Artificial intelligence in chronic kidney diseases: methodology and potential applications.

Chronic kidney disease (CKD) represents a significant global health challenge, characterized by kidney damage and decreased function. Its prevalence has steadily increased, necessitating a comprehensive understanding of its epidemiology, risk factors, and management strategies. While traditional prognostic markers such as estimated glomerular filtration rate (eGFR) and albuminuria provide valuable insights, they may not fully capture the complexity of CKD progression and associated cardiovascular (CV) risks.This paper reviews the current state of renal and CV risk prediction in CKD, highlighting the limitations of traditional models and the potential for integrating artificial intelligence (AI) techniques. AI, particularly machine learning (ML) and deep learning (DL), offers a promising avenue for enhancing risk prediction by analyzing vast and diverse patient data, including genetic markers, biomarkers, and imaging. By identifying intricate patterns and relationships within datasets, AI algorithms can generate more comprehensive risk profiles, enabling personalized and nuanced risk assessments.Despite its potential, the integration of AI into clinical practice faces challenges such as the opacity of some algorithms and concerns regarding data quality, privacy, and bias. Efforts towards explainable AI (XAI) and rigorous data governance are essential to ensure transparency, interpretability, and trustworthiness in AI-driven predictions.

The G Protein Estrogen Receptor (GPER) is involved in the resistance to the CDK4/6 inhibitor palbociclib in breast cancer.

BACKGROUND: The cyclin D1-cyclin dependent kinases (CDK)4/6 inhibitor palbociclib in combination with endocrine therapy shows remarkable efficacy in the management of estrogen receptor (ER)-positive and HER2-negative advanced breast cancer (BC). Nevertheless, resistance to palbociclib frequently arises, highlighting the need to identify new targets toward more comprehensive therapeutic strategies in BC patients. METHODS: BC cell lines resistant to palbociclib were generated and used as a model system. Gene silencing techniques and overexpression experiments, real-time PCR, immunoblotting and chromatin immunoprecipitation studies as well as cell viability, colony and 3D spheroid formation assays served to evaluate the involvement of the G protein-coupled estrogen receptor (GPER) in the resistance to palbociclib in BC cells. Molecular docking simulations were also performed to investigate the potential interaction of palbociclib with GPER. Furthermore, BC cells co-cultured with cancer-associated fibroblasts (CAFs) isolated from mammary carcinoma, were used to investigate whether GPER signaling may contribute to functional cell interactions within the tumor microenvironment toward palbociclib resistance. Finally, by bioinformatics analyses and k-means clustering on clinical and expression data of large cohorts of BC patients, the clinical significance of novel mediators of palbociclib resistance was explored. RESULTS: Dissecting the molecular events that characterize ER-positive BC cells resistant to palbociclib, the down-regulation of ERα along with the up-regulation of GPER were found. To evaluate the molecular events involved in the up-regulation of GPER, we determined that the epidermal growth factor receptor (EGFR) interacts with the promoter region of GPER and stimulates its expression toward BC cells resistance to palbociclib treatment. Adding further cues to these data, we ascertained that palbociclib does induce pro-inflammatory transcriptional events via GPER signaling in CAFs. Of note, by performing co-culture assays we demonstrated that GPER contributes to the reduced sensitivity to palbociclib also facilitating the functional interaction between BC cells and main components of the tumor microenvironment named CAFs. CONCLUSIONS: Overall, our results provide novel insights on the molecular events through which GPER may contribute to palbociclib resistance in BC cells. Additional investigations are warranted in order to assess whether targeting the GPER-mediated interactions between BC cells and CAFs may be useful in more comprehensive therapeutic approaches of BC resistant to palbociclib.

Genes selection using deep learning and explainable artificial intelligence for chronic lymphocytic leukemia predicting the need and time to therapy.

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, ß2-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.

Nutrition Education Program and Physical Activity Improve the Adherence to the Mediterranean Diet: Impact on Inflammatory Biomarker Levels in Healthy Adolescents From the DIMENU Longitudinal Study.

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 (ß = 0.868, p < 0.0001 and ß = 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 (ß = -14.763, p < 0.001 and ß = -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.