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2. Gundelia tournefortii L. (Akkoub): a review of a valuable wild vegetable from Eastern Mediterranean.

Author

Hani, N., Abulaila, K., Howes, M.-J. R., Mattana, E., Bacci, S., Sleem, K., Sarkis, L., Eddine, N. Saed, Baydoun, S., Apostolides, N. Arnold, and Ulian, T.

Abstract

Gundelia tournefortii L. (Asteraceae) is an artichoke-like wild edible vegetable that grows in the semi-arid climate of the East Mediterranean. Due to its high cultural and economic values for culinary and therapeutic uses, this plant is exposed to overharvesting driven by household consumption and trade, threatening the survival of natural populations. Some limited data on the nutrient composition of G. tournefortii exists indicating presence of folic acid and several essential amino acids. Research on seed germination reports that mechanical scarification, gibberellic acid, and cold stratification are all effective treatments for seed dormancy breaking and therefore to propagate plants from seed. Successful vegetative propagation from the plant meristems is also available. However, despite some exceptions, the species is still not widely cultivated due to its thorny habit and complex seed germination requirements, and the ability to ensure seed germination under natural field conditions remains to be addressed. [ABSTRACT FROM AUTHOR]

Published
2024
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3. MASLD, hepatic steatosis and fibrosis are associated with the prevalence of chronic kidney disease and retinopathy in adults with type 1 diabetes mellitus

Author

Mantovani, A, Morieri, M, Aldigeri, R, Palmisano, L, Masulli, M, Bonomo, K, Baroni, M, Cossu, E, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Cas, A, de Kreutzenberg, S, Targher, G, Mantovani, Alessandro, Morieri, Mario Luca, Aldigeri, Raffaella, Palmisano, Luisa, Masulli, Maria, Bonomo, Katia, Baroni, Marco Giorgio, Cossu, Efisio, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, Targher, Giovanni, Mantovani, A, Morieri, M, Aldigeri, R, Palmisano, L, Masulli, M, Bonomo, K, Baroni, M, Cossu, E, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Cas, A, de Kreutzenberg, S, Targher, G, Mantovani, Alessandro, Morieri, Mario Luca, Aldigeri, Raffaella, Palmisano, Luisa, Masulli, Maria, Bonomo, Katia, Baroni, Marco Giorgio, Cossu, Efisio, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, and Targher, Giovanni

Abstract

Aim: We examined whether metabolic dysfunction-associated steatotic liver disease (MASLD) with or without significant fibrosis (assessed by validated non-invasive biomarkers) was associated with an increased risk of prevalent chronic kidney disease (CKD) or diabetic retinopathy in people with type 1 diabetes mellitus (T1DM). Methods: We performed a retrospective multicenter cross-sectional study involving 1,409 adult outpatients with T1DM, in whom hepatic steatosis index (HSI) and fibrosis (FIB)-4 index were calculated for non-invasively detecting hepatic steatosis (defined by HSI > 36), with or without coexisting significant fibrosis (FIB-4 index ≥ 1.3 or < 1.3). CKD was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 or urine albumin/creatinine ratio ≥ 3.0 mg/mmol. The presence of diabetic retinopathy was also recorded in all participants. Results: Patients with MASLD and significant fibrosis (n = 93) had a remarkably higher prevalence of CKD and diabetic retinopathy than their counterparts with MASLD without fibrosis (n = 578) and those without steatosis (n = 738). After adjustment for sex, diabetes duration, hemoglobin A1c, hypertension, and use of antihypertensive or lipid-lowering medications, patients with SLD and significant fibrosis had a higher risk of prevalent CKD (adjusted-odds ratio 1.76, 95 % confidence interval 1.05–2.96) than those without steatosis. Patients with MASLD without fibrosis had a higher risk of prevalent retinopathy (adjusted-odds ratio 1.49, 95 % CI 1.13–1.46) than those without steatosis. Conclusion: This is the largest cross-sectional study showing that MASLD with and without coexisting significant fibrosis was associated, independently of potential confounders, with an increased risk of prevalent CKD and retinopathy in adults with T1DM.

Published
2024

4. Magnetic Raman Scattering of Insulating Cuprates

Author

Eroles, J. M., Batista, C. D., Bacci, S. B., and Gagliano, E. R.

Subjects
Condensed Matter
Abstract

We study the $B_{1g}$ and $A_{1g}$ Raman profiles of M$_{2}$Cu$O_{4}$ (with M= La, Pr, Nd, Sm, Gd), Bi$_{2}$Sr$_{2}$Ca$_{0.5}$Y$_{0.5}$Cu$_{2}$O$_{8+y}$%, YBa$_{2}$Cu$_{3}$O$_{6.2}$ and PrBa$_{2}$Cu$_{2.7}$Al$_{0.3}$O$_{7}$ insulating cuprates within the Loudon-Fleury theory, in the framework of an extended Hubbard model for moderate on-site Coulomb interaction $U$. We calculate the non-resonant contribution to these Raman profiles by using exact diagonalization techniques and analyze two types of contributing mechanisms to the line shapes: 4-spin cyclic exchange and spin-phonon interactions. Although these interactions contribute to different parts of the spectra, together, they account for the enhanced linewidth and asymmetry of the $B_{1g}$ mode, as well as the non-negligible intensity of the $A_{1g}$ Raman line observed in these materials., Comment: 8 pages, 2 eps figures. To be published in PRB

Published
1998
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5. Theory of Nuclear Spin-Lattice Relaxation in La$_2$CuO$_4$ at High Temperatures

Author

Sokol, A., Gagliano, E., and Bacci, S.

Subjects
Condensed Matter
Abstract

The problem of the nuclear spin-lattice relaxation in La$_2$CuO$_4$ is revisited in connection with the recent measurements of the NQR relaxation rate for temperatures up to $ 900\mbox{K} $ [T.\ Imai {\em et al.}, Phys.\ Rev.\ Lett., in press]. We use an approach based on the exact diagonalization for the Heisenberg model to calculate the short wavelength contribution to the relaxation rate in the high temperature region, $ T \! \agt \! J/2 $. It is shown that the spin diffusion accounts for approximately 10\% of the total relaxation rate at $900 \mbox{K} $ and would become dominant for $T\! >\! J$. The calculated $1/T_1$ is in good agreement with the experiment both in terms of the absolute value and temperature dependence., Comment: 12 pages, RevTex (2 PostScript figures appended), UIUC-P-93-02-016

Published
1993
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6. Association between different modalities of insulin administration and metabolic dysfunction-associated fatty liver disease in adults with type 1 diabetes mellitus

Author

Csermely, A, Mantovani, A, Morieri, M, Palmisano, L, Masulli, M, Cossu, E, Baroni, M, Bonomo, K, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Aldigeri, R, Cas, A, de Kreutzenberg, S, Targher, G, Csermely, Alessandro, Mantovani, Alessandro, Morieri, Mario Luca, Palmisano, Luisa, Masulli, Maria, Cossu, Efisio, Baroni, Marco Giorgio, Bonomo, Katia, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Aldigeri, Raffaella, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, Targher, Giovanni, Csermely, A, Mantovani, A, Morieri, M, Palmisano, L, Masulli, M, Cossu, E, Baroni, M, Bonomo, K, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Aldigeri, R, Cas, A, de Kreutzenberg, S, Targher, G, Csermely, Alessandro, Mantovani, Alessandro, Morieri, Mario Luca, Palmisano, Luisa, Masulli, Maria, Cossu, Efisio, Baroni, Marco Giorgio, Bonomo, Katia, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Aldigeri, Raffaella, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, and Targher, Giovanni

Abstract

Aim: We examined whether different insulin administration modalities, i.e., multiple daily injections (MDI) or continuous subcutaneous insulin infusion (CSII by insulin pumps), are differently associated with the risk of having metabolic dysfunction-associated fatty liver disease (MAFLD), with or without coexisting significant liver fibrosis (assessed by validated non-invasive biomarkers), in adults with type 1 diabetes mellitus (T1DM). Methods: We conducted a retrospective, multicenter, cross-sectional study involving 1,417 adult individuals with established T1DM treated with MDI or CSII. We calculated hepatic steatosis index (HSI) and fibrosis (FIB)-4 index for non-invasively detecting MAFLD (defined by HSI >36), with or without coexisting significant fibrosis (defined by FIB-4 index ≥ 1.3 or <1.3, respectively). Results: Compared to the MDI group (n = 1,161), insulin-pump users (n = 256; 18.1%) were more likely to be younger (mean age: 40 vs. 48 years, P < 0.001), had better glycemic control (mean hemoglobin A1c: 7.7% vs. 7.9%, P = 0.025) and a markedly lower prevalence of MAFLD with coexisting significant fibrosis (2.7% vs. 8.1%, P = 0.010), but a comparable prevalence of MAFLD without fibrosis. In multinomial logistic regression analysis, CSII therapy was associated with a ∼70%-lower risk of MAFLD with significant fibrosis (unadjusted odds ratio 0.32, 95% confidence interval 0.14–0.70; P = 0.004), but this association was no longer significant after adjustment for age, hemoglobin A1c and other potential confounders. Conclusion: The lower prevalence of MAFLD with coexisting significant fibrosis we observed in adults with T1DM using CSII therapy, compared to those using MDI therapy, is primarily mediated by inter-group differences in age.

Published
2023

7. Hepatic steatosis with significant fibrosis is associated with an increased 10-year estimated risk of cardiovascular disease in adults with type 1 diabetes mellitus

Author

Mantovani, A, Morieri, M, Palmisano, L, Masulli, M, Cossu, E, Baroni, M, Bonomo, K, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Aldigeri, R, Cas, A, de Kreutzenberg, S, Targher, G, Mantovani, Alessandro, Morieri, Mario Luca, Palmisano, Luisa, Masulli, Maria, Cossu, Efisio, Baroni, Marco Giorgio, Bonomo, Katia, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Aldigeri, Raffaella, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, Targher, Giovanni, Mantovani, A, Morieri, M, Palmisano, L, Masulli, M, Cossu, E, Baroni, M, Bonomo, K, Cimini, F, Cavallo, G, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Pollis, R, Aldigeri, R, Cas, A, de Kreutzenberg, S, Targher, G, Mantovani, Alessandro, Morieri, Mario Luca, Palmisano, Luisa, Masulli, Maria, Cossu, Efisio, Baroni, Marco Giorgio, Bonomo, Katia, Cimini, Flavia Agata, Cavallo, Gisella, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Pollis, Riccardo Maria, Aldigeri, Raffaella, Cas, Alessandra Dei, de Kreutzenberg, Saula Vigili, and Targher, Giovanni

Abstract

Background: We assessed whether hepatic steatosis with or without significant fibrosis (determined by validated non-invasive biomarkers) is associated with an increased 10-year estimated risk for cardiovascular disease (CVD) in people with type 1 diabetes mellitus (T1DM). Methods: We conducted a retrospective, multicenter, cross-sectional study involving 1,254 adults with established T1DM without pre-existing CVD. We used the hepatic steatosis index (HSI) and fibrosis (FIB)-4 index for non-invasively detecting hepatic steatosis (defined as HSI > 36), with or without coexisting significant fibrosis (defined as FIB-4 index ≥ 1.3 or < 1.3). We calculated the Steno type 1 risk engine and the atherosclerotic CVD (ASCVD) risk score to estimate the 10-year risk of developing a first fatal or nonfatal CVD event. Results: Using the Steno type 1 risk engine, a significantly greater proportion of patients with hepatic steatosis and significant fibrosis (n = 91) had a high 10-year estimated CVD risk compared to those with hepatic steatosis alone (n = 509) or without steatosis (n = 654) (75.8% vs. 23.2% vs. 24.9%, p < 0.001). After adjustment for sex, BMI, diabetes duration, hemoglobin A1c, chronic kidney disease, and lipid-lowering medication use, patients with hepatic steatosis and significant fibrosis had an increased 10-year estimated risk of developing a first fatal or nonfatal CVD event (adjusted-odds ratio 11.4, 95% confidence interval 3.54–36.9) than those without steatosis. We observed almost identical results using the ASCVD risk calculator. Conclusions: The 10-year estimated CVD risk is remarkably greater in T1DM adults with hepatic steatosis and significant fibrosis than in their counterparts with hepatic steatosis alone or without steatosis.

