22 research outputs found
Nuovi spazi abitati nel mondo post industriale: La voce dei colori di Jimmy Liao
No full textThe post-industrial era brought new spaces whose lack of habitability soon led to call them non-places. These are transit spaces that often exceeded the human dimension, architectures that were frequently hostile to the body. Literature did not take long to incorporate them, also children’s literature. Airports, train stations, subway, began to populate the pages of books for children. The Sound of Colors, by Jimmy Liao, is an example of this. In this paper, this book has been analyzed, with an attempt to show the complexity of author treatment of the space. In this picturebook, a blind girl faces the everyday life in the subway stations. The whole narrative is developed between two interconnected worlds, the real one depicted by this non-place, and the other, poetic, by the girl. A qualitative methodology has been employed to study this work. Findings lead to thinking that current children’s literature spaces are increasingly multifaceted.L’era postindustriale ha portato con sé nuovi spazi, presto chiamati “non luoghi” per la loro inabitabilità. Si tratta di zone di transito non a misura d’uomo, spazi architettonici spesso ostili al corpo. I non luoghi presto compaiono in letteratura, anche quella per l’infanzia: aeroporti, stazioni ferroviarie, metropolitana cominciano ad apparire nelle pagine dei libri per bambini. The Sound of Colors di Jimmy Liao è un esempio di questo fenomeno. Nel presente lavoro, si analizza l’opera di Liao nel tentativo di mostrare la complessità del trattamento dello spazio da parte dell’autore. In quest’albo illustrato, una ragazzina cieca affronta la vita di tutti i giorni nelle stazioni della metropolitana. La narrazione si svolge tra due mondi collegati: quello reale del non luogo, e quello poetico della ragazzina. Nello studio dell’opera abbiamo utilizzato una metodologia qualitativa. I risultati inducono a pensare che nella letteratura per bambini attuale gli spazi siano sempre più multiformi
Unlocking the future: Machine learning sheds light on prognostication for early-stage hepatocellular carcinoma: Editorial on “Conventional and machine learning- based risk scores for patients with early-stage hepatocellular carcinoma”
No full textValidation of a data-driven clustering model for MASLD: Evidence from three large-scale Asian cohorts
No full textBackground & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a heterogeneous condition that presents varying risks for liver-related and cardiovascular complications. Clustering methods have identified distinct MASLD subtypes, yet their applicability to Asian populations remains unclear. This study aims to validate a MASLD clustering model using clinical variables from three Asian cohorts: Wenzhou Real-World (WRW), Hong Kong Clinical Data Analysis and Reporting System (CDARS), and SingHealth Diabetes Registry. Methods: Clustering analysis was conducted based on age, BMI, hemoglobin A(1c), alanine aminotransferase, LDL-cholesterol, and triglycerides. Outcomes included major adverse cardiovascular events (MACE), liver-related events (LRE), and new-onset type 2 diabetes (T2DM). They were analyzed using Cox regression risk models and Kaplan-Meier analyses to assess risk and incident events across MASLD clusters. Results: Across the three cohorts, distinct risk patterns emerged for MACE, LRE, and T2DM among various MASLD clusters. For MACE, the cardiometabolic cluster exhibited the highest risk in all cohorts: WRW (hazard ratio [HR] 1.315, p <0.001), Hong Kong CDARS (HR 1.559, p <0.001), and SingHealth Diabetes Registry (HR 1.262, p <0.001). For LRE, the liver-specific cluster showed the highest risk in the WRW (HR 1.578, p = 0.002) and SingHealth Diabetes Registry cohorts (HR 2.403, p <0.001). In contrast, in the Hong Kong CDARS cohort, both the cardiometabolic (HR 1.818, p <0.001) and liver-specific clusters (HR 1.557, p <0.001) exhibited similarly increased risks. For T2DM, the cardiometabolic cluster showed the highest risk in the WRW (HR 3.418, p <0.001) and Hong Kong CDARS cohorts (HR 2.761, p <0.001). Conclusions: The proposed MASLD clustering model is applicable to Asian populations, facilitating personalized treatment and optimizing outcomes
Deep reinforcement learning for intraday power trading
No full textLAUREA MAGISTRALEIn questa tesi l’autore presenta un’implementazione di un Sistema automatico per il commercio energetico , basato su un algoritmo di deep reinforcement learning. Il modello osserva il mercato nell’ intervallo di tempo immediatamente antecedente al momento di consegna dell’energia, momento sempre più importante dalla diffusione delle fonti di energia rinnovabili, spesso intermittenti.
