BACKGROUND: Inflammation is associated with adverse cardiovascular events. Data from recent trials suggest that colchicine reduces the risk of cardiovascular events. METHODS: In this multicenter trial with a 2-by-2 factorial design, we randomly assigned patients who had myocardial infarction to receive either colchicine or placebo and either spironolactone or placebo. The results of the colchicine trial are reported here. The primary efficacy outcome was a composite of death from cardiovascular causes, recurrent myocardial infarction, stroke, or unplanned ischemia-driven coronary revascularization, evaluated in a time-to-event analysis. C-reactive protein was measured at 3 months in a subgroup of patients, and safety was also assessed. RESULTS: A total of 7062 patients at 104 centers in 14 countries underwent randomization; at the time of analysis, the vital status was unknown for 45 patients (0.6%), and this information was most likely missing at random. A primary-outcome event occurred in 322 of 3528 patients (9.1%) in the colchicine group and 327 of 3534 patients (9.3%) in the placebo group over a median follow-up period of 3 years (hazard ratio, 0.99; 95% confidence interval [CI], 0.85 to 1.16; P = 0.93). The incidence of individual components of the primary outcome appeared to be similar in the two groups. The least-squares mean difference in C-reactive protein levels between the colchicine group and the placebo group at 3 months, adjusted according to the baseline values, was -1.28 mg per liter (95% CI, -1.81 to -0.75). Diarrhea occurred in a higher percentage of patients with colchicine than with placebo (10.2% vs. 6.6%; P<0.001), but the incidence of serious infections did not differ between groups. CONCLUSIONS: Among patients who had myocardial infarction, treatment with colchicine, when started soon after myocardial infarction and continued for a median of 3 years, did not reduce the incidence of the composite primary outcome (death from cardiovascular causes, recurrent myocardial infarction, stroke, or unplanned ischemia-driven coronary revascularization). (Funded by the Canadian Institutes of Health Research and others; CLEAR ClinicalTrials.gov number, NCT03048825.).

• MeSH
• C-Reactive Protein * analysis   MeSH
• Stroke prevention & control   MeSH
• Double-Blind Method MeSH
• Myocardial Infarction * prevention & control   mortality   MeSH
• Kaplan-Meier Estimate MeSH
• Colchicine * therapeutic use   adverse effects   MeSH
• Middle Aged MeSH
• Humans MeSH
• Recurrence MeSH
• Secondary Prevention MeSH
• Aged MeSH
• Spironolactone therapeutic use   adverse effects   MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH
• Randomized Controlled Trial MeSH

The Global Alliance for Genomics and Health (GA4GH) Phenopacket Schema was released in 2022 and approved by ISO as a standard for sharing clinical and genomic information about an individual, including phenotypic descriptions, numerical measurements, genetic information, diagnoses, and treatments. A phenopacket can be used as an input file for software that supports phenotype-driven genomic diagnostics and for algorithms that facilitate patient classification and stratification for identifying new diseases and treatments. There has been a great need for a collection of phenopackets to test software pipelines and algorithms. Here, we present Phenopacket Store. Phenopacket Store v.0.1.19 includes 6,668 phenopackets representing 475 Mendelian and chromosomal diseases associated with 423 genes and 3,834 unique pathogenic alleles curated from 959 different publications. This represents the first large-scale collection of case-level, standardized phenotypic information derived from case reports in the literature with detailed descriptions of the clinical data and will be useful for many purposes, including the development and testing of software for prioritizing genes and diseases in diagnostic genomics, machine learning analysis of clinical phenotype data, patient stratification, and genotype-phenotype correlations. This corpus also provides best-practice examples for curating literature-derived data using the GA4GH Phenopacket Schema.

• MeSH
• Algorithms MeSH
• Databases, Genetic MeSH
• Phenotype * MeSH
• Genomics * methods   MeSH
• Humans MeSH
• Software * MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. METHODS: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model's performance was compared against four expert clinicians using 14 previously unseen MRI scans. RESULTS: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% ± 3.4%, with a weighted top-3 accuracy of 84.7% ± 1.8% and top-5 accuracy of 90.2% ± 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% ± 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. CONCLUSIONS: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform.

