Dyani Gaudilliere

All Publications

• Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Maric, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020

  View details for DOI 10.1038/s42256-020-00232-8

  View details for Web of Science ID 000579336000001

• Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor. Stelzer, I., Ghaemi, M., Han, X., Ando, K., Peterson, L., Contrepois, K., Ganio, E., Tsai, A., Tsai, E., Rumer, K., Stanley, N., Fallazadeh, R., Becker, M., Culos, A., Gaudilliere, D., Wong, R., Winn, V., Shaw, G., Stevenson, D., Snyder, M., Angst, M., Aghaeepour, N., Gaudilliere, B. SPRINGER HEIDELBERG. 2020: 89A

  View details for Web of Science ID 000525432600082

• Surgical Treatment of Osteonecrosis of the Jaw: An Emerging Problem in the Era of Bisphosphonates. Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons Hung, K. S., Sheckter, C. C., Gaudilliere, D., Suarez, P., Curtin, C. 2020

  View details for DOI 10.1016/j.joms.2019.12.018

  View details for PubMedID 32004467

• Multiomics Characterization of Preterm Birth in Low- and Middle-Income Countries. JAMA network open Jehan, F., Sazawal, S., Baqui, A. H., Nisar, M. I., Dhingra, U., Khanam, R., Ilyas, M., Dutta, A., Mitra, D. K., Mehmood, U., Deb, S., Mahmud, A., Hotwani, A., Ali, S. M., Rahman, S., Nizar, A., Ame, S. M., Moin, M. I., Muhammad, S., Chauhan, A., Begum, N., Khan, W., Das, S., Ahmed, S., Hasan, T., Khalid, J., Rizvi, S. J., Juma, M. H., Chowdhury, N. H., Kabir, F., Aftab, F., Quaiyum, A., Manu, A., Yoshida, S., Bahl, R., Rahman, A., Pervin, J., Winston, J., Musonda, P., Stringer, J. S., Litch, J. A., Ghaemi, M. S., Moufarrej, M. N., Contrepois, K., Chen, S., Stelzer, I. A., Stanley, N., Chang, A. L., Hammad, G. B., Wong, R. J., Liu, C., Quaintance, C. C., Culos, A., Espinosa, C., Xenochristou, M., Becker, M., Fallahzadeh, R., Ganio, E., Tsai, A. S., Gaudilliere, D., Tsai, E. S., Han, X., Ando, K., Tingle, M., Maric, I., Wise, P. H., Winn, V. D., Druzin, M. L., Gibbs, R. S., Darmstadt, G. L., Murray, J. C., Shaw, G. M., Stevenson, D. K., Snyder, M. P., Quake, S. R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 3 (12): e2029655

  Abstract

  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.To investigate the ability of transcriptomics and proteomics profiling of plasma and metabolomics analysis of urine to identify early biological measurements associated with PTB.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.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.The PTB phenotype was defined as the delivery of a live infant before completing 37 weeks of gestation.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.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.

  View details for DOI 10.1001/jamanetworkopen.2020.29655

  View details for PubMedID 33337494

• Author Correction: Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N., Lindberg-Larsen, V., Einhaus, J., Tsai, A. S., Verdonk, F., Culos, A., Ghaemi, S., Rumer, K. K., Stelzer, I. A., Gaudilliere, D., Tsai, E., Fallahzadeh, R., Choisy, B., Kehlet, H., Aghaeepour, N., Angst, M. S., Gaudilliere, B. 2020; 11 (1): 4495

  Abstract

  An amendment to this paper has been published and can be accessed via a link at the top of the paper.

  View details for DOI 10.1038/s41467-020-18410-y

  View details for PubMedID 32883978

• Preferential inhibition of adaptive immune system dynamics by glucocorticoids in patients after acute surgical trauma. Nature communications Ganio, E. A., Stanley, N., Lindberg-Larsen, V., Einhaus, J., Tsai, A. S., Verdonk, F., Culos, A., Gahemi, S., Rumer, K. K., Stelzer, I. A., Gaudilliere, D., Tsai, E., Fallahzadeh, R., Choisy, B., Kehlet, H., Aghaeepour, N., Angst, M. S., Gaudilliere, B. 2020; 11 (1): 3737