Published
2023

8. Sex differences in cardiovascular disease and cardiovascular risk estimation in patients with type 1 diabetes

Author

Dei Cas, A, Aldigeri, R, Mantovani, A, Masulli, M, Palmisano, L, Cavalot, F, Bonomo, K, Baroni, M, Cossu, E, Cavallo, G, Cimini, F, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Morieri, M, Pollis, R, Targher, G, Vigili de Kreutzenberg, S, Dei Cas, Alessandra, Aldigeri, Raffaella, Mantovani, Alessandro, Masulli, Maria, Palmisano, Luisa, Cavalot, Franco, Bonomo, Katia, Baroni, Marco Giorgio, Cossu, Efisio, Cavallo, Gisella, Cimini, Flavia Agata, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Morieri, Mario Luca, Pollis, Riccardo Maria, Targher, Giovanni, Vigili de Kreutzenberg, Saula, Dei Cas, A, Aldigeri, R, Mantovani, A, Masulli, M, Palmisano, L, Cavalot, F, Bonomo, K, Baroni, M, Cossu, E, Cavallo, G, Cimini, F, Buzzetti, R, Mignogna, C, Leonetti, F, Bacci, S, Trevisan, R, Morieri, M, Pollis, R, Targher, G, Vigili de Kreutzenberg, S, Dei Cas, Alessandra, Aldigeri, Raffaella, Mantovani, Alessandro, Masulli, Maria, Palmisano, Luisa, Cavalot, Franco, Bonomo, Katia, Baroni, Marco Giorgio, Cossu, Efisio, Cavallo, Gisella, Cimini, Flavia Agata, Buzzetti, Raffaella, Mignogna, Carmen, Leonetti, Frida, Bacci, Simonetta, Trevisan, Roberto, Morieri, Mario Luca, Pollis, Riccardo Maria, Targher, Giovanni, and Vigili de Kreutzenberg, Saula

Abstract

Aims: Patients with type 1 diabetes (T1D) have higher cardiovascular disease (CVD) risk compared to the general population. This observational study aims to evaluate sex-related differences in CVD prevalence and CVD risk estimates in a large cohort of T1D adults. Materials and methods: We conducted a multicenter, cross-sectional study involving 2,041 T1D patients (mean age 46 years; 44.9% women). In patients without pre-existing CVD (primary prevention), we calculated the Steno type 1 risk engine to estimate the 10-year risk of developing CVD events. Results: CVD prevalence (n=116) was higher in men than in women aged ≥55 years (19.2 vs 12.8%, p=0.036), but comparable between the two sexes in those aged <55 years (p=0.91). In patients without pre-existing CVD (n=1,925), mean 10-year estimated CVD risk was 15.4±0.4% without any significant sex difference. However, stratifying this patient group by age, the 10-year estimated CVD risk was significantly higher in men than in women until age 55 years (p<0.001), but this risk equalized after this age. Carotid-artery plaque burden was significantly associated with age ≥55 years and with a medium and high 10-year estimated CVD risk, without any significant sex difference. Diabetic retinopathy and sensory-motor neuropathy were also associated with higher 10-year CVD risk and female sex. Conclusions: Both men and women with T1D are at high CVD risk. The 10-year estimated CVD risk was higher in men aged <55 years than in women of similar age, but these sex differences disappeared at age ≥55 years, suggesting that female sex was no longer protective.

Published
2023

9. Latent trait models for perceived risk assessment using a Covid-19 data survey.

Author

Bacci, S., Fabbricatore, R., and Iannario, Maria

Subjects
*ITEM response theory, *COVID-19, *LATENT class analysis (Statistics), *RISK assessment, *NUCLEAR weapons
Abstract

Aim of the contribution is analyzing potential events that may negatively impact individuals, assets, and/or the environment, and making judgments about the perceived personal and social riskiness of Covid-19 compared to other hazards belonging to health (AIDS, cancer, infarction), environmental (climate change), behavioral (serious car accidents), and technological (nuclear weapons) domains. The comparative risk analysis has been performed on a survey data collected during the first Italian Covid-19 lockdown. An item response theory model for polytomously scored items has been implemented for the analysis of the positioning of Covid-19 with respect to the other hazards in terms of perceived risk. Among the attributes determining the hazard's perceived risk, Covid-19 distinguishes for the knowledge of risks from the hazard, media attention, and fear caused by the hazard in the peers. Besides, through a latent regression analysis, the role of some individual characteristics on the perceived risk for Covid-19 has been examined. Our contribution allows us to disentangle among several aspects of hazards and describe the main factors affecting the perceived risk. It also contributes to determine if existing control measures are perceived as adequate and the interest for new media with related impact on a person's reaction. [ABSTRACT FROM AUTHOR]

Published
2023
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10. The ANS Collaboration Monitoring Program

Author

Munari U., Bacci S., Baldinelli L., Castellani F., Cetrulo G., Cherini G., Dallaporta S., Dallavia G., Englaro A., Frigo A., Graziani M., Luppi V., Maitan A., Marangoni C., Milani A., Moretti S., Moschini F., Ochner P., Siviero A., Righetti G. L., Tomaselli S., Tomasoni S., Vagnozzi A., and Valisa P.

Subjects
stars, symbiotic binaries, novae, photometry, spectroscopy, Astronomy, QB1-991
Abstract

ANS Collaboration is a growing Italian network of small and medium size telescopes performing spectroscopy (low and medium resolution single dispersion, and Echelle high resolution modes) and UBV RCIC CCD photometry of symbiotic stars and novae mainly, but with interest also on other types of objects like optical transients, eclipsing binaries, X-ray source counterparts and on-call follow-up observations of selected targets from some surveys like IPHAS or RAVE. In the present form ANS started operations in 2005, and at the time of writing has logged on symbiotic stars 14602 photometric runs and a rich ensemble of low and high resolution, absolutely fluxed spectra. the paper describes the internal organization, operation modes and procedures, and presents sample data and performance statistics.

Published
2012
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15. Incidence and severity of pertussis hospitalisations in infants aged less than 1 year in 37 hospitals of six EU/EEA countries, results of PERTINENT sentinel pilot surveillance system, December 2015 to December 2018

Author

Merdrignac L, Aït El Belghiti F, Pandolfi E, Jané M, Murphy J, Fabiánová K, García Cenoz M, Flem E, Guillot S, Tozzi AE, Carmona G, Habington A, Zavadilová J, Navasués A, Bøås H, Lévy-Brühl D, Ferretti B, Miguel Lanaspa Pérez, O'Sullivan N, Krížová P, Fernandino L, Bekkevold T, Hanslik T, Munoz-Almagro C, Bacci S, Spiteri G, Valenciano M, Moren A, PERTINENT Group, and PERTINENT group

Subjects
pertussis, active surveillance, hospital surveillance, pertussis incidence
Abstract

IntroductionPERTINENT is a pilot active surveillance system of infants hospitalised with pertussis in six European Union/European Economic Area countries (37 hospitals, seven sites).AimThis observational study aimed to estimate annual pertussis incidence per site from 2016 to 2018 and respective trends between 2017 and 2018. Pertussis cases were described, including their severity.MethodsWe developed a generic protocol and laboratory guidelines to harmonise practices across sites. Cases were hospitalised infants testing positive for Bordetella pertussis by PCR or culture. Sites collected demographic, clinical, laboratory data, vaccination status, and risk/protective factors. We estimated sites' annual incidences by dividing case numbers by the catchment populations.ResultsFrom December 2015 to December 2018, we identified 469 cases (247 males; 53%). The median age, birthweight and gestational age were 2.5 months (range: 0-11.6; interquartile range (IQR): 2.5), 3,280 g (range: 700-4,925; IQR: 720) and 39 weeks (range: 25-42; IQR: 2), respectively. Thirty cases (6%) had atypical presentation either with cough or cyanosis only or with absence of pertussis-like symptoms. Of 330 cases with information, 83 (25%) were admitted to intensive care units including five deceased infants too young to be vaccinated. Incidence rate ratios between 2018 and 2017 were 1.43 in Czech Republic (p = 0.468), 0.25 in Catalonia (p = 0.002), 0.71 in France (p = 0.034), 0.14 in Ireland (p = 0.002), 0.63 in Italy (p = 0.053), 0.21 in Navarra (p = 0.148) and zero in Norway.ConclusionsIncidence appeared to decrease between 2017 and 2018 in all but one site. Enhanced surveillance of hospitalised pertussis in Europe is essential to monitor pertussis epidemiology and disease burden.

Published
2021

20. Savoirs, entre éthos et pathos. Le cas de Roberto Saviano

Author

Bacci, S. (Simone), Heiden, L. (editor), and Tarrade, L. (editor)

Subjects
Sciences de l'Homme et Société/Linguistique
Abstract

Dans cet article nous mobiliserons la notion d’éthos discursif dans le cadre d’une analyse des stratégies syntactico-énonciatives mises en place par Roberto Saviano, journaliste expert de mafias, lorsqu’il prend la parole dans la presse et dans les réseaux sociaux. Nous voulons montrer comment Saviano transmet ses connaissances du milieux mafieux en adressant sa force persuasive en deux directions : vers soi, pour construire son image (l’éthos) et vers l’auditoire, pour susciter son émotion (le pathos).

Published
2022
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25. Dietary intake and major food sources of polyphenols in people with type 2 diabetes: The TOSCA.IT Study

