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Recent developments in managing luminal microbial ecology in patients with inflammatory bowel disease: from evidence to microbiome-based diagnostic and personalized therapy
No full textIntroduction: Inflammatory bowel disease (IBD), including Crohn’s disease and ulcerative colitis, is a chronic condition characterized by abnormal immune responses and intestinal inflammation. Emerging evidence highlights the vital role of gut microbiota in IBD’s onset and progression. Recent advances have shaped diagnostic and therapeutic strategies, increasingly focusing on microbiome-based personalized care. Methodology: this review covers studies from 2004 to 2024, reflecting the surge in research on luminal microbial ecology in IBD. Human studies were prioritized, with select animal studies included for mechanistic insights. Only English-language, peer-reviewed articles–clinical trials, systematic reviews, and meta-analyses–were considered. Studies without clinical validation were excluded unless offering essential insights. Searches were conducted using PubMed, Scopus, and Web of Science. Areas covered: we explore mechanisms for managing IBD-related microbiota, including microbia..
Integrated Bedload Monitoring for Mobility Threshold Analysis in an Alpine Stream
No full textMonitoring bedload is challenging, particularly after extreme events, when the equilibrium state of mountain systems shifts, and the sediment dynamics changes. This study, conducted in the Rio Cordon (Dolomites, BL), aims at detect-ing the changes in bedload transport thresholds during minor events after the Vaia storm (October 2018). Three periods were analyzed during spring-summer 2023, adopting different field-based techniques: bedload tracing (95 Passive Integrated Transponders, PITs), and sediment trap (Bunte sampler). The three periods exhib-ited a Qp equal to 1.52, 0.21, and 0.35 m3s-1, respectively. Despite the recovery rates constantly > 85%, only the first period recorded grains displacements, with an average distance of 0.31 m (moved + not moved) and a transported Dmax of 92 mm. Regarding the data collected from the Bunte trap, transport rates of 0.29, 0.01, and 0.17 g s-1 were recorded for the first, second, and third period, respec-tively. The material transported during the first period registered a Dmax of 84 mm and a Dmedian of 57 mm. A Dmax of 16 mm and 42 mm with Dmedian of 8 mm and 24 mm were recorded in the second and third period, respectively. The integrated approach of both monitoring techniques enabled the identification of transport thresholds for each grain size class (i.e., D = 45.3 mm requires Qc ≥ 0.35 m3s-1; D = 128 mm requires Qc ≥ 1.52 m3s-1). These results underline the good performance of the Bunte approach as it overcomes the technical limitation of PITs forvalues<45.3mm
Are AI-based surveillance systems for healthcare-associated infections ready for clinical practice? A systematic review and meta-analysis
Get PDFHealthcare-associated infections (HAIs) are a global public health concern, imposing significant clinical and financial burdens. Despite advancements, surveillance methods remain largely manual and resource-intensive, often leading to underreporting. In this context, automation, particularly through Artificial Intelligence (AI), shows promise in optimizing clinical workflows. However, adoption challenges persist. This study aims to evaluate the current performance and impact of AI in HAI surveillance, considering technical, clinical, and implementation aspects. We conducted a systematic review of Scopus and Embase databases following PRISMA guidelines. AI-based models' performances, accuracy, AUC, sensitivity, and specificity, were pooled using a random-effect model, stratifying by detected HAI type. Our study protocol was registered in PROSPERO (CRD42024524497). Of 2834 identified citations, 249 studies were reviewed. The performances of AI models were generally high but with significant heterogeneity between HAI types. Overall pooled sensitivity, specificity, AUC, and accuracy were respectively 0.835, 0.899, 0.864, and 0.880. About 35.7 % of studies compared AI system performance with alternative automated or standard-of-care surveillance methods, with most achieving better or comparable re-sults to clinical scores or manual surveillance. <7.6 % explicitly measured AI impact in terms of improved patient outcomes, workload reduction, and cost savings, with the majority finding benefits. Only 30 studies deployed the model in a user-friendly tool, and 9 tested it in real clinical practice. In this systematic review, AI shows promising performance in HAI surveillance, although its routine appli-cation in clinical practice remains uncommon. Despite over a decade, retrieved studies offer scant evidence on reducing burden, costs, and resource use. This prevents their potential superiority over traditional or simpler automated surveillance systems from being fully evaluated. Further research is necessary to assess impact, enhance interpretability, and ensure reproducibility
Rethinking Europeanness through travelling imaginative geographies
No full textTourism mobilities play a significant role in how European identity and the European space are imagined, encountered, and negotiated, but this role has rarely been interrogated. To address this gap, the present article discusses the nexus between tourism and Europeanness, drawing from the concept of ‘travelling imaginative geographies of Europe’. This concept was developed from literature that identifies Europeanness as everyday practice, geographical imaginations, tourism, and mobilities. To test the concept, a limited number of tourist pictures that were shared in a pilot study conducted with 24 ‘students-cum-tourists’–international Master’s-level students who had gone on tourist trips across Europe–are presented. The pilot study results revealed that travelling imaginative geographies reproduces and challenges consolidated narratives on Europeanness, including the role of cultural heritage and tourism mobilities, urban sociability and public space, embodied semiotics, and perceived ..
