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15 results on '"Dambra, P."'

1. C/EBPβ-dependent autophagy inhibition hinders NK cell function in cancer

Author

Portale, Federica, Carriero, Roberta, Iovino, Marta, Kunderfranco, Paolo, Pandini, Marta, Marelli, Giulia, Morina, Nicolò, Lazzeri, Massimo, Casale, Paolo, Colombo, Piergiuseppe, De Simone, Gabriele, Camisaschi, Chiara, Lugli, Enrico, Basso, Gianluca, Cibella, Javier, Marchini, Sergio, Bordi, Matteo, Meregalli, Greta, Garbin, Anna, Dambra, Monica, Magrini, Elena, Rackwitz, Wiebke, Cecconi, Francesco, Corbelli, Alessandro, Fiordaliso, Fabio, Eitler, Jiri, Tonn, Torsten, and Di Mitri, Diletta

Published
2024
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2. Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance

Author

Dambra, Savino, Han, Yufei, Aonzo, Simone, Kotzias, Platon, Vitale, Antonino, Caballero, Juan, Balzarotti, Davide, and Bilge, Leyla

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Many studies have proposed machine-learning (ML) models for malware detection and classification, reporting an almost-perfect performance. However, they assemble ground-truth in different ways, use diverse static- and dynamic-analysis techniques for feature extraction, and even differ on what they consider a malware family. As a consequence, our community still lacks an understanding of malware classification results: whether they are tied to the nature and distribution of the collected dataset, to what extent the number of families and samples in the training dataset influence performance, and how well static and dynamic features complement each other. This work sheds light on those open questions. by investigating the key factors influencing ML-based malware detection and classification. For this, we collect the largest balanced malware dataset so far with 67K samples from 670 families (100 samples each), and train state-of-the-art models for malware detection and family classification using our dataset. Our results reveal that static features perform better than dynamic features, and that combining both only provides marginal improvement over static features. We discover no correlation between packing and classification accuracy, and that missing behaviors in dynamically-extracted features highly penalize their performance. We also demonstrate how a larger number of families to classify make the classification harder, while a higher number of samples per family increases accuracy. Finally, we find that models trained on a uniform distribution of samples per family better generalize on unseen data.

Published
2023

3. Are SPAC Revenue Forecasts Informative?

Author

Dambra, Michael, Even-Tov, Omri, and Munevar, Kimberlyn

Subjects
Accounting, Auditing and Accountability, Banking, Finance and Investment, Commerce, Management, Tourism and Services, SPACs, forward-looking statements, IPOs, retail investors., Accounting, Accounting, auditing and accountability, Banking, finance and investment
Abstract

ABSTRACT: This paper examines the informativeness of special purpose acquisition company (SPAC) revenue forecasts. We document a positive association between the compound annual growth rate in revenue forecasts and abnormal returns, retail trading, and Twitter activity in the five-day window surrounding the disclosure of a merger announcement. By contrast, we find limited evidence that institutional investors and traditional information intermediaries respond to SPAC revenue forecasts. We also find evidence that SPAC revenue forecasts positively predict future operating underperformance, stock underperformance, and class action lawsuits. Overall, our results affirm the SEC’s concerns about the attractiveness of aggressive revenue projections to retail investors. JEL Classifications: G34; G32; M40; M48.