Author

Vitale, M, Masulli, M, Rivellese, A, Bonora, E, Cappellini, F, Nicolucci, A, Squatrito, S, Antenucci, D, Barrea, A, Bianchi, C, Bianchini, F, Fontana, L, Fornengo, P, Giorgino, F, Gnasso, A, Mannucci, E, Mazzotti, A, Nappo, R, Palena, A, Pata, P, Perriello, G, Potenziani, S, Radin, R, Ricci, L, Romeo, F, Santini, C, Scarponi, M, Serra, R, Timi, A, Turco, A, Vedovato, M, Zavaroni, D, Grioni, S, Riccardi, G, Vaccaro, O, Cocozza, S, Auciello, S, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Sinagra, T, Longhitano, S, Tropea, V, Ballardini, G, Babini, A, Ripani, R, Gregori, G, Dolci, M, Bruselli, L, Salutini, I, Mori, M, Baccetti, F, Lapolla, A, Sartore, G, Burlina, S, Chilelli, N, Buzzetti, R, Venditti, C, Carlone, A, Galluzzo, A, Giordano, C, Torregrossa, V, Corsi, L, Cuneo, G, Corsi, S, Tizio, B, Galluzzo, G, Citro, G, Natale, M, Salvatore, V, Di Cianni, G, Lacaria, E, Russo, L, Iannarelli, R, De Gregorio, A, Sciarretta, F, D'Andrea, S, Montani, V, Cannarsa, E, Dolcetti, K, Cordera, R, Bonabello, L, Mazzucchelli, C, Giorda, C, Bonetto, C, Baldassarre, M, Iovine, C, Ciano, O, Dall'Aglio, E, Mancastroppa, G, Grimaldi, F, Tonutti, L, Boemi, M, D'Angelo, F, Leotta, S, Lauro, D, Rinaldi, M, Cignarelli, M, La Macchia, O, Fariello, S, Tomasi, F, Zamboni, C, Dozio, N, Trevisan, R, Scaranna, C, Del Prato, S, Miccoli, R, Garofolo, M, Pugliese, G, Salvi, L, Rangel, G, Anichini, R, Tedeschi, A, Corsini, E, Cucinotta, D, Di Benedetto, A, Giunta, L, Ruffo, M, Bossi, A, Carpinter, R, Dotta, F, Ceccarelli, E, Bartolo, P, Caselli, C, Luberto, A, Calbucci, G, Consoli, A, Ginestra, F, Calabrese, M, Zogheri, A, Laviola, L, Ippolito, C, Tarantino, L, Avogaro, A, Carallo, C, Scicchitano, C, Livraga, S, Perin, P, Forrnengo, P, Prinzis, T, De Cosmo, S, Bacci, S, Lamanna, C, Lettina, G, Aiello, A, Lalli, C, Franzetti, I, Petrachi, F, Asprino, V, Capra, C, Forte, E, Reggiani, G, Forlani, G, Montesi, L, Mazzella, N, Piatti, P, Monti, L, Stuccillo, M, Auletta, P, Petraroli, E, Capobianco, G, Romano, G, Cutolo, M, De Simone, G, Caiazzo, G, Nunziata, P, Sorrentino, S, Amelia, U, Calatola, P, Capuano, G, Vitale M., Masulli M., Rivellese A. A., Bonora E., Cappellini F., Nicolucci A., Squatrito S., Antenucci D., Barrea A., Bianchi C., Bianchini F., Fontana L., Fornengo P., Giorgino F., Gnasso A., Mannucci E., Mazzotti A., Nappo R., Palena A. P., Pata P., Perriello G., Potenziani S., Radin R., Ricci L., Romeo F., Santini C., Scarponi M., Serra R., Timi A., Turco A. A., Vedovato M., Zavaroni D., Grioni S., Riccardi G., Vaccaro O., Cocozza S., Auciello S., Cigolini M., Pichiri I., Brangani C., Tomasetto E., Sinagra T., Longhitano S., Tropea V., Ballardini G., Babini A. C., Ripani R., Gregori G., Dolci M., Bruselli L., Salutini I., Mori M., Baccetti F., Lapolla A., Sartore G., Burlina S., Chilelli N. C., Buzzetti R., Venditti C., Carlone A., Galluzzo A., Giordano C., Torregrossa V., Corsi L., Cuneo G., Corsi S., Tizio B., Galluzzo G., Citro G., Natale M., Salvatore V., Di Cianni G., Lacaria E., Russo L., Iannarelli R., De Gregorio A., Sciarretta F., D'Andrea S., Montani V., Cannarsa E., Dolcetti K., Cordera R., Bonabello L. A., Mazzucchelli C., Giorda C. B., Bonetto C., Baldassarre M. P. A., Iovine C., Ciano O., Dall'Aglio E., Mancastroppa G., Grimaldi F., Tonutti L., Boemi M., D'Angelo F., Leotta S., Lauro D., Rinaldi M. E., Cignarelli M., La Macchia O., Fariello S., Tomasi F., Zamboni C., Dozio N., Trevisan R., Scaranna C., Del Prato S., Miccoli R., Garofolo M., Pugliese G., Salvi L., Rangel G., Anichini R., Tedeschi A., Corsini E., Cucinotta D., Di Benedetto A., Giunta L., Ruffo M. C., Bossi A. C., Carpinter R., Dotta F., Ceccarelli E., Bartolo P. D., Caselli C., Luberto A., Calbucci G., Consoli A., Ginestra F., Calabrese M., Zogheri A., Laviola L., Ippolito C., Tarantino L., Avogaro A., Carallo C., Scicchitano C., Livraga S., Perin P. C., Forrnengo P., Prinzis T., De Cosmo S., Bacci S., Lamanna C., Lettina G., Aiello A., Lalli C., Franzetti I., Petrachi F., Asprino V., Capra C., Forte E., Reggiani G. M., Forlani G., Montesi L., Mazzella N., Piatti P. M., Monti L., Stuccillo M., Auletta P., Petraroli E., Capobianco G., Romano G., Cutolo M., De Simone G., Caiazzo G., Nunziata P., Sorrentino S., Amelia U., Calatola P., Capuano G., Vitale, M, Masulli, M, Rivellese, A, Bonora, E, Cappellini, F, Nicolucci, A, Squatrito, S, Antenucci, D, Barrea, A, Bianchi, C, Bianchini, F, Fontana, L, Fornengo, P, Giorgino, F, Gnasso, A, Mannucci, E, Mazzotti, A, Nappo, R, Palena, A, Pata, P, Perriello, G, Potenziani, S, Radin, R, Ricci, L, Romeo, F, Santini, C, Scarponi, M, Serra, R, Timi, A, Turco, A, Vedovato, M, Zavaroni, D, Grioni, S, Riccardi, G, Vaccaro, O, Cocozza, S, Auciello, S, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Sinagra, T, Longhitano, S, Tropea, V, Ballardini, G, Babini, A, Ripani, R, Gregori, G, Dolci, M, Bruselli, L, Salutini, I, Mori, M, Baccetti, F, Lapolla, A, Sartore, G, Burlina, S, Chilelli, N, Buzzetti, R, Venditti, C, Carlone, A, Galluzzo, A, Giordano, C, Torregrossa, V, Corsi, L, Cuneo, G, Corsi, S, Tizio, B, Galluzzo, G, Citro, G, Natale, M, Salvatore, V, Di Cianni, G, Lacaria, E, Russo, L, Iannarelli, R, De Gregorio, A, Sciarretta, F, D'Andrea, S, Montani, V, Cannarsa, E, Dolcetti, K, Cordera, R, Bonabello, L, Mazzucchelli, C, Giorda, C, Bonetto, C, Baldassarre, M, Iovine, C, Ciano, O, Dall'Aglio, E, Mancastroppa, G, Grimaldi, F, Tonutti, L, Boemi, M, D'Angelo, F, Leotta, S, Lauro, D, Rinaldi, M, Cignarelli, M, La Macchia, O, Fariello, S, Tomasi, F, Zamboni, C, Dozio, N, Trevisan, R, Scaranna, C, Del Prato, S, Miccoli, R, Garofolo, M, Pugliese, G, Salvi, L, Rangel, G, Anichini, R, Tedeschi, A, Corsini, E, Cucinotta, D, Di Benedetto, A, Giunta, L, Ruffo, M, Bossi, A, Carpinter, R, Dotta, F, Ceccarelli, E, Bartolo, P, Caselli, C, Luberto, A, Calbucci, G, Consoli, A, Ginestra, F, Calabrese, M, Zogheri, A, Laviola, L, Ippolito, C, Tarantino, L, Avogaro, A, Carallo, C, Scicchitano, C, Livraga, S, Perin, P, Forrnengo, P, Prinzis, T, De Cosmo, S, Bacci, S, Lamanna, C, Lettina, G, Aiello, A, Lalli, C, Franzetti, I, Petrachi, F, Asprino, V, Capra, C, Forte, E, Reggiani, G, Forlani, G, Montesi, L, Mazzella, N, Piatti, P, Monti, L, Stuccillo, M, Auletta, P, Petraroli, E, Capobianco, G, Romano, G, Cutolo, M, De Simone, G, Caiazzo, G, Nunziata, P, Sorrentino, S, Amelia, U, Calatola, P, Capuano, G, Vitale M., Masulli M., Rivellese A. A., Bonora E., Cappellini F., Nicolucci A., Squatrito S., Antenucci D., Barrea A., Bianchi C., Bianchini F., Fontana L., Fornengo P., Giorgino F., Gnasso A., Mannucci E., Mazzotti A., Nappo R., Palena A. P., Pata P., Perriello G., Potenziani S., Radin R., Ricci L., Romeo F., Santini C., Scarponi M., Serra R., Timi A., Turco A. A., Vedovato M., Zavaroni D., Grioni S., Riccardi G., Vaccaro O., Cocozza S., Auciello S., Cigolini M., Pichiri I., Brangani C., Tomasetto E., Sinagra T., Longhitano S., Tropea V., Ballardini G., Babini A. C., Ripani R., Gregori G., Dolci M., Bruselli L., Salutini I., Mori M., Baccetti F., Lapolla A., Sartore G., Burlina S., Chilelli N. C., Buzzetti R., Venditti C., Carlone A., Galluzzo A., Giordano C., Torregrossa V., Corsi L., Cuneo G., Corsi S., Tizio B., Galluzzo G., Citro G., Natale M., Salvatore V., Di Cianni G., Lacaria E., Russo L., Iannarelli R., De Gregorio A., Sciarretta F., D'Andrea S., Montani V., Cannarsa E., Dolcetti K., Cordera R., Bonabello L. A., Mazzucchelli C., Giorda C. B., Bonetto C., Baldassarre M. P. A., Iovine C., Ciano O., Dall'Aglio E., Mancastroppa G., Grimaldi F., Tonutti L., Boemi M., D'Angelo F., Leotta S., Lauro D., Rinaldi M. E., Cignarelli M., La Macchia O., Fariello S., Tomasi F., Zamboni C., Dozio N., Trevisan R., Scaranna C., Del Prato S., Miccoli R., Garofolo M., Pugliese G., Salvi L., Rangel G., Anichini R., Tedeschi A., Corsini E., Cucinotta D., Di Benedetto A., Giunta L., Ruffo M. C., Bossi A. C., Carpinter R., Dotta F., Ceccarelli E., Bartolo P. D., Caselli C., Luberto A., Calbucci G., Consoli A., Ginestra F., Calabrese M., Zogheri A., Laviola L., Ippolito C., Tarantino L., Avogaro A., Carallo C., Scicchitano C., Livraga S., Perin P. C., Forrnengo P., Prinzis T., De Cosmo S., Bacci S., Lamanna C., Lettina G., Aiello A., Lalli C., Franzetti I., Petrachi F., Asprino V., Capra C., Forte E., Reggiani G. M., Forlani G., Montesi L., Mazzella N., Piatti P. M., Monti L., Stuccillo M., Auletta P., Petraroli E., Capobianco G., Romano G., Cutolo M., De Simone G., Caiazzo G., Nunziata P., Sorrentino S., Amelia U., Calatola P., and Capuano G.

Abstract

Purpose: Proper evaluation of polyphenols intake at the population level is a necessary step in order to establish possible associations with health outcomes. Available data are limited, and so far no study has been performed in people with diabetes. The aim of this work was to document the intake of polyphenols and their major food sources in a cohort of people with type 2 diabetes and in socio-demographic subgroups. Methods: We studied 2573 men and women aged 50–75 years. Among others, anthropometry was measured by standard protocol and dietary habits were investigated by food frequency questionnaire (EPIC). The intake of polyphenols was evaluated using US Department of Agriculture and Phenol-Explorer databases. Results: The mean total polyphenol intake was 683.3 ± 5.8 mg/day. Non-alcoholic beverages represented the main food source of dietary polyphenols and provided 35.5% of total polyphenol intake, followed by fruits (23.0%), alcoholic beverages (14.0%), vegetables (12.4%), cereal products and tubers (4.6%), legumes (3.7%) and oils (2.1%); chocolate, cakes and nuts are negligible sources of polyphenols in this cohort. The two most important polyphenol classes contributing to the total intake were flavonoids (47.5%) and phenolic acids (47.4%). Polyphenol intake increased with age and education level and decreased with BMI; furthermore, in the northern regions of Italy, the polyphenol intake was slightly, but significantly higher than in the central or southern regions. Conclusions: The study documents for the first time the intake of polyphenols and their main food sources in people with diabetes using validated and complete databases of the polyphenol content of food. Compared with published data, collected in people without diabetes, these results suggest a lower intake and a different pattern of intake in people with diabetes.

Published
2018

27. Increased expression of iNOS by Langerhans cells in hanging marks.

Author

Focardi, M., Bugelli, V., Venturini, M., Bianchi, I., Defraia, B., Pinchi, V., and Bacci, S.

Subjects
LANGERHANS cells, CELL populations, CELLULAR control mechanisms, NITRIC oxide
Abstract

Recent studies show that Langerhans cell density increases in vital lesions and ligature marks when compared with post-mortal wounds. The enzyme, iNOS, has been established as a marker for estimating time of agony and serves in the regulation of dendritic cell behaviour. It is the aim of this paper, therefore, to evaluate the expression of this enzyme by Langerhans cells and the possible consequences that may be related to the production of nitric oxide by these cells in other types of lesions, including hanging furrows. The results show a greater expression of iNOS by Langerhans cells at the level of the hanging furrow when compared with other examined groups. Apart from an increase in the expression of iNOS, a large fraction of the mast cell population in the class II MHC molecules was observed in the hanging furrow. This corroborates that interactions between mast cells and dendritic cells are critical for the differentiation of these latter cellular types. Hence, the results suggest that iNOS plays a crucial role in the forensic practice of establishing the time interval in defining the vitality in hanging marks. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Multilevel Model-Based Clustering: A New Proposal of Maximum-A-Posteriori Assignment

Author

Imaizumi, T, Okada, A, Miyamoto, S, Sakaori, F, Yamamoto, Y, Vichi, M, Bacci, S, Bartolucci, F, Pennoni, F, Imaizumi, T, Okada, A, Miyamoto, S, Sakaori, F, Yamamoto, Y, Vichi, M, Bacci, S, Bartolucci, F, and Pennoni, F

Abstract

We deal with the problem of latent variable prediction in the context of multilevel latent class models for categorical responses provided by individuals nested in groups. In particular, we propose a posterior assignment rule that jointly predicts the individual- and group-level latent variables. This proposal is alternative to the common maximum- a-posteriori rule, which is based on first predicting the latent variables at cluster level and, then, those at individual level. To illustrate the proposal, we show the results of two simulation studies and two applications on data related to the national and the international assessment of student skills.

