Reply from M.P. Hermans, P. Selvais and M. van Ypersele de Strihou

Hermans, Michel P.; Selvais, P. L.; Strihou, M. van Ypersele

doi:10.1111/j.1464-5491.1995.tb00494.x

Cited by 11 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among those produced by resident cells, there are the surfactant-associated proteins (SP). Pulmonary surfactant is composed of specific phospholipids, and several SP, such as SP-A (the most abundant), SP-B and SP-D, that together with Clara cell protein and mucin-associated antigens are secreted by different lung epithelial cells and can be found in BAL [28,29]. Detection of SP in BAL provides information about the changes in lung epithelial cell function or clearance during lung diseases [25,30,31].…”

Section: Bal Protein Compositionmentioning

confidence: 99%

Proteome analysis of bronchoalveolar lavage in lung diseases

et al. 2006

View full text Add to dashboard Cite

The proteomic approach is complementary to genomics and enables protein composition to be investigated under various clinical conditions. Its application to the study of bronchoalveolar lavage (BAL) is extremely promising. BAL proteomic studies were initially based on two-dimensional electrophoretic separation of complex protein samples and subsequent identification of proteins by different methods. With the techniques available today it is possible to attain many different research objectives. BAL proteomics can contribute to the identification of proteins in alveolar spaces with possible insights into pathogenesis and clinical application for diagnosis, prognosis and therapy. Many proteins with different functions have already been identified in BAL. Some could be biomarkers that need to be individually confirmed by correlation with clinical parameters and validation by other methods on larger cohorts of patients. The standardization of BAL sample preparation and processing for proteomic studies is an important goal that would promote and facilitate clinical applications. Here, we review the principal literature on BAL proteomic analysis applied to the study of lung diseases.

show abstract

Section: Bal Protein Compositionmentioning

confidence: 99%

Proteome analysis of bronchoalveolar lavage in lung diseases

et al. 2006

View full text Add to dashboard Cite

show abstract

“…It is also known that atherogenic dyslipidemia (AD) poses a cardiovascular risk (CV) risk due to elevated TG levels and accompanied by low HDL-C. AD is associated with insulin resistance (IR), and confers a marked increase in residual vascular risk, even when LDL-C is low (Hermans et al 2010). It has been reported that TG/ HDL-C ratio may be used as a surrogate marker for assessing CV risk (Hermans et al 2010;Nicholls et al 2011).…”

Section: Discussionmentioning

confidence: 99%

“…AD is associated with insulin resistance (IR), and confers a marked increase in residual vascular risk, even when LDL-C is low (Hermans et al 2010). It has been reported that TG/ HDL-C ratio may be used as a surrogate marker for assessing CV risk (Hermans et al 2010;Nicholls et al 2011). In HF-HC fed hamster model, Saroglitazar treatment significantly reduced the TG/HDL-C ratio, which suggests that Saroglitazar may provide significant benefits to patients with atherogenic dyslipidemia.…”

Section: Discussionmentioning

confidence: 99%

Saroglitazar, a novel PPARα/γ agonist with predominant PPARα activity, shows lipid‐lowering and insulin‐sensitizing effects in preclinical models

Jain

Giri

Trivedi

et al. 2015

Pharmacology Res & Perspec

View full text Add to dashboard Cite

Saroglitazar is a novel nonthiazolidinediones (TZD) and nonfibric acid derivative designed to act as dual regulator of lipids and glucose metabolism by activating peroxisome proliferator-activated receptors (PPAR). These studies evaluate the efficacy and safety profile of Saroglitazar in preclinical in vitro and in vivo models. The EC50 values of Saroglitazar assessed in HepG2 cells using PPAR transactivation assay for hPPARα and hPPARγ were 0.65 pmol/L and 3 nmol/L, respectively. In db/db mice, 12-day treatment with Saroglitazar (0.01–3 mg/kg per day, orally) caused dose-dependent reductions in serum triglycerides (TG), free fatty acids (FFA), and glucose. The ED50 for these effects was found to be 0.05, 0.19, and 0.19 mg/kg, respectively with highly significant (91%) reduction in serum insulin and AUC-glucose following oral glucose administration (59%) at 1 mg/kg dose. Significant reduction in serum TG (upto 90%) was also observed in Zucker fa/fa rats, Swiss albino mice, and in high fat -high cholesterol (HF-HC)-fed Golden Syrian hamsters. LDL cholesterol was significantly lowered in hApoB100/hCETP double transgenic mice and HF-HC diet fed Golden Syrian Hamsters. Hyperinsulinemic-Euglycemic clamp study in Zucker fa/fa rats demonstrated potent insulin-sensitizing activity. Saroglitazar also showed a significant decrease in SBP (22 mmHg) and increase (62.1%) in serum adiponectin levels in Zucker fa/fa rats. A 90-day repeated dose comparative study in Wistar rats and marmosets confirmed efficacy (TG lowering) potential of Saroglitazar and has indicated low risk of PPAR-associated side effects in humans. Based on efficacy and safety profile, Saroglitazar appears to have good potential as novel therapeutic agent for treatment of dyslipidemia and diabetes.

show abstract

“…This is indeed possible for the technologies of Refs. [2,13,14] and has also been demonstrated e.g. in memristive devices [3,15].…”

Section: Introductionmentioning

confidence: 92%

Training of Photonic Neural Networks through In Situ Backpropagation

Hughes¹,

Minkov²,

Williamson³

et al. 2019

Conference on Lasers and Electro-Optics

View full text Add to dashboard Cite

Recently, integrated optics has gained interest as a hardware platform for implementing machine learning algorithms. Of particular interest are artificial neural networks, since matrix-vector multiplications, which are used heavily in artificial neural networks, can be done efficiently in photonic circuits. The training of an artificial neural network is a crucial step in its application. However, currently on the integrated photonics platform there is no efficient protocol for the training of these networks. In this work, we introduce a method that enables highly efficient, in situ training of a photonic neural network. We use adjoint variable methods to derive the photonic analogue of the backpropagation algorithm, which is the standard method for computing gradients of conventional neural networks. We further show how these gradients may be obtained exactly by performing intensity measurements within the device. As an application, we demonstrate the training of a numerically simulated photonic artificial neural network. Beyond the training of photonic machine learning implementations, our method may also be of broad interest to experimental sensitivity analysis of photonic systems and the optimization of reconfigurable optics platforms. arXiv:1805.09943v1 [physics.optics]

show abstract

Reply from M.P. Hermans, P. Selvais and M. van Ypersele de Strihou

Cited by 11 publications

References 0 publications

Proteome analysis of bronchoalveolar lavage in lung diseases

Proteome analysis of bronchoalveolar lavage in lung diseases

Saroglitazar, a novel PPARα/γ agonist with predominant PPARα activity, shows lipid‐lowering and insulin‐sensitizing effects in preclinical models

Training of Photonic Neural Networks through In Situ Backpropagation

Contact Info

Product

Resources

About