Detecting Cerebral Ischemia From Electroencephalography During Carotid Endarterectomy Using Machine Learning.

Monitoring cerebral neuronal activity via electroencephalography (EEG) during surgery can detect ischemia, a precursor to stroke. However, current neurophysiologist-based monitoring is prone to error. In this study, we evaluated machine learning (ML) for efficient and accurate ischemia detection. We trained supervised ML models on a dataset of 802 patients with intraoperative ischemia labels and evaluated them on an independent validation dataset of 30 patients with refined labels from five neurophysiologists. Our results show moderate-to-substantial agreement between neurophysiologists, with Cohen's kappa values between 0.59 and 0.74. Neurophysiologist performance ranged from 58-93% for sensitivity and 83-96% for specificity, while ML models demonstrated comparable ranges of 63-89% and 85-96%. Random Forest (RF), LightGBM (LGBM), and XGBoost RF achieved area under the receiver operating characteristic curve (AUROC) values of 0.92-0.93 and area under the precision-recall curve (AUPRC) values of 0.79-0.83. ML has the potential to improve intraoperative monitoring, enhancing patient safety and reducing costs.

Author Correction: γδ T cells producing interleukin-17A regulate adipose regulatory T cell homeostasis and thermogenesis.

In the version of this article initially published, three authors (Hui-Fern Kuoy, Adam P. Uldrich and Dale. I. Godfrey) and their affiliations, acknowledgments and contributions were not included. The correct information is as follows:Ayano C. Kohlgruber1,2, Shani T. Gal-Oz3, Nelson M. LaMarche1,2, Moto Shimazaki1, Danielle Duquette4, Hui-Fern Koay5,6, Hung N. Nguyen1, Amir I. Mina4, Tyler Paras1, Ali Tavakkoli7, Ulrich von Andrian2,8, Adam P. Uldrich5,6, Dale I. Godfrey5,6, Alexander S. Banks4, Tal Shay3, Michael B. Brenner1,10* and Lydia Lynch1,4,9,10*1Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, USA. 2Division of Medical Sciences, Harvard Medical School, Boston, MA, USA. 3Department of Life Sciences, Ben-Gurion University of the Negev, Beersheba, Israel. 4Division of Endocrinology, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. 5Department of Microbiology and Immunology, Peter Doherty Institute for Infection and Immunity, University of Melbourne, Parkville, Australia. 6ARC Centre of Excellence in Advanced Molecular Imaging, University of Melbourne, Parkville, Australia. 7Department of General and Gastrointestinal Surgery, Brigham and Women's Hospital, Boston, MA, USA. 8Department of Microbiology and Immunology, Harvard Medical School, Boston, MA, USA. 9School of Biochemistry and Immunology, Trinity College, Dublin, Ireland. 10These authors jointly supervised this work: Michael B. Brenner, Lydia Lynch. *e-mail: mbrenner@research.bwh.harvard.edu; llynch@bwh.harvard.eduAcknowledgementsWe thank A.T. Chicoine, flow cytometry core manager at the Human Immunology Center at BWH, for flow cytometry sorting. We thank D. Sant'Angelo (Rutgers Cancer Institute) for providing Zbtb16-/- mice and R. O'Brien (National Jewish Health) for providing Vg4/6-/- mice. Supported by NIH grant R01 AI11304603 (to M.B.B.), ERC Starting Grant 679173 (to L.L.), the National Health and Medical Research Council of Australia (1013667), an Australian Research Council Future Fellowship (FT140100278 for A.P.U.) and a National Health and Medical Research Council of Australia Senior Principal Research Fellowship (1117766 for D.I.G.).Author contributionsA.C.K., L.L., and M.B.B. conceived and designed the experiments, and wrote the manuscript. A.C.K., N.M.L., L.L., H.N.N., M.S., T.P., and D.D. performed the experiments. S.T.G.-O. and T.S. performed the RNA-seq analysis. A.S.B. and A.I.M. provided advice and performed the CLAMS experiments. A.T. provided human bariatric patient samples. Parabiosis experiments were performed in the laboratory of U.v.A. H.-F.K., A.P.U. and D.I.G provided critical insight into the TCR chain usage of PLZF+ γδ T cells. M.B.B., N.M.L., and L.L. critically reviewed the manuscript.The errors have been corrected in the HTML and PDF version of the article.Correction to: Nature Immunology doi:10.1038/s41590-018-0094-2 (2018), published online 18 April 2018.

Timing of Lymphedema After Treatment for Breast Cancer: When Are Patients Most At Risk?

