Solid epidemiological evidences connect obesity with incidence, stage and survival in pancreatic cancer. However, the underlying mechanistic basis linking adipocytes to pancreatic cancer progression ...remain largely elusive. We hypothesized that factors secreted by adipocytes could be responsible for epithelial-to-mesenchymal transition (EMT) induction and, in turn, a more aggressive phenotype in models of pancreatic preneoplastic lesions.
We studied the role of factors secreted by two adipogenic model systems from primary human bone marrow stromal cells (hBMSCs) in an in vitro experimental cell transformation model system of human pancreatic ductal epithelial (HPDE) cell stably expressing activated KRAS (HPDE/KRAS),Results:We measured a significant induction of EMT and aggressiveness in HPDE and HPDE/KRAS cell lines when cultured with medium conditioned by fully differentiated adipocytes (ADIPO
) if compared with the same cells cultured with medium conditioned by hBMSC (hBMSC
) from two different healthy donors. Several genes coding for soluble modulators of the non-canonical WNT signaling pathway, including FRZB, SFRP2, RSPO1, WNT5A and 5B were significantly overexpressed in fully differentiated adipocytes than in their respective in hBMSC. ADIPO
induced the overexpression and the nuclear translocation of the Frizzled family member receptor tyrosine kinase-like orphan receptor (Ror) 2 in HPDE and HPDE/KRAS cells. Vantictumab, an anti-Frizzled monoclonal antibody, reduced ROR2 nuclear translocation and in turn the EMT and aggressiveness in HPDE and HPDE/KRAS cells.
We demonstrated that adipocytes could induce EMT and aggressiveness in models of pancreatic preneoplastic lesions by orchestrating a complex paracrine signaling of soluble modulators of the non-canonical WNT signaling pathway that determine, in turn, the activation and nuclear translocation of ROR2. This signaling pathway could represent a novel target for pancreatic cancer chemoprevention. Most importantly, these factors could serve as novel biomarkers to select a risk population among obese subjects for screening and, thus, early diagnosis of pancreatic cancer.
•Thermal decomposition of automotive polyurethane is investigated by neural network.•Kinetic of solid thermal decomposition is accurately determined as a combined event.•Traditional kinetic models ...are mathematically corrected by the neural network.•Rn and Dn models are associated by the network in the solid decomposition process.•Kinetic studies by traditional methods are confronted by the neural network results.
Thermal decomposition of automotive polyurethane was investigated by thermogravimetry under non-isothermal and isothermal conditions. For isothermal treatment, a neural network (ANN) was adopted with kinetic models as activation functions for neurons in the hidden layer. In this network architecture, rate constants represent weights between the input and intermediate layer and the learning process occurs by optimizing only the weights in output layer. Polyurethane sample was collected from an automotive intake manifold and the Diffusion and Contraction models were selected for better describe the decomposition as a combined event. Due to mathematical corrections, the accuracy of ANN is greater than individual model analysis. To validate the isothermal results, the non-isothermal analysis was performed and activation energy was calculated by Friedman, Flynn-Wall-Ozawa and Kissinger-Akahira-Sunose methods. The Ea calculated is 185–198 kJ mol−1 for all the methods. The results were used in a critical analysis between the both methods.
Despite its proven activity as third-line treatment in gastrointestinal stromal tumors (GIST), regorafenib can present a poor tolerability profile which often leads to treatment modifications and ...transient or permanent discontinuation; thus, in clinical practice physicians usually adopt various dosing and interval schedules to counteract regorafenib-related adverse events and avoid treatment interruption. The aim of this real-world study was to investigate the efficacy and safety of personalized schedules of regorafenib in patients with metastatic GIST, in comparison with the standard schedule (160 mg daily, 3-weeks-on, 1-week-off).
Institutional registries across seven Italian reference centers were retrospectively reviewed and data of interest retrieved to identify patients with GIST who had received regorafenib from February 2013 to January 2021. The Kaplan–Meier method was used to estimate survival and the log-rank test to make comparisons.
Of a total of 152 patients with GIST, 49 were treated with standard dose, while 103 received personalized schedules. At a median follow-up of 36.5 months, median progression-free survival was 5.6 months 95% confidence interval (CI) 3.73-11.0 months versus 9.7 months (95% CI 7.9-14.5 months) in the standard-dose and the personalized schedule groups, respectively hazard ratio (HR) 0.51; 95% CI 0.34-0.75; P = 0.00052. Median overall survival was 16.6 months (95% CI 14.1-21.8 months) versus 20.5 months (95% CI 15.0-25.4 months), respectively (HR 0.75; 95% CI 0.49-1.22; P = 0.16).
Regorafenib-personalized schedules are commonly adopted in daily clinical practice of high-volume GIST expert centers and correlate with significant improvement of therapeutic outcomes. Therefore, regorafenib treatment optimization in patients with GIST may represent the best strategy to maximize long-term therapy.
•Regorafenib-personalized schedules are commonly adopted in daily clinical practice.•Regorafenib-personalized schedules correlate with statistically significant improvement of therapeutic outcomes.•A prompt personalization of regorafenib could help clinicians avoid early treatment discontinuation due to adverse events.•A patient-tailored approach could be applied to other metastatic solid tumors treated with regorafenib.
Thermogravimetric analysis (TG) was used to investigate kinetic thermal decomposition process of thalidomide under non-isothermal and isothermal conditions. For the isothermal treatment, a ...methodology based on multilayer perceptron neural networks was adopted using four isothermal experimental data. These temperatures were chosen once they are near to the most important loss of mass of the material. Fifteen well-established models for solid thermal decomposition were used in the network to describe the process. The contraction models were selected by the network as a preferable set as they presented small residual error to fit the data. The neural network architecture allows quantifying the contribution of each model in the process description and also corrects the kinetic functions. Nitrogen atmosphere was used, and the experiments were performed at 293, 304, 314 and 324 °C. In the non-isothermal analysis, the activation energy was calculated through Friedman, Vyazovkin and Kissinger–Akahira–Sunose methods. For these experiments, N
2
atmosphere was also used at four heating rates: 2.5, 5.0, 10 and 20 °C min
−1
. The activation energy was calculated as 98–108 kJ mol
−1
for all the methods. The results were compared and analyzed to predict thermal decomposition for the medicines.
Context
Aiming at accurately predicting electro-optical properties of biomolecules, this work presents distributed atomic and functional-group polarizability tensors for a series of polypeptides and ...peptide clusters constructed from glycine and its residuals. By partitioning the electron density using the quantum theory of atoms in molecules, we demonstrated a very good transferability of the group polarizabilities. We were able to identify and extract the most efficient functional groups capable of generating the largest electrical susceptibility in condensed phases. Both the isotropic polarizability and its anisotropy were used to understand the way functional groups act as sources of linear optical responses, how they interact with each other reinforcing the macroscopic optical behavior within the material, and how covalent bonds and non-covalent interactions, such as hydrogen bonds, determine refractive indices and birefringence. Particular attention is devoted to the peptide bonds as they provide links to build biomacromolecules or polymers. An adequate quantum–mechanical treatment of at least the first interaction sphere of a given functional group is required to properly describe the effects of mutual polarization, but we identified optimum cluster size and shape to better estimate polarizabilities and dipole moments of larger molecules or molecular aggregates from the knowledge of the electron density of a central molecule or amino acid residual that is representative of the bulk. The strategy outlined here is a fast yet effective tool for estimating the optical properties of proteins but could eventually find application in the rational design of optical organic materials as well.
Methods
Electronic-structure calculations were performed on the Gaussin16 program at the DFT level using the CAMB3LYP functional and the double-ζ quality Dunning basis set aug-cc-pVDZ. Electron density partitioning followed the concepts of the Quantum Theory of Atoms and Molecules (QTAIM) and was performed using the AIMAll program. The locally developed Polaber routine was applied to calculate dipole moment vectors and polarizability tensors. It was amended to include the effects of the local field on a given central molecule by means of a modified Atom-Dipole Interaction Model (ADIM).
Since atomic or functional‐group properties in the bulk are generally not available from experimental methods, computational approaches based on partitioning schemes have emerged as a rapid yet ...accurate pathway to estimate the materials behavior from chemically meaningful building blocks. Among several applications, a comprehensive and systematically built database of atomic or group polarizabilities and related opto‐electronic quantities would be very useful not only to envisage linear or non‐linear optical properties of biomacromolecules but also to improve the accuracy of classical force fields devoted to simulate biochemical processes. In this work, we propose the first entries of such database that contains distributed polarizabilities and dipole moments extracted from fragments of peptides. Twenty three prototypical conformers of the dipeptides alanine–alanine and glycine–glycine were used to extract functional groups such as CH2, CHCH3, NH2, COOH, CONH, thus allowing construction of a diversity of chemically relevant environments. To evaluate the accuracy of our database, reconstructed properties of larger peptides containing up to six residues of alanine and glycine were tested against density functional theory calculations at the M06‐HF/aug‐cc‐pVDZ level of theory. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%. In order to include solvent effects explicitly, the peptides were also surrounded by a box of water molecules whose distribution was optimized using the CHARMM force field. Solvent effects introduced by a classical dipole–dipole interaction model were compared to those obtained from polarizable‐continuum model calculations.
A database of functional‐group polarizabilities and dipole moments is useful to envisage optical properties of biomacromolecules and to improve the accuracy of classical force fields devoted to simulate biochemical processes. Here, the first entries are extracted from fragments of dipeptides. To evaluate its accuracy, reconstructed properties of larger peptides were benchmarked against density functional theory calculations. The procedure is particularly accurate for the diagonal components of the polarizability tensor with errors up to 15%.
PDF
