Late Endocrine and Metabolic Sequelae and Long‐Term Monitoring of Classical Hodgkin Lymphoma and Diffuse Large B‐Cell Lymphoma Survivors: A Systematic Review by the Fondazione Italiana Linfomi

Background: Overall survival after lymphoma has improved in recent years, but the high prevalence of late treatment‐related sequelae has been observed as a counterpart. Method: In this systematic review, FIL researchers aimed to: (i) estimate the incidence or prevalence of late endocrine‐metabolic sequelae, (ii) evaluate the effects of modern therapeutic approaches on incidence or prevalence of late endocrine‐metabolic sequelae, and (iii) determine whether there is evidence of follow‐up schemes for their screening/early diagnosis in the subset of long‐term classical Hodgkin lymphoma (cHL) and diffuse large B‐cell lymphoma (DLBCL) survivors treated at adult age. The MEDLINE, Embase and the Cochrane Library databases were searched for relevant articles published up to October, 2020. The study selection process was conducted by three independent reviewers and was reported according to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines. A risk of bias assessment was performed using the Cochrane tool for randomized trials and the Newcastle‐Ottawa Scale for observational studies. Results: In the final analysis, eight studies were included, four of which focused on thyroid disease, two on gonadal dysfunction, one on bone disease and one on metabolic syndrome. Hypothyroidism was reported in up to 60% of adult cHL survivors and was frequently recorded even with modern radiotherapy approaches. Menopause occurred in 52–72% of women after chemotherapy. An 86% reduction in vertebral density was reported following R‐CHOP‐like chemotherapy. Sarcopenia and metabolic syndrome were reported in 37.9% and 60% of patients, respectively. No validated screening protocols were found for the early diagnosis of long‐term treatment‐related endocrine and metabolic sequelae, thus the authors finally suggest the execution of screening exams according to the risk category which were identified in the epidemiologic studies

Structural characterization of the Xi class glutathione transferase from the haloalkaliphilic archaeon Natrialba magadii

Xi class glutathione transferases (GSTs) are a recently identified group, within this large superfamily of enzymes, specifically endowed with glutathione-dependent reductase activity on glutathionyl-hydroquinone. Enzymes belonging to this group are widely distributed in bacteria, fungi, and plants but not in higher eukaryotes. Xi class GSTs are also frequently found in archaea and here we focus on the enzyme produced by the extreme haloalkaliphilic archaeon Natrialba magadii (NmGHR). We investigated its function and stability and determined its 3D structure in the apo form by X-ray crystallography. NmGHR displays the same fold of its mesophilic counterparts, is enriched in negatively charged residues, which are evenly distributed along the surface of the protein, and is characterized by a peculiar distribution of hydrophobic residues. A distinctive feature of haloalkaliphilic archaea is their preference for γ-glutamyl-cysteine over glutathione as a reducing thiol. Indeed we found that the N. magadii genome lacks a gene coding for glutathione synthase. Analysis of NmGHR structure suggests that the thiol binding site (G-site) of the enzyme is well suited for hosting γ-glutamyl-cysteine

Clinical Management of Long-Term Survivors after Classical Hodgkin Lymphoma and Diffuse Large B-Cell Lymphoma

Compared to other patients suffering from hematological malignancies, classical Hodgkin lymphoma (cHL) and diffuse large B-cell lymphoma (DLBCL) patients have a long life expectancy when in complete remission at the end of first, or sometimes second, line treatments [...]

Escherichia coli in Europe: An Overview

Escherichia coli remains one of the most frequent causes of several common bacterial infections in humans and animals. E. coli is the prominent cause of enteritis, urinary tract infection, septicaemia and other clinical infections, such as neonatal meningitis. E. coli is also prominently associated with diarrhoea in pet and farm animals. The therapeutic treatment of E. coli infections is threatened by the emergence of antimicrobial resistance. The prevalence of multidrug-resistant E. coli strains is increasing worldwide principally due to the spread of mobile genetic elements, such as plasmids. The rise of multidrug-resistant strains of E. coli also occurs in Europe. Therefore, the spread of resistance in E. coli is an increasing public health concern in European countries. This paper summarizes the current status of E. coli strains clinically relevant in European countries. Furthermore, therapeutic interventions and strategies to prevent and control infections are presented and discussed. The article also provides an overview of the current knowledge concerning promising alternative therapies against E. coli diseases