L’obiettivo è imparare la strategia ottimale di scambio che massimizza il profitto dell’agente e minimizza il rischio.
Il processo decisionale è basato su una MDP and è risolto tramite l’algoritmo A2C. La configurazione che garantisce la migliore performan.ce è ampiamente superiore alle strategie basate sul Prezzo medio pesato sul volume. Inoltre il modello potrebbe essere facilmente generalizzato ad altri beni o mercati finanziari.In this thesis, the author presents an implementation of an automated trading system for power trading based on deep reinforcement learning.
The model focuses on the market closer to the delivery time of the power, which became more important with the widespread introduction of renewable intermittent production.
The objective is to learn the optimal trading strategy that maximizes the profit of the agent while reducing the risk exposure.
The sequential decision making is formulated as an MDP and solved with advantage actor-critic algorithm (A2C).
The best performance configuration outperforms baselines strategies based on the volume-weighted average price.
Furthermore, this model could be extended to other commodity or financial markets
Increasing antiviral treatment uptake improves survival in patients with HBV-related HCC
No full textBackground & Aims: Antiviral treatment is known to improve survival in patients with chronic hepatitis B (CHB)-related hepatocellular carcinoma (HCC). Yet, the treatment uptake in CHB patients remains low. We aimed to report the secular trend in antiviral treatment uptake from 2007-2017, and to compare the effect of different nucleos(t)ide analogue (NA) initiation times (before vs. after HCC diagnosis) on survival. Methods: A 3-month landmark analysis was used to compare overall survival in patients not receiving NA treatment (i.e. no NA), patients receiving NAs after their first HCC treatment (i.e. post-HCC NA), and patients receiving NAs <= 3 months before their first HCC treatment (i.e. pre-HCC NA). A propensity score-weighted Cox proportional hazards model was used to balance clinical characteristics between the 3 groups and to estimate hazard ratios (HRs). Results: The uptake of antiviral treatment in HCC patients increased from 47.3% in 2007 to 98.3% in 2017. The pre-HCC NA group contributed mostly to the uptake rate, which increased from 72.7% to 96.0% in the past decade. In addition, 3,843 CHB patients (407 no NA; 2,932 pre-HCC NA; 504 post-HCC NA) with HCC, receiving at least 1 type of HCC treatment, were included in the analysis. Lack of NA treatment at the time of HCC diagnosis increased the risk of death (weighted HR 3.05; 95% CI 2.70-3.44; p<0.001). The impact of the timing of NA treatment was insignificant (weighted HR 0.90; 95% CI 0.78-1.04; p = 0.161). Conclusions: The uptake of antiviral treatment in HCC patients increased over the past decade. NA treatment, regardless of whether it was initiated before or after HCC diagnosis, improved survival. It is never too late to initiate NA treatment, even after HCC diagnosis. (C) 2020 The Author(s). Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL).ope
Hepatocellular carcinoma surveillance after HBsAg seroclearance
No full textHepatitis B surface antigen (HBsAg) seroclearance is considered the functional cure and the optimal treatment endpoint for chronic hepatitis B (CHB). Patients with CHB who cleared HBsAg generally have a favorable clinical course with minimal risk of developing hepatocellular carcinoma (HCC) or cirrhotic complications. Nevertheless, a minority of patients still develop HCC despite HBsAg seroclearance. While patients with liver cirrhosis are still recommended for HCC surveillance, whether other non-cirrhotic patients who achieved HBsAg seroclearance should remain on HCC surveillance remains unclear. This review provides an overview of the incidence of HBsAg seroclearance, the factors associated with the occurrence of HBsAg seroclearance, the durability of HBsAg seroclearance, the risk of developing HCC after HBsAg seroclearance, the risk factors associated with HCC development after HBsAg seroclearance, the role of HCC risk scores, and the implications on HCC surveillance. Existing HCC risk scores have a reasonably good performance in patients after HBsAg seroclearance. In the era of artificial intelligence, future HCC risk prediction models based on artificial intelligence and longitudinal clinical data may further improve the prediction accuracy to establish a foundation of a risk score-based HCC surveillance strategy. As different novel hepatitis B virus (HBV) antiviral agents aiming at HBsAg seroclearance are under active development, new knowledge is anticipated on the natural history and HCC risk prediction of patients treated with new HBV drugs
