Identification of brown adipose tissue in mice with fat-water IDEAL-MRI

Purpose: To investigate the feasibility of using IDEAL (Iterative Decomposition with Echo Asymmetry and Least squares estimation) fat-water imaging and the resultant fat fraction metric in detecting brown adipose tissue (BAT) in mice, and in differentiating BAT from white adipose tissue (WAT). Materials and Methods: Excised WAT and BAT samples and whole-mice carcasses were imaged with a rapid three-dimensional fat-water IDEAL-SPGR sequence on a 3 Tesla scanner using a single-channel wrist coil. An isotropic voxel size of 0.6 mm was used. Excised samples were also scanned with single-voxel proton spectroscopy. Fat fraction images from IDEAL were reconstructed online using research software, and regions of WAT and BAT were quantified. Results: A broad fat fraction range for BAT was observed (40-80%), in comparison to a tighter and higher WAT range of 90-93%, in both excised tissue samples and in situ. Using the fat fraction metric, the interscapular BAT depot in each carcass could be clearly identified, as well as peri-renal and inguinal depots that exhibited a mixed BAT and WAT phenotype appearance. Conclusion: Due to BAT's multi-locular fat distribution and extensive mitochondrial, cytoplasm, and vascular supply, its fat content is significantly less than that of WAT. We have demonstrated that the fat fraction metric from IDEAL-MRI is a sensitive and quantitative approach to noninvasively characterize BAT

Characterization of T/B cell antigen specific-engineered tumors for studying T cell and GC B cell function in a murine model of NSCLC

View full abstracthttps://openworks.mdanderson.org/leading-edge/1013/thumbnail.jp

Effects of Hepatocyte Growth Factor on Viability and Biotransformation Functions of Hepatocytes in Gel Entrapped and Monolayer Culture.

OBJECTIVES: An extracorporeal bioartificial liver device must maintain viability and differentiated function of hepatocytes cultivated at high cell density. Growth factors, such as hepatocyte growth factor, found in high concentrations in the plasma of patients with fulminant hepatic failure, have the potential to promote hepatocyte dedifferentiation and thus, decrease function. We tested the hypothesis that hepatocyte growth factor would improve viable cell density and decrease biotransformation functions of liver cells in monolayer culture and in hepatocytes entrapped in collagen cylindrical gel noodles as found in the extracorporeal bioartificial liver. DESIGN: In vitro, controlled study. SETTING: University research laboratory. SUBJECTS: Adult Sprague Dawley Rats. INTERVENTIONS: Hepatocytes were harvested by a two-step collagenase technique. Harvested hepatocytes were plated onto type 1 collagen coated plates or entrapped in type 1 collagen cylindrical gels and cultured in different concentrations of hepatocyte growth factor. Interval measurements of 3H-thymidine incorporation, albumin synthesis, biotransformation functions, and viability were made. MEASUREMENTS AND MAIN RESULTS: In monolayer culture, the addition of hepatocyte growth factor caused a dramatic increase in 3H-thymidine incorporation. This increase was accompanied by a decrease in the appearance of the lidocaine metabolite, monoethyglycinexylidide. Albumin production was unchanged. In cylindrical gel entrapment cultures, hepatocyte growth factor caused a significant increase in 2-day viability but had no effect on the metabolite appearance of lidocaine or 4-methyl umbelliferone or albumin production. CONCLUSIONS: Hepatocyte growth factor induces dedifferentiation of hepatocytes in monolayer culture. Collagen matrix entrapment appears to abrogate this effect and improve liver cell viability. There may be reciprocal regulation of hepatocyte reproductive and differentiated functions, such as biotransformation, which can be influenced by the entrapment of hepatocytes in an extracellular type 1 collagen matrix

Ambient air pollution in relation to diabetes and glucose-homoeostasis markers in China: a cross-sectional study with findings from the 33 Communities Chinese Health Study