Published
2020

34. Effects on the incidence of cardiovascular events of the addition of pioglitazone versus sulfonylureas in patients with type 2 diabetes inadequately controlled with metformin (TOSCA.IT): a randomised, multicentre trial

Author

Vaccaro, O, Masulli, M, Nicolucci, A, Bonora, E, Del Prato, S, Maggioni, A, Rivellese, A, Squatrito, S, Giorda, C, Sesti, G, Mocarelli, P, Lucisano, G, Sacco, M, Signorini, S, Cappellini, F, Perriello, G, Babini, A, Lapolla, A, Gregori, G, Giordano, C, Corsi, L, Buzzetti, R, Clemente, G, Di Cianni, G, Iannarelli, R, Cordera, R, La Macchia, O, Zamboni, C, Scaranna, C, Boemi, M, Iovine, C, Lauro, D, Leotta, S, Dall'Aglio, E, Cannarsa, E, Tonutti, L, Pugliese, G, Bossi, A, Anichini, R, Dotta, F, Di Benedetto, A, Citro, G, Antenucci, D, Ricci, L, Giorgino, F, Santini, C, Gnasso, A, De Cosmo, S, Zavaroni, D, Vedovato, M, Consoli, A, Calabrese, M, di Bartolo, P, Fornengo, P, Riccardi, G, D'Angelo, F, Giansanti, R, Tanase, L, Lanari, L, Testa, I, Pancani, F, Ranchelli, A, Vagheggi, P, Scatona, A, Fontana, L, Laviola, L, Tarantino, L, Ippolito, C, Gigantelli, V, Manicone, M, Conte, E, Trevisan, R, Rota, R, Dodesini, A, Reggiani, G, Montesi, L, Mazzella, N, Forlani, G, Caselli, C, Di Luzio, R, Mazzotti, A, Aiello, A, Barrea, A, Musto, A, D'Amico, F, Sinagra, T, Longhitano, S, Trowpea, V, Sparti, M, Italia, S, Lisi, E, Grasso, G, Pezzino, V, Insalaco, F, Carallo, C, Scicchitano, C, De Franceschi, M, Calbucci, G, Ripani, R, Cuneo, G, Corsi, S, Romeo, F, Lesina, A, Comoglio, M, Bonetto, C, Robusto, A, Nada, E, Asprino, V, Cetraro, R, Impieri, M, Lucchese, G, Donnarumma, G, Tizio, B, Lenza, L, Paraggio, P, Tomasi, F, Dozio, N, Scalambra, E, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, O, Fariello, S, Sorrentino, M, Franzetti, I, Radin, R, Annunziata, F, Bonabello, L, Durante, A, Dolcino, M, Gallo, F, Mazzucchelli, C, Aleo, A, Melga, P, Briatore, L, Maggi, D, Storace, D, Cecoli, F, D'Ugo, E, Pupillo, M, Baldassarre, M, Salvati, F, Minnucci, A, De Luca, A, Zugaro, A, Santarelli, L, Bosco, A, Petrella, V, La Verghetta, G, D'Andrea, S, Giuliani, A, Polidoro, W, Sperandio, A, Sciarretta, F, Pezzella, A, Carlone, A, Potenziani, S, Venditti, C, Foffi, C, Carbone, S, Cipolloni, L, Moretti, C, Leto, G, Serra, R, Petrachi, F, Romano, I, Lacaria, E, Russo, L, Goretti, C, Sannino, C, Dolci, M, Bruselli, L, Mori, M, Baccetti, F, Del Freo, M, Cucinotta, D, Giunta, L, Ruffo, M, Cannizzaro, D, Pintaudi, B, Perrone, G, Pata, P, Ragonese, F, Lettina, G, Mancuso, T, Coppolino, A, Piatti, P, Monti, L, Stuccillo, M, Lucotti, P, Setola, M, Crippa, G, Loi, C, Oldani, M, Bottalico, M, Pellegata, B, Bonomo, M, Menicatti, L, Resi, V, Bertuzzi, F, Disoteo, E, Pizzi, G, Annuzzi, G, Capaldo, B, Nappo, R, Auciello, S, Turco, A, Costagliola, L, Corte, G, Vallefuoco, P, Nappi, F, Vitale, M, Cocozza, S, Ciano, O, Massimino, E, Garofalo, N, Avogaro, A, Guarneri, G, Fedele, D, Sartore, G, Chilelli, N, Burlina, S, Bonsembiante, B, Galluzzo, A, Torregrossa, V, Mancastroppa, G, Arsenio, L, Cioni, F, Caronna, S, Papi, M, Santeusanio, F, Calagreti, G, Timi, A, Tantucci, A, Marino, C, Ginestra, F, Di Biagio, R, Taraborelli, M, Miccoli, R, Bianchi, C, Garofolo, M, Politi, K, Penno, G, Livraga, S, Calzoni, F, Corsini, E, Tedeschi, A, Gagliano, M, Ippolito, G, Salutini, E, Cervellino, F, Natale, M, Salvatore, V, Zampino, A, Sinisi, R, Arcangeli, A, Zogheri, A, Guizzotti, S, Longo, R, Pellicano, F, Scolozzi, P, Termine, S, Luberto, A, Ballardini, G, Trojani, C, Mazzuca, P, Bruglia, M, Ciamei, M, Genghini, S, Zannoni, C, Rangel, G, Salvi, L, Zappaterreno, A, Cordone, S, Simonelli, P, Meggiorini, M, Frasheri, A, Di Pippo, C, Maglio, C, Mazzitelli, G, Rinaldi, M, Galli, A, Romano, M, D'Angelo, P, Suraci, C, Bacci, S, Palena, A, Genovese, S, Mancino, M, Rondinelli, M, Capone, F, Calabretto, E, Bulgheroni, M, Bucciarelli, L, Ceccarelli, E, Fondelli, C, Santacroce, C, Guarino, E, Nigi, L, Lalli, C, Di Vizia, G, Scarponi, M, Montani, V, Di Bernardino, P, Romagni, P, Dolcetti, K, Forte, E, Tamburo, L, Perin, P, Prinzis, T, Gruden, G, Bruno, G, Zucco, C, Perotta, M, Marena, S, Monsignore, S, Panero, F, Ponzi, F, Carpinteri, R, Casagrande, M, Coletti, M, Balini, A, Filopanti, M, Madaschi, S, Pulcina, A, Grimaldi, F, Venturini, G, Agus, S, Pagnutti, S, Guidotti, F, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Fainelli, G, Tomasetto, E, Zoppini, G, Galletti, A, Perrone, D, Capra, C, Bianchini, F, Ceseri, M, Di Nardo, B, Sasso, E, Bartolomei, B, Suliman, I, Fabbri, G, Romano, G, Maturo, N, Nunziata, G, Capobianco, G, De Simone, G, Villa, V, Rota, G, Pentangelo, C, Carbonara, O, Caiazzo, G, Cutolo, M, Sorrentino, T, Mastrilli, V, Amelia, U, Masi, S, Corigliano, G, Gaeta, I, Armentano, V, Calatola, P, Capuano, G, Angiulli, B, Auletta, P, Petraroli, E, Iodice, C, Agrusta, M, Vaccaro O., Masulli M., Nicolucci A., Bonora E., Del Prato S., Maggioni A. P., Rivellese A. A., Squatrito S., Giorda C. B., Sesti G., Mocarelli P., Lucisano G., Sacco M., Signorini S., Cappellini F., Perriello G., Babini A. C., Lapolla A., Gregori G., Giordano C., Corsi L., Buzzetti R., Clemente G., Di Cianni G., Iannarelli R., Cordera R., La Macchia O., Zamboni C., Scaranna C., Boemi M., Iovine C., Lauro D., Leotta S., Dall'Aglio E., Cannarsa E., Tonutti L., Pugliese G., Bossi A. C., Anichini R., Dotta F., Di Benedetto A., Citro G., Antenucci D., Ricci L., Giorgino F., Santini C., Gnasso A., De Cosmo S., Zavaroni D., Vedovato M., Consoli A., Calabrese M., di Bartolo P., Fornengo P., Riccardi G., D'Angelo F., Giansanti R., Tanase L., Lanari L., Testa I., Pancani F., Ranchelli A., Vagheggi P., Scatona A., Fontana L., Laviola L., Tarantino L., Ippolito C., Gigantelli V., Manicone M., Conte E., Trevisan R., Rota R., Dodesini A. R., Reggiani G. M., Montesi L., Mazzella N., Forlani G., Caselli C., Di Luzio R., Mazzotti A., Aiello A., Barrea A., Musto A., D'Amico F., Sinagra T., Longhitano S., Trowpea V., Sparti M., Italia S., Lisi E., Grasso G., Pezzino V., Insalaco F., Carallo C., Scicchitano C., De Franceschi M. S., Calbucci G., Ripani R., Cuneo G., Corsi S., Romeo F., Lesina A., Comoglio M., Bonetto C., Robusto A., Nada E., Asprino V., Cetraro R., Impieri M., Lucchese G., Donnarumma G., Tizio B., Lenza L., Paraggio P., Tomasi F., Dozio N., Scalambra E., Mannucci E., Lamanna C., Cignarelli M., Macchia O. L., Fariello S., Sorrentino M. R., Franzetti I., Radin R., Annunziata F., Bonabello L. A., Durante A., Dolcino M., Gallo F., Mazzucchelli C., Aleo A., Melga P., Briatore L., Maggi D., Storace D., Cecoli F., D'Ugo E., Pupillo M., Baldassarre M. P. A., Salvati F., Minnucci A., De Luca A., Zugaro A., Santarelli L., Bosco A., Petrella V., La Verghetta G. G., D'Andrea S., Giuliani A. E., Polidoro W. L., Sperandio A., Sciarretta F., Pezzella A., Carlone A., Potenziani S., Venditti C., Foffi C., Carbone S., Cipolloni L., Moretti C., Leto G., Serra R., Petrachi F., Romano I., Lacaria E., Russo L., Goretti C., Sannino C., Dolci M., Bruselli L., Mori M. L., Baccetti F., Del Freo M., Cucinotta D., Giunta L., Ruffo M. C., Cannizzaro D., Pintaudi B., Perrone G., Pata P., Ragonese F., Lettina G., Mancuso T., Coppolino A., Piatti P. M., Monti L., Stuccillo M., Lucotti P., Setola M., Crippa G. V., Loi C., Oldani M., Bottalico M. L., Pellegata B., Bonomo M., Menicatti L. S. M., Resi V., Bertuzzi F., Disoteo E. O., Pizzi G., Annuzzi G., Capaldo B., Nappo R., Auciello S. M., Turco A. A., Costagliola L., Corte G. D., Vallefuoco P., Nappi F., Vitale M., Cocozza S., Ciano O., Massimino E., Garofalo N., Avogaro A., Guarneri G., Fedele D., Sartore G., Chilelli N. C., Burlina S., Bonsembiante B., Galluzzo A., Torregrossa V., Mancastroppa G., Arsenio L., Cioni F., Caronna S., Papi M., Santeusanio F., Calagreti G., Timi A., Tantucci A., Marino C., Ginestra F., Di Biagio R., Taraborelli M., Miccoli R., Bianchi C., Garofolo M., Politi K. S., Penno G., Livraga S., Calzoni F., Mancastroppa G. L. F., Corsini E., Tedeschi A., Gagliano M. S., Ippolito G., Salutini E., Cervellino F., Natale M., Salvatore V., Zampino A., Sinisi R., Arcangeli A., Zogheri A., Guizzotti S., Longo R., Pellicano F., Scolozzi P., Termine S., Luberto A., Ballardini G., Trojani C., Mazzuca P., Bruglia M., Ciamei M., Genghini S., Zannoni C., Rangel G., Salvi L., Zappaterreno A., Cordone S., Simonelli P., Meggiorini M., Frasheri A., Di Pippo C., Maglio C., Mazzitelli G., Rinaldi M. E., Galli A., Romano M., D'Angelo P., Suraci C., Bacci S., Palena A. P., Genovese S., Mancino M., Rondinelli M., Capone F., Calabretto E., Bulgheroni M., Bucciarelli L., Ceccarelli E., Fondelli C., Santacroce C., Guarino E., Nigi L., Lalli C., Di Vizia G., Scarponi M., Montani V., Di Bernardino P., Romagni P., Dolcetti K., Forte E., Tamburo L., Perin P. C., Prinzis T., Gruden G., Bruno G., Zucco C., Perotta M., Marena S., Monsignore S., Panero F., Ponzi F., Carpinteri R., Casagrande M. L., Coletti M. F., Balini A., Filopanti M., Madaschi S., Pulcina A., Grimaldi F., Venturini G., Agus S., Pagnutti S., Guidotti F., Cavarape A., Cigolini M., Pichiri I., Brangani C., Fainelli G., Tomasetto E., Zoppini G., Galletti A., Perrone D., Capra C., Bianchini F., Ceseri M., Di Nardo B., Sasso E., Bartolomei B., Suliman I., Fabbri G., Romano G., Maturo N., Nunziata G., Capobianco G., De Simone G., Villa V., Rota G., Pentangelo C., Carbonara O., Caiazzo G., Cutolo M., Sorrentino T., Mastrilli V., Amelia U., Masi S., Corigliano