Abstract The majority of spontaneous preterm births (sPTB) occurs without identifiable clinical indications or apparent risk factors. A dysregulated maternal immune adaptation at delivery has been associated with sPTB. Yet, a precise understanding of maternal immune dynamics preceding sPTB remains lacking. Here we show, in a nested case-control study within a low-risk, population-based pregnancy cohort, that an abnormal immune adaptation in mothers’ blood precedes sPTB by weeks to months and discriminates sPTB cases from term controls (AUROC: 0.7). Prominent features include enhanced immune cell responses to an adrenergic stimulus during the first and second trimesters, followed by increased production of pro-inflammatory cytokines in the third trimester in sPTB vs. term pregnancies. Transcriptome analysis of second trimester single-cell CD4 + T cells reveals a Th17-skewed, neuroactive-protein responsive phenotype in sPTB pregnancies. Our study provides a multi-omics resource and a conceptual framework for early identification of individuals at increased risk for sPTB with broad translational implications for advancing targeted preventive measures.

Background:The molecular mechanisms governing adaptive neuroprotection during the postpartum period remain unknown. We hypothesized that circulating exosomes contain bioactive cargo (such as Hsp20) that confer neuroprotection against ischemic injury during the postpartum period. Methods:Exosomes were isolated from plasma of postpartum female mice (ppExos) and control female mice, and from serial blood samples obtained from healthy human volunteers during pregnancy (3rd trimester) and again on postpartum day 2. Exosomal size and protein markers were confirmed via nanoparticle tracking analysis and Western blotting. Neuroprotection with exosome treatment was assessed in vitro using mouse neuronal and astrocyte cultures and human retinal pigment cell line subjected to simulated ischemia. In vivo neuroprotection was assessed using transient middle cerebral artery occlusion (MCAO) in young adult and aged mice. Mitochondrial integrity and reactive oxygen species (ROS) production were evaluated by live cell imaging. In vivo neuroprotection was evaluated by assessing infarct volume and neurobehavioral scores. Changes in mitochondrial dynamics were measured by immunofluorescence and Western blot. Human exosomes were sent for proteomic assessment (SomaScan™) followed by differential expression analysis (R software v4). Protein quantification was validated by immunoblot. Results:ppExos significantly reduced infarct volumes and improved neurological deficits post-MCAO in both young and aged female mice. In vitro, ppExos reduced ROS generation in all cell types after simulated ischemia and reduced mitochondrial fragmentation in astrocytes. Mitochondrial fusion proteins Mfn2 and Opa1 proteins were elevated in maternal postpartum brains, and preserved in both astrocyte and neuronal cell cultures after in vitro ischemia with ppExo treatment. Proteomics revealed significant upregulation of heat shock protein 20 (Hsp20) in human ppExos, while Western blot validated elevated Hsp20 in both human and mouse ppExos. Simulated ischemia significantly reduced Hsp20 in astrocyte and neuronal cultures which was reversed by treatment with ppExos. In conclusion, ppExos represent a previously unrecognized, naturally optimized neuroprotective agent that enhances mitochondrial resilience and antioxidant defenses associated with enhanced Hsp20 expression. These findings establish a novel platform for sex-informed, cell-free therapies in ischemic cerebrovascular accidents.

Digital twin models can accelerate therapeutic development by enabling low-risk testing of candidate interventions. In preterm labor (PTL), a major pregnancy complication where clinical trials face unique ethical and financial barriers, digital twins are especially valuable for evaluating new therapies targeting immune dysfunctions driving PTL. Yet, current models lack single-cell resolution, limiting detection of cell-type-specific mechanisms, off-target effects, and the design of personalized interventions. We present Simulated Immunome Modeling of Clinical Outcomes (SIMCO), a single-cell-level digital twin framework that models immunomodulatory treatment effects on the timing of labor using immunome-wide, single-cell simulations. SIMCO's digital twins are trained and validated on a newly generated mass cytometry atlas of the pregnant immunome exposed to nine candidate drugs preselected for PTL prevention. Applying SIMCO to an independent cohort of pregnant individuals, we simulate treatment effects on gestational length, screening for candidate drugs that delay labor timing and providing system-level mechanistic insight for each drug candidate. Tetrahydrofolate, maprotiline, and the combination of aspirin and lansoprazole emerged as top candidates for PTL prevention, delaying labor onset primarily through enhanced mTOR signaling in innate immune cells and attenuated JAK/STAT signaling in naïve CD4+ T cells. The codebase is available at https://github.com/ofondeur/SIMCO/.

