An Extensive Assessment of Network Embedding in PPI Network Alignment.

Network alignment is a fundamental task in network analysis. In the biological field, where the protein-protein interaction (PPI) is represented as a graph, network alignment allowed the discovery of underlying biological knowledge such as conserved evolutionary pathways and functionally conserved proteins throughout different species. A recent trend in network science concerns network embedding, i.e., the modelling of nodes in a network as a low-dimensional feature vector. In this survey, we present an overview of current PPI network embedding alignment methods, a comparison among them, and a comparison to classical PPI network alignment algorithms. The results of this comparison highlight that: (i) only five network embeddings for network alignment algorithms have been applied in the biological context, whereas the literature presents several classical network alignment algorithms; (ii) there is a need for developing an evaluation framework that may enable a unified comparison between different algorithms; (iii) the majority of the proposed algorithms perform network embedding through matrix factorization-based techniques; (iv) three out of five algorithms leverage external biological resources, while the remaining two are designed for domain agnostic network alignment and tested on PPI networks; (v) two algorithms out of three are stated to perform multi-network alignment, while the remaining perform pairwise network alignment.

A Comprehensive Machine-Learning-Based Software Pipeline to Classify EEG Signals: A Case Study on PNES vs. Control Subjects.

The diagnosis of psychogenic nonepileptic seizures (PNES) by means of electroencephalography (EEG) is not a trivial task during clinical practice for neurologists. No clear PNES electrophysiological biomarker has yet been found, and the only tool available for diagnosis is video EEG monitoring with recording of a typical episode and clinical history of the subject. In this paper, a data-driven machine learning (ML) pipeline for classifying EEG segments (i.e., epochs) of PNES and healthy controls (CNT) is introduced. This software pipeline consists of a semiautomatic signal processing technique and a supervised ML classifier to aid clinical discriminative diagnosis of PNES by means of an EEG time series. In our ML pipeline, statistical features like the mean, standard deviation, kurtosis, and skewness are extracted in a power spectral density (PSD) map split up in five conventional EEG rhythms (delta, theta, alpha, beta, and the whole band, i.e., 1-32 Hz). Then, the feature vector is fed into three different supervised ML algorithms, namely, the support vector machine (SVM), linear discriminant analysis (LDA), and Bayesian network (BN), to perform EEG segment classification tasks for CNT vs. PNES. The performance of the pipeline algorithm was evaluated on a dataset of 20 EEG signals (10 PNES and 10 CNT) that was recorded in eyes-closed resting condition at the Regional Epilepsy Centre, Great Metropolitan Hospital of Reggio Calabria, University of Catanzaro, Italy. The experimental results showed that PNES vs. CNT discrimination tasks performed via the ML algorithm and validated with random split (RS) achieved an average accuracy of 0.97 ± 0.013 (RS-SVM), 0.99 ± 0.02 (RS-LDA), and 0.82 ± 0.109 (RS-BN). Meanwhile, with leave-one-out (LOO) validation, an average accuracy of 0.98 ± 0.0233 (LOO-SVM), 0.98 ± 0.124 (LOO-LDA), and 0.81 ± 0.109 (LOO-BN) was achieved. Our findings showed that BN was outperformed by SVM and LDA. The promising results of the proposed software pipeline suggest that it may be a valuable tool to support existing clinical diagnosis.

Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.

Through an adequate survey of the history of the disease, Narrative Medicine (NM) aims to allow the definition and implementation of an effective, appropriate, and shared treatment path. In the present study different topic modeling techniques are compared, as Latent Dirichlet Allocation (LDA) and topic modeling based on BERT transformer, to extract meaningful insights in the Italian narration of COVID-19 pandemic. In particular, the main focus was the characterization of Post-acute Sequelae of COVID-19, (i.e., PASC) writings as opposed to writings by health professionals and general reflections on COVID-19, (i.e., non-PASC) writings, modeled as a semi-supervised task. The results show that the BERTopic-based approach outperforms the LDA-base approach by grouping in the same cluster the 97.26% of analyzed documents, and reaching an overall accuracy of 91.97%.

COVID-19 Community Temporal Visualizer: a new methodology for the network-based analysis and visualization of COVID-19 data.