  Abstract

  Glucocorticoids (GC) are a controversial yet commonly used intervention in the clinical management of acute inflammatory conditions, including sepsis or traumatic injury. In the context of major trauma such as surgery, concerns have been raised regarding adverse effects from GC, thereby necessitating a better understanding of how GCs modulate the immune response. Here we report the results of a randomized controlled trial (NCT02542592) in which we employ a high-dimensional mass cytometry approach to characterize innate and adaptive cell signaling dynamics after a major surgery (primary outcome) in patients treated with placebo or methylprednisolone (MP). A robust, unsupervised bootstrap clustering of immune cell subsets coupled with random forest analysis shows profound (AUC = 0.92, p-value = 3.16E-8) MP-induced alterations of immune cell signaling trajectories, particularly in the adaptive compartments. By contrast, key innate signaling responses previously associated with pain and functional recovery after surgery, including STAT3 and CREB phosphorylation, are not affected by MP. These results imply cell-specific and pathway-specific effects of GCs, and also prompt future studies to examine GCs' effects on clinical outcomes likely dependent on functional adaptive immune responses.

  View details for DOI 10.1038/s41467-020-17565-y

  View details for PubMedID 32719355

• VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R., Ganio, E., Becker, M., Phongpreecha, T., Nassar, H., Ghaemi, S., Maric, I., Culos, A., Chang, A. L., Xenochristou, M., Han, X., Espinosa, C., Rumer, K., Peterson, L., Verdonk, F., Gaudilliere, D., Tsai, E., Feyaerts, D., Einhaus, J., Ando, K., Wong, R. J., Obermoser, G., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 11 (1): 3738

  Abstract

  High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

  View details for DOI 10.1038/s41467-020-17569-8

  View details for PubMedID 32719375

• Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nature machine intelligence Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Marić, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 2 (10): 619–28

  Abstract

  The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

  View details for DOI 10.1038/s42256-020-00232-8

  View details for PubMedID 33294774

  View details for PubMedCentralID PMC7720904

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia FRONTIERS IN IMMUNOLOGY Han, X., Ghaemi, M. S., Ando, K., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J., Bertrand, B., Stanley, N., Culos, A., Tanada, A., Hedou, J., Tsai, E. S., Fallahzadeh, R., Wong, R. J., Judy, A. E., Winn, V. D., Druzins, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2019; 10

  View details for DOI 10.3389/fimmu.2019.01305

  View details for Web of Science ID 000470999000001

• A YEAR-LONG IMMUNE PROFILE OF THE SYSTEMIC RESPONSE IN ACUTE STROKE SURVIVORS Tsai, A., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Culos, A., Ghaemi, M. S., Choisy, B., Djebali, K., Einhaus, J. F., Bertrand, B., Tanada, A., Stanley, N., Fallahzadeh, R., Baca, Q., Quach, L. N., Osborn, E., Drag, L., Lansberg, M., Angst, M., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2019: 155

  View details for Web of Science ID 000480793600335

• A year-long immune profile of the systemic response in acute stroke survivors. Brain : a journal of neurology Tsai, A. S., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Culos, A., Ghaemi, M. S., Choisy, B., Djebali, K., Einhaus, J. F., Bertrand, B., Tanada, A., Stanley, N., Fallahzadeh, R., Baca, Q., Quach, L. N., Osborn, E., Drag, L., Lansberg, M. G., Angst, M. S., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. 2019

  Abstract

  Stroke is a leading cause of cognitive impairment and dementia, but the mechanisms that underlie post-stroke cognitive decline are not well understood. Stroke produces profound local and systemic immune responses that engage all major innate and adaptive immune compartments. However, whether the systemic immune response to stroke contributes to long-term disability remains ill-defined. We used a single-cell mass cytometry approach to comprehensively and functionally characterize the systemic immune response to stroke in longitudinal blood samples from 24 patients over the course of 1 year and correlated the immune response with changes in cognitive functioning between 90 and 365 days post-stroke. Using elastic net regularized regression modelling, we identified key elements of a robust and prolonged systemic immune response to ischaemic stroke that occurs in three phases: an acute phase (Day 2) characterized by increased signal transducer and activator of transcription 3 (STAT3) signalling responses in innate immune cell types, an intermediate phase (Day 5) characterized by increased cAMP response element-binding protein (CREB) signalling responses in adaptive immune cell types, and a late phase (Day 90) by persistent elevation of neutrophils, and immunoglobulin M+ (IgM+) B cells. By Day 365 there was no detectable difference between these samples and those from an age- and gender-matched patient cohort without stroke. When regressed against the change in the Montreal Cognitive Assessment scores between Days 90 and 365 after stroke, the acute inflammatory phase Elastic Net model correlated with post-stroke cognitive trajectories (r = -0.692, Bonferroni-corrected P = 0.039). The results demonstrate the utility of a deep immune profiling approach with mass cytometry for the identification of clinically relevant immune correlates of long-term cognitive trajectories.