Teaching scientific practices through low-cost tools: an experiment with high school students
Get PDFThe study regards the development of a teaching-learning sequence (TLS) for students in a technical institute, aimed at enhancing scientific practices. The TLS was designed following a design-based research methodology, undergoing two cycles of design and test. The study describes the process and analyses students' achievement of a specific learning outcome (the development of the practice 'developing and using models') during the latest version of the TLS. To obtain a more accurate evaluation, an assessment strategy based on the triangulation of multiples sources (students' work and an observation grid filled in by two independent observers) was adopted. The findings suggest that the TLS had a positive impact on the development of the target scientific practice, particularly in some sub-abilities such as the mathematization of motion, its representation through space-time graphs and motion diagrams, and the translation between different representations. On the other hand, students had difficulties in constructing multiple representations and discussing the limitations of models. The study emphasizes the importance of continued refinement of teaching proposals to address emergent challenges and optimize learning support
Coupling biogas upgrading with biopolymers accumulation through cyanobacteria CO2fixation
No full textBiogas, primarily composed of carbon dioxide (CO2) and methane, requires upgrading to biomethane by removing the CO2 to improve its usability, making it suitable for direct injection into gas grid and serving as a renewable alternative to fossil-derived methane. This study investigates the potential of the recently isolated cyanobacterial strain Synechocystis sp. B12, selected for its robustness and tolerance to high light intensity, in biogas upgrading. Synechocystis sp. B12 demonstrated exceptional tolerance to high CO2 concentrations as in the biogas, utilising it for photosynthetic growth without any detrimental effects from CH4 or other contaminants. This establishes it as a promising candidate for biogas upgrading applications. The strain successfully fixed over 99 % of the CO2 present both in synthetic gas mixture and industrial biogas. Moreover, Synechocystis sp. B12 converted the captured CO2 into polyhydroxybutyrate, a biodegradable bioplastic compound, achieving productivities of approximately 80 mg L-1. This approach provides the dual advantage of enhancing biogas quality while simultaneously transforming CO2 into valuable bioproducts
A systematic review of developments in mHealth smartphone applications for Transgender and Gender Diverse individuals
Get PDFTransgender and gender diverse (TGD) individuals face significant healthcare barriers, resulting in inequities and unmet needs. Mobile health (mHealth) applications offer promising solutions by providing accessible, cost-effective, personalized, and gender-affirming care. This systematic review, conducted using PRISMA 2020 guidelines and the PICO framework, screened 5005 records from 4 databases and included 11 articles. The review aimed to identify key features of mHealth apps developed for TGD individuals, focusing on theoretical frameworks, design strategies, and their approaches to addressing healthcare barriers. Key challenges in developing mHealth apps include implementing systemic changes in healthcare settings to combat stigma and discrimination, grounding app development in TGD-specific theoretical frameworks, adequately addressing stressors and protective factors, and overcoming methodological limitations that hinder the evaluation of health outcomes. Overcoming these challen..
Acalabrutinib in High-Risk Chronic Lymphocytic Leukaemia Naïve Patients: An Italian Multicenter Retrospective Observational Real-Life Experience
No full textAn innovative computer-based model for the generation of expressive lead guitar performances
Get PDFPerformative musical expressiveness can be attributed to the manipulation of parameters associated with the macro categories of timing, dynamics, and timbre.
