The predictive role of NLR and PLR for solid non-AIDS defining cancer incidence in HIV-infected subjects: a MASTER cohort study

Patients’ characteristics according to lost to follow-up at 3-years. Table S2. Distribution of non-AIDS defining cancer. Table S3. Multivariate analysis: time dependent Cox regression model. Variables included in the full model. (DOCX 20 kb

The burden of chronic diseases and cost-of-care in subjects with HIV infection in a Health District of Northern Italy over a 12-year period compared to that of the general population

The increase in life expectancy of HIV-infected patients has driven increased costs due to life-long HIV treatment and concurrent age-related comorbidities. This population-based study aimed to investigate the burden of chronic diseases and health costs for HIV(+) subjects compared to the general population living in Brescia Local health Agency (LHA) over a 12-year period

Chronic HBV infection in pregnant immigrants: a multicenter study of the Italian Society of Infectious and Tropical Diseases

The aims of the study were to estimate the clinical impact of HBV infection in pregnant immigrants and their family members and to identify a useful approach to managing the healthcare of HBsAg-positive immigrants. Included in this study were 143 HBsAg-positive pregnant immigrants of the 1,970 from countries with intermediate/high HBV endemicity who delivered in 8 Italian hospitals in 2012-2013. In addition, 172 family members of 96 HBsAg-positive pregnant immigrants were tested for serum HBsAg. The median age of the 143 HBsAg-positive pregnant immigrants was 31.0±12.1 years and the length of stay in Italy 5.0±4.1 years; 56.5% were unaware of their HBsAg positivity. HBV DNA was detected in 74.5% of the pregnant immigrants, i.e., 94.3% from Eastern Europe, 72.2% from East Asia and 58.1% from Sub-Saharan Africa. HBV DNA ≥2000 IU/mL was detected in 47.8% of pregnant immigrants, associated with ALT ≥1.5 times the upper normal value in 15% of cases. Anti-HDV was detected in 10% of cases. HBsAg was detected in 31.3% of the 172 family members. All HBsAg-positive immigrants received counseling on HBV infection and its prevention, and underwent a complete clinical evaluation. The findings validate the approach used for the healthcare management of the HBsAg-positive immigrant population

Cross-talk between chronic lymphocytic leukemia (CLL) tumor B cells and mesenchymal stromal cells (MSCs): implications for neoplastic cell survival

Leukemic cells from Chronic Lymphocytic Leukemia (CLL) patients interact with stromal cells of the surrounding microenvironment. Mesenchymal Stromal Cells (MSCs) represent the main population in CLL marrow stroma, which may play a key role for disease support and progression. In this study we evaluated whether MSCs influence in vitro CLL cell survival. MSCs were isolated from the bone marrow of 46 CLL patients and were characterized by flow cytometry analysis. Following co-culture of MSCs and leukemic B cells, we demonstrated that MSCs were able to improve leukemic B cell viability, this latter being differently dependent from the signals coming from MSCs. In addition, we found that the co-culture of MSCs with leukemic B cells induced an increased production of IL-8, CCL4, CCL11, and CXCL10 chemokines.As far as drug resistance is concerned, MSCs counteract the cytotoxic effect of Fludarabine/Cyclophosphamide administration in vivo, whereas they do not protect CLL cells from the apoptosis induced by the kinase inhibitors Bafetinib and Ibrutinib. The evidence that leukemic clones are conditioned by environmental stimuli suggest new putative targets for therapy in CLL patients

Antagonism of the prokineticin system prevents and reverses allodynia and inflammation in a mouse model of diabetes