  View details for DOI 10.1093/brain/awz022

  View details for PubMedID 30860258

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Han, X., Ghaemi, M. S., Ando, K., Peterson, L., Ganio, E. A., Tsai, A. S., Gaudilliere, D., Einhaus, J., Tsai, E. S., Stanley, N. M., Culos, A., Taneda, A. H., Fallahzadeh, R., Wong, R. J., Winn, V. D., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. SAGE PUBLICATIONS INC. 2019: 271A

  View details for Web of Science ID 000459610400616

• Deep Immune Profiling of the Post-Stroke Peripheral Immune Response Reveals Tri-phasic Response and Correlations With Long-Term Cognitive Outcomes Tsai, A. S., Berry, K., Beneyto, M. M., Gaudilliere, D., Ganio, E. A., Choisy, B., Djebali, K., Baca, Q., Quach, L., Drag, L., Lansberg, M. G., Angst, M. S., Gaudilliere, B., Buckwalter, M. S., Aghaeepour, N. LIPPINCOTT WILLIAMS & WILKINS. 2019

  View details for Web of Science ID 000478733402484

• Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy. Bioinformatics (Oxford, England) Ghaemi, M. S., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. T., Lee-McMullen, B., Lehallier, B., Robaczewska, A., Mcilwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., Gibbs, R., Darmstadt, G. L., Lewis, D. B., Partovi Nia, V., Agard, B., Tibshirani, R., Nolan, G., Snyder, M. P., Relman, D. A., Quake, S. R., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2019; 35 (1): 95–103

  Abstract

  Motivation: Multiple biological clocks govern a healthy pregnancy. These biological mechanisms produce immunologic, metabolomic, proteomic, genomic and microbiomic adaptations during the course of pregnancy. Modeling the chronology of these adaptations during full-term pregnancy provides the frameworks for future studies examining deviations implicated in pregnancy-related pathologies including preterm birth and preeclampsia.Results: We performed a multiomics analysis of 51 samples from 17 pregnant women, delivering at term. The datasets included measurements from the immunome, transcriptome, microbiome, proteome and metabolome of samples obtained simultaneously from the same patients. Multivariate predictive modeling using the Elastic Net (EN) algorithm was used to measure the ability of each dataset to predict gestational age. Using stacked generalization, these datasets were combined into a single model. This model not only significantly increased predictive power by combining all datasets, but also revealed novel interactions between different biological modalities. Future work includes expansion of the cohort to preterm-enriched populations and in vivo analysis of immune-modulating interventions based on the mechanisms identified.Availability and implementation: Datasets and scripts for reproduction of results are available through: https://nalab.stanford.edu/multiomics-pregnancy/.Supplementary information: Supplementary data are available at Bioinformatics online.

  View details for PubMedID 30561547

• Differential Dynamics of the Maternal Immune System in Healthy Pregnancy and Preeclampsia. Frontiers in immunology Han, X., Ghaemi, M. S., Ando, K., Peterson, L. S., Ganio, E. A., Tsai, A. S., Gaudilliere, D. K., Stelzer, I. A., Einhaus, J., Bertrand, B., Stanley, N., Culos, A., Tanada, A., Hedou, J., Tsai, E. S., Fallahzadeh, R., Wong, R. J., Judy, A. E., Winn, V. D., Druzin, M. L., Blumenfeld, Y. J., Hlatky, M. A., Quaintance, C. C., Gibbs, R. S., Carvalho, B., Shaw, G. M., Stevenson, D. K., Angst, M. S., Aghaeepour, N., Gaudilliere, B. 2019; 10: 1305

