ABSTRACT
Background: Colorectal cancer (CRC) is a type of tumor caused by the uncontrolled growth of cells in the mucosa lining the last part of the intestine. Emerging evidence underscores an association between CRC and gut microbiome dysbiosis. The high mortality rate of this cancer has made it necessary to develop new early diagnostic methods. Machine learning (ML) techniques can represent a solution to evaluate the interaction between intestinal microbiota and host physiology. Through explained artificial intelligence (XAI) it is possible to evaluate the individual contributions of microbial taxonomic markers for each subject. Our work also implements the Shapley Method Additive Explanations (SHAP) algorithm to identify for each subject which parameters are important in the context of CRC. Results: The proposed study aimed to implement an explainable artificial intelligence framework using both gut microbiota data and demographic information from subjects to classify a cohort of control subjects from those with CRC. Our analysis revealed an association between gut microbiota and this disease. We compared three machine learning algorithms, and the Random Forest (RF) algorithm emerged as the best classifier, with a precision of 0.729 ± 0.038 and an area under the Precision-Recall curve of 0.668 ± 0.016. Additionally, SHAP analysis highlighted the most crucial variables in the model's decision-making, facilitating the identification of specific bacteria linked to CRC. Our results confirmed the role of certain bacteria, such as Fusobacterium, Peptostreptococcus, and Parvimonas, whose abundance appears notably associated with the disease, as well as bacteria whose presence is linked to a non-diseased state. Discussion: These findings emphasizes the potential of leveraging gut microbiota data within an explainable AI framework for CRC classification. The significant association observed aligns with existing knowledge. The precision exhibited by the RF algorithm reinforces its suitability for such classification tasks. The SHAP analysis not only enhanced interpretability but identified specific bacteria crucial in CRC determination. This approach opens avenues for targeted interventions based on microbial signatures. Further exploration is warranted to deepen our understanding of the intricate interplay between microbiota and health, providing insights for refined diagnostic and therapeutic strategies.
ABSTRACT
OBJECTIVES: Multiple myositis-specific antibodies have been identified, each associated with different clinical subsets of dermatomyositis (DM). Anti-SAE associated DM is considered the least studied subset. Our study aimed to evaluate the clinical and histological characteristics of DM patients with anti-SAE antibodies. As reference, patients with anti-Mi2 antibodies associated DM, representing a well-characterised subset, were analysed. METHODS: We recorded data from our DM cohort in the INflammatory MYositis REgistry (INMYRE). Patients were divided into two groups: those positive for anti-SAE and those positive for anti-Mi2 antibodies. Clinical characteristics, including skin, muscle, and extra-muscular involvements, were recorded. Available muscle biopsies were compared between the two groups. RESULTS: Of 92 DM patients, 10 (10.9%) were positive for anti-SAE and 17 (18.5%) for anti-Mi2. Anti-SAE positive DM patients showed classic DM findings but were characterised by a higher prevalence of skin itching (60% vs. 11.8%, p<0.01), shawl sign (40% vs. 5.9%, p<0.05) and lung involvement (30% vs. 0%, p<0.05) compared to anti-Mi2 positive patients. Furthermore, anti-SAE positive DM patients showed lower creatine kinase levels than those with anti-Mi2 (median [IQR]: 101 [58-647] vs. 1984 [974-3717], p<0.05) and a lower percentage of muscle fibre degeneration and necrosis (1.5%±1.7 vs. 5.9%±3.2, p<0.05) in muscle biopsies. No other differences were observed. CONCLUSIONS: Anti-SAE DM represents a disease subset characterised by classic cutaneous involvement often associated with itching, less severe muscle involvement, but potential pulmonary involvement that should always be investigated in these patients.
Subject(s)
Dermatomyositis , Myositis , Humans , Dermatomyositis/diagnosis , Dermatomyositis/drug therapy , Dermatomyositis/complications , Autoantibodies , Pruritus/complications , Italy/epidemiologyABSTRACT
The presented study protocol outlines a comprehensive investigation into the interplay among the human microbiota, volatilome, and disease biomarkers, with a specific focus on Behçet's disease (BD) using methods based on explainable artificial intelligence. The protocol is structured in three phases. During the initial three-month clinical study, participants will be divided into control and experimental groups. The experimental groups will receive a soluble fiber-based dietary supplement alongside standard therapy. Data collection will encompass oral and fecal microbiota, breath samples, clinical characteristics, laboratory parameters, and dietary habits. The subsequent biological data analysis will involve gas chromatography, mass spectrometry, and metagenetic analysis to examine the volatilome and microbiota composition of salivary and fecal samples. Additionally, chemical characterization of breath samples will be performed. The third phase introduces Explainable Artificial Intelligence (XAI) for the analysis of the collected data. This novel approach aims to evaluate eubiosis and dysbiosis conditions, identify markers associated with BD, dietary habits, and the supplement. Primary objectives include establishing correlations between microbiota, volatilome, phenotypic BD characteristics, and identifying patient groups with shared features. The study aims to identify taxonomic units and metabolic markers predicting clinical outcomes, assess the supplement's impact, and investigate the relationship between dietary habits and patient outcomes. This protocol contributes to understanding the microbiome's role in health and disease and pioneers an XAI-driven approach for personalized BD management. With 70 recruited BD patients, XAI algorithms will analyze multi-modal clinical data, potentially revolutionizing BD management and paving the way for improved patient outcomes.
ABSTRACT
Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccination plays a crucial role as pivotal strategy to curb the coronavirus disease-19 (COVID-19) pandemic. The present study described the clinical status of patients affected by idiopathic inflammatory myopathies (IIM) after COVID-19 vaccination to assess the number of relapses. We included all patients affected by IIM and followed by Myositis Clinic, Rheumatology and Respiratory Diseases Units, Siena University Hospital, Bari University Hospital, Policlinico Umberto I, Sapienza University, Rome, and Policlinico Paolo Giaccone, Palermo. They underwent a telephone survey. A total of 119 IIM patients (median, IQR 58 (47-66) years; 32males; 50 dermatomyositis, 39 polymyositis and 30 anti-synthetase syndrome) were consecutively enrolled. Except four patients who refused the vaccination, 94 (81.7%) received Comirnaty, 16 (13.9%) Spikevax, 5 (4.4%) Vaxzevria. Seven (6.1%) patients had flare after vaccination. One of them had life-threatening systemic involvement and died two months after second dose of COVID-19 vaccination. From logistic regression analysis, Chi2-log ratio = 0.045,the variable that most influences the development of flare was the number of organs involved (p = 0.047). Sixty-eight patients received the third dose of COVID-19 vaccination: 51(75%) Comirnaty and 17 (25%) Moderna. No patients had flares after third dose. Our study represents the largest cohort of IIM patients in which the incidence of recurrence after anti-SARS-CoV-2 vaccine was assessed. In line with real-life data from other diseases, we found a clinical non-statistically significant risk of relapse in our patients, which occurred seldom, usually mild and in patients with a more severe and aggressive course of disease.
