Cardiac troponin is a specific and sensitive biomarker to identify and quantify myocardial injury. Sumatriptan purchase Myocardial injury is frequently detected after acute ischemic stroke and strongly associated with unfavorable outcomes. Concomitant acute coronary syndrome is only one of several possible differential diagnoses that may cause elevation of cardiac troponin after stroke. As a result, there are uncertainties regarding the correct interpretation and optimal management of stroke patients with myocardial injury in clinical practice. Elevation of cardiac troponin may occur as part of a 'Stroke-Heart Syndrome'. The term 'Stroke-Heart Syndrome' subsumes a clinical spectrum of cardiac complications after stroke including cardiac injury, dysfunction, and arrhythmia which may relate to disturbances of autonomic function and the brain-heart axis. In this review, we provide an up-to-date overview about prognostic implications, mechanisms, and management of elevated cardiac troponin levels in patients with acute ischemic stroke.The article aims at analysing online depression forums enabling lay reinterpretation and criticism of expert biomedical discourses. Firstly, two contrasting interpretations of depression are reconstructed expert psy-discourses are confronted with the phenomenological descriptions of lay experiences, with a special emphasis on online forums as empirical platforms hosting such debates. After clarifying the general theoretical stakes concerning contested 'depression narratives', the results of an online ethnography are introduced the main topics appearing in online discussions are summarised (analysing how the abstract tensions between lay and expert discourses appear in the actual discussions), along with the idealtypical discursive logics (analysing pragmatic advises, attempts of reframing self-narratives and expressions of unconditional recognition). Finally, based on these analyses an attempt is made to explore the latent functionality of online depression forums by referring to a secular 'ritual healing' existing as an unreflected, contingent potential. Patient-centered care (PCC) is crucial for value-based care. We aimed to assess PCC dimensions addressed in hepatitis C virus direct-acting antiviral treatment delivery to people who inject drugs. We conducted a scoping review to identify the studies that described hepatitis C virus treatment delivery to people who inject drugs in the direct-acting antiviral treatment era. We analyzed the included studies against eight PCC dimensions (1) access to care; (2) coordination and integration of care; (3) continuity and translation; (4) physical comfort; (5) information, education, and communication; (6) emotional support; (7) involvement of family and friends; and (8) respect for individual patient preferences, perceived needs, and values. Additionally, we assessed the use of patient-centered terminology and the recognition of PCC importance and its relevance to treatment outcomes. None of the identified 36 studies addressed all PCC dimensions (highest seven, lowest two). Our findings revealed that PCC dimenss emphasizes the need for more patient-centered hepatitis C virus treatment for people who inject drugs.The current COVID-19 pandemic is an exceptional health situation including for drug use. As there was no known effective drug for COVID-19 at the beginning of the pandemic, different candidates were proposed. In this short article, we present the French public pharmacovigilance activities during this health crisis. Although COVID-19 is a confounding factor per se, owing to its potential for multi-organ damage including the heart and kidney, the quality of the transmitted data in adverse drug reaction reports, the timeliness of feedback from clinicians, and the real-time pharmacological and medical analysis by the French network of the regional pharmacovigilance centers made it possible to swiftly identify relevant safety signals. The French National Agency of Medicine was thus able to validate the data and convey their findings very early. This decentralized organization based on medical and pharmacological evaluation of case reports has proven to be efficient and responsive in this unique and challenging healthcare emergency. Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) pneumonia from other types of pneumonia and validate it with a real-world dataset (RWD). A total of 563 chest CT scans of 380 patients (227/380 were diagnosed with COVID-19 pneumonia) from 5 hospitals were collected to train our deep learning (DL) model. Lung regions were extracted by U-net, then transformed and fed to pre-trained ResNet-50-based IDANNet (Identification and Analysis of New covid-19 Net) to produce a diagnostic probability. Fivefold cross-validation was employed to validate the application of our model. Another 318 scans of 316 patients (243/316 were diagnosed with COVID-19 pneumonia) from 2 other hospitals were enrolled prospectively as the RWDs to testify our DL model's performance and compared it with that from 3 experienced radiologists. A three-dimensional DL model was successfully established. The diagnostic threshold to differentiate COVID-19 an radiologists. • The attention heatmaps were fully generated by the model without additional manual annotation and the attention regions were highly aligned with the ROIs acquired by human radiologists for diagnosis.• In an internal validation set, our DL model achieved the best performance to differentiate COVID-19 from non-COVID-19 pneumonia with a sensitivity of 0.836, a specificity of 0.800, and an AUC of 0.906 (95% CI 0.886-0.913) when the threshold was set at 0.685. • In the prospective RWD cohort, our DL diagnostic model achieved a sensitivity of 0.811, a specificity of 0.822, and AUC of 0.868 (95% CI 0.851-0.876), non-inferior to the performance of 3 experienced radiologists. • The attention heatmaps were fully generated by the model without additional manual annotation and the attention regions were highly aligned with the ROIs acquired by human radiologists for diagnosis.