Análisis de diversidad de la familia Poaceae en la región austral de America del Sur

337-351Analysis of Poaceae biodiversity in austral South America. The Poaceae is one of the best represented families in austral South America with a total of 206 genera comprising 1523 species arranged in 10 different subfamilies. Here we analyzed the distribution of these taxa in Argentina, southern Brazil (Paraná, Rio Grande do Sul and Santa Catarina), Chile, Paraguay, and Uruguay; we discuss the species richness of the different subfamilies, tribes and genera, its distribution, endemics, annual and perennial species as well as Kranz and non Kranz taxa, its geographical distribution in relation to temperature and rainfall, disjunct genera, and proportion of taxa in relation to the different ecoregions in the area

Modeling provincial Covid-19 epidemic data in Italy using an adjusted time-dependent SIRD model

In this paper we develop a predictive model for the spread of COVID-19 infection at a provincial (i.e. EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and from local newspaper websites. This integration is mainly concerned with COVID-19 cause specific death data which are not available at NUTS-3 level from open official data data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic, specifically the number of susceptible, infected, deceased and recovered people. Predictive model performance is evaluated using comparison with real data

Emetogenicity of Antibody-Drug Conjugates (ADCs) in Solid Tumors with a Focus on Trastuzumab Deruxtecan: Insights from an Italian Expert Panel

In the past decade, nine antibody-drug conjugates (ADCs) have been approved for the treatment of various tumors, four of which specifically for solid malignancies. ADCs deliver the cytotoxic payload to the cancer site, thereby improving chemotherapy efficacy while reducing systemic drug exposure and toxicity. With their high selectivity, ADCs are associated with a manageable side-effect profile, with nausea and vomiting being among the most frequent toxicities, although this may vary according to the respective ADC and the associated payload. Information about the emetic risk of the new ADC compounds is limited. Three virtual focus groups of Italian oncologists were held to raise awareness on the importance of an antiemetic prophylaxis regimen to prevent and mitigate ADC-associated emesis and its sequelae. After reviewing published evidence and guidelines, the three expert panels shared their experience on the early use of ADCs gained through the participation in specific clinical trials and their clinical practice. The following issues were discussed: antiemetic therapy during trastuzumab deruxtecan treatment, with a protocol adopted at the San Raffaele Hospital (Milan, Italy); the use of steroids; the management of anticipatory nausea during trastuzumab deruxtecan therapy; nutritional counselling; and effective doctor\u2013patient communication. The experts acknowledged that recommendations should be drug-specific, and formulated opinion-based advice intended to guide physicians in their daily practice until further evidence emerges

Modeling the covariates effects on the hazard function by piecewise exponential artificial neural networks : an application to a controlled clinical trial on renal carcinoma

BACKGROUND: In exploring the time course of a disease to support or generate biological hypotheses, the shape of the hazard function provides relevant information. For long follow-ups the shape of hazard function may be complex, with the presence of multiple peaks. In this paper we present the use of a neural network extension of the piecewise exponential model to study the shape of the hazard function in time in dependence of covariates. The technique is applied to a dataset of 247 renal cell carcinoma patients from a randomized clinical trial. RESULTS: An interaction effect of treatment with number of metastatic lymph nodes but not with pathologic T-stage is highlighted. CONCLUSIONS: Piecewise Exponential Artificial Neural Networks demonstrate a clinically useful and flexible tool in assessing interaction or time-dependent effects of the prognostic factors on the hazard function

Las reservas privadas ¿son efectivas para conservar las propiedades de los ecosistemas?

185-199To evaluate the contribution to conservation of private refuges, we compared the ecosystem functioning of this type of conservation (Refuge) with contiguous national parks (Park) and rural ranches and farms under traditional management (Traditional). We assume the Park as the reference situation, in as much they constitute the areas with the lowest human intervention. The analysis was based on three descriptors of the seasonal dynamics of the carbon gains, derived from spectral data provided by the MODIS sensor: the Normalized Difference Vegetation Index integral (NDVI-I), a spectral index related to the total carbon gains of the ecosystem; the relative range (RRel: (maximum NDVI-minimum NDVI) /(NDVI-I), and the month of occurrence of the maximum value of NDVI (MMax). These last two attributes characterize the seasonality and phenology of vegetation. We worked on three contrasting biogeographic regions of Argentina (Paranaense forest (Misiones province), Mesopotamic Pampa (Entre Ríos province), Interior Pampa (Córdoba province). In the Paranaense forest, the Refuge presented a lower NDVI-I value than the Park, but higher than the Traditional (P greather than 0.05). The seasonality of the vegetation carbon gains (RRel) was lower on the Park than for the other two managements. In the Mesopotamic Pampa, the gallery forests were the most productive unit (higher NDVI-I). The Refuge presented an intermediate behavior. Grasslands presented the highest NDVI-I on the Refuge (P greather than 0.05). The emblematic vegetation of this region (the palm savannas), did not differ among managements. The seasonality was lower in the gallery forests of the Park. In the Interior Pampa, the differences between Refuge and Traditional, both in NDVI-I and RRel, were not significantly different among vegetation types or managements. The MMax did not differ between managements for any of the regions studied. Our results showed that for most of the ecosystem attributes considered, the Refuge presented values more similar to those of the reference situation (the Park) than to the traditional management alternatives. Private conservation therefore has being efficient for the conservation and maintenance of the ecosystemic processes and services bounded to the dynamic of the vegetation carbon gains

