Stony Brook Research Articles

A New Dressing System Reduces the Number of Dressing Changes in the Primary Total Knee Arthroplasty: A Randomized Controlled Trial.

PurposeWe devised a novel dressing system to accelerate the recovery after total knee arthroplasty (TKA). The purpose of this study was to assess the clinical outcomes and economic expenses of the new dressing system.MethodsIn this randomized and controlled trial, we enrolled 98 patients who underwent the first unilateral TKA between September 2020 and June 2021. The patients were randomly assigned to one of two groups: the intervention (the new dressing system group) or the control (the traditional gauze dressing) group. We gathered and evaluated patient data including age, gender, body mass index, surgical side, number of dressing changes, post-operative hospital stay, dressing-related expense, satisfaction, pain and function scores, wound scores and wound-related complications.ResultsThe number of dressing changes and post-operative hospital stay in the intervention group were significantly less than in the control group (p = 0.000, p = 0.002). Satisfaction in the intervention group was significantly higher than in the control group's (p = 0.000). There were no significant differences between the two groups in dressing-related expense, pain and function scores. During the one month follow-up, the intervention group's Stony Brook Scar Evaluation Scale (SBSES) was considerably higher than the control group's (p = 0.012).ConclusionThe new dressing system can reduce the number of dressing changes and post-operative hospital stays while increasing patient satisfaction with no difference in medical costs in TKA. This wound dressing system has potential for application in TKAClinical Trial Registrationhttps://clinicaltrials.gov, identifier ChiCTR2000033814.

The impact of bariatric surgery on the risk of coronary revascularization.

Metabolic surgery is associated with improved cardiovascular risk profile. Randomized and observational studies exploring the impact of bariatric surgery on follow-up coronary revascularization (CR) as a primary endpoint are limited. To identify the impact of metabolic surgery on the risk of follow-up CR, including percutaneous coronary revascularization (PCI) and coronary artery bypass grafting (CABG) SETTING: Stony Brook Department of Surgery, Stony Brook University Hospital, New York, United States. A retrospective analysis was performed for patients with obesity between 2006 and September 2015. Patients were divided into those with history of metabolic surgery and those without. Patient were also stratified by bariatric surgery type. All study groups were followed till 2018 and for at least 3 years to monitor the development of the primary endpoint-any CR including PCI or CABG. The study population with obesity was 515,307 patients; 95,901 with history of surgery versus 419,406 matched patients without. A total of 12,873 (13.4%) with surgery and 51,478 (12.27%) without were lost to follow-up by 2018. The group with history of surgery had a reduced risk of future CR (hazard ratio [HR], .46; 95% confidence interval [CI]: .42-.50; P < .0001), PCI (HR, .45; 95% CI: .41-.49; P < .0001) and CABG (HR, .49; 95% CI:.42-.56; P < .0001). In subgroup analysis, laparoscopic adjustable gastric banding compared with Roux-en-Y gastric bypass (RYGB) was associated with higher follow-up CR (HR, 1.34; 95% CI: 1.11-1.63; P < .01) and PCI (HR, 1.34; 95% CI: 1.07-1.68; P < .05). Bariatric surgery is associated with reduced risk of future CR, PCI, and CABG. Upon subgroup analysis, RYGB was associated with reduced risk of PCI and CR.

Abstract 241: Length Of Hospital Stay Is Lessened In Patients That Present With Chest Pain When Serial Troponins Are Measured Every Three Hours Compared To Every Six Hours

