Drugs form the mainstay of therapy in rheumatoid arthritis (RA). Five main classes of drugs are currently used: analgesics, non-steroidal anti-inflammatories (NSAIDs), glucocorticoids, nonbiologic and biologic disease-modifying antirheumatic drugs. Current clinical practice guidelines recommend that clinicians start biologic agents if patients have suboptimal response or intolerant to one or two traditional disease modifying agents (DMARDs). Methotrexate, sulfasalazine, leflunomide and hydroxychloroquine are the commonly used DMARDs. Currently, anti-TNF is the commonly used first line biologic worldwide followed by abatacept and it is usually combined with MTX. There is some evidence that tocilizumab is the most effective biologic as a monotherapy agent. Rituximab is generally not used as a first line biologic therapy due to safety issues but still as effective as anti-TNF. The long term data for the newer oral small molecule biologics such as tofacitinib is not available and hence used only as a last resort.

Objective. Stroke is the commonest cause of epileptic seizures in older adults. Risk factors for post-stroke seizure (PSS) are well known, however, predicting PSS risk is clinically challenging. This study aimed to evaluate the predictive accuracy of PSS risk prediction models developed to date. Methods. We performed a systematic review and meta-analysis of studies using MEDLINE and EMBASE from database inception to 28 th December 2020. The search criteria included all peer-reviewed research articles, in which PSS risk prediction models were developed or validated for ischaemic and/or haemorrhagic stroke. Random-effects meta-analysis was used to generate summary statistics of model performance and receiver operating characteristic curves. Quality appraisal of studies was conducted using PROBAST. Results. Thirteen original studies involving 182,673 stroke patients (mean age: 38-74.9 years; 29.4-60.9% males), reporting 15 PSS risk prediction models were included. The incidence of early PSS (occurring one week from stroke onset) and late PSS (occurring >one week from stroke onset) was 4.5% and 2.1%, respectively. Cortical involvement, functional deficits, increasing lesion size, early seizures, younger age, and haemorrhage were the commonest predictors across the models. SeLECT demonstrated greatest predictive accuracy (AUC 0.77 [95% CI: 0.71-0.82]) for late PSS following ischaemic stroke, and CAVE for predicting late PSS following haemorrhagic stroke (AUC 0.81 [0.76-0.86]). Fourteen of 15 studies demonstrated a high risk of bias, with lack of model validation and reporting of performance measures on calibration and discrimination being the commonest reasons. Significance. Although risk factors for PSS are widely documented, this review identified few multivariate models with low risk of bias, synthetising single variables into an individual prediction of seizure risk. Such models may help personalise clinical management and serve as useful research tools by identifying stroke patients at high risk of developing PSS for recruitment into studies of anti-epileptic drug prophylaxis.

Purpose: The purpose of the study is to investigate the dispersion of droplet nuclei/aerosol which are produced during coughing and continuous talking in order to quantify the risk of infection due to airborne disease transmission. Methods: A three-dimensional modelling of aerosol transport due to human respiratory activities such as coughing and talking within a room environment has been simulated using CFD technique. An inert scalar transport equation was used to represent aerosol cloud, while turbulence was modelled with the \(k-ϵ\) turbulence model. A modified Wells-Riley equation was used to calculate the risk of infection based on quanta emission concept. Results The spatial and temporal distribution of aerosol cloud within the room is initially driven by the upward flowing thermal plume surrounding the human, but later driven by the flow field constrained by the walls and cooler air movement. While the cough generated aerosols are concentrated in a smaller space within the room, the continuous talk generated aerosols are distributed throughout the room. Conclusion Within an indoor environment, 2m distancing will not be enough to protect healthy people from aerosols coming from an infected person due to continuous talking with prolonged exposure.

The purpose of the study is to investigate the dispersion of droplet nuclei/aerosol which are produced during coughing and continuous talking to quantify the risk of infection due to airborne disease transmission. A three-dimensional modelling of aerosol transport due to human respiratory activities such as coughing and talking within a room environment has been simulated using CFD technique. An inert scalar transport equation was used to represent aerosol cloud, while turbulence was modelled with the $$k-\epsilon $$ k - ϵ turbulence model. A modified Wells–Riley equation was used to calculate the risk of infection based on quanta emission concept. The spatial and temporal distribution of aerosol cloud within the room is initially driven by the upward flowing thermal plume surrounding the human, but later driven by the flow field constrained by the walls and cooler air movement. While the cough generated aerosols are concentrated in a smaller space within the room, the continuous talk generated aerosols are distributed throughout the room. Within an indoor environment, 2 m distancing will not be enough to protect healthy people from aerosols coming from an infected person due to continuous talking with prolonged exposure.