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

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

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%

An observational retrospective/horizontal study to compare oxygen-ozone therapy and/or global postural re-education in complicated chronic low back pain

Acute low back pain (LBP) is the fifth most common reason for physician visits and about nine out of ten adults experience back pain at some point in their life. In a large number of patients LBP is associated with disc herniation (DH). Recently, oxygen-ozone (O2O3) therapy has been used successfully in the treatment of LBP, reducing pain after the failure of other conservative treatments. The aim of this study was to assess the effects of O2O3 therapy in back pain rehabilitation, comparing three groups of patients suffering from chronic back pain associated with DH submitted to three different treatments: intramuscular O2O3 infiltrations, global postural re-education (GPR), or a combination of the two (O2O3+GPR). The data show that pain severity before treatment was significantly lower in the patients treated with GPR alone (VAS score 7.4) than in the O2O3+GPR patients (VAS score 8.5) and the O2O3 patients (VAS score 8.6). At the end of treatment, pain severity was lower in the O2O3 patients than in the GPR-alone patients. After some years of follow-up only the difference between O2O3+GPR and GPR-alone remained significant

Development and Validation of Machine-Learning Models to Support Clinical Diagnosis for Non-Epileptic Psychogenic Seizures

Electroencephalographic (EEG) signal processing and machine learning can support neurologists’ work in discriminating Psychogenic Non-Epileptic Seizure (PNES) from epilepsy. PNES represents a neurological disease often misdiagnosed. Although the symptoms of PNES patients can be similar to those exhibited by epileptic patients, EEG signals during a psychogenic seizure do not show ictal patterns such as in epilepsy. Therefore, PNES diagnosis requires long-term EEG video. Applying signal processing and machine-learning methodologies could help clinicians find helpful information in the clinical diagnosis of PNES by analyzing EEG signals registered in resting conditions and in a short time. These methodologies should prevent long EEG recording sessions and avoid inducing seizures in the subjects. The aim of our study is to develop and validate several machine-learning models on a larger dataset, consisting of 225 EEGs (75 healthy, 75 PNES, and 75 subjects with epilepsy). A deep analysis of our results shows that changes in the evaluation strategy led to changes in accuracy from 45% to 83.98% for a standard Light Gradient Boosting Machine (LGBM) classifier. Our findings suggest that it is necessary to operate a very rigorous control in terms of experimental data collection (patient selection, signal acquisition) and terms of validation strategies to obtain and reproducible results