Climate action after COP26
In: IPPR progressive review, Band 28, Heft 4, S. 317-323
ISSN: 2573-2331
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In: IPPR progressive review, Band 28, Heft 4, S. 317-323
ISSN: 2573-2331
In: info:eu-repo/semantics/altIdentifier/doi/10.2147/JN.S74135
Alok Sharma,1,2 Ziad M Al Zoubi3 1Department of Neurosurgery, Lokmanya Tilak Municipal General (LTMG) Hospital and LTM Medical College, Mumbai, India; 2NeuroGen Brain and Spine Institute, Mumbai, India; 3Jordan Orthopedic and Spinal Centre, Amman, Jordan Abstract: Ethics, regulations, and evidence-based practices form the foundation of modern medicine. However, in recent years, and particularly in reference to cellular therapy, they have become obstacles to the growth and development of this new form of treatment. Based on four important documents, it is proposed that regulatory bodies and medical associations recommend an alternate way of looking at regulations for cell therapy, so as to ensure that only safe and effective treatments are offered to patients, and that greater availability of these new treatment options is also encouraged. The four documents on which these recommendations are based are: 1) World Medical Association Declaration of Helsinki – Ethical Principles for Medical Research Involving Human Subjects; 2) The International Society for Cellular Therapy "White paper" published in 2010; 3) The Beijing Declaration of the International Association of Neurorestoratology; and 4) New legislation passed in Japan in 2014 on regenerative medicine. These recommendations are: greater permissiveness for the use of cell therapy in incurable conditions, identify legitimate cell therapy services, promote medical innovation, respect the rights of patients to choose treatments, recognize the valid compassionate use of unapproved therapies, recognize the significance of small functional gains, give importance to practice-based evidence and existing published literature, have differing regulations for the different types of cell therapies, and adapt the new Japanese legislation for regenerative medicine. Keywords: cellular therapy, stem cells, ethics, regulations, evidence-based medicine, practice-based evidence, Japan regulations, Korea regulations
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In: International review of administrative sciences: an international journal of comparative public administration, Band 67, Heft 4, S. 733-739
ISSN: 1461-7226
In: International review of administrative sciences: an international journal of comparative public administration, Band 67, Heft 4, S. 733-739
ISSN: 0020-8523
In: International review of administrative sciences: an international journal of comparative public administration, Band 67, Heft 4, S. 733-740
ISSN: 0020-8523
In: GFNPSS Global Nursing Journal of India, Band 3, Heft 1
SSRN
BACKGROUND: The novel coronavirus (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2, and within a few months, it has become a global pandemic. This forced many affected countries to take stringent measures such as complete lockdown, shutting down businesses and trade, as well as travel restrictions, which has had a tremendous economic impact. Therefore, having knowledge and foresight about how a country might be able to contain the spread of COVID-19 will be of paramount importance to the government, policy makers, business partners and entrepreneurs. To help social and administrative decision making, a model that will be able to forecast when a country might be able to contain the spread of COVID-19 is needed. RESULTS: The results obtained using our long short-term memory (LSTM) network-based model are promising as we validate our prediction model using New Zealand's data since they have been able to contain the spread of COVID-19 and bring the daily new cases tally to zero. Our proposed forecasting model was able to correctly predict the dates within which New Zealand was able to contain the spread of COVID-19. Similarly, the proposed model has been used to forecast the dates when other countries would be able to contain the spread of COVID-19. CONCLUSION: The forecasted dates are only a prediction based on the existing situation. However, these forecasted dates can be used to guide actions and make informed decisions that will be practically beneficial in influencing the real future. The current forecasting trend shows that more stringent actions/restrictions need to be implemented for most of the countries as the forecasting model shows they will take over three months before they can possibly contain the spread of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04224-2.
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In: JCOU-D-22-00834
SSRN
The INHAND (International Harmonization of Nomenclature and Diagnostic Criteria for Lesions Project (www.toxpath.org/inhand.asp) is a joint initiative of the Societies of Toxicologic Pathology from Europe (ESTP), Great Britain (BSTP), Japan (JSTP) and North America (STP) to develop an internationally accepted nomenclature for proliferative and nonproliferative lesions in laboratory animals. The purpose of this publication is to provide a standardized nomenclature for classifying microscopic lesions observed in most tissues and organs from the nonhuman primate used in nonclinical safety studies. Some of the lesions are illustrated by color photomicrographs. The standardized nomenclature presented in this document is also available electronically on the internet (http://www.goreni.org/). Sources of material included histopathology databases from government, academia, and industrial laboratories throughout the world. Content includes spontaneous lesions as well as lesions induced by exposure to test materials. Relevant infectious and parasitic lesions are included as well. A widely accepted and utilized international harmonization of nomenclature for lesions in laboratory animals will provide a common language among regulatory and scientific research organizations in different countries and increase and enrich international exchanges of information among toxicologists and pathologists.
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WOS: 000471758500010 ; PubMed ID: 31209238 ; The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZenecaAstraZeneca; European Union Horizon 2020 research [668858 PrECISE]; Joint Research Center for Computational Biomedicine (Bayer AG); National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences; Wellcome TrustWellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
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The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; AstraZeneca ; European Union Horizon 2020 research [668858 PrECISE] ; Joint Research Center for Computational Biomedicine (Bayer AG) ; National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences ; Wellcome Trust [102696, 206194] ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
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The effectiveness of most cancer targeted therapies is short-lived. Tumors often develop resistance that might be overcome with drug combinations. However, the number of possible combinations is vast, necessitating data-driven approaches to find optimal patient-specific treatments. Here we report AstraZeneca's large drug combination dataset, consisting of 11,576 experiments from 910 combinations across 85 molecularly characterized cancer cell lines, and results of a DREAM Challenge to evaluate computational strategies for predicting synergistic drug pairs and biomarkers. 160 teams participated to provide a comprehensive methodological development and benchmarking. Winning methods incorporate prior knowledge of drug-target interactions. Synergy is predicted with an accuracy matching biological replicates for >60% of combinations. However, 20% of drug combinations are poorly predicted by all methods. Genomic rationale for synergy predictions are identified, including ADAM17 inhibitor antagonism when combined with PIK3CB/D inhibition contrasting to synergy when combined with other PI3K-pathway inhibitors in PIK3CA mutant cells. ; We thank the Genomics of Drug Sensitivity in Cancer and COSMIC teams at the Wellcome Trust Sanger Institute for help with the preparation of the molecular data, Denes Turei for help with Omnipath, and Katjusa Koler for help with matching drug names across combination screens. We thank AstraZeneca for funding and provision of data to the DREAM Consortium to run the challenge, and funding from the European Union Horizon 2020 research (under grant agreement No 668858 PrECISE to J.S.R.), the Joint Research Center for Computational Biomedicine (which is partially funded by Bayer AG) to J.S.R., National Institute for Health Research (NIHR) Sheffield Biomedical Research Center, Premium Postdoctoral Fellowship Program of the Hungarian Academy of Sciences. M.G lab is supported by Wellcome Trust (102696 and 206194).
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