PURPOSE: The purpose of the study was to determine when the risk of lymphedema is highest after treatment of breast cancer and which factors influence the time course of lymphedema development. METHODS AND MATERIALS: Between 2005 and 2017, 2171 women (with 2266 at-risk arms) who received surgery for unilateral or bilateral breast cancer at our institution were enrolled. Perometry was used to objectively assess limb volume preoperatively, and lymphedema was defined as a ≥10% relative arm-volume increase arising >3 months postoperatively. Multivariable regression was used to uncover risk factors associated with lymphedema, the Cox proportional hazards model was used to calculate lymphedema incidence, and the semiannual hazard rate of lymphedema was calculated. RESULTS: With a median follow-up of 4 years, the overall estimated 5-year cumulative incidence of lymphedema was 13.7%. Significant factors associated with lymphedema on multivariable analysis were high preoperative body mass index, axillary lymph node dissection (ALND), and regional lymph node radiation (RLNR). Patients receiving ALND with RLNR experienced the highest 5-year rate of lymphedema (31.2%), followed by those receiving ALND without RLNR (24.6%) and sentinel lymph node biopsy with RLNR (12.2%). Overall, the risk of lymphedema peaked between 12 and 30 months postoperatively; however, the time course varied as a function of therapy received. Early-onset lymphedema (<12 months postoperatively) was associated with ALND (HR [hazard ratio], 4.75; P < .0001) but not with RLNR (HR, 1.21; P = .55). In contrast, late-onset lymphedema (>12 months postoperatively) was associated with RLNR (HR, 3.86; P = .0001) and, to a lesser extent, ALND (HR, 1.86; P = .029). The lymphedema risk peaked between 6 and 12 months in the ALND-without-RLNR group, between 18 and 24 months in the ALND-with-RLNR group, and between 36 and 48 months in the group receiving sentinel lymph node biopsy with RLNR. CONCLUSIONS: The time course for lymphedema development depends on the breast cancer treatment received. ALND is associated with early-onset lymphedema, and RLNR is associated with late-onset lymphedema. These results can influence clinical practice to guide lymphedema surveillance strategies and patient education.

γδ T cells producing interleukin-17A regulate adipose regulatory T cell homeostasis and thermogenesis.

γδ T cells are situated at barrier sites and guard the body from infection and damage. However, little is known about their roles outside of host defense in nonbarrier tissues. Here, we characterize a highly enriched tissue-resident population of γδ T cells in adipose tissue that regulate age-dependent regulatory T cell (Treg) expansion and control core body temperature in response to environmental fluctuations. Mechanistically, innate PLZF+ γδ T cells produced tumor necrosis factor and interleukin (IL) 17 A and determined PDGFRα+ and Pdpn+ stromal-cell production of IL-33 in adipose tissue. Mice lacking γδ T cells or IL-17A exhibited decreases in both ST2+ Treg cells and IL-33 abundance in visceral adipose tissue. Remarkably, these mice also lacked the ability to regulate core body temperature at thermoneutrality and after cold challenge. Together, these findings uncover important physiological roles for resident γδ T cells in adipose tissue immune homeostasis and body-temperature control.

Association Between Precautionary Behaviors and Breast Cancer-Related Lymphedema in Patients Undergoing Bilateral Surgery.

Purpose This study examined the lifestyle and clinical risk factors for lymphedema in a cohort of patients who underwent bilateral breast cancer surgery. Patients and Methods Between 2013 and 2016, 327 patients who underwent bilateral breast cancer surgery were prospectively screened for arm lymphedema as quantified by the weight-adjusted volume change (WAC) formula. Arm perometry and subjective data were collected preoperatively and at regular intervals postoperatively. At the time of each measurement, patients completed a risk assessment survey that reported the number of blood draws, injections, blood pressure readings, trauma to the at-risk arm, and number of flights since the previous measurement. Generalized estimating equations were applied to ascertain the association among arm volume changes, clinical factors, and risk exposures. Results The cohort comprised 327 patients and 654 at-risk arms, with a median postoperative follow-up that ranged from 6.1 to 68.2 months. Of the 654 arms, 83 developed lymphedema, defined as a WAC ≥ 10% relative to baseline. On multivariable analysis, none of the lifestyle risk factors examined through the risk assessment survey were significantly associated with increased WAC. Multivariable analysis demonstrated that having a body mass index ≥ 25 kg/m2 at the time of breast cancer diagnosis ( P = .0404), having undergone axillary lymph node dissection ( P = .0464), and receipt of adjuvant chemotherapy ( P = .0161) were significantly associated with increased arm volume. Conclusion Blood pressure readings, blood draws, injections, and number or duration of flights were not significantly associated with increases in arm volume in this cohort. These findings may help to guide patient education about lymphedema risk reduction strategies for those who undergo bilateral breast cancer surgery.