The CONSORT 2010 statement provides minimum guidelines for reporting randomised 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. This Consensus Statement describes the methods used to identify and evaluate candidate items and gain consensus. In addition, it also provides the CONSORT-AI checklist, which includes the new extension items and their accompanying explanations. Methods The SPIRIT-AI and CONSORT-AI extensions were simultaneously developed for clinical trial protocols and trial reports. An announcement for the SPIRIT-AI and CONSORT-AI initiative was published in October 2019, 35 and the two guidelines were registered as reporting

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.

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.

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.

Background:  Sleep disturbances and excessive daytime somnolence are common and disabling features in adult-onset myotonic dystrophy type 1 (DM1). Methods:  Our study used questionnaires, ambulatory polysomnography and the multiple sleep latency test to evaluate sleep-wake cycle and daytime sleepiness in unselected adult-onset DM1 patients. We recruited 18 patients affected by adult-onset DM1 and 18 matched controls. Results:  Sleep efficiency was <90% in 16/18 patients, and it was significantly reduced when compared with controls. Reduced sleep efficiency was associated with abnormal respiratory events (5/18 patients) and/or periodic limb movements (11/18 patients). The Periodic Limb Movement Index was significantly increased in DM1 versus controls. A significantly lower mean MSLT sleep latency was detected in DM1 versus controls, but it did not reach pathological levels. Conclusions:  Our controlled study demonstrated sleep alterations in unselected consecutive DM1 patients. Periodic limb movements in sleep are commonly associated with sleep disturbance in adult-onset DM1, and it may represent a marker of CNS neurodegenerative processes in DM1

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.

(2010) Neuropathology and Applied Neurobiology 36, 275-284 The myotonic dystrophy type 2 (DM2) gene product zinc finger protein 9 (ZNF9) is associated with sarcomeres and normally localized in DM2 patients' muscles Aims: Myotonic dystrophy type 2 (DM2) is caused by a [CCTG]n intronic expansion in the zinc finger protein 9 (ZNF9) gene. As for DM1, sharing with DM2 a similar phenotype, the pathogenic mutation involves a transcribed but untranslated genomic region, suggesting that RNA toxicity may have a role in the pathogenesis of these multisystem disorders by interfering with common cellular mechanisms. However, haploinsufficiency has been described in DM1 and DM2 animal models, and might contribute to pathogenesis. The aim of the present work was therefore to assess ZNF9 protein expression in rat tissues and in human muscle, and ZNF9 subcellular distribution in normal and DM2 human muscles. Methods: Polyclonal anti-ZNF9 antibodies were obtained in rabbit, high pressure liquid chromatography-purified, and used for Western blot, standard and confocal immunofluorescence and immunogold labelling electron microscopy on a panel of normal rat tissues and on normal and DM2 human muscles. Results: Western blot analysis showed that ZNF9 is ubiquitously expressed in mammalian tissues, and that its signal is not substantially modified in DM2 muscles. Immunofluorescence studies showed a myofibrillar distribution of ZNF9, and double staining with two non-repetitive epitopes of titin located it in the I bands. This finding was confirmed by the visualization of ZNF9 in close relation with sarcomeric thin filaments by immunogold labelling electron microscopy. ZNF9 distribution was unaltered in DM2 muscle fibres. Conclusions: ZNF9 is abundantly expressed in human myofibres, where it is located in the sarcomeric I bands, and no modification of this pattern is observed in DM2 muscles.

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