Two-color, two-photon imaging at long excitation wavelengths using a diamond Raman laser

We demonstrate that the second-Stokes output from a diamond Raman laser, pumped by a femtosecond Ti:Sapphire laser, can be used to efficiently excite red-emitting dyes by two-photon excitation at 1080 nm and beyond. We image red fluorescent protein (RFP) expressing HeLa cells, as well as dyes such as Texas Red and Mitotracker Red. We demonstrate the potential for simultaneous two-color, two-photon imaging with this laser by using the residual pump beam for excitation of a green-emitting dye. We demonstrate this for the combination of AlexaFluor 488 and AlexaFluor 568. Because the Raman laser extends the wavelength range of the Ti:Sapphire laser, resulting in a laser system tunable 680 nm-1200 nm it can be used for two-photon excitation of a large variety and combination of dyes

DataSHIELD: taking the analysis to the data, not the data to the analysis

Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK's proposed 'care.data' initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data. Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC. Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach. DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property-the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis

Associations between depressive symptoms and disease progression in older patients with chronic kidney disease: results of the EQUAL study

Background Depressive symptoms are associated with adverse clinical outcomes in patients with end-stage kidney disease; however, few small studies have examined this association in patients with earlier phases of chronic kidney disease (CKD). We studied associations between baseline depressive symptoms and clinical outcomes in older patients with advanced CKD and examined whether these associations differed depending on sex. Methods CKD patients (>= 65 years; estimated glomerular filtration rate <= 20 mL/min/1.73 m(2)) were included from a European multicentre prospective cohort between 2012 and 2019. Depressive symptoms were measured by the five-item Mental Health Inventory (cut-off <= 70; 0-100 scale). Cox proportional hazard analysis was used to study associations between depressive symptoms and time to dialysis initiation, all-cause mortality and these outcomes combined. A joint model was used to study the association between depressive symptoms and kidney function over time. Analyses were adjusted for potential baseline confounders. Results Overall kidney function decline in 1326 patients was -0.12 mL/min/1.73 m(2)/month. A total of 515 patients showed depressive symptoms. No significant association was found between depressive symptoms and kidney function over time (P = 0.08). Unlike women, men with depressive symptoms had an increased mortality rate compared with those without symptoms [adjusted hazard ratio 1.41 (95% confidence interval 1.03-1.93)]. Depressive symptoms were not significantly associated with a higher hazard of dialysis initiation, or with the combined outcome (i.e. dialysis initiation and all-cause mortality). Conclusions There was no significant association between depressive symptoms at baseline and decline in kidney function over time in older patients with advanced CKD. Depressive symptoms at baseline were associated with a higher mortality rate in men

Machine Learning to Assess for Acute Myocardial Infarction within 30 Minutes

Variations in high-sensitivity cardiac troponin I (hs-cTnI) by age and sex along with various sampling times can make the evaluation for acute myocardial infarction (AMI) challenging. Machine learning integrates these variables to allow a more accurate evaluation for possible AMI. The goal was to test the diagnostic and prognostic utility of a machine learning algorithm in the evaluation of possible AMI. We applied a machine learning algorithm (myocardial-ischemic-injury-index [MI3]) that incorporates age, sex, and hs-cTnI levels at time 0 and 30 minutes in 529 patients evaluated for possible AMI in a single urban emergency department. MI3 generates an index value from 0-100 reflecting the likelihood of AMI. Patients were followed at 30-45 days for major adverse cardiac events (MACE). There were 42 (7.9%) patients that had an AMI. Patients were divided into 3 groups by the MI3 score: low-risk (≤ 3.13), intermediate-risk (\u3e 3.13-51.0), and high-risk (\u3e 51.0). The sensitivity for AMI was 100% with a MI3 value ≤ 3.13 and 353 (67%) ruled-out for AMI at 30 minutes. At 30-45 days there were 2 (0.6%) MACEs (2 non-cardiac deaths) in the low-risk group, in the intermediate-risk group 4 (3.0%) MACEs (3 AMIs, 1 cardiac death), and in the high-risk group 4 (9.1%) MACEs (4 AMIs, 2 cardiac deaths). The MI3 algorithm had 100% sensitivity for AMI at 30 minutes and identified a low-risk cohort who may be considered for early discharge

Machine Learning to Assess for Acute Myocardial Infarction within 30 Minutes

Variations in high-sensitivity cardiac troponin I (hs-cTnI) by age and sex along with various sampling times can make the evaluation for acute myocardial infarction (AMI) challenging. Machine learning integrates these variables to allow a more accurate evaluation for possible AMI. The goal was to test the diagnostic and prognostic utility of a machine learning algorithm in the evaluation of possible AMI. We applied a machine learning algorithm (myocardial-ischemic-injury-index [MI3]) that incorporates age, sex, and hs-cTnI levels at time 0 and 30 minutes in 529 patients evaluated for possible AMI in a single urban emergency department. MI3 generates an index value from 0-100 reflecting the likelihood of AMI. Patients were followed at 30-45 days for major adverse cardiac events (MACE). There were 42 (7.9%) patients that had an AMI. Patients were divided into 3 groups by the MI3 score: low-risk (≤ 3.13), intermediate-risk (\u3e 3.13-51.0), and high-risk (\u3e 51.0). The sensitivity for AMI was 100% with a MI3 value ≤ 3.13 and 353 (67%) ruled-out for AMI at 30 minutes. At 30-45 days there were 2 (0.6%) MACEs (2 non-cardiac deaths) in the low-risk group, in the intermediate-risk group 4 (3.0%) MACEs (3 AMIs, 1 cardiac death), and in the high-risk group 4 (9.1%) MACEs (4 AMIs, 2 cardiac deaths). The MI3 algorithm had 100% sensitivity for AMI at 30 minutes and identified a low-risk cohort who may be considered for early discharge