Lapatinib in II linea dopo il doppio blocco pertuzumab-trastuzumab: un caso clinico

We report the case of a young, 36-year-old patient diagnosed with multifocal breast carcinoma, undergoing neoadjuvant chemotherapy, surgery and adjuvant treatment. The patient presented an early recovery of liver disease after only 7 months of free interval. Histological reevaluation after liver biopsy revealed a HER2-positive disease, for which it was treated with first line chemotherapy including double anti-HER2 block. After 3 cycles the disease progressed at liver and encephalic level. It was therefore decided to treat brain localizations with whole-brain radiotherapy and to start a second line with capecitabine associated with lapatinib. This chemo-radiotherapy approach allowed to control the disease for 8 months. Following further progression (lymph node and bone), a third line was chosen with trastuzumab emtansine (TDM1). The encephalic disease remained stable for another 8 months, when due to visceral progression and worsening of clinical picture, TDM1 was interrupted and supportive therapies were started (Oncology).

A Dynamic Approach to Natech Risk Assessment Applied to an LPG Storage Facility in a Landslides Sensitive Italian Area

Due to the climate change, extreme weather phenomena are becoming increasingly intense and occur with higher frequencies, even in unusual areas. Nevertheless, historical data showed as Natech accidents can be triggered not only by natural disasters, like earthquakes or tornadoes, but even by natural phenomena that are considered of minor importance, such as rain and lightning. Only recently, the Natech issue has gained a great deal of attention, but there is still a lack of consolidated Natech risk-analysis methodologies and tools. The focus of this work is to include natural hazards into a dynamic risk assessment system beside the typical parameters of process risks. In Italy, rainfall represents the most common triggering factor for landslides. Generally, the determination of trigger and propagation can rely on physically-based approaches, which require the calibration of many parameters and are often difficult to apply, or on empirical correlations between rainfall and landslide built from historical data. On the other hand, by using a data driven approach, available data can be exploited to define the system state over time, anticipate the systems outcome, support decision-making, and adopt the most appropriate adjustments, allowing to enhance system resilience and knowledge. The actual capability of the proposed approach was evaluated on a simple case-study represented by an LPG storage facility located in landslides sensitive zone of Liguria Region

Congenital myopathies: Clinical phenotypes and new diagnostic tools

Congenital myopathies are a group of genetic muscle disorders characterized clinically by hypotonia and weakness, usually from birth, and a static or slowly progressive clinical course. Historically, congenital myopathies have been classified on the basis of major morphological features seen on muscle biopsy. However, different genes have now been identified as associated with the various phenotypic and histological expressions of these disorders, and in recent years, because of their unexpectedly wide genetic and clinical heterogeneity, next-generation sequencing has increasingly been used for their diagnosis. We reviewed clinical and genetic forms of congenital myopathy and defined possible strategies to improve cost-effectiveness in histological and imaging diagnosis

A Dynamic Approach to Natech Risk Assessment Applied to an LPG Storage Facility in a Landslides Sensitive Italian Area

Due to the climate change, extreme weather phenomena are becoming increasingly intense and occur with higher frequencies, even in unusual areas. Nevertheless, historical data showed as Natech accidents can be triggered not only by natural disasters, like earthquakes or tornadoes, but even by natural phenomena that are considered of minor importance, such as rain and lightning. Only recently, the Natech issue has gained a great deal of attention, but there is still a lack of consolidated Natech risk-analysis methodologies and tools. The focus of this work is to include natural hazards into a dynamic risk assessment system beside the typical parameters of process risks. In Italy, rainfall represents the most common triggering factor for landslides. Generally, the determination of trigger and propagation can rely on physically-based approaches, which require the calibration of many parameters and are often difficult to apply, or on empirical correlations between rainfall and landslide built from historical data. On the other hand, by using a data driven approach, available data can be exploited to define the system state over time, anticipate the systems outcome, support decision-making, and adopt the most appropriate adjustments, allowing to enhance system resilience and knowledge. The actual capability of the proposed approach was evaluated on a simple case-study represented by an LPG storage facility located in landslides sensitive zone of Liguria Region

Air Quality in the Italian Northwestern Alps during Year 2020: Assessment of the COVID-19 «Lockdown Effect» from Multi-Technique Observations and Models

The effect of COVID-19 confinement regulations on air quality in the northwestern Alps is assessed here based on measurements at five valley sites in different environmental contexts. Surface concentrations of nitrogen oxides (NO and NO2), ozone (O3), particulate matter (PM2.5 and PM10), together with a thorough microphysical (size), chemical, and optical (light absorption) aerosol characterisation, complemented by observations along the vertical column are considered. Even in the relatively pristine environment of the Alps, the «lockdown effect» is well discernible, both in the early confinement phase and in late 2020. The variations observed during the first confinement period in the city of Aosta (−61% NO, −43% NO2, +5% O3, +9% PM2.5, −12% PM10, relative to average 2015–2019 conditions) are attributed to the competing effects of air pollution lockdown-induced changes (−74%, −52%, +18%, −13%, −27%, relative to the counterfactual scenario for 2020 provided by a predictive statistical model trained on past measurements) and meteorology (+52%, +18%, −11%, +25%, +20%, relative to average conditions). These changes agree well with the ones obtained from a chemical transport model with modified emissions according to the restrictions. With regard to column-integrated quantities and vertical profiles, the NO2 column density decreases by >20% due to the lockdown, whereas tropospheric aerosols are mainly influenced by large-scale dynamics (transport of secondary particles from the Po basin and mineral dust from the Sahara desert and the Caspian Sea), except a shallow layer about 500 m thick close to the surface, possibly sensitive to curtailed emissions (especially exhaust and non-exhaust particles from road traffic and fugitive emissions from the industry)

Event Detection in Optical Signals via Domain Adaptation

Data-driven models trained in an end-to-end manner can reliably detect events within optical signals. Unfortunately, event detection models poorly generalize when monitoring signals collected from devices with different acquisition procedures. We overcome this limitation by presenting a novel domain adaptation solution for event detection networks that enables inference across multiple types of devices. Rather than training a black-box detection network, we decouple event localization and classification tasks. Localization is performed by the Interval Proposal Algorithm (IPA), which leverages signal processing techniques to localize candidate events and derive context features. These events are then standardized and fed to a feature extractor to obtain morphological features. By combining domain-specific context features with domain-invariant morphological features, the classifier achieves good generalization capabilities through different domains. Our method can successfully detect events in OTDR traces achieving a [email protected] of 75.33% on traces from the source domain and generalizing well ([email protected] of 69.27%) on traces from the target domain, despite being trained solely from the source domain

Gas6 as a putative noninvasive biomarker of hepatic fibrosis

To evaluate serum growth arrest-specific gene 6 (Gas6) concentration as a biomarker of liver fibrosis progression