  Abstract

  Preeclampsia is one of the most severe pregnancy complications and a leading cause of maternal death. However, early diagnosis of preeclampsia remains a clinical challenge. Alterations in the normal immune adaptations necessary for the maintenance of a healthy pregnancy are central features of preeclampsia. However, prior analyses primarily focused on the static assessment of select immune cell subsets have provided limited information for the prediction of preeclampsia. Here, we used a high-dimensional mass cytometry immunoassay to characterize the dynamic changes of over 370 immune cell features (including cell distribution and functional responses) in maternal blood during healthy and preeclamptic pregnancies. We found a set of eight cell-specific immune features that accurately identified patients well before the clinical diagnosis of preeclampsia (median area under the curve (AUC) 0.91, interquartile range [0.82-0.92]). Several features recapitulated previously known immune dysfunctions in preeclampsia, such as elevated pro-inflammatory innate immune responses early in pregnancy and impaired regulatory T (Treg) cell signaling. The analysis revealed additional novel immune responses that were strongly associated with, and preceded the onset of preeclampsia, notably abnormal STAT5ab signaling dynamics in CD4+T cell subsets (AUC 0.92, p = 8.0E-5). These results provide a global readout of the dynamics of the maternal immune system early in pregnancy and lay the groundwork for identifying clinically-relevant immune dysfunctions for the prediction and prevention of preeclampsia.

  View details for DOI 10.3389/fimmu.2019.01305

  View details for PubMedID 31263463

  View details for PubMedCentralID PMC6584811

• Multiomics modeling of the immunome, transcriptome, microbiome, proteome and metabolome adaptations during human pregnancy BIOINFORMATICS Ghaemi, M., DiGiulio, D. B., Contrepois, K., Callahan, B., Ngo, T. M., Lee-McMullen, B., Lehallier, B., Robaczewska, A., Mcilwain, D., Rosenberg-Hasson, Y., Wong, R. J., Quaintance, C., Culos, A., Stanley, N., Tanada, A., Tsai, A., Gaudilliere, D., Ganio, E., Han, X., Ando, K., McNeil, L., Tingle, M., Wise, P., Maric, I., Sirota, M., Wyss-Coray, T., Winn, V. D., Druzin, M. L., Gibbs, R., Darmstadt, G. L., Lewis, D. B., Nia, V., Agard, B., Tibshirani, R., Nolan, G., Snyder, M. P., Relman, D. A., Quake, S. R., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2019; 35 (1): 95–103

  View details for DOI 10.1093/bioinformatics/bty537

  View details for Web of Science ID 000459313900012

• Mass Cytometry and Proteomic Based Prediction of the Onset of Labor. Ando, K., Han, X., Ghaemi, S., Tsai, A., Ganio, E., Gaudilliere, D., Culos, T., Shaw, G., Wong, R., Stevenson, D., Carvalho, B., Tingle, M., Angst, M., Aghaeepor, N., Gaudilliere, B., Stanford March Dimes Prematurity SAGE PUBLICATIONS INC. 2018: 153A

  View details for Web of Science ID 000429928200292

• An immune clock of human pregnancy. Science immunology Aghaeepour, N., Ganio, E. A., Mcilwain, D., Tsai, A. S., Tingle, M., Van Gassen, S., Gaudilliere, D. K., Baca, Q., McNeil, L., Okada, R., Ghaemi, M. S., Furman, D., Wong, R. J., Winn, V. D., Druzin, M. L., El-Sayed, Y. Y., Quaintance, C., Gibbs, R., Darmstadt, G. L., Shaw, G. M., Stevenson, D. K., Tibshirani, R., Nolan, G. P., Lewis, D. B., Angst, M. S., Gaudilliere, B. 2017; 2 (15)

  Abstract

  The maintenance of pregnancy relies on finely tuned immune adaptations. We demonstrate that these adaptations are precisely timed, reflecting an immune clock of pregnancy in women delivering at term. Using mass cytometry, the abundance and functional responses of all major immune cell subsets were quantified in serial blood samples collected throughout pregnancy. Cell signaling-based Elastic Net, a regularized regression method adapted from the elastic net algorithm, was developed to infer and prospectively validate a predictive model of interrelated immune events that accurately captures the chronology of pregnancy. Model components highlighted existing knowledge and revealed previously unreported biology, including a critical role for the interleukin-2-dependent STAT5ab signaling pathway in modulating T cell function during pregnancy. These findings unravel the precise timing of immunological events occurring during a term pregnancy and provide the analytical framework to identify immunological deviations implicated in pregnancy-related pathologies.

