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Big Data holds the promise of fundamentally transforming the manner in which adverse drug reactions can be identified and evaluated. This commentary discusses new data sources that are envisioned to form a Big Data-enabled pharmacovigilance system and the role of these data in powering the future of adverse drug reactions detection. [ABSTRACT FROM AUTHOR]

Background: Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.Results: This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics.Conclusions: This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies. [ABSTRACT FROM AUTHOR]

Objective: To characterise empirical instances of Unified Medical Language System (UMLS) Metathesaurus term strings in a large clinical corpus, and to illustrate what types of term characteristics are generalisable across data sources.Design: Based on the occurrences of UMLS terms in a 51 million document corpus of Mayo Clinic clinical notes, this study computes statistics about the terms' string attributes, source terminologies, semantic types and syntactic categories. Term occurrences in 2010 i2b2/VA text were also mapped; eight example filters were designed from the Mayo-based statistics and applied to i2b2/VA data.Results: For the corpus analysis, negligible numbers of mapped terms in the Mayo corpus had over six words or 55 characters. Of source terminologies in the UMLS, the Consumer Health Vocabulary and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) had the best coverage in Mayo clinical notes at 106426 and 94788 unique terms, respectively. Of 15 semantic groups in the UMLS, seven groups accounted for 92.08% of term occurrences in Mayo data. Syntactically, over 90% of matched terms were in noun phrases. For the cross-institutional analysis, using five example filters on i2b2/VA data reduces the actual lexicon to 19.13% of the size of the UMLS and only sees a 2% reduction in matched terms.Conclusion: The corpus statistics presented here are instructive for building lexicons from the UMLS. Features intrinsic to Metathesaurus terms (well formedness, length and language) generalise easily across clinical institutions, but term frequencies should be adapted with caution. The semantic groups of mapped terms may differ slightly from institution to institution, but they differ greatly when moving to the biomedical literature domain. [ABSTRACT FROM AUTHOR]

Background: New guidelines propose a minimum 5-year survival of 60% for hepatocellular carcinoma (HCC) with living donor liver transplantation (LDLT). This study aimed to evaluate the 5- and 10-year survival rates after transplantation for the expanded criteria for HCC., Methods: This single-center retrospective cohort study included 208 patients who underwent LDLT for the expanded criteria (the largest tumor diameter of ≤10 cm, any tumor number, and alpha-fetoprotein [AFP] level of <1000 ng/mL) and analyzed 5- and 10-year overall survival (OS) and recurrence risk (RR) rates., Results: With a median follow-up of 65.1 months (IQR, 19.1-80.2), the 5- and 10-year OS and RR rates were 67.0% and 61.0% and 20.5% and 22.5%, respectively. The largest tumor diameter of >6 cm (hazard ratio [HR], 3.7; 95% CI, 1.7-8.2; P = .001) and AFP level of >400 ng/mL (HR, 4.0; 95% CI, 1.8-9.0; P = .001) were predictors of recurrence. Patients outside the Milan criteria (MC) were grouped into low- and high-risk HCC based on tumor size and AFP level. For low-risk HCC (tumor size of <6 cm, any tumor number, and AFP level of <400 ng/mL), the 5-year RR was comparable to the MC and increased the transplant pool by 35.7% (P > .5). The median number of tumors and the rate of microvascular invasion in the high-risk group, low-risk group, and MC were 2.0 (1.0-3.2), 4.0 (2.0-5.0), and 1.0 (1.0-2.0) (P < .001) and 72.2% (13/18), 44.0% (22/50), and 22.8% (32/140) (P < .001), respectively., Conclusion: The expanded criteria met the benchmark for 5-year survival. LDLT for the low-risk HCC in the expanded criteria was associated with an acceptable RR., (Copyright © 2024 Society for Surgery of the Alimentary Tract. Published by Elsevier Inc. All rights reserved.)