Limbo: A Flexible High-performance Library for Gaussian Processes modeling and Data-Efficient Optimization

Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 2015Limbo (LIbrary for Model-Based Optimization) is an open-source C++11 library for Gaussian Processes and data-efficient optimization (e.g., Bayesian optimization) that is designed to be both highly flexible and very fast. It can be used as a state-of-the-art optimization library or to experiment with novel algorithms with “plugin” components. Limbo is currently mostly used for data-efficient policy search in robot learning and online adaptation because computation time matters when using the low-power embedded computers of robots. For example, Limbo was the key library to develop a new algorithm that allows a legged robot to learn a new gait after a mechanical damage in about 10-15 trials (2 minutes), and a 4-DOF manipulator to learn neural networks policies for goal reaching in about 5 trials. The implementation of Limbo follows a policy-based design that leverages C++ templates: this allows it to be highly flexible without the cost induced by classic object-oriented designs (cost of virtual functions). The regression benchmarks show that the query time of Limbo’s Gaussian processes is several orders of magnitude better than the one of GPy (a state-of-the-art Python library for Gaussian processes) for a similar accuracy (the learning time highly depends on the optimization algorithm chosen to optimize the hyper-parameters). The black-box optimization benchmarks demonstrate that Limbo is about 2 times faster than BayesOpt (a C++ library for data-efficient optimization) for a similar accuracy and data-efficiency. In practice, changing one of the components of the algorithms in Limbo (e.g., changing the acquisition function) usually requires changing only a template definition in the source code. This design allows users to rapidly experiment and test new ideas while keeping the software as fast as specialized code. Limbo takes advantage of multi-core architectures to parallelize the internal optimization processes (optimization of the acquisition function, optimization of the hyper-parameters of a Gaussian process) and it vectorizes many of the linear algebra operations (via the Eigen 3 library and optional bindings to Intel’s MKL). The library is distributed under the CeCILL-C license via a Github repository. The code is standard-compliant but it is currently mostly developed for GNU/Linux and Mac OS X with both the GCC and Clang compilers. New contributors can rely on a full API reference, while their developments are checked via a continuous integration platform (automatic unit-testing routines). Limbo is currently used in the ERC project ResiBots, which is focused on data-efficient trial-and-error learning for robot damage recovery, and in the H2020 projet PAL, which uses social robots to help coping with diabetes. It has been instrumental in many scientific publications since 201

Proteus mirabilis glutathione S-transferase B1-1 is involved in protective mechanisms against oxidative and chemical stresses.

We investigated the effects of several xenobiotics, including antimicrobial agents and general stress factors such as starvation, heat and osmotic shock, on the modulation of expression of Proteus mirabilis glutathione S-transferase B1-1 (PmGST B1-1). The level of expression of PmGST B1-1 was established by both Western- and Northern-blot experiments. Our results show that several compounds can modulate expression of PmGST B1-1. The level of PmGST B1-1 increased when bacterial cells were exposed to a variety of stresses such as 1-chloro-2,4-dinitrobenzene, H(2)O(2), fosfomycin or tetracycline. A knock-out gst B gene was also constructed using the suicide vector pKNOCKlox-Ap. Successful inactivation of the wild-type gene was confirmed by PCR, DNA sequence analysis and Western blotting. Under normal culture conditions, this mutant was viable and displayed no significant phenotypic differences compared with the wild-type. However, viability tests revealed that the null mutant was more sensitive to oxidative stress in the form of H(2)O(2) and to several antimicrobial drugs when compared with the wild-type. These results suggest that PmGST B1-1 has an active role in the protection against oxidative stress generated by H(2)O(2) and it appears to be involved in the detoxification of antimicrobial agents

The amino acid sequence of glutathione transferase from Proteus mirabilis, a prototype of a new class of enzymes.

The complete amino acid sequence of glutathione transferase from Proteus mirabilis was determined. The sequence was reconstructed by analysis of peptides obtained after cleavage by trypsin, Glu-C and Asp-N endoproteinases. The enzyme subunit is composed of 203 amino acid residues corresponding to a molecular mass of 22856 Da. Comparison of this sequence with other known primary structures of the corresponding enzyme from different sources shows a low level of identity (17-26%) with only seven conserved residues in all the sequences considered. This novel glutathione transferase could represent the prototype of a new class, possibly including other bacterial enzymes