  View details for PubMedID 28864494

• Freehand Versus Guided Surgery: Factors Influencing Accuracy of Dental Implant Placement. Implant dentistry Choi, W., Nguyen, B. C., Doan, A., Girod, S., Gaudilliere, B., Gaudilliere, D. 2017

  Abstract

  Patient anatomy, practitioner experience, and surgical approach are all factors that influence implant accuracy. However, the relative importance of each factor is poorly understood. The present study aimed to identify which factors most critically determine implant accuracy to aid the practitioner in case selection for guided versus freehand surgery.One practitioner's ideal implant angulation and position was compared with his achieved position radiographically for 450 implants placed using a conventional freehand method. The relative contribution of 11 demographic, anatomical, and surgical factors to the accuracy of implant placement was systematically quantified.The most important predictors of angulation and position accuracy were the number of adjacent implants placed and the tooth-borne status of the site. Immediate placement also significantly increased position accuracy, whereas cases with narrow sites were significantly more accurate in angulation. Accuracy also improved with the practitioner's experience.These results suggest tooth-borne, single-implant cases performed later in the practitioner's experience are most appropriate for freehand placement, whereas guided surgery should be considered to improve accuracy for multiple-implant cases in edentulous or partially edentulous sites.

  View details for DOI 10.1097/ID.0000000000000620

  View details for PubMedID 28753195

• Freehand Versus Guided Surgery: Factors Influencing Accuracy of Dental Implant Placement. Implant dentistry Choi, W., Nguyen, B. C., Doan, A., Girod, S., Gaudilliere, B., Gaudilliere, D. 2017; 26 (4): 500–509

  Abstract

  Patient anatomy, practitioner experience, and surgical approach are all factors that influence implant accuracy. However, the relative importance of each factor is poorly understood. The present study aimed to identify which factors most critically determine implant accuracy to aid the practitioner in case selection for guided versus freehand surgery.One practitioner's ideal implant angulation and position was compared with his achieved position radiographically for 450 implants placed using a conventional freehand method. The relative contribution of 11 demographic, anatomical, and surgical factors to the accuracy of implant placement was systematically quantified.The most important predictors of angulation and position accuracy were the number of adjacent implants placed and the tooth-borne status of the site. Immediate placement also significantly increased position accuracy, whereas cases with narrow sites were significantly more accurate in angulation. Accuracy also improved with the practitioner's experience.These results suggest tooth-borne, single-implant cases performed later in the practitioner's experience are most appropriate for freehand placement, whereas guided surgery should be considered to improve accuracy for multiple-implant cases in edentulous or partially edentulous sites.

  View details for PubMedID 28731896

• A Comparative Assessment of Implant Site Viability in Humans and Rats. Journal of dental research Chen, C. H., Pei, X., Tulu, U. S., Aghvami, M., Chen, C. T., Gaudillière, D., Arioka, M., Maghazeh Moghim, M., Bahat, O., Kolinski, M., Crosby, T. R., Felderhoff, A., Brunski, J. B., Helms, J. A. 2017: 22034517742631

  Abstract

  Our long-term objective is to devise methods to improve osteotomy site preparation and, in doing so, facilitate implant osseointegration. As a first step in this process, we developed a standardized oral osteotomy model in ovariectomized rats. There were 2 unique features to this model: first, the rats exhibited an osteopenic phenotype, reminiscent of the bone health that has been reported for the average dental implant patient population. Second, osteotomies were produced in healed tooth extraction sites and therefore represented the placement of most implants in patients. Commercially available drills were then used to produce osteotomies in a patient cohort and in the rat model. Molecular, cellular, and histologic analyses demonstrated a close alignment between the responses of human and rodent alveolar bone to osteotomy site preparation. Most notably in both patients and rats, all drilling tools created a zone of dead and dying osteocytes around the osteotomy. In rat tissues, which could be collected at multiple time points after osteotomy, the fate of the dead alveolar bone was followed. Over the course of a week, osteoclast activity was responsible for resorbing the necrotic bone, which in turn stimulated the deposition of a new bone matrix by osteoblasts. Collectively, these analyses support the use of an ovariectomy surgery rat model to gain insights into the response of human bone to osteotomy site preparation. The data also suggest that reducing the zone of osteocyte death will improve osteotomy site viability, leading to faster new bone formation around implants.