G., Gaeta I., Armentano V., Calatola P., Capuano G., Angiulli B., Auletta P., Petraroli E., Iodice C. E., Agrusta M., Vaccaro, O, Masulli, M, Nicolucci, A, Bonora, E, Del Prato, S, Maggioni, A, Rivellese, A, Squatrito, S, Giorda, C, Sesti, G, Mocarelli, P, Lucisano, G, Sacco, M, Signorini, S, Cappellini, F, Perriello, G, Babini, A, Lapolla, A, Gregori, G, Giordano, C, Corsi, L, Buzzetti, R, Clemente, G, Di Cianni, G, Iannarelli, R, Cordera, R, La Macchia, O, Zamboni, C, Scaranna, C, Boemi, M, Iovine, C, Lauro, D, Leotta, S, Dall'Aglio, E, Cannarsa, E, Tonutti, L, Pugliese, G, Bossi, A, Anichini, R, Dotta, F, Di Benedetto, A, Citro, G, Antenucci, D, Ricci, L, Giorgino, F, Santini, C, Gnasso, A, De Cosmo, S, Zavaroni, D, Vedovato, M, Consoli, A, Calabrese, M, di Bartolo, P, Fornengo, P, Riccardi, G, D'Angelo, F, Giansanti, R, Tanase, L, Lanari, L, Testa, I, Pancani, F, Ranchelli, A, Vagheggi, P, Scatona, A, Fontana, L, Laviola, L, Tarantino, L, Ippolito, C, Gigantelli, V, Manicone, M, Conte, E, Trevisan, R, Rota, R, Dodesini, A, Reggiani, G, Montesi, L, Mazzella, N, Forlani, G, Caselli, C, Di Luzio, R, Mazzotti, A, Aiello, A, Barrea, A, Musto, A, D'Amico, F, Sinagra, T, Longhitano, S, Trowpea, V, Sparti, M, Italia, S, Lisi, E, Grasso, G, Pezzino, V, Insalaco, F, Carallo, C, Scicchitano, C, De Franceschi, M, Calbucci, G, Ripani, R, Cuneo, G, Corsi, S, Romeo, F, Lesina, A, Comoglio, M, Bonetto, C, Robusto, A, Nada, E, Asprino, V, Cetraro, R, Impieri, M, Lucchese, G, Donnarumma, G, Tizio, B, Lenza, L, Paraggio, P, Tomasi, F, Dozio, N, Scalambra, E, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, O, Fariello, S, Sorrentino, M, Franzetti, I, Radin, R, Annunziata, F, Bonabello, L, Durante, A, Dolcino, M, Gallo, F, Mazzucchelli, C, Aleo, A, Melga, P, Briatore, L, Maggi, D, Storace, D, Cecoli, F, D'Ugo, E, Pupillo, M, Baldassarre, M, Salvati, F, Minnucci, A, De Luca, A, Zugaro, A, Santarelli, L, Bosco, A, Petrella, V, La Verghetta, G, D'Andrea, S, Giuliani, A, Polidoro, W, Sperandio, A, Sciarretta, F, Pezzella, A, Carlone, A, Potenziani, S, Venditti, C, Foffi, C, Carbone, S, Cipolloni, L, Moretti, C, Leto, G, Serra, R, Petrachi, F, Romano, I, Lacaria, E, Russo, L, Goretti, C, Sannino, C, Dolci, M, Bruselli, L, Mori, M, Baccetti, F, Del Freo, M, Cucinotta, D, Giunta, L, Ruffo, M, Cannizzaro, D, Pintaudi, B, Perrone, G, Pata, P, Ragonese, F, Lettina, G, Mancuso, T, Coppolino, A, Piatti, P, Monti, L, Stuccillo, M, Lucotti, P, Setola, M, Crippa, G, Loi, C, Oldani, M, Bottalico, M, Pellegata, B, Bonomo, M, Menicatti, L, Resi, V, Bertuzzi, F, Disoteo, E, Pizzi, G, Annuzzi, G, Capaldo, B, Nappo, R, Auciello, S, Turco, A, Costagliola, L, Corte, G, Vallefuoco, P, Nappi, F, Vitale, M, Cocozza, S, Ciano, O, Massimino, E, Garofalo, N, Avogaro, A, Guarneri, G, Fedele, D, Sartore, G, Chilelli, N, Burlina, S, Bonsembiante, B, Galluzzo, A, Torregrossa, V, Mancastroppa, G, Arsenio, L, Cioni, F, Caronna, S, Papi, M, Santeusanio, F, Calagreti, G, Timi, A, Tantucci, A, Marino, C, Ginestra, F, Di Biagio, R, Taraborelli, M, Miccoli, R, Bianchi, C, Garofolo, M, Politi, K, Penno, G, Livraga, S, Calzoni, F, Corsini, E, Tedeschi, A, Gagliano, M, Ippolito, G, Salutini, E, Cervellino, F, Natale, M, Salvatore, V, Zampino, A, Sinisi, R, Arcangeli, A, Zogheri, A, Guizzotti, S, Longo, R, Pellicano, F, Scolozzi, P, Termine, S, Luberto, A, Ballardini, G, Trojani, C, Mazzuca, P, Bruglia, M, Ciamei, M, Genghini, S, Zannoni, C, Rangel, G, Salvi, L, Zappaterreno, A, Cordone, S, Simonelli, P, Meggiorini, M, Frasheri, A, Di Pippo, C, Maglio, C, Mazzitelli, G, Rinaldi, M, Galli, A, Romano, M, D'Angelo, P, Suraci, C, Bacci, S, Palena, A, Genovese, S, Mancino, M, Rondinelli, M, Capone, F, Calabretto, E, Bulgheroni, M, Bucciarelli, L, Ceccarelli, E, Fondelli, C, Santacroce, C, Guarino, E, Nigi, L, Lalli, C, Di Vizia, G, Scarponi, M, Montani, V, Di Bernardino, P, Romagni, P, Dolcetti, K, Forte, E, Tamburo, L, Perin, P, Prinzis, T, Gruden, G, Bruno, G, Zucco, C, Perotta, M, Marena, S, Monsignore, S, Panero, F, Ponzi, F, Carpinteri, R, Casagrande, M, Coletti, M, Balini, A, Filopanti, M, Madaschi, S, Pulcina, A, Grimaldi, F, Venturini, G, Agus, S, Pagnutti, S, Guidotti, F, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Fainelli, G, Tomasetto, E, Zoppini, G, Galletti, A, Perrone, D, Capra, C, Bianchini, F, Ceseri, M, Di Nardo, B, Sasso, E, Bartolomei, B, Suliman, I, Fabbri, G, Romano, G, Maturo, N, Nunziata, G, Capobianco, G, De Simone, G, Villa, V, Rota, G, Pentangelo, C, Carbonara, O, Caiazzo, G, Cutolo, M, Sorrentino, T, Mastrilli, V, Amelia, U, Masi, S, Corigliano, G, Gaeta, I, Armentano, V, Calatola, P, Capuano, G, Angiulli, B, Auletta, P, Petraroli, E, Iodice, C, Agrusta, M, Vaccaro O., Masulli M., Nicolucci A., Bonora E., Del Prato S., Maggioni A. P., Rivellese A. A., Squatrito S., Giorda C. B., Sesti G., Mocarelli P., Lucisano G., Sacco M., Signorini S., Cappellini F., Perriello G., Babini A. C., Lapolla A., Gregori G., Giordano C., Corsi L., Buzzetti R., Clemente G., Di Cianni G., Iannarelli R., Cordera R., La Macchia O., Zamboni C., Scaranna C., Boemi M., Iovine C., Lauro D., Leotta S., Dall'Aglio E., Cannarsa E., Tonutti L., Pugliese G., Bossi A. C., Anichini R., Dotta F., Di Benedetto A., Citro G., Antenucci D., Ricci L., Giorgino F., Santini C., Gnasso A., De Cosmo S., Zavaroni D., Vedovato M., Consoli A., Calabrese M., di Bartolo P., Fornengo P., Riccardi G., D'Angelo F., Giansanti R., Tanase L., Lanari L., Testa I., Pancani F., Ranchelli A., Vagheggi P., Scatona A., Fontana L., Laviola L., Tarantino L., Ippolito C., Gigantelli V., Manicone M., Conte E., Trevisan R., Rota R., Dodesini A. R., Reggiani G. M., Montesi L., Mazzella N., Forlani G., Caselli C., Di Luzio R., Mazzotti A., Aiello A., Barrea A., Musto A., D'Amico F., Sinagra T., Longhitano S., Trowpea V., Sparti M., Italia S., Lisi E., Grasso G., Pezzino V., Insalaco F., Carallo C., Scicchitano C., De Franceschi M. S., Calbucci G., Ripani R., Cuneo G., Corsi S., Romeo F., Lesina A., Comoglio M., Bonetto C., Robusto A., Nada E., Asprino V., Cetraro R., Impieri M., Lucchese G., Donnarumma G., Tizio B., Lenza L., Paraggio P., Tomasi F., Dozio N., Scalambra E., Mannucci E., Lamanna C., Cignarelli M., Macchia O. L., Fariello S., Sorrentino M. R., Franzetti I., Radin R., Annunziata F., Bonabello L. A., Durante A., Dolcino M., Gallo F., Mazzucchelli C., Aleo A., Melga P., Briatore L., Maggi D., Storace D., Cecoli F., D'Ugo E., Pupillo M., Baldassarre M. P. A., Salvati F., Minnucci A., De Luca A., Zugaro A., Santarelli L., Bosco A., Petrella V., La Verghetta G. G., D'Andrea S., Giuliani A. E., Polidoro W. L., Sperandio A., Sciarretta F., Pezzella A., Carlone A., Potenziani S., Venditti C., Foffi C., Carbone S., Cipolloni L., Moretti C., Leto G., Serra R., Petrachi F., Romano I., Lacaria E., Russo L., Goretti C., Sannino C., Dolci M., Bruselli L., Mori M. L., Baccetti F., Del Freo M., Cucinotta D., Giunta L., Ruffo M. C., Cannizzaro D., Pintaudi B., Perrone G., Pata P., Ragonese F., Lettina G., Mancuso T., Coppolino A., Piatti P. M., Monti L., Stuccillo M., Lucotti P., Setola M., Crippa G. V., Loi C., Oldani M., Bottalico M. L., Pellegata B., Bonomo M., Menicatti L. S. M., Resi V., Bertuzzi F., Disoteo E. O., Pizzi G., Annuzzi G., Capaldo B., Nappo R., Auciello S. M., Turco A. A., Costagliola L., Corte G. D., Vallefuoco P., Nappi F., Vitale M., Cocozza S., Ciano O., Massimino E., Garofalo N., Avogaro A., Guarneri G., Fedele D., Sartore G., Chilelli N. C., Burlina S., Bonsembiante B., Galluzzo A., Torregrossa V., Mancastroppa G., Arsenio L., Cioni F., Caronna S., Papi M., Santeusanio F., Calagreti G., Timi A., Tantucci A., Marino C., Ginestra F., Di Biagio R., Taraborelli M., Miccoli R., Bianchi C., Garofolo M., Politi K. S., Penno G., Livraga S., Calzoni F., Mancastroppa G. L. F., Corsini E., Tedeschi A., Gagliano M. S., Ippolito G., Salutini E., Cervellino F., Natale M., Salvatore V., Zampino A., Sinisi R., Arcangeli A., Zogheri A., Guizzotti S., Longo R., Pellicano F., Scolozzi P., Termine S., Luberto A., Ballardini G., Trojani C., Mazzuca P., Bruglia M., Ciamei M., Genghini S., Zannoni C., Rangel G., Salvi L., Zappaterreno A., Cordone S., Simonelli P., Meggiorini M., Frasheri A., Di Pippo C., Maglio C., Mazzitelli G., Rinaldi M. E., Galli A., Romano M., D'Angelo P., Suraci C., Bacci S., Palena A. P., Genovese S., Mancino M., Rondinelli M., Capone F., Calabretto E., Bulgheroni M., Bucciarelli L., Ceccarelli E., Fondelli C., Santacroce C., Guarino E., Nigi L., Lalli C., Di Vizia G., Scarponi M., Montani V., Di Bernardino P., Romagni P., Dolcetti K., Forte E., Tamburo L., Perin P. C., Prinzis T., Gruden G., Bruno G., Zucco C., Perotta M., Marena S., Monsignore S., Panero F., Ponzi F., Carpinteri R., Casagrande M. L., Coletti M. F., Balini A., Filopanti M., Madaschi S., Pulcina A., Grimaldi F., Venturini G., Agus S., Pagnutti S., Guidotti F., Cavarape A., Cigolini M., Pichiri I., Brangani C., Fainelli G., Tomasetto E., Zoppini G., Galletti A., Perrone D., Capra C., Bianchini F., Ceseri M., Di Nardo B., Sasso E., Bartolomei B., Suliman I., Fabbri G., Romano G., Maturo N., Nunziata G., Capobianco G., De Simone G., Villa V., Rota G., Pentangelo C., Carbonara O., Caiazzo G., Cutolo M., Sorrentino T., Mastrilli V., Amelia U., Masi S., Corigliano G., Gaeta I., Armentano V., Calatola P., Capuano G., Angiulli B., Auletta P., Petraroli E., Iodice C. E., and Agrusta M.

