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Deepening Cisplatin sensitivity on Oral Squamous cell Carcinoma cell lines after PON2 knockdown: A FTIRM investigation
Get PDFCisplatin is a platinum-based chemotherapy drug with antimicrobial and antitumoral activity, largely used for a long time in the treatment of several cancers, including the Oral Squamous Cell Carcinoma (OSCC), which is one of the most frequent neoplasms of the oral cavity. Due to its aggressiveness and metastatic invasion, OSCC is characterized by poor outcome, often related also to chemoresistance mechanisms. The intracellular enzyme paraoxonase-2 (PON2) normally acts defending cells from the damages induced by Reactive Oxygen Species. Hence, in cancer cells, this enzyme can shield the potential of cisplatin, triggering a resistance mechanism. Based on this evidence, PON2 knockdown seems to be a valuable way to enhance the effects of chemotherapy, escaping this resistance. In this study, HOC621 and HSC-3 OSCC cell lines submitted to PON2 silencing were analyzed by Fourier Transform Infrared Microspectroscopy to evaluate the time-dependent changes occurring in these cells after cisplatin treatment. Spectral data were statistically analyzed by multivariate and univariate analyses and compared with MTT results. Positive feedback on cisplatin efficacy was found in both cell lines submitted to PON2 knockdown, even if with a different response. In particular, a less growth was found in PON2 silenced HOC 621 cells, respect to HSC-3 ones. Moreover, specific spectral markers (A1172/ATOT, A1053/ATOT, A967/A1080, and A992/ATOT band area ratios) were identified and statistically analyzed (p < 0.05): cellular alterations mainly in nucleic acids and carbohydrates were found in both cell lines, although more evident in HOC 621 ones, which therefore appeared to be more affected by chemotherapy treatmen
A Conceptual Costing Software Tool Based on Machine Learning for Turbomachines
No full textThe contemporary design landscape emphasizes the importance of product cost alongside performance, sustainability, and quality. Early determination of production costs, particularly in preliminary design phases like conceptual design, is crucial for maintaining competitiveness. Parametric cost estimation methods are preferred during these stages, leveraging identifiable relationships between design variables (cost drivers) and costs. Industry 4.0 innovations offer solutions to challenges posed by traditional Cost Estimation Relationship methods, with machine learning (ML) applications emerging as efficient tools for industrial processes. In the scientific literature, several approaches aim to create cost models using regression analysis, neural networks, and decision trees. The methods are implemented using data analysis tools that are not entirely suitable for product designers or cost engineers. The potential benefits of ML methods are not wholly exploited due to the limited effectiveness of the software tools. Addressing this gap, the paper introduces a software tool with an administrative module enabling cost engineers to develop parametric cost models using ML algorithms based on the CRISP-DM method. The tool facilitates data collection, model training, evaluation, and benchmarking. Cost models are stored in a database accessible to design engineers, allowing quick and accurate cost estimations by providing relevant cost drivers. Feature importance algorithms highlight critical cost drivers. The tool was tested in collaboration with a company that designs turbomachinery. During the experimentation, cost models were developed for one of the main rotor parts of an axial compressor (discs and spacer). The tool is a prototype to be further developed to manage an entire bill of materials, integrating risk management concepts
Circulating MicroRNAs in Patients with Psoriasis Treated with Anti-IL-23: A Cohort Study
No full textIntroduction: Psoriasis is characterized by aberrant keratinocyte activity and immune cell infiltration, driven by immune-mediated pathways. MicroRNAs (miRNAs) play crucial roles in regulating these processes, offering insights into disease mechanisms and therapeutic targets.ObjectivesThis study aimed to investigate changes in circulating miRNAs in psoriasis patients undergoing risankizumab therapy, an anti-IL-23 monoclonal antibody, to understand its impact on disease pathogenesis and treatment response.MethodsPlasma samples from 12 psoriasis patients were collected before (T0) and after 1 year (T1) of risankizumab treatment and analyzed using small RNA sequencing. Findings were validated in a separate cohort of 23 patients using quantitative real-time PCR (qRT-PCR). T-regulatory cell (Treg) numbers and pro-inflammatory cytokine levels were also assessed.ResultsSignificant clinical improvement was observed in all patients after 1 year of treatment, accompanied by increased Treg counts and reduced levels of pro-inflammatory cytokines. Twenty-four miRNAs exhibited differential expression post-treatment; 9 were downregulated and 15 upregulated. Notably, miR-200a-3p showed a significant correlation with baseline Psoriasis Area Severity Index (PASI), indicating its potential as a severity marker. Risankizumab therapy also decreased peripheral blood levels of IL-23, IL-1 beta, and IL-8.ConclusionsThis study identifies specific circulating miRNAs, including miR-200a-3p, as potential biomarkers for monitoring treatment responses in psoriasis patients. The findings underscore the therapeutic efficacy of risankizumab in modulating miRNA profiles and immune pathways associated with psoriasis pathogenesis. Overall, these results provide new insights into the mechanisms of risankizumab action and highlight miRNAs as promising candidates for personalized medicine approaches in psoriasis management
Responding to natural disasters: What do monthly remittance data tell us?