Extended stays in the postanesthesia care unit (PACU) pose challenges in high-volume endoscopies. This study investigates the impact of intraoperative fentanyl use on PACU duration, postoperative pain, and financial implications in outpatient endoscopy. A retrospective analysis of upper/lower endoscopies at our facility (2020–2022) was conducted, focusing on the relationship between fentanyl use, PACU duration, and pain scales. Financial impacts were also assessed. Among 11,488 patients, 5787 (50.4

BackgroundThe expansion of artificial intelligence (AI) within large language models (LLMs) has the potential to streamline healthcare delivery. Despite the increased use of LLMs, disparities in their performance particularly in different languages, remain underexplored. This study examines the quality of ChatGPT responses in English and Japanese, specifically to questions related to anaesthesiology.MethodsAnaesthesiologists proficient in both languages were recruited as experts in this study. Ten frequently asked questions in anaesthesia were selected and translated for evaluation. Three non-sequential responses from ChatGPT were assessed for content quality (accuracy, comprehensiveness, and safety) and communication quality (understanding, empathy/tone, and ethics) by expert evaluators.ResultsEight anaesthesiologists evaluated English and Japanese LLM responses. The overall quality for all questions combined was higher in English compared with Japanese responses. Content and communication quality were significantly higher in English compared with Japanese LLMs responses (both P<0.001) in all three responses. Comprehensiveness, safety, and understanding were higher scores in English LLM responses. In all three responses, more than half of the evaluators marked overall English responses as better than Japanese responses.ConclusionsEnglish LLM responses to anaesthesia-related frequently asked questions were superior in quality to Japanese responses when assessed by bilingual anaesthesia experts in this report. This study highlights the potential for language-related disparities in healthcare information and the need to improve the quality of AI responses in underrepresented languages. Future studies are needed to explore these disparities in other commonly spoken languages and to compare the performance of different LLMs.

BACKGROUND:Postoperative cognitive decline (POCD) is the predominant complication affecting patients over 60 years old following major surgery, yet its prediction and prevention remain challenging. Understanding the biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This study aimed to provide a comprehensive analysis of immune cell trajectories differentiating patients with and without POCD and to derive a predictive score enabling the identification of high-risk patients during the preoperative period. MATERIAL AND METHODS:Twenty-six patients aged 60 years old and older undergoing elective major orthopedic surgery were enrolled in a prospective longitudinal study, and the occurrence of POCD was assessed 7 days after surgery. Serial samples collected before surgery, and 1, 7, and 90 days after surgery were analyzed using a combined single-cell mass cytometry and plasma proteomic approach. Unsupervised clustering of the high-dimensional mass cytometry data was employed to characterize time-dependent trajectories of all major innate and adaptive immune cell frequencies and signaling responses. Sparse machine learning coupled with data-driven feature selection was applied to the presurgery immunological dataset to classify patients at risk for POCD. RESULTS:The analysis identified cell-type and signaling-specific immune trajectories differentiating patients with and without POCD. The most prominent trajectory features revealed early exacerbation of JAK/STAT and dampening of inhibitory κB and nuclear factor-κB immune signaling responses in patients with POCD. Further analyses integrating immunological and clinical data collected before surgery identified a preoperative predictive model comprising one plasma protein and 10 immune cell features that classified patients at risk for POCD with excellent accuracy (AUC=0.80, P =2.21e-02 U -test). CONCLUSION:Immune system-wide monitoring of patients over 60 years old undergoing surgery unveiled a peripheral immune signature of POCD. A predictive model built on immunological data collected before surgery demonstrated greater accuracy in predicting POCD compared to known clinical preoperative risk factors, offering a concise list of biomarker candidates to personalize perioperative management.

Postoperative cognitive decline (POCD) is the predominant complication affecting elderly patients following major surgery, yet its prediction and prevention remain challenging. Understanding biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This longitudinal study involving 26 elderly patients undergoing orthopedic surgery aimed to characterize the impact of peripheral immune cell responses to surgical trauma on POCD. Trajectory analyses of single-cell mass cytometry data highlighted early JAK/STAT signaling exacerbation and diminished MyD88 signaling post-surgery in patients who developed POCD. Further analyses integrating single-cell and plasma proteomic data collected before surgery with clinical variables yielded a sparse predictive model that accurately identified patients who would develop POCD (AUC = 0.80). The resulting POCD immune signature included one plasma protein and ten immune cell features, offering a concise list of biomarker candidates for developing point-of-care prognostic tests to personalize perioperative management of at-risk patients. The code and the data are documented and available at https://github.com/gregbellan/POCD . Teaser:Modeling immune cell responses and plasma proteomic data predicts postoperative cognitive decline.