  View details for PubMedID 29202640

• Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery. Journal of immunology (Baltimore, Md. : 1950) Aghaeepour, N., Kin, C., Ganio, E. A., Jensen, K. P., Gaudilliere, D. K., Tingle, M., Tsai, A., Lancero, H. L., Choisy, B., McNeil, L. S., Okada, R., Shelton, A. A., Nolan, G. P., Angst, M. S., Gaudilliere, B. L. 2017

  Abstract

  Application of high-content immune profiling technologies has enormous potential to advance medicine. Whether these technologies reveal pertinent biology when implemented in interventional clinical trials is an important question. The beneficial effects of preoperative arginine-enriched dietary supplements (AES) are highly context specific, as they reduce infection rates in elective surgery, but possibly increase morbidity in critically ill patients. This study combined single-cell mass cytometry with the multiplex analysis of relevant plasma cytokines to comprehensively profile the immune-modifying effects of this much-debated intervention in patients undergoing surgery. An elastic net algorithm applied to the high-dimensional mass cytometry dataset identified a cross-validated model consisting of 20 interrelated immune features that separated patients assigned to AES from controls. The model revealed wide-ranging effects of AES on innate and adaptive immune compartments. Notably, AES increased STAT1 and STAT3 signaling responses in lymphoid cell subsets after surgery, consistent with enhanced adaptive mechanisms that may protect against postsurgical infection. Unexpectedly, AES also increased ERK and P38 MAPK signaling responses in monocytic myeloid-derived suppressor cells, which was paired with their pronounced expansion. These results provide novel mechanistic arguments as to why AES may exert context-specific beneficial or adverse effects in patients with critical illness. This study lays out an analytical framework to distill high-dimensional datasets gathered in an interventional clinical trial into a fairly simple model that converges with known biology and provides insight into novel and clinically relevant cellular mechanisms.

  View details for PubMedID 28794234

• Mapping the Fetomaternal Peripheral Immune System at Term Pregnancy. Journal of immunology Fragiadakis, G. K., Baca, Q. J., Gherardini, P. F., Ganio, E. A., Gaudilliere, D. K., Tingle, M., Lancero, H. L., McNeil, L. S., Spitzer, M. H., Wong, R. J., Shaw, G. M., Darmstadt, G. L., Sylvester, K. G., Winn, V. D., Carvalho, B., Lewis, D. B., Stevenson, D. K., Nolan, G. P., Aghaeepour, N., Angst, M. S., Gaudilliere, B. L. 2016

  Abstract

  Preterm labor and infections are the leading causes of neonatal deaths worldwide. During pregnancy, immunological cross talk between the mother and her fetus is critical for the maintenance of pregnancy and the delivery of an immunocompetent neonate. A precise understanding of healthy fetomaternal immunity is the important first step to identifying dysregulated immune mechanisms driving adverse maternal or neonatal outcomes. This study combined single-cell mass cytometry of paired peripheral and umbilical cord blood samples from mothers and their neonates with a graphical approach developed for the visualization of high-dimensional data to provide a high-resolution reference map of the cellular composition and functional organization of the healthy fetal and maternal immune systems at birth. The approach enabled mapping of known phenotypical and functional characteristics of fetal immunity (including the functional hyperresponsiveness of CD4(+) and CD8(+) T cells and the global blunting of innate immune responses). It also allowed discovery of new properties that distinguish the fetal and maternal immune systems. For example, examination of paired samples revealed differences in endogenous signaling tone that are unique to a mother and her offspring, including increased ERK1/2, MAPK-activated protein kinase 2, rpS6, and CREB phosphorylation in fetal Tbet(+)CD4(+) T cells, CD8(+) T cells, B cells, and CD56(lo)CD16(+) NK cells and decreased ERK1/2, MAPK-activated protein kinase 2, and STAT1 phosphorylation in fetal intermediate and nonclassical monocytes. This highly interactive functional map of healthy fetomaternal immunity builds the core reference for a growing data repository that will allow inferring deviations from normal associated with adverse maternal and neonatal outcomes.