Published
2023

4. Quantifying Carbon Emissions due to Online Third-Party Tracking

Author

Pachilakis, Michalis, Dambra, Savino, Sanchez-Rola, Iskander, and Bilge, Leyla

Subjects
Computer Science - Computers and Society
Abstract

In the past decade, global warming made several headlines and turned the attention of the whole world to it. Carbon footprint is the main factor that drives greenhouse emissions up and results in the temperature increase of the planet with dire consequences. While the attention of the public is turned to reducing carbon emissions by transportation, food consumption and household activities, we ignore the contribution of CO2eq emissions produced by online activities. In the current information era, we spend a big amount of our days browsing online. This activity consumes electricity which in turn produces CO2eq. While website browsing contributes to the production of greenhouse gas emissions, the impact of the Internet on the environment is further exacerbated by the web-tracking practice. Indeed, most webpages are heavily loaded by tracking content used mostly for advertising, data analytics and usability improvements. This extra content implies big data transmissions which results in higher electricity consumption and thus higher greenhouse gas emissions. In this work, we focus on the overhead caused by web tracking and analyse both its network and carbon footprint. By leveraging the browsing telemetry of 100k users and the results of a crawling experiment of 2.7M websites, we find that web tracking increases data transmissions upwards of 21%, which in turn implies the additional emission of around 11 Mt of greenhouse gases in the atmosphere every year. We find such contribution to be far from negligible, and comparable to many activities of modern life, such as meat production, transportation, and even cryptocurrency mining. Our study also highlights that there exist significant inequalities when considering the footprint of different countries, website categories, and tracking organizations, with a few actors contributing to a much greater extent than the remaining ones.

Published
2023

5. One Size Does not Fit All: Quantifying the Risk of Malicious App Encounters for Different Android User Profiles

Author

Dambra, Savino, Bilge, Leyla, Kotzias, Platon, Shen, Yun, and Caballero, Juan

Subjects
Computer Science - Cryptography and Security, Computer Science - Computers and Society
Abstract

Previous work has investigated the particularities of security practices within specific user communities defined based on country of origin, age, prior tech abuse, and economic status. Their results highlight that current security solutions that adopt a one-size-fits-all-users approach ignore the differences and needs of particular user communities. However, those works focus on a single community or cluster users into hard-to-interpret sub-populations. In this work, we perform a large-scale quantitative analysis of the risk of encountering malware and other potentially unwanted applications (PUA) across user communities. At the core of our study is a dataset of app installation logs collected from 12M Android mobile devices. Leveraging user-installed apps, we define intuitive profiles based on users' interests (e.g., gamers and investors), and fit a subset of 5.4M devices to those profiles. Our analysis is structured in three parts. First, we perform risk analysis on the whole population to measure how the risk of malicious app encounters is affected by different factors. Next, we create different profiles to investigate whether risk differences across users may be due to their interests. Finally, we compare a per-profile approach for classifying clean and infected devices with the classical approach that considers the whole population. We observe that features such as the diversity of the app signers and the use of alternative markets highly correlate with the risk of malicious app encounters. We also discover that some profiles such as gamers and social-media users are exposed to more than twice the risks experienced by the average users. We also show that the classification outcome has a marked accuracy improvement when using a per-profile approach to train the prediction models. Overall, our results confirm the inadequacy of one-size-fits-all protection solutions.

Published
2023

6. 'Real Attackers Don't Compute Gradients': Bridging the Gap Between Adversarial ML Research and Practice

Author

Apruzzese, Giovanni, Anderson, Hyrum S., Dambra, Savino, Freeman, David, Pierazzi, Fabio, and Roundy, Kevin A.

Subjects
Computer Science - Cryptography and Security, Computer Science - Machine Learning
Abstract

Recent years have seen a proliferation of research on adversarial machine learning. Numerous papers demonstrate powerful algorithmic attacks against a wide variety of machine learning (ML) models, and numerous other papers propose defenses that can withstand most attacks. However, abundant real-world evidence suggests that actual attackers use simple tactics to subvert ML-driven systems, and as a result security practitioners have not prioritized adversarial ML defenses. Motivated by the apparent gap between researchers and practitioners, this position paper aims to bridge the two domains. We first present three real-world case studies from which we can glean practical insights unknown or neglected in research. Next we analyze all adversarial ML papers recently published in top security conferences, highlighting positive trends and blind spots. Finally, we state positions on precise and cost-driven threat modeling, collaboration between industry and academia, and reproducible research. We believe that our positions, if adopted, will increase the real-world impact of future endeavours in adversarial ML, bringing both researchers and practitioners closer to their shared goal of improving the security of ML systems.

