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AbstractBackgroundChronic pain affects one-fifth of US adults. Reductions in opioid prescribing have been associated with increased non-prescription opioid use and, chronologically, increased stimulant (methamphetamine and cocaine) use. While non-prescription opioid use is commonly attributed to pain self-management, the role of stimulants in managing pain is unclear.MethodsWe analyzed baseline data from a longitudinal study of patients with chronic non-cancer pain in an urban safety-net healthcare system who had been prescribed an opioid for ≥3 of the last 12 months, and had a history of non-prescription opioid, cocaine, or amphetamine use (N = 300). We estimated the prevalence and identified correlates of stimulant use to treat pain among a subgroup of patients who reported past-year stimulant use (N = 105). Data sources included computer-assisted questionnaire (demographics, substance use, pain), clinical exam and procedures (pain, pain tolerance), and chart abstraction (opioid prescriptions). We conducted bivariate analyses to assess associations between demographics, pain characteristics, non-opioid therapies, substance use, opioid prescriptions, and self-reported symptoms, with reporting using stimulants to treat pain. Demographic variables and those with significant bivariate associations were included in a multivariable logistic regression model.ResultsFifty-two percent of participants with past-year stimulant use reported using stimulants in the past year to treat pain. Participants who used stimulants for pain reported slightly higher average pain in the past 3 months (median of 8 (IQR: 6–8) vs 7 (7–9) out of 10, p = 0.049). In the multivariable analysis, female gender (AOR= 3.20, 95% CI: 1.06–9.63, p = 0.039) and higher score on the Douleur Neuropathique 4neuropathic pain questionnaire (AOR = 1.34, 95% CI: 1.05–1.70, p = 0.017) were associated with past-year stimulant use to treat pain.ConclusionStimulants may be used for pain self-management, particularly for neuropathic pain and among women. Our findings suggest an underexplored motivation for stimulant use in an era of reduced access to prescribed opioids.

Background Chronic pain affects one-fifth of US adults. Reductions in opioid prescribing have been associated with increased non-prescription opioid use and, chronologically, increased stimulant (methamphetamine and cocaine) use. While non-prescription opioid use is commonly attributed to pain self-management, the role of stimulants in managing pain is unclear.Methods We analyzed baseline data from a longitudinal study of patients with chronic non-cancer pain in an urban safety-net healthcare system who had been prescribed an opioid for ≥3 of the last 12 months, and had a history of non-prescription opioid, cocaine, or amphetamine use (N= 300). We estimated the prevalence and identified correlates of stimulant use to treat pain among a subgroup of patients who reported past-year stimulant use (N= 105). Data sources included computer-assisted questionnaire (demographics, substance use, pain), clinical exam and procedures (pain, pain tolerance), and chart abstraction (opioid prescriptions). We conducted bivariate analyses to assess associations between demographics, pain characteristics, non-opioid therapies, substance use, opioid prescriptions, and self-reported symptoms, with reporting using stimulants to treat pain. Demographic variables and those with significant bivariate associations were included in a multivariable logistic regression model.Results Fifty-two percent of participants with past-year stimulant use reported using stimulants in the past year to treat pain. Participants who used stimulants for pain reported slightly higher average pain in the past 3 months (median of 8 (IQR: 6–8) vs 7 (7–9) out of 10, p= 0.049). In the multivariable analysis, female gender (AOR= 3.20, 95% CI: 1.06–9.63, p= 0.039) and higher score on the Douleur Neuropathique 4neuropathic pain questionnaire (AOR = 1.34, 95% CI: 1.05–1.70, p= 0.017) were associated with past-year stimulant use to treat pain.Conclusion Stimulants may be used for pain self-management, particularly for neuropathic pain and among women. Our findings suggest an underexplored motivation for stimulant use in an era of reduced access to prescribed opioids.