Get PDFIdentifying the insurance role of remittances against natural disasters through aggregate annual data is challenging due to the dynamics of remittances and disasters throughout the year and possible intertemporal substitution effects. In an event-study setting based on monthly remittance flows from Italy to 81 developing countries for 2005–2015, we investigate their dynamics in the aftermath of disasters. We find that monthly remittances positively respond to natural disasters in migrants’ home countries. The response is immediate and significant up to 3–4 months after the event. Later on remittances return to pre-disaster levels but there is no evidence of intertemporal substitution. We observe some anticipation effects, which could be related to the recurrent nature of some types of disasters. The intensity and timing of remittances’ responsiveness are heterogeneous according to the nature of disasters, to the receiving country's characteristics, and to migrants’ socio-economic conditions in the host countr
A Framework for Investigating Discording Communities on Social Platforms
Get PDFIn recent years, polarization on social media has risen significantly. Social platforms often feature a range of topics that give rise to communities of users with diametrically opposed views, who tend to avoid engaging with others having different perspectives. We call these types of communities “diverging communities”. Examples include communities of supporters and skeptics of climate change or COVID-19 vaccines. In this paper, we aim to investigate this phenomenon. To do so, we first propose a formal definition of discording communities. We then present a framework for investigating the behavior of users of discording communities on a social platform. Our framework is general in that it can be adapted to any social platform where users discuss a topic that polarizes them into communities with diametrically opposed viewpoints rejecting confrontation. Our framework considers not only the structure of communities but also the content of the messages posted by their users. Finally, it can also handle the temporal evolution of the polarization level of both communities and their users. In addition to proposing a formal definition of diverging communities and presenting our framework, we illustrate the results of an extensive experimental campaign carried out on two case studies involving Reddit and X and show how our framework is able to identify a number of features that distinguish the users of one diverging community from the users of the othe
Producing agri-food derived composts from coffee husk as primary feedstock at different temperature conditions
No full textThere is a great global concern about agricultural wastes from food and feed crop processing that have significant environmental impacts. Composting is the most environmentally friendly, cost-effective, and efficient processes that can solve the problems of accumulation and toxicity of agricultural waste. The aim of this study is the detoxification of coffee husk by composting at two temperature conditions (“warm” and “cold”). In the greenhouse, the ambient temperature was changed day by day to mimic the situation of a spring to summer “warm” period (≈16–34 °C) and a spring “cold” period (≈7–20 °C) typical of central Italy. The coffee industry should accept the responsibility for the large amount of organic waste production, which presents toxicity and mass accumulation problems. Coffee husk as the main raw material is not used directly as bio-fertilizer in agriculture sector due to the leaching of phenolic compounds and high pH value. The brewing industry is famous for its mass production, and the brewer residues as a by-product have an extremely acidic pH that makes them an unsuitable material for direct composting, but the mixture of these materials can optimize pH. The addition of cow manure accelerates microbial activity and is a strategy to improve composting rate and maturity. The following mixtures were tested: coffee husk and brewer spent grains in a proportion of 2:1 (Compost 1), coffee husk and cow manure in a proportion of 4:1 (Compost 2), and coffee husk, brewer spent grain, and cow manure in a proportion of 5:3:2 (Compost 3). Quality and maturity of the final composts appeared to be affected by the ambient temperature conditions, which remarkably affected pH, C/N ratio, nutrient and trace elements availability, germination index, microbial biomass carbon, and FDA hydrolysis. Results showed that both sets of temperatures produced composts to be considered standard compost, but “warm” conditions compost showed greater maturity, while the composts produced under “cold” conditions were able to increase seed gemination
Sensor-Based Monitoring of Physical Activity for Glucose Management in Diabetic Patients: A Review
No full textContinuous glucose monitoring makes it possible to forecast the trajectory of future glucose concentrations. Meals, insulin, and other physiological and metabolic changes, such as physical activity, all impact glucose concentration. Devices monitoring patients’ physical activity are being developed to solve these problems. This review focuses on non-invasive sensors used to enhance glucose monitoring in patients with type 1 diabetes by utilising physiological characteristics associated with physical exercise. The search yielded 37 original research publications, from which we selected the most significant aspects regarding the devices, the various types of sensors and data acquired, the physiological signal, and the methodologies applied to analyze and use this data. The capacity to evaluate physiological data in real-time has been transformed by the growing integration of embedded artificial intelligence systems, enabling a more precise and prompt assessment of patient circumstances, including measuring glucose level