Summary: Background: Health effects of air pollution on diabetes have been scarcely studied in developing countries. We aimed to explore the associations of long-term exposure to ambient particulate matter (PM) and gaseous pollutants with diabetes prevalence and glucose-homoeostasis markers in China. Methods: Between April 1 and Dec 31, 2009, we recruited a total of 15 477 participants aged 18–74 years using a random number generator and a four-staged, stratified and cluster sampling strategy from a large cross-sectional study (the 33 Communities Chinese Health Study) from three cities in Liaoning province, northeastern China. Fasting and 2 h insulin and glucose concentrations and the homoeostasis model assessment of insulin resistance index and β-cell function were used as glucose-homoeostasis markers. Diabetes was defined according to the American Diabetes Association's recommendations. We calculated exposure to air pollutants using data from monitoring stations (PM with an aerodynamic diameter of 10 μm or less [PM10], sulphur dioxide, nitrogen dioxide, and ozone) and a spatial statistical model (PM with an aerodynamic diameter of 1 μm or less [PM1] and 2·5 μm or less [PM2·5]). We used two-level logistic regression and linear regression analyses to assess associations between exposure and outcomes, controlling for confounders. Findings: All the studied pollutants were significantly associated with increased diabetes prevalence (eg, the adjusted odds ratios associated with an increase in IQR for PM1, PM2·5, and PM10 were 1·13, 95% CI 1·04–1·22; 1·14, 1·03–1·25; and 1·20, 1·12–1·28, respectively). These air pollutants were also associated with higher concentrations of fasting glucose (0·04–0·09 mmol/L), 2 h glucose (0·10–0·19 mmol/L), and 2 h insulin (0·70–2·74 μU/L). No association was observed for the remaining biomarkers. Stratified analyses indicated greater effects on the individuals who were younger (<50 years) or overweight or obese. Interpretation: Long-term exposure to air pollution was associated with increased risk of diabetes in a Chinese population, particularly in individuals who were younger or overweight or obese. Funding: The National Key Research and Development Program of China, the National Natural Science Foundation of China, the Fundamental Research Funds for the Central Universities, the Guangdong Province Natural Science Foundation, the Career Development Fellowship of Australian National Health and Medical Research Council, and the Early Career Fellowship of Australian National Health and Medical Research Council

Appearance of cerebral infarct fogging on CT perfusion

Fogging is a deceptive phenomenon that can partially or completely obscure a subacute infarct on noncontrast head CT. We present the appearance of infarct fogging on CT perfusion through 3 cases. At time of fogging, the subacute infarctions demonstrated variable mean transit time with increased cerebral blood volume and cerebral blood flow on CT perfusion. Fogging occurred within 6-10 days, sooner than the previously described 2-3 weeks in classic fogging. At time of fogging, CT perfusion demonstrated a “luxury-like” perfusion pattern and augmented the identification of the true extent of the infarction at time of fogging. Keywords: Fogging effect, Cerebral infarct/stroke, Vasospasm, CT perfusion, Noncontrast head C

Design and Performance of a COVID-19 Hospital Recovery Model

Objective:. To determine the accuracy of a predictive model for inpatient occupancy that was implemented at a large New England hospital to aid hospital recovery planning from the COVID-19 surge. Background:. During recovery from COVID surges, hospitals must plan for multiple patient populations vying for inpatient capacity, so that they maintain access for emergency department (ED) patients while enabling time-sensitive scheduled procedures to go forward. To guide pandemic recovery planning, we implemented a model to predict hospital occupancy for COVID and non-COVID patients. Methods:. At a quaternary care hospital in New England, we included hospitalizations from March 10 to July 12, 2020 and subdivided them into COVID, non-COVID nonscheduled (NCNS), and non-COVID scheduled operating room (OR) hospitalizations. For the recovery period from May 25 to July 12, the model made daily hospital occupancy predictions for each population. The primary outcome was the daily mean absolute percentage error (MAPE) and mean absolute error (MAE) when comparing the predicted versus actual occupancy. Results:. There were 444 COVID, 5637 NCNS, and 1218 non-COVID scheduled OR hospitalizations during the recovery period. For all populations, the MAPE and MAE for total occupancy were 2.8% or 22.3 hospitalizations per day; for general care, 2.6% or 17.8 hospitalizations per day; and for intensive care unit, 9.7% or 11.0 hospitalizations per day. Conclusions:. The model was accurate in predicting hospital occupancy during the recovery period. Such models may aid hospital recovery planning so that enough capacity is maintained to care for ED hospitalizations while ensuring scheduled procedures can efficiently return

A mixed methods study of clinical information availability in obstetric triage and prenatal offices.

