Understanding response to rituximab treatment in rheumatoid arthritis through immune fingerprinting of T and B cells

Despite the great research advances in dissecting the mechanisms underlying rheumatoid arthritis onset and development, the exact pathophysiology of this disease remains unsolved. In the past years, the introduction of biologicals, and in particular of therapeutic monoclonal antibodies (mAb), has constituted a major breakthrough in the clinical management of the disease. Although these new drugs have proven effective, in some patients, remission or low disease activity is only partially or temporally achieved. Understanding the mechanism behind this incomplete response might lead to improved therapies and in ultimately to improved quality of life for patients. This thesis describes our efforts to elucidate the mechanisms behind response to B-cell depletion therapy using rituximab in rheumatoid arthritis. We evaluated how both direct and indirect effects can influence clinical response to the treatment. To achieve this we applied AIRR sequencing or Adaptive Immune Receptor Repertoire sequencing, which allows to identify and quantify all T- and B-cell receptors within an individual. This methodology allowed us to fingerprint and monitor ongoing immune responses in cohorts of rheumatoid arthritis patients undergoing rituximab treatment

Where Are You, Congress?: Silence Rings in Congress as Juvenile Offenders Remain in Prison for Life

Over the last decade, Supreme Court precedent has changed the way courts have sentenced juveniles in the United States. It has failed, however, to clearly establish the proper handling of cases in which juveniles are sentenced to extended periods of time in prison that equate to a de facto sentence of life in prison without parole. Congress has also remained noticeably silent on the issue. Children are not considered mature enough to vote, to drink alcohol, to serve on a jury, and yet, courts treat juvenile offenders as mature enough to pay for their crimes for the remainder of their lives. Without a clear remedy in sight, juvenile offenders face uncertain fates and unequal treatment in the justice system, both on the state and federal level. Therefore, despite residing in the same circuit, a juvenile in New Jersey, for example, will face different sentencing consequences than a juvenile in Pennsylvania for similar crimes. This note proposes a solution for Congress to start the trend by banning de facto LWOP to fully establish that children are, in fact, treated differently than adults in the United States

Understanding response to rituximab treatment in rheumatoid arthritis through immune fingerprinting of T and B cells

Despite the great research advances in dissecting the mechanisms underlying rheumatoid arthritis onset and development, the exact pathophysiology of this disease remains unsolved. In the past years, the introduction of biologicals, and in particular of therapeutic monoclonal antibodies (mAb), has constituted a major breakthrough in the clinical management of the disease. Although these new drugs have proven effective, in some patients, remission or low disease activity is only partially or temporally achieved. Understanding the mechanism behind this incomplete response might lead to improved therapies and in ultimately to improved quality of life for patients. This thesis describes our efforts to elucidate the mechanisms behind response to B-cell depletion therapy using rituximab in rheumatoid arthritis. We evaluated how both direct and indirect effects can influence clinical response to the treatment. To achieve this we applied AIRR sequencing or Adaptive Immune Receptor Repertoire sequencing, which allows to identify and quantify all T- and B-cell receptors within an individual. This methodology allowed us to fingerprint and monitor ongoing immune responses in cohorts of rheumatoid arthritis patients undergoing rituximab treatment

Arzanol, a prenylated heterodimeric phloroglucinyl pyrone, inhibits eicosanoid biosynthesis and exhibits anti-inflammatory efficacy in vivo.

Based on its capacity to inhibit in vitro HIV-1 replication in T cells and the release of pro-inflammatory cytokines in monocytes, the prenylated heterodimeric phloroglucinyl α-pyrone arzanol was identified as the major anti-inflammatory and anti-viral constituent from Helichrysum italicum. We have now investigated the activity of arzanol on the biosynthesis of pro-inflammatory eicosanoids, evaluating its anti-inflammatory efficacy in vitro and in vivo. Arzanol inhibited 5-lipoxygenase (EC 7.13.11.34) activity and related leukotriene formation in neutrophils, as well as the activity of cyclooxygenase (COX)-1 (EC 1.14.99.1) and the formation of COX-2-derived prostaglandin (PG)E(2)in vitro (IC(50)=2.3-9μM). Detailed studies revealed that arzanol primarily inhibits microsomal PGE(2) synthase (mPGES)-1 (EC 5.3.99.3, IC(50)=0.4μM) rather than COX-2. In fact, arzanol could block COX-2/mPGES-1-mediated PGE(2) biosynthesis in lipopolysaccharide-stimulated human monocytes and human whole blood, but not the concomitant COX-2-derived biosynthesis of thromboxane B(2) or of 6-keto PGF(1α), and the expression of COX-2 or mPGES-1 protein was not affected. Arzanol potently suppressed the inflammatory response of the carrageenan-induced pleurisy in rats (3.6mg/kg, i.p.), with significantly reduced levels of PGE(2) in the pleural exudates. Taken together, our data show that arzanol potently inhibits the biosynthesis of pro-inflammatory lipid mediators like PGE(2)in vitro and in vivo, providing a mechanistic rationale for the anti-inflammatory activity of H. italicum, and a rationale for further pre-clinical evaluation of this novel anti-inflammatory lead

Pharmacogenomics of Drug Response in Type 2 Diabetes: Toward the Definition of Tailored Therapies?