Abstract

Background The best treatment option for patients with type 2 diabetes in whom treatment with metformin alone fails to achieve adequate glycaemic control is debated. We aimed to compare the long-term effects of pioglitazone versus sulfonylureas, given in addition to metformin, on cardiovascular events in patients with type 2 diabetes. Methods TOSCA.IT was a multicentre, randomised, pragmatic clinical trial, in which patients aged 50–75 years with type 2 diabetes inadequately controlled with metformin monotherapy (2–3 g per day) were recruited from 57 diabetes clinics in Italy. Patients were randomly assigned (1:1), by permuted blocks randomisation (block size 10), stratified by site and previous cardiovascular events, to add-on pioglitazone (15–45 mg) or a sulfonylurea (5–15 mg glibenclamide, 2–6 mg glimepiride, or 30–120 mg gliclazide, in accordance with local practice). The trial was unblinded, but event adjudicators were unaware of treatment assignment. The primary outcome, assessed with a Cox proportional-hazards model, was a composite of first occurrence of all-cause death, non-fatal myocardial infarction, non-fatal stroke, or urgent coronary revascularisation, assessed in the modified intention-to-treat population (all randomly assigned participants with baseline data available and without any protocol violations in relation to inclusion or exclusion criteria). This study is registered with ClinicalTrials.gov, number NCT00700856. Findings Between Sept 18, 2008, and Jan 15, 2014, 3028 patients were randomly assigned and included in the analyses. 1535 were assigned to pioglitazone and 1493 to sulfonylureas (glibenclamide 24 [2%], glimepiride 723 [48%], gliclazide 745 [50%]). At baseline, 335 (11%) participants had a previous cardiovascular event. The study was stopped early on the basis of a futility analysis after a median follow-up of 57·3 months. The primary outcome occurred in 105 patients (1·5 per 100 person-years) who were given pioglitazone and 108 (1·5 p

Published
2017

37. Optimal model-based clustering with multilevel data

Author

Bacci, S, Bartolucci, F, PENNONI, FULVIA, Yoshiro Yamamoto, Takafumi Kubota, Koji Kurihara, Masahiro Mizuta, Miki Nakai, Junji Nakano, Atsuho Nakayama, Makiko Oda, Takuya Ohmori, Kosuke Okusa, Fumitake Sakaori, Kumiko Shiina, Akinobu Takeuchi, Makoto Tomita, Yuki Toyoda, Hiroshi Yadohisa, Satoru Yokoyama, Bacci, S, Bartolucci, F, and Pennoni, F

Subjects
SECS-S/01 - STATISTICA, Expectation-Maximization algorithm, maximum a-posteriori probability, Viterbi algorithm, Educational effectiveness studie
Abstract

In many contexts, sample units are clustered in groups according to a certain criterion, for instance employees in firms, students in classes, or patients in hospitals. These data are analyzed by multilevel models (Goldstein, 2011) and have important applications in the evaluation of public services, particularly in education and health. For instance, it may be of interest to make comparisons between schools or classes at national and international level on the basis of the students’acquired knowledge. Accountability systems in education have been promoted in the statistical literature mainly since the 90’s by Goldstein and Spiegelhalter (1996), who supported the idea that the performance monitoring approach may improve efficiency. In this work, we focus on models in which the multilevel structure is accounted for by a hierarchical set of discrete latent variables, even in the presence of multivariate responses; these latent variables are used to represent the unobserved heterogeneity between clusters (i.e., groups) of units and between units in each cluster, extending the Latent Class (LC) approach (Lazarsfeld and Henry, 1968) to the multilevel setting. In particular, two cases are of interest. The first is when the observed outcomes are polytomous, as they correspond to item responses, and data are collected at the same time occasion. This approach has been applied by many authors in the educational context, see among others Vermunt (2008) and Gnaldi et al. (2016). The second case of interest is when the data have a longitudinal dimension and heterogeneity between units is represented in a dynamic fashion by a Latent Markov (LM) chain, as proposed in Bartolucci et al. (2011); see also Bartolucci et al. (2013). While maximum likelihood estimation through the Expectation-Maximization algorithm (Dempster et al. 1977) of the models mentioned above is already well established, an issue that still deserves attention is that of predicting the latent variables at cluster and individual level on the basis of the observed data. In the LC literature, the Maximum A-Posteriori (MAP) approach is commonly used for this aim; for each latent variable, it consists in selecting the value having the highest posterior probability, which corresponds to the conditional distribution of this variable given the observed data. For the models at issue, the MAP approach may be applied in two different ways: (i) the latent variables at cluster and unit levels are separately dealt with for each cluster and unit; (ii) we first predict the latent variable for each cluster and then we predict each individual-specific latent variable (or variables in longitudinal case) conditional on the value predicted for the corresponding cluster-level latent variable. Both approaches may lead to suboptimal predictions, in the sense that the predictions may not correspond to the MAP probability of all latent variables. A similar problem exists in the LM model literature, where the sequence of latent states predicted by the local decoding method may not correspond to the MAP sequence of latent states that may be found by the global decoding method (Viterbi, 1967, Juang and Rabiner, 1991). We propose an alternative rule for the posterior classification that jointly considers individuals and groups. More in detail, the proposed rule is built by formulating the multilevel LC model in terms of an LM model (Bartolucci et al. 2013) and, then, considering a suitable adaptation of the Viterbi algorithm. The Viterbi algorithm applied in the hidden Markov literature has the advantage to have a linear complexity since it consists in finding the most likely sequence of latent classes on the basis of a forward and a backward recursion. The involved quantities may be interpreted as posterior probabilities by which we allocate each individual and cluster of individuals to a latent class. To illustrate the proposed approach, we show the results of some applications related to two educational effectiveness studies by considering data collected with the purpose to assess differences in the education level. The first dataset is a collection of measures related to the entire Italian population of schools and classes at the end of the compulsory education period (having at least 10 years of education). These Italian data have been collected by the National Institute of Evaluation of the Educational System of Instruction and Training (INVALSI). They refer to the competences assessed in 2009 by a set of multiple choice items which are dichotomously scored and concern Italian reading and grammar and mathematics; the student gender is available as well as the geographical location of the school. Another type of measurement on reading, mathematics, and science competences has been collected on the large-scale assessment surveys TIMSS (Trends in International Mathematics and Science Study) and PIRLS (Progress in International Reading Literacy Study). The surveys have been conducted in 2011 according to a sampling design that also accounts for the geographical area. We consider the achievement scores at the fourth grade when the Italian pupils are 9 to 10 years old. They have been related to a set of covariates collected by the background parents’ questionnaires and by the principals’ questionnaire of the schools (see also Grilli et al. 2016). The data are released according to five achievement scores for each subject and their variability should be due to the estimation process. These scores known as plausible values (Von Davier and Sinharay, 2013) result from the expected quantities calculated by the E step of the EM algorithm and they are an approximation of the conditional distribution of proficiency when the generalized partial credit model (Muraki, 1992) is used to estimate the performance of examinee subgroups. Main references Bartolucci, F., Farcomeni, A., and Pennoni, F. (2013). Latent Markov Models for Longitudinal Data. Chapman and Hall/CRC press, Boca Raton. Bartolucci, F., Pennoni, F., and Vittadini, G. (2011). Assessment of school performance through a multilevel latent Markov Rasch model. Journal of Educational and Behavioral Statistics, 36, 491– 522. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1–38. Gnaldi, M., Bacci, S., and Bartolucci, F. (2016). A multilevel finite mixture item response model to cluster examinees and schools. Advances in Data Analysis and Classification, 10, 53-70. Grilli, L., Pennoni, F., Rampichini, C., and Romeo, I. (2016). Exploiting TIMSS and PIRLS combined data: Multivariate multilevel modelling of student achievement. The Annals of Applied Statistics, 10, 2405-2426. Goldstein, H. (2011). Multilevel Statistical Models, John Wiley & Sons, Chichester, UK. Goldstein, H. and Spiegelhalter, D. J. (1996). League tables and their limitations: Statistical issues in comparisons of institutional performance. Journal of the Royal Statistical Society, Series A, 3, 385-443. Juang, B. H. and Rabiner, L. R. (1991). Hidden Markov models for speech recognition. Technometrics, 33, 251–272. Lazarsfeld, P. F and Henry, N. W. (1968). Latent Structure Analysis. Houghton Mifflin, Boston. Muraki, E. (1992). A generalized partial credit model: Application of the EM algorithm. Applied Psychological Measurement, 16, 159-177. Vermunt, J. K. (2008). Multilevel latent variable modeling: an application in education testing, Austrian Journal of Statistics, 37, 285–299. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13, 260–269. Von Davier, M. and Sinharay, S. (2013). Analytics in international large-scale assessments: Item response theory and population models. Handbook of international large-scale assessment: Background, technical issues, and methods of data analysis, 155-174

Published
2017

38. A new possible treatment for skin fibrosis with blue light: an in vitro study

Author

Magni G, Rossi F, Tatini F, Fraccalvieri M, Coppi E, Cherchi F, Pugliese AM, Alfieri D, Tripodi C, Targetti L, Bacci S, De Siena G, Cicchi, Pavone F, and e Pini R

Subjects
fibroblasts, fibrosis, wound healing, blue light
Abstract

Wound healing is a dynamic process consisting in four overlapped phases: haemostasis, inflammation, proliferation and remodelling. In our previous in vivo studies on superficial abrasions model, we analysed some cellular type of inflammatory infiltration and tissue remodelling after healing in treated and untreated samples with blue LED light. We pointed out that the blue light carried out an early enter in inflammatory phase and it accelerates the differentiation of fibroblasts into myofibroblasts, improving collagen morphology, leading to a faster healing of the treated tissues. Fibroblasts are the main cells involved in collagen deposition and their overactivity conducts on scars formation and keloid development. The purpose of this work is to investigate the effects of blue light on fibroblasts. In our in vitro studies we irradiated with a blue light primary culture from human keloid fibroblasts which are then analysed by the use of WST8 and SRB assays, electrophysiology and confocal microscopy in order to assess its influence on cells viability and metabolism, membrane potential response and myofibroblast activity. Results show that the blue light has an irradiation time- dependent modulation effect on keloids fibroblasts metabolism but not on viability, and that after irradiation there is a membrane potential response.