OBJECTIVE: To determine the effect of availability of clinical information from an integrated electronic health record system on pregnancy outcomes at the point of care. MATERIALS AND METHODS: We used provider interviews and surveys to evaluate the availability of pregnancy-related clinical information in ambulatory practices and the hospital, and applied multiple regression to determine whether greater clinical information availability is associated with improvements in pregnancy outcomes and changes in care processes. Our regression models are risk adjusted and include physician fixed effects to control for unobservable characteristics of physicians that are constant across patients and time. RESULTS: Making nonstress test results, blood pressure data, antenatal problem lists, and tubal sterilization requests from office records available to hospital-based providers is significantly associated with reductions in the likelihood of obstetric trauma and other adverse pregnancy outcomes. Better access to prenatal records also increases the probability of labor induction and decreases the probability of Cesarean section (C-section). Availability of lab test results and new diagnoses generated in the hospital at ambulatory offices is associated with fewer preterm births and low-birth-weight babies. DISCUSSION AND CONCLUSIONS: Increased availability of specific clinical information enables providers to deliver better care and improve outcomes, but some types of clinical data are more important than others. More available information does not always result from automated integration of electronic records, but rather from the availability of the source records. Providers depend upon information that they trust to be reliable, complete, consistent, and easily retrievable, even if this requires multiple interfaces

Intensity Modulated Proton Therapy for Hepatocellular Carcinoma: Initial Clinical Experience

Purpose: Our purpose was to assess the safety and efficacy of intensity modulated proton therapy (IMPT) for the treatment of hepatocellular carcinoma (HCC). Methods and Materials: A retrospective review was conducted on all patients who were treated with IMPT for HCC with curative intent from June 2015 to December 2018. All patients had fiducials placed before treatment. Inverse treatment planning used robust optimization with 2 to 3 beams. The majority of patients were treated in 15 fractions (n = 30, 81%, 52.5-67.5 Gy, relative biological effectiveness), whereas the remainder were treated in 5 fractions (n = 7, 19%, 37.5-50 Gy, relative biological effectiveness). Daily image guidance consisted of orthogonal kilovoltage x-rays and use of a 6° of freedom robotic couch. Outcomes (local control, progression free survival, and overall survival) were determined using Kaplan-Meier methods. Results: Thirty-seven patients were included. The median follow-up for living patients was 21 months (Q1-Q3, 17-30 months). Pretreatment Child-Pugh score was A5-6 in 70% of patients and B7-9 in 30% of patients. Nineteen patients had prior liver directed therapy for HCC before IMPT. Eight patients (22%) required a replan during treatment, most commonly due to inadequate clinical target volume coverage. One patient (3%) experienced a grade 3 acute toxicity (pain) with no recorded grade 4 or 5 toxicities. An increase in Child-Pugh score by ≥ 2 within 3 months of treatment was observed in 6 patients (16%). At 1 year, local control was 94%, intrahepatic control was 54%, progression free survival was 35%, and overall survival was 78%. Conclusions: IMPT is safe and feasible for treatment of HCC

Evaluation of classification and regression tree (CART) model in weight loss prediction following head and neck cancer radiation therapy

Objective: We explore whether a knowledge–discovery approach building a Classification and Regression Tree (CART) prediction model for weight loss (WL) in head and neck cancer (HNC) patients treated with radiation therapy (RT) is feasible. Methods and materials: HNC patients from 2007 to 2015 were identified from a prospectively collected database Oncospace. Two prediction models at different time points were developed to predict weight loss ≥5 kg at 3 months post-RT by CART algorithm: (1) during RT planning using patient demographic, delineated dose data, planning target volume–organs at risk shape relationships data and (2) at the end of treatment (EOT) using additional on-treatment toxicities and quality of life data. Results: Among 391 patients identified, WL predictors during RT planning were International Classification of Diseases diagnosis; dose to masticatory and superior constrictor muscles, larynx, and parotid; and age. At EOT, patient-reported oral intake, diagnosis, N stage, nausea, pain, dose to larynx, parotid, and low-dose planning target volume–larynx distance were significant predictive factors. The area under the curve during RT and EOT was 0.773 and 0.821, respectively. Conclusions: We demonstrate the feasibility and potential value of an informatics infrastructure that has facilitated insight into the prediction of WL using the CART algorithm. The prediction accuracy significantly improved with the inclusion of additional treatment-related data and has the potential to be leveraged as a strategy to develop a learning health system