Neuropathic pain is a severe diabetes complication and its treatment is not satisfactory. It is associated with neuroinflammation-related events that participate in pain generation and chronicization. Prokineticins are a new family of chemokines that has emerged as critical players in immune system, inflammation and pain. We investigated the role of prokineticins and their receptors as modulators of neuropathic pain and inflammatory responses in experimental diabetes. In streptozotocin-induced-diabetes in mice, the time course expression of prokineticin and its receptors was evaluated in spinal cord and sciatic nerves, and correlated with mechanical allodynia. Spinal cord and sciatic nerve pro- and anti-inflammatory cytokines were measured as protein and mRNA, and spinal cord GluR subunits expression studied. The effect of preventive and therapeutic treatment with the prokineticin receptor antagonist PC1 on behavioural and biochemical parameters was evaluated. Peripheral immune activation was assessed measuring macrophage and T-helper cytokine production. An up-regulation of the Prokineticin system was present in spinal cord and nerves of diabetic mice, and correlated with allodynia. Therapeutic PC1 reversed allodynia while preventive treatment blocked its development. PC1 normalized prokineticin levels and prevented the up-regulation of GluN2B subunits in the spinal cord. The antagonist restored the pro-/anti-inflammatory cytokine balance altered in spinal cord and nerves and also reduced peripheral immune system activation in diabetic mice, decreasing macrophage proinflammatory cytokines and the T-helper 1 phenotype. The prokineticin system contributes to altered sensitivity in diabetic neuropathy and its inhibition blocked both allodynia and inflammatory events underlying disease

High-efficiency all-solution-processed light-emitting diodes based on anisotropic colloidal heterostructures with polar polymer injecting layers

Colloidal quantum dots (QDs) are emerging as true candidates for light-emitting diodes with ultrasaturated colors. Here, we combine CdSe/CdS dot-in-rod hetero-structures and polar/polyelectrolytic conjugated polymers to demonstrate the first example of fully solution-based quantum dot light-emitting diodes (QD-LEDs) incorporating all-organic injection/transport layers with high brightness, very limited roll-off and external quantum efficiency as high as 6.1%, which is 20 times higher than the record QD-LEDs with all-solution processed organic interlayers and exceeds by over 200% QD-LEDs embedding vacuum-deposited organic molecules

A metric learning approach for splicing localization based on synthetic speech detection