  View details for PubMedID 27793998

• Gender disparities in scholarly productivity of US academic surgeons JOURNAL OF SURGICAL RESEARCH Mueller, C. M., Gaudilliere, D. K., Kin, C., Menorca, R., Girod, S. 2016; 203 (1): 28-33

  Abstract

  Female surgeons have faced significant challenges to promotion over the past decades, with attrition rates supporting a lack of improvement in women's position in academia. We examine gender disparities in research productivity, as measured by the number of citations, publications, and h-indices, across six decades.The online profiles of full-time faculty members of surgery departments of three academic centers were reviewed. Faculty members were grouped into six cohorts by decade, based on year of graduation from medical school. Differences between men and women across cohorts as well as by academic rank were examined.The profiles of 978 surgeons (234 women and 744 men) were reviewed. The number of female faculty members in the institutions increased significantly over time, reaching the current percentage of 35.3%. Significant differences in number of articles published were noted at the assistant and full but not at the associate, professor level. Women at these ranks had fewer publications than men. Gender differences were also found in all age cohorts except among the most recent who graduated in the 2000s. The impact of publications, as measured by h-index and number of citations, was not consistently significantly different between the genders at any age or rank.We identified a consistent gender disparity in the number of publications for female faculty members across a 60-year span. Although the youngest cohort, those who graduated in the 2000s, appeared to avoid the gender divide, our data indicate that overall women still struggle with productivity in the academic arena.

  View details for DOI 10.1016/j.jss.2016.03.060

  View details for Web of Science ID 000378170200005

  View details for PubMedID 27338531

• Haptic feedback improves surgeons' user experience and fracture reduction in facial trauma simulation JOURNAL OF REHABILITATION RESEARCH AND DEVELOPMENT Girod, S., Schvartzman, S. C., Gaudilliere, D., Salisbury, K., Silva, R. 2016; 53 (5): 561-569

  Abstract

  Computer-assisted surgical (CAS) planning tools are available for craniofacial surgery, but are usually based on computer-aided design (CAD) tools that lack the ability to detect the collision of virtual objects (i.e., fractured bone segments). We developed a CAS system featuring a sense of touch (haptic) that enables surgeons to physically interact with individual, patient-specific anatomy and immerse in a three-dimensional virtual environment. In this study, we evaluated initial user experience with our novel system compared to an existing CAD system. Ten surgery resident trainees received a brief verbal introduction to both the haptic and CAD systems. Users simulated mandibular fracture reduction in three clinical cases within a 15 min time limit for each system and completed a questionnaire to assess their subjective experience. We compared standard landmarks and linear and angular measurements between the simulated results and the actual surgical outcome and found that haptic simulation results were not significantly different from actual postoperative outcomes. In contrast, CAD results significantly differed from both the haptic simulation and actual postoperative results. In addition to enabling a more accurate fracture repair, the haptic system provided a better user experience than the CAD system in terms of intuitiveness and self-reported quality of repair.

  View details for DOI 10.1682/JRRD.2015.03.0043

  View details for Web of Science ID 000387593000005

  View details for PubMedID 27898160

• Caries Management By Risk Assessment in a Cleft and Craniofacial Center JOURNAL OF CRANIOFACIAL SURGERY Gaudilliere, D., Thakur, Y., Ku, M., Kaur, A., Shrestha, P., Girod, S. C. 2014; 25 (6): E529-E536

  Abstract

  Patients with craniofacial anomalies have an increased incidence of dental caries. The prevention program "Caries Management By Risk Assessment" (CAMBRA) has been previously validated but has not yet been introduced at a widespread level in a medical setting, particularly for this high-risk population.In this cross-sectional study, we aimed to evaluate the feasibility of implementing CAMBRA during the medical visit at an institutional tertiary care center, which treats children with craniofacial anomalies. The study included 161 participants aged 1 to 18 years. Patients and parents received a personalized educational session, toothbrushing tutorial, and fluoride varnish application. We assessed the prevalence of dental caries, caries risk factors, and knowledge of oral hygiene in this patient population.The overall caries prevalence in this group was higher than average (57% compared with 42%, according to the Centers for Disease Control and Prevention). The most prevalent risk factors were developmental delay, deep pits/fissures, low socioeconomic status, orthodontic appliances, and carbohydrate snacks. The greatest predictors of dental caries were having 1 or more risk factors and having low socioeconomic status. In summary, children with craniofacial anomalies were at high risk for dental caries, with high rates of risk factors and low rates of preventive factors.Our findings revealed that basic oral hygiene standards are not being met in this high-risk population, highlighting the need for implementation of protocols such as CAMBRA. The results of this study can aid healthcare workers in craniofacial centers and children's hospitals to improve the understanding of oral hygiene and dental care of their patients.

