RESUMO
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ABSTRACT The CONSORT 2010 statement provides minimum guidelines for reporting randomized trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human-AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.
RESUMO A declaração CONSORT 2010 apresenta diretrizes mínimas para relatórios de ensaios clínicos randomizados. Seu uso generalizado tem sido fundamental para garantir a transparência na avaliação de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence) é uma nova diretriz para relatórios de ensaios clínicos que avaliam intervenções com um componente de IA. Ela foi desenvolvida em paralelo à sua declaração complementar para protocolos de ensaios clínicos, a SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 29 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão CONSORT-AI inclui 14 itens novos que, devido à sua importância para as intervenções de IA, devem ser informados rotineiramente juntamente com os itens básicos da CONSORT 2010. A CONSORT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA está inserida, considerações sobre o manuseio dos dados de entrada e saída da intervenção de IA, a interação humano-IA e uma análise dos casos de erro. A CONSORT-AI ajudará a promover a transparência e a integralidade nos relatórios de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente a qualidade do desenho do ensaio clínico e o risco de viés nos resultados relatados.
RESUMO
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ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.
RESUMO
resumen está disponible en el texto completo
ABSTRACT The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human-AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret and critically appraise the design and risk of bias for a planned clinical trial.
RESUMO A declaração SPIRIT 2013 tem como objetivo melhorar a integralidade dos relatórios dos protocolos de ensaios clínicos, fornecendo recomendações baseadas em evidências para o conjunto mínimo de itens que devem ser abordados. Essas orientações têm sido fundamentais para promover uma avaliação transparente de novas intervenções. Recentemente, tem-se reconhecido cada vez mais que intervenções que incluem inteligência artificial (IA) precisam ser submetidas a uma avaliação rigorosa e prospectiva para demonstrar seus impactos sobre os resultados de saúde. A extensão SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials - Artificial Intelligence) é uma nova diretriz de relatório para protocolos de ensaios clínicos que avaliam intervenções com um componente de IA. Essa diretriz foi desenvolvida em paralelo à sua declaração complementar para relatórios de ensaios clínicos, CONSORT-AI (Consolidated Standards of Reporting Trials - Artificial Intelligence). Ambas as diretrizes foram desenvolvidas por meio de um processo de consenso em etapas que incluiu revisão da literatura e consultas a especialistas para gerar 26 itens candidatos. Foram feitas consultas sobre esses itens a um grupo internacional composto por 103 interessados diretos, que participaram de uma pesquisa Delphi em duas etapas. Chegou-se a um acordo sobre os itens em uma reunião de consenso que incluiu 31 interessados diretos, e os itens foram refinados por meio de uma lista de verificação piloto que envolveu 34 participantes. A extensão SPIRIT-AI inclui 15 itens novos que foram considerados suficientemente importantes para os protocolos de ensaios clínicos com intervenções que utilizam IA. Esses itens novos devem constar dos relatórios de rotina, juntamente com os itens básicos da SPIRIT 2013. A SPIRIT-AI preconiza que os pesquisadores descrevam claramente a intervenção de IA, incluindo instruções e as habilidades necessárias para seu uso, o contexto no qual a intervenção de IA será integrada, considerações sobre o manuseio dos dados de entrada e saída, a interação humano-IA e a análise de casos de erro. A SPIRIT-AI ajudará a promover a transparência e a integralidade nos protocolos de ensaios clínicos com intervenções que utilizam IA. Seu uso ajudará editores e revisores, bem como leitores em geral, a entender, interpretar e avaliar criticamente o delineamento e o risco de viés de um futuro estudo clínico.
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Most cancer-related deaths are a result of metastasis, and thus the importance of this process as a target of therapy cannot be understated. By asking 'how can we effectively treat cancer?', we do not capture the complexity of a disease encompassing >200 different cancer types - many consisting of multiple subtypes - with considerable intratumoural heterogeneity, which can result in variable responses to a specific therapy. Moreover, we have much less information on the pathophysiological characteristics of metastases than is available for the primary tumour. Most disseminated tumour cells that arrive in distant tissues, surrounded by unfamiliar cells and a foreign microenvironment, are likely to die; however, those that survive can generate metastatic tumours with a markedly different biology from that of the primary tumour. To treat metastasis effectively, we must inhibit fundamental metastatic processes and develop specific preclinical and clinical strategies that do not rely on primary tumour responses. To address this crucial issue, Cancer Research UK and Cancer Therapeutics CRC Australia formed a Metastasis Working Group with representatives from not-for-profit, academic, government, industry and regulatory bodies in order to develop recommendations on how to tackle the challenges associated with treating (micro)metastatic disease. Herein, we describe the challenges identified as well as the proposed approaches for discovering and developing anticancer agents designed specifically to prevent or delay the metastatic outgrowth of cancer.