The purpose of an expressive performance may vary depending on the specific musician and the sociocultural and stylistic context in which the performance takes place. Among the objectives commonly identified in the literature are the expression (and/or induction in the listener) of emotions, the clarification of the piece’s formal structure, the sonic rendering of concepts and sensory perceptions, and adherence to a specific stylistic current. Additionally, it is evident for some musicians that they imprint their performances with distinctive expressive traits that make them recognizable to the listener and that transcend the aforementioned objectives. The field of computational musical expressiveness aims to develop automatic models that are ideally capable of generating virtual musical performances endowed with expressiveness, similar to what occurs with human musicians. From the information engineering perspective, computational performative musical expressiveness has historically involved the fields of affective computing, artificial intelligence, human-computer interaction, biomechanical simulation, and robotics. Although these fields are undoubtedly at the heart of computational musical expressiveness, this research area is also inherently highly multidisciplinary, requiring expertise at least also in psychological and musicological areas.
The stylistic reference domain has been, at least in a largely predominant manner, that of European art music from the 17th, 18th, and 19th centuries. This can be partially explained by a general academic cultural bias toward music commonly called “classical”. Another reason Euro-classical music has historically been a protagonist in the field of computational expressiveness is that it is based on the score/performance dualism, thus allowing a precise analysis of the performance’s expressive deviations compared to the score’s prescriptive dimension. The musical instrument most often referred to has been the piano.
This research work, on the other hand, addresses the stylistic domain of contemporary popular music, specifically lead electric guitar parts, a field so far explored only very partially. This has firstly required understanding what “expressive performance” means in the reference context. Also, the physical characteristics of the instrument, the performative techniques used by guitarists, and the biomechanical limits or constraints that may arise in the interaction between performer and guitar had to be analyzed. The following step was an in-depth review of the scientific literature on computational musical expressiveness, also to understand which of the previous works could serve as inspiration, possibly with specific modifications, in the new application context. The development of the expressive model was thus based on the use of optimization techniques for the automatic generation of the basic virtual fingering, on a rule-based system for the automatic insertion of articulations and expressive techniques, on a Machine Learning approach for the simulation of timing and dynamics deviations primarily due to involuntary biomechanical and psycho-perceptive components, and finally on the user’s introduction of indications regarding the deliberate expressive component (specifically concerning loudness and positioning of the notes relative to the beat). The various modules of the model, based on an input melody in MIDI format, generate a detailed MIDI description of the guitar performance, which can then be sonified through a virtual instrument.
The potential applications are numerous and include, in particular, the use of the model in educational and music production sectors
EEG as a predictive biomarker of neurotoxicity in anti-CD19 CAR T-cell therapy
Get PDFObjective
Immune effector cell-associated neurotoxicity syndrome (ICANS) is a potentially fatal complication of CD19-directed CAR T-cell therapy. The aim of this study was to investigate the role of EEG as a predictive biomarker of ICANS.
Methods
In this prospective, monocentric, cohort study, consecutive refractory B-cell non-Hodgkin lymphoma patients undergoing CAR T-cell therapy had EEG assessments at fixed time points pre- and post-infusion. The risk of ICANS was evaluated according to EEG findings detected qualitatively, using a grading scale ranging from 0 (normal) to 3 (severely abnormal), and quantitatively, using power spectral and connectivity measures.
Results
307 EEGs from 68 patients have been qualitatively evaluated, of whom 238 were eligible for quantitative analysis. Neurotoxicity manifested in 22/68 (32.4%) patients. Pre-infusion EEG abnormalities (grade 1 and 2) were qualitatively detected in 8/68 (11.7%) patients, emerging as a risk factor for ICANS [HR 5.8 (95%CI 2.6–12.9)]. Quantitative analysis of pre-infusion EEGs did not yield significative results. Post-infusion qualitative EEG abnormalities were associated to a higher risk of ICANS development [HR 11.6 (4.4–30.5) for grade 2; HR 9.7 (2.6–36.6) for grade 3]. Concerning the quantitative analysis, in post-infusion EEGs higher theta energy [HR 1.10 (1.03–1.16)] and delta + theta/alfa ratio [HR 1.37 (1.11–1.67)] were associated to higher risk of ICANS, while higher beta energy resulted protective [HR 0.91 (0.85–0.97)].
Conclusions
Our study establishes EEG as a predictive tool for identifying patients at risk for ICANS before CAR T-cell infusion, who may benefit from prophylactic treatments, and anticipating ICANS onset following infusion, enabling early intervention