Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection ...(DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder’s reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell’s c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.
The increased availability of high quality data from post disaster field reconnaissance, enabled the use of deep learning algorithms in the field of geotechnical earthquake engineering. The 2010-2011 ...Canterbury earthquake sequence in New Zealand caused significant damage due to abundant manifestation of liquefaction induced lateral spreading. The data available from this sequence is an ideal case study for deep learning analyses due to the amount and quality of information available through the New Zealand Geotechnical Database (NZGD). A dataset of about 7500 datapoints was collected and organized by the authors to develop a new Graph Neural Network (GNN) algorithm for lateral spreading in the Canterbury area. The comparison between predicted and observed data is performed using feed forward Neural Network. Several GNN models with different hyperparameters are explored and the best model is presented in this paper, and Explainable Artificial Intelligence is applied to the model that provides the best performance. These computationally expensive analyses were carried out utilizing cloud based computing capabilities offered by the Texas Advanced Computing Center (TACC) available to the natural hazard community through the cyberinfrastructure DesignSafe.
Adherence to Mediterranean diet (MD) and physical activity (PA) in adolescence represent powerful indicators of healthy lifestyles in adulthood. The aim of this longitudinal study was to investigate ...the impact of nutrition education program (NEP) on the adherence to the MD and on the inflammatory status in healthy adolescents, categorized into three groups according to their level of PA (inactivity, moderate intensity, and vigorous intensity). As a part of the DIMENU (Dieta Mediterranea & Nuoto) study, 85 adolescents (aged 14–17 years) participated in the nutrition education sessions provided by a team of nutritionists and endocrinologists at T0. All participants underwent anthropometric measurements, bio-impedentiometric analysis (BIA), and measurements of inflammatory biomarkers such as ferritin, erythrocyte sedimentation rate (ESR), and C-reactive protein (CRP) levels. Data were collected at baseline (T0) and 6 months after NEP (T1). To assess the adherence to the MD, we used KIDMED score. In our adolescents, we found an average MD adherence, which was increased at T1 compared with T0 (T0: 6.03 ± 2.33 vs. T1: 6.96 ± 2.03,
p
= 0.002), with an enhanced percentage of adolescents with optimal (≥8 score) MD adherence over the study period (T0: 24.71% vs. T1: 43.52%,
p
= 0.001). Interestingly, in linear mixed-effects models, we found that NEP and vigorous-intensity PA levels independently influenced KIDMED score (β = 0.868,
p
< 0.0001 and β = 1.567,
p
= 0.009, respectively). Using ANOVA, NEP had significant effects on serum ferritin levels (
p
< 0.001), while either NEP or PA influenced ESR (
p
= 0.035 and 0.002, respectively). We also observed in linear mixed-effects models that NEP had a negative effect on ferritin and CRP (β = −14.763,
p
< 0.001 and β = −0.714,
p
= 0.02, respectively). Our results suggest the usefulness to promote healthy lifestyle, including either nutrition education interventions, or PA to improve MD adherence and to impact the inflammatory status in adolescence as a strategy for the prevention of chronic non-communicable diseases over the entire lifespan.
In the field of functional genomics, the analysis of gene expression profiles through Machine and Deep Learning is increasingly providing meaningful insight into a number of diseases. The paper ...proposes a novel algorithm to perform Feature Selection on genomic-scale data, which exploits the reconstruction capabilities of autoencoders and an ad-hoc defined Explainable Artificial Intelligence-based score in order to select the most informative genes for diagnosis, prognosis, and precision medicine. Results of the application on a Chronic Lymphocytic Leukemia dataset evidence the effectiveness of the algorithm, by identifying and suggesting a set of meaningful genes for further medical investigation.
Automatic Medical Report Generation via Latent Space Conditioning and Transformers Adornetto, Carlo; Guzzo, Antonella; Vasile, Andrea
2023 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech),
2023-Nov.-14
Conference Proceeding
This paper presents a comprehensive exploration of integrating artificial intelligence (AI) in the healthcare sector, focusing on the development and implementation of a novel framework called ...VAE-GPT. Our architecture combines Variational Autoencoder (VAE) and Generative Pre-trained Transformer (GPT), to generate high-quality medical reports. The VAE component enables the model to learn a latent space representation of the images, capturing the underlying patterns and structures. The GPT component leverages the power of transformer-based language models to generate coherent and contextually relevant text. Additionally, a novel metric, Medical Embeddings Attention Distance (MEAD), is proposed in order to capture the semantic similarity between the generated and training medical reports, taking into account the importance of specific words determined by the attention module. Experiments on real dataset demonstrate that our framework achieves state-of-the-art comparable performances in generating accurate and informative medical reports.
