Comparative Evaluation of Whole Body and Hepatic Insulin Resistance Using Indices from Oral Glucose Tolerance Test in Morbidly Obese Subjects with Nonalcoholic Fatty Liver Disease

Nonalcoholic Fatty Liver Disease (NAFLD) is the hepatic manifestation of metabolic syndrome and is a marker of Insulin Resistance (IR). Euglycemic-hyperinsulinemic clamp is the gold standard for measuring whole body IR (hepatic + peripheral IR). However, it is an invasive and expensive procedure. Homeostasis Model Assessment Index for Insulin Sensitivity (HOMA-IS), Quantitative Insulin Sensitivity Check Index (QUICKI) for hepatic IR and Insulin Sensitivity Index (ISI0,120), and Whole Body Insulin Sensitivity Index (WBISI) for whole body IR are the indices calculated after Oral Glucose Tolerance Test (OGTT). We used these indices as noninvasive methods of IR (inverse of insulin sensitivity) estimation and compared hepatic/peripheral components of whole body IR in NAFLD. Methods. 113 morbidly obese, nondiabetic subjects who underwent gastric bypass surgery and intraoperative liver biopsy were included in the study. OGTT was performed preoperatively and the indices were calculated. Subjects were divided into closely matched groups as normal, fatty liver (FL) and Non-Alcoholic Steatohepatitis (NASH) based on histology. Results. Whole body IR was significantly higher in both FL and NASH groups (NAFLD) as compared to Normal, while hepatic IR was higher only in NASH from Normal. Conclusions. FL is a manifestation of peripheral IR but not hepatic IR

Perturbations in neuroinflammatory pathways are associated with paclitaxel-induced peripheral neuropathy in breast cancer survivors.

Paclitaxel is a common chemotherapy drug associated with the development of chronic paclitaxel-induced peripheral neuropathy (PIPN). PIPN is associated with neuroinflammatory mechanisms in pre-clinical studies. Here, we evaluated for differential gene expression (DGE) in peripheral blood between breast cancer survivors with and without PIPN and for neuroinflammatory (NI) related signaling pathways and whole-transcriptome profiles from other experiments. Pathway impact analysis identified 8 perturbed NI related pathways. Expression profile analysis found 15 experiments having similar whole-transcriptome profiles of DGE related to neuroinflammation and PIPN. These findings suggest that perturbations in pathways associated with neuroinflammation are found in cancer survivors with PIPN

Improving Pain Assessment Using Vital Signs and Pain Medication for Patients With Sickle Cell Disease: Retrospective Study

Background: Sickle cell disease (SCD) is the most common inherited blood disorder affecting millions of people worldwide. Most patients with SCD experience repeated, unpredictable episodes of severe pain. These pain episodes are the leading cause of emergency department visits among patients with SCD and may last for several weeks. Arguably, the most challenging aspect of treating pain episodes in SCD is assessing and interpreting a patient\u27s pain intensity level. Objective: This study aims to learn deep feature representations of subjective pain trajectories using objective physiological signals collected from electronic health records. Methods: This study used electronic health record data collected from 496 Duke University Medical Center participants over 5 consecutive years. Each record contained measures for 6 vital signs and the patient\u27s self-reported pain score, with an ordinal range from 0 (no pain) to 10 (severe and unbearable pain). We also extracted 3 features related to medication: medication type, medication status (given or applied, or missed or removed or due), and total medication dosage (mg/mL). We used variational autoencoders for representation learning and designed machine learning classification algorithms to build pain prediction models. We evaluated our results using an accuracy and confusion matrix and visualized the qualitative data representations. Results: We designed a classification model using raw data and deep representational learning to predict subjective pain scores with average accuracies of 82.8%, 70.6%, 49.3%, and 47.4% for 2-point, 4-point, 6-point, and 11-point pain ratings, respectively. We observed that random forest classification models trained on deep represented features outperformed models trained on unrepresented data for all pain rating scales. We observed that at varying Likert scales, our models performed better when provided with medication data along with vital signs data. We visualized the data representations to understand the underlying latent representations, indicating neighboring representations for similar pain scores with a higher resolution of pain ratings. Conclusions: Our results demonstrate that medication information (the type of medication, total medication dosage, and whether the medication was given or missed) can significantly improve subjective pain prediction modeling compared with modeling with only vital signs. This study shows promise in data-driven estimated pain scores that will help clinicians with additional information about the patient\u27s condition, in addition to the patient\u27s self-reported pain scores

