186 research outputs found
A simplified estimate of the Effective Reproduction Number using its relation with the doubling time and application to Italian COVID-19 data
A simplified method to compute , the Effective Reproduction Number, is
presented. The method relates the value of to the estimation of the
doubling time performed with a local exponential fit. The condition
corresponds to a growth rate equal to zero or equivalently an infinite doubling
time. Different assumptions on the probability distribution of the generation
time are considered. A simple analytical solution is presented in case the
generation time follows a gamma distribution.Comment: Submitted to: The European Physical Journal Plus. Revised version
according to reviewer's comment
A study on the possible merits of using symptomatic cases to trace the development of the COVID-19 pandemic
In a recent work we introduced a novel method to compute the effective
reproduction number and we applied it to describe the development of the
COVID-19 outbreak in Italy. The study is based on the number of daily positive
swabs as reported by the Italian Dipartimento di Protezione Civile. Recently,
the Italian Istituto Superiore di Sanit\`a made available the data relative of
the symptomatic cases, where the reporting date is the date of beginning of
symptoms instead of the date of the reporting of the positive swab. In this
paper we will discuss merits and drawbacks of this data, quantitatively
comparing the quality of the pandemic indicators computed with the two samples
A statistical analysis of death rates in Italy for the years 2015-2020 and a comparison with the casualties reported for the COVID-19 pandemic
We analyze the data about casualties in Italy in the period 01/01/2015 to
30/09/2020 released by the Italian National Institute of Statistics (ISTAT).
The data exhibit a clear sinusoidal behavior, whose fit allows for a robust
subtraction of the baseline trend of casualties in Italy, with a surplus of
mortality in correspondence to the flu epidemics in winter and to the hottest
periods in summer. While these peaks are symmetric in shape, the peak in
coincidence with the COVID-19 pandemics is asymmetric and more pronounced. We
fit the former with a Gaussian function and the latter with a Gompertz
function, in order to quantify number of casualties, the duration and the
position of all causes of excess deaths. The overall quality of the fit to the
data turns out to be very good. We discuss the trend of casualties in Italy by
different classes of ages and for the different genders. We finally compare the
data-subtracted casualties as reported by ISTAT with those reported by the
Italian Department for Civil Protection (DPC) relative to the deaths directly
attributed to COVID-19, and we discuss the differences.Comment: 16 pages, 13 figure
New molecular targets in bone metastases
Bone metastases have a major impact on morbidity and on mortality in cancer patients. Despite its clinical relevance, metastasis remains the most poorly elucidated aspect of carcinogenesis. The biological mechanisms leading to bone metastasis establishment have been referred as " vicious circle," a complex network between cancer cells and the bone microenvironment. This review is aimed to underline the new molecular targets in bone metastases management other than bisphosphonates. Different pathways or molecules such as RANK/RANKL/OPG, cathepsin K, endothelin-1, Wnt/DKK1, Src have recently emerged as potential targets and nowadays preclinical and clinical trials are underway. The results from those in the advanced clinical phases are encouraging and underlined the need to design large randomised clinical trials to validate these results in the next future.Targeting the bone by preventing skeletal related events (SREs) and bone metastases has major clinical impact in improving survival in bone metastatic patients and in preventing disease relapse in adjuvant setting. © 2010 Elsevier Ltd
Dicer and Drosha expression and response to Bevacizumab-based therapy in advanced colorectal cancer patients
PURPOSE:
The miRNA-regulating enzymes Dicer and Drosha exhibit aberrant expression in several cancer types. Dicer and Drosha play a crucial role during the angiogenetic process in vitro and, for Dicer, in vivo. We aimed to investigate the potential role of Dicer and Drosha in predicting response to Bevacizumab-based therapy in advanced colorectal cancer (CRC) patients.
METHODS:
Dicer and Drosha mRNA levels were analysed in formalin-fixed paraffin-embedded specimens from patients affected by advanced CRC treated with or without Bevacizumab-containing regimens (n=116 and n=50, respectively) and from patients with diverticulosis as control group (n=20). The experimental data were obtained using qRT-PCR, analysed comparing Dicer and Drosha expression levels in tumour samples versus normal mucosa and then compared to clinical outcome.
RESULTS:
The tumour samples from Bevacizumab-treated patients showed a significantly higher Drosha expression (P<.001) versus normal mucosa, while Dicer levels did not differ. Intriguingly, we found that low Dicer levels predicted a longer progression-free survival (PFS) (P<.0001) and overall survival (OS) (P=.009). In addition, low Dicer levels were associated with better response to Bevacizumab-based treatments versus high Dicer levels (1.7% complete responses and 53.4% partial responses versus 0% and 32.7%, respectively; P=.0067). Multivariate analysis identified three independent predictors of improved OS: high performance status (PS) (relative risk (RR) 1.45; P=.011), lower organs involvement (RR 0.79; P=.034) and low Dicer expression (RR 0.71; P=.008). Conversely, Drosha levels were not associated with prognosis and outcome associated with treatment. In non-Bevacizumab-treated patients, Dicer and Drosha expression did not correlate with outcome.
