Rapid Optical Cavity PCR.

Recent outbreaks of deadly infectious diseases, such as Ebola and Middle East respiratory syndrome coronavirus, have motivated the research for accurate, rapid diagnostics that can be administered at the point of care. Nucleic acid biomarkers for these diseases can be amplified and quantified via polymerase chain reaction (PCR). In order to solve the problems of conventional PCR--speed, uniform heating and cooling, and massive metal heating blocks--an innovative optofluidic cavity PCR method using light-emitting diodes (LEDs) is accomplished. Using this device, 30 thermal cycles between 94 °C and 68 °C can be accomplished in 4 min for 1.3 μL (10 min for 10 μL). Simulation results show that temperature differences across the 750 μm thick cavity are less than 2 °C and 0.2 °C, respectively, at 94 °C and 68 °C. Nucleic acid concentrations as low as 10(-8) ng μL(-1) (2 DNA copies per μL) can be amplified with 40 PCR thermal cycles. This simple, ultrafast, precise, robust, and low-cost optofluidic cavity PCR is favorable for advanced molecular diagnostics and precision medicine. It is especially important for the development of lightweight, point-of-care devices for use in both developing and developed countries

The SCWISh network is essential for survival under mechanical pressure

International audienceCells that proliferate within a confined environment build up mechanical compressive stress. For example, mechanical pressure emerges in the naturally space-limited tumor environment. However, little is known about how cells sense and respond to mechanical compression. We developed microfluidic bioreactors to enable the investigation of the effects of compressive stress on the growth of the genetically tractable model organism Saccharomyces cerevisiae. We used this system to determine that compressive stress is partly partly sensed through a module consisting of the mucin Msb2, and the cell wall protein Sho1, which act together as a sensor module in one of the two major osmosensing pathways in budding yeast. This signal is transmitted via the MAPKKK kinase Ste11. Thus, we term this mechanosensitive pathway the SMuSh pathway, for Ste11 through Mucin / Sho1 pathway. The SMuSh pathway delays cells in the G1 phase of the cell cycle and improves cell survival in response to growth-induced pressure. We also found that the Cell Wall Integrity (CWI) pathway contributes to the response to mechanical compres-sive stress. These latter results are confirmed in complimentary experiments in the accompanying manuscript from Mishra et al. When both the SMuSh and the CWI pathways are deleted, cells fail to adapt to compressive stress and all cells lyse at relatively low pressure when grown in confinement. Thus, we define a network that is essential for cell survival during growth under pressure. We term this new mechanosensory system the SCWISh (Survival through the CWI and SMuSh) network. compressive stress| mechanosensing | microfluidic

SCWISh network is essential for survival under mechanical pressure.

Cells that proliferate within a confined environment build up mechanical compressive stress. For example, mechanical pressure emerges in the naturally space-limited tumor environment. However, little is known about how cells sense and respond to mechanical compression. We developed microfluidic bioreactors to enable the investigation of the effects of compressive stress on the growth of the genetically tractable model organism Saccharomyces cerevisiae We used this system to determine that compressive stress is partly sensed through a module consisting of the mucin Msb2 and the cell wall protein Sho1, which act together as a sensor module in one of the two major osmosensing pathways in budding yeast. This signal is transmitted via the MAPKKK kinase Ste11. Thus, we term this mechanosensitive pathway the "SMuSh" pathway, for Ste11 through Mucin/Sho1 pathway. The SMuSh pathway delays cells in the G1 phase of the cell cycle and improves cell survival in response to growth-induced pressure. We also found that the cell wall integrity (CWI) pathway contributes to the response to mechanical compressive stress. These latter results are confirmed in complimentary experiments in Mishra et al. [Mishra R, et al. (2017) Proc Natl Acad Sci USA, 10.1073/pnas.1709079114]. When both the SMuSh and the CWI pathways are deleted, cells fail to adapt to compressive stress, and all cells lyse at relatively low pressure when grown in confinement. Thus, we define a network that is essential for cell survival during growth under pressure. We term this mechanosensory system the SCWISh (survival through the CWI and SMuSh) network

