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Abstract Background Lymphocyte function and immune competence can be measured by proliferative response to stimuli and is useful in several clinical contexts including primary (or genetic) and secondary immunodeficiencies (e.g., immunosuppression following transplantation). Proliferation assays have traditionally been prepared manually due to the difficulty in maintaining the sterile environment required to prevent bacterial and fungal impact on cell growth. On most automated liquid handlers, the sample and reagents are placed inside a cabinet whose inner surfaces include rails, pullies, pumps, and circuitry that face the sample and are not amenable to sterilization. The Andrew+ (Waters, MA) is a small surface cleanable liquid handler with all machinery enclosed. We demonstrate here the use of an Andrew+ placed in a biological safety cabinet (BSC) to perform sterile assay setup. Methods Peripheral blood monocytes (PBMCs) from five individuals were manually generated via buffy coat isolation, then brought to standardized concentration. An Andrew+ was placed in a 4’ BSC and all surfaces disinfected with Oxivir Tb (Diversey, SC). The Andrew+ prepared three serial dilutions of two stimulant reagents (phytohemagglutinin (PHA) and pokeweed (PWM)), then dispensed stimulant and control reagents horizontally and PBMCs vertically to a 48 well plate. This process was duplicated manually from the same specimens to create a reference plate. The plates were incubated at 37°C and 5% CO2 for three days, then pulsed with EdU (Thermo Fisher, MA) and incubated for one additional day. Cells were harvested and counted via flow cytometry. Overall time and hands-on time were measured. Results A total of five samples were tested inside a using both manual and automation setup under eight conditions including with isotype control, media without stimulant, three concentrations of PHA, and three concentrations of PWM. The percent proliferation for various cell types as induced by each stimulant was calculated from the difference of the highest stimulant signal from the background (media-only) signal. Comparing the automated and manual method results showed a slope of 1.0217 and R2 of 0.9966. For a simulated run of 6 samples, technologist time for automated steps was under 8 minutes with a total time of 56 minutes. When these steps were performed manually, the time was 30 minutes. Automation increased total time by 26 minutes but saved 22 minutes of technologist hands-on time. Conclusion The Andrew+ was able to perform sterile setup of a cell culture assay. While slower overall than manual processing, technologist time was reduced. Ergonomic, inter-tech variability, and sample transcription error issues were subjectively observed to be less likely. The Andrew+ can work with up to four “dominos” (reagent or tip blocks) in width and up to three dominos in depth. Non-custom, standard depth BSCs from a variety of manufacturers (Labconco, Nuaire, ThermoFisher) have room for one domino in depth so, except for timings performed in this evaluation, all steps were broken down to be performed with a max of four dominos. Efficient sterile use of the system would require an extended depth BSC.

Background: Substance use disorders (SUDs) are highly prevalent, affecting over 48.5 million Americans. Available treatments for SUD remain insufficient, and many patients do not respond to existing interventions despite adequate adherence to treatments. While novel therapies for SUD are urgently needed, the use of psychedelic drugs for the treatment of SUDs has shown promise. Objectives: This overview of systematic reviews summarizes existing evidence on hallucinogens—serotonergic psychedelics and ketamine—for the treatment of SUD. Methods: A comprehensive search of the literature was conducted to identify relevant evidence for using serotonergic and non-serotonergic psychedelics for the treatment of SUDs. After initial screening (n = 468 studies), 62 studies were retrieved and assessed for eligibility, and a total of 16 systematic reviews were included. Conclusions: Although preliminary, evidence suggests that the use of serotonergic and non-serotonergic psychedelics for the treatment of SUD may provide advantages over traditional therapeutics, and these compounds may eventually become part of the next generation of treatments for SUD under specific circumstances. Research with these drugs has faced significant challenges, though, and caution when interpreting results is warranted, given high risk of bias and several other methodological limitations from the studies to date. Furthermore, risks associated with these drugs are not negligible. For now, the use of psychedelic drugs for the treatment of SUDs remains experimental, and existing evidence is insufficient to support its use in clinical practice.

Abstract Background Delirium is often underdiagnosed in clinical practice and is not routinely coded for billing. While manual chart review can identify delirium, it is labor-intensive and impractical for large-scale studies. Natural language processing (NLP) can analyze unstructured text in electronic health records (EHRs) to extract meaningful clinical information. Methods To support national integration of NLP for EHR-based delirium identification across different institutions, we launched the Delirium Interest Group within the national Evolve to Next-Gen Accrual to Clinical Trials (ENACT) NLP Working Group. This paper outlines our initial efforts to standardize, evaluate, and translate an NLP-based delirium detection model into the i2b2/ENACT platform. Results Multisite contextual inquiry identified several key challenges, including variations in local screening practices (eg, tools used, documentation frequency, and quality control), the need for harmonized definitions in the context of EHRs, and the complexity of modeling temporal logic. Multisite NLP evaluation revealed variable performance degradation driven by differences in delirium screening practices, clinical documentation patterns and semantics, and note syntactic structures. Conclusion Our work represents an important first step toward enabling scalable and standardized NLP-based delirium detection across institutions. By engaging diverse institutions through the ENACT NLP Working Group, we identified shared challenges and site-specific variations that impact model implementation and performance. Our collaborative approach enabled the development of a more robust framework for delirium identification across heterogeneous EHR systems. Future efforts will build on this foundation to enhance the validity, usability, and translational impact of delirium detection.