Published
2022

8. DAYDREAMS - Development of Prescriptive Analytics based on Artificial Intelligence for Railways Intelligent Asset Management Systems

Author

Oneto, L, Anastasopoulos, M, Anguita, D, Baroni, I, Canepa, R, Dambra, C, Gogos, S, Jentner, W, and Petralli, S

Abstract

DAYDREAMS, which started its activities in December 2020, is a Horizon2020 project within Shift2Rail's 3rd Innovation Programme (IP3). The project's overall objective is to advance - in line with Shift2Rail's vision (now called Europe's Rail Join Undertaking) - on the integration and use of data and artificial/human trustworthy intelligence, together with context-driven Human Machine Interface (HMI) for prescriptive Intelligent Asset Management Systems (IAMS) in railway.

Published
2023
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9. Adherence issues related to sublingual immunotherapy as perceived by allergists

Author

Scurati, S., Frati, F., Passalacqua, G., Puccinelli, P., Hilaire, C., Incorvaia, C., D Avino, G., Comi, R., Lo Schiavo, M., Pezzuto, F., Montera, C., Pio, A., Teresa Ielpo, M., Cellini, F., Vicentini, L., Pecorari, R., Aresu, T., Capra, L., Benedictis, E., Bombi, C., Zauli, D., Vanzi, A., Alberto Paltrinieri, C., Bondioli, A., Paletta, I., Ventura, D., Mei, F., Paolini, F., Colangelo, C., Cavallucci, E., Cucinelli, F., Tinari, R., Ermini, G., Beltrami, V., Novembre, E., Begliomini, C., Marchese, E., Solito, E., Ammannati, V., Molino, G., Galli, E., Baldassini, M., Di Michele, L., Calvani, M., Gidaro, M., Venuti, A., Li Bianchi, E., Benassi, F., Pocobelli, D., Zangari, P., Rocco, M. G., Lo Vecchio, A., Pingitore, G., Grimaldi, O., Schiavino, D., Perrone, N., Antonietta Frieri, M., Di Rienzo, V., Tripodi, S., Scarpa, A., Tomsic, M., Bonaguro, R., Enrico Senna, G., Sirena, A., Turatello, F., Crescioli, S., Favero, E., Billeri, L., Chieco Bianchi, F., Gemignani, C., Zanforlin, M., Angiola Crivellaro, M., Hendrick, B., Maltauro, A., Masieri, S., Elisabetta Conte, M., Fama, M., Pozzan, M., Bonadonna, P., Casanova, S., Vallerani, E., Schiappoli, M., Borghesan, F., Giro, G., Casotto, S., Berardino, L., Zanoni, G., Ariano, R., Aquilina, R., Pellegrino, R., Marsico, P., Del Giudice, A., Narzisi, G., Tomaselli, V., Fornaca, G., Favro, M., Loperfido, B., Gallo, C., Buffoni, S., Gani, F., Raviolo, P., Faggionato, S., Truffelli, T., Vivalda, L., Albano, M., Enzo Rossi, R., Lattuada, G., Bona, F., Quaglio, L., Chiesa, A., Trapani, M., Seminara, R., Cucchi, B., Oderda, S., Borio, G., Galeasso, G., Garbaccio, P., Marco, A., Marengo, F., Cadario, G., Manzoni, S., Vinay, C., Curcio, A., Silvestri, A., Peduto, A., Riario-Sforza, G. G., Maria Forgnone, A., Barocelli, P., Tartaglia, N., Feyles, G., Giacone, A., Ricca, V., Guida, G., Nebiolo, F., Bommarito, L., Heffler, E., Vietti, F., Galimberti, M., Savi, E., Pappacoda, A., Bottero, P., Porcu, S., Felice, G., Berra, D., Francesca Spina, M., Pravettoni, V., Calamari, A. M., Varin, E., Iemoli, E., Lietti, D., Ghiglioni, D., Alessandro Fiocchi, Tosi, A., Poppa, M., Caviglia, A., Restuccia, M., Russello, M., Alciato, P., Manzotti, G., Ranghino, E., Luraschi, G., Rapetti, A., Rivolta, F., Allegri, F., Terracciano, L., Agostinis, F., Paolo