Improving real-time detection of laryngeal lesions in endoscopic images using a decoupled super-resolution enhanced YOLO
No full textBackground and Objective: Laryngeal Cancer (LC) constitutes approximately one third of head and neck cancers. Detecting early-stage lesions in this anatomical region is crucial for achieving a high survival rate. However, it poses significant diagnostic challenges owing to the varied appearance of lesions and the need for precise characterization for appropriate clinical management. Conventional diagnostic approaches rely heavily on endoscopic examination, which often requires expert interpretation and may be limited by subjective assessment. Deep learning (DL) approaches offer promising opportunities for automating lesion detection, but their efficacy in handling multi-modal imaging data and accurately localizing small lesions remains a subject of investigation. Furthermore, the clinical domain may largely benefit from the deployment of efficient DL methods that can ensure equitable access to advanced technologies, regardless of the availability of resources that can often be limited. In this study, a DL-based approach, named SRE-YOLO, was introduced to provide real-time assistance to less-experienced personnel during laryngeal assessment, by automatically detecting lesions at different scales from endoscopic White Light (WL) and Narrow-Band Imaging (NBI) images. Methods: During the training, the SRE-YOLO integrates a YOLOv8 nano (YOLOv8n) baseline with a Super-Resolution (SR) branch to enhance lesion detection. This last component is decoupled during inference to preserve the low computational demand of the YOLOv8n baseline. The evaluation was conducted on a multi-center dataset, encompassing diverse laryngeal pathologies and acquisition modalities. Results: The SRE-YOLO method improved the Average Precision (AP @IoU=0.5) in lesion detection by 5% with respect to the YOLOv8n baseline, while maintaining the inference speed of 58.8 Frames Per Second (FPS). Comparative analyses against state-of-the-art DL methods highlighted the efficacy of the SRE-YOLO approach in balancing detection accuracy, computational efficiency, and real-time applicability. Conclusions: This research underscores the potential of SRE-YOLO in developing efficient DL-driven decision support systems for real-time detection of laryngeal lesions at different scales from both WL and NBI endoscopic dat
An international expert survey on the worldwide digitalization in psychiatry: Global findings from the WPA survey
Get PDFBackground: The World Psychiatric Association (WPA) Working Group on Digital Psychiatry aims to digitally supplement, support and improve mental health and care literacy, acceptance and accessibility across WPA member countries and National Psychiatric Associations (NPAs). To help with this goal, the present study was set to explore first the global status of digital mental health and care across NPAs METHODS: An international expert survey on the digitalization level across all 145 WPA NPAs was electronically distributed through Qualtrics. Descriptive statistics were carried out on the global dataset. Results: Across all 145 responses, 57 were included for analysis (39.3 % response rate). Most NPAs reported lacking an official section on digital mental health (73.7 %), missing national (59.6 %) or regional policies (82.5 %), clinical guidelines (>60 % depending on the digital tool/program), and education/training in both medicine (77.2 %) either and psychiatry training programs (71.9 %). Telemedicine seemed to be the most regulated digital tool in more than half of all included NPAs. Telemedicine (45.6 %) and telemental healthcare (38.6 %) were generally reimbursed. The reported highest priority areas for future actions across WPA Regions were education and training, and the development of guidelines. Conclusion: This study represents a benchmark in the work of the WG on Digital Psychiatry. It presents clear priority areas that will guide the delivery of targeted actions aimed to promote digital mental health and care, and ultimately, equitable mental health outcomes around the world. Overall, the highest priorities to be globally implemented are represented by education/training and evidence-based clinical practice guidelines
2D temperature distribution reconstruction of steel bars under thermal transient from sequences of occluded infrared images
Get PDFIn-line thermography evaluates surface temperature distributions for quality control. This paper addresses the issue when the thermal imaging camera cannot fully capture a moving component undergoing successive stages of processing with transient thermal behavior. Reconstruction of the 2D temperature distribution requires stitching sequential partial images. When the object is moving and undergoing a thermal transient, simple stitching of sequential images leads to discontinuities and erroneous temperature distribution because the images are framed at different times. The correction of such artifacts is demonstrated using steel bars coming out of an induction furnace as an example. Two strategies are compared: temporal alignment and spatial alignment. Spatial alignment considers cooling relative to the distance from the furnace, requiring knowledge of the transient thermal pattern of the bar. The performance of the method is discussed in terms of effectiveness, uncertainty, and practical implementatio