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

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

Unsupervised neural networks as a support tool for pathology diagnosis in MALDI-MSI experiments:A case study on thyroid biopsies

Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets for training and validation, their application is limited in clinical fields since ground truth information can hardly be obtained on a sizeable cohort of patients. Unsupervised neural networks - such as Self-Organizing Maps (SOMs) - represent an alternative approach to identifying hidden patterns in biomedical data. Here we investigate the feasibility of SOMs for the identification of malignant and non-malignant regions in liquid biopsies of thyroid nodules, on a patient-specific basis. MALDI-ToF (Matrix Assisted Laser Desorption Ionization -Time of Flight) mass spectrometry-imaging (MSI) was used to measure the spectral profile of bioptic samples. SOMs were then applied for the analysis of MALDI-MSI data of individual patients' samples, also testing various pre-processing and agglomerative clustering methods to investigate their impact on SOMs' discrimination efficacy. The final clustering was compared against the sample's probability to be malignant, hyperplastic or related to Hashimoto thyroiditis as quantified by multinomial regression with LASSO. Our results show that SOMs are effective in separating the areas of a sample containing benign cells from those containing malignant cells. Moreover, they allow to overlap the different areas of cytological glass slides with the corresponding proteomic profile image, and inspect the specific weight of every cellular component in bioptic samples. We envision that this approach could represent an effective means to assist pathologists in diagnostic tasks, avoiding the need to manually annotate cytological images and the effort in creating labeled datasets

The routine use of a digital tool for the tumor cell fraction quantification in molecular pathology: an international validation of QuANTUM

Objective. The absolute and relative quantification of tumor cell fraction (TCF) in tissue samples for molecular pathology testing is time-consuming and poorly reproducible. Methods. Here we report the results of an international survey on non-small cell lung cancer (NSCLC), validating the Qupath Analysis of Nuclei from Tumor to Uniform Molecular tests (QuANTUM) automated computational pipeline for TCF quantification. Results. The TCF obtained with QuANTUM is reliable, as demonstrated by the comparison with the manual counting of cells (ground truth, GT) in cell blocks, small biopsies and surgical specimens (overall correlation of 0.89). The visual evaluation of QuANTUMprocessed images increased the pathologists’ agreement with GT and QuANTUM of +0.16, +0.21, +0.09 and +0.17, +0.29, +0.21 across the three sample types, respectively. An overall increase in cases classified as containing ≥100 tumor cells for all sample types was noted after QuANTUM (from 75 cases, 63% to 96 cases, 80% among cell blocks, p = 0.003). Conclusions. QuANTUM is an easy-to-use and reliable tool for the TCF assessment and its employment significantly modifies the visual estimation by pathologists, improving the assessment of NSCLC cases for molecular analysi

Biomarkers discovery through multivariate statistical methods to face clinical issues concerning thyroid tumour variants classification

Thyroid nodules are common among Western populations, with an estimated prevalence of 50% among individuals aged above 60. However, only 5-10% of the nodules are cancerous, making identifying malignant lesions a substantial health concern for pathologists searching for novel and more accurate diagnostic tools and techniques. Machine Learning (ML) algorithms have emerged as a transformative force in healthcare, improving medical practice in several aspects, including diagnosing tumours. Among the possible biomarkers of thyroid cancer, molecular features obtained through Matrix Assisted Laser Desorption Ionization Mass Spectrometry Imaging (MALDI-MSI) are the most promising. This work presents an application of several ML algorithms using molecular features to build an accurate diagnostic tool for the classification of thyroid nodules [1]. The primary goal of this research is to discover discriminatory molecular signals that can serve as valuable biomarkers. These tumour markers play a crucial role in accurately classifying undefined thyroid cancer variants, such as the Non Invasive Follicular Thyroid Neoplasm with Papillary-like nuclear features type (NIFTP), shedding light on their behaviour and establishing connections to malignancy or benignity. Regarding the ML methods considered for the task, the implementation and comparison of Linear Discriminant Analysis (LDA), Diagonal Discriminant Analysis (DDA), and sparse Partial Least Squares Discriminant Analysis (sPLS-DA) in this work have provided valuable insights into understanding the behaviour of NIFTP. The noteworthy aspect is that all three techniques discover common and relevant features as biomarkers for the NIFTP class, thus improving the reliability of the results from a statistical point of view. These supervised approaches have enabled the identification of specific molecular signals that effectively distinguish thyroid tumour classes, shedding light on NIFTP-type characteristics within this context, achieving accuracy greater than 0.9. This synergy between the medical and machine learning domains can also catalyze further exploration in biomarker discovery. Expanding the applications of supervised learning approaches to address clinical issues in the omics field is a pivotal aspect that can foster cutting-edge research and provide a reliable starting point for researchers to implement and enhance machine learning techniques