Understanding the evolution of the spread of the COVID-19 pandemic requires the analysis of several data at the spatial and temporal levels. Here, we present a new network-based methodology to analyze COVID-19 data measures containing spatial and temporal features and its application on a real dataset. The goal of the methodology is to analyze sets of homogeneous datasets (i.e. COVID-19 data taken in different periods and in several regions) using a statistical test to find similar/dissimilar datasets, mapping such similarity information on a graph and then using a community detection algorithm to visualize and analyze the spatio-temporal evolution of data. We evaluated diverse Italian COVID-19 data made publicly available by the Italian Protezione Civile Department at https://github.com/pcm-dpc/COVID-19/. Furthermore, we considered the climate data related to two periods and we integrated them with COVID-19 data measures to detect new communities related to climate changes. In conclusion, the application of the proposed methodology provides a network-based representation of the COVID-19 measures by highlighting the different behaviour of regions with respect to pandemics data released by Protezione Civile and climate data. The methodology and its implementation as R function are publicly available at https://github.com/mmilano87/analyzeC19D.

COVID-WAREHOUSE: A Data Warehouse of Italian COVID-19, Pollution, and Climate Data.

The management of the COVID-19 pandemic presents several unprecedented challenges in different fields, from medicine to biology, from public health to social science, that may benefit from computing methods able to integrate the increasing available COVID-19 and related data (e.g., pollution, demographics, climate, etc.). With the aim to face the COVID-19 data collection, harmonization and integration problems, we present the design and development of COVID-WAREHOUSE, a data warehouse that models, integrates and stores the COVID-19 data made available daily by the Italian Protezione Civile Department and several pollution and climate data made available by the Italian Regions. After an automatic ETL (Extraction, Transformation and Loading) step, COVID-19 cases, pollution measures and climate data, are integrated and organized using the Dimensional Fact Model, using two main dimensions: time and geographical location. COVID-WAREHOUSE supports OLAP (On-Line Analytical Processing) analysis, provides a heatmap visualizer, and allows easy extraction of selected data for further analysis. The proposed tool can be used in the context of Public Health to underline how the pandemic is spreading, with respect to time and geographical location, and to correlate the pandemic to pollution and climate data in a specific region. Moreover, public decision-makers could use the tool to discover combinations of pollution and climate conditions correlated to an increase of the pandemic, and thus, they could act in a consequent manner. Case studies based on data cubes built on data from Lombardia and Puglia regions are discussed. Our preliminary findings indicate that COVID-19 pandemic is significantly spread in regions characterized by high concentration of particulate in the air and the absence of rain and wind, as even stated in other works available in literature.

Oral Hypoglycemic Drugs: Pathophysiological Basis of Their Mechanism of ActionOral Hypoglycemic Drugs: Pathophysiological Basis of Their Mechanism of Action.

Type 2 diabetes is a syndrome characterized by relative insulin deficiency, insulin resistance and increased hepatic glucose output. Medications used to treat the disease are designed to correct one or more of these metabolic abnormalities. Current recommendations of the American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD) include diet and exercise as first-line therapy plus hypoglycemic drugs. Actually there are seven distinct classes of anti-hyperglicemic agents, each of them displaying unique pharmacologic properties. The aim of this review is to describe the pathophysiological basis of their mechanism of action, a necessary step to individualize treatment of diabetic people, taking into proper consideration potential benefits and secondary effects of drugs.

Fasting plasma C-peptide and micro- and macrovascular complications in a large clinic-based cohort of type 1 diabetic patients.

OBJECTIVE: A protective effect of residual beta-cell function on microvascular complications of type 1 diabetes has been suggested. Our aim was to retrospectively evaluate the association of fasting plasma C-peptide values with micro- and macrovascular complications. RESEARCH DESIGN AND METHODS: We recruited a clinic-based cohort of 471 type 1 diabetic patients born after 1945 and cared for in the period 1994-2004. Centralized measurements and standardized procedures of ascertainment of micro- and macrovascular complications were employed. Individual cumulative averages of A1C up to 2007 were calculated. RESULTS: Residual beta-cell secretion was detected even many years after diabetes diagnosis. In multivariate linear regression analysis, fasting plasma C-peptide values were positively associated with age at diagnosis (beta = 0.02; P < 0.0001) and triglycerides (beta = 0.20; P = 0.05) and inversely associated with diabetes duration (beta = -0.03; P < 0.0001) and HDL cholesterol (beta = -0.006; P = 0.03). The final model explained 21% of fasting C-peptide variability. With respect to fasting C-peptide values in the lowest tertile (<0.06 nmol/l), higher values were associated with lower prevalence of microvascular complications (odds ratio [OR] 0.59 [95% CI 0.37-0.94]) independently of age, sex, diabetes duration, individual cumulative A1C average during the study period, hypertension, and cardiovascular diseases. No association was evident with macrovascular complications (0.77 [0.38-1.58]). CONCLUSIONS: Our study shows an independent protective effect of residual beta-cell function on the development of microvascular complications in type 1 diabetes, suggesting the potential beneficial effect of treatment that allows the preservation of even modest beta-cell function over time.

