Activity

5 have witnessed maximum reduction (>50%) in compare to the pre-lockdown phase. In compare to the last year (i.e. 2019) during the said time period the reduction of PM10 and PM2.5 is as high as about 60% and 39% respectively. Among other pollutants, NO2 (-52.68%) and CO (-30.35%) level have also reduced during-lockdown phase. About 40% to 50% improvement in air quality is identified just after four days of commencing lockdown. About 54%, 49%, 43%, 37% and 31% reduction in NAQI have been observed in Central, Eastern, Southern, Western and Northern parts of the megacity. Overall, the study is thought to be a useful supplement to the regulatory bodies since it showed the pollution source control can attenuate the air quality. Temporary such source control in a suitable time interval may heal the environment. BACKGROUND Adverse cardiac remodeling is a major risk factor for the development of post myocardial infarction (MI) heart failure (HF). This study investigates the effects of the chymase inhibitor fulacimstat on adverse cardiac remodeling after acute ST-segment-elevation myocardial infarction (STEMI). METHODS In this double-blind, randomized, placebo-controlled trial patients with first STEMI were eligible. To preferentially enrich patients at high risk of adverse remodeling, main inclusion criteria were a left-ventricular ejection fraction (LVEF) ≤45% and an infarct size >10% on day 5 to 9 post MI as measured by cardiac MRI. Patients were then randomized to 6 months treatment with either 25 mg fulacimstat (n = 54) or placebo (n = 53) twice daily on top of standard of care starting day 6 to 12 post MI. The changes in LVEF, LV end-diastolic volume index (LVEDVI), and LV end-systolic volume index (LVESVI) from baseline to 6 months were analyzed by a central blinded cardiac MRI core laboratory. RESULTS Fulacimstat was safe and well tolerated and achieved mean total trough concentrations that were approximately tenfold higher than those predicted to be required for minimal therapeutic activity. Comparable changes in LVEF (fulacimstat 3.5% ± 5.4%, placebo 4.0% ± 5.0%, P = .69), LVEDVI (fulacimstat 7.3 ± 13.3 mL/m2, placebo 5.1 ± 18.9 mL/m2, P = .54), and LVESVI (fulacimstat 2.3 ± 11.2 mL/m2, placebo 0.6 ± 14.8 mL/m2, P = .56) were observed in both treatment arms. CONCLUSION Fulacimstat was safe and well tolerated in patients with left-ventricular dysfunction (LVD) after first STEMI but had no effect on cardiac remodeling. BACKGROUND An amassing body of evidence exists to support an association between the use of immune checkpoint inhibitors (ICIs) and the development of tuberculosis (TB). METHODS We performed a systematic review of the literature to assess the nature of this relationship using PubMed, EMBASE and meeting proceedings. RESULTS We have identified 16 patients who developed active TB during immunotherapy. Median age was 61 (range 49-87). Twelve (75%) were male and 4 (25%) were female. Lung cancer was the most common type of cancer (n = 8), followed by melanoma (n = 3) and head and neck cancer (n = 3). Median time to TB reactivation after initiation of ICI therapy was 6.3 months (range 1-24 months). Two (13%) patients died of complications of TB (spinal cord compression, GI perforation). TB reactivation in organs (pericardium, bone, liver, and GI track; one each) other than the lungs has been documented. We did not find any cases of TB reactivation that occurred during anti-CTLA-4 therapy. CONCLUSION Findings from our systematic review indicate that PD-(L)1 inhibitors are linked to TB reactivation. TB activation can occur in various organs and TB-related fatalities have been reported. TB screening before starting immunotherapy should be considered in high-risk patient populations. Further research, including prospective studies with patients whose baseline TB status is known, is necessary to better understand the incidence of TB reactivation during ICI therapy and how best to manage TB that develops during immunotherapy. Recent technological developments have resulted in the availability of miniaturised spectral imaging sensors capable of operating in the multi- (MSI) and hyperspectral imaging (HSI) regimes. Simultaneous advances in image-processing techniques and artificial intelligence (AI), especially in machine learning and deep learning, have made these data-rich modalities highly attractive as a means of extracting biological information non-destructively. Surgery in particular is poised to benefit from this, as spectrally-resolved tissue optical properties can offer enhanced contrast as well as diagnostic and guidance information during interventions. click here This is particularly relevant for procedures where inherent contrast is low under standard white light visualisation. This review summarises recent work in surgical spectral imaging (SSI) techniques, taken from Pubmed, Google Scholar and arXiv searches spanning the period 2013-2019. New hardware, optimised for use in both open and minimally-invasive surgery (MIS), is described, and recent commercial activity is summarised. Computational approaches to extract spectral information from conventional colour images are reviewed, as tip-mounted cameras become more commonplace in MIS. Model-based and machine learning methods of data analysis are discussed in addition to simulation, phantom and clinical validation experiments. A wide variety of surgical pilot studies are reported but it is apparent that further work is needed to quantify the clinical value of MSI/HSI. The current trend toward data-driven analysis emphasises the importance of widely-available, standardised spectral imaging datasets, which will aid understanding of variability across organs and patients, and drive clinical translation. V.Contrast-enhanced magnetic resonance imaging (CEMRI) is crucial for the diagnosis of patients with liver tumors, especially for the detection of benign tumors and malignant tumors. However, it suffers from high-risk, time-consuming, and expensive in current clinical diagnosis due to the use of the gadolinium-based contrast agent (CA) injection. If the CEMRI can be synthesized without CA injection, there is no doubt that it will greatly optimize the diagnosis. In this study, we propose a Tripartite Generative Adversarial Network (Tripartite-GAN) as a non-invasive, time-saving, and inexpensive clinical tool by synthesizing CEMRI to detect tumors without CA injection. Specifically, our innovative Tripartite-GAN combines three associated-networks (an attention-aware generator, a convolutional neural network-based discriminator, and a region-based convolutional neural network-based detector) for the first time, which achieves CEMRI synthesis and tumor detection promoting each other in an end-to-end framework. The generator facilitates detector for accurate tumor detection via synthesizing tumor-specific CEMRI.