PyMT-Maclow: A novel, inducible, murine model for determining the role of CD68 positive cells in breast tumor development

CD68+ tumor-associated macrophages (TAMs) are pro-tumorigenic, pro-angiogenic and are associated with decreased survival rates in patients with cancer, including breast cancer. Non-specific models of macrophage ablation reduce the number of TAMs and limit the development of mammary tumors. However, the lack of specificity and side effects associated with these models compromise their reliability. We hypothesized that specific and controlled macrophage depletion would provide precise data on the effects of reducing TAM numbers on tumor development. In this study, the MacLow mouse model of doxycycline-inducible and selective CD68+ macrophage depletion was crossed with the murine mammary tumor virus (MMTV)-Polyoma virus middle T antigen (PyMT) mouse model of spontaneous ductal breast adenocarcinoma to generate the PyMT-MacLow line. In doxycycline-treated PyMT-MacLow mice, macrophage numbers were decreased in areas surrounding tumors by 43%. Reducing the number of macrophages by this level delayed tumor progression, generated less proliferative tumors, decreased the vascularization of carcinomas and down-regulated the expression of many pro-angiogenic genes. These results demonstrate that depleting CD68+ macrophages in an inducible and selective manner delays the development of mammary tumors and that the PyMT-MacLow model is a useful and unique tool for studying the role of TAMs in breast cancer

Current evidence-based therapy does not restore plasma apelin level in phenotypically diverse chronic heart failure patients

Background: Apelin, endogenous peptide acting through its receptor (APJ), is the most potent inotropic agent known to man. Plasma apelin and cardiac APJ mRNA levels rise in early stages of chronic heart failure (CHF) but fall later in decompensated CHF. The effect of current evidence-based management of CHF on plasma apelin level is not known. We estimated change in plasma apelin level in CHF patients of diverse phenotypes treated with standard pharmacotherapy and compared it with the corresponding change in left ventricular ejection fraction (LVEF), plasma brain natriuretic peptide (BNP) level and quality of life (QoL).Methods: With ethical approval and written informed consent, venous blood samples were collected from 39 CHF [dilated cardiomyopathy (DCM) (n=21), restrictive cardiomyopathy (RCM) (n=9) and chronic constrictive pericarditis (CCP) (n=9)] patients and 10 age-matched healthy controls, at baseline and after 12 weeks. Plasma apelin and BNP were estimated by competitive ELISA (RayBiotech Inc.) and an auto-analyzer (Triage, Allere Inc.), respectively. QoL was assessed using Kansas City Cardiomyopathy Questionnaire (KCCQ). Nonparametric tests were applied and p-value <0.05 was considered significant.Results: Low LVEF, KCCQ score and high BNP levels were observed in all CHF patients compared to controls. Plasma apelin level was depressed in RCM and CCP patients compared to controls but not in DCM patients. These parameters did not change in any group after 3 months of standard pharmacotherapy.Conclusions: Current evidence-based management of CHF does not restore the depressed apelin-APJ axis. New drugs are required for specifically modulating this promising therapeutic target in CHF

Acute electrocardiographic changes during smoking: An observational study

Objective To study the temporal relationship of smoking with electrophysiological changes. Design Prospective observational study. Setting Tertiary cardiac center. Participants Male smokers with atypical chest pain were screened with a treadmill exercise test (TMT). A total of 31 such patients aged 49.8±10.5 years, in whom TMT was either negative or mildly positive were included. Heart rate variability (HRV) parameters of smokers were compared to those of 15 healthy non-smoking participants. Interventions All patients underwent a 24 h Holter monitoring to assess ECG changes during smoking periods. Results Heart rate increased acutely during smoking. Mean heart rate increased from 83.8±13.7 bpm 10 min before smoking, to 90.5±16.4 bpm during smoking, (p &#60;0.0001) and returned to baseline after 30 min. Smoking was also associated with increased ectopic beats (mean of 5.3/h prior to smoking to 9.8/h during smoking to 11.3/h during the hour after smoking; p &#60;0.001). Three patients (9.7%) had significant ST–T changes after smoking. HRV index significantly decreased in smokers (15.2±5.3) as compared to non-smoking controls participants (19.4±3.6; p=0.02), but the other spectral HRV parameters were comparable. Conclusions Heart rate and ectopic beats increase acutely following smoking. Ischaemic ST–T changes were also detected during smoking. Spectral parameters of HRV analysis of smokers remained in normal limits, but more importantly geometrical parameter—HRV index—showed significant abnormality

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

Purpose: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. Materials and Methods: In this retrospective study, 369,071 chest radiographs and associated radiology reports from 64,581 (mean age, 51.71; 54.51% women) patients from the MIMIC-CXR chest radiograph dataset were included. This dataset was split into patients with and without congestive heart failure (CHF). Pulmonary edema severity labels from the associated radiology reports were extracted from patients with CHF as four different ordinal levels: 0, no edema; 1, vascular congestion; 2, interstitial edema; and 3, alveolar edema. Deep learning models were developed using two approaches: a semi-supervised model using a variational autoencoder and a pre-trained supervised learning model using a dense neural network. Receiver operating characteristic curve analysis was performed on both models. Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0.99 for the semi-supervised model and 0.87 for the pre-trained models. Performance of the algorithm was inversely related to the difficulty in categorizing milder states of pulmonary edema (shown as AUCs for semi-supervised model and pre-trained model, respectively): 2 versus 0, 0.88 and 0.81; 1 versus 0, 0.79 and 0.66; 3 versus 1, 0.93 and 0.82; 2 versus 1, 0.69 and 0.73; and, 3 versus 2, 0.88 and 0.63. Conclusion: Deep learning models were trained on a large chest radiograph dataset and could grade the severity of pulmonary edema on chest radiographs with high performance.Comment: The two first authors contributed equall