  View details for DOI 10.1097/SCS.0000000000001040

  View details for Web of Science ID 000345012000008

• Computer-Aided Trauma Simulation System With Haptic Feedback Is Easy and Fast for Oral-Maxillofacial Surgeons to Learn and Use JOURNAL OF ORAL AND MAXILLOFACIAL SURGERY Schvartzman, S. C., Silva, R., Salisbury, K., Gaudilliere, D., Girod, S. 2014; 72 (10): 1984-1993

  Abstract

  Computer-assisted surgical (CAS) planning tools have become widely available in craniomaxillofacial surgery, but are time consuming and often require professional technical assistance to simulate a case. An initial oral and maxillofacial (OM) surgical user experience was evaluated with a newly developed CAS system featuring a bimanual sense of touch (haptic).Three volunteer OM surgeons received a 5-minute verbal introduction to the use of a newly developed haptic-enabled planning system. The surgeons were instructed to simulate mandibular fracture reductions of 3 clinical cases, within a 15-minute time limit and without a time limit, and complete a questionnaire to assess their subjective experience with the system. Standard landmarks and linear and angular measurements between the simulated results and the actual surgical outcome were compared.After the 5-minute instruction, all 3 surgeons were able to use the system independently. The analysis of standardized anatomic measurements showed that the simulation results within a 15-minute time limit were not significantly different from those without a time limit. Mean differences between measurements of surgical and simulated fracture reductions were within current resolution limitations in collision detection, segmentation of computed tomographic scans, and haptic devices. All 3 surgeons reported that the system was easy to learn and use and that they would be comfortable integrating it into their daily clinical practice for trauma cases.A CAS system with a haptic interface that capitalizes on touch and force feedback experience similar to operative procedures is fast and easy for OM surgeons to learn and use.

  View details for DOI 10.1016/j.joms.2014.05.007

  View details for Web of Science ID 000342466500018

• Body dysmorphic disorder and psychological distress in orthognathic surgery patients. Journal of oral and maxillofacial surgery Collins, B., Gonzalez, D., Gaudilliere, D. K., Shrestha, P., Girod, S. 2014; 72 (8): 1553-1558

  Abstract

  Body dysmorphic disorder (BDD) is a distressing condition involving preoccupation with an imagined or exaggerated deformity. The purpose of our study was to investigate the presence of BDD and its comorbidity with anxiety, depression, and obsessive-compulsive disorder (OCD) in patients undergoing orthognathic surgery (OS).The present prospective study included 99 patients from the outpatient oral and maxillofacial surgery clinic at Stanford University who requested OS. The incidence of BDD, depression, anxiety, and OCD was assessed preoperatively using validated self-report measures. To determine the prevalence of Axis I psychological symptoms among patients, the descriptive and bivariate statistics were computed. P < .05 was considered significant.In our sample, 13 patients (13%) screened positive for BDD. We did not find any significant correlations between the presence of BDD and gender, race, age, or marital status. Depressive symptoms were reported by 42% of the patients, OCD symptoms by 29%, and mild, moderate, and severe anxiety by 14%, 5%, and 4%, respectively. Using Spearman correlations, we found significant correlations between BDD and anxiety, depression, and OCD (P < .01).The results of the present study suggest that the rates of BDD, depression, anxiety, and OCD are high in patients undergoing OS. Furthermore, we found a strong correlation between BDD and anxiety, OCD, and depression in these patients. Future studies are necessary to determine the postoperative changes in these psychological disorders and whether these changes are affected by having positive BDD screening results at baseline.

  View details for DOI 10.1016/j.joms.2014.01.011

  View details for PubMedID 24582136