BACKGROUND:Inpatient postpartum recovery trajectories following cesarean delivery and spontaneous vaginal delivery are underexplored.OBJECTIVE:This study primarily aimed to compare recovery following cesarean delivery and spontaneous vaginal delivery in the first postpartum week, and secondarily to evaluate psychometrically the Japanese version of the Obstetric Quality of Recovery-10 scoring tool.STUDY DESIGN:Following institutional review board approval, the EQ-5D-3L (EuroQoL 5-Dimension 3-Level) questionnaire and a Japanese version of the Obstetric Quality of Recovery-10 measure were used to evaluate inpatient postpartum recovery in uncomplicated nulliparous parturients delivering via scheduled cesarean delivery or spontaneous vaginal delivery.RESULTS:A total of 48 and 50 women who delivered via cesarean delivery and spontaneous vaginal delivery, respectively, were recruited. Women delivering via scheduled cesarean delivery experienced significantly worse quality of recovery on days 1 and 2 compared with those who had spontaneous vaginal delivery. Quality of recovery significantly improved daily, plateauing at days 4 and 3 for cesarean delivery and spontaneous vaginal delivery groups, respectively. Compared with cesarean delivery, spontaneous vaginal delivery was associated with prolonged time to analgesia requirement, decreased opioid consumption, reduced antiemetic requirement, and reduced times to liquid/solid intake, ambulation, and discharge. Obstetric Quality of Recovery-10-Japanese is a valid (correlates with the EQ-5D-3L including a global health visual analog scale, gestational age, blood loss, opioid consumption, time until first analgesic request, liquid/solid intake, ambulation, catheter removal, and discharge), reliable (Cronbach alpha=0.88; Spearman-Brown reliability estimate=0.94; and intraclass correlation coefficient=0.89), and clinically feasible (98% 24-hour response rate) measure.CONCLUSION:Inpatient postpartum recovery is significantly better in the first 2 postpartum days following spontaneous vaginal delivery compared with scheduled cesarean delivery. Inpatient recovery is largely achieved within 4 and 3 days following scheduled cesarean delivery and spontaneous vaginal delivery, respectively. Obstetric Quality of Recovery-10-Japanese is a valid, reliable, and feasible measure of inpatient postpartum recovery.

OBJECTIVE:This study aimed to conduct a systematic review and to evaluate the psychometric measurement properties of instruments for postpartum anxiety using the Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines to identify the best available patient-reported outcome measure. DATA SOURCES:We searched 4 databases (CINAHL, Embase, PubMed, and Web of Science in July 2022) and included studies that evaluated at least 1 psychometric measurement property of a patient-reported outcome measurement instrument. The protocol was registered with the International Prospective Register for Systematic Reviews under identifier CRD42021260004 and followed the Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines for systematic reviews. STUDY ELIGIBILITY:Studies eligible for inclusion were those that assessed the performance of a patient-reported outcome measure for screening for postpartum anxiety. We included studies in which the instruments were subjected to some form of psychometric property assessment in the postpartum maternal population, consisted of at least 2 questions, and were not subscales. METHODS:This systematic review used the Consensus-Based Standards for the Selection of Health Measurement Instruments and the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines to identify the best patient-reported outcome measurement instrument for examining postpartum anxiety. A risk of bias assessment was performed, and a modified GRADE approach was used to assess the level of evidence with recommendations being made for the overall quality of each instrument. RESULTS:A total of 28 studies evaluating 13 instruments in 10,570 patients were included. Content validity was sufficient in 9 with 5 instruments receiving a class A recommendation (recommended for use). The Postpartum Specific Anxiety Scale, Postpartum Specific Anxiety Scale Research Short Form, Postpartum Specific Anxiety Scale Research Short Form Covid, Postpartum Specific Anxiety Scale-Persian, and the State-Trait Anxiety Inventory demonstrated adequate content validity and sufficient internal consistency. Nine instruments received a recommendation of class B (further research required). No instrument received a class C recommendation (not recommended for use). CONCLUSION:Five instruments received a class A recommendation, all with limitations, such as not being specific to the postpartum population, not assessing all domains, lacking generalizability, or evaluation of cross-cultural validity. There is currently no freely available instrument that assess all domains of postpartum anxiety. Future studies are needed to determine the optimum current instrument or to develop and validate a more specific measure for maternal postpartum anxiety.

