Switching from the bio-originators to biosimilar: is it premature to recommend this procedure?

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Additional comment to: 'Switching from the bio-originators to biosimilar: is it premature to recommend this procedure?' by Scherlinger and Schaeverbeke

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Detecting mental disorder on social media: a ChatGPT-augmented explainable approach

In the digital era, the prevalence of depressive symptoms expressed on social media has raised serious concerns, necessitating advanced methodologies for timely detection. This paper addresses the challenge of interpretable depression detection by proposing a novel methodology that effectively combines Large Language Models (LLMs) with eXplainable Artificial Intelligence (XAI) and conversational agents like ChatGPT. In our methodology, explanations are achieved by integrating BERTweet, a Twitter-specific variant of BERT, into a novel self-explanatory model, namely BERT-XDD, capable of providing both classification and explanations via masked attention. The interpretability is further enhanced using ChatGPT to transform technical explanations into human-readable commentaries. By introducing an effective and modular approach for interpretable depression detection, our methodology can contribute to the development of socially responsible digital platforms, fostering early intervention and support for mental health challenges under the guidance of qualified healthcare professionals

Block size estimation for data partitioning in HPC applications using machine learning techniques

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, finding an effective partitioning, i.e. a suitable size for data blocks, is a key strategy to speed-up parallel data-intensive applications and increase scalability. This paper describes a methodology for data block size estimation in HPC applications, which relies on supervised machine learning techniques. The implementation of the proposed methodology was evaluated using as a testbed dislib, a distributed computing library highly focused on machine learning algorithms built on top of the PyCOMPSs framework. We assessed the effectiveness of our solution through an extensive experimental evaluation considering different algorithms, datasets, and infrastructures, including the MareNostrum 4 supercomputer. The results we obtained show that the methodology is able to efficiently determine a suitable way to split a given dataset, thus enabling the efficient execution of data-parallel applications in high performance environments

Alternative biomarkers of tuberculosis infection in patients with immune-mediated inflammatory diseases

IntroductionIFN-γ release assays (IGRAs) are one of the referral tests for diagnosing tuberculosis infection (TBI). To improve IGRAs accuracy, several markers have been investigated. Patients with immune-mediated inflammatory diseases (IMID), taking biological drugs, have a higher risk to progress to TB-disease compared to the general population. In several guidelines, annual TBI screening is recommended for patients undergoing biological therapy. Aim of this study was to investigate, within the QuantiFERON-TB-Plus (QFT-Plus) platform, if beside IFN-γ, alternative biomarkers help to diagnose TBI-IMID patients.MethodsWe enrolled 146 subjects: 46 with TB disease, 20 HD, 35 with TBI and 45 with TBI and IMID. Thirteen IMID subjects with a QFT-Plus negative result were diagnosed as TBI based on radiological evidence of TBI. We evaluated the IP-10 level in response to TB1 and TB2 peptides of QFT-Plus assay and we compared these results with the standardized assay based on IFN-γ. Multiplex immune assay was performed on plasma from TB1 and TB2 tubes and results were analyzed by a gradient boosting machine (GBM) as learning technique.ResultsTBI-IMID showed a significant decreased IP-10 level in response to TB1 and TB2 stimulation compared to TBI-NO IMID (p < 0.0001 and p = 0.0002). The TBI-IMID showed a moderate agreement between the IP-10-based assay and QFT-Plus scores. In TBI-IMID, QFT-Plus showed 70% sensitivity for TBI detection whereas the IP-10-based assay reached 61%. Tests combination increased the sensitivity for TBI diagnosis up to 77%. By a GBM, we explored alternative biomarkers for diagnosing TBI in IMID population reaching 89% sensitivity. In particular, the signature based on IL-2, IP-10, and IL-9 detection was associated with TB status (infection/disease). However, by applying the cut-off identified by ROC analysis, comparing TB and TBI with the HD group, within the IMID population, we did not improve the accuracy for TBI-diagnosis. Similarly, this signature did not improve TBI diagnosis in IMID with radiological evidence of TBI but negative QFT-Plus score.DiscussionTo develop alternative strategies for TBI immune-diagnosis, future studies are needed to evaluate the memory response of TBI defined by radiological tools. These results may help in tuberculosis management of patients taking lifelong immune-suppressive drugs

Inflamed shoulder structures in polymyalgia rheumatica with normal erythrocyte sedimentation rate

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The Third Dose of BNT162b2 COVID-19 Vaccine Does Not “Boost” Disease Flares and Adverse Events in Patients with Rheumatoid Arthritis

Data on the risk of adverse events (AEs) and disease flares in autoimmune rheumatic diseases (ARDs) after the third dose of COVID-19 vaccine are scarce. The aim of this multicenter, prospective study is to analyze the clinical and immunological safety of BNT162b2 vaccine in a cohort of rheumatoid arthritis (RA) patients followed-up from the first vaccine cycle to the third dose. The vaccine showed an overall good safety profile with no patient reporting serious AEs, and a low percentage of total AEs at both doses (40/78 (51.3%) and 13/47 (27.7%) patients after the second and third dose, respectively (p < 0.002). Flares were observed in 10.3% of patients after the end of the vaccination cycle and 12.8% after the third dose. Being vaccinated for influenza was inversely associated with the onset of AEs after the second dose, at both univariable (p = 0.013) and multivariable analysis (p = 0.027). This result could allow identification of a predictive factor of vaccine tolerance, if confirmed in larger patient populations. A higher disease activity at baseline was not associated with a higher incidence of AEs or disease flares. Effectiveness was excellent after the second dose, with only 1/78 (1.3%) mild breakthrough infection (BI) and worsened after the third dose, with 9/47 (19.2%) BI (p < 0.002), as a probable expression of the higher capacity of the Omicron variants to escape vaccine recognition