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Computational protein design is advancing rapidly. Here we describe efficient routes starting from validated parallel and antiparallel peptide assemblies to design two families of α-helical barrel proteins with central channels that bind small molecules. Computational designs are seeded by the sequences and structures of defined de novo oligomeric barrel-forming peptides, and adjacent helices are connected by loop building. For targets with antiparallel helices, short loops are sufficient. However, targets with parallel helices require longer connectors; namely, an outer layer of helix-turn-helix-turn-helix motifs that are packed onto the barrels. Throughout these computational pipelines, residues that define open states of the barrels are maintained. This minimizes sequence sampling, accelerating the design process. For each of six targets, just two to six synthetic genes are made for expression in Escherichia coli. On average, 70% of these genes express to give soluble monomeric proteins that are fully characterized, including high-resolution structures for most targets that match the design models with high accuracy.

Background: Hospital discharge summaries play an essential role in informing GPs of recent admissions to ensure excellent continuity of care and prevent adverse events; however, they are notoriously poorly written, time-consuming, and can result in delayed discharge. Aim: To evaluate the potential of artificial intelligence (AI) to produce high-quality discharge summaries equivalent to the level of a doctor who has completed the UK Foundation Programme. Design & setting: Feasibility study using 25 mock patient vignettes. Method: Twenty-five mock patient vignettes were written by the authors. Five junior doctors wrote discharge summaries from the case vignettes (five each). The same case vignettes were input into ChatGPT. In total, 50 discharge summaries were generated; 25 by Al and 25 by junior doctors. Quality and suitability were determined through both independent GP evaluators and adherence to a minimum dataset. Results: Of the 25 AI-written discharge summaries 100% were deemed by GPs to be of an acceptable quality compared with 92% of the junior doctor summaries. They both showed a mean compliance of 97% with the minimum dataset. In addition, the ability of GPs to determine if the summary was written by ChatGPT was poor, with only a 60% accuracy of detection. Similarly, when run through an AI-detection tool all were recognised as being very unlikely to be written by AI. Conclusion: AI has proven to produce discharge summaries of equivalent quality to a junior doctor who has completed the UK Foundation Programme; however, larger studies with real-world patient data with NHS-approved AI tools will need to be conducted.

I argue that Helen Knight and Margaret Macdonald expressed views on the nature of ‘good’ in aesthetic contexts which anticipate to a striking extent the dispute between Peter Geach and R. M. Hare over ethical uses of ‘good’ several years later. I show that Knight introduced a distinction between uses of adjectives later drawn also by Geach, and that she employed that distinction, as Geach did, in order to defend a descriptivist approach to ‘good’ according to which ‘good’ is not ambiguous. I also show that Macdonald conceived of evaluative statements as commendatory, and that her conception of evaluative language therefore closely resembles the position which Hare would later advance. [ABSTRACT FROM AUTHOR]

In this article, I offer a detailed examination of Margaret Macdonald's response to the Cartesian sceptical argument from dreaming. I show that Macdonald's views were not well understood by her contemporaries, and I suggest that this misunderstanding has led to her omission from subsequent discussions of this subject. I end with a brief demonstration of the fact that Macdonald's central claims have re-emerged in contemporary epistemology. [ABSTRACT FROM AUTHOR]

In this article I describe the contributions made by Samuel Alexander to the issue of relations which so vexed Bertrand Russell and F. H. Bradley in the late nineteenth and early twentieth centuries. I provide a novel understanding of Alexander's position concerning relations and describe the way in which he viewed his position as superior to those of Bradley and Russell. I offer, therefore, a more complete picture of a philosophical debate central to the relevant period, through the introduction of a third character whose views have been largely omitted from available narratives. [ABSTRACT FROM AUTHOR]

In this discussion I argue that Peter Sullivan is wrong to suggest that Wittgenstein's position in the Philosophical Investigations involves a commitment to transcendental idealism. I show that Sullivan's interpretation involves holding that transcendental idealism was employed by Wittgenstein in the attempt to combat a Platonist mythology. I show, through a detailed appraisal of Wittgenstein's discussion of samples, that Wittgenstein's approach to Platonism does not involve any such employment of transcendental idealism. I conclude that there is no such motivation as Sullivan finds in Wittgenstein for endorsing transcendental idealism, and that we ought not, therefore, ascribe to him such a view. [ABSTRACT FROM AUTHOR]