Conditional independence relations among biological markers may improve clinical decision as in the case of triple negative breast cancers

The associations existing among different biomarkers are important in clinical settings because they contribute to the characterisation of specific pathways related to the natural history of the disease, genetic and environmental determinants. Despite the availability of binary/linear (or at least monotonic) correlation indices, the full exploitation of molecular information depends on the knowledge of direct/indirect conditional independence (and eventually causal) relationships among biomarkers, and with target variables in the population of interest. In other words, that depends on inferences which are performed on the joint multivariate distribution of markers and target variables. Graphical models, such as Bayesian Networks, are well suited to this purpose. Therefore, we reconsidered a previously published case study on classical biomarkers in breast cancer, namely estrogen receptor (ER), progesterone receptor (PR), a proliferative index (Ki67/MIB-1) and to protein HER2/neu (NEU) and p53, to infer conditional independence relations existing in the joint distribution by inferring (learning) the structure of graphs entailing those relations of independence. We also examined the conditional distribution of a special molecular phenotype, called triple-negative, in which ER, PR and NEU were absent. We confirmed that ER is a key marker and we found that it was able to define subpopulations of patients characterized by different conditional independence relations among biomarkers. We also found a preliminary evidence that, given a triple-negative profile, the distribution of p53 protein is mostly supported in 'zero' and 'high' states providing useful information in selecting patients that could benefit from an adjuvant anthracyclines/alkylating agent-based chemotherapy

Correlation of HER2 status between primary tumors and corresponding circulating tumor cells in advanced breast cancer patients

International audienceBiocharacterization of circulating tumor cells (CTCs) in the peripheral blood of advanced breast cancer (ABC) patients may represent a real-time tumor biopsy. We assessed HER2 status on CTCs from blood samples of ABC patients. CTCs were separated and stained using the CellSearch System. HER2 status was assessed by immunofluorescence and, when technically feasible, by fluorescence in situ hybridization. Blood samples were obtained from 66 ABC patients. Forty patients had a positive CTC sample (61%) and of these, 15 (37%) had HER2 + CTCs. We found non-concordant results in 32% of cases: 29% (8/28) of HER2-negative primary tumors had HER2-positive CTCs and 42% (5/12) of HER2-positive primary tumors had HER2-negative CTCs ( = 0.278). Our study suggests that a subset of patients with HER2-negative primary tumors develops HER2-positive CTCs during disease progression

CoViD-19, learning from the past: A wavelet and cross-correlation analysis of the epidemic dynamics looking to emergency calls and Twitter trends in Italian Lombardy region

The first case of Coronavirus Disease 2019 in Italy was detected on February the 20th in Lombardy region. Since that date, Lombardy has been the most affected Italian region by the epidemic, and its healthcare system underwent a severe overload during the outbreak. From a public health point of view, therefore, it is fundamental to provide healthcare services with tools that can reveal possible new health system stress periods with a certain time anticipation, which is the main aim of the present study. Moreover, the sequence of law decrees to face the epidemic and the large amount of news generated in the population feelings of anxiety and suspicion. Considering this whole complex context, it is easily understandable how people "overcrowded"social media with messages dealing with the pandemic, and emergency numbers were overwhelmed by the calls. Thus, in order to find potential predictors of possible new health system overloads, we analysed data both from Twitter and emergency services comparing them to the daily infected time series at a regional level. Particularly, we performed a wavelet analysis in the time-frequency plane, to finely discriminate over time the anticipation capability of the considered potential predictors. In addition, a cross-correlation analysis has been performed to find a synthetic indicator of the time delay between the predictor and the infected time series. Our results show that Twitter data are more related to social and political dynamics, while the emergency calls trends can be further evaluated as a powerful tool to potentially forecast new stress periods. Since we analysed aggregated regional data, and taking into account also the huge geographical heterogeneity of the epidemic spread, a future perspective would be to conduct the same analysis on a more local basis. Copyright