LAUREA MAGISTRALECon il rapido progresso delle tecniche di intelligenza artificiale degli ultimi anni, la possibilità di generare video, immagini e audio estremamente realistici è alla portata di tutti. Queste tecnologie hanno aperto la strada alla creazione di deepfakes, contenuti multimediali prodotti utilizzando tecniche di deep learning che rappresentano realisticamente persone in comportamenti ingannevoli. Anche se principalmente utilizzati per puro intrattenimento, è da subito apparso evidente che il loro impiego potesse causare problemi e danni alle persone rappresentate. Infatti, al giorno d'oggi non è difficile trovare online casi di diffusione di fake news, frodi e bufale che coinvolgono i deepfake. In questo contesto, la componente audio gioca un ruolo importante, poiché la maggior parte dei deepfake si presenta sotto forma di video in cui l'audio riveste un ruolo di assoluta importanza per il realismo del risultato finale. Inoltre, sta diventando sempre più comune l'uso di deepfake composti da solo audio. Questo sviluppo viene sostenuto anche dalla continua evoluzione dei sistemi di sintesi vocale, che sono ormai in grado di generare audio altamente realistici con pochissimi secondi di parlato della persona che si vuole imitare. È quindi evidente l'importanza di un metodo automatico per la rilevazione di questi falsi. Tuttavia, un ulteriore minaccia è costituita dalla combinazione di audio splicing, una tecnica utilizzata per sostituire solo parti di un audio con l'obbiettivo di cambiarne l'intero significato, e deepfake. In questa tesi consideriamo il problema del rilevamento e della localizzazione dello splicing basato sulla produzione sintetica della voce. Siamo interessati solo ad audio creati sostituendo parti del segnale originale con porzioni di audio generate sinteticamente. Se usata per scopi malevoli, questa tecnica può produrre tracce incredibilmente realistiche che possono facilmente ingannare i migliori rilevatori automatici, poiché l'audio contiene ancora sezioni del parlato originale. Affrontiamo il problema dividendo il segnale sospetto in sezioni, anche sovrapposte, che vengono poi mappate in uno spazio di embeddings grazie all'utilizzo di reti neurali allenate per distinguere audio reali da parlato falso. Andiamo poi a calcolare la funzione di novelty partendo dagli embeddings estratti. Questa ci consente di analizzare la somiglianza tra i vari embeddings. Quando la funzione di novelty mostra picchi con prominenza significativa, viene rilevato e localizzato un punto di splicing all'interno dell'audio. In particolare, sfruttiamo l'importanza del metric learning nella fase di allenamento della rete neurale. Crediamo infatti che uno spazio di embeddings ben definito sia di cruciale importanza per la risoluzione del problema di splicing, sia in termini di rilevamento che in termini di localizzazione. Per validare il nostro metodo abbiamo creato due dataset di grandi dimensioni contenenti audio non-spliced e audio spliced, andando a considerare anche audio contenenti più di una singola sezione modificata. Tutti i vocali generati sinteticamente ed utilizzati per la creazione degli audio nei due dataset sono prodotti utilizzando sistemi di sintesi vocale all'avanguardia. Il metodo proposto è stato validato sia sul problema di rilevamento che di localizzazione dei punti di splicing. L'uso di metric learning mostra risultati molto promettenti nel campo del rilevamento e della localizzazione dello splicing, suggerendo alcuni sviluppi futuri in questa direzione. Abbiamo inoltre analizzato l'importanza dell'utilizzo di una funzione di novelty e il suo contributo nel rilevamento e nelle prestazioni complessive della localizzazione dei punti di splicing.With the rapid progress of artificial intelligence techniques over the last few years, the possibility to generate extremely realistic video, image and audio is within everyone's reach. These technologies have paved the way for deepfakes' creation, data produced using deep learning techniques that realistically represent people in deceptive behaviors. Even if they have been largely employed for entertainment purposes, it quickly became evident that their use can cause harm to the people represented. Indeed, nowadays it is not hard to find examples of fake news spreading and frauds that involve deepfake content. In this context, audio plays an important role since the majority of deepfakes come in a video form containing an audio component. Moreover, it is becoming increasingly common the use of deepfake with audio only. This is driven by the continuous evolution of speech synthesis systems, which nowadays can generate highly realistic content with very few seconds of audio of the target person. It is thus clear the importance of an anti-spoofing method for detecting fake speech content. However, an even greater threat comes from the combination of audio splicing, substituting only sections of an original speech to change its entire meaning, and deepfake. In this work, we consider the problem of splicing detection and localization based on synthetic voice production. We are interested only in spliced audio created by substituting parts of a pristine speech with synthetically generated speech. When used for malicious purposes, audio splicing can produce incredibly realistic spliced tracks that can fool the state-of-the-art detectors, since the audio still contains sections of real signals. We address the problem by dividing a suspect speech signal into overlapping frames and mapping them to a high-dimensional embedding space using state-of-the-art deep learning systems (extractors) trained to detect signals as real or fake. Then, we employ the novelty function to analyze the similarity between the embeddings. When the novelty function shows peaks of significant prominence, a splicing point is detected and localized. In particular, we exploit the importance of metric learning in the training phase of the extractor. We believe that a well-defined embedding space is of crucial importance for the problem of splicing detection and localization. To evaluate our method we construct two large datasets of spliced and non-spliced signal speech signals, exploiting also the possibility of multiple spliced regions in the audio. All the synthetically generated data employed are produced using state-of-the-art speech synthesis systems. The method is evaluated both for the detection and localization tasks. The use of metric learning shows promising results in the field of splicing detection and localization, suggesting some future developments in this direction. Moreover, we also analyzed the importance of defining an effective novelty function and its contribution to splicing detection and localization's overall performances