A battery of tests was established to determine the oestrogenic, mutagenic and genotoxic potential of two categories of endocrine disrupting chemicals (EDCs), phthalates and alkylphenols. Diisononylphthalate (DINP), d...A battery of tests was established to determine the oestrogenic, mutagenic and genotoxic potential of two categories of endocrine disrupting chemicals (EDCs), phthalates and alkylphenols. Diisononylphthalate (DINP), diethylhexylphthalate (DEHP), dibutylphthalate (DBP), diisododecylphthalate (DIDP) and 4-nonylphenol (4-NP) were oestrogenic in the yeast estrogen screen (YES) assay and potently oestrogenic in the MVLN and E-SCREEN assays at environmentally relevant concentrations. DINP and 4-NP were mutagenic in the Ames assay and also induced significant levels of unscheduled DNA synthesis and DNA strand breakage. Significant induction in the percentage of cells containing micronuclei was observed after treatment with DINP, DEHP and 4-NP. In addition, sewage effluents from sewage treatment plants (STPs) in the Border, Midlands and Western (BMW) region of Ireland were significantly oestrogenic in the YES assay. Moreover, analysis of levels of phthalates and alkylphenol identified in Irish rivers receiving treated effluent showed potent oestrogenicity in the YES assay. The proliferative and genotoxic ability of the phthalates and alkylphenol, and the oestrogenicity of the treated effluents reported here, is significant as these EDCs and EDCs within the effluent may play a role in the etiology of human abnormalities.展开更多
The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods...The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods: A literature review was undertaken to identify the hurdles and steps required to introduce supported clinical decision-making using AI within hospitals. This was supported by a survey of local hospital practice within the Midlands of the United Kingdom to see what systems had been introduced and were functioning effectively. Results: It is unclear how to practically implement systems using AI within medicine easily. Algorithmic medicine based on a set of rules calculated from data only takes a clinician so far to deliver patient centred optimal treatment. AI facilitates a clinician’s ability to assimilate data from disparate sources and can help with some of the analysis and decision making. However, learning remains organic and the subtleties of difference between patients, care providers who exhibit non-verbal communication for instance make it difficult for an AI to capture all the pertinent information required to make the correct clinical decision for any given individual. Hence it assists rather than controls any process in clinical practice. It also must continually renew and adapt considering changes in practise and trends as the goalposts change to meet fluctuations in resources and workload. Precision surgery is benefiting from robotic-assisted surgery in parts driven by AI and being used in 80% of trusts locally. Conclusion: The use of AI in clinical practice remains patchy with it being adopted where research groups have studied a more effective method of monitoring or treatment. The use of robotic-assisted surgery on the other hand has been more rapid as the precision of treatment that this provides appears attractive in improving clinical care.展开更多
文摘A battery of tests was established to determine the oestrogenic, mutagenic and genotoxic potential of two categories of endocrine disrupting chemicals (EDCs), phthalates and alkylphenols. Diisononylphthalate (DINP), diethylhexylphthalate (DEHP), dibutylphthalate (DBP), diisododecylphthalate (DIDP) and 4-nonylphenol (4-NP) were oestrogenic in the yeast estrogen screen (YES) assay and potently oestrogenic in the MVLN and E-SCREEN assays at environmentally relevant concentrations. DINP and 4-NP were mutagenic in the Ames assay and also induced significant levels of unscheduled DNA synthesis and DNA strand breakage. Significant induction in the percentage of cells containing micronuclei was observed after treatment with DINP, DEHP and 4-NP. In addition, sewage effluents from sewage treatment plants (STPs) in the Border, Midlands and Western (BMW) region of Ireland were significantly oestrogenic in the YES assay. Moreover, analysis of levels of phthalates and alkylphenol identified in Irish rivers receiving treated effluent showed potent oestrogenicity in the YES assay. The proliferative and genotoxic ability of the phthalates and alkylphenol, and the oestrogenicity of the treated effluents reported here, is significant as these EDCs and EDCs within the effluent may play a role in the etiology of human abnormalities.
文摘The use of Artificial Intelligence (AI) for clinical pathway management and decision making is believed to improve clinical care and has been used to improve pathways for treatment in most medical disciplines. Methods: A literature review was undertaken to identify the hurdles and steps required to introduce supported clinical decision-making using AI within hospitals. This was supported by a survey of local hospital practice within the Midlands of the United Kingdom to see what systems had been introduced and were functioning effectively. Results: It is unclear how to practically implement systems using AI within medicine easily. Algorithmic medicine based on a set of rules calculated from data only takes a clinician so far to deliver patient centred optimal treatment. AI facilitates a clinician’s ability to assimilate data from disparate sources and can help with some of the analysis and decision making. However, learning remains organic and the subtleties of difference between patients, care providers who exhibit non-verbal communication for instance make it difficult for an AI to capture all the pertinent information required to make the correct clinical decision for any given individual. Hence it assists rather than controls any process in clinical practice. It also must continually renew and adapt considering changes in practise and trends as the goalposts change to meet fluctuations in resources and workload. Precision surgery is benefiting from robotic-assisted surgery in parts driven by AI and being used in 80% of trusts locally. Conclusion: The use of AI in clinical practice remains patchy with it being adopted where research groups have studied a more effective method of monitoring or treatment. The use of robotic-assisted surgery on the other hand has been more rapid as the precision of treatment that this provides appears attractive in improving clinical care.