Troponins have close to 100% sensitivity when drawn 6 to 12 hours after the start of chest pain in acute coronary syndrome (ACS). Length of hospital stay (LOS) is a marker of efficient hospital management. Since troponin measurements at 3 (Q3H) and 6-hour (Q6H) intervals have comparable efficacy for ACS, we suggest considering Q3H troponins as opposed to Q6H in chest pain admissions to shorten LOS. A retrospective review of 389 patients who presented to Stony Brook Southampton Hospital in 2020 and 2021 with chest pain was conducted. Two patient care strategies were examined, one involved collecting troponins Q6H and the other Q3H. 319 patients with the Q6H strategy were analyzed and 61.1% were ruled in for ACS. For the Q3H strategy, 70 patients were analyzed in which 42.9% were ruled in for ACS. LOS was defined as hours between triage and submission of discharge instructions. A two-sample t-test was performed to find correlation between frequency of troponins and LOS. A two-sample t-test assuming unequal variance was conducted to compare LOS in hours between Q6H and Q3H strategies. There was a significant difference in LOS for Q6H (M = 123, SD = 201) and Q3H (M = 66, SD = 135) troponins; t = 2.91, p = 0.002. These results suggest that LOS is lessened when troponin intervals are shorter. The variance was elevated (40249,18343), possibly due to patients who stayed longer for other needs, but mean LOS and standard deviation remained decreased both when excluding patients that stayed longer than one week, and when including all patients in the analysis. Optimizing LOS has a variety of benefits. In patients with chest pain, shortening the interval of troponin collection can lessen LOS. Studies of comparable efficacy of shorter troponin intervals in evaluation of ACS have led to the practice of trending at Q3H intervals in Europe. Our study demonstrates that trending troponins Q3H is viable in a community hospital in the United States and should be further investigated and considered in the future.

Abstract 243: Length Of Hospital Stay Is Lessened In Patients That Present With Chest Pain When Admitted To The Cardiology Service Compared To Other Services Within Our Teaching Hospital

Length of stay (LOS) in the hospital for patients presenting with chest pain can be influenced by several factors. In patients evaluated for acute coronary syndrome (ACS), admission to a cardiology service may allow patients to have a shorter LOS, while admission to a non-specialty service has been shown to potentially cause worse outcomes. In this analysis, we evaluate whether specialized care decreases LOS in patients who present with chest pain. A retrospective review of 389 patients who presented to Stony Brook Southampton Hospital in 2020 and 2021 with chest pain and underwent ACS evaluation was conducted. The two strategies involved admissions to the cardiology service or to other hospital services. We analyzed 95 cardiology admissions, 56.8% of which ruled in for ACS, and 294 admissions to other hospital services, 58.2% of which ruled in for ACS. LOS was defined as hours between triage and submission of discharge instructions. A two-sample t-test was performed to find correlations between admission service and length of hospital stay. A two-sample t-test assuming unequal variance was conducted to compare LOS in hours between patients admitted to the cardiology and non-cardiology services. There was a significant difference in LOS for the cardiology service (M = 48, SD = 55) and non-cardiology service (M = 134, SD = 214) conditions; t = 6.30, p &lt; 0.001. These results suggest that LOS is lessened when chest pain patients are admitted to the cardiology service. The variance was elevated (2985, 45849), possibly due to patients who stayed longer for other needs, but mean LOS and SD remained decreased when excluding patients that stayed longer than one week, and when including all patients in the analysis. When admitting a patient, there are many ways to optimize LOS. Admission of patients who present with chest pain to a specialty cardiology service may allow patients to have a more efficient LOS and should be further investigated and considered in the future.

Abstract 82: Post Coronary Angioplasty; Why Did My Patient Die?

Introduction: PCI interventions carry the risk of mortality. Minimizing by appropriate selection of patients, procedural technique, aftercare is critical. Despite advances, mortality risk has not significantly changed. Registries have been developed to assess and report outcomes/quality of care. Methods: Mortalities identified in the National Cardiovascular Disease Registry [NCDR] database from 2017-2020 @ Stony Brook University Hospital were identified and reviewed. The 2017 mortalities were compared with Vizient [Analytics providing cost/quality/value benchmarking &amp; practice improvement feedback] predictors of mortality (case mix index [CMI]) and NYS PCI database which use different risk adjusters. Results: There were 79 mortalities over 4 years (average age 79, M:F ratio 2.07 [54/26], 76 died in-house). Cause of death [59 cardiac, 8 neurologic, 4 infection, 1 hemorrhage, 1 pulmonary, 3 unknown; 10 cases attributed to surgery]. Mortality rates remained constant over time. Vizient expected mortality rates show poor correlation with NCDR predictions, which may reflect in part patient selection and unadjusted comorbidity. NCDR correlation with the more restrictive risk adjusted NYS DOH mortality rates is better than for Vizient. NCDR mortality for 2017 was 1.4% [21/1503]; NYS DOH 30 day reported OMR1.76%, EMR 1.36%, RAMR of 1.46% for all [26/1,476] cases and RAMR of 0.95% for non-emergent [1,150] cases. Conclusions: The NCDR provides a useful tool to benchmark PCI mortality, but unadjusted comorbidity, as demonstrated by mismatches in predicted outcomes] adversely affects observed mortality. Increased attention to patient selection may provide improvements in outcomes, decrease mortality risks, and improve value. Value: Improving patient selection improves NCDR mortality and result in lower costs and better reported outcomes. Patients, Providers and Payers may benefit from the provision of appropriate services and improved outcomes..

