Impact of Age of Packed RBC Transfusion on Oxygenation in Patients Receiving Extracorporeal Membrane Oxygenation.

OBJECTIVE: To determine the effect of age of packed RBCs on tissue oxygenation in children receiving extracorporeal membrane oxygenation support. DESIGN: A retrospective study was done between March 2013 and August 2015. The following biomarkers were examined 6 hours before and 6 hours after the conclusion of a transfusion: serum and circuit hematocrits, serum and circuit venous saturations, serum lactate levels (mg/dL), and cerebral saturation via near-infrared spectroscopy. Biomarkers were examined with respect to time relative to transfusion using four discrete categories (< 3, -3 to 0, 0-3, and > 3 hr). The association between age of blood transfusion and change in biomarkers was accessed analyzing time relative to transfusion as described above. In addition, the age of blood transfusion was analyzed similarly, using four discrete categories (0-7, 7-14, 14-21, and > 21 d). SETTING: Twenty-four bed mixed pediatric medical and cardiac ICU at a tertiary care center. SUBJECTS: Zero- to 18-year-old patients of required extracorporeal membrane oxygenation support. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Circuit venous saturation demonstrated an increase of 2.5% (p < 0.001) in first 3 hours posttransfusion. This was followed by a 1.4% decrease after the initial 3 hours posttransfusion. Serum venous saturation showed no statistically significant change with relation to transfusions. Neither lactate levels nor near-infrared spectroscopy demonstrated any observed statistical change with relation to transfusion. With regards to the relationship between the age of RBC transfusion and tissue oxygenation biomarkers, none of the biomarkers exhibited a consistent interaction. CONCLUSIONS: Our study demonstrates that the age of packed RBC transfusion does not affect the degree tissue oxygenation in children receiving extracorporeal membrane oxygenation support, as measured by mixed venous oxygen saturation, lactate, and near-infrared spectroscopy. In addition, packed RBC transfusion, in general, did not produce any meaningful change in these markers of tissue oxygenation.

Respiratory syncytial virus increases the virulence of Streptococcus pneumoniae by binding to penicillin binding protein 1a. A new paradigm in respiratory infection.

RATIONALE: Respiratory syncytial virus (RSV) and Streptococcus pneumoniae are major respiratory pathogens. Coinfection with RSV and S. pneumoniae is associated with severe and often fatal pneumonia but the molecular basis for this remains unclear. OBJECTIVES: To determine if interaction between RSV and pneumococci enhances pneumococcal virulence. METHODS: We used confocal microscopy and Western blot to identify the receptors involved in direct binding of RSV and pneumococci, the effects of which were studied in both in vivo and in vitro models of infection. Human ciliated respiratory epithelial cell cultures were infected with RSV for 72 hours and then challenged with pneumococci. Pneumococci were collected after 2 hours exposure and changes in gene expression determined using quantitative real-time polymerase chain reaction. MEASUREMENTS AND MAIN RESULTS: Following incubation with RSV or purified G protein, pneumococci demonstrated a significant increase in the inflammatory response and bacterial adherence to human ciliated epithelial cultures and markedly increased virulence in a pneumonia model in mice. This was associated with extensive changes in the pneumococcal transcriptome and significant up-regulation in the expression of key pneumococcal virulence genes, including the gene for the pneumococcal toxin, pneumolysin. We show that mechanistically this is caused by RSV G glycoprotein binding penicillin binding protein 1a. CONCLUSIONS: The direct interaction between a respiratory virus protein and the pneumococcus resulting in increased bacterial virulence and worsening disease outcome is a new paradigm in respiratory infection.

Adaptive continuation based smooth l0-norm approximation for compressed sensing MR image reconstruction.