Type 2 diabetes is one of the major causes of mortality with rapidly increasing prevalence. Pharmacological treatment is the first recommended approach after failure in lifestyle changes. However, a significant number of patients shows—or develops along time and disease progression—drug resistance. In addition, not all type 2 diabetic patients have the same responsiveness to drug treatment. Despite the presence of nongenetic factors (hepatic, renal, and intestinal), most of such variability is due to genetic causes. Pharmacogenomics studies have described association between single nucleotide variations and drug resistance, even though there are still conflicting results. To date, the most reliable approach to investigate allelic variants is Next-Generation Sequencing that allows the simultaneous analysis, on a genome-wide scale, of nucleotide variants and gene expression. Here, we review the relationship between drug responsiveness and polymorphisms in genes involved in drug metabolism (CYP2C9) and insulin signaling (ABCC8, KCNJ11, and PPARG). We also highlight the advancements in sequencing technologies that to date enable researchers to perform comprehensive pharmacogenomics studies. The identification of allelic variants associated with drug resistance will constitute a solid basis to establish tailored therapeutic approaches in the treatment of type 2 diabetes

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself.Comment: Accepted to be presented at XAI.it 2022 - Italian Workshop on Explainable Artificial Intelligenc

On The Effects Of Data Normalisation For Domain Adaptation On EEG Data

In the Machine Learning (ML) literature, a well-known problem is the Dataset Shift problem where, differently from the ML standard hypothesis, the data in the training and test sets can follow different probability distributions, leading ML systems toward poor generalisation performances. This problem is intensely felt in the Brain-Computer Interface (BCI) context, where bio-signals as Electroencephalographic (EEG) are often used. In fact, EEG signals are highly non-stationary both over time and between different subjects. To overcome this problem, several proposed solutions are based on recent transfer learning approaches such as Domain Adaption (DA). In several cases, however, the actual causes of the improvements remain ambiguous. This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods. In particular, using \textit{SEED}, \textit{DEAP}, and \textit{BCI Competition IV 2a} EEG datasets, we experimentally evaluated the impact of different normalization strategies applied with and without several well-known DA methods, comparing the obtained performances. It results that the choice of the normalisation strategy plays a key role on the classifier performances in DA scenarios, and interestingly, in several cases, the use of only an appropriate normalisation schema outperforms the DA technique.Comment: Published in its final version on Engineering Applications of Artificial Intelligence (EAAI) https://doi.org/10.1016/j.engappai.2023.10620

Fatty Acid Ratios as Parameters to Discriminate Between Normal and Tumoral Cells and Compare Drug Treatments in Cancer Cells

The fatty acid (FA) composition of cell membranes represents a metabolic biomarker. However, the FA profile reproducibility in cell cultures remains a significant challenge. In this study, cell FA ratios are validated as metabolic markers alternative to cell FA. To this goal, cell samples belonging to cancer HeLa cells and normal 3T3 fibroblasts, from various experimental sets, are analyzed by a high-performance liquid chromatography system coupled with a photodiode array detector and evaporative light scattering detector (HPLC-DAD/ELSD), and the ratios among the main FA are calculated. Principal component analysis (PCA) separately performed on FA and FA ratio data indicates similar clustering of cell samples concerning the cell type. Moreover, similar scores values t[1] and t[2] and graphical distances are calculated in the PCA plots separately performed on FA and FA ratios measured in cancer HeLa cells subjected to various antitumoral compounds. Last, PCA applied to selected FA ratios measured in various cell lines, obtained in similar experimental conditions, allows to discriminate between normal and tumoral cells. The results substantiate FA ratios as a cell-specific fingerprint, characterized by reproducibility across intra-laboratory conditions, useful for cell characterization, discrimination between normal and tumoral cells, and the comparison of different drug treatments. Practical Applications: The reproducibility of the fatty acid (FA) profile in cell cultures remains a significant challenge. Results obtained from this study improve knowledge about the role of the FA ratio profile as a cell-specific fingerprint characterized by reproducibility across intra-laboratory conditions. The characterization of the specific FA ratio profile of a cell culture, under standardized experimental conditions, can facilitate the comparative evaluation of cell data sets for nutritional, metabolic, and pharmacological studies, overcoming differences in cell culture conditions and FA extraction/analytical procedures

Toward the application of XAI methods in EEG-based systems

An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI). The non-stationarity of EEG signals can lead to poor generalisation performance in BCI classification systems used in different sessions, also from the same subject. In this paper, we start from the hypothesis that the Dataset Shift problem can be alleviated by exploiting suitable eXplainable Artificial Intelligence (XAI) methods to locate and transform the relevant characteristics of the input for the goal of classification. In particular, we focus on an experimental analysis of explanations produced by several XAI methods on an ML system trained on a typical EEG dataset for emotion recognition. Results show that many relevant components found by XAI methods are shared across the sessions and can be used to build a system able to generalise better. However, relevant components of the input signal also appear to be highly dependent on the input itself

Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine

In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the training stage, a decision rule is manually defined to detect anomalous data. However, this process could be made automatic using machine learning methods, whom performances are maximised using hyperparameter optimization techniques. The paper proposes a semi-supervised method with a data-driven approach to detect structural anomalies. The methodology consists of: (i) a Variational Autoencoder (VAE) to approximate undamaged data distribution and (ii) a One-Class Support Vector Machine (OC-SVM) to discriminate different health conditions using damage sensitive features extracted from VAE's signal reconstruction. The method is applied to a scale steel structure that was tested in nine damage's scenarios by IASC-ASCE Structural Health Monitoring Task Group