Effects of metformin and exercise training, alone or in association, on cardio-pulmonary performance and quality of life in insulin resistance patients

BACKGROUND: Metformin (MET) therapy exerts positive effects improving glucose tolerance and preventing the evolution toward diabetes in insulin resistant patients. It has been shown that adding MET to exercise training does not improve insulin sensitivity. The aim of this study was to determine the effect of MET and exercise training alone or in combination on maximal aerobic capacity and, as a secondary end-point on quality of life indexes in individuals with insulin resistance. METHODS: 75 insulin resistant patients were enrolled and subsequently assigned to MET (M), MET with exercise training (MEx), and exercise training alone (Ex). 12-weeks of supervised exercise-training program was carried out in both Ex and MEx groups. Cardiopulmonary exercise test and SF-36 to evaluate Health-Related Quality of Life (HRQoL) was performed at basal and after 12-weeks of treatment. RESULTS: Cardiopulmonary exercise test showed a significant increase of peak VO2 in Ex and MEx whereas M showed no improvement of peak VO2 (∆ VO2 [CI 95%] Ex +0.26 [0.47 to 0.05] l/min; ∆ VO2 MEx +0.19 [0.33 to 0.05] l/min; ∆ VO2 M -0.09 [-0.03 to -0.15] l/min; M vs E p < 0.01; M vs MEx p < 0.01; MEx vs Ex p = ns). SF-36 highlighted a significant increase in general QoL index in the MEx (58.3 ± 19 vs 77.3 ± 16; p < 0.01) and Ex (62.1 ± 17 vs 73.7 ± 12; p < 0.005) groups. CONCLUSIONS: We evidenced that cardiopulmonary negative effects showed by MET therapy may be counterbalanced with the combination of exercise training. Given that exercise training associated with MET produced similar effects to exercise training alone in terms of maximal aerobic capacity and HRQoL, programmed exercise training remains the first choice therapy in insulin resistant patients

Efficacy of Spectral Signatures for the Automatic Classification of Abnormal Ventricular Potentials in Substrate-Guided Mapping Procedures

Several peculiar spectral signatures of post-ischaemic ventricular tachycardia (VT) electrograms (EGMs) have been recently published in the scientific literature. However, despite they were claimed as potentially useful for the automatic identification of arrhythmogenic targets for the VT treatment by trans-catheter ablation, their exploitation in machine learning (ML) applications has been not assessed yet. The aim of this work is to investigate the impact of the information retrieved from these frequency-domain signatures in modelling supervised ML tools for the identification of physiological and abnormal ventricular potentials (AVPs). As such, 1504 bipolar intracardiac EGMs from nine electroanatomic mapping procedures of post-ischaemic VT patients were retrospectively labelled as AVPs or physiological by an expert electrophysiologist. In order to assess the efficacy of the proposed spectral features for AVPs recognition, two different classifiers were adopted in a 10-time 10-fold cross-validation scheme. In both classifiers, the adoption of spectral signatures led to recognition accuracy values above 81%, suggesting that the use of the frequency-domain characteristics of these signals can be successfully considered for the computer-aided recognition of AVPs in substrate-guided mapping procedures

Interpretability of fingerprint presentation attack detection systems: a look at the “representativeness” of samples against never-seen-before attacks

Nowadays, fingerprint Presentation Attack Detection systems (PADs) are primarily based on deep learning architectures subjected to massive training. However, their performance decreases to never-seen-before attacks. With the goal of contributing to explaining this issue, we hypothesized that this limited ability to generalize is due to the lack of "representativeness" of the samples available for the PAD training. "Representativeness" is treated here from a geometrical perspective: the spread of samples into the feature space, especially near the decision boundaries. In particular, we explored the possibility of adopting three-dimensionality reduction methods to make the problem affordable through visual inspection. These methods enable visual inspection and interpretation by projecting data into two-dimensional spaces, facilitating the identification of weak areas in the decision regions estimated after the training phase. Our analysis delineates the benefits and drawbacks of each dimensionality reduction method and leads us to make substantial recommendations in the crucial phase of the training design

Meta-analysis of genome-wide association studies from the CHARGE consortium identifies common variants associated with carotid intima media thickness and plaque

Carotid intima media thickness (cIMT) and plaque determined by ultrasonography are established measures of subclinical atherosclerosis that each predicts future cardiovascular disease events. We conducted a meta-analysis of genome-wide association data in 31,211 participants of European ancestry from nine large studies in the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. We then sought additional evidence to support our findings among 11,273 individuals using data from seven additional studies. In the combined meta-analysis, we identified three genomic regions associated with common carotid intima media thickness and two different regions associated with the presence of carotid plaque (P < 5 × 10 -8). The associated SNPs mapped in or near genes related to cellular signaling, lipid metabolism and blood pressure homeostasis, and two of the regions were associated with coronary artery disease (P < 0.006) in the Coronary Artery Disease Genome-Wide Replication and Meta-Analysis (CARDIoGRAM) consortium. Our findings may provide new insight into pathways leading to subclinical atherosclerosis and subsequent cardiovascular events

Texture and artifact decomposition for improving generalization in deep-learning-based deepfake detection