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The complexity of preterm birth (PTB), both spontaneous and medically indicated, and its various etiologies and associated risk factors pose a significant challenge for developing tools to accurately predict risk. This review focuses on the discovery of proteomics signatures that might be useful for predicting spontaneous PTB or preeclampsia (PE), which often results in PTB. We describe methods for proteomics analyses, proteomics biomarker candidates that have so far been identified, obstacles for discovering biomarkers that are sufficiently accurate for clinical use, and the derivation of composite signatures including clinical parameters to increase predictive power. We conclude with an outlook including the derivation of biosignatures with highly multiplexed and sensitive proteomics platforms and the integration of proteomics results with other omics with aid of advanced computational algorithms. Finally, we point to the importance of conducting future biosignature and composite signature studies in large and diverse populations to derive accurate, reliable, generalizable, and clinically useful prediction tools.

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

As part of the American Society of Anesthesiology Brain Health Initiative goal of improving perioperative brain health for older patients, over 30 experts met at the fifth International Perioperative Neurotoxicity Workshop in San Francisco, CA, in May 2016, to discuss best practices for optimizing perioperative brain health in older adults (ie, >65 years of age). The objective of this workshop was to discuss and develop consensus solutions to improve patient management and outcomes and to discuss what older adults should be told (and by whom) about postoperative brain health risks. Thus, the workshop was provider and patient oriented as well as solution focused rather than etiology focused. For those areas in which we determined that there were limited evidence-based recommendations, we identified knowledge gaps and the types of scientific knowledge and investigations needed to direct future best practice. Because concerns about perioperative neurocognitive injury in pediatric patients are already being addressed by the SmartTots initiative, our workshop discussion (and thus this article) focuses specifically on perioperative cognition in older adults. The 2 main perioperative cognitive disorders that have been studied to date are postoperative delirium and cognitive dysfunction. Postoperative delirium is a syndrome of fluctuating changes in attention and level of consciousness that occurs in 20%–40% of patients >60 years of age after major surgery and inpatient hospitalization. Many older surgical patients also develop postoperative cognitive deficits that typically last for weeks to months, thus referred to as postoperative cognitive dysfunction. Because of the heterogeneity of different tools and thresholds used to assess and define these disorders at varying points in time after anesthesia and surgery, a recent article has proposed a new recommended nomenclature for these perioperative neurocognitive disorders. Our discussion about this topic was organized around 4 key issues: preprocedure consent, preoperative cognitive assessment, intraoperative management, and postoperative follow-up. These 4 issues also form the structure of this document. Multiple viewpoints were presented by participants and discussed at this in-person meeting, and the overall group consensus from these discussions was then drafted by a smaller writing group (the 6 primary authors of this article) into this manuscript. Of course, further studies have appeared since the workshop, which the writing group has incorporated where appropriate. All participants from this in-person meeting then had the opportunity to review, edit, and approve this final manuscript; 1 participant did not approve the final manuscript and asked for his/her name to be removed.

Ageing drives changes in neuronal and cognitive function, the decline of which is a major feature of many neurological disorders. The hippocampus, a brain region subserving roles of spatial and episodic memory and learning, is sensitive to the detrimental effects of ageing at morphological and molecular levels. With advancing age, synapses in various hippocampal subfields exhibit impaired long-term potentiation, an electrophysiological correlate of learning and memory. At the molecular level, immediate early genes are among the synaptic plasticity genes that are both induced by long-term potentiation and downregulated in the aged brain. In addition to revitalizing other aged tissues, exposure to factors in young blood counteracts age-related changes in these central nervous system parameters, although the identities of specific cognition-promoting factors or whether such activity exists in human plasma remains unknown. We hypothesized that plasma of an early developmental stage, namely umbilical cord plasma, provides a reservoir of such plasticity-promoting proteins. Here we show that human cord plasma treatment revitalizes the hippocampus and improves cognitive function in aged mice. Tissue inhibitor of metalloproteinases 2 (TIMP2), a blood-borne factor enriched in human cord plasma, young mouse plasma, and young mouse hippocampi, appears in the brain after systemic administration and increases synaptic plasticity and hippocampal-dependent cognition in aged mice. Depletion experiments in aged mice revealed TIMP2 to be necessary for the cognitive benefits conferred by cord plasma. We find that systemic pools of TIMP2 are necessary for spatial memory in young mice, while treatment of brain slices with TIMP2 antibody prevents long-term potentiation, arguing for previously unknown roles for TIMP2 in normal hippocampal function. Our findings reveal that human cord plasma contains plasticity-enhancing proteins of high translational value for targeting ageing- or disease-associated hippocampal dysfunction.