Published
2018

40. Immunohistochemical localization of Langerhans cells as a tool for vitality in hanging mark wounds: a pilot study.

Author

Focardi, M., Puliti, E., Grifoni, R., Palandri, M., Bugelli, V., Pinchi, V., Norelli, G.A., and Bacci, S.

Subjects
LANGERHANS cells, VITALITY, DIGITAL images, PILOT projects, HANGING (Death)
Abstract

Hanging deaths are common and characterized by their brief survival time. Macroscopic and histological exams are frequently unable to distinguish vital lesions from post-mortem lesions. This pilot study investigates the dendritic and mast cells in hanging marks to establish vitality lesions. Skin specimens were taken from vital wounds, ligature marks, and post-mortem lesions. Cryosections were stained for haematoxylin eosin, avidin, CD1a and MHC class II+ antigens and examined under light or fluorescence microscopies. Using digitized photomicrographs the images were then analysed. Differences were found in the Langerhans cells and epidermal MHC density, the vital lesions and ligature marks and the other specimens. The results may prove useful in forensic practice when neither macro nor microscopic objective evidence of vitality is available. The results could very possibly support the hypothesis that the amount of time it takes to die from hanging is less than currently believed (< 5 min). [ABSTRACT FROM AUTHOR]

Published
2020
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42. Chapter Assessing the predictive capability of Invalsi tests on high school final mark

Author

Bacci, Silvia, Bertaccini, Bruno, Petrucci, Alessandra, and Tocchioni, Valentina

Subjects
University students, academic performance, educational achievement, multilevel models, bic Book Industry Communication::J Society & social sciences
Abstract

Educational achievement can be considered a multifaceted issue, which comprises different domains of learning. In Italy, on one hand the INVALSI tests administered to students through the schooling years aim to measure the ability of students in numeracy, literacy and English reading and listening competencies, separately. On the other hand, the high school final mark may be considered an overall performance outcome, formed by the combination of several marks in different subjects. Finally, at university academic achievement may be represented by the number of credits earned during the first year of enrolment, usually considered a good predictor of successful academic performances. The aim of the present work is to understand if the INVALSI scores, the high school final mark and the number of credits earned in the first academic year are associated. More specifically, our objective is twofold: first, we intend to verify if and how the INVALSI scores are associated with students’ high school final mark; second, we aim to check if the INVALSI scores and / or the high school final mark are predictive of students’ career in terms of credits earned in the first year. We will interpret our results concentrating our attention on eventual differences depending on type of school, university, field of study, student’s geographical area of residence. We use the MOBYSU.it database, selecting nearly 200.000 students who obtained their high school diploma in Italy in 2019 and enrolled in an Italian university in academic year 2019/2020. For the first objective, we estimate a multilevel ordered logit models, with students nested within high schools; as for the second objective, we estimate a cross-classified multilevel models, with students nested within high schools and athenaeums. We interpret our results in the light of assessing eventual divergences in students’ performances during the transition from high school to university.

Published
2023
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44. Optimal model-based clustering with multilevel data

Author

Yoshiro Yamamoto, Takafumi Kubota, Koji Kurihara, Masahiro Mizuta, Miki Nakai, Junji Nakano, Atsuho Nakayama, Makiko Oda, Takuya Ohmori, Kosuke Okusa, Fumitake Sakaori, Kumiko Shiina, Akinobu Takeuchi, Makoto Tomita, Yuki Toyoda, Hiroshi Yadohisa, Satoru Yokoyama, Bacci, S, Bartolucci, F, Pennoni, F, PENNONI, FULVIA, Yoshiro Yamamoto, Takafumi Kubota, Koji Kurihara, Masahiro Mizuta, Miki Nakai, Junji Nakano, Atsuho Nakayama, Makiko Oda, Takuya Ohmori, Kosuke Okusa, Fumitake Sakaori, Kumiko Shiina, Akinobu Takeuchi, Makoto Tomita, Yuki Toyoda, Hiroshi Yadohisa, Satoru Yokoyama, Bacci, S, Bartolucci, F, Pennoni, F, and PENNONI, FULVIA

Abstract

In many contexts, sample units are clustered in groups according to a certain criterion, for instance employees in firms, students in classes, or patients in hospitals. These data are analyzed by multilevel models (Goldstein, 2011) and have important applications in the evaluation of public services, particularly in education and health. For instance, it may be of interest to make comparisons between schools or classes at national and international level on the basis of the students’acquired knowledge. Accountability systems in education have been promoted in the statistical literature mainly since the 90’s by Goldstein and Spiegelhalter (1996), who supported the idea that the performance monitoring approach may improve efficiency. In this work, we focus on models in which the multilevel structure is accounted for by a hierarchical set of discrete latent variables, even in the presence of multivariate responses; these latent variables are used to represent the unobserved heterogeneity between clusters (i.e., groups) of units and between units in each cluster, extending the Latent Class (LC) approach (Lazarsfeld and Henry, 1968) to the multilevel setting. In particular, two cases are of interest. The first is when the observed outcomes are polytomous, as they correspond to item responses, and data are collected at the same time occasion. This approach has been applied by many authors in the educational context, see among others Vermunt (2008) and Gnaldi et al. (2016). The second case of interest is when the data have a longitudinal dimension and heterogeneity between units is represented in a dynamic fashion by a Latent Markov (LM) chain, as proposed in Bartolucci et al. (2011); see also Bartolucci et al. (2013). While maximum likelihood estimation through the Expectation-Maximization algorithm (Dempster et al. 1977) of the models mentioned above is already well established, an issue that still deserves attention is that of predicting the latent variables at cluster

Published
2017

47. Addition of either pioglitazone or a sulfonylurea in type 2 diabetic patients inadequately controlled with metformin alone: impact on cardiovascular events. A randomized controlled trial

Author

Vaccaro, O, Masulli, M, Bonora, E, Del Prato, S, Giorda, Cb, Maggioni, Ap, Mocarelli, P, Nicolucci, A, Rivellese, Aa, Squatrito, S, Riccardi, G, IT study group, T. O. S. C. A., Sud, Cm, Imbaro, S, Garofalo, N, Ferrannini, E, Howard, B, Gerdts, E, Imperatore, G, Tavazzi, L, Pellegrini, F, Fabbri, G, Levantesi, G, Turazza, F, Gentile, S, Panico, S, Brambilla, P, Signorini, S, Cappellini, F, Parma, C, D'Alonzo, D, Di Nardo, B, Ferrari, S, Franciosi, M, Pecce, R, Valentini, M, Ceseri, M, Bianchini, F, Baldini, E, Atzori, A, Boemi, M, D'Angelo, F, Giansanti, R, Ricci, L, Ranchelli, A, Di Berardino, P, Cannarsa, E, Giorgino, F, Manicone, M, Tarantino, L, Trevisan, R, Scaranna, C, Forlani, G, Montesi, L, Aiello, A, Barrea, A, Sinagra, T, Longhitano, S, Sesti, G, Gnasso, A, Carallo, C, Scicchitano, C, Santini, C, Calbucci, G, Ripani, R, Corsi, L, Corsi, S, Romeo, F, Asprino, V, Donnarumma, G, Tizio, B, Clemente, G, Tomasi, F, Dozio, N, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, Ol, Fariello, S, Cordera, R, Mazzucchelli, C, Pupillo, M, Zugaro, A, Bosco, A, De Luca, A, Iannarelli, R, Giuliani, A, Polidoro, L, Sperandio, A, Sciarretta, F, Raffaella, B, Venditti, C, Di Cianni, G, Goretti, C, Dolci, Ma, Bruselli, L, Mori, M, Baccetti, F, Gregori, G, Venezia, A, Cucinotta, D, Pintaudi, B, Ragonese, F, Pata, P, Piatti, Pm, Luccotti, P, Orsi, E, Bonomo, M, Menicatti, L, Turco, Aa, Ciano, O, Vallefuoco, P, Corigliano, G, Pentangelo, C, Petraroli, E, Auletta, P, Carbonara, O, Capobianco, G, Caiazzo, G, Angiulli, B, De Simone, G, Michele, C, Mastrilli, V, Nunziata, G, Romano, G, Gaeta, I, Sorrentino, T, Iovine, C, Nappi, F, Paolisso, G, Rizzo, Mr, Avogaro, A, Vedovato, M, Lapolla, A, Sartore, G, Burlina, S, Chilelli, Nc, Galluzzoy, A, Giordano, C, Torregrossa, V, Arsenio, L, Dall'Aglio, E, Cioni, F, Babini, M, Moncastroppa, G, Perriello, G, Timi, A, Consoli, A, Ginestra, F, Zavaroni, D, Calzoni, F, Miccoli, R, Bianchi, C, Politi, S, Anichini, R, Tedeschi, A, Citro, G, Zampino, A, Rosa, S, Natale, M, Giocoli, Cl, Caruso, E, Tramontano, L, Imbroinise, A, Perna, Cd, Calabrese, M, Zogheri, A, Luberto, A, Ballardini, G, Babini, Ac, Zannoni, C, Pugliese, G, Salvi, L, Mazzitelli, G, Zappaterreno, A, Frontoni, S, Ventricini, A, Lauro, D, Galli, A, Rinaldi, Me, Leotta, S, Fontana, L, Goretti, S, Pozzilli, P, Leonetti, F, Morano, S, Filetti, S, Cosmo, Sd, Bacci, S, Palena, Ap, Calatola, P, Capuano, G, Amelia, U, Dotta, Francesco, Guarino, E, Ceccarelli, E, Lalli, C, Scarponi, M, Forte, E, Potenziani, S, Perin, Pc, Marena, S, Zucco, C, Perotto, M, Bossi, A, Filopanti, M, Grimaldi, F, Tonutti, L, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Capra, C, Cigolini, M., Vaccaro, O1, Masulli, M, Bonora, E, Del Prato, S, Giorda, Cb, Maggioni, Ap, Mocarelli, P, Nicolucci, A, Rivellese, Aa, Squatrito, S, Riccardi, G, Collaborators Riccardi G, T. O. S. C. A. IT study g. r. o. u. p., Sud, Cm, Imbaro, S, Vaccaro, O, Garofalo, N, Ferrannini, E, Howard, B, Gerdts, E, Imperatore, G, Tavazzi, L, Pellegrini, F, Fabbri, G, Levantesi, G, Turazza, F, Gentile, Sandro, Panico, S, Brambilla, P, Signorini, S, Cappellini, F, Parma, C, D'Alonzo, D, Di Nardo, B, Ferrari, S, Franciosi, M, Pecce, R, Valentini, M, Ceseri, M, Bianchini, F, Baldini, E, Atzori, A, Boemi, M, D'Angelo, F, Giansanti, R, Ricci, L, Ranchelli, A, Di Berardino, P, Cannarsa, E, Giorgino, F, Manicone, M, Tarantino, L, Trevisan, R, Scaranna, C, Forlani, G, Montesi, L, Aiello, A, Barrea, A, Sinagra, T, Longhitano, S, Sesti, G, Gnasso, A, Carallo, C, Scicchitano, C, Santini, C, Calbucci, G, Ripani, R, Corsi, L, Corsi, S, Romeo, F, Asprino, V, Donnarumma, G, Tizio, B, Clemente, G, Tomasi, F, Dozio, N, Mannucci, E, Lamanna, C, Cignarelli, M, Macchia, Ol, Fariello, S, Cordera, R, Mazzucchelli, C, Pupillo, M, Zugaro, A, Bosco, A, De Luca, A, Iannarelli, R, Giuliani, A, Polidoro, L, Sperandio, A, Sciarretta, F, Raffaella, B, Venditti, C, Di Cianni, G, Goretti, C, Dolci, Ma, Bruselli, L, Mori, M, Baccetti, F, Gregori, G, Venezia, A, Cucinotta, D, Pintaudi, B, Ragonese, F, Pata, P, Piatti, Pm, Luccotti, P, Orsi, E, Bonomo, M, Menicatti, L, Turco, Aa, Ciano, O, Vallefuoco, P, Corigliano, G, Pentangelo, C, Petraroli, E, Auletta, P, Carbonara, O, Capobianco, G, Caiazzo, G, Angiulli, B, De Simone, G, Michele, C, Mastrilli, V, Nunziata, G, Romano, G, Gaeta, I, Sorrentino, T, Iovine, C, Nappi, F, Paolisso, Giuseppe, Rizzo, Maria Rosaria, Avogaro, A, Vedovato, M, Lapolla, A, Sartore, G, Burlina, S, Chilelli, Nc, Galluzzoy, A, Giordano, C, Torregrossa, V, Arsenio, L, Dall'Aglio, E, Cioni, F, Babini, M, Moncastroppa, G, Perriello, G, Timi, A, Consoli, A, Ginestra, F, Zavaroni, D, Calzoni, F, Miccoli, R, Bianchi, C, Politi, S, Anichini, R, Tedeschi, A, Citro, G, Zampino, A, Rosa, S, Natale, M, Giocoli, Cl, Caruso, E, Tramontano, L, Imbroinise, A, Perna, Cd, Calabrese, M, Zogheri, A, Luberto, A, Ballardini, G, Babini, Ac, Zannoni, C, Pugliese, G, Salvi, L, Mazzitelli, G, Zappaterreno, A, Frontoni, S, Ventricini, A, Lauro, D, Galli, A, Rinaldi, Me, Leotta, S, Fontana, L, Goretti, S, Pozzilli, P, Leonetti, F, Morano, S, Filetti, S, Cosmo, Sd, Bacci, S, Palena, Ap, Calatola, P, Capuano, G, Amelia, U, Dotta, F, Guarino, E, Ceccarelli, E, Lalli, C, Scarponi, M, Forte, E, Potenziani, S, Perin, Pc, Marena, S, Zucco, C, Perotto, M, Bossi, A, Filopanti, M, Grimaldi, F, Tonutti, L, Cavarape, A, Cigolini, M, Pichiri, I, Brangani, C, Tomasetto, E, Capra, C, Cigolini M., Author information, Vaccaro, Olga, Masulli, Maria, Rivellese, ANGELA ALBAROSA, Riccardi, Gabriele, Giorda, C, Maggioni, A, Rivellese, A, Giorda, CB, Maggioni, AP, and Rivellese, AA