Co‐expression of Zeb‐1 and K17 provides new insights into the role of EMT in pancreatic cancer progression

INTRODUCTIONDespite improvements in screening and therapeutic options, pancreatic ductal adenocarcinoma (PDAC) has the third‐worst mortality rate, highlighting the need for better diagnostic and therapeutic approaches. Epithelial‐mesenchymal transition (EMT) has been conferred with a greater likelihood of metastasis in multiple cancers, specifically identified through markers like Zeb‐1 and Twist‐1. Zeb‐1 (Zinc Finger E‐Box Binding Homeobox 1) was shown to repress E‐cadherin expression, consequently inducing EMT.Our lab has previously identified K17 (Keratin 17) as an independent prognostic biomarker of poor outcomes in PDAC patients. While increasing evidence has suggested that EMT drives pancreatic cancer migration and metastasis, there have not been any well‐established connections between K17 expression and EMT. This study aims to provide new insights into the role of K17 in EMT by evaluating the co‐expression of K17 and Zeb‐1 in human PDAC biopsies. We hypothesized that tumor cells co‐express K17 and Zeb‐1 as a hallmark of EMT in PDAC. In addition, our study aims to establish a novel method of scoring Zeb‐1 expression in stromal fibroblasts via immunofluorescence to remove variability in the stromal constituency.METHODSPDAC tissues obtained from patients who underwent surgical resection between 2013 and 2017 at Stony Brook University (n=20) were selected for serial sectioning. Double immunofluorescence and single immunohistochemical staining were achieved by incubation with Zeb‐1 and K17 antibodies followed by incubation of fluorescent or biotin conjugated secondary antibodies, respectively. Histologic examination was performed using immunofluorescence staining along with H&amp;E and immunohistochemical staining (IHC) to determine concordance.RESULTS● IHC for Zeb‐1 was detected predominantly in the nuclei of centroacinar cells, benign proliferative ducts, and stromal fibroblasts. Focal nuclear staining in tumor epithelial was also detected in some cases.● High levels of K17 and but decreased expression of Zeb‐1 were detected in diffusely infiltrative cells with morphologic features of EMT.● Both high Zeb‐1 and high K17 protein expression were associated with poor survival of PDAC patients.● Using confocal immunofluorescence microscopy, we detected co‐localization of Zeb‐1 and K17 in PDAC.● We quantified Zeb‐1 protein expression in PDAC samples from 20 patients and confirmed significantly higher expression in the tumor‐associated stroma of high‐grade tumors compared to stromal fibroblasts in areas devoid of tumor.CONCLUSIONOur results provide evidence that K17 is upregulated in tumor epithelial cells that show morphologic immunohistochemical properties of EMT. The loss of Zeb‐1 in diffusely infiltrative, K17‐positive tumor cells suggests that EMT upregulates K17, driving tumor aggression. Our results suggest that K17 may enhance PDAC aggression by mediating EMT. Hence, novel therapeutic agents targeting K17 may improve clinical care for this biologically distinct patient subgroup.

An Examination of Electroconvulsive Therapy and Delivery of Care in Delirious Mania.