Piras, P., Ronchi, G., Gaspardini, G., Caria, V., Tolu, F., Fantasia, D., Carta, P., Moraschini, A., Quilleri, R., Santelli, A., Prandini, P., Del Giudice, G., Apollonio, A., Bonazza, L., Teresa Franzini, M., Branchi, S., Zanca, M., Rinaldi, S., Catelli, L., Zanoletti, T., Cosentino, C., Della Torre, F., Cremonte, L., Musazzi, D., Suli, C., Rivolta, L., Ottolenghi, A., Marino, G., Sterza, G., Sambugaro, R., Orlandini, A., Minale, P., Voltolini, S., Bignardi, D., Omodeo, P., Tiri, A., Milani, S., Ronchi, B., Licardi, G., Bruni, P., Scibilia, J., Schroeder, J., Crosti, F., Maltagliati, A., Alesina, M. R., Mosca, M., Leone, G., Napolitano, G., Di Gruttola, G., Scala, G., Mascio, S., Valente, A., Marchetiello, I., Catello, R., Gazulli, A., Del Prete, A., Varricchio, A. M., Carbone, A., Forestieri, A., Stillitano, M., Leonetti, L., Tirroni, E., Castellano, F., Abbagnara, F., Romano, F., Levanti, C., Cilia, M., Longo, R., Ferrari, A., Merenda, R., Di Ponti, A., Guercio, E., Surace, L., Ammendola, G., Tansella, F., Peccarisi, L., Stragapede, L., Minenna, M., Granato, M., Fuiano, N., Pannofino, A., Ciuffreda, S., Giannotta, A., Morero, G., D Oronzio, L., Taddeo, G., Nettis, E., Cinquepalmi, G., Lamanna, C., Mastrandrea, F., Minelli, M., Salamino, F., Muratore, L., Latorre, F., Quarta, C., Ventura, M., D Ippolito, G., Giannoccaro, F., Dambra, P., Pinto, L., Triggiani, M., Munno, G., Manfredi, G., Lonero, G., Damiano, V., Errico, G., Di Leo, E., Manzari, F., Spagna, V., Arsieni, A., Matarrese, A., Mazzarella, G., Scarcia, G., Scarano, R., Ferrannini, A., Pastore, A., Maionchi, P., Filannino, L., Tria, M., Giuliano, G., Damiani, E., Scichilone, N., Marchese, M., Lucania, A., Marino, M., Strazzeri, L., Tumminello, S., Vitale, G. I., Gulotta, S., Gragotto, G., Zambito, M., Greco, D., Valenti, G., Licitra, G., Cannata, E., Filpi, R., Contraffatto, M., Sichili, S., Randazzo, S., Scarantino, G., Lo Porto, B., Pavone, F., Di Bartolo, C., Paternò, A., Rapisarda, F., Laudani, E., Leonardi, S., Padua, V., Cabibbo, G., Marino Guzzardi, G., Deluca, F., Agozzino, C., Pettinato, R., Ghini, M., Scurati S., Frati F., Passalacqua G., Puccinelli P., Hilaire C., Incorvaia C., D'Avino G., Comi R., Lo Schiavo M., Pezzuto F., Montera C., Pio A., Teresa Ielpo M., Cellini F., Vicentini L., Pecorari R., Aresu T., Capra L., De Benedictis E., Bombi C., Zauli D., Vanzi A., Alberto Paltrinieri C., Bondioli A., Paletta I., Ventura D., Mei F., Paolini F., Colangelo C., Cavallucci E., Cucinelli F., Tinari R., Ermini G., Beltrami V., Novembre E., Begliomini C., Marchese E., Solito E., Ammannati V., Molino G., Galli E., Baldassini M., Di Michele L., Calvani M., Gidaro M., Venuti A., Li Bianchi E., Benassi F., Pocobelli D., Zangari P., De Rocco M.G., Lo Vecchio A., Pingitore G., Grimaldi O., Schiavino D., Perrone N., Antonietta Frieri M., Di Rienzo V., Tripodi S., Scarpa A., Tomsic M., Bonaguro R., Enrico Senna G., Sirena A., Turatello F., Crescioli S., Favero E., Billeri L., Chieco Bianchi F., Gemignani C., Zanforlin M., Angiola Crivellaro M., Hendrick B., Maltauro A., Masieri S., Elisabetta Conte M., Fama M., Pozzan M., Bonadonna P., Casanova S., Vallerani E., Schiappoli M., Borghesan F., Giro G., Casotto