Subjects
Blood Glucose, Male, BIO/12 - BIOCHIMICA CLINICA E BIOLOGIA MOLECOLARE CLINICA, Endocrinology, Diabetes and Metabolism, pioglitazone, sulfonylurea, type 2 diabetes, metformin, cardiovascular events, Medicine (miscellaneous), Type 2 diabetes, Settore MED/13 - Endocrinologia, Body Mass Index, law.invention, Randomized controlled trial, Risk Factors, law, Surveys and Questionnaires, Cardiovascular Disease, pioglitazone, piogllitazone, Stroke, Diabetes, Therapy, Pioglitazone, Nutrition and Dietetics, Diabetes, Thiazolidinedione, cardiovascular events, Type 2 Diabetes Mellitus, sulphonylureas, Middle Aged, Metformin, Sulfonylurea Compound, Treatment Outcome, Tolerability, Cardiovascular Diseases, Drug Therapy, Combination, Female, type 2 diabetes, Cardiology and Cardiovascular Medicine, Human, medicine.drug, medicine.medical_specialty, Endpoint Determination, sulfonylurea, cardiovascualr event, Sudden death, Follow-Up Studie, Internal medicine, Diabetes mellitus, medicine, Humans, Hypoglycemic Agents, sulfonylureas, interventio trial, randomized controlled trial, Aged, Hypoglycemic Agent, Questionnaire, business.industry, Risk Factor, medicine.disease, Surgery, Sulfonylurea Compounds, Diabetes Mellitus, Type 2, Quality of Life, Thiazolidinediones, Therapy, business, metformin, Pioglitazone, Follow-Up Studies
Abstract

Background and aims Metformin is the first-line therapy in type 2 diabetes. In patients inadequately controlled with metformin, the addition of a sulfonylurea or pioglitazone are equally plausible options to improve glycemic control. However, these drugs have profound differences in their mechanism of action, side effects, and impact on cardiovascular risk factors. A formal comparison of these two therapies in terms of cardiovascular morbidity and mortality is lacking. The TOSCA.IT study was designed to explore the effects of adding pioglitazone or a sulfonylurea on cardiovascular events in type 2 diabetic patients inadequately controlled with metformin. Methods Multicentre, randomized, open label, parallel group trial of 48 month duration. Type 2 diabetic subjects, 50–75 years, BMI 20–45 Kg/m 2 , on secondary failure to metformin monotherapy will be randomized to add-on a sulfonylurea or pioglitazone. The primary efficacy outcome is a composite endpoint of all-cause mortality, nonfatal myocardial infarction, nonfatal stroke, and unplanned coronary revascularization. Principal secondary outcome is a composite ischemic endpoint of sudden death, fatal and non-fatal myocardial infarction and stroke, endovascular or surgical intervention on the coronary, leg or carotid arteries, major amputations. Side effects, quality of life and economic costs will also be evaluated. Efficacy, safety, tolerability, and study conduct will be monitored by an independent Data Safety Monitoring Board. End points will be adjudicated by an independent external committee. Conclusions TOSCA.IT is the first on-going study investigating the head-to-head comparison of adding a sulfonylurea or pioglitazone to existing metformin treatment in terms of hard cardiovascular outcomes. Registration: Clinicaltrials.gov ID NCT00700856.

Published
2012

48. Le condizioni di alloggio degli studenti fuori sede nell’esperienza dei laureati a Bologna

Author

Bacci S., CAMMELLI, ANDREA, GASPERONI, Giancarlo, DI FRANCIA, ANGELO, AUTORI VARI, Bacci S., Cammelli A., Gasperoni G., and Di Francia A.

Subjects
UNIVERSITÀ, CONDIZIONE ABITATIVA, STUDENTI
Abstract

Obiettivo del lavoro – realizzato dall'Osservatorio Statistico dell'Università di Bologna – è l’analisi delle condizioni di alloggio dei laureati fuori sede presso la sede centrale di Bologna dell’Ateneo omonimo, ponendo particolare attenzione sulle caratteristiche delle abitazioni private. Il testo è articolato in 4 sezioni. Nella prima si inquadra la problematica oggetto d’interesse e viene definita la popolazione degli studenti fuori sede che hanno conseguito il titolo di studio nella sede centrale di Bologna dell’Alma Mater Studiorum; quindi le caratteristiche salienti del campione di intervistati vengono presentate e confrontate con le medesime caratteristiche a livello di popolazione, in modo da avere indicazioni sulla rappresentatività e, quindi, sull’affidabilità del campione stesso; la sezione si chiude con una descrizione delle modalità di indagine e del questionario impiegato. La seconda sezione è dedicata alle esperienze abitative dei fuori sede. Vengono prese in considerazione i principali tipi di alloggio, quali studentati o collegi gestiti dall’Azienda Regionale per il Diritto allo Studio (Arstud), gestiti da enti religiosi, gestiti da altri tipi di enti, le pensioni e gli alberghi e gli alloggi privati (appartamenti, case), e si analizza la distribuzione dei fuori sede per tipo alloggio scelto, tenendo conto altresì della (even-tualmente) diversa dislocazione sul territorio, della durata complessiva dell’esperienza vissuta in ciascun tipo di alloggio e del numero di periodi distinti che hanno caratterizzato ciascuna esperienza. In particolare, viene introdotta la distinzione tra prima esperienza abitativa ed esperienza più duratura. La distinzione tra prima esperienza ed esperienza più duratura viene mantenuta anche nelle sezioni successive, entrambe dedicate ad approfondimenti sulle sole esperienze abitative in alloggi privati. Nella terza vengono prese in considerazione le caratteristiche distintive delle abitazioni private (quella relativa alla prima esperienza e quella relativa all’esperienza più duratura), in termini, tra l’altro, di distribuzione geografica sul territorio, di numero di coinquilini per casa e per stanza, di tipo di contratto d’affitto e di importo del canone mensile, così da pervenire a una visione d’insieme delle condi-zioni di alloggio degli studenti fuori sede. La quarta sezione è dedicata all’analisi della qualità degli alloggi privati, così come risulta dalle dichiarazioni degli intervistati; in particolare, dopo un primo paragrafo che si occupa della qualità complessiva degli alloggi, ci si concentra sulla qualità delle sole abitazioni private, approfondendo gli aspetti inerenti alle problematiche e alle comodità presenti nelle abitazioni e la disponibilità del proprietario a venire incontro alle esigenze dei suoi inquilini; le analisi vengono svolte in funzione di caratteristiche individuali e di contesto in modo da identificare eventuali associazioni significative tra la qualità degli alloggi e tali caratteristiche. Alcune considerazioni finali chiudono il lavoro, riepilogando i principali risultati conseguiti con l’indagine ed evidenziando i punti critici della problematica trattata.

Published
2009

49. Blood-borne cells involved in arterial repair upon experimental incision injury

Author

Pieri, L., Rinaldi, B., Domenici, L., Bacci, S., Filippelli, A., Annalisa Capuano, Rossi, F., Romagnoli, P., Pieri, L, Rinaldi, Barbara, Domenici, L, Bacci, S, Filippelli, A, Capuano, Annalisa, Rossi, Francesco, and Romagnoli, P.

Subjects
metabolism/pathology, Male, Rats, Inbred WKY, Dendritic cells, Muscle, Smooth, Vascular, Endothelial cell, Vascular, Animals, CD43, Dendritic cells, Endothelial cells, Hemangioblasts, Hsp47, Smooth muscle cells, Endothelium, Lymphocytes, Inbred WKY, Antigens, Animals, Antigens, metabolism, Blood Cells, cytology, Carotid Arteries, metabolism/pathology, Carotid Artery Injuries, pathology, Dendritic Cells, cytology, Endothelium, cytology, HSP47 Heat-Shock Proteins, metabolism, Lymphocytes, cytology, Male, Muscle, Smooth, cytology, Rats, Rats, Inbred WKY, Tunica Intima, pathology, Wound Healing, physiology, HSP47 Heat-Shock Proteins, Wound Healing, Leukosialin, Blood Cells, Dendritic Cells, Rats, 616 - Patología. Medicina clínica. Oncología, Carotid Arteries, cytology, Muscle, pathology, Endothelium, Vascular, Carotid Artery Injuries, Tunica Intima, metabolism
Abstract

Summary. We had previously shown that microscopically detectable infiltration of dendritic cells and expression of Hsp47 in tissue lysates occur during repair upon experimental arterial injury. We have further analysed here the cell types involved in the repair process by histology, electron microscopy and immunofluorescence. Rat carotid arteries were subjected to brief crushing and full thickness incision and were analysed up to 21 d thereafter. Adhesion and activation of platelets occurred 3 h after surgery. A neointima had formed 7 d after surgery, where immature cells entered from the lumen and gave rise to cells rich in organelles of the secretory pathway and endowed with bundles of phalloidin-binding microfilaments. Alpha smooth muscle-positive, secretory and contractile smooth muscle cells were found in the neointima 14 and 21 d after injury. Seven to 21 d after surgery, endothelial cells appeared immature and the newly formed tissue contained MHC-II positive, CD43 positive dendritic cells which clustered with lymphocytes, a few macrophages containing apoptotic remnants and cells labelled for Hsp47. Thin elastic fibrils appeared in the neointima 21 d after injury. The results suggest that the response to acute arterial incision injury is mediated by blood borne cells which differentiate along multiple pathways; the process evolves without reaching stabilization within the observed time lapse; the secretion of extracellular matrix is marked by the expression of Hsp47; and the constant presence of dendritic cells clustered with lymphocytes makes these cells candidate to a pivotal role in the tissue response to injury.

Published
2008

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