Delirious mania is a severe life-threatening syndrome, often misdiagnosed, and eminently treatable as a variant of catatonia. Our aim is to provide a comprehensive examination of electroconvulsive therapy (ECT) parameters and clinical features, as well as describe the delivery of care of the patients with delirious mania. A retrospective study was conducted of the ECT records at Stony Brook University Hospital from years 2014 to 2021. We characterized demographic and clinical variables, including psychiatric diagnoses and ECT parameters of patients identified with delirious mania. We identified 8 cases (3 women) of delirious mania with 8 corresponding acute treatment series. The mean age was 43.2 ± 12.6 years (range, 23-59 years). There were a total of 55 sessions performed with an average of 6.9 ± 2.6 (range, 5-13); 45 (82%) were bilateral (bifrontal or bitemporal) and 10 (18%) were right unilateral electrode placement. In 40 (73%) of the sessions, a high-energy stimulus was used (>60%, or 302 millicoulombs). Seizure duration measured on electroencephalogram was 47.4 ± 25.9 seconds (range, 0-143 seconds). Motor seizure duration measured on electromyogram was 32.7 ± 14.9 seconds (range, 0-66 seconds). In 6 cases, ECT was delayed for 10 days, and patients were given inappropriate treatments. High-dose antipsychotics caused worsened aggression and hemodynamic instability requiring physical restraints in 50% of cases. The clinical presentation of delirious mania remains poorly recognized, and its treatment is often delayed, which may result in negative outcomes. Bilateral ECT with high-energy dosing yielded a rapid remission of symptoms.

Society Awards 2021.

Society Awards 2021.

Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1

Abstract. The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation, and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia, and Brazil. The temporal correlation of simulated monthly accumulated precipitation against in situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e. elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but enforces a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and, finally, precipitation. Hence, this setup provides the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered a biodiversity hotspot of Peru. The robustness of this particular configuration of the model is backed up by similar results obtained during wet climate conditions observed in 2012.

Implementasi Algoritma Convolutional Neural Network Dalam Mengklasifikasi Kesegaran Buah Berdasarkan Citra Buah

The development of Information Technology today, which continues to grow, can help overcome various problems because matters relating to the advancement of Information Technology have spread to almost all levels of Indonesian society. Along with the development of Information Technology, it is also marked by Artificial Intelligence which can simulate human intelligence and help handle tasks in the real world. By utilizing Information Technology, one of them can be used in terms of the classification of fruit freshness. Where this classification will be very useful and help farmers and fruit consumers. This study describes the use of the Convolutional Neural Network to classify the freshness of the following fruits: apples, oranges, and bananas. And also using six classes, namely fresh apples, fresh oranges, fresh bananas, unfresh apples, unfresh oranges, and unfresh bananas. The first thing to do is Convolutional Neuronal Network training using an image dataset as input using data sources from Kaggle.com, published by "Student at Stony Brook University, New York, United States". To determine the performance of the various models produced, the following Confusion Matrix is used: accuracy, precision, and recall. The best average obtained is 93%.

Conversations with the Dead

Conversations with the Dead

Contributing Authors

Other| April 01 2022 Contributing Authors Women, Gender, and Families of Color (2022) 10 (1): 103–104. https://doi.org/10.5406/23260947.10.1.06 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Contributing Authors. Women, Gender, and Families of Color 1 April 2022; 10 (1): 103–104. doi: https://doi.org/10.5406/23260947.10.1.06 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All Scholarly Publishing CollectiveUniversity of Illinois PressWomen, Gender, and Families of Color Search Advanced Search AISHAH SCOTT is an assistant professor of health sciences and Black studies at Providence College. She received her PhD in twentieth-century American history at Stony Brook University, specializing in history of race and medicine. Her manuscript “Respectability Can't Save You: The AIDS Epidemic in Urban Black America” focuses on the impact of respectability politics on the Black community during the HIV/AIDS epidemic. She previously earned her Master's Degree in Public Policy from Stony Brook.VERONICA POPP is a visiting professor of English at the University of St. Francis in Chicagoland. Popp has been published in Still Point Arts Quarterly, Peitho, Bitch Media, Films for the Feminst Classroom, Gender Forum, and The Last Line. Her creative dissertation, Sick, was longlisted for the New Welsh Review (NWR) AmeriCymru Prize. She served as the graduate assistant for the Jane Nelson Institute for Women's Leadership and Center... You do not currently have access to this content.

Patient survey superior to EMR extraction for eliciting positive symptoms at COVID-19 illness onset.