Although prematurity is the single largest cause of death in children under 5 years of age, the current definition of prematurity, based on gestational age, lacks the precision needed for guiding care decisions. Here, we propose a longitudinal risk assessment for adverse neonatal outcomes in newborns based on a deep learning model that uses electronic health records (EHRs) to predict a wide range of outcomes over a period starting shortly before conception and ending months after birth. By linking the EHRs of the Lucile Packard Children’s Hospital and the Stanford Healthcare Adult Hospital, we developed a cohort of 22,104 mother-newborn dyads delivered between 2014 and 2018. Maternal and newborn EHRs were extracted and used to train a multi-input multitask deep learning model, featuring a long short-term memory neural network, to predict 24 different neonatal outcomes. An additional cohort of 10,250 mother-newborn dyads delivered at the same Stanford Hospitals from 2019 to September 2020 was used to validate the model. Areas under the receiver operating characteristic curve at delivery exceeded 0.9 for 10 of the 24 neonatal outcomes considered and were between 0.8 and 0.9 for 7 additional outcomes. Moreover, comprehensive association analysis identified multiple known associations between various maternal and neonatal features and specific neonatal outcomes. This study used linked EHRs from more than 30,000 mother-newborn dyads and would serve as a resource for the investigation and prediction of neonatal outcomes. An interactive website is available for independent investigators to leverage this unique dataset: https://maternal-child-health-associations.shinyapps.io/shiny_app/.

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

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.

Controversies surround the possible long-term physiological and psychological consequences of opioid use. Analgesic tolerance and addiction are commonly at the center of these controversies, but other concerns exist as well. A growing body of evidence suggests that hyperalgesia caused by the chronic administration of opioids can occur in laboratory animals and in humans. In these studies we describe a murine model of opioid-induced hyperalgesia (OIH). After the treatment of mice for 6 days with implanted morphine pellets followed by their removal, both thermal hyperalgesia and mechanical allodynia were documented. Additional experiments demonstrated that prior morphine treatment also increased formalin-induced licking behavior. These effects were intensified by intermittent abstinence accomplished through administration of naloxone during morphine treatment. Experiments designed to determine if the μ-opioid receptor mediated OLH in our model revealed that the relatively-selective μ-opioid receptor agonist fentanyl induced the thermal hyperalgesia and mechanical allodynia characteristic of OIH when administered in intermittent boluses over 6 days. In complimentary experiments we found that CXBK mice which have reduced μ-opioid receptor binding displayed no significant OIH after morphine treatment. Finally, we explored the pharmacological sensitivities of OIH. We found that the N-methyl-d-aspartate (NMDA) receptor antagonist MK-801, the nitric oxide synthase (NOS) inhibitor NG-nitro-l-arginine methyl ester (l-NAME) and the heme oxygenase (HO) inhibitor tin protoporphyrin (Sn-P) dose-dependently reduced OIH in this model while the NSAID indomethacin had no effect. Thus we have characterized a murine model of OIH which will be useful in the pursuit of the molecular mechanisms underlying this phenomenon.

Purpose:To report the anesthetic management of a patient with carcinoid tumour metastatic to the liver who presented for orthotopic liver transplantation. Anesthetic implications of metastatic carcinoid tumour on liver transplantation and the use of octreotide are discussed.

