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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. The CONSORT-AI and SPIRIT-AI extensions improve the transparency of clinical trial design and trial protocol reporting for artificial intelligence interventions.

At least four articles credit the AI tool as a co-author, as publishers scramble to regulate its use. At least four articles credit the AI tool as a co-author, as publishers scramble to regulate its use. Credit: Iryna Imago/Shutterstock Hands typing on a laptop keyboard with screen showing artificial intelligence chatbot ChatGPT

A high concentration of ammonium (NH₄⁺) as the sole source of nitrogen in the growth medium often is toxic to plants. The nitrate transporter NRT1.1 is involved in mediating the effects of NH₄⁺ toxicity; however, the mechanism remains undefined. In this study, wild-type Arabidopsis (Arabidopsis thaliana Columbia-0 [Col-0]) and NRT1.1 mutants (chl1-1 and chl1-5) were grown hydroponically in NH₄NO₃ and (NH₄)₂SO₄ media to assess the function of NRT1.1 in NH₄⁺ stress responses. All the plants grew normally in medium containing mixed nitrogen sources, but Col-0 displayed more chlorosis and lower biomass and photosynthesis than the NRT1.1 mutants in (NH₄)₂SO₄ medium. Grafting experiments between Col-0 and chl1-5 further confirmed that NH₄⁺ toxicity is influenced by NRT1.1. In (NH₄)₂SO₄ medium, NRT1.1 induced the expression of NH₄⁺ transporters, increasing NH₄⁺ uptake. Additionally, the activities of glutamine synthetase and glutamate synthetase in roots of Col-0 plants decreased and soluble sugar accumulated significantly, whereas pyruvate kinase-mediated glycolysis was not affected, all of which contributed to NH₄⁺ accumulation. By contrast, the NRT1.1 mutants showed reduced NH₄⁺ accumulation and enhanced NH₄⁺ assimilation through glutamine synthetase, glutamate synthetase, and glutamate dehydrogenase. Moreover, the up-regulation of genes involved in ethylene synthesis and senescence in Col-0 plants treated with (NH₄)₂SO₄ suggests that ethylene is involved in NH₄⁺ toxicity responses. This study showed that NH₄⁺ toxicity is related to a nitrate-independent signaling function of NRT1.1 in Arabidopsis, characterized by enhanced NH₄⁺ accumulation and altered NH₄⁺ metabolism, which stimulates ethylene synthesis, leading to plant senescence.

Reviewing and accepting study plans before results are known can counter perverse incentives. Chris Chambers sets out three ways to improve the approach. Reviewing and accepting study plans before results are known can counter perverse incentives. Chris Chambers sets out three ways to improve the approach.

Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson’s correlation with the reference system was moderate for swing times (r = 0.4–0.66), but stronger for stance and double support time (r = 0.93–0.95). Cadence mean error was −0.25 steps/min ± 3.9 steps/min (r = 0.97), while walking speed mean error was −0.02 ± 0.11 m/s (r = 0.92). These preliminary results suggest that single camera videos and pose estimation models based on deep networks could be used to quantify clinically relevant gait metrics in individuals poststroke, even while using assistive devices in uncontrolled environments. Such development opens the door to applications for gait analysis both inside and outside of clinical settings, without the need of sophisticated equipment.

Changing atmospheric CO₂ levels, climate, and air humidity affect plant gas exchange that is controlled by stomata, small pores on plant leaves and stems formed by guard cells. Evolution has shaped the morphology and regulatory mechanisms governing stomatal movements to correspond to the needs of various land plant groups over the past 400 million years. Stomata close in response to the plant hormone abscisic acid (ABA), elevated CO₂ concentration, and reduced air humidity. Whether the active regulatory mechanisms that control stomatal closure in response to these stimuli are present already in mosses, the oldest plant group with stomata, or were acquired more recently in angiosperms remains controversial. It has been suggested that the stomata of the basal vascular plants, such as ferns and lycophytes, close solely hydropassively. On the other hand, active stomatal closure in response to ABA and CO₂ was found in several moss, lycophyte, and fern species. Here, we show that the stomata of two temperate fern species respond to ABA and CO₂ and that an active mechanism of stomatal regulation in response to reduced air humidity is present in some ferns. Importantly, fern stomatal responses depend on growth conditions. The data indicate that the stomatal behavior of ferns is more complex than anticipated before, and active stomatal regulation is present in some ferns and has possibly been lost in others. Further analysis that takes into account fern species, life history, evolutionary age, and growth conditions is required to gain insight into the evolution of land plant stomatal responses.