THE MANAGEMENT OF HAEMOGLOBIN INTERFERENCE FOR THE MALDI-MSI ANALYSIS OF IN VIVO THYROID BIOPSIES

Introduction Fine Needle Aspiration biopsy (FNAB) is the gold standard exam to determine the malignant nature of thyroid nodules[1]. Contamination of FNAB samples with red blood cells is problematic for proteomics analysis, given that large amounts of haemoglobin (Hb) suppress other protein signals[2]. Hence, it is paramount to standardise the sample preparation of ex-vivo and in-vivo thyroid FNABs for proteomic MALDI-MSI analysis, in order to minimise Hb interference. Methods Human FNABs were collected and deposited onto conductive glass slides from both ex-vivo(n=9), surgically removed thyroid specimens, and in-vivo(n=12) thyroid specimens for intact proteins MALDI-MSI analysis. Three protocols were compared using ex-vivo biopsies collected from the same thyroid: a) conventional air dried smear; b) cytological smear immediately fixed in ethanol; c) ThinPrep (TP) cytological preparation using a ThinPrep 2000 system. Results The spectral profiles of both ex-vivo and in-vivo conventional air-dried smears were characterized by high 917 inter-patient variability related to the abundance of the Hb peaks. In particular, the strong vascularization of some thyroid nodules is reflected in FNABs with a high content of Hb. The amount of Hb was markedly decreased in TP preparation with respect to both conventional air-dried and fixed smears. On the other hand, the absolute intensity of other protein signals, suppressed with the other two methods, were significantly increased in TP samples. Furthermore, the management of Hb interference of ex-vivo and in vivo TP samples was comparable, indicating the opportunity to use in-vivo TP specimens for MALDI-MSI proteomic analysis and biomarker discovery. The MALDI-MSI approach combined with virtual microdissection permitted to extract specific protein signatures from different histotypes of both benign and malignant thyroid cell clusters. Conclusions The Thin Prep procedure for thyroid FNABs samples preparation combined with MALDI-MSI proteomic analysis allow us to obtain high-quality spectra, follicular cells specific protein profiles and to manage the haemoglobin interference. The application of this reproducible technique to in-vivo cytological samples can help cytopathologists in the diagnosis of thyroid nodules combining both morphological and proteomics information. Novel Aspect This study represents the first example of MALDI-MSI applied to ex-vivo and in-vivo thyroid FNABs, prepared using the ThinPrep preparation, for proteomic analysis. References 1. Russ G, Bonnema SJ, Erdogan MF, Durante C, Ngu R, Leenhardt L. European Thyroid Journal, 6, 225-237 (2017). 2. Amann JM, Chaurand P, Gonzalez A, Mombley J, Massion PP, Carbone DP, Caprioli RM, Clinical Cancer Research, 12, 5142–5150 (2006). Funding: This work was funded thanks to AIRC (AssociazioneItaliana per la RicercasulCancro) MFAG GRANT 2016 - Id. 18445

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

AbstractMathematical 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.Author summarySystems Biology is an interdisciplinary research area focusing on the integration of biological data with mathematical and computational methods in order to unravel and predict the emergent behavior of complex biological systems. The ultimate goal is the understanding of the complex mechanisms at the basis of biological processes, together with the formulation of novel hypotheses that can be then tested by means of laboratory experiments. In such a context, mechanistic models can be used to describe and investigate biochemical reaction networks by taking into account all the details related to their stoichiometry and kinetics. Unfortunately, these models can be characterized by hundreds or thousands of molecular species and biochemical reactions, making their simulation unfeasible with classic simulators running on Central Processing Units (CPUs). In addition, a large number of simulations might be required to calibrate the models and/or to test the effect of perturbations. In order to overcome the limitations imposed by CPUs, Graphics Processing Units (GPUs) can be effectively used to accelerate the simulations of these models. We thus designed and developed a novel GPU-based tool, called FiCoS, to speed-up the computational analyses typically required in Systems Biology.</jats:sec

Detecting latent spatial patterns in mass spectrometry brain imaging data via Bayesian mixtures

Mass spectrometry methods can record biomolecule abundance for a broad set of molecular masses given a sample of a specific biological tissue. In particular, the MALDI-MSI technique produces imaging data where, for each pixel, a mass spectrum is recorded. There is the urge to rely on suited statistical methods to model these data, fully addressing their morphological characteristics. Here, we investigate the use of Bayesian mixture models to segment these real biomedical images. We aim to detect groups of pixels that present similar patterns to extract interesting insights, such as anomalies that one cannot capture from the original pictures. This task is particularly challenging given the high dimensionality of the data and the spatial correlation among pixels. To account for the spatial nature of the dataset, we rely on Hidden Markov Random Field