CONCLUSION:
These findings suggest that low Dicer mRNA levels seem to be independent predictors of favourable outcome and response in patients affected by advanced CRCs treated with Bevacizumab-based therapy
ICSC: The Italian National Research Centre on HPC, Big Data and Quantum computing
ICSC (“Italian Center for SuperComputing”) is one of the five Italian National Centres created within the framework of the NextGenerationEU funding by the European Commission. The aim of ICSC, designed and approved through 2022 and eventually started in September 2022, is to create the national digital infrastructure for research and innovation, leveraging existing HPC, HTC and Big Data infrastructures and evolving towards a cloud data-lake model. It will be available to the scientific and industrial communities through flexible and uniform cloud web interfaces and will be relying on a high-level support team; as such, it will form a globally attractive ecosystem based on strategic public-private partnerships to fully exploit top level digital infrastructure for scientific and technical computing and promote the development of new computing technologies. The ICSC IT infrastructure is built upon existing scientific digital infrastructures as provided by the major national players: GARR, the Italian NREN, provides the network infrastructure, whose capacity will be upgraded to multiples of Tbps; CINECA hosts Leonardo, one of the world largest HPC systems, with a power of over 250 Pflops, to be further increased and complemented with a quantum computer; INFN contributes with its distributed Big Data cloud infrastructure, built in the last decades to respond to the needs of the HEP community. On top of the IT infrastructure, several thematic activities will be funded and will focus on the development of tools and applications in several research domains. Of particular relevance to this audience are the activities on "Fundamental Research and Space Economy" and "Astrophysics and Cosmos Observations", strictly aligned with the INFN and HEP core activities. Finally, two technological research activities will foster research on "Future HPC and Big Data" and "Quantum Computing"
Prognostic significance of KRAS mutation rate in metastatic colorectal cancer patients.
No abstract availabl
Prognostic significance of K-Ras mutation rate in metastatic colorectal cancer patients
none24noIntroduction: Activating mutations of K-Ras gene have a well-established role as predictors of resistance to anti-EGFR monoclonal antibodies in metastatic colorectal cancer (mCRC) patients. Their prognostic value is controversial, and no data regarding the prognostic value of mutation rate, defined as the percentage of mutated alleles/ tumor sample, are available. We aimed to evaluate the prognostic value of K-Ras mutation rate in a homogenous cohort of mCRC patients receiving first-line doublet plus bevacizumab. Patients and Methods: This retrospective study enrolled 397 K-Ras mutant mCRC patients from 6 Italian centers, and 263 patients were fully evaluable for our analysis. K-Ras mutation rate was assessed by pyrosequencing. Patients with less than 60% of cancer cells in tumor tissue were excluded. No patients received anti-EGFR containing anticancer therapy, at any time. Median mutation rate was 40% and was adopted as cut-off. The primary and secondary endpoints were PFS and OS respectively. Results: At univariate analysis, K-Ras mutation rate higher than 40% was significantly associated with lower PFS (7.3 vs 9.1 months; P < 0.0001) and OS (21 vs 31 months; P = 0.004). A multivariate model adjusted for age at diagnosis, site of origin of tumor tissue (primary vs metastases), referral center, number of metastatic sites, and first-line chemotherapy backbone, showed that K-Ras mutation rate remained a significant predictor of PFS and OS in the whole population. Discussion: Our data demonstrate an association between K-Ras mutation rate and prognosis in mCRC patients treated with bevacizumab-containing first-line therapy. These data deserve to be verified in an independent validation set.openVincenzi B.; Cremolini C.; Sartore-Bianchi A.; Russo A.; Mannavola F.; Perrone G.; Pantano F.; Loupakis F.; Rossini D.; Ongaro E.; Bonazzina E.; Dell'Aquila E.; Imperatori M.; Zoccoli A.; Bronte G.; Maglio G.D.; Fontanini G.; Natoli C.; Falcone A.; Santini D.; Onetti-Muda A.; Siena S.; Tonini G.; Aprile G.Vincenzi, B.; Cremolini, C.; Sartore-Bianchi, A.; Russo, A.; Mannavola, F.; Perrone, G.; Pantano, F.; Loupakis, F.; Rossini, D.; Ongaro, E.; Bonazzina, E.; Dell'Aquila, E.; Imperatori, M.; Zoccoli, A.; Bronte, G.; Maglio, G. D.; Fontanini, G.; Natoli, C.; Falcone, A.; Santini, D.; Onetti-Muda, A.; Siena, S.; Tonini, G.; Aprile, G
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