Purpose: There are a number of algorithms for smooth l0-norm (SL0) approximation. In most of the cases, sparsity level of the reconstructed signal is controlled by using a decreasing sequence of the modulation parameter values. However, predefined decreasing sequences of the modulation parameter values cannot produce optimal sparsity or best reconstruction performance, because the best choice of the parameter values is often data-dependent and dynamically changes in each iteration. Approach: We propose an adaptive compressed sensing magnetic resonance image reconstruction using the SL0 approximation method. The SL0 approach typically involves one-step gradient descent of the SL0 approximating function parameterized with a modulation parameter, followed by a projection step onto the feasible solution set. Since the best choice of the parameter values is often data-dependent and dynamically changes in each iteration, it is preferable to adaptively control the rate of decrease of the parameter values. In order to achieve this, we solve two subproblems in an alternating manner. One is a sparse regularization-based subproblem, which is solved with a precomputed value of the parameter, and the second subproblem is the estimation of the parameter itself using a root finding technique. Results: The advantage of this approach in terms of speed and accuracy is illustrated using a compressed sensing magnetic resonance image reconstruction problem and compared with constant scale factor continuation based SL0-norm and adaptive continuation based l1-norm minimization approaches. The proposed adaptive estimation is found to be at least twofold faster than automated parameter estimation based iterative shrinkage-thresholding algorithm in terms of CPU time, on an average improvement of reconstruction performance 15% in terms of normalized mean squared error. Conclusions: An adaptive continuation-based SL0 algorithm is presented, with a potential application to compressed sensing (CS)-based MR image reconstruction. It is a data-dependent adaptive continuation method and eliminates the problem of searching for appropriate constant scale factor values to be used in the CS reconstruction of different types of MRI data.

A wavelet subband based LSTM model for 12-lead ECG synthesis from reduced lead set.

Synthesis of a 12-lead electrocardiogram from a reduced lead set has previously been extensively studied in order to meet patient comfort, minimise complexity, and enable telemonitoring. Traditional methods relied solely on the inter-lead correlation between the standard twelve leads for learning the models. The 12-lead ECG possesses not only inter-lead correlation but also intra-lead correlation. Learning a model that can exploit this spatio-temporal information in the ECG could generate lead signals while preserving important diagnostic information. The proposed approach takes leverage of the enhanced inter-lead correlation of the ECG signal in the wavelet domain. Long-short-term memory (LSTM) networks, which have emerged as a powerful tool for sequential data mining, are a type of recurrent neural network architecture with an inherent capability to capture the spatiotemporal information of the heart signal. This work proposes the deep learning architecture that utilizes the discrete wavelet transform and the LSTM to reconstruct a generic 12-lead ECG from a reduced lead set. The experimental results are evaluated using different diagnostic measures and similarity metrics. The proposed framework is well founded, and accurate reconstruction is possible as it can capture clinically significant features and provides a robust solution against noise.

A novel strategy to downstage breast cancer: impact of a phone helpline.

Breast cancer incidence rates in India are rising. The majority of breast cancers are still diagnosed in later stages. There is also a burden of neglected cancers in India, where patients neglect their symptoms due to fear, ignorance, financial insecurity and lack of access to medical care. This results in greater morbidity and mortality from breast cancer. Systematic screening programs have been tested in an Indian setting, with limited success. An effective strategy to downstage breast cancer is an area of unmet need. We aimed to explore the effectiveness of an anonymous nurse-led telephone helpline in identifying patients with possible breast malignancies and to encourage them to seek healthcare. We created a telephone helpline system by training junior public health nurses (JPHNs) to provide counselling to women who may call with breast-related symptoms. We then created a short video message on the initiative and disseminated it using social media platforms. During the 1-year study period, 434 calls were received from individuals who reported having some breast symptoms. Among them, 28% (122 callers) had never consulted a doctor for their symptoms. 78 callers consulted a nearby doctor upon the advice of the JPHN. Among them, 14 callers (18%) were advised by the doctor to undergo investigations to rule out malignancy, while 64 (82%) of them were found to have some benign/normal breast conditions. 3 (21%) out of the 14 patients who underwent further evaluation were eventually diagnosed with breast cancer. Our study provides evidence that an anonymous nurse-led telephone helpline can be an effective strategy to reduce the incidence of neglected breast cancers and downstage the diagnoses.