S., Berardino L., Zanoni G., Ariano R., Aquilina R., Pellegrino R., Marsico P., Del Giudice A., Narzisi G., Tomaselli V., Fornaca G., Favro M., Loperfido B., Gallo C., Buffoni S., Gani F., Raviolo P., Faggionato S., Truffelli T., Vivalda L., Albano M., Enzo Rossi R., Lattuada G., Bona F., Quaglio L., Chiesa A., Trapani M., Seminara R., Cucchi B., Oderda S., Borio G., Galeasso G., Garbaccio P., De Marco A., Marengo F., Cadario G., Manzoni S., Vinay C., Curcio A., Silvestri A., Peduto A., Riario-Sforza G.G., Maria Forgnone A., Barocelli P., Tartaglia N., Feyles G., Giacone A., Ricca V., Guida G., Nebiolo F., Bommarito L., Heffler E., Vietti F., Galimberti M., Savi E., Pappacoda A., Bottero P., Porcu S., Felice G., Berra D., Francesca Spina M., Pravettoni V., Calamari A.M., Varin E., Iemoli E., Lietti D., Ghiglioni D., Fiocchi A., Tosi A., Poppa M., Caviglia A., Restuccia M., Russello M., Alciato P., Manzotti G., Ranghino E., Luraschi G., Rapetti A., Rivolta F., Allegri F., Terracciano L., Agostinis F., Paolo Piras P., Ronchi G., Gaspardini G., Caria V., Tolu F., Fantasia D., Carta P., Moraschini A., Quilleri R., Santelli A., Prandini P., Del Giudice G., Apollonio A., Bonazza L., Teresa Franzini M., Branchi S., Zanca M., Rinaldi S., Catelli L., Zanoletti T., Cosentino C., Della Torre F., Cremonte L., Musazzi D., Suli C., Rivolta L., Ottolenghi A., Marino G., Sterza G., Sambugaro R., Orlandini A., Minale P., Voltolini S., Bignardi D., Omodeo P., Tiri A., Milani S., Ronchi B., Licardi G., Bruni P., Scibilia J., Schroeder J., Crosti F., Maltagliati A., Alesina M.R., Mosca M., Leone G., Napolitano G., Di Gruttola G., Scala G., Mascio S., Valente A., Marchetiello I., Catello R., Gazulli A., Del Prete A., Varricchio A.M., Carbone A., Forestieri A., Stillitano M., Leonetti L., Tirroni E., Castellano F., Abbagnara F., Romano F., Levanti C., Cilia M., Longo R., Ferrari A., Merenda R., Di Ponti A., Guercio E., Surace L., Ammendola G., Tansella F., Peccarisi L., Stragapede L., Minenna M., Granato M., Fuiano N., Pannofino A., Ciuffreda S., Giannotta A., Morero G., D'Oronzio L., Taddeo G., Nettis E., Cinquepalmi G., Lamanna C., Mastrandrea F., Minelli M., Salamino F., Muratore L., Latorre F., Quarta C., Ventura M., D'Ippolito G., Giannoccaro F., Dambra P., Pinto L., Triggiani M., Munno G., Manfredi G., Lonero G., Damiano V., Errico G., Di Leo E., Manzari F., Spagna V., Arsieni A., Matarrese A., Mazzarella G., Scarcia G., Scarano R., Ferrannini A., Pastore A., Maionchi P., Filannino L., Tria M., Giuliano G., Damiani E., Scichilone N., Marchese M., Lucania A., Marino M., Strazzeri L., Tumminello S., Vitale G.I., Gulotta S., Gragotto G., Zambito M., Greco D., Valenti G., Licitra G., Cannata E., Filpi R., Contraffatto M., Sichili S., Randazzo S., Scarantino G., Lo Porto B., Pavone F., Di Bartolo C., Paterno A., Rapisarda F., Laudani E., Leonardi S., Padua V., Cabibbo G., Marino Guzzardi G., Deluca F., Agozzino C., Pettinato R., Ghini M., Scurati S, Frati F, Passalacqua G, Puccinelli P, Hilaire C, Incorvaia I, D'Avino G, Comi R, Lo Schiavio M, Pezzuto F, Montera C, Pio A, Ielpo MT, Cellini F, Vicentini L, Pecorari R, Aresu T, Capra L, De Benedictis E, Bombi C, Zauli D, and et al