Objective: Two clinical trials investigated the pharmacokinetics of human corticotropin-releasing factor (hCRF), resulting cortisol release, and associated hemodynamic changes.Methods: In a 3 × 3 Latin square design, subjects were randomized to receive a single dose of 5 μg · kg−1hCRF as a 10-minute intravenous infusion, a 180-minute infusion, and a subcutaneous injection in separate study sessions 7 days apart. Twelve additional subjects obtained a subcutaneous dose of either 300, 600, or 1200 μg hCRF on 3 consecutive days. Noncompartmental and compartmental pharmacokinetic analysis was performed. Hemodynamic response was characterized with use of pharmacodynamic models.Results: The volume of distribution at steady state was 9.81 ± 3.0 and 15.61 ± 2.9, and the clearance was 256 ± 40 mL · min−1and 345 ± 90 mL · min−1for the 10-minute and 180-minute intravenous infusion, respectively (P < .05). Corresponding elimination half-life was 45 ± 7 minutes and 37 ± 10 minutes. Two-compartment and 1-compartment models adequately described the 10-minute and 180-minute infusions, respectively. The bioavailability of hCRF after subcutaneous administration was 67% ± 17%. Apparent clearance remained unchanged for different subcutaneous doses. Peak plasma cortisol concentrations were similar after subcutaneous and intravenous administration of hCRF. Repetitive administration of hCRF did not result in accumulation but produced a reduced plasma cortisol response. A sigmoidal model related plasma hCRF concentrations to increase in heart rate (maximum, 39 beats · min−1). The relationship between the modest decrease in diastolic blood pressure and plasma hCRF concentrations was linear.Conclusion: The pharmacokinetics of intravenously administered hCRF were nonlinear, but apparent clearance was constant for various subcutaneous doses. An excellent bioavailability and preserved bioactivity make the subcutaneous route an attractive choice. Repetitive administration of hCRF probably caused tolerance of the cortisol response.

IMPORTANCE: Worldwide, preterm birth (PTB) is the single largest cause of deaths in the perinatal and neonatal period and is associated with increased morbidity in young children. The cause of PTB is multifactorial, and the development of generalizable biological models may enable early detection and guide therapeutic studies. OBJECTIVE: To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic/prognostic study analyzed plasma and urine samples collected from May 2014 to June 2017 from pregnant women in 5 biorepository cohorts in low- and middle-income countries (LMICs; ie, Matlab, Bangladesh; Lusaka, Zambia; Sylhet, Bangladesh; Karachi, Pakistan; and Pemba, Tanzania). These cohorts were established to study maternal and fetal outcomes and were supported by the Alliance for Maternal and Newborn Health Improvement and the Global Alliance to Prevent Prematurity and Stillbirth biorepositories. Data were analyzed from December 2018 to July 2019. EXPOSURES: Blood and urine specimens that were collected early during pregnancy (median sampling time of 13.6 weeks of gestation, according to ultrasonography) were processed, stored, and shipped to the laboratories under uniform protocols. Plasma samples were assayed for targeted measurement of proteins and untargeted cell-free ribonucleic acid profiling; urine samples were assayed for metabolites. MAIN OUTCOMES AND MEASURES: The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation. RESULTS: Of the 81 pregnant women included in this study, 39 had PTBs (48.1%) and 42 had term pregnancies (51.9%) (mean [SD] age of 24.8 [5.3] years). Univariate analysis demonstrated functional biological differences across the 5 cohorts. A cohort-adjusted machine learning algorithm was applied to each biological data set, and then a higher-level machine learning modeling combined the results into a final integrative model. The integrated model was more accurate, with an area under the receiver operating characteristic curve (AUROC) of 0.83 (95% CI, 0.72-0.91) compared with the models derived for each independent biological modality (transcriptomics AUROC, 0.73 [95% CI, 0.61-0.83]; metabolomics AUROC, 0.59 [95% CI, 0.47-0.72]; and proteomics AUROC, 0.75 [95% CI, 0.64-0.85]). Primary features associated with PTB included an inflammatory module as well as a metabolomic module measured in urine associated with the glutamine and glutamate metabolism and valine, leucine, and isoleucine biosynthesis pathways. CONCLUSIONS AND RELEVANCE: This study found that, in LMICs and high PTB settings, major biological adaptations during term pregnancy follow a generalizable model and the predictive accuracy for PTB was augmented by combining various omics data sets, suggesting that PTB is a condition that manifests within multiple biological systems. These data sets, with machine learning partnerships, may be a key step in developing valuable predictive tests and intervention candidates for preventing PTB.

Single-cell mass cytometry revealed immune correlates of patient-associated variability in surgical recovery.