Assuntos
Antineoplásicos/uso terapêutico , Desenvolvimento de Medicamentos/organização & administração , Metástase Neoplásica/tratamento farmacológico , Animais , Antineoplásicos/farmacologia , Humanos , Terapia de Alvo Molecular , Reino Unido , Ensaios Antitumorais Modelo de XenoenxertoRESUMO
Amyloid-ß protein precursor (AßPP) is a ubiquitous protein found in all cell types, suggesting basic and yet important roles, which still remain to be fully elucidated. Loss of function of AßPP has been linked to abnormal neuronal morphology and synaptic function within the hippocampus and alterations in spatial learning, suggesting a neurotrophic role for this protein. Besides AßPP, nerve growth factor (NGF) and other neurotrophins have also been shown to finely modulate neuronal excitability, synaptic plasticity, and cognitive functions. In addition, recent data support the hypothesis of a functional interconnection between AßPP and NGF pathway. Here, we demonstrated that loss of AßPP function, leading to progressive decrease of choline acetyltransferase expression in the septum, correlates with age-related impairment of long-term potentiation (LTP) in the dentate gyrus. We next addressed whether impaired hippocampal plasticity in AßPP-null mice can be restored upon NGF treatment. Notably, NGF, as well as Pro-NGF, can fully revert LTP deficits in AßPP-null mice through p75NTR and JNK pathway activation. Overall the present study may unveil a new mechanism by which, in the absence of AßPP, NGF treatment may preferentially direct p75-neurotrophin-dependent JNK activation toward regeneration and plasticity in functionally relevant brain circuits.
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Envelhecimento/genética , Precursor de Proteína beta-Amiloide/deficiência , Hipocampo/metabolismo , Fator de Crescimento Neural/fisiologia , Plasticidade Neuronal/genética , Receptores de Fator de Crescimento Neural/fisiologia , Envelhecimento/metabolismo , Precursor de Proteína beta-Amiloide/genética , Animais , Potenciais Pós-Sinápticos Excitadores/efeitos dos fármacos , Potenciais Pós-Sinápticos Excitadores/genética , Potenciação de Longa Duração/genética , Sistema de Sinalização das MAP Quinases/genética , Masculino , Camundongos , Camundongos da Linhagem 129 , Camundongos Endogâmicos C57BL , Camundongos Knockout , Fator de Crescimento Neural/genética , Receptor trkA/deficiência , Receptor trkA/genética , Receptores de Fator de Crescimento Neural/genética , Transdução de Sinais/genética , Sinapses/genética , Sinapses/metabolismoRESUMO
B6-Tg/Thy1APP23Sdz (APP23) mutant mice exhibit neurohistological hallmarks of Alzheimer's disease but show intact basal hippocampal neurotransmission and synaptic plasticity. Here, we examine whether spatial learning differently modifies the structural and electrophysiological properties of hippocampal synapses in APP23 and wild-type mice. While no genotypic difference was found in the pseudotrained mice, training elicited a stronger increase in spine density and a more rapid decay of long-term potentiation (LTP) in APP23 mutants. Thus, learning discloses mutation-related abnormalities regarding dendritic spine formation and LTP persistence, thereby suggesting that although unaltered in naïve synapses, plasticity becomes defective at the time it comes into play.
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Doença de Alzheimer , Precursor de Proteína beta-Amiloide/genética , Hipocampo/fisiopatologia , Deficiências da Aprendizagem/etiologia , Potenciação de Longa Duração/genética , Doença de Alzheimer/complicações , Doença de Alzheimer/genética , Doença de Alzheimer/patologia , Análise de Variância , Animais , Biofísica/métodos , Espinhas Dendríticas/patologia , Espinhas Dendríticas/ultraestrutura , Modelos Animais de Doenças , Estimulação Elétrica/métodos , Potenciais Pós-Sinápticos Excitadores/genética , Hipocampo/patologia , Humanos , Técnicas In Vitro , Deficiências da Aprendizagem/genética , Locomoção/genética , Aprendizagem em Labirinto/fisiologia , Camundongos , Camundongos Transgênicos , Mutação/genética , Neurônios/patologia , Neurônios/ultraestrutura , Técnicas de Patch-Clamp/métodos , Tempo de Reação/genética , Coloração pela Prata/métodos , Fatores de TempoRESUMO
The clinical features of myotonic dystrophy type 1 (DM1) and type 2 (DM2) may present striking similarity, whereas, in some cases, the DM2 phenotype may be so mild that the diagnosis may be missed. Therefore, the identification of disease-specific histopathological patterns for DM1 and DM2 may help clinicians to correctly address genetic studies. We performed a comparative morphological and morphometric analysis on muscle biopsies from 10 DM1 and 11 DM2 patients, comparing type 1 and type 2 fibers as to: fiber type predominance, transverse diameter, atrophy and hypertrophy factors, and prevalence of central nuclei. In DM1 cases we observed preferential type 1 fiber atrophy and a higher prevalence of central nucleation among type 1 fibers in all cases. In DM2 muscle biopsies, high rates of atrophic and hypertrophic type 2 fibers were observed in most cases, and preferential central nucleation in type 2 fibers was present in all cases. As opposed to DM1, in which type 1 fibers display most of the histological changes, preferential atrophy and hypertrophy of type 2 fibers may be considered as markers of DM2. A higher prevalence of central nuclei among hypertrophic type 2 fibers has a predictive value for the diagnosis of DM2. Thus, morphometric and fiber type-based histological analysis of muscle biopsies may help differentiate between DM1 and DM2 and guide molecular analysis.
