A hepatoprotective Lindera obtusiloba extract suppresses growth and attenuates insulin like growth factor-1 receptor signaling and NF-kappaB activity in human liver cancer cell lines

<p>Abstract</p> <p>Background</p> <p>In traditional Chinese and Korean medicine, an aqueous extract derived from wood and bark of the Japanese spice bush <it>Lindera obtusiloba </it>(<it>L.obtusiloba</it>) is applied to treat inflammations and chronic liver diseases including hepatocellular carcinoma. We previously demonstrated anti-fibrotic effects of <it>L.obtusiloba </it>extract in hepatic stellate cells. Thus, we here consequently examine anti-neoplastic effects of <it>L.obtusiloba </it>extract on human hepatocellular carcinoma (HCC) cell lines and the signaling pathways involved.</p> <p>Methods</p> <p>Four human HCC cell lines representing diverse stages of differentiation were treated with <it>L.obtusiloba </it>extract, standardized according to its known suppressive effects on proliferation and TGF-β-expression. Beside measurement of proliferation, invasion and apoptosis, effects on signal transduction and NF-κB-activity were determined.</p> <p>Results</p> <p><it>L.obtusiloba </it>extract inhibited proliferation and induced apoptosis in all HCC cell lines and provoked a reduced basal and IGF-1-induced activation of the IGF-1R signaling cascade and a reduced transcriptional NF-κB-activity, particularly in the poorly differentiated SK-Hep1 cells. Pointing to anti-angiogenic effects, <it>L.obtusiloba </it>extract attenuated the basal and IGF-1-induced expression of hypoxia inducible factor-1α, vascular endothelial growth factor, peroxisome proliferator-activated receptor-γ, cyclooxygenase-2 and inducible nitric oxide synthase.</p> <p>Conclusions</p> <p>The traditional application of the extract is confirmed by our experimental data. Due to its potential to inhibit critical receptor tyrosine kinases involved in HCC progression via the IGF-1 signaling pathway and NF-κB, the standardized <it>L.obtusiloba </it>extract should be further analysed for its active compounds and explored as (complementary) treatment option for HCC.</p

ENETS Consensus Guidelines for the Standards of Care in Neuroendocrine Tumors: Pathology: Diagnosis and Prognostic Stratification.

The European Neuroendocrine Tumor Society (ENETS) proposed standard of care guidelines for pathology in 2009. Since then, profound changes in the classification have been made, dividing neuroendocrine neoplasia (NEN) into well-differentiated neuroendocrine tumors (NET) and poorly differentiated neuroendocrine carcinomas (NEC) in the 2010 WHO classification. The 7th edition of the TNM classification (2009) included NEN for the first time, widely adapting ENETS proposals but with some differences for NEC and for NET of the pancreas and the appendix. Therapy guidelines for gastroenteropancreatic NET were updated in 2016. The need for an update of the standards of care prompted the ENETS to organize a consensus conference which was held in Antibes in 2015; a working group was designated to propose pathological standards of car

Novel Gemcitabine Conjugated Albumin Nanoparticles: a Potential Strategy to Enhance Drug Efficacy in Pancreatic Cancer Treatment

Purpose: The present study reports a novel conjugate of gemcitabine (GEM) with bovine serum albumin (BSA) and thereof nanoparticles (GEM-BSA NPs) to potentiate the therapeutic efficacy by altering physicochemical properties, improving cellular uptake and stability of GEM. Methods: The synthesized GEM-BSA conjugate was extensively characterized by NMR, FTIR, MALDI-TOF and elemental analysis. Conjugation mediated changes in structural conformation and physicochemical properties were analysed by fluorescence, Raman and CD spectroscopy, DSC and contact angle analysis. Further, BSA nanoparticles were developed from BSA-GEM conjugate and extensively evaluated against in-vitro pancreatic cancer cell lines to explore cellular uptake pathways and therapeutic efficacy. Results: Various characterization techniques confirmed covalent conjugation of GEM with BSA. GEM-BSA conjugate was then transformed into NPs via high pressure homogenization technique with particle size 147.2 ± 7.3, PDI 0.16 ± 0.06 and ZP -19.2 ± 1.4. The morphological analysis by SEM and AFM revealed the formation of smooth surface spherical nanoparticles. Cellular uptake studies in MIA PaCa-2 (GEM sensitive) and PANC-1 (GEM resistant) pancreatic cell lines confirmed energy dependent clathrin internalization/endocytosis as a primary mechanism of NPs uptake. In-vitro cytotoxicity studies confirmed the hNTs independent transport of GEM in MIA PaCa-2 and PANC-1 cells. Moreover, DNA damage and annexin-V assay revealed significantly higher apoptosis level in case of cells treated with GEM-BSA NPs as compared to free GEM. Conclusions: GEM-BSA NPs were found to potentiate the therapeutic efficacy by altering physicochemical properties, improving cellular uptake and stability of GEM and thus demonstrated promising therapeutic potential over free drug