Role of modifying genes on the severity of rare mutation of MYH7 gene in hypertrophic obstructive cardiomyopathy

Hypertrophic Cardiomyopathy (HCM) is an autosomal dominant disorder due to mutations in sacromeric genes with variable penetrance. Hypertrophic Obstructive Cardiomyopathy (HOCM) is a major complication of Hypertrophic Cardiomyopathy. Unexplained hypertrophy in the Left Ventricle (LV) or Intraventricular Septum (IVS) had been the diagnostic criterion for HCM which is more often confirmed by the echocardiography. The frequency of HCM in general population is 1:500 and about 60-70% genetic predisposition is known. It has been observed that mutations in the Cardiac myosin binding proteinC (MYBPC3) gene causes late onset of disease with mild symptoms while mutations in the Beta Myosin Heavy chain (MYH7) gene leads to early onset with severe symptoms. Apart from Epigenetic and Environmental factors, modifier genes further complicate the situation leading to altered clinical outcome even among the same family members having identical mutation

Indian system of medicine used concurrently with standard conventional medicine improves quality of life in patients of cardio vascular diseases (C.V.D)

Worldwide there is increased shift towards usage of traditional medicine in patients of chronic diseases like Cardio Vascular Disorders. In India, these medicines are used concurrently. Objective of the study was to ascertain prevalence and effect of concurrent traditional drug therapy with standard pharmacotherapy in patients with CVD. The present study used a cross sectional study design to assess the prevalence and a prospective cohort design to assess the effect of concurrent Ayurvedic medicines with standard pharmacotherapy in terms of quality of life. After screening 600 patients, 128 were found taking such medicines. Out of these, 100 were recruited as cases (Group-I), while 100 who were matched in terms of age, body mass index, ejection fraction, and receiving standard therapy only were recruited as controls (Group-II). Assessment parameters included demographic, biochemical, ejection fraction through echocardiography, distance covered in six Minute walk Test (6MWT), Quality of Life (QOL) through Kansas City Cardio-myopathy Questionnaire (KCCQ) and Seattle Angina Questionnaire (SAQ) with follow up at 6 months. Prakriti as mentioned in Ayurveda was also assessed using a questionnaire. Both groups were comparable at base line. Total 87 in Group-I and 91 in Group-II completed the study. Further, 76% patients were diagnosed with heart failure (HF) and 24% with coronary artery disease (CAD). There was no change in distance covered in 6MWT in both HF or CAD groups. But there was improvement in all cases in domains of KCCQ and SAQ as compared to controls. To conclude, concurrent use of traditional medicine with standard conventional care in CVD may improve quality of life in cardiovascular disorders.

Nature inspired antibody design and optimization

The biotech industry has seen an explosion in the development of therapeutic antibodies in the last decade. The advantages of antibodies as therapeutics – namely their high affinity, specificity, potency, stability, manufacturability and low toxicity – are compelling. Nevertheless, there are many challenges associated with antibody discovery and development that require key technical advances to improve the rational and reliable generation of potent antibody therapeutics. We have made three key discoveries that address some of these fundamental challenges related to the design and selection of antibodies with high affinity, specificity, stability and solubility. First, we find that the accumulation of affinity-enhancing mutations in the complementaritydetermining regions (CDRs) during affinity maturation is often a destabilizing process. Surprisingly, mutations that enhance antibody binding affinity are commonly destabilizing. Second, we have developed novel yeast surface display methods for co-evolving antibody affinity and stability to address the general problem of antibody destabilization during affinity maturation. Our approach simultaneously evaluates antibody binding to both antigen and a conformational ligand that acts as a folding sensor to rapidly identify sets of mutations that promote both high antibody affinity and stability. This methodology has enabled us to identify novel compensatory mutations that offset the destabilizing effects of affinity-enhancing mutations and lead to affinitymaturated antibodies with high thermodynamic stability. Interestingly, our directed evolution method appears to mimic some aspects of natural antibody evolution, as natural antibodies also accumulate similar types of compensatory mutations to maintain thermodynamic stability during in vivo affinity maturation. Third, we have developed novel antibody library design and selection methods for generating antibodies with high specificity. It is common for antibody specificity to be compromised during in vitro affinity maturation. We have developed innovative methods for designing antibody libraries based on natural antibody diversity to simultaneously sample residues at many sites in the CDRs and framework regions that are most likely to promote high specificity. By coupling these nature-inspired antibody libraries with novel positive and negative selection methods, we have isolated antibodies with specificities that rival those of natural antibodies and which are much higher than typical antibodies identified using in vitro selection methods. Interestingly, we find that antibodies with improved specificity also possess excellent biophysical properties, including high solubility and stability. We are currently using computational methods to understand how rare antibody variants are able to maintain high specificity and stability during affinity maturation. Our long-term goal is to develop systematic and robust design methods to rapidly generate and optimize antibodies for use in a range of diagnostic and therapeutic applications