Assuntos
Núcleo Celular/patologia , Fibras Musculares Esqueléticas/patologia , Distrofia Miotônica/classificação , Distrofia Miotônica/diagnóstico , Adenosina Trifosfatases/metabolismo , Adulto , Idoso , Biópsia/métodos , Núcleo Celular/metabolismo , Diagnóstico Diferencial , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Fibras Musculares Esqueléticas/metabolismo , Cadeias Pesadas de Miosina/metabolismo , Distrofia Miotônica/metabolismoRESUMO
Axon-derived neuregulins (NRGs) are a family of growth factors whose binding to ErbB tyrosine kinase receptors promotes the maturation, proliferation and survival of Schwann cells (SCs). Correct NRG/ErbB signaling is essential for the homeostasis of axonal-glial complexes and seems to play a role in peripheral nerve repair. The potential involvement of ErbB receptors in human peripheral neuropathies has not been clarified. Therefore, we assessed the immunoreactivity for EGFR (ErbB1), ErbB2, and ErbB3 in nerve biopsies from patients with different forms of Charcot-Marie-Tooth disease, type 1, (CMT1), as compared to others with inflammatory neuropathies and controls. The most notable changes consisted in the overexpression of ErbB2 and ErbB3 by SCs of nerves from CMT1A patients. These findings are consistent with an impairment of SC differentiation and expand the molecular phenotype of CMT1A. The upregulation of these receptors may play a role in the inhibition of myelination or in the promotion of recurrent demyelination and axonal damage.
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Doença de Charcot-Marie-Tooth/genética , Doença de Charcot-Marie-Tooth/metabolismo , Genes erbB-2/genética , Receptor ErbB-3/genética , Células de Schwann/metabolismo , Animais , Axotomia , Doença de Charcot-Marie-Tooth/patologia , Conexinas/biossíntese , Conexinas/genética , Expressão Gênica , Humanos , Imuno-Histoquímica , Masculino , Ratos , Ratos Wistar , Receptor ErbB-3/biossíntese , Nervo Isquiático/fisiologia , Nervo Sural/patologia , Degeneração Walleriana/fisiopatologia , Proteína beta-1 de Junções ComunicantesRESUMO
Charcot-Marie-Tooth type 1 disease (CMT1) is a group of inherited demyelinating neuropathies caused by mutations in genes expressed by myelinating Schwann cells. Rather than demyelination per se, alterations of Schwann cell-axon interactions have been suggested as the main cause of motor-sensory impairment in CMT1 patients. In an attempt to identify molecules that may be involved in such altered interactions, the extracellular matrix (ECM) remodeling occurring in CMT1 sural nerves was studied. For comparison, both normal sural nerves and sural nerves affected by neuropathies of different origin were used. The study was performed by immunohistochemical analysis using antibodies against collagen types I, III, IV, V, and VI and the glycoproteins fibronectin, laminin, vitronectin and tenascin. Up-regulation of collagens, fibronectin and laminin was commonly found in nerve biopsy specimens from patients affected by CMT1 and control diseases, but higher levels of overexpression were usually observed in CMT1 cases. On the other hand, vitronectin and tenascin appeared preferentially induced in CMT1 compared to other pathologies investigated here. Vitronectin, whose expression in normal nerves was limited to perineurial layers and to the walls of epineurial and endoneurial vessels, became strongly and diffusely expressed in the endoneurium in most CMT1 biopsy specimens. The expression of tenascin, confined to the perineurium, to vessel walls and to the nodes of Ranvier in normal nerves, was displaced and extended along the internodes of several nerve fibers in the majority of CMT1 nerves. Thus, compared with our pathological controls CMT1 seemed to determine the most extensive remodeling of peripheral nerve ECM.