Competing Interests: Funding Support and Author Disclosures Dr Jain has received consulting fees from Bristol Myers Squibb, ARTIS Ventures, and Broadview Ventures outside of the submitted work. Dr Sarraju holds stock in Medeloop. Dr Ashley is a founder of Personalis, DeepCell, and Svexa; is a founding advisor of Nuevocor; is a nonexecutive director at AstraZeneca; and has served as an advisor for SequenceBio, Novartis, Medical Excellence Capital, Foresite Capital, and Third Rock Ventures. Dr Harrington has served on the Board of Directors for the American Heart Association and Cytokinetics; has served as a consultant or advisor for the National Heart, Lung, and Blood Institute, Atropos Health, Bitterroot Bio, Bristol Myers Squibb, Bridge Bio, Chiesi, CPC Clinical Research/Colorado Prevention Center, CSL Behring, Edwards Lifesciences, Element Science, Foresight, Merck, and WebMD; has received research grants/contracts for the randomized controlled trials from the National Heart, Lung, and Blood Institute (ISCHEMIA), Duke/Patient-Centered Outcomes Research Institute (ADAPTABLE), Janssen (Factor XIa inhibitor), and CSL (HDL); and has served on the data safety and monitoring board for the Baim Institute and the University of Colorado. Dr Mahaffey has received research support from the American Heart Association, Apple Inc, Bayer Pharmaceuticals, the California Institute for Regenerative Medicine, Commonwealth Serum Laboratories (CSL) Behring, Eidos, Ferring, Gilead, Verily (Google), Idorsia, Johnson & Johnson (J&J), Luitpold, Novartis, the PAC-12, Precordior, and Sanifit; has received consulting fees from Applied Therapeutics, Bayer, Bristol Myers Squibb, BridgeBio, CSL Behring, Elsevier, Fosun Pharma, Human, Johnson & Johnson, Moderna, Myokardia, Novartis, Novo Nordisk, Otsuka, Phasebio, Portola, Quidel, and Theravance; and has equity holdings in Human, Medeloop, Precordior, and Regencor. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

The rapid identification of protein-protein interactions has been significantly enabled by mass spectrometry (MS) proteomics-based methods, including affinity purification-MS, crosslinking-MS, and proximity-labeling proteomics. While these methods can reveal networks of interacting proteins, they cannot reveal how specific protein-protein interactions alter protein function or cell signaling. For instance, when two proteins interact, there can be emergent signaling processes driven purely by the individual activities of those proteins being co-localized. Alternatively, protein-protein interactions can allosterically regulate function, enhancing or suppressing activity in response to binding. In this work, we investigate the interaction between the tyrosine phosphatase PTP1B and the adaptor protein Grb2, which have been annotated as binding partners in a number of proteomics studies. This interaction has been postulated to co-localize PTP1B with its substrate IRS-1 by forming a ternary complex, thereby enhancing the dephosphorylation of IRS-1 to suppress insulin signaling. Here, we report that Grb2 binding to PTP1B also allosterically enhances PTP1B catalytic activity. We show that this interaction is dependent on the proline-rich region of PTP1B, which interacts with the C-terminal SH3 domain of Grb2. Using NMR spectroscopy and hydrogen-deuterium exchange mass spectrometry (HDX-MS) we show that Grb2 binding alters PTP1B structure and/or dynamics. Finally, we use MS proteomics to identify other interactors of the PTP1B proline-rich region that may also regulate PTP1B function similarly to Grb2. This work presents one of the first examples of a protein allosterically regulating the enzymatic activity of PTP1B and lays the foundation for discovering new mechanisms of PTP1B regulation in cell signaling., Competing Interests: Competing Interests The authors declare no competing interests.

Importance: Large language models (LLMs) can assist in various health care activities, but current evaluation approaches may not adequately identify the most useful application areas., Objective: To summarize existing evaluations of LLMs in health care in terms of 5 components: (1) evaluation data type, (2) health care task, (3) natural language processing (NLP) and natural language understanding (NLU) tasks, (4) dimension of evaluation, and (5) medical specialty., Data Sources: A systematic search of PubMed and Web of Science was performed for studies published between January 1, 2022, and February 19, 2024., Study Selection: Studies evaluating 1 or more LLMs in health care., Data Extraction and Synthesis: Three independent reviewers categorized studies via keyword searches based on the data used, the health care tasks, the NLP and NLU tasks, the dimensions of evaluation, and the medical specialty., Results: Of 519 studies reviewed, published between January 1, 2022, and February 19, 2024, only 5% used real patient care data for LLM evaluation. The most common health care tasks were assessing medical knowledge such as answering medical licensing examination questions (44.5%) and making diagnoses (19.5%). Administrative tasks such as assigning billing codes (0.2%) and writing prescriptions (0.2%) were less studied. For NLP and NLU tasks, most studies focused on question answering (84.2%), while tasks such as summarization (8.9%) and conversational dialogue (3.3%) were infrequent. Almost all studies (95.4%) used accuracy as the primary dimension of evaluation; fairness, bias, and toxicity (15.8%), deployment considerations (4.6%), and calibration and uncertainty (1.2%) were infrequently measured. Finally, in terms of medical specialty area, most studies were in generic health care applications (25.6%), internal medicine (16.4%), surgery (11.4%), and ophthalmology (6.9%), with nuclear medicine (0.6%), physical medicine (0.4%), and medical genetics (0.2%) being the least represented., Conclusions and Relevance: Existing evaluations of LLMs mostly focus on accuracy of question answering for medical examinations, without consideration of real patient care data. Dimensions such as fairness, bias, and toxicity and deployment considerations received limited attention. Future evaluations should adopt standardized applications and metrics, use clinical data, and broaden focus to include a wider range of tasks and specialties.

