Renal replacement therapy is after mechanical ventilation one of the most important and frequently used organ replacement therapies in daily routine intensive care practice. In contrast to mechanical ventilation, quality standards for renal replacement therapy are less well known and defined. In this position paper of the German Interdisciplinary Association for Intensive Care and Emergency Medicine, we describe quality standards of renal replacement procedures in order to improve therapy of patients with severe acute kidney injury.There has been a rapid progress in developing genetically engineered T cells in recent years both in basic and clinical cancer studies. Chimeric antigen receptor (CAR)-T cells exert an immune response against various cancers, including the non-small-cell lung cancer (NSCLC). As novel agents of immunotherapy, CAR-T cells show great promise for NSCLC. However, targeting specific antigens in NSCLC with engineered CAR-T cells is complicated because of a lack of tumor-specific antigens, the immunosuppressive tumor microenvironment, low levels of infiltration of CAR-T cells into tumor tissue, and tumor antigen escape. Meanwhile, the clinical application of CAR-T cells remains limited due to the cases of on-target/off-tumor and neurological toxicity, as well as cytokine release syndrome. Hence, optimal CAR-T-cell design against NSCLC is urgently needed. In this review, we describe the basic structure and generation of CAR-T cells and summarize the common tumor-associated antigens targeted in clinical trials on CAR-T-cell therapy for NSCLC, as well as point out current challenges and novel strategies. Although many obstacles remain, the new/next generation of CARs show much promise. Taken together, research on CAR-T cells for the treatment of NSCLC is underway and has yielded promising preliminary results both in basic and pre-clinical medicine. More pre-clinical experiments and clinical trials are, therefore, warranted.The coronavirus disease (COVID-19) is caused by Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and presents with respiratory symptoms which can be life threatening in severe cases. At the start of the pandemic, allergy, asthma, and chronic obstructive pulmonary disease (COPD) were considered as risk factors for COVID-19 as they tend to exacerbate during respiratory viral infections. Recent literature has not shown that airway allergic diseases is a high-risk factor or that it increases the severity of COVID-19. This is due to a decrease in Angiotensin-converting enzyme 2 (ACE2) gene expression in the nose and bronchial cells of allergic airway diseases. Conventional asthma treatment includes inhaled corticosteroids (ICS), allergen immunotherapy (AIT), and biologics, and should be continued as they might reduce the risks of asthmatics for coronavirus infection by enhancing antiviral defence and alleviating inflammation.
  There is a lack of data on patients' and diagnostic factors for prognostication of complete recovery in patients with non-idiopathic peripheral facial palsy (FP).
 
  Cohort register-based study of 264 patients with non-idiopathic peripheral FP and uniform diagnostics and standardized treatment in a university hospital from 2007 to 2017 (47% female, median age 57years). Clinical data, facial grading, electrodiagnostics, motor function tests, non-motor function tests, and onset of prednisolone therapy were assessed for their impact on the probability of complete recovery using univariable and multivariable statistics.
 
  The most frequent reason for a non-idiopathic peripheral FP was a reactivation of Varicella Zoster Virus (VZV; 36.4%). Traumatic origin had a higher proportion of complete FP (52.9%). Furthermore, in traumatic FP, the mean interval between onset and start of prednisolone therapy was longer than in other cases (5.6 ± 6.2days). Patients with reactivation of VZV, Lyme disease or otogenic FP had a significant higher recovery rate (p = 0.002, p < 0.0001, p = 0.018, respectively), whereas patients with post-surgery FP and other reasons had a significant lower recovery rate (p < 0.0001). After multivariate analyses voluntary activity in first EMG, Lyme disease and post-surgery cause were identified as independent diagnostic and prognostic factors on the probability of complete recovery (all p < 0.05).
 
  Infectious causes for non-idiopathic FP like VZV reactivation and Lyme disease had best probability for complete recovery.  https://www.selleckchem.com/products/Erlotinib-Hydrochloride.html  Post-surgery FP had a worse prognosis.
 
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  To elucidate the effect of deep learning-based computer-assisted detection (CAD) on the performance of different-level physicians in detecting intracranial haemorrhage using CT.
 
  A total of 40 head CT datasets (normal, 16; haemorrhagic, 24) were evaluated by 15 physicians (5 board-certificated radiologists, 5 radiology residents, and 5 medical interns). The physicians attended 2 reading sessions without and with CAD. All physicians annotated the haemorrhagic regions with a degree of confidence, and the reading time was recorded in each case. Our CAD system was developed using 433 patients' head CT images (normal, 203; haemorrhagic, 230), and haemorrhage rates were displayed as corresponding probability heat maps using U-Net and a machine learning-based false-positive removal method. Sensitivity, specificity, accuracy, and figure of merit (FOM) were calculated based on the annotations and confidence levels.
 
  In patient-based evaluation, the mean accuracy of all physicians significantly increased from 83.7 to 89.7% (p < 0.001) after using CAD. Additionally, accuracies of board-certificated radiologists, radiology residents, and interns were 92.5, 82.5, and 76.0% without CAD and 97.5, 90.5, and 81.0% with CAD, respectively. The mean FOM of all physicians increased from 0.78 to 0.82 (p = 0.004) after using CAD. The reading time was significantly lower when CAD (43 s) was used than when it was not (68 s, p < 0.001) for all physicians.
 
  The CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage.
 The CAD system developed using deep learning significantly improved the diagnostic performance and reduced the reading time among all physicians in detecting intracranial haemorrhage.