Context: COVID-19 has ravaged nations around the world, with New York City (NYC) and the NYC suburbs being particular epicenters of COVID-19 infection. Suffolk County, NY, has reported over 200,000 cases and 3,000 deaths to date. Most initial COVID-19 research concerned hospitalized patients. Presenting symptomology in the outpatient setting was poorly characterized, as were the implications of specific presenting symptoms, beyond respiratory distress or hypoxia, for eventual disease severity. This made it difficult for primary care physicians to predict which patients would require hospitalization for COVID-19 disease or decompensate while being managed at home during a time when hospital and ICU beds were limited. Objective: To characterize presenting symptoms of COVID-19 infection in the outpatient setting and evaluate for correlation with severity, duration, and chronicity of disease. Study Design and Analysis: We collected survey data from both patient telephone interviews and electronic medical record (EMR) extraction. Patient characteristics were described using means and percentages when appropriate. Percentage of symptoms by severity level, symptom duration, COVID-19 testing and escalating medical care were calculated. To evaluate association of risk factors with positive testing, severity, duration and chronicity of symptoms, logistic regression was used. Patient characteristics, medications and repeat measures were evaluated as risk factors in logistic regression. Setting or Dataset: 107 patients with suspected and confirmed COVID-19 cases at the 3 primary care practices of Stony Brook University Hospital between March and December, 2020. Population Studied: adult, English speaking primary care patients with suspected or confirmed COVID-19 Intervention/Instrument: patient self report telephone survey, EMR data extraction survey Outcome Measures: symptom duration, symptom severity, persistence of symptoms at 3 month time point Significant Results: Patient self-report survey elicited nearly twice as many symptoms described at illness onset vs. those recorded in the EMR. Conclusions: Early in the setting of newly emerging infectious diseases, particularly those such as COVID-19 which involve multiple organ systems, patient self report of symptoms of illness rather than EMR extraction alone may be crucial both for identifying cases and in order to characterize pathophysiology of disease in real time.

Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients.

The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios.

The Corevent 2020: An Open-source, Rapid Design-build-Test Emergency Ventilator Developed for Covid-19

In the first quarter of 2020, SARs-CoV-2 (COVID-19) infections began to grow at an alarming rate despite drastic measures to reduce infection rates. Severe COVID-19 cases required mechanical ventilation, resulting in ventilator shortages worldwide. To address the ventilator shortages, the authors developed the CoreVent 2020, an emergency-use ventilator for adult patients that was designed, built, and tested in ten days. The CoreVent 2020 is a pressure-cycled, time-limited ventilator with a breath-assist mode that operates on standard pressurized oxygen and medical air. It provides adjustable peak inspiratory pressure (PIP) and positive end-expiratory pressure (PEEP). A medical-grade commercially available breathing circuit is used to minimize non-medical component requirements. The CoreVent 2020 was fabricated in-house at Stony Brook University Hospital and tested on three mechanical lung simulators in which the operating modes and alarm features were demonstrated. Animal studies were also performed in both normal breathing mode and breath-assisted modes. Arterial blood gas measurements confirmed that the ventilator provided satisfactory ventilation for the test subjects. The COVID-19 pandemic presented unique constraints on the design and innovation process not normally encountered in typical practice. Design decisions such as component choice, delivery time, and ease of high-volume, rapid manufacturing influenced all aspects of the design process. This aspect of the design/innovation process is also discussed, as well as an introductory discussion on how training and simulations can be developed so that innovation can occur efficiently in future crises situations.

First person – Hoi Tong Wong

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Hoi Tong Wong is first author on ‘ Decoupling SARS-CoV-2 ORF6 localization and interferon antagonism’, published in JCS. Hoi Tong is a PhD student in the lab of Dr Daniel Salamango at Stony Brook University, New York, investigating host–pathogen interactions at the molecular level.

Scar Perception After Two Surgical Approaches for Total Hip Arthroplasty.

