Three-Dimensional Structure of Conotoxin tx3a: A m-1 Branch Peptide of the M-Superfamily

The M-superfamily, one of eight major conotoxin superfamilies found in the venom of the cone snail, contains a Cys framework with disulfide-linked loops labeled 1, 2, and 3 (- CC1C2C3CC-). M-superfamily conotoxins can be divided into the m-1, -2, -3 and -4 branches, based upon the number of residues located in the third Cys loop between the fourth and fifth Cys residues. Here we provide a three-dimensional solution structure for the m-1 conotoxin tx3a found in the venom of Conus textile. The 15 amino acid peptide, CCSWDVCDHPSCTCC, has disulfide bonds between Cys1 and Cys14, Cys2 and Cys12, and Cys7 and Cys15 typical of the C1- C5, C2-C4, and C3-C6 connectivity pattern seen in m-1 branch peptides. The tertiary structure of tx3a was determined by 2D 1H NMR in combination with the combined assignment and dynamics algorithm for nuclear magnetic resonance (NMR) applications CYANA program. Input for structure calculations consisted of 62 inter- and intraproton, 5 phi angle, and 4 hydrogen bond constraints. The root-mean-square deviation values for the 20 final structures are 0.32 +/- 0.07 Å and 0.84 +/- 0.11 Å for the backbone and heavy atoms, respectively. Surprisingly, the structure of tx3a has a “triple-turn” motif seen in the m-2 branch conotoxin mr3a, which is absent in mr3e, the only other member of the m-1 branch of the M-superfamily whose structure is known. Interestingly, injection of tx3a into mice elicits an excitatory response similar to that of the m-2 branch peptide mr3a, even though the conotoxins have different disulfide connectivity patterns

Acceptance and understanding of artificial intelligence in medical research among orthopaedic surgeons

AimsThe principles of evidence-based medicine (EBM) are the foundation of modern medical practice. Surgeons are familiar with the commonly used statistical techniques to test hypotheses, summarize findings, and provide answers within a specified range of probability. Based on this knowledge, they are able to critically evaluate research before deciding whether or not to adopt the findings into practice. Recently, there has been an increased use of artificial intelligence (AI) to analyze information and derive findings in orthopaedic research. These techniques use a set of statistical tools that are increasingly complex and may be unfamiliar to the orthopaedic surgeon. It is unclear if this shift towards less familiar techniques is widely accepted in the orthopaedic community. This study aimed to provide an exploration of understanding and acceptance of AI use in research among orthopaedic surgeons.MethodsSemi-structured in-depth interviews were carried out on a sample of 12 orthopaedic surgeons. Inductive thematic analysis was used to identify key themes.ResultsThe four intersecting themes identified were: 1) validity in traditional research, 2) confusion around the definition of AI, 3) an inability to validate AI research, and 4) cautious optimism about AI research. Underpinning these themes is the notion of a validity heuristic that is strongly rooted in traditional research teaching and embedded in medical and surgical training.ConclusionResearch involving AI sometimes challenges the accepted traditional evidence-based framework. This can give rise to confusion among orthopaedic surgeons, who may be unable to confidently validate findings. In our study, the impact of this was mediated by cautious optimism based on an ingrained validity heuristic that orthopaedic surgeons develop through their medical training. Adding to this, the integration of AI into everyday life works to reduce suspicion and aid acceptance.Cite this article: Bone Jt Open 2023;4(9):696–703

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Safety and efficacy of fluoxetine on functional outcome after acute stroke (AFFINITY): a randomised, double-blind, placebo-controlled trial

Background Trials of fluoxetine for recovery after stroke report conflicting results. The Assessment oF FluoxetINe In sTroke recoverY (AFFINITY) trial aimed to show if daily oral fluoxetine for 6 months after stroke improves functional outcome in an ethnically diverse population. Methods AFFINITY was a randomised, parallel-group, double-blind, placebo-controlled trial done in 43 hospital stroke units in Australia (n=29), New Zealand (four), and Vietnam (ten). Eligible patients were adults (aged ≥18 years) with a clinical diagnosis of acute stroke in the previous 2–15 days, brain imaging consistent with ischaemic or haemorrhagic stroke, and a persisting neurological deficit that produced a modified Rankin Scale (mRS) score of 1 or more. Patients were randomly assigned 1:1 via a web-based system using a minimisation algorithm to once daily, oral fluoxetine 20 mg capsules or matching placebo for 6 months. Patients, carers, investigators, and outcome assessors were masked to the treatment allocation. The primary outcome was functional status, measured by the mRS, at 6 months. The primary analysis was an ordinal logistic regression of the mRS at 6 months, adjusted for minimisation variables. Primary and safety analyses were done according to the patient's treatment allocation. The trial is registered with the Australian New Zealand Clinical Trials Registry, ACTRN12611000774921. Findings Between Jan 11, 2013, and June 30, 2019, 1280 patients were recruited in Australia (n=532), New Zealand (n=42), and Vietnam (n=706), of whom 642 were randomly assigned to fluoxetine and 638 were randomly assigned to placebo. Mean duration of trial treatment was 167 days (SD 48·1). At 6 months, mRS data were available in 624 (97%) patients in the fluoxetine group and 632 (99%) in the placebo group. The distribution of mRS categories was similar in the fluoxetine and placebo groups (adjusted common odds ratio 0·94, 95% CI 0·76–1·15; p=0·53). Compared with patients in the placebo group, patients in the fluoxetine group had more falls (20 [3%] vs seven [1%]; p=0·018), bone fractures (19 [3%] vs six [1%]; p=0·014), and epileptic seizures (ten [2%] vs two [<1%]; p=0·038) at 6 months. Interpretation Oral fluoxetine 20 mg daily for 6 months after acute stroke did not improve functional outcome and increased the risk of falls, bone fractures, and epileptic seizures. These results do not support the use of fluoxetine to improve functional outcome after stroke

p53-mediated activation of miRNA34 candidate tumor-suppressor genes.

Taken together, the data suggest the miRNA34s might be key effectors of p53 tumor-suppressor function, and their inactivation might contribute to certain cancers

Anesthesia and the developing brain : A way forward for clinical research

It is now well established that many general anesthetics have a variety of effects on the developing brain in animal models. In contrast, human cohort studies show mixed evidence for any association between neurobehavioural outcome and anesthesia exposure in early childhood. In spite of large volumes of research, it remains very unclear if the animal studies have any clinical relevance; or indeed how, or if, clinical practice needs to be altered. Answering these questions is of great importance given the huge numbers of young children exposed to general anesthetics. A recent meeting in Genoa brought together researchers and clinicians to map a path forward for future clinical studies. This paper describes these discussions and conclusions. It was agreed that there is a need for large, detailed, prospective, observational studies, and for carefully designed trials. It may be impossible to design or conduct a single study to completely exclude the possibility that anesthetics can, under certain circumstances, produce long-term neurobehavioural changes in humans; however, observational studies will improve our understanding of which children are at greatest risk, and may also suggest potential underlying etiologies, and clinical trials will provide the strongest evidence to test the effectiveness of different strategies or anesthetic regimens with respect to better neurobehavioral outcome