Development and validation of a score to predict postoperative respiratory failure in a multicentre European cohort : A prospective, observational study

BACKGROUND Postoperative respiratory failure (PRF) is the most frequent respiratory complication following surgery. OBJECTIVE The objective of this study was to build a clinically useful predictive model for the development of PRF. DESIGN A prospective observational study of a multicentre cohort. SETTING Sixty-three hospitals across Europe. PATIENTS Patients undergoing any surgical procedure under general or regional anaesthesia during 7-day recruitment periods. MAIN OUTCOME MEASURES Development of PRF within 5 days of surgery. PRF was defined by a partial pressure of oxygen in arterial blood (PaO2) less than 8 kPa or new onset oxyhaemoglobin saturation measured by pulse oximetry (SpO(2)) less than 90% whilst breathing room air that required conventional oxygen therapy, noninvasive or invasive mechanical ventilation. RESULTS PRF developed in 224 patients (4.2% of the 5384 patients studied). In-hospital mortality [95% confidence interval (95% CI)] was higher in patients who developed PRF [10.3% (6.3 to 14.3) vs. 0.4% (0.2 to 0.6)]. Regression modelling identified a predictive PRF score that includes seven independent risk factors: low preoperative SpO(2); at least one preoperative respiratory symptom; preoperative chronic liver disease; history of congestive heart failure; open intrathoracic or upper abdominal surgery; surgical procedure lasting at least 2 h; and emergency surgery. The area under the receiver operating characteristic curve (c-statistic) was 0.82 (95% CI 0.79 to 0.85) and the Hosmer-Lemeshow goodness-of-fit statistic was 7.08 (P = 0.253). CONCLUSION A risk score based on seven objective, easily assessed factors was able to predict which patients would develop PRF. The score could potentially facilitate preoperative risk assessment and management and provide a basis for testing interventions to improve outcomes. The study was registered at ClinicalTrials.gov (identifier NCT01346709)

Development of a prediction model for postoperative pneumonia A multicentre prospective observational study

BACKGROUND Postoperative pneumonia is associated with increased morbidity, mortality and costs. Prediction models of pneumonia that are currently available are based on retrospectively collected data and administrative coding systems. OBJECTIVE To identify independent variables associated with the occurrence of postoperative pneumonia. DESIGN A prospective observational study of a multicentre cohort (Prospective Evaluation of a RIsk Score for postoperative pulmonary COmPlications in Europe database). SETTING Sixty-three hospitals in Europe. PATIENTS Patients undergoing surgery under general and/or regional anaesthesia during a 7-day recruitment period. MAIN OUTCOME MEASURE The primary outcome was postoperative pneumonia. Definition: the need for treatment with antibiotics for a respiratory infection and at least one of the following criteria: new or changed sputum; new or changed lung opacities on a clinically indicated chest radiograph; temperature more than 38.3 degrees C; leucocyte count more than 12 000 mu l(-1). RESULTS Postoperative pneumonia occurred in 120 out of 5094 patients (2.4%). Eighty-two of the 120 (68.3%) patients with pneumonia required ICU admission, compared with 399 of the 4974 (8.0%) without pneumonia (P < 0.001). We identified five variables independently associated with postoperative pneumonia: functional status [odds ratio (OR) 2.28, 95% confidence interval (CI) 1.58 to 3.12], pre-operative SpO(2) values while breathing room air (OR 0.83, 95% CI 0.78 to 0.84), intra-operative colloid administration (OR 2.97, 95% CI 1.94 to 3.99), intra-operative blood transfusion (OR 2.19, 95% CI 1.41 to 4.71) and surgical site (open upper abdominal surgery OR 3.98, 95% CI 2.19 to 7.59). The model had good discrimination (c-statistic 0.89) and calibration (Hosmer-Lemeshow P = 0.572). CONCLUSION We identified five variables independently associated with postoperative pneumonia. The model performed well and after external validation may be used for risk stratification and management of patients at risk of postoperative pneumonia