ImportanceMaternal depression is frequently reported in the postpartum period, with an estimated prevalence of approximately 15% during the first postpartum year. Despite the high prevalence of postpartum depression, there is no consensus regarding which patient-reported outcome measure (PROM) should be used to screen for this complex, multidimensional construct.ObjectiveTo evaluate psychometric measurement properties of existing PROMs of maternal postpartum depression using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guideline and identify the best available patient-reported screening measure.Evidence ReviewThis systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. PubMed, CINAHL, Embase, and Web of Science were searched on July 1, 2019, for validated PROMs of postpartum depression, and an additional search including a hand search of references from eligible studies was conducted in June 2021. Included studies evaluated 1 or more psychometric measurement properties of the identified PROMs. A risk-of-bias assessment was performed to evaluate methods of each included study. Psychometric measurement properties of each PROM were rated according to COSMIN criteria. A modified Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the level of evidence supporting each rating, and a recommendation class (A, recommended for use; B, further research required; or C, not recommended) was given based on the overall quality of each included PROM.FindingsAmong 10 264 postpartum recovery studies, 27 PROMs were identified. Ten PROMs (37.0%) met the inclusion criteria and were used in 43 studies (0.4%) involving 22 095 postpartum women. At least 1 psychometric measurement property was assessed for each of the 10 validated PROMs identified. Content validity was sufficient in all PROMs. The Edinburgh Postnatal Depression Scale (EPDS) demonstrated adequate content validity and a moderate level of evidence for sufficient internal consistency (with sufficient structural validity), resulting in a recommendation of class A. The other 9 PROMs evaluated received a recommendation of class B.Conclusions and RelevanceThe findings of this systematic review suggest that the EPDS is the best available patient-reported screening measure of maternal postpartum depression. Future studies should focus on evaluating the cross-cultural validity, reliability, and measurement error of the EPDS to improve understanding of its psychometric properties and utility.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

INTRODUCTION:Preoperative optimization programs have demonstrated positive effects on perioperative physical function and surgical outcomes. In nonsurgical populations, physical activity and healthy diet may reduce pain and pain medication requirement, but this has not been studied in surgical patients. Our aim was to determine whether a preoperative diet and exercise intervention affects postoperative pain and pain medication use. METHODS:Patients undergoing abdominal colorectal surgery were invited to participate in a web-based patient engagement program. Those enrolling in the first and third time periods received information on the standard perioperative pathway (enhanced recovery after surgery [ERAS]). Those enrolling in the second time period also received reminders on nutrition and exercise (PREHAB + ERAS). The primary outcome was postoperative inpatient opioid use. The secondary outcomes were inpatient postoperative pain scores and nonopioid pain medication use. RESULTS:The ERAS and PREHAB + ERAS groups were similar in demographic and operative characteristics. Subgroup analysis of patients who activated their accounts demonstrated that the two groups had similar average maximum daily pain scores, but the PREHAB + ERAS group (n = 158) used 15.9 fewer oral morphine equivalents per postoperative inpatient day than the ERAS group (n = 92), representing a 30% decrease (53 mg versus 37.1 mg, P = 0.04). The two groups used comparable amounts of acetaminophen, gabapentin, and ketorolac. Generalized linear models demonstrated that PREHAB + ERAS, minimally invasive surgery, and older age were associated with lower inpatient opioid use. CONCLUSIONS:Access to a web-based preoperative diet and exercise program may reduce inpatient opioid use after major elective colorectal surgery. Further studies are necessary to determine whether the degree of adherence to nutrition and physical activity recommendations has a dose-dependent effect on opioid use.

Objective:The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery.Summary Background Data:SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs.Methods:Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30days of surgery.Results:A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82).Conclusions:The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

Abstract Background Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. Objective The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. Study design Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. Results The model derived in the Stanford cohort was highly significant (p = 3.9E–15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E–01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E–21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E–02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E–454, R = 0.92) and Detroit cohorts (p = 1.1.E–92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. Conclusions Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the “same” clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.