Background: Advance care planning/serious illness conversations can help clinicians understand patients' values and preferences. There are limited data on how to increase these conversations, and their effect on care patterns. We hypothesized that using a machine learning survival model to select patients for serious illness conversations, along with trained care coaches to conduct the conversations, would increase uptake in cancer patients at high risk of short-term mortality., Methods: We conducted a cluster-randomized stepped wedge study on the physician level. Oncologists entered the intervention condition in a random order over six months. Adult patients with metastatic cancer were included. Patients with <2 year computer-predicted survival and no prognosis documentation were classified as high-priority for serious illness conversations. In the intervention condition, providers received automated weekly emails highlighting high-priority patients and were asked to document prognosis for them. Care coaches reached out to these patients to conduct the remainder of the conversation. The primary endpoint was proportion of visits with prognosis documentation within 14 days., Results: 6,372 visits in 1,825 patients were included in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than control condition: 2.9% vs 1.1% (adjusted odds ratio 4.3, p < .0001). The proportion of visits with advance care planning documentation was also higher in the intervention condition: 7.7% vs 1.8% (adjusted odds ratio 14.2, p < .0001). In high-priority visits, advance care planning documentation rate in intervention/control visits was 24.2% vs 4.0%., Conclusion: The intervention increased documented conversations, with contributions by both providers and care coaches., (© The Author(s) 2024. Published by Oxford University Press. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Introduction: Dental sealants applied to occlusal pit-and-fissure surfaces have been shown to prevent caries and arrest occlusal noncavitated carious lesions (NCCLs). The American Dental Association (ADA) recommends that oral healthcare providers apply sealants on occlusal NCCLs. Though the evidence is clear that sealants are effective, few studies have examined the adoption of the ADA guideline by dentists and the duration of protection provided by sealants in a large real-world setting., Methods: This study used observational electronic health record (EHR) data from a network of dental clinics to follow teeth over a 2 year time period from when they were diagnosed as having an occlusal NCCL until either they were treated with a restoration or the time period ended with no restoration. The objectives of the study were to determine: (1) the degree to which dentists adopted the guideline, (2) whether the duration of protection was different for teeth that received a sealant from teeth that did not receive a sealant, and (3) whether dentists' experience placing sealants was associated with the duration of protection., Results: Overall, there were 7,299 teeth in the sample. Of those, dentists restored 591 teeth and applied sealants on 164. The sealant application rate for eligible teeth was 2.2%. Sealant application was associated with provider, with 1.9% of providers placing more than half of the sealants. By the end of the observation period, the proportion of teeth progressing to restorations was 8.2% for teeth that had not received a sealant and 3.0% for teeth that had received one (RR = 0.37; 95% CI: 0.16-0.88; p = 0.02). Multilevel survival analysis showed that teeth that had not received a sealant were restored sooner than teeth that had received a sealant (aHR = 0.11; 95% CI: 0.03-0.36; p &lt; 0.01). Overall, teeth that received a sealant had an 89% reduced hazard of restoration within 2 years compared with teeth that did not receive sealants., Conclusion: This study found that by arresting decay, the presence of sealants led to fewer restorations and delayed restorations compared with teeth not receiving a sealant or restoration in the 2 years following diagnosis of occlusal NCCL in clinical settings., (© 2024 S. Karger AG, Basel.)

Protein molecular interactions and post-translational modifications (PTMs), such as phosphorylation, can be co-dependent and reciprocally co-regulate each other. Although this interplay is central for many biological processes, a systematic method to simultaneously study assembly-states and PTMs from the same sample is critically missing. Here, we introduce SEC-MX (Size Exclusion Chromatography fractions MultipleXed), a global quantitative method combining Size Exclusion Chromatography and PTM-enrichment for simultaneous characterization of PTMs and assembly-states. SEC-MX enhances throughput, allows phosphopeptide enrichment, and facilitates quantitative differential comparisons between biological conditions. Applying SEC-MX to HEK293 and HCT116 cells, we generated a proof-of-concept dataset mapping thousands of phosphopeptides and their assembly-states. Our analysis revealed intricate relationships between phosphorylation events and assembly-states and generated testable hypotheses for follow-up studies. Overall, we establish SEC-MX as a valuable tool for exploring protein functions and regulation beyond abundance changes.

Contemporary developments in the field of peptide macrocyclization methodology are imperative for enabling the advance of drug design in medicinal chemistry. This report discloses a Rh(III)-catalyzed macrocyclization via carboamidation, reacting acryloyl-peptide-dioxazolone precursors and arylboronic acids to form complex cyclic peptides with concomitant incorporation of noncanonical α-amino acids. The diverse and modular technology allows for expedient access to a wide variety of cyclic peptides from 4 to 15 amino acids in size and features simultaneous formation of unnatural phenylalanine and tyrosine derivatives with up to >20:1 diastereoselectivity. The reaction showcases an expansive substrate scope with 45 examples and is compatible with the majority of standard protected amino acids used in Fmoc-solid phase peptide synthesis. The methodology is applied to the synthesis of multiple peptidomimetic macrocyclic analogs, including derivatives of cyclosomatostatin and gramicidin S.