Subjects
medicine.medical_specialty, Pathology, genetic structures, efficacy, Alternative medicine, Medicine (miscellaneous), Adherence, Cost, Efficacy, Side effects, Sublingual immunotherapy, Settore MED/10 - Malattie Dell'Apparato Respiratorio, sublingual immunotherapy, ALLERGEN, cost, medicine, Subcutaneous immunotherapy, Sublingual immunotherapy, adherence, Clinical efficacy, Intensive care medicine, Pharmacology, Toxicology and Pharmaceutics (miscellaneous), sublingual immunoterapy, Original Research, Asthma, AEROALLERGENS, side effects, business.industry, Health Policy, medicine.disease, Slit, eye diseases, Clinical trial, Patient Preference and Adherence, immunotherapy, sense organs, Allergists, ADHERENCE TO TREATMENT, business, Social Sciences (miscellaneous)
Abstract

Silvia Scurati1, Franco Frati1, Gianni Passalacqua2, Paola Puccinelli1, Cecile Hilaire1, Cristoforo Incorvaia3, Italian Study Group on SLIT Compliance 1Scientific and Medical Department, Stallergenes, Milan, Italy; 2Allergy and Respiratory Diseases, Department of Internal Medicine, Genoa; 3Allergy/Pulmonary Rehabilitation, ICP Hospital, Milan, ItalyObjectives: Sublingual immunotherapy (SLIT) is a viable alternative to subcutaneous immunotherapy to treat allergic rhinitis and asthma, and is widely used in clinical practice in many European countries. The clinical efficacy of SLIT has been established in a number of clinical trials and meta-analyses. However, because SLIT is self-administered by patients without medical supervision, the degree of patient adherence with treatment is still a concern. The objective of this study was to evaluate the perception by allergists of issues related to SLIT adherence.Methods: We performed a questionnaire-based survey of 296 Italian allergists, based on the adherence issues known from previous studies. The perception of importance of each item was assessed by a VAS scale ranging from 0 to 10.Results: Patient perception of clinical efficacy was considered the most important factor (ranked 1 by 54% of allergists), followed by the possibility of reimbursement (ranked 1 by 34%), and by the absence of side effects (ranked 1 by 21%). Patient education, regular follow-up, and ease of use of SLIT were ranked first by less than 20% of allergists.Conclusion: These findings indicate that clinical efficacy, cost, and side effects are perceived as the major issues influencing patient adherence to SLIT, and that further improvement of adherence is likely to be achieved by improving the patient information provided by prescribers.Keywords: adherence, sublingual immunotherapy, efficacy, cost, side effects