Nontransgenic genome editing in regenerable protoplasts, plant cells free of their cell wall, could revolutionize crop improvement because it reduces regulatory and technical complexity. However, plant tissue culture is known to engender frequent unwanted variation, termed somaclonal variation. To evaluate the contribution of large-scale genome instability to this phenomenon, we analyzed potatoes (Solanum tuberosum) regenerated from either protoplasts or stem explants for copy number changes by comparison of Illumina read depth. Whereas a control set of eight plants that had been propagated by cuttings displayed no changes, all 15 protoplast regenerants tested were affected by aneuploidy or structural chromosomal changes. Certain chromosomes displayed segmental deletions and duplications ranging from one to many. Resampling different leaves of the same plant found differences in three regenerants, indicating frequent persistence of instability. By comparison, 33 regenerants from stem explants used for Agrobacterium-mediated transformation displayed less frequent but still considerable (18%) large-scale copy number changes. Repetition of certain instability patterns suggested greater susceptibility in specific genomic sites. These results indicate that tissue culture, depending on the protocol used, can induce genomic instability resulting in large-scale changes likely to compromise final plant phenotype.

BackgroundDespite a concerted policy effort in Europe, social inequalities in health are a persistent problem. Developing a standardised measure of socioeconomic level across Europe will improve the understanding of the underlying mechanisms and causes of inequalities. This will facilitate developing, implementing and assessing new and more effective policies, and will improve the comparability and reproducibility of health inequality studies among countries. This paper presents the extension of the European Deprivation Index (EDI), a standardised measure first developed in France, to four other European countries—Italy, Portugal, Spain and England, using available 2001 and 1999 national census data.Methods and resultsThe method previously tested and validated to construct the French EDI was used: first, an individual indicator for relative deprivation was constructed, defined by the minimal number of unmet fundamental needs associated with both objective (income) poverty and subjective poverty. Second, variables available at both individual (European survey) and aggregate (census) levels were identified. Third, an ecological deprivation index was constructed by selecting the set of weighted variables from the second step that best correlated with the individual deprivation indicator.ConclusionsFor each country, the EDI is a weighted combination of aggregated variables from the national census that are most highly correlated with a country-specific individual deprivation indicator. This tool will improve both the historical and international comparability of studies, our understanding of the mechanisms underlying social inequalities in health and implementation of intervention to tackle social inequalities in health.

•Due to the risks of identification and recruitment bias, opting for a cluster design when individual randomization would be feasible needs a strong justification. Concerns around contamination are unlikely to be acceptable justifications; although estimation of indirect effects might be.•When cluster randomization is adopted, we recommend that authors provide a clear justification for the choice of cluster randomization and clearly outline strategies to mitigate increased risks of bias. This should include identification and recruitment by someone blind to the treatment allocation and minimal or objective individual-level eligibility criteria.•Other good conduct procedures which are routinely implemented in individually randomized trials should be followed. These include implementation of the randomization using an accepted method of allocation concealment, for example, by using an independent statistician to generate the allocation sequence; blind outcome assessment when outcomes are subjective; and clear pre-specification (in a protocol or trial registration site) of the primary outcome including primary assessment time and method of primary analysis.•All these aspects should be clearly reported as per CONSORT guidelines. To ensure particular clarity around identification and recruitment, authors should also provide a timeline-cluster diagram. To describe the prevalence of risks of bias in cluster-randomized trials of individual-level interventions, according to the Cochrane Risk of Bias tool. Review undertaken in duplicate of a random sample of 40 primary reports of cluster-randomized trials of individual-level interventions. The most common reported reasons for adopting cluster randomization were the need to avoid contamination (17, 42.5%) and practical considerations (14, 35%). Of the 40 trials all but one was assessed as being at risk of bias. A majority (27, 67.5%) were assessed as at risk due to the timing of identification and recruitment of participants; many (21, 52.5%) due to an apparent lack of adequate allocation concealment; and many due to selectively reported results (22, 55%), arising from a mixture of reasons including lack of documentation of primary outcome. Other risks mostly occurred infrequently. Many cluster-randomized trials evaluating individual-level interventions appear to be at risk of bias, mostly due to identification and recruitment biases. We recommend that investigators carefully consider the need for cluster randomization; follow recommended procedures to mitigate risks of identification and recruitment bias; and adhere to good reporting practices including clear documentation of primary outcome and allocation concealment methods.