Pharmacological Treatment of Ascites: Challenges and Controversies
No full textAscites is the most common complication from cirrhosis related to portal hypertension and depicts the onset of hepatic decompensation. Ranging from uncomplicated to refractory ascites, the progression carries prognostic value by reflecting the deterioration of underlying cirrhosis and portal hypertension. Diuretics have been the mainstay of treatment to control ascites, but the side effects heighten when the dosage is escalated. Non-selective beta-blockers (NSBBs) are widely used nowadays to prevent hepatic decompensation and variceal hemorrhage. However, with worsening systemic vasodilation and inflammation when ascites progresses, patients on NSBBs are at risk of hemodynamic collapse leading to renal hypoperfusion and thus hepatorenal syndrome. Long-term albumin infusion was studied to prevent the progression of ascites. However, the results were conflicting. Sodium-glucose cotransporter-2 inhibitors are under investigation to control refractory ascites. With that, patients with refractory ascites may require regular large-volume paracentesis. With an aging population, more patients are put on anti-thrombotic agents and their risks in decompensated cirrhosis and invasive procedures have to be considered. In general, decompensated cirrhosis with ascites poses multiple issues to pharmacological treatment. In the present review, we discuss the challenges and controversies in the pharmacological treatment of ascites
Novel machine learning models outperform risk scores in predicting hepatocellular carcinoma in patients with chronic viral hepatitis
No full textBackground & aims: Accurate hepatocellular carcinoma (HCC) risk prediction facilitates appropriate surveillance strategy and reduces cancer mortality. We aimed to derive and validate novel machine learning models to predict HCC in a territory-wide cohort of patients with chronic viral hepatitis (CVH) using data from the Hospital Authority Data Collaboration Lab (HADCL).
Methods: This was a territory-wide, retrospective, observational, cohort study of patients with CVH in Hong Kong in 2000-2018 identified from HADCL based on viral markers, diagnosis codes, and antiviral treatment for chronic hepatitis B and/or C. The cohort was randomly split into training and validation cohorts in a 7:3 ratio. Five popular machine learning methods, namely, logistic regression, ridge regression, AdaBoost, decision tree, and random forest, were performed and compared to find the best prediction model.
Results: A total of 124,006 patients with CVH with complete data were included to build the models. In the training cohort (n = 86,804; 6,821 HCC), ridge regression (area under the receiver operating characteristic curve [AUROC] 0.842), decision tree (0.952), and random forest (0.992) performed the best. In the validation cohort (n = 37,202; 2,875 HCC), ridge regression (AUROC 0.844) and random forest (0.837) maintained their accuracy, which was significantly higher than those of HCC risk scores: CU-HCC (0.672), GAG-HCC (0.745), REACH-B (0.671), PAGE-B (0.748), and REAL-B (0.712) scores. The low cut-off (0.07) of HCC ridge score (HCC-RS) achieved 90.0% sensitivity and 98.6% negative predictive value (NPV) in the validation cohort. The high cut-off (0.15) of HCC-RS achieved high specificity (90.0%) and NPV (95.6%); 31.1% of patients remained indeterminate.
Conclusions: HCC-RS from the ridge regression machine learning model accurately predicted HCC in patients with CVH. These machine learning models may be developed as built-in functional keys or calculators in electronic health systems to reduce cancer mortality.