Published
2010

10. Phenotypic changes of leukemic cells in a patient with prolympho-plasmacytic leukaemia after treatment with fludarabine

Author

Loria, M. P., adriana dell'osso, Grande, M., Dambra, P., D Oronzio, L., Crollo, R., Lasaracina, E., Lucivero, G., Loria, Mp, Dell'Osso, A, Grande, M, Dambra, P, D'Oronzio, L, Crollo, R, Lasaracina, E, and Lucivero, Giacomo

Subjects
Male, Phenotype, Antigens, CD, Leukemia, Prolymphocytic, Humans, Antineoplastic Agents, Lymphocytes, Immunosuppressive Agents, Vidarabine, Aged, Immunophenotyping, Leukemia, Plasma Cell

11. Centyrin ligands for extrahepatic delivery of siRNA

Author

Klein, Donna, Goldberg, Shalom, Theile, Christopher S., Dambra, Richard, Haskell, Kathleen, Kuhar, Elise, Lin, Tricia, Parmar, Rubina, Manoharan, Muthiah, Richter, Mark, Wu, Meizhen, Mendrola Zarazowski, Jeannine, Jadhav, Vasant, Maier, Martin A., Sepp-Lorenzino, Laura, O’Neil, Karyn, and Dudkin, Vadim

Abstract

RNA interference offers the potential to treat disease at the earliest onset by selectively turning off the expression of target genes, such as intracellular oncogenes that drive cancer growth. However, the development of RNAi therapeutics as anti-cancer drugs has been limited by both a lack of efficient and target cell-specific delivery systems and the necessity to overcome numerous intracellular barriers, including serum/lysosomal instability, cell membrane impermeability, and limited endosomal escape. Here, we combine two technologies to achieve posttranscriptional gene silencing in tumor cells: Centyrins, alternative scaffold proteins binding plasma membrane receptors for targeted delivery, and small interfering RNAs (siRNAs), chemically modified for high metabolic stability and potency. An EGFR Centyrin known to internalize in EGFR-positive tumor cells was site-specifically conjugated to a beta-catenin (CTNNb1) siRNA and found to drive potent and specific target knockdown by free uptake in cell culture and in mice inoculated with A431 tumor xenografts (EGFR amplified). The generalizability of this approach was further demonstrated with Centyrins targeting multiple receptors (e.g., BCMA, PSMA, and EpCAM) and siRNAs targeting multiple genes (e.g., CD68, KLKb1, and SSB1). Moreover, by installing multiple conjugation handles, two different siRNAs were fused to a single Centyrin, and the conjugate was shown to simultaneously silence two different targets. Finally, by specifically pairing EpCAM-binding Centyrins that exhibited optimized internalization profiles, we present data showing that an EpCAM Centyrin CTNNb1 siRNA conjugate suppressed tumor cell growth of a colorectal cancer cell line containing an APC mutation but not cells with normal CTNNb1 signaling. Overall, these data demonstrate the potential of Centyrin-siRNA conjugates to target cancer cells and silence oncogenes, paving the way to a new class of anticancer drugs.

Published
2021
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12. MODELLING POPULATION DISTRIBUTION FOR EPIDEMIOLOGICAL STUDIES USING NIGHT-TIME SATELLITE DATA.