X-ray computed microtomography ({\mu}-CT) is a non-destructive technique that can generate high-resolution 3D images of the internal anatomy of medical and biological samples. These images enable ...clinicians to examine internal anatomy and gain insights into the disease or anatomical morphology. However, extracting relevant information from 3D images requires semantic segmentation of the regions of interest, which is usually done manually and results time-consuming and tedious. In this work, we propose a novel framework that uses a convolutional neural network (CNN) to automatically segment the full morphology of the heart of Carassius auratus. The framework employs an optimized 2D CNN architecture that can infer a 3D segmentation of the sample, avoiding the high computational cost of a 3D CNN architecture. We tackle the challenges of handling large and high-resoluted image data (over a thousand pixels in each dimension) and a small training database (only three samples) by proposing a standard protocol for data normalization and processing. Moreover, we investigate how the noise, contrast, and spatial resolution of the sample and the training of the architecture are affected by the reconstruction technique, which depends on the number of input images. Experiments show that our framework significantly reduces the time required to segment new samples, allowing a faster microtomography analysis of the Carassius auratus heart shape. Furthermore, our framework can work with any bio-image (biological and medical) from {\mu}-CT with high-resolution and small dataset size
Autophagy, the cellular process responsible for degradation and recycling of cytoplasmic components through the autophagosomal-lysosomal pathway, is fundamental for neuronal homeostasis and its ...deregulation has been identified as a hallmark of neurodegeneration. Retinal hypoxic-ischemic events occur in several sight-treating disorders, such as central retinal artery occlusion, diabetic retinopathy, and glaucoma, leading to degeneration and loss of retinal ganglion cells. Here we analyzed the autophagic response in the retinas of mice subjected to ischemia induced by transient elevation of intraocular pressure, reporting a biphasic and reperfusion time-dependent modulation of the process. Ischemic insult triggered in the retina an acute induction of autophagy that lasted during the first hours of reperfusion. This early upregulation of the autophagic flux limited RGC death, as demonstrated by the increased neuronal loss observed in mice with genetic impairment of basal autophagy owing to heterozygous ablation of the autophagy-positive modulator Ambra1 (Ambra1
). Upregulation of autophagy was exhausted 24 h after the ischemic event and reduced autophagosomal turnover was associated with build up of the autophagic substrate SQSTM-1/p62, decreased ATG12-ATG5 conjugate, ATG4 and BECN1/Beclin1 expression. Animal fasting or subchronic systemic treatment with rapamycin sustained and prolonged autophagy activation and improved RGC survival, providing proof of principle for autophagy induction as a potential therapeutic strategy in retinal neurodegenerative conditions associated with hypoxic/ischemic stresses.
Glaucoma, a leading cause of irreversible blindness worldwide, is an optic neuropathy characterized by the progressive death of retinal ganglion cells (RGCs). Elevated intraocular pressure (IOP) is ...recognized as the main risk factor. Despite effective IOP-lowering therapies, the disease progresses in a significant number of patients. Therefore, alternative IOP-independent strategies aiming at halting or delaying RGC degeneration is the current therapeutic challenge for glaucoma management. Here, we review the literature on the neuroprotective activities, and the underlying mechanisms, of natural compounds and dietary supplements in experimental and clinical glaucoma.
Glaucoma, a progressive age-related optic neuropathy characterized by the death of retinal ganglion cells, is the most common neurodegenerative cause of irreversible blindness worldwide. The ...therapeutic management of glaucoma, which is limited to lowering intraocular pressure, is still a challenge since visual loss progresses in a significant percentage of treated patients. Restricted dietary regimens have received considerable attention as adjuvant strategy for attenuating or delaying the progression of neurodegenerative diseases. Here we discuss the literature exploring the effects of modified eating patterns on retinal aging and resistance to stressor stimuli.
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