Expert consensus document:Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA)

Cholangiocarcinoma (CCA) is a heterogeneous group of malignancies with features of biliary tract differentiation. CCA is the second most common primary liver tumour and the incidence is increasing worldwide. CCA has high mortality owing to its aggressiveness, late diagnosis and refractory nature. In May 2015, the "European Network for the Study of Cholangiocarcinoma" (ENS-CCA: www.enscca.org or www.cholangiocarcinoma.eu) was created to promote and boost international research collaboration on the study of CCA at basic, translational and clinical level. In this Consensus Statement, we aim to provide valuable information on classifications, pathological features, risk factors, cells of origin, genetic and epigenetic modifications and current therapies available for this cancer. Moreover, future directions on basic and clinical investigations and plans for the ENS-CCA are highlighted

Magnetic resonance spectroscopy: quantitative analysis of brain metabolites and macromolecules

Proton magnetic resonance spectroscopy (1H-MRS) allows non-invasive quantification of the human brain's metabolism in vivo. 1H-MRS measures the interaction of the 1H- hydrogen isotope with oscillating electromagnetic fields in the presence of a strong electromagnetic field. The measured MRS signal of the 1H-hydrogen atoms reflects the concentration of the metabolites present in the tissue. Metabolites are small molecules reflecting the metabolism. Each 1H-hydrogen atom present in a metabolite has a specific resonance frequency, which depends on the chemical structure of the metabolite. The ensemble of the resonance frequencies of all metabolites present in the measured tissue creates the MRS signal. The MRS signal is Fourier transformed, producing an MRS spectrum, where each resonance frequency appears as a distinct peak. The most abundant molecule in the human tissue is water. The resonance frequency of water is suppressed in 1H-MRS to permit the quantification of other metabolites, which are present with significantly lower concentrations. In the MRS spectrum, protons with lower resonance frequencies than water form the upfield spectrum, whereas protons with higher resonance frequencies form the downfield spectrum. This work focused on the modelling of the MRS spectrum. The first part is focused on the accurate determination of metabolite concentrations. The upfield spectrum contains most brain metabolites of clinical interest. However, there is a severe spectral overlap between the metabolite resonances, and therefore dedicated software calculates the contributions of individual metabolites. The modelling of the individual metabolite contributions to the measured spectrum is referred to as spectral fitting. Through this spectral fitting, the metabolite concentrations needed for clinical diagnostics are determined. The most significant overlap in MRS spectra originates from the signals underlying the metabolite resonances, referred to as the macromolecular spectrum. The macromolecular spectrum contains the resonance frequencies of protons in proteins and peptides, which have a slightly faster signal decay than the smaller molecules (metabolites). Other contributors to the spectral overlap are residuals of the not entirely suppressed water signal or lipid signals originating from outside the volume of interest. A spline baseline is typically used in the fitting software to model these contributors. This work firstly investigated the impacts of different macromolecular spectra and spline baselines used in spectral fitting. Significant effects in the quantified metabolite concentrations were noticed, when the spline baseline flexibility was altered in the community “gold standard” software, LCModel. Therefore, the newly developed fitting algorithm proposed in this work, ProFit-v3, incorporates an automatic adaptive baseline flexibility determination. The ProFit-v3 software was then systematically evaluated to different perturbations and baseline effects. The quantified concentrations were compared to the ground truth (when known) and the LCModel software results. The second part of this work focuses on the modelling of the less investigated regions of the MRS spectrum. The downfield spectrum contains many resonance peaks unassigned to metabolite contributions. In this work, downfield spectral peaks were used to quantify intracellular pH. Additionally, for all downfield peaks T2 relaxation times, peak linewidths, and concentrations were calculated. Lastly, based on the quantified peak properties combined with previous literature measurements, the contributing molecules to the downfield peaks were assigned. The macromolecular spectrum was attributed by previous literature to contributions of amino acids in proteins and peptides, based on in vitro measurement of dialyzed cytosol. Moreover, the resonance frequencies of protein amino acids have been extensively collected into a protein database by the NMR community. Hence, this work proposes a modelling approach to quantify the in vivo measured macromolecular spectrum to individual amino acids. In conclusion, the investigation results and the proposed fitting software ProFit-v3 from this work should lead to improved quantification of 1H-MRS spectra. Lastly, the peak assignments in the downfield spectra and the proposed amino acid model promises possible future biomarkers for disease

Simultaneous Detection of Metabolite Concentration Changes, Water BOLD Signal and pH Changes during Visual Stimulation in the Human Brain at 9.4T