Multi-domain enzymes can be regulated by both inter-domain interactions and structural features intrinsic to the catalytic domain. The tyrosine phosphatase SHP2 is a quintessential example of a multi-domain protein that is regulated by inter-domain interactions. This enzyme has a protein tyrosine phosphatase (PTP) domain and two phosphotyrosine-recognition domains (N-SH2 and C-SH2) that regulate phosphatase activity through autoinhibitory interactions. SHP2 is canonically activated by phosphoprotein binding to the SH2 domains, which causes large inter-domain rearrangements, but autoinhibition can also be disrupted by disease-associated mutations. Many details of the SHP2 activation mechanism are still unclear, the physiologically-relevant active conformations remain elusive, and hundreds of human variants of SHP2 have not been functionally characterized. Here, we perform deep mutational scanning on both full-length SHP2 and its isolated PTP domain to examine mutational effects on inter-domain regulation and catalytic activity. Our experiments provide a comprehensive map of SHP2 mutational sensitivity, both in the presence and absence of inter-domain regulation. Coupled with molecular dynamics simulations, our investigation reveals novel structural features that govern the stability of the autoinhibited and active states of SHP2. Our analysis also identifies key residues beyond the SHP2 active site that control PTP domain dynamics and intrinsic catalytic activity. This work expands our understanding of SHP2 regulation and provides new insights into SHP2 pathogenicity., Competing Interests: Competing interests The authors declare no competing interests.

Mutations in the tyrosine phosphatase Src homology-2 domain-containing protein tyrosine phosphatase-2 (SHP2) are associated with a variety of human diseases. Most mutations in SHP2 increase its basal catalytic activity by disrupting autoinhibitory interactions between its phosphatase domain and N-terminal SH2 (phosphotyrosine recognition) domain. By contrast, some disease-associated mutations located in the ligand-binding pockets of the N- or C-terminal SH2 domains do not increase basal activity and likely exert their pathogenicity through alternative mechanisms. We lack a molecular understanding of how these SH2 mutations impact SHP2 structure, activity, and signaling. Here, we characterize five SHP2 SH2 domain ligand-binding pocket mutants through a combination of high-throughput biochemical screens, biophysical and biochemical measurements, and molecular dynamics simulations. We show that while some of these mutations alter binding affinity to phosphorylation sites, the T42A mutation in the N-SH2 domain is unique in that it also substantially alters ligand-binding specificity, despite being 8 to 10 Å from the specificity-determining region of the SH2 domain. This mutation exerts its effect on sequence specificity by remodeling the phosphotyrosine-binding pocket, altering the mode of engagement of both the phosphotyrosine and surrounding residues on the ligand. The functional consequence of this altered specificity is that the T42A mutant has biased sensitivity toward a subset of activating ligands and enhances downstream signaling. Our study highlights an example of a nuanced mechanism of action for a disease-associated mutation, characterized by a change in protein-protein interaction specificity that alters enzyme activation., Competing Interests: Competing interests statement:The authors declare no competing interest.