Background: Hospital discharge summaries play an essential role in informing GPs of recent admissions to ensure excellent continuity of care and prevent adverse events; however, they are notoriously poorly written, time-consuming, and can result in delayed discharge. Aim: To evaluate the potential of artificial intelligence (AI) to produce high-quality discharge summaries equivalent to the level of a doctor who has completed the UK Foundation Programme. Design & setting: Feasibility study using 25 mock patient vignettes. Method: Twenty-five mock patient vignettes were written by the authors. Five junior doctors wrote discharge summaries from the case vignettes (five each). The same case vignettes were input into ChatGPT. In total, 50 discharge summaries were generated; 25 by Al and 25 by junior doctors. Quality and suitability were determined through both independent GP evaluators and adherence to a minimum dataset. Results: Of the 25 AI-written discharge summaries 100% were deemed by GPs to be of an acceptable quality compared with 92% of the junior doctor summaries. They both showed a mean compliance of 97% with the minimum dataset. In addition, the ability of GPs to determine if the summary was written by ChatGPT was poor, with only a 60% accuracy of detection. Similarly, when run through an AI-detection tool all were recognised as being very unlikely to be written by AI. Conclusion: AI has proven to produce discharge summaries of equivalent quality to a junior doctor who has completed the UK Foundation Programme; however, larger studies with real-world patient data with NHS-approved AI tools will need to be conducted. [ABSTRACT FROM AUTHOR]

Background: The anti-IgE monoclonal antibody omalizumab is widely used for severe asthma. This study aimed to identify biomarkers that predict clinical improvement during 1 year of omalizumab treatment. Methods: One-year open-label Study of Mechanisms of action of Omalizumab in Severe Asthma (SoMOSA) involving 216 patients with severe (Global Initiative for Asthma step 4/5) uncontrolled atopic asthma (at least two severe exacerbations in the previous year) taking high-dose inhaled corticosteroids and long-acting β-agonists with or without maintenance oral corticosteroids. It had two phases: 0-16 weeks, to assess early clinical improvement by Global Evaluation of Therapeutic Effectiveness (GETE); and 16-52 weeks, to assess late responses based on ⩾50% reduction in exacerbations or mOCS dose. All participants provided samples (exhaled breath, blood, sputum, urine) before and after 16 weeks of omalizumab treatment. Measurements and Main Results: A total of 191 patients completed phase 1; 63% had early improvement. Of 173 who completed phase 2, 69% had reduced exacerbations by ⩾50% and 57% (37 of 65) taking mOCSs had reduced their dose by ⩾50%. The primary outcomes 2,3-dinor-11-β-PGF2α, GETE score, and standard clinical biomarkers (blood and sputum eosinophils, exhaled nitric oxide, serum IgE) did not predict either clinical response. Five volatile organic compounds and five plasma lipid biomarkers strongly predicted the ⩾50% reduction in exacerbations (receiver operating characteristic areas under the curve of 0.780 and 0.922, respectively) and early responses (areas under the curve of 0.835 and 0.949, respectively). In an independent cohort, gas chromatography/mass spectrometry biomarkers differentiated between severe and mild asthma. Conclusions: This is the first discovery of omics biomarkers that predict improvement in asthma with biologic agent treatment. Prospective validation and development for clinical use is justified.

Most childhood cancers possess distinct clinicopathological profiles from those seen in adulthood, reflecting their divergent mechanisms of carcinogenesis. Rather than depending on the decades-long, stepwise accumulation of changes within a mature cell that defines adult carcinomas, many pediatric malignancies emerge rapidly as the consequence of random errors during development. These errors-whether they be genetic, epigenetic, or microenvironmental-characteristically block maturation, resulting in phenotypically primitive neoplasms. Only an event that falls within a narrow set of spatiotemporal parameters will forge a malignant clone; if it occurs too soon then the event might be lethal, or negatively selected against, while if it is too late or in an incorrectly primed precursor cell then the necessary intracellular conditions for transformation will not be met. The precise characterization of these changes, through the study of normal tissues and tumors from patients and model systems, will be essential if we are to develop new strategies to diagnose, treat, and perhaps even prevent childhood cancer., (Copyright © 2024 Cold Spring Harbor Laboratory Press; all rights reserved.)