• MeSH
• Adult MeSH
• Internet MeSH
• Middle Aged MeSH
• Humans MeSH
• Magnetic Resonance Imaging * methods   MeSH
• Neuromuscular Diseases * diagnosis   diagnostic imaging   MeSH
• Machine Learning * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: There is a paucity of data on treatment outcomes following stereotactic radiosurgery (SRS) for brain metastases from sarcoma primaries. METHODS: The International Radiosurgery Research Foundation member-sites were queried for patients with brain metastases from sarcoma primaries treated with SRS. Overall survival (OS) and local control (LC) were calculated via Kaplan-Meier analysis. Univariate analyses examined prognostic factors associated with LC and OS via log-rank t-tests and multivariate analyses (MVA) via Cox proportional hazards model. RESULTS: A total of 146 patients with 309 brain metastases were identified. Two-hundred and thirty lesions were treated with single-fraction SRS with a median dose of 20 Gy (15-24 Gy). Ninety-five patients had extracranial metastases, including 75 oligometastatic patients. One- and 2-year OS and LC rates were 47.7% and 37.3%, and 78.3% and 62.2%, respectively. On univariate analyses, superior 1-year OS was noted among leiomyosarcomas (69.7% vs. 42.6%; p = .02) with poorer outcomes among pleomorphic histologies (10.5% vs. 50.7%; p = .002). Pleomorphic histologies were associated with poorer OS on MVA (hazard ratio [HR], 3.13; p = .006). On MVA, LC was inferior among patients of age ≥45 years (HR, 3.78; p < .001) and superior among leiomyosarcomas (HR, 0.31; p = .03). OS was prognosticated based on adverse factors (ie, nonleiomyosarcoma histology and progressive extracranial metastases). Two-year OS for patients with and without adverse features were 78.6% and 31.5%, respectively. CONCLUSIONS: LC outcomes were driven by histology and age with superior LC among leiomyosarcomas and patients of age <45 years. OS was driven by nonleiomyosarcoma histology and the presence of progressive extracranial disease.

• MeSH
• Adult MeSH
• Kaplan-Meier Estimate MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Brain Neoplasms * secondary   radiotherapy   mortality   surgery   MeSH
• Prognosis MeSH
• Radiosurgery * methods   MeSH
• Retrospective Studies MeSH
• Sarcoma * pathology   mortality   radiotherapy   secondary   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Treatment Outcome MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Young Adult MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Multicenter Study MeSH

IMPORTANCE: The successful implementation of artificial intelligence (AI) in health care depends on its acceptance by key stakeholders, particularly patients, who are the primary beneficiaries of AI-driven outcomes. OBJECTIVES: To survey hospital patients to investigate their trust, concerns, and preferences toward the use of AI in health care and diagnostics and to assess the sociodemographic factors associated with patient attitudes. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study developed and implemented an anonymous quantitative survey between February 1 and November 1, 2023, using a nonprobability sample at 74 hospitals in 43 countries. Participants included hospital patients 18 years of age or older who agreed with voluntary participation in the survey presented in 1 of 26 languages. EXPOSURE: Information sheets and paper surveys handed out by hospital staff and posted in conspicuous hospital locations. MAIN OUTCOMES AND MEASURES: The primary outcome was participant responses to a 26-item instrument containing a general data section (8 items) and 3 dimensions (trust in AI, AI and diagnosis, preferences and concerns toward AI) with 6 items each. Subgroup analyses used cumulative link mixed and binary mixed-effects models. RESULTS: In total, 13 806 patients participated, including 8951 (64.8%) in the Global North and 4855 (35.2%) in the Global South. Their median (IQR) age was 48 (34-62) years, and 6973 (50.5%) were male. The survey results indicated a predominantly favorable general view of AI in health care, with 57.6% of respondents (7775 of 13 502) expressing a positive attitude. However, attitudes exhibited notable variation based on demographic characteristics, health status, and technological literacy. Female respondents (3511 of 6318 [55.6%]) exhibited fewer positive attitudes toward AI use in medicine than male respondents (4057 of 6864 [59.1%]), and participants with poorer health status exhibited fewer positive attitudes toward AI use in medicine (eg, 58 of 199 [29.2%] with rather negative views) than patients with very good health (eg, 134 of 2538 [5.3%] with rather negative views). Conversely, higher levels of AI knowledge and frequent use of technology devices were associated with more positive attitudes. Notably, fewer than half of the participants expressed positive attitudes regarding all items pertaining to trust in AI. The lowest level of trust was observed for the accuracy of AI in providing information regarding treatment responses (5637 of 13 480 respondents [41.8%] trusted AI). Patients preferred explainable AI (8816 of 12 563 [70.2%]) and physician-led decision-making (9222 of 12 652 [72.9%]), even if it meant slightly compromised accuracy. CONCLUSIONS AND RELEVANCE: In this cross-sectional study of patient attitudes toward AI use in health care across 6 continents, findings indicated that tailored AI implementation strategies should take patient demographics, health status, and preferences for explainable AI and physician oversight into account.

