A regional early warning system for debris flows

In this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the drainage basins in which at least one debris-flow event was generated during the time period considered (2009-2019). The performances of the models obtained with different machine learning techniques were evaluated with the ROC analysis. The most suitable model was then experimentally implemented in the existing early warning system of the Aosta Valley Region. The model provides daily values of debris-flow probability (DFP) for individual basins, based on the input geo-morphological and hydro-meteorological variables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of the region

LGALS3BP antibody-drug-conjugate and its use for the treatment of cancer

The present invention relates to a special type of non-internalizing binding moiety- drug-conjugates that specifically target LGALS3BP. From one aspect, the invention relates to an antibody-drug-conjugate comprising an antibody capable of binding to LGALS3BP, said antibody being conjugated to cytotoxic drugs. The invention also comprises methods of the treatment of LGALS3BP-expressing cancer, including administering to a patient the disclosed drug conjugates and pharmaceutical preparations

Secreted Gal-3BP is a novel promising target for non-internalizing Antibody–Drug Conjugates

Abstract Galectin-3-binding protein (Gal-3BP) has been identified as a cancer and metastasis-associated, secreted protein that is expressed by the large majority of cancers. The present study describes a special type of non-internalizing antibody-drug-conjugates that specifically target Gal-3BP. Here, we show that the humanized 1959 antibody, which specifically recognizes secreted Gal-3BP, selectively localized around tumor but not normal cells. A site specific disulfide linkage with thiol-maytansinoids to unpaired cysteine residues of 1959, resulting in a drug-antibody ratio of 2, yielded an ADC product, which cured A375m melanoma bearing mice. ADC products based on the non-internalizing 1959 antibody may be useful for the treatment of several human malignancies, as the cognate antigen is abundantly expressed and secreted by several cancers, while being present at low levels in most normal adult tissues

Antibody-Drug Conjugates: The New Frontier of Chemotherapy

In recent years, antibody-drug conjugates (ADCs) have become promising antitumor agents to be used as one of the tools in personalized cancer medicine. ADCs are comprised of a drug with cytotoxic activity cross-linked to a monoclonal antibody, targeting antigens expressed at higher levels on tumor cells than on normal cells. By providing a selective targeting mechanism for cytotoxic drugs, ADCs improve the therapeutic index in clinical practice. In this review, the chemistry of ADC linker conjugation together with strategies adopted to improve antibody tolerability (by reducing antigenicity) are examined, with particular attention to ADCs approved by the regulatory agencies (the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA)) for treating cancer patients. Recent developments in engineering Immunoglobulin (Ig) genes and antibody humanization have greatly reduced some of the problems of the first generation of ADCs, beset by problems, such as random coupling of the payload and immunogenicity of the antibody. ADC development and clinical use is a fast, evolving area, and will likely prove an important modality for the treatment of cancer in the near future

A regional early warning system for debris flows

In this study, we have developed a predictive model for debris flows using machine learning techniques on a detailed dataset composed by a variety of geomorphological and hydro-meteorological variables. The variables of the dataset were collected from daily measured and modelled data for all of the drainage basins in which at least one debris-flow event was generated during the time period considered (2009-2019). The performances of the models obtained with different machine learning techniques were evaluated with the ROC analysis. The most suitable model was then experimentally implemented in the existing early warning system of the Aosta Valley Region. The model provides daily values of debris-flow probability (DFP) for individual basins, based on the input geo-morphological and hydro-meteorological variables. These results can be used to issue specific debris-flow alerts at the scale of the alert areas of the region

A random forest approach to quality-checking automatic snow-depth sensor measurements

State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models

Optimizing systemic therapy for advanced hepatocellular carcinoma: the key role of liver function

The number of effective systemic therapies for the treatment of advanced hepatocellular carcinoma (HCC) is rapidly increasing, and the advent of immunotherapy has changed the treatment paradigm for these patients, leading to significantly improved survival outcomes. However, many patients with HCC will continue to receive tyrosine kinase inhibitors, partly because of contraindications to immune checkpoint inhibitors. Currently, the best sequential first- and second-line systemic treatment remains elusive. Maintenance of optimal liver function is crucial, it is likely to impinge on temporary or permanent treatment discontinuation, and should also be considered when defining the treatment sequence. Hepatic decompensation, which does not always coincide with disease progression, is part of this complex dynamically evolving system, and must be promptly recognized and adequately managed to allow the patient to continue in the therapeutic course. The purpose of this review is to highlight and summarize the evidence on the efficacy and safety of systemic agents, with a focus on the impact of underlying cirrhosis, and to suggest new clinical outcomes for randomized controlled trials for advanced HCC to better assess the net health benefit in this specific setting