It can adopt different shapes in response to cellular demands and changes of the inner membrane morphology often accompany severe diseases, including neurodegenerative- and metabolic diseases and cancer. In recent years, progress was made in the identification of molecules that are important for the aforementioned membrane remodeling events. In this review, we will sum up recent results and discuss the main players of membrane remodeling processes that lead to the mitochondrial inner membrane ultrastructure and in clathrin-mediated endocytosis. We will compare differences and similarities between the molecular mechanisms that peripheral and integral membrane proteins use to deform membranes.Over the past decade, changes in the diagnosis and management of Legionella pneumonia occurred and risk factors for severe infection and increased mortality were identified. Previous reports found that nosocomial infection is associated with higher mortality while others showed no differences. We aimed to evaluate the differences in the clinical course and mortality rates between hospital-acquired pneumonia (HAP) and community-acquired pneumonia (CAP) caused by Legionella pneumophila. A retrospective cohort study of patients admitted due to Legionella pneumonia between January 2012 through November 2019 was conducted in a tertiary referral center (Rambam Health Care Campus, Haifa, Israel). The primary outcome was 30-day Legionella pneumonia-related mortality. A multivariable logistic regression was performed to determine whether a nosocomial infection is an independent predictor of mortality. One hundred nine patients were included. Seventy (64.2%) had CAP and 39 (35.8%) had HAP. The groups were comparable regarding age, gender, and comorbidities. Time to diagnosis was longer and the number of patients receiving initial empiric anti-Legionella spp. treatment was smaller in the HAP group (8 days [IQR 5.5-12.5] vs. 5 days [IQR 3-8], p less then 0.001 and 65.5% vs. 78.6%, p = 0.003, respectively). Patients with HAP had higher 30-day mortality, 41% vs. 18.6%, p = 0.02.  https://www.selleckchem.com/products/bms-927711.html  In a multivariable logistic regression model, only pneumonia severity index and nosocomial source were independently associated with increased mortality. HAP caused by Legionella spp. is independently associated with increased mortality when compared to CAP caused by the same pathogen. The possible reasons for this increased mortality include late diagnosis and delayed initiation of appropriate treatment.
  Intensive care unit-acquired weakness syndrome (ICUAWS) can be a consequence of long-term mechanical ventilation. Despite recommendations of early patient mobilisation, little is known about the feasibility, safety and benefit of interval training in early rehabilitation facilities (ERF) after long-term invasive ventilation.
 
  We retrospectively analysed two established training protocols of bicycle ergometry in ERF patients after long-term (> 7 days) invasive ventilation (n = 46). Patients conducted moderate continuous (MCT, n = 24, mean age 70.3 ± 10.1 years) or high-intensity interval training (HIIT, n = 22, mean age 63.6 ± 12.6 years). The intensity of training was monitored with the BORG CR10 scale (intense phases ≥ 7/10 and moderate phases ≤ 4/10 points). The primary outcome was improvement (∆-values) of six-minute-walk-test (6 MWT), while the secondary outcomes were improvement of vital capacity (VC
  ), forced expiratory volume in 1 s (FEV
  ), maximal inspiratory pressure (PI
  ) and functional capabilities (functional independence assessment measure, FIM/FAM and Barthel scores) after 3 weeks of training. No adverse events were observed. There was a trend towards a greater improvement of 6 MWT in HIIT than MCT (159.5 ± 64.9 m vs. 120.4 ± 60.4 m; p = .057), despite more days of invasive ventilation (39.6 ± 16.8 days vs. 26.8 ± 16.2 days; p = .009). VC
  (∆0.5l ± 0.6 vs. ∆0.5l ± 0.3; p = .462), FEV
  (∆0.2l ± 0.3 vs. ∆0.3l ± 0.2; p = .218) PI
  (∆0.8 ± 1.1 kPa vs. ∆0.7 ± 1.3pts; p = .918) and functional status (FIM/FAM ∆29.0 ± 14.8pts vs. ∆30.9 ± 16.0pts; p = .707; Barthel ∆28.9 ± 16.0 pts vs. ∆25.0 ± 10.5pts; p = .341) improved in HIIT and MCT.
 
  We demonstrate the feasibility and safety of HIIT in the early rehabilitation of ICUAWS patients. Larger trials are necessary to find adequate dosage of HIIT in ICUAWS patients.
 We demonstrate the feasibility and safety of HIIT in the early rehabilitation of ICUAWS patients. Larger trials are necessary to find adequate dosage of HIIT in ICUAWS patients.
  We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy.
 
  We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard.
 
  At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74-0.81), 0.82 specificity (95% CI 0.78-0.85), and 0.89 area under the curve (AUC) (95% CI 0.86-0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72-0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73-0.87), and 0.81 AUC (95% CI 0.73-0.87). Radiologists' reading obtained 0.63 sensitivity (95% CI 0.52-0.74) and 0.78 specificity (95% CI 0.61-0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52-0.74) and 0.86 specificity (95% CI 0.71-0.95) in Centre 2.
 
  This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.
 This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.