• MeSH
• Adult MeSH
• Trust MeSH
• Internationality MeSH
• Middle Aged MeSH
• Humans MeSH
• Hospitals MeSH
• Delivery of Health Care * MeSH
• Cross-Sectional Studies MeSH
• Surveys and Questionnaires MeSH
• Aged MeSH
• Artificial Intelligence * MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Flow cytometry immunophenotyping is critical for the diagnostic classification of mature/peripheral B-cell neoplasms/B-cell chronic lymphoproliferative disorders (B-CLPD). Quantitative driven classification approaches applied to multiparameter flow cytometry immunophenotypic data can be used to extract maximum information from a multidimensional space created by individual parameters (e.g., immunophenotypic markers), for highly accurate and automated classification of individual patient (sample) data. Here, we developed and compared five diagnostic classification algorithms, based on a large set of EuroFlow multicentric flow cytometry data files from a cohort 659 B-CLPD patients. These included automatic population separators based on Principal Component Analysis (PCA), Canonical Variate Analysis (CVA), Neighbourhood Component Analysis (NCA), Support Vector Machine algorithms (SVM) and a variant of the CA(Canonical Analysis) algorithm, in which the number of SDs (Standard Deviations) varied for each of the comparisons of different pairs of diseases (CA-vSD). All five classification approaches are based on direct prospective interrogation of individual B-CLPD patients against the EuroFlow flow cytometry B-CLPD database composed of tumor B-cells of 659 individual patients stained in an identical way and classified a priori by the World Health Organization (WHO) criteria into nine diagnostic categories. Each classification approach was evaluated in parallel in terms of accuracy (% properly classified cases), precision (multiple or single diagnosis/case) and coverage (% cases with a proposed diagnosis). Overall, average rates of correct diagnosis (for the nine B-CLPD diagnostic entities) of between 58.9 % and 90.6 % were obtained with the five algorithms, with variable percentages of cases being either misclassified (4.1 %-14.0 %) or unclassifiable (0.3 %-37.0 %). Automatic population separators based on CA, SVM and PCA showed a high average level of correctness (90.6 %, 86.8 %, and 86.0 %, respectively). Nevertheless, this was at the expense of proposing a considerable number of multiple diagnoses for a significant proportion of the test cases (54.5 %, 53.5 %, and 49.6 %, respectively). The CA-vSD algorithm generated the smaller average misclassification rate (4.1 %), but with 37.0 % of cases for which no diagnosis was proposed. In contrast, the NCA algorithm left only 2.7 % of cases without an associated diagnosis but misclassified 14.0 %. Among correctly classified cases (83.3 % of total), 91.2 % had a single proposed diagnosis, 8.6 % had two possible diagnoses, and 0.2 % had three. We demonstrate that the proposed AI algorithms provide an acceptable level of accuracy for the diagnostic classification of B-CLPD patients and, in general, surpass other algorithms reported in the literature.

• MeSH
• Algorithms MeSH
• B-Lymphocytes * pathology   MeSH
• Immunophenotyping * methods   MeSH
• Middle Aged MeSH
• Humans MeSH
• Lymphoproliferative Disorders * diagnosis   classification   MeSH
• Flow Cytometry * methods   MeSH
• Aged MeSH
• Support Vector Machine MeSH
• Check Tag
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Comparative Study MeSH