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

With the rapid growth of interest in and use of large language models (LLMs) across various industries, we are facing some crucial and profound ethical concerns, especially in the medical field. The unique technical architecture and purported emergent abilities of LLMs differentiate them substantially from other artificial intelligence (AI) models and natural language processing techniques used, necessitating a nuanced understanding of LLM ethics. In this Viewpoint, we highlight ethical concerns stemming from the perspectives of users, developers, and regulators, notably focusing on data privacy and rights of use, data provenance, intellectual property contamination, and broad applications and plasticity of LLMs. A comprehensive framework and mitigating strategies will be imperative for the responsible integration of LLMs into medical practice, ensuring alignment with ethical principles and safeguarding against potential societal risks., Competing Interests: Declaration of interests DSWT holds patents on a deep-learning system for the detection of retinal diseases. AJB is a cofounder and consultant for Personalis and NuMedii; is a consultant to Mango Tree Corporation; has previously been a consultant for Samsung, 10x Genomics, Helix, Pathway Genomics, and Verinata (Illumina); has served on paid advisory panels or boards for Geisinger Health, Regenstrief Institute, Gerson Lehman Group, AlphaSights, Covance, Novartis, Genentech, and Merck, and Roche; is a shareholder in Personalis and NuMedii; is a minor shareholder in Apple, Meta (Facebook), Alphabet (Google), Microsoft, Amazon, Snap, 10x Genomics, Illumina, Regeneron, Sanofi, Pfizer, Royalty Pharma, Moderna, Sutro, Doximity, BioNtech, Invitae, Pacific Biosciences, Editas Medicine, Nuna Health, Assay Depot, and Vet24seven, and several other non-health related companies and mutual funds; has received honoraria and travel reimbursement for invited talks from Johnson and Johnson, Roche, Genentech, Pfizer, Merck, Lilly, Takeda, Varian, Mars, Siemens, Optum, Abbott, Celgene, AstraZeneca, AbbVie, Westat, and many academic institutions, medical-specific or disease-specific foundations and associations, and health systems; receives royalty payments through Stanford University for several patents and other disclosures licensed to NuMedii and Personalis; has done research funded by NIH, Peraton (as the prime on an NIH contract), Genentech, Johnson and Johnson, FDA, Robert Wood Johnson Foundation, Leon Lowenstein Foundation, Intervalien Foundation, Priscilla Chan and Mark Zuckerberg, and the Barbara and Gerson Bakar Foundation; and has previously done research funded by the March of Dimes, Juvenile Diabetes Research Foundation, California Governor's Office of Planning and Research, California Institute for Regenerative Medicine, L’Oreal, and Progenity. NL is a scientific advisor to TIIM SG. NHS is a cofounder of Prealize Health (a predictive analytics company) and Atropos Health (an on-demand evidence generation company); receives funding from the Gordon and Betty Moore Foundation for developing virtual model deployments; and is a member of working groups of the Coalition for Health AI (CHAI), a consensus-building organisation providing guidelines for the responsible use of artificial intelligence in health care. JS, through his involvement with the Murdoch Children's Research Institute, receives funding from the Victorian State Government through the Operational Infrastructure Support (OIS) programme. JCLO is supported by grants from the National Medical Research Council Singapore (MOH-CIAINV21nov-001) and AI Singapore OTTIC (AISG2-TC-2022-006). All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: This article aims to examine how generative artificial intelligence (AI) can be adopted with the most value in health systems, in response to the Executive Order on AI., Materials and Methods: We reviewed how technology has historically been deployed in healthcare, and evaluated recent examples of deployments of both traditional AI and generative AI (GenAI) with a lens on value., Results: Traditional AI and GenAI are different technologies in terms of their capability and modes of current deployment, which have implications on value in health systems., Discussion: Traditional AI when applied with a framework top-down can realize value in healthcare. GenAI in the short term when applied top-down has unclear value, but encouraging more bottom-up adoption has the potential to provide more benefit to health systems and patients., Conclusion: GenAI in healthcare can provide the most value for patients when health systems adapt culturally to grow with this new technology and its adoption patterns., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: With the capability to render prediagnoses, consumer wearables have the potential to affect subsequent diagnoses and the level of care in the health care delivery setting. Despite this, postmarket surveillance of consumer wearables has been hindered by the lack of codified terms in electronic health records (EHRs) to capture wearable use., Objective: We sought to develop a weak supervision-based approach to demonstrate the feasibility and efficacy of EHR-based postmarket surveillance on consumer wearables that render atrial fibrillation (AF) prediagnoses., Methods: We applied data programming, where labeling heuristics are expressed as code-based labeling functions, to detect incidents of AF prediagnoses. A labeler model was then derived from the predictions of the labeling functions using the Snorkel framework. The labeler model was applied to clinical notes to probabilistically label them, and the labeled notes were then used as a training set to fine-tune a classifier called Clinical-Longformer. The resulting classifier identified patients with an AF prediagnosis. A retrospective cohort study was conducted, where the baseline characteristics and subsequent care patterns of patients identified by the classifier were compared against those who did not receive a prediagnosis., Results: The labeler model derived from the labeling functions showed high accuracy (0.92; F1-score=0.77) on the training set. The classifier trained on the probabilistically labeled notes accurately identified patients with an AF prediagnosis (0.95; F1-score=0.83). The cohort study conducted using the constructed system carried enough statistical power to verify the key findings of the Apple Heart Study, which enrolled a much larger number of participants, where patients who received a prediagnosis tended to be older, male, and White with higher CHA2DS2-VASc (congestive heart failure, hypertension, age ≥75 years, diabetes, stroke, vascular disease, age 65-74 years, sex category) scores (P<.001). We also made a novel discovery that patients with a prediagnosis were more likely to use anticoagulants (525/1037, 50.63% vs 5936/16,560, 35.85%) and have an eventual AF diagnosis (305/1037, 29.41% vs 262/16,560, 1.58%). At the index diagnosis, the existence of a prediagnosis did not distinguish patients based on clinical characteristics, but did correlate with anticoagulant prescription (P=.004 for apixaban and P=.01 for rivaroxaban)., Conclusions: Our work establishes the feasibility and efficacy of an EHR-based surveillance system for consumer wearables that render AF prediagnoses. Further work is necessary to generalize these findings for patient populations at other sites., (© Richard M Yoo, Ben T Viggiano, Krishna N Pundi, Jason A Fries, Aydin Zahedivash, Tanya Podchiyska, Natasha Din, Nigam H Shah. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Objectives: To investigate the genomic diversity and β-lactam susceptibilities of Enterococcus faecalis collected from patients with infective endocarditis (IE)., Methods: We collected 60 contemporary E. faecalis isolates from definite or probable IE cases identified between 2018 and 2021 at the University of Pittsburgh Medical Center. We used whole-genome sequencing to study bacterial genomic diversity and employed antibiotic checkerboard assays and a one-compartment pharmacokinetic-pharmacodynamic (PK/PD) model to investigate bacterial susceptibility to ampicillin and ceftriaxone both alone and in combination., Results: Genetically diverse E. faecalis were collected, however, isolates belonging to two STs, ST6 and ST179, were collected from 21/60 (35%) IE patients. All ST6 isolates encoded a previously described mutation upstream of penicillin-binding protein 4 (pbp4) that is associated with pbp4 overexpression. ST6 isolates had higher ceftriaxone MICs and higher fractional inhibitory concentration index values for ampicillin and ceftriaxone (AC) compared to other isolates, suggesting diminished in vitro AC synergy against this lineage. Introduction of the pbp4 upstream mutation found among ST6 isolates caused increased ceftriaxone resistance in a laboratory E. faecalis isolate. PK/PD testing showed that a representative ST6 isolate exhibited attenuated efficacy of AC combination therapy at humanized antibiotic exposures., Conclusions: We find evidence for diminished in vitro AC activity among a subset of E. faecalis IE isolates with increased pbp4 expression. These findings suggest that alternate antibiotic combinations against diverse contemporary E. faecalis IE isolates should be evaluated., (© The Author(s) 2024. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