BackgroundThere is a paucity of literature examining patients’ and health-care providers’ perception of surgical scars after total hip arthroplasty (THA). This study examined perception of surgical scars after direct anterior (DAA) or posterior approach (PA) for THA using validated scar-assessment scales.Material and methodsSeventy-five DAA and 75 PA THA patients underwent scar assessment using the Patient Observer Scar Assessment Scale (POSAS) and Stony Brook Scar Evaluation Scales. Mean age was different between the cohorts (DAA 67 vs PA 62 years, P = .01). All patients had subcuticular running closure, secured with skin adhesive glue. Mean time from THA to scar assessment was 3.1 and 3.6 years for the DAA and PA groups, respectively (P = .18).ResultsScar opinion on the POSAS patient-reported scale was graded closer to normal skin more often for DAA than for PA patients (P = .03). More irregularities were graded for the DAA scars on the POSAS observer scale (P = .02) and the Stony Brook Scar Evaluation Scales (P = .04). Age did not predict scar opinion on any of the scales (P > .05). Female gender and a history of keloids predicted poorer scar appearance (P = .001 and P = .02). Overall scar appearance was rated as “good” in 93% of the DAA and 91% of the PA patients (P = .63).ConclusionDifferences exist in DAA and PA scar perception based on validated scales. Future randomized trials in scar assessment may control for confounding variables such as age and gender, as well as potential biases when using scar assessment scales.

Risks and rewards of big-data in epigenomics research: an interview with Melanie Ehrlich.

Melanie Ehrlich, PhD, is a professor in the Tulane Cancer Center, the Tulane Center for Medical Bioinformatics and Genomics and the Hayward Human Genetics Program at Tulane Medical School, New Orleans, LA. She obtained her PhD in molecular biology in 1971 from the State University of New York at Stony Brook and completed postdoctoral research at Albert Einstein College of Medicine in 1972. She has been working on various aspects of epigenetics, starting with DNA methylation, since 1973. Her group made many first findings about DNA methylation (see below). For example, in 1982 and 1983, in collaboration with Charles Gehrke at the University of Missouri, she was the first to report tissue-specific and cancer-specific differences in overall DNA methylation in humans. In 1985, Xian-Yang Zhang and Richard Wang in her lab discovered a class of human DNA sequences specifically hypomethylated in sperm. In 1998, her group was the first to describe extensive losses of DNA methylation in pericentromeric and centromeric DNA repeats in human cancer. Her lab's many publications on the prevalence of both DNA hypermethylation and hypomethylation in the same cancers brought needed balance to our understanding of the epigenetics of cancer and to its clinical implications [1]. Besides working on cancer epigenetics, her research group has helped elucidate cytogenetic and gene expression abnormalities in the immunodeficiency, centromeric and facial anomalies (ICF) syndrome, a rare recessive disease often caused by mutations in DNMT3B. Her group also studied the epigenetics and transcriptomics of facioscapulohumeral muscular dystrophy (FSHD), whose disease locus is a tandem 3.3-kb repeat at subtelomeric 4q (that happens to be hypomethylated in ICF DNA [2]). Her study of FSHD has taken her in the direction of muscle (skeletal muscle, heart and aorta) epigenetics [3-6]. Recently, she has led research that applies epigenetics much more rigorously than usual to the evaluation of genetic variants from genome-wide association studies (GWAS) of osteoporosis and obesity. In continued collaboration with Sriharsa Pradhan at New England Biolabs and Michelle Lacey at Tulane University, she has compared 5-hydroxymethylcytosine and 5-methylcytosine clustering in various human tissues [7] and is studying myoblast methylomes that they generated by a new high-resolution enzymatic technique (enzymatic methyl-seq).

Index to Titles and Authors/Editors of Books Reviewed

Index to Titles and Authors/Editors of Books Reviewed

“There Will Be an End, But We Don’t Know When”: Preserving Diverse COVID-19 Pandemic Experiences Through Oral History

In March 2020, Stony Brook University Libraries began documenting campus communications and the events locally unfolding as the COVID-19 pandemic emerged across the world. These efforts evolved into a larger rapid-response collecting project planned and managed remotely. Documenting COVID-19: Stony Brook University Experiences was developed to preserve institutional history and experiences. With a collecting scope of personal narratives, photographs, and oral histories, the initiative presented new opportunities and challenges for Stony Brook University Libraries. This study discusses the oral history methods for the project from concept to implementation. It describes the planning and managing processes for the oral histories and considers the ethics of recording trauma experiences contemporaneously during the pandemic. The authors offer practical guidance for developing and processing a rapid-response collection of oral histories in a remote environment.