Previous studies investigated relationships between the BOLD signal and metabolite concentration changes during visual stimulation by sequential or interleaved fMRI/fMRS measurements. The purpose of this study was to simultaneously investigate the dynamics of BOLD signal and metabolite levels in the activated human brain at 9.4T using the metabolite-cycling (MC) technique. A correlation between the MC water dynamics and concentration increases of lactate and glutamate during activation could be verified. Besides, it could be shown that the high spectral quality of fMRS at 9.4T facilitates separate fitting of creatine and phosphocreatine thereby enabling the calculation of pH dynamics during visual stimulation

Estimation of T2 Relaxation Times and Absolute Quantification of Metabolites in the Human Brain at 9.4 T

Purpose/Introduction: For the absolute quantification1 of metabolites, appropriate T2 relaxation times must be used in the calculation. T2 relaxation times are usually specific to the sequence, tissue type, and region of interest in the human brain. Deelchand et al.2, reported T2 relaxation times of singlets at 9.4 T. In this work, T2 relaxation times of both singlets and J-coupled metabolites are reported for a GM rich voxel in the occipital region in the human brain at 9.4 T for the first time. The absolute concentrations mmolal (mmol/kg) of the metabolite peaks are also calculated. Subjects and Methods: A TE series of metabolite-cycled semi- LASER3 spectra were acquired on 11 healthy volunteers (TE:24, 32, 40, 52, 60 ms; TR: 6000 ms; NEX:96) with the transmit reference frequency set at 7.0 ppm and another spectra (TE/TR: 24/6000 ms, NEX:32) was acquired with the transmit reference frequency set at 2.4 ppm for absolute quantification. Tissue volume fractions were obtained from MP2RAGE image segmentation. Also, water reference spectra (NEX: 16) were acquired using semi-LASER sequence in order to avoid any influence of MC pulses. The raw data were pre-processed and were fit using LCModel-v6.3- 1L4. The concentrations were fit to a mono-exponential decay across the TE series in order to estimate the T2 relaxation times of the metabolite peaks. The concentrations of the metabolites were absolutely quantified using the formula given by Gasparovic et al.1 including correction factors. T1 and T2 relaxation times of water at 9.4 T were taken from Hagberg et al.5. T1 relaxation times of metabolites were considered from Wright et al.6, and T2 relaxation times calculated from this work were taken. The calculated T2 relaxation times for the summed spectra from 11 healthy volunteers and the mean from T2 values for individual spectra with standard deviations are reported in Table 1, which are overall in a good agreement. All reported T2 relaxation times show the expected negative correlation with increasing the field strength7,8. Absolute concentrations in mmolal with and without T2 correction are shown in Table 2 for a fair comparison between this work and other studies2,9 which do not include T2 correction. The values with T2 correction also agree with Marjanska et al.,8 except for minor discrepancies which could be from small deviations in the calculation of T2 values

Estimation of Tp2 Relaxation Times of Macromolecules in Human Brain Spectra at 9.4 T

Previous studies have shown, that the inclusion of a macromolecular baseline in the basis set for fitting can influence the quantification 1,2, while other studies have shown the possible clinical relevance of macromolecules (MM) in clinical diagnostics 3. Hence, a characterization of MMs is of crucial importance 4, and knowing the apparent T2 (2ρ) relaxation times can lead to a better understanding of these MMs

Towards a Fitting Model of Macromolecular Spectra: Amino Acids

Broad signals underlying in vivo 1H MRS spectra are referred to in literature as macromolecules, and have been assigned to amino acids by Behar et al. These amino acids are creating proteins through chemical bonds. Depending on the protein structure and sequence of amino acids, they have different chemical shifts as published in protein NMR databases. This work uses these published chemical shifts to create a fitting model for the macromolecular baselines of human brain using amino acids

Phosphorus transversal relaxation times and metabolite concentrations in the human brain at 9.4 T

A method to estimate phosphorus (31 P) transversal relaxation times (T2 s) of coupled spin systems is demonstrated. Additionally, intracellular and extracellular pH and relaxation-corrected metabolite concentrations are reported. Echo time (TE) series of 31 P metabolite spectra were acquired using stimulated echo acquisition mode (STEAM) localization. Spectra were fitted using LCModel with accurately modeled Versatile Simulation, Pulses and Analysis (VeSPA) basis sets accounting for J-evolution of the coupled spin systems. T2 s were estimated by fitting a single exponential two-parameter model across the TE series. Fitted inorganic phosphate frequencies were used to calculate pH, and estimated relaxation times were used to determine the relaxation-corrected brain metabolite concentrations on an assumption of 3 mM γ-ATP. The method was demonstrated in healthy human brain at a field strength of 9.4 T. T2 times of ATP and nicotinamide adenine dinucleotide (NAD) were shortest between 8 and 20 ms, followed by T2 s of inorganic phosphate between 25 and 50 ms, and phosphocreatine with a T2 of 100 ms. Phosphomonoesters and phosphodiesters had the longest T2 s of about 130 ms. The measured T2 s are comparable with literature values and fit in a decreasing trend with increasing field strengths. Calculated pHs and metabolite concentrations are also comparable with literature values