Gene therapy of thyroid cancer via retrovirally-driven combined expression of human interleukin-2 and herpes simplex virus thymidine kinase

OBJECTIVE AND DESIGN: Based on our clinical experience with combined gene therapy of glioblastoma, we developed a retroviral vector expressing two therapeutic genes (i.e. thymidine kinase of herpes simplex virus, HSV-TK, and interleukin-2, IL-2) and evaluated its efficiency in vitro and in vivo. METHODS: Expression of therapeutic genes in transduced thyroid carcinoma cells was analyzed by real-time RT-PCR. Ganciclovir sensitivity of infected cells was assessed in vitro in thyroid carcinoma cell lines and in vivo in nude mice bearing xenografted thyroid cancers. The combined effect of IL-2/HSV-TK was compared with the effect of IL-2 alone. RESULTS: Expression of therapeutic genes was higher in differentiated than in anaplastic thyroid carcinoma cells. Ganciclovir treatment led to dose- and time-dependent killing of transduced cells in vitro. A bystander effect was demonstrated by using mixtures of infected and non-infected cells. In vivo studies showed a significant reduction of growth and the presence of an inflammatory infiltrate in transduced thyroid tumors expressing IL-2 alone, as compared with non-infected tumors. By using the retroviral vector expressing IL-2/HSV-TK, treatment with ganciclovir led to complete eradication of anaplastic tumors and a >80% reduction of the size of differentiated thyroid carcinomas. Histological analysis of tumor specimens showed extensive necrosis and inflammatory cell infiltrates. The combination of IL-2/HSV-TK plus ganciclovir was significantly more efficient than IL-2 alone in eradicating tumor masses. The bystander effect was also obtained in vivo. CONCLUSIONS: These findings demonstrate the feasibility and efficiency of a combined immunomodulating and suicide gene therapy approach for thyroid carcinomas

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Modelling Human Regulatory Variation in Mouse: Finding the Function in Genome-Wide Association Studies and Whole-Genome Sequencing

An increasing body of literature from genome-wide association studies and human whole-genome sequencing highlights the identification of large numbers of candidate regulatory variants of potential therapeutic interest in numerous diseases. Our relatively poor understanding of the functions of non-coding genomic sequence, and the slow and laborious process of experimental validation of the functional significance of human regulatory variants, limits our ability to fully benefit from this information in our efforts to comprehend human disease. Humanized mouse models (HuMMs), in which human genes are introduced into the mouse, suggest an approach to this problem. In the past, HuMMs have been used successfully to study human disease variants; e.g., the complex genetic condition arising from Down syndrome, common monogenic disorders such as Huntington disease and β-thalassemia, and cancer susceptibility genes such as BRCA1. In this commentary, we highlight a novel method for high-throughput single-copy site-specific generation of HuMMs entitled High-throughput Human Genes on the X Chromosome (HuGX). This method can be applied to most human genes for which a bacterial artificial chromosome (BAC) construct can be derived and a mouse-null allele exists. This strategy comprises (1) the use of recombineering technology to create a human variant–harbouring BAC, (2) knock-in of this BAC into the mouse genome using Hprt docking technology, and (3) allele comparison by interspecies complementation. We demonstrate the throughput of the HuGX method by generating a series of seven different alleles for the human NR2E1 gene at Hprt. In future challenges, we consider the current limitations of experimental approaches and call for a concerted effort by the genetics community, for both human and mouse, to solve the challenge of the functional analysis of human regulatory variation