State-of-the-art methods in photoproximity labeling center on the targeted generation and capture of short-lived reactive intermediates to provide a snapshot of local protein environments. Diazirines are the current gold standard for high-resolution proximity labeling, generating short-lived aryl(trifluoromethyl) carbenes. Here, we present a method to access aryl(trifluoromethyl) carbenes from a stable diazo source via tissue-penetrable, deep red to near-infrared light (600-800 nm). The operative mechanism of this activation involves Dexter energy transfer from photoexcited osmium(II) photocatalysts to the diazo, thus revealing an aryl(trifluoromethyl) carbene. The labeling preferences of the diazo probe with amino acids are studied, showing high reactivity toward heteroatom-H bonds. Upon the synthesis of a biotinylated diazo probe, labeling studies are conducted on native proteins as well as proteins conjugated to the Os photocatalyst. Finally, we demonstrate that the conjugation of a protein inhibitor to the photocatalyst also enables selective protein labeling in the presence of spectator proteins and achieves specific labeling of a membrane protein on the surface of mammalian cells via a two-antibody photocatalytic system.

Importance: Given the importance of rigorous development and evaluation standards needed of artificial intelligence (AI) models used in health care, nationwide accepted procedures to provide assurance that the use of AI is fair, appropriate, valid, effective, and safe are urgently needed., Observations: While there are several efforts to develop standards and best practices to evaluate AI, there is a gap between having such guidance and the application of such guidance to both existing and new AI models being developed. As of now, there is no publicly available, nationwide mechanism that enables objective evaluation and ongoing assessment of the consequences of using health AI models in clinical care settings., Conclusion and Relevance: The need to create a public-private partnership to support a nationwide health AI assurance labs network is outlined here. In this network, community best practices could be applied for testing health AI models to produce reports on their performance that can be widely shared for managing the lifecycle of AI models over time and across populations and sites where these models are deployed.

Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.

Background: Despite decreases in opioid prescribing from 2016 through 2019, some dentists (general, specialists, oral and maxillofacial surgeons) in the United States continue to prescribe opioids at high rates. The authors' objective was to define dentists' trajectories of opioid prescribing., Methods: The authors identified actively prescribing dentists from the IQVIA Longitudinal Prescription data set, from 2015 through 2019. Group-based trajectory modeling identified opioid prescribing trajectories on the basis of dentists' annual prescribing rates for the overall sample (model 1) and for high prescribers (model 2). The authors used χ 2 or Mann-Whitney U tests to characterize the model 2 trajectory groups., Results: In model 1 (n = 199,145 prescribers), group-based trajectory modeling identified 8 trajectories that were grouped into 5 categories. A total of 14.8% were nonprescribers who composed less than 1% of all prescriptions, low prescribers (3 groups; 46.0%) prescribed at low rates (2015: 5.5%-16.9%; 2019: 1.5%-11.9%), decliners (7.3%) decreased prescribing rapidly (2015: 29.4%; 2019: 5.1%), moderately high prescribers (2 groups; 28.5%) prescribed moderately (2015: 28.7% and 39.2%; 2019: 18.1% and 28.8%), and consistently high prescribers (3.4%) prescribed at high rates (2015: 54.6%; 2019: 44.7%). In model 2, from consistently high prescribers (n = 6,845), 4 trajectories were identified. Of these 4 groups, 1 group (7.5%) declined prescribing rapidly. The groups did not differ meaningfully; however, the rapid decliners included fewer oral and maxillofacial surgeons (13.0% vs 18.4%), saw more Medicaid patients (2.5% vs 1.0%), and had higher opioid prescribing rates in 2015 (95.5% vs 91.6%) (P < .001 for all)., Conclusions: The authors identified variations in dentists' opioid prescribing rates. Although 60% of dentists decreased prescribing rates by 30% through 83%, 3.4% of dentists consistently prescribed at high rates., Practical Implications: Some dentists continue to prescribe opioids at high levels, indicating that additional information is needed to better inform policy and clinical decision making., Competing Interests: Disclosures None of the authors reported any disclosures., (Copyright © 2024 American Dental Association. Published by Elsevier Inc. All rights reserved.)