Lay summary: Novel machine learning models generated accurate risk scores for hepatocellular carcinoma (HCC) in patients with chronic viral hepatitis. HCC ridge score was consistently more accurate than existing HCC risk scores. These models may be incorporated into electronic medical health systems to develop appropriate cancer surveillance strategies and reduce cancer death.ope
Role of noninvasive tests in the prognostication of metabolic dysfunction-associated steatotic liver disease
No full textIn managing metabolic dysfunction-associated steatotic liver disease, which affects over 30% of the general population, effective noninvasive biomarkers for assessing disease severity, monitoring disease progression, predicting the development of liver-related complications, and assessing treatment response are crucial. The advantage of simple fibrosis scores lies in their widespread accessibility through routinely performed blood tests and extensive validation in different clinical settings. They have shown reasonable accuracy in diagnosing advanced fibrosis and good performance in excluding the majority of patients with a low risk of liver-related complications. Among patients with elevated serum fibrosis scores, a more specific fibrosis and imaging biomarker has proved useful to accurately identify patients at risk of liver-related complications. Among specific fibrosis blood biomarkers, enhanced liver fibrosis is the most widely utilized and has been approved in the United States as a prognostic biomarker. For imaging biomarkers, the availability of vibration-controlled transient elastography has been largely improved over the past years, enabling the use of liver stiffness measurement (LSM) for accurate assessment of significant and advanced fibrosis, and cirrhosis. Combining LSM with other routinely available blood tests enhances the ability to diagnose at-risk metabolic dysfunction-associated steatohepatitis and predict liver-related complications, some reaching an accuracy comparable to that of liver biopsy. Magnetic resonance imaging-based modalities provide the most accurate quantification of liver fibrosis, though the current utilization is limited to research settings. Expanding their future use in clinical practice depends on factors such as cost and facility availability
Impact of cardiometabolic risk factors on hepatic fibrosis and clinical outcomes in MASLD: A population-based multi-cohort study
No full textBackground & Aims: Evaluating five cardiometabolic risk factors (CMRFs) is crucial for diagnosing metabolic dysfunction-associated steatotic liver disease (MASLD). This study investigated the impact of CMRFs on hepatic fibrosis and long-term clinical outcomes in patients with MASLD. Methods: Two cross-sectional cohorts (Korean magnetic resonance elastography [n = 6,684] and US vibration-controlled transient elastography [n = 6,230]) were included to assess the impact of five CMRFs and their combinations on hepatic fibrosis. Two longitudinal cohorts (UK Biobank [n = 408,544; mean follow-up, 14.3 years] and Korea National Health Insurance data [n = 355,640; mean follow-up, 11.7 years]) were included to evaluate long-term outcomes, including liver-related events, hepatocellular carcinoma events, and overall, cardiovascular, and liver-related death. The risk of MASLD associated with CMRFs was assessed using logistic or Cox regression analysis, referencing participants without steatotic liver disease. Results: Across all four cohorts, patients with type 2 diabetes mellitus had the highest risk of hepatic fibrosis and long-term clinical outcomes. Among the five CMRFs, impaired fasting glucose (CMRF2) was the most significant risk factor for both hepatic fibrosis and long-term clinical outcomes. High blood pressure (CMRF3) was the second most significant risk factor for hepatic fibrosis, following CMRF2. Low high-density lipoprotein cholesterol level (CMRF5) exhibited comparable significance for long-term clinical outcomes. These clinical outcomes worsened with increasing severity of glucose abnormalities (normal and impaired fasting glucose levels and type 2 diabetes mellitus). Patients with MASLD and CMRF2 exhibited a two-to-four times higher risk of hepatic fibrosis and liver-related events compared with those without impaired fasting glucose levels, similar to MASLD accompanied by any four CMRFs. Conclusions: The impact of the five CMRFs on hepatic fibrosis and long-term clinical outcomes varied across different clinical outcomes and population characteristics. However, impaired fasting glucose (CMRF2) consistently demonstrated the highest risk. Impact and implications: Understanding the impact of the five cardiometabolic risk factors (CMRFs) used in the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) on hepatic fibrosis and long-term clinical outcomes can improve the quality of care in the general population by facilitating the identification of at-risk individuals with MASLD. In our results, although the impact of each of the five CMRFs on hepatic fibrosis and long-term clinical outcomes varied depending on the type of clinical outcomes and the characteristics of the population, impaired fasting glucose (CMRF2) consistently showed the highest risk. Patients with MASLD and CMRF2 exhibited a two-to-four times higher risk of hepatic fibrosis and liver-related events compared with those without impaired fasting glucose levels, similar to MASLD accompanied by any four CMRFs. The utilization of impaired fasting glucose (CMRF2) can raise awareness among primary care providers regarding high-risk groups at the time of MASLD diagnosis