Digital transformation is widely understood as a process where technology is used to modify an organization's products and services and to create new ones. It is rapidly advancing in all sectors of society. Researchers have shown that it is a multidimensional process determined by human decisions based on ideologies, ideas, beliefs, goals, and the ways in which technology is used. In health care and health, the end result of digital transformation is digital health. In this study, a detailed literature review covering 560 research articles published in major journals was performed, followed by an analysis of ideas, beliefs, and goals guiding digital transformation and their possible consequences for privacy, human rights, dignity, and autonomy in health care and health. Results of literature analyses demonstrated that from the point of view of privacy, dignity, and human rights, the current laws, regulations, and system architectures have major weaknesses. One possible model of digital health is based on the dominant ideas and goals of the business world related to the digital economy and neoliberalism, including privatization of health care services, monetization and commodification of health data, and personal responsibility for health. These ideas represent meaningful risks to human rights, privacy, dignity, and autonomy. In this paper, we present an alternative solution for digital health called human-centric digital health (HCDH). Using system thinking and system modeling methods, we developed a system model for HCDH. It uses 5 views (ideas, health data, principles, regulation, and organizational and technical innovations) to align with human rights and values and support dignity, privacy, and autonomy. To make HCDH future proof, extensions to human rights, the adoption of the principle of restricted informational ownership of health data, and the development of new duties, responsibilities, and laws are needed. Finally, we developed a system-oriented, architecture-centric, ontology-based, and policy-driven approach to represent and manage HCDH ecosystems.

• MeSH
• Digital Technology MeSH
• Digital Health * MeSH
• Humans MeSH
• Human Rights MeSH
• Patient-Centered Care * MeSH
• Privacy MeSH
• Check Tag
• Humans MeSH
• Publication type
• Journal Article MeSH
• Review MeSH

Bacterial proton pumps, proteorhodopsins (PRs), are a major group of light-driven membrane proteins found in marine bacteria. They are functionally and structurally distinct from archaeal and eukaryotic proton pumps. To elucidate the proton transfer mechanism by PRs and understand the differences to nonbacterial pumps on a molecular level, high-resolution structures of PRs' functional states are needed. In this work, we have determined atomic-resolution structures of MAR, a PR from marine actinobacteria, in various functional states, notably the challenging late O intermediate state. These data and information from recent atomic-resolution structures on an archaeal outward proton pump bacteriorhodopsin and bacterial inward proton pump xenorhodopsin allow for deducing key universal elements for light-driven proton pumping. First, long hydrogen-bonded chains characterize proton pathways. Second, short hydrogen bonds allow proton storage and inhibit their backflow. Last, the retinal Schiff base is the active proton donor and acceptor to and from hydrogen-bonded chains.

• MeSH
• Protein Conformation MeSH
• Models, Molecular MeSH
• Proton Pumps chemistry   metabolism   MeSH
• Protons * MeSH
• Rhodopsins, Microbial * chemistry   metabolism   MeSH
• Hydrogen Bonding MeSH
• Publication type
• Journal Article MeSH