T cell responses are inhibited by acidic environments. T cell receptor (TCR)-induced protein phosphorylation is negatively regulated by dephosphorylation and/or ubiquitination, but the mechanisms underlying sensitivity to acidic environments are not fully understood. Here, we found that TCR stimulation induced a molecular complex of Cbl-b, an E3-ubiquitin ligase, with STS1, a pH-sensitive unconventional phosphatase. The induced interaction depended upon a proline motif in Cbl-b interacting with the STS1 SH3 domain. STS1 dephosphorylated Cbl-b interacting phosphoproteins. The deficiency of STS1 or Cbl-b diminished the sensitivity of T cell responses to the inhibitory effects of acid in an autocrine or paracrine manner in vitro or in vivo. Moreover, the deficiency of STS1 or Cbl-b promoted T cell proliferative and differentiation activities in vivo and inhibited tumor growth, prolonged survival, and improved T cell fitness in tumor models. Thus, a TCR-induced STS1-Cbl-b complex senses intra- or extra-cellular acidity and regulates T cell responses, presenting a potential therapeutic target for improving anti-tumor immunity., Competing Interests: Declaration of interests A.W. is a co-founder and a scientific advisory board (SAB) member of Nurix Therapeutics, Inc., which has a Cbl-b inhibitor in phase I clinical trials. Dr. Weiss has founder shares and receives payment for his role on the Nurix SAB. A.W. also serves on the advisory boards of BlueSphere Bio, BridGene Biologics, EpiBiologics, Genentech, Imidomics, and Jasper Therapeutics. L.F. has received research support from Roche/Genentech, Abbvie, Bavarian Nordic, Bristol Myers Squibb, Dendreon, Janssen, Merck, and Partner Therapeutics. L.F. has served on the SABs of Actym, Astra Zeneca, Atreca, Bioatla, Bolt, Bristol Myer Squibb, Daiichi Sankyo, Immunogenesis, Innovent, Merck, Merck KGA, Nutcracker, RAPT, Scribe, Senti, Sutro, and Roche/Genentech., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Importance: Ranitidine, the most widely used histamine-2 receptor antagonist (H2RA), was withdrawn because of N-nitrosodimethylamine impurity in 2020. Given the worldwide exposure to this drug, the potential risk of cancer development associated with the intake of known carcinogens is an important epidemiological concern., Objective: To examine the comparative risk of cancer associated with the use of ranitidine vs other H2RAs., Design, Setting, and Participants: This new-user active comparator international network cohort study was conducted using 3 health claims and 9 electronic health record databases from the US, the United Kingdom, Germany, Spain, France, South Korea, and Taiwan. Large-scale propensity score (PS) matching was used to minimize confounding of the observed covariates with negative control outcomes. Empirical calibration was performed to account for unobserved confounding. All databases were mapped to a common data model. Database-specific estimates were combined using random-effects meta-analysis. Participants included individuals aged at least 20 years with no history of cancer who used H2RAs for more than 30 days from January 1986 to December 2020, with a 1-year washout period. Data were analyzed from April to September 2021., Exposure: The main exposure was use of ranitidine vs other H2RAs (famotidine, lafutidine, nizatidine, and roxatidine)., Main Outcomes and Measures: The primary outcome was incidence of any cancer, except nonmelanoma skin cancer. Secondary outcomes included all cancer except thyroid cancer, 16 cancer subtypes, and all-cause mortality., Results: Among 1 183 999 individuals in 11 databases, 909 168 individuals (mean age, 56.1 years; 507 316 [55.8%] women) were identified as new users of ranitidine, and 274 831 individuals (mean age, 58.0 years; 145 935 [53.1%] women) were identified as new users of other H2RAs. Crude incidence rates of cancer were 14.30 events per 1000 person-years (PYs) in ranitidine users and 15.03 events per 1000 PYs among other H2RA users. After PS matching, cancer risk was similar in ranitidine compared with other H2RA users (incidence, 15.92 events per 1000 PYs vs 15.65 events per 1000 PYs; calibrated meta-analytic hazard ratio, 1.04; 95% CI, 0.97-1.12). No significant associations were found between ranitidine use and any secondary outcomes after calibration., Conclusions and Relevance: In this cohort study, ranitidine use was not associated with an increased risk of cancer compared with the use of other H2RAs. Further research is needed on the long-term association of ranitidine with cancer development.

Importance: There is increased interest in and potential benefits from using large language models (LLMs) in medicine. However, by simply wondering how the LLMs and the applications powered by them will reshape medicine instead of getting actively involved, the agency in shaping how these tools can be used in medicine is lost., Observations: Applications powered by LLMs are increasingly used to perform medical tasks without the underlying language model being trained on medical records and without verifying their purported benefit in performing those tasks., Conclusions and Relevance: The creation and use of LLMs in medicine need to be actively shaped by provisioning relevant training data, specifying the desired benefits, and evaluating the benefits via testing in real-world deployments.

Objective: Heatlhcare institutions are establishing frameworks to govern and promote the implementation of accurate, actionable, and reliable machine learning models that integrate with clinical workflow. Such governance frameworks require an accompanying technical framework to deploy models in a resource efficient, safe and high-quality manner. Here we present DEPLOYR, a technical framework for enabling real-time deployment and monitoring of researcher-created models into a widely used electronic medical record system., Materials and Methods: We discuss core functionality and design decisions, including mechanisms to trigger inference based on actions within electronic medical record software, modules that collect real-time data to make inferences, mechanisms that close-the-loop by displaying inferences back to end-users within their workflow, monitoring modules that track performance of deployed models over time, silent deployment capabilities, and mechanisms to prospectively evaluate a deployed model's impact., Results: We demonstrate the use of DEPLOYR by silently deploying and prospectively evaluating 12 machine learning models trained using electronic medical record data that predict laboratory diagnostic results, triggered by clinician button-clicks in Stanford Health Care's electronic medical record., Discussion: Our study highlights the need and feasibility for such silent deployment, because prospectively measured performance varies from retrospective estimates. When possible, we recommend using prospectively estimated performance measures during silent trials to make final go decisions for model deployment., Conclusion: Machine learning applications in healthcare are extensively researched, but successful translations to the bedside are rare. By describing DEPLOYR, we aim to inform machine learning deployment best practices and help bridge the model implementation gap., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Objective: To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research., Materials and Methods: The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training., Results: The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies., Discussion: Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users., Conclusion: Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure., Competing Interests: None declared., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Introduction: Pharmacovigilance programs protect patient health and safety by identifying adverse event signals through postmarketing surveillance of claims data and spontaneous reports. Electronic health records (EHRs) provide new opportunities to address limitations of traditional approaches and promote discovery-oriented pharmacovigilance., Methods: To evaluate the current state of EHR-based medication safety signal identification, we conducted a scoping literature review of studies aimed at identifying safety signals from routinely collected patient-level EHR data. We extracted information on study design, EHR data elements utilized, analytic methods employed, drugs and outcomes evaluated, and key statistical and data analysis choices., Results: We identified 81 eligible studies. Disproportionality methods were the predominant analytic approach, followed by data mining and regression. Variability in study design makes direct comparisons difficult. Studies varied widely in terms of data, confounding adjustment, and statistical considerations., Conclusion: Despite broad interest in utilizing EHRs for safety signal identification, current efforts fail to leverage the full breadth and depth of available data or to rigorously control for confounding. The development of best practices and application of common data models would promote the expansion of EHR-based pharmacovigilance., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The success of foundation models such as ChatGPT and AlphaFold has spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. In this narrative review, we examine 84 foundation models trained on non-imaging EMR data (i.e., clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g., MIMIC-III) or broad, public biomedical corpora (e.g., PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. Considering these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare., (© 2023. The Author(s).)

Sonodynamic therapy (SDT) is an emerging non-invasive cancer treatment method in the field of nanomedicine, which has the advantages of deep penetration, good therapeutic efficacy, and minimal damage to normal tissues. Sonosensitizers play a crucial role in the process of SDT, as their structure and properties directly determine the treatment outcome. Inorganic sonosensitizers, with their high stability and longer circulation time in the human body, have great potential in SDT. In this review, the possible mechanisms of SDT including the ultrasonic cavitation, reactive oxygen species generation, and activation of immunity are briefly discussed. Then, the latest research progress on inorganic sonosensitizers is systematically summarized. Subsequently, strategies for optimizing treatment efficacy are introduced, including combination therapy and image-guided therapy. The challenges and future prospects of sonodynamic therapy are discussed. It is hoped that this review will provide some guidance for the screening of inorganic sonosensitizers.