Author

Briggs, D, Gulliver, J, Dambra, C, and Petrakis, M

Subjects
POLLUTION, POPULATION health, POPULATION density
Abstract

One of the main requirements for investigation of health risks to environmental pollution is accurate information on population distribution. Although detailed data do exist in many parts of the world, based on national censuses at household or census tract level, in many others the level of spatial aggregation of these data is too coarse to allow reliable population estimates to be derived at the small area scale. Nor do census data typically provide accurate, small-area estimates of changes in population distribution in inter-censual years. One important, but as yet little-used source of information on population distribution is night-time satellite data, such as those provided by the Defense Meteorological Satellite Program (DMSP). These provide complete Earth coverage every night (subject to cloud cover) at a nominal spatial resolution of ca. 500 to 2700 metres. Previous studies have demonstrated strong associations between light emissions and population density at broad (e.g. national) scales, and the capability to use the data to detect and map changes in urban area over periods of a few years. This study assessed the ability to extend the use of these data to give local estimates of population distribution and to detect short-term variations in population as inputs to epidemiological studies. In a European-wide analysis, DMSP data were first linked to detailed land cover data within a Geographical Information System, to enhance their spatial resolution, using a combination of kriging and inverse distance weighting techniques. Associations between light emissions from relevant land cover classes and population numbers at different levels of aggregation were then analysed, both across the European Union and by country. Results showed that country-specific models typically explained 70-90% of variation in population distribution at the regional (e.g. county) scale and 50-85% of variation at the small-area (census tract) scale. DMSP data were also analysed to explore seasonal and year-to-year variations in population in sub-areas of the EU. Clear differences could be detected in both seasonal patterns of activity and long-term urban growth. Further exploration and use of these data as part of epidemiological investigations is therefor warranted. [ABSTRACT FROM AUTHOR]

Published
2003
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13. IgE-mediated Urticaria/Angioedema After Ingestion of Mussels.

Author

Nettis, E., Pannofino, A., Dambra, P., Loria, M. P., Di Maggio, G., Damiani, E., Ferrannini, A., and Tursi, A.

Subjects
URTICARIA, ANGIONEUROTIC edema, MUSSELS, IMMUNOGLOBULIN E, HEALTH, PHYSIOLOGY
Abstract

Reports on a study of 11 cases of immunoglobulin E (IgE)-mediated urticaria/angioedema caused by mussels. Allergic reactions after ingestion of raw and/or cooked mussels; Skin-prick tests; Possible significance of cooking procedure in the incidence of positive skin tests to unprepared cooked mussel and/or clam preparations; Poor relationship between clinical symptoms and test results.

Published
2001
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14. MODELLING POPULATION DISTRIBUTION FOR EPIDEMIOLOGICAL STUDIES USING NIGHTTIME SATELLITE DATA

Author

Briggs, D, Gulliver, J, Dambra, C, and Petrakis, M

Abstract

One of the main requirements for investigation of health risks to environmental pollution is accurate information on population distribution. Although detailed data do exist in many parts of the world, based on national censuses at household or census tract level, in many others the level of spatial aggregation of these data is too coarse to allow reliable population estimates to be derived at the small area scale. Nor do census data typically provide accurate, small-area estimates of changes in population distribution in inter-censual years. One important, but as yet little-used source of information on population distribution is night-time satellite data, such as those provided by the Defense Meteorological Satellite Program (DMSP). These provide complete Earth coverage every night (subject to cloud cover) at a nominal spatial resolution of ca. 500 to 2700 metres. Previous studies have demonstrated strong associations between light emissions and population density at broad (e.g. national) scales, and the capability to use the data to detect and map changes in urban area over periods of a few years. This study assessed the ability to extend the use of these data to give local estimates of population distribution and to detect short-term variations in population as inputs to epidemiological studies. In a European-wide analysis, DMSP data were first linked to detailed land cover data within a Geographical Information System, to enhance their spatial resolution, using a combination of kriging and inverse distance weighting techniques. Associations between light emissions from relevant land cover classes and population numbers at different levels of aggregation were then analysed, both across the European Union and by country. Results showed that country-specific models typically explained 70–90 of variation in population distribution at the regional (e.g. county) scale and 50–85 of variation at the small-area (census tract) scale. DMSP data were also analysed to explore seasonal and year-to-year variations in population in sub-areas of the EU. Clear differences could be detected in both seasonal patterns of activity and long-term urban growth. Further exploration and use of these data as part of epidemiological investigations is therefor warranted.

Published
2003

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