Background: Metabolic and bariatric surgery (MBS) is an established treatment for severe obesity and its related comorbidities. While long-term outcomes are often emphasized, early postoperative complications remain a critical metric of safety and resource utilization. Large database studies have reported national trends in MBS outcomes but are limited by lack of granularity, inconsistent definitions, and inclusion of heterogeneous surgical experience. This study aimed to provide a detailed analysis of 30-day postoperative complications following sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and duodenal switch (DS) over a 17-year period at a single high-volume quaternary academic center. Materials and Methods: A retrospective cohort study of adult patients who underwent primary laparoscopic SG, RYGB, or DS between 2008 and 2024. All procedures were performed by fellowship-trained, minimally invasive bariatric surgeons. Complications occurring within 30 days postoperatively were identified through comprehensive chart review and classified using the Clavien-Dindo grading system. Primary outcomes included incidence, severity, and type of complications, as well as rates of readmission, reoperation, and reintervention. Multivariable logistic regression was used to identify predictors of overall and major complications. Results: Of 2190 patients included, 413 (18.9%) experienced at least 1 early complication, totaling 467 discrete events. DS had the highest complication rate (29.3%), followed by RYGB (18.4%) and SG (17.5%). RYGB was associated with a high burden of major complications, including anastomotic strictures, GI bleeding, and reoperations for obstruction or leak. The 30-day rates of readmission (2.0%) and reoperation (1.3%) were low across all groups. Independent predictors of major complications included venous stasis (aOR=3.22), renal insufficiency (aOR=2.65), and type 2 diabetes (aOR=1.96); both RYGB (aOR=2.25) and DS (aOR=2.77) carried higher risk than SG. Conclusion: This study provides a granular, procedure-specific profile of early complications after SG, RYGB, and DS. While all 3 procedures demonstrated acceptable safety profiles, DS had the highest overall complication rate, and RYGB carried the greatest burden of major complications. These findings underscore the variability in early postoperative outcomes by procedure type and reflect the value of institutional experience in characterizing complication profiles.

This retrospective propensity-matched cohort study assessed the association between sodium-glucose co-transporter-2 inhibitor (SGLT2i) use and cardiovascular outcomes in patients with acute myocardial infarction (AMI) without a history of heart failure. Using the TriNetX U.S. Collaborative Network, we identified 1440 matched patients (720 SGLT2i+ vs 720 SGLT2i−) with preserved ejection fraction and no prior heart failure. Nearly 90% of patients had diabetes. The primary outcome was major adverse cardiovascular events (MACE), defined as a composite of MI, stroke, heart failure, and all-cause mortality. At 1 year, MACE occurred in 17.4% of SGLT2i+ versus 22.0% of SGLT2i− patients (odds ratio [OR] 0.746, 95% CI 0.574-0.969; P = .028), and all-cause mortality occurred in 3.1% versus 7.6%, respectively (OR 0.389, 95% CI 0.234-0.645; P < .001). SGLT2i use was also associated with lower risks of major bleeding. Findings were consistent over long-term follow-up. While the observed associations suggest potential cardioprotective effects of SGLT2i post-MI in patients without heart failure, residual confounding cannot be excluded. The observed reduction in bleeding events is novel and warrants further investigation in prospective studies.

Abstract Objective: Electronic Health Record (EHR) data are critical for advancing translational research and AI technologies. The ENACT network offers access to structured EHR data across 57 CTSA hubs. However, substantial information is contained in clinical narratives, requiring natural language processing (NLP) for research. The ENACT NLP Working Group was formed to make NLP-derived clinical information accessible and queryable across the network. Methods: We established the ENACT NLP Working Group with 13 sites selected based on criteria including clinical notes access, IT infrastructure, NLP expertise, and institutional support. We divided sites into five focus groups targeting clinical tasks within disease contexts. Each focus group consisted of two development sites and two validation sites. We extended the ENACT ontology to standardize NLP-derived data and conducted multisite evaluations using the Open Health Natural Language Processing (OHNLP) Toolkit. Results: The working group achieved 100% site retention and deployed NLP infrastructure across all sites. We developed and validated NLP algorithms for rare disease phenotyping, social determinants of health, opioid use disorder, sleep phenotyping, and delirium phenotyping. Performance varied across sites (F1 scores 0.53–0.96), highlighting data heterogeneity impacts. We extended the ENACT common data model and ontology to incorporate NLP-derived data while maintaining Shared Health Research Informatics NEtwork (SHRINE) compatibility. Conclusion: This demonstrates feasibility of deploying NLP infrastructure across large, federated networks. The focus group approach proved more practical than general-purpose approaches. Key lessons include the challenge of data heterogeneity and importance of collaborative governance. This work also provides a foundation that other networks can build on to implement NLP capabilities for translational research.