BACKGROUND: Through the agnostic screening of patients with uncharacterised disease phenotypes for an upregulation of type I interferon (IFN) signalling, we identified a cohort of individuals heterozygous for mutations in PTPN1, encoding the protein-tyrosine phosphatase 1B (PTP1B). We aimed to describe the clinical phenotype and molecular and cellular pathology of this new disease. METHODS: In this case series, we identified patients and collected clinical and neuroradiological data through collaboration with paediatric neurology and clinical genetics colleagues across Europe (Czechia, France, Germany, Italy, Slovenia, and the UK) and Israel. Variants in PTPN1 were identified by exome and directed Sanger sequencing. The expression of IFN-stimulated genes was determined by quantitative (q) PCR or NanoString technology. Experiments to assess RNA and protein expression and to investigate type 1 IFN signalling were undertaken in patient fibroblasts, hTERT-immortalised BJ-5ta fibroblasts, and RPE-1 cells using CRISPR-Cas9 editing and standard cell biology techniques. FINDINGS: Between Dec 20, 2013, and Jan 11, 2023, we identified 12 patients from 11 families who were heterozygous for mutations in PTPN1. We found ten novel or very rare variants in PTPN1 (frequency on gnomAD version 4.1.0 of <1·25 × 10:sup>-6). Six variants were predicted as STOP mutations, two involved canonical splice-site nucleotides, and two were missense substitutions. In three patients, the variant occurred de novo, whereas in nine affected individuals, the variant was inherited from an asymptomatic parent. The clinical phenotype was characterised by the subacute onset (age range 1-8 years) of loss of motor and language skills in the absence of seizures after initially normal development, leading to spastic dystonia and bulbar involvement. Neuroimaging variably demonstrated cerebral atrophy (sometimes unilateral initially) or high T2 white matter signal. Neopterin in CSF was elevated in all ten patients who were tested, and all probands demonstrated an upregulation of IFN-stimulated genes in whole blood. Although clinical stabilisation and neuroradiological improvement was seen in both treated and untreated patients, in six of eight treated patients, high-dose corticosteroids were judged clinically to result in an improvement in neurological status. Of the four asymptomatic parents tested, IFN signalling in blood was normal (three patients) or minimally elevated (one patient). Analysis of patient blood and fibroblasts showed that tested PTPN1 variants led to reduced levels of PTPN1 mRNA and PTP1B protein, and in-vitro assays demonstrated that loss of PTP1B function was associated with impaired negative regulation of type 1 IFN signalling. INTERPRETATION: PTPN1 haploinsufficiency causes a type 1 IFN-driven autoinflammatory encephalopathy. Notably, some patients demonstrated stabilisation, and even recovery, of neurological function in the absence of treatment, whereas in others, the disease appeared to be responsive to immune suppression. Prospective studies are needed to investigate the safety and efficacy of specific immune suppression approaches in this disease population. FUNDING: The UK Medical Research Council, the European Research Council, and the Agence Nationale de la Recherche.

• MeSH
• Child MeSH
• Haploinsufficiency * genetics   MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Mutation genetics   MeSH
• Brain Diseases genetics   MeSH
• Neuroinflammatory Diseases genetics   MeSH
• Child, Preschool MeSH
• Protein Tyrosine Phosphatase, Non-Receptor Type 1 * genetics   MeSH
• Check Tag
• Child MeSH
• Infant MeSH
• Humans MeSH
• Adolescent MeSH
• Male MeSH
• Child, Preschool MeSH
• Female MeSH
• Publication type
• Journal Article MeSH

Histone deacetylases (HDACs) are frequently deregulated in cancer, and several HDAC inhibitors (HDACi) have gained approval for treating peripheral T cell lymphomas. Here, we investigated the effects of pharmacological or genetic HDAC inhibition on NPM::ALK positive anaplastic large cell lymphoma (ALCL) development to assess the potential use of HDACi for the treatment of this disease. Short-term systemic pharmacological inhibition of HDACs using the HDACi Entinostat in a premalignant ALCL mouse model postponed or even abolished lymphoma development, despite high expression of the NPM::ALK fusion oncogene. To further disentangle the effects of systemic HDAC inhibition from thymocyte intrinsic effects, conditional genetic deletions of HDAC1 and HDAC2 enzymes were employed. In sharp contrast, T cell-specific deletion of Hdac1 or Hdac2 in the ALCL mouse model significantly accelerated NPM::ALK-driven lymphomagenesis, with Hdac1 loss having a more pronounced effect. Integration of gene expression and chromatin accessibility data revealed that Hdac1 deletion selectively perturbed cell type-specific transcriptional programs, crucial for T cell differentiation and signaling. Moreover, multiple oncogenic signaling pathways, including PDGFRB signaling, were highly upregulated. Our findings underscore the tumor-suppressive function of HDAC1 and HDAC2 in T cells during ALCL development. Nevertheless, systemic pharmacological inhibition of HDACs could still potentially improve current therapeutic outcomes.

• MeSH
• Anaplastic Lymphoma Kinase * metabolism   genetics   MeSH
• Lymphoma, Large-Cell, Anaplastic * drug therapy   pathology   genetics   metabolism   MeSH
• Benzamides pharmacology   MeSH
• Histone Deacetylase 1 * genetics   antagonists & inhibitors   physiology   metabolism   MeSH
• Histone Deacetylase 2 genetics   MeSH
• Histone Deacetylase Inhibitors * pharmacology   therapeutic use   MeSH
• Humans MeSH
• Mice MeSH
• Pyridines pharmacology   MeSH
• Genes, Tumor Suppressor * MeSH
• Animals MeSH
• Check Tag
• Humans MeSH
• Mice MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH