Selective extraction of intracellular components from the microalga Chlorella vulgaris by combined pulsed electric field–temperature treatment

Author's accepted version (post-print).Available from 16/12/2017

FiCoS: A fine-grained and coarse-grained GPU-powered deterministic simulator for biochemical networks.

Mathematical models of biochemical networks can largely facilitate the comprehension of the mechanisms at the basis of cellular processes, as well as the formulation of hypotheses that can be tested by means of targeted laboratory experiments. However, two issues might hamper the achievement of fruitful outcomes. On the one hand, detailed mechanistic models can involve hundreds or thousands of molecular species and their intermediate complexes, as well as hundreds or thousands of chemical reactions, a situation generally occurring in rule-based modeling. On the other hand, the computational analysis of a model typically requires the execution of a large number of simulations for its calibration, or to test the effect of perturbations. As a consequence, the computational capabilities of modern Central Processing Units can be easily overtaken, possibly making the modeling of biochemical networks a worthless or ineffective effort. To the aim of overcoming the limitations of the current state-of-the-art simulation approaches, we present in this paper FiCoS, a novel "black-box" deterministic simulator that effectively realizes both a fine-grained and a coarse-grained parallelization on Graphics Processing Units. In particular, FiCoS exploits two different integration methods, namely, the Dormand-Prince and the Radau IIA, to efficiently solve both non-stiff and stiff systems of coupled Ordinary Differential Equations. We tested the performance of FiCoS against different deterministic simulators, by considering models of increasing size and by running analyses with increasing computational demands. FiCoS was able to dramatically speedup the computations up to 855×, showing to be a promising solution for the simulation and analysis of large-scale models of complex biological processes

Cytomolecular Classification of Thyroid Nodules Using Fine-Needle Washes Aspiration Biopsies

Fine-needle aspiration biopsies (FNA) represent the gold standard to exclude the malignant nature of thyroid nodules. After cytomorphology, 20-30% of cases are deemed "indeterminate for malignancy" and undergo surgery. However, after thyroidectomy, 70-80% of these nodules are benign. The identification of tools for improving FNA's diagnostic performances is explored by matrix-assisted laser-desorption ionization mass spectrometry imaging (MALDI-MSI). A clinical study was conducted in order to build a classification model for the characterization of thyroid nodules on a large cohort of 240 samples, showing that MALDI-MSI can be effective in separating areas with benign/malignant cells. The model had optimal performances in the internal validation set (n = 70), with 100.0% (95% CI = 83.2-100.0%) sensitivity and 96.0% (95% CI = 86.3-99.5%) specificity. The external validation (n = 170) showed a specificity of 82.9% (95% CI = 74.3-89.5%) and a sensitivity of 43.1% (95% CI = 30.9-56.0%). The performance of the model was hampered in the presence of poor and/or noisy spectra. Consequently, restricting the evaluation to the subset of FNAs with adequate cellularity, sensitivity improved up to 76.5% (95% CI = 58.8-89.3). Results also suggest the putative role of MALDI-MSI in routine clinical triage, with a three levels diagnostic classification that accounts for an indeterminate gray zone of nodules requiring a strict follow-up

In-Depth Mapping of the Urinary N-Glycoproteome: Distinct Signatures of ccRCC-related Progression

Protein N-glycosylation is one of the most important post-translational modifications and is involved in many biological processes, with aberrant changes in protein N-glycosylation patterns being closely associated with several diseases, including the progression and spreading of tumours. In light of this, identifying these aberrant protein glycoforms in tumours could be useful for understanding the molecular mechanism of this multifactorial disease, developing specific biomarkers and finding novel therapeutic targets. We investigated the urinary N-glycoproteome of clear cell renal cell carcinoma (ccRCC) patients at different stages (n = 15 at pT1 and n = 15 at pT3), and of non-ccRCC subjects (n = 15), using an N-glyco-FASP-based method. Using label-free nLC-ESI MS/MS, we identified and quantified several N-glycoproteins with altered expression and abnormal changes affecting the occupancy of the glycosylation site in the urine of RCC patients compared to control. In particular, nine of them had a specific trend that was directly related to the stage progression: CD97, COCH and P3IP1 were up-expressed whilst APOB, FINC, CERU, CFAH, HPT and PLTP were down-expressed in ccRCC patients. Overall, these results expand our knowledge related to the role of this post-translational modification in ccRCC and translation of this information into pre-clinical studies could have a significant impact on the discovery of novel biomarkers and therapeutic target in kidney cancer

Selective extraction of intracellular components from the microalga Chlorella vulgaris by combined pulsed electric field–temperature treatment

The synergistic effect of temperature (25–65 C) and total specific energy input (0.55–1.11 kWh kgDW 1 ) by pulsed electric field (PEF) on the release of intracellular components from the microalgae Chlorella vulgaris was studied. The combination of PEF with temperatures from 25 to 55 C resulted in a conductivity increase of 75% as a result of cell membrane permeabilization. In this range of temperatures, 25–39% carbohydrates and 3–5% proteins release occurred and only for carbohydrate release a synergistic effect was observed at 55 C. Above 55 C spontaneous cell lysis occurred without PEF. Combined PEF–temperature treatment does not sufficiently disintegrate the algal cells to release both carbohydrates and proteins at yields comparable to the benchmark bead milling (40–45% protein, 48–58% carbohydrates)

Precision Nephrology Is a Non-Negligible State of Mind in Clinical Research:Remember the Past to Face the Future

CKD is a major public health problem. It is characterized by a multitude of risk factors that, when aggregated, can strongly modify outcome. While major risk factors, namely, albuminuria and low estimated glomerular filtration rate (eGFR) have been well analyzed, a large variability in disease progression still remains. This happens because (1) the weight of each risk factor varies between populations (general population or CKD cohort), countries, and single individuals and (2) response to nephroprotective drugs is so heterogeneous that a non-negligible part of patients maintains a high cardiorenal risk despite optimal treatment. Precision nephrology aims at individualizing cardiorenal prognosis and therapy. The purpose of this review is to focus on the risk stratification in different areas, such as clinical practice, population research, and interventional trials, and to describe the strategies used in observational or experimental studies to afford individual-level evidence. The future of precision nephrology is also addressed. Observational studies can in fact provide more adequate findings by collecting more information on risk factors and building risk prediction models that can be applied to each individual in a reliable fashion. Similarly, new clinical trial designs can reduce the individual variability in response to treatment and improve individual outcomes