks facilitates data-driven techniques for handling large amounts of data, either obtained through experiments or simulations at multiple spatio-temporal scales, thereby finding the hidden patterns underlying these data and promoting efficient research methods. The main purpose of this paper is to extend the capabilities of a new solver called realFluidReactingNNFoam, under development at the University of Perugia, in OpenFOAM with a neural network algorithm for replacing complex real-fluid thermophysical property evaluations, using the approach of coupling OpenFOAM and Python-trained neural network models. Currently, neural network models are trained against data generated using the Peng–Robinson equation of state assuming a mixture’s frozen temperature. The OpenFOAM solver, where needed, calls the neural network models in each grid cell with appropriate inputs, and the returned results are used and stored in suitable OpenFOAM data structures. Such inference for thermophysical properties is achieved via the “Neural Network Inference in C made Easy (NNICE)” library, which proved to be very efficient and robust. The overall model is validated considering a liquid-rocket benchmark comprised of liquid-oxygen (LOX) and gaseous-hydrogen (GH2) streams. The model accounts for real-fluid thermodynamics and transport properties, making use of the Peng–Robinson equation of state and the Chung transport model. First, the development of a real-fluid model with an artificial neural network is described in detail. Then, the numerical results of the transcritical mixing layer (LOX/GH2) benchmark are presented and analyzed in terms of accuracy and computational efficiency. The results of the overall implementation indicate that the combined OpenFOAM and machine learning approach provides a speed-up factor higher than seven, while preserving the original solver accuracy.

Therapeutic options for children with multiple sclerosis are scarce. Teriflunomide is approved in more than 80 countries for the treatment of adults with relapsing multiple sclerosis. The TERIKIDS study examined the safety and efficacy of teriflunomide in children with relapsing multiple sclerosis. The TERIKIDS trial was a multicentre, phase 3, double-blind, parallel-group, randomised, placebo-controlled study conducted at 57 clinical centres in 22 countries in Asia, Europe, the Middle East, North Africa, and North America. The trial enrolled patients aged 10–17 years, diagnosed with relapsing multiple sclerosis and with at least one relapse in the year preceding screening or at least two relapses in the 2 years preceding screening. Patients were randomly assigned (2:1) to oral teriflunomide (dosage equivalent to 14 mg in adults) or matching placebo, using an interactive web and voice response system, for up to 96 weeks. Personnel in all sites and all patients were masked to study treatment in the double-blind period. Early entry into a subsequent 96-week open-label extension phase was possible before the end of the double-blind period for patients with confirmed clinical relapse or high MRI activity (at least five new or enlarged T2 lesions at week 24, followed by at least nine new or enlarged T2 lesions at week 36, or at least five new or enlarged T2 lesions at weeks 36 and 48, or at weeks 48 and 72). The primary endpoint was time to first confirmed clinical relapse by the end of the double-blind period. Key secondary imaging endpoints were number of new or enlarged T2 lesions and number of gadolinium-enhancing lesions per MRI scan. Efficacy endpoints were analysed in the intention-to-treat population, and safety was assessed in all patients randomly assigned to treatment and exposed to the double-blind study medication. This study is registered with ClinicalTrials.gov (trial number NCT02201108) and is closed to recruitment, but an additional optional open-label extension is ongoing. Between July 24, 2014, and the date of last patient visit on Oct 25, 2019, 185 patients were screened for eligibility, 166 (90%) were enrolled, and 109 were randomly assigned teriflunomide and 57 were randomly assigned placebo. 102 (94%) of 109 and 53 (93%) of 57 completed the double-blind period. Switch to the ongoing open-label extension because of high MRI activity was more frequent than anticipated in the placebo group (14 [13%] of 109 patients in the teriflunomide group vs 15 [26%] of 57 in the placebo group), decreasing the power of the study. After 96 weeks, there was no difference in time to first confirmed clinical relapse with teriflunomide compared with placebo (hazard ratio 0·66, 95% CI 0·39–1·11; p=0·29). Teriflunomide reduced the number of new or enlarged T2 lesions versus placebo by 55% (relative risk 0·45, 95% CI 0·29–0·71; p=0·00061), and the number of gadolinium-enhancing lesions by 75% (relative risk 0·25, 0·13–0·51; p<0·0001). Adverse events occurred in 96 (88%) patients in the teriflunomide group and 47 (82%) patients in the placebo group; serious adverse events occurred in 12 (11%) patients in the teriflunomide group and 6 (11%) patients in the placebo group. Nasopharyngitis, upper-respiratory-tract infection, alopecia, paraesthesia, abdominal pain, and increased blood creatine phosphokinase were more frequent with teriflunomide than with placebo. During the double-blind phase, four patients in the teriflunomide group had pancreatic adverse events (two with acute pancreatitis and two with pancreatic enzyme elevation), of which three events led to treatment discontinuation. No significant difference in time to first confirmed clinical relapse was found, possibly because more patients than expected switched from the double-blind to the open-label treatment period because of high MRI activity. Key secondary imaging analyses and a prespecified sensitivity analysis of probability of relapse or high MRI activity suggest that teriflunomide might have beneficial effects in children with relapsing multiple sclerosis by reducing the risk of focal inflammatory activity. Sanofi.

IntroductionTocilizumab randomized clinical trial results are heterogeneous because of the heterogenous population included in them.MethodsWe conducted a meta-analysis with subgroup meta-analysis (PRISMA guidelines) between severe and non-severe COVID-19.ResultsWe included nine trials. Overall, the mortality rate was 24.5% (821/3357) in the tocilizumab group and 29.1% (908/3125) in the control group at day 28–30 (pooled OR, 0.85; 95% CI 0.76–0.96; p = 0.006). Considering the subgroup analysis, this benefit on mortality was confirmed and amplified in the severe COVID-19 group (pooled OR, 0.82; 95% CI 0.73–0.93; p = 0.001) but not in the non-severe COVID-19 group (pooled OR, 1.46; 95% CI 0.91–2.34; p = 0.12). For patients who were not mechanically ventilated at baseline (5523/6482), the pooled OR (0.74; 95% CI 0.64–0.85; p < 0.0001) for mechanical ventilation incidence at day 28–30 was in favor of tocilizumab (cumulative incidence of 14.8% versus 19.4% in tocilizumab and control arm, respectively). This benefit was confirmed in both subgroups, i.e., severe and non-severe COVID-19.ConclusionTocilizumab is an effective treatment in hospitalized patients with COVID-19 and hypoxemia by improving survival and decreasing mechanical ventilation requirement. The greatest benefit is observed in severe COVID-19.