Adherence issues related to sublingual immunotherapy as perceived by allergists

Objectives: Sublingual immunotherapy (SLIT) is a viable alternative to subcutaneous immunotherapy to treat allergic rhinitis and asthma, and is widely used in clinical practice in many European countries. The clinical efficacy of SLIT has been established in a number of clinical trials and meta-analyses. However, because SLIT is self-administered by patients without medical supervision, the degree of patient adherence with treatment is still a concern. The objective of this study was to evaluate the perception by allergists of issues related to SLIT adherence. Methods: We performed a questionnaire-based survey of 296 Italian allergists, based on the adherence issues known from previous studies. The perception of importance of each item was assessed by a VAS scale ranging from 0 to 10. Results: Patient perception of clinical efficacy was considered the most important factor (ranked 1 by 54% of allergists), followed by the possibility of reimbursement (ranked 1 by 34%), and by the absence of side effects (ranked 1 by 21%). Patient education, regular follow-up, and ease of use of SLIT were ranked first by less than 20% of allergists. Conclusion: These findings indicate that clinical efficacy, cost, and side effects are perceived as the major issues influencing patient adherence to SLIT, and that further improvement of adherence is likely to be achieved by improving the patient information provided by prescribers. © 2010 Scurati et al, publisher and licensee Dove Medical Press Ltd

Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients

BACKGROUND: The SARS-CoV-2 pandemic is currently leading to increasing numbers of COVID-19 patients all over the world. Clinical presentations range from asymptomatic, mild respiratory tract infection, to severe cases with acute respiratory distress syndrome, respiratory failure, and death. Reports on a dysregulated immune system in the severe cases call for a better characterization and understanding of the changes in the immune system. METHODS: In order to dissect COVID-19-driven immune host responses, we performed RNA-seq of whole blood cell transcriptomes and granulocyte preparations from mild and severe COVID-19 patients and analyzed the data using a combination of conventional and data-driven co-expression analysis. Additionally, publicly available data was used to show the distinction from COVID-19 to other diseases. Reverse drug target prediction was used to identify known or novel drug candidates based on finding from data-driven findings. RESULTS: Here, we profiled whole blood transcriptomes of 39 COVID-19 patients and 10 control donors enabling a data-driven stratification based on molecular phenotype. Neutrophil activation-associated signatures were prominently enriched in severe patient groups, which was corroborated in whole blood transcriptomes from an independent second cohort of 30 as well as in granulocyte samples from a third cohort of 16 COVID-19 patients (44 samples). Comparison of COVID-19 blood transcriptomes with those of a collection of over 3100 samples derived from 12 different viral infections, inflammatory diseases, and independent control samples revealed highly specific transcriptome signatures for COVID-19. Further, stratified transcriptomes predicted patient subgroup-specific drug candidates targeting the dysregulated systemic immune response of the host. CONCLUSIONS: Our study provides novel insights in the distinct molecular subgroups or phenotypes that are not simply explained by clinical parameters. We show that whole blood transcriptomes are extremely informative for COVID-19 since they capture granulocytes which are major drivers of disease severity

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Environmental analysis of sustainable production practices applied to cyclamen and zonal geranium

Italian floriculture is facing structural changes. Possible options to maintain competitiveness of the involved companies include promotion of added values, from local production to environmental sustainability. To quantify value and benefits of cleaner production processes and choices, a holistic view is necessary and could be provided by life cycle assessment (LCA) methodology. Previous studies on ornamental products generally focused on data from one company or a small sample. The aim of this study was a gate-to-gate life cycle assessment of two ornamental species, cyclamen (Cyclamen persicum Mill.) and zonal geranium (Pelargonium 7 hortorum Bailey), using data from a sample of 20 companies belonging to a floriculture district in the Treviso, Veneto region. We also assessed the potential benefits of the environmental impact of alternative management choices regarding plant protection and reuse of composted waste biomass. Life cycle impact assessment showed higher impact scores for the zonal geranium, mainly as a consequence of greenhouse heating with fossil fuels. This factor, along with higher uniformity of production practices and technological levels of equipment, translated to a lower variability in comparison with cyclamen production, which showed a wider results range, in particular for eutrophication, acidification and human toxicity potential. The application of integrated pest management with cyclamen had significant benefits by reducing acidification and human toxicity, while reducing use of mineral nutrients through amending growing media with compost resulted in a reduction in eutrophication potential. Similar achievable benefits for zonal geranium were not observed because of the dominant contribution of energy inputs