The harmful utilization of DeepFake technology poses a significant threat to public welfare, precipitating a crisis in public opinion. Existing detection methodologies, predominantly relying on convolutional neural networks and deep learning paradigms, focus on achieving high in-domain recognition accuracy amidst many forgery techniques. However, overseeing the intricate interplay between textures and artifacts results in compromised performance across diverse forgery scenarios. This paper introduces a groundbreaking framework, denoted as Texture and Artifact Detector (TAD), to mitigate the challenge posed by the limited generalization ability stemming from the mutual neglect of textures and artifacts. Specifically, our approach delves into the similarities among disparate forged datasets, discerning synthetic content based on the consistency of textures and the presence of artifacts. Furthermore, we use a model ensemble learning strategy to judiciously aggregate texture disparities and artifact patterns inherent in various forgery types, thereby enabling the model’s generalization ability. Our comprehensive experimental analysis, encompassing extensive intra-dataset and cross-dataset validations along with evaluations on both video sequences and individual frames, confirms the effectiveness of TAD. The results from four benchmark datasets highlight the significant impact of the synergistic consideration of texture and artifact information, leading to a marked improvement in detection capabilities

Data generation via diffusion models for crowd anomaly detection

Crowd analysis is a critical aspect of public security and video surveillance. One of the primary challenges in developing effective crowd anomaly detectors is the lack of comprehensive training data. To address this issue, we investigate using synthetic data to enhance training for anomaly detection in crowded environments by generating a dataset of synthetic videos using two open-source diffusion models. Each synthetic video depicts typical crowded scenes that may be either normal or anomalous. To assess the effectiveness of our approach, we compare the model’s performance across three training scenarios: using only real videos, only synthetic videos, and a combination of both. This preliminary analysis highlights the potential of data generated via diffusion models to improve crowd anomaly detectors’ stability and classification capabilities

Exploring Transfer Learning for Ventricular Tachycardia Electrophysiology Studies

Arrhythmogenic sites in post-ischemic ventricular tachycardia (VT) are usually identified by looking for abnormal ventricular potentials (AVPs) in intracardiac electrograms (EGMs). Unfortunately, the accurate recognition of AVPs is a challenging problem for different reasons, including the intrinsic variability in the A VP waveform. Given the high performance of deep neural networks in several scenarios, in this work, we explored the use of transfer learning (TL) for AVPs detection in intracardiac electrophysiology. A balanced set of 1504 bipolar intracardiac EGMs was collected from nine post-ischemic VT patients. The time-frequency representation was generated for each EGM by computing the synchrosqueezed wavelet transform to be used in the re-training of the convolutional neural network. The proposed approach allows obtaining high recognition results, above 90% for all the investigated performance indexes, demonstrating the effectiveness of deep learning in the recognition of AVPs in post-ischemic VT EGMs and paving the way for its use in supporting clinicians in targeting arrhythmogenic sites. In addition, this study further confirms the efficacy of the TL approach even in case of limited dataset sizes

LivDet2023 - Fingerprint Liveness Detection Competition: Advancing Generalization

The International Fingerprint Liveness Detection Competition (LivDet) is a biennial event that invites academic and industry participants to prove their advancements in Fingerprint Presentation Attack Detection (PAD). This edition, LivDet2023, proposed two challenges, "Liveness Detection in Action" and "Fingerprint Representation", to evaluate the efficacy of PAD embedded in verification systems and the effectiveness and compactness of feature sets. A third, "hidden" challenge is the inclusion of two subsets in the training set whose sensor information is unknown, testing participants' ability to generalize their models. Only bona fide fingerprint samples were provided to participants, and the competition reports and assesses the performance of their algorithms suffering from this limitation in data availability

Fingerprint Presentation Attacks: Tackling the Ongoing Arms Race in Biometric Authentication

The widespread use of Automated Fingerprint Identification Systems (AFIS) in consumer electronics opens for the development of advanced presentation attacks, i.e. procedures designed to bypass an AFIS using a forged fingerprint. As a consequence, AFIS are often equipped with a fingerprint presentation attack detection (FPAD) module, to recognize live fingerprints from fake replicas, in order to both minimize the risk of unauthorized access and avoid pointless computations. The ongoing arms race between attackers and detector designers demands a comprehensive understanding of both the defender’s and attacker’s perspectives to develop robust and efficient FPAD systems. This paper proposes a dual-perspective approach to FPAD, which encompasses the presentation of a new technique for carrying out presentation attacks starting from perturbed samples with adversarial techniques and the presentation of a new detection technique based on an adversarial data augmentation strategy. In this case, attack and defence are based on the same assumptions demonstrating that this dual research approach can be exploited to enhance the overall security of fingerprint recognition systems against spoofing attacks

Glucose-6-phosphate dehydrogenase deficiency accelerates arterial aging in diabetes

Aims High glucose levels and Glucose-6-Phosphate Dehydrogenase deficiency (G6PDd) have both tissue inflammatory effects. Here we determined whether G6PDd accelerates arterial aging (information linked stiffening) in diabetes.MethodsPlasma glucose, interleukin 6 (IL6), and arterial stiffness (indexed as carotid-femoral Pulse Wave Velocity, PWV) and red blood cell G6PD activity were assessed in a large (4448) Sardinian population.ResultsAlthough high plasma glucose in diabetics, did not differ by G6DP status (178.2 +/- 55.1 vs 169.0 +/- 50.1 mg/dl) in G6DPd versus non-G6PDd subjects, respectively, IL6, and PWV (adjusted for age and glucose) were significantly increased in G6PDd vs non-G6PDd subjects (PWV, 8.0 +/- 0.4 vs 7.2 +/- 0.2 m/sec) and (IL6, 6.9 +/- 5.0 vs 4.2 +/- 3.0 pg/ml). In non-diabetics, neither fasting plasma glucose, nor IL6, nor PWV were impacted by G6PDd.ConclusionG6PDd in diabetics is associated with increased inflammatory markers and accelerated arterial aging