What is the clinical usefulness of the metabolic syndrome? The Casale Monferrato study.

OBJECTIVE: Data on the clinical usefulness of the metabolic syndrome with respect to cardiovascular risk are not conclusive. We have assessed this issue in a large population-based cohort of diabetic and nondiabetic people in Southern Europe. METHODS: An Italian population-based cohort of 3729 individuals (2211 without diabetes and 1518 with diabetes) was examined, with centralized measurements, including the Homeostasis Model Assessment (HOMA) index in nondiabetic people. The usefulness of the metabolic syndrome (ATP III criteria) as an indicator of cardiovascular disease (CVD), independently of classical and novel risk factor [C-reactive protein (CRP) and albumin excretion rate (AER)] was assessed by using unconditional logistic regression. RESULTS: One thousand, seven hundred and fifty-three individuals (47.0%) had neither diabetes nor the metabolic syndrome, 458 (12.3%) had the metabolic syndrome only, 442 (11.8%) had type 2 diabetes only and 1076 (28.9%) had both diabetes and the metabolic syndrome. The highest likelihood of having CVD was conferred by both diabetes and the metabolic syndrome [odds ratio (OR) = 4.37, 95% confidence interval (CI) 3.25-5.87], independently of age, sex, low-density lipoprotein-cholesterol, smoke, AER, and CRP values. After further adjustment for its individual components, the association between CVD and the metabolic syndrome was no more evident. Among people with CRP 3 mg/l or less, ORs were similar in nondiabetic people with the metabolic syndrome and in diabetic people without it, whereas among those with CRP greater than 3 mg/l OR was two-fold higher in the latter. Values in upper quartiles of the HOMA-IR conferred a significant two-fold increased OR of CVD, even after adjustment for individual components of the metabolic syndrome, CRP and AER. CONCLUSIONS: The additional information provided by the metabolic syndrome is limited, in both diabetic and nondiabetic people, whereas the HOMA index is a useful indicator of CVD, independently of individual components of the metabolic syndrome, classical and novel risk factors.

C-reactive protein and 5-year survival in type 2 diabetes: the Casale Monferrato Study.

OBJECTIVE: To determine to what extent plasma C-reactive protein (CRP) values influence 5-year all-cause and cardiovascular mortality in type 2 diabetic individuals, independently of albumin excretion rate (AER) and other cardiovascular risk factors, and its incremental usefulness for predicting individual risk of mortality. RESEARCH DESIGN AND METHODS: Measurements of CRP were performed in 2,381 of 3,249 (73.3%) subjects as part of the population-based Casale Monferrato Study. Its association with 5-year all-cause and cardiovascular mortality was assessed with multivariate Cox proportional hazards modeling. The C statistic and measures of calibration and global fit were also assessed. RESULTS: Results are based on 496 deaths in 11.717 person-years of observations (median follow-up 5.4 years). With respect to subjects with CRP < or =3 mg/l, those with higher values had an adjusted hazard ratio (HR) of 1.51 (95% CI 1.18-1.92) for all-cause mortality and 1.44 (0.99-2.08) for cardiovascular mortality. In normoalbuminuric subjects, respective HRs of CRP were 1.56 (1.13-2.15) and 1.65 (1.00-2.74), AER being neither a modifier nor a confounder of CRP association. In analysis limited to diabetic subjects without cardiovascular disease (CVD), adjusted HRs were 1.67 (1.24-2.24) for all-cause mortality and 1.36 (0.83-2.24) for cardiovascular mortality. The improvement in individual risk assessment was marginal when measured with various statistical measures of model discrimination, calibration, and global fit. CONCLUSIONS: CRP measurement is independently associated with short-term mortality risk in type 2 diabetic individuals, even in normoalbuminuric subjects and in those without a previous diagnosis of CVD. Its clinical usefulness in individual assessment of 5-year risk of mortality, however, is limited.