Clustering of Pain Dynamics in Sickle Cell Disease from Sparse, Uneven Samples

Irregularly sampled time series data are common in a variety of fields. Many typical methods for drawing insight from data fail in this case. Here we attempt to generalize methods for clustering trajectories to irregularly and sparsely sampled data. We first construct synthetic data sets, then propose and assess four methods of data alignment to allow for application of spectral clustering. We also repeat the same process for real data drawn from medical records of patients with sickle cell disease -- patients whose subjective experiences of pain were tracked for several months via a mobile app. We find that different methods for aligning irregularly sampled sparse data sets can lead to different optimal numbers of clusters, even for synthetic data with known properties. For the case of sickle cell disease, we find that three clusters is a reasonable choice, and these appear to correspond to (1) a low pain group with occasionally acute pain, (2) a group which experiences moderate mean pain that fluctuates often from low to high, and (3) a group that experiences persistent high levels of pain. Our results may help physicians and patients better understand and manage patients\u27 pain levels over time, and we expect that the methods we develop will apply to a wide range of other data sources in medicine and beyond

Pain Intensity Assessment in Sickle Cell Disease patients using Vital Signs during Hospital Visits

Pain in sickle cell disease (SCD) is often associated with increased morbidity, mortality, and high healthcare costs. The standard method for predicting the absence, presence, and intensity of pain has long been self-report. However, medical providers struggle to manage patients based on subjective pain reports correctly and pain medications often lead to further difficulties in patient communication as they may cause sedation and sleepiness. Recent studies have shown that objective physiological measures can predict subjective self-reported pain scores for inpatient visits using machine learning (ML) techniques. In this study, we evaluate the generalizability of ML techniques to data collected from 50 patients over an extended period across three types of hospital visits (i.e., inpatient, outpatient and outpatient evaluation). We compare five classification algorithms for various pain intensity levels at both intra-individual (within each patient) and inter-individual (between patients) level. While all the tested classifiers perform much better than chance, a Decision Tree (DT) model performs best at predicting pain on an 11-point severity scale (from 0-10) with an accuracy of 0.728 at an inter-individual level and 0.653 at an intra-individual level. The accuracy of DT significantly improves to 0.941 on a 2-point rating scale (i.e., no/mild pain: 0-5, severe pain: 6-10) at an intra-individual level. Our experimental results demonstrate that ML techniques can provide an objective and quantitative evaluation of pain intensity levels for all three types of hospital visits.Comment: Accepted for presentation at the FIRST WORKSHOP ON COMPUTATIONAL & AFFECTIVE INTELLIGENCE IN HEALTHCARE APPLICATIONS (VULNERABLE POPULATIONS) In Conjunction with the International Conference on Pattern Recognition (ICPR) 202

Maternal Food Insecurity Is Associated with Increased Risk of Certain Birth Defects

Food insecurity represents a lack of access to enough food to meet basic needs. We hypothesized that food insecurity may increase birth defect risks, because it is an indicator of increased stress or compromised nutrition, which are both implicated in birth defect etiologies. This study used population-based case-control data. Included in the analysis were 1,189 case mothers and 695 control mothers who were interviewed by telephone. We calculated a food insecurity score as the number of affirmative responses to 5 questions from a shortened instrument designed to measure food insecurity. OR for the food insecurity score specified as a linear term indicated that a higher score was associated with increased risk of cleft palate, d-transposition of the great arteries, tetralogy of Fallot, spina bifida, and anencephaly, but not with cleft lip with or without cleft palate, after adjustment for maternal race-ethnicity, education, BMI, intake of folic acid-containing supplements, dietary intake of folate and energy, neighborhood crime, and stressful life events. In addition, several models suggested effect modification by certain factors. For example, for anencephaly, among women with the worst score for neighborhood crime (i.e. 6), the OR associated with a 1-unit change in the food insecurity score was 1.57 (95% CI 1.06, 2.33), whereas among women with a low crime score (i.e. 2), the corresponding OR was 1.16 (95% CI 0.96, 1.38). This study suggests that increased risks of certain birth defects may be included among the negative consequences of food insecurity

Requirements of SLP76 tyrosines in ITAM and integrin receptor signaling and in platelet function in vivo

Src homology 2 domain–containing leukocyte phosphoprotein of 76 kD (SLP76), an adaptor that plays a critical role in platelet activation in vitro, contains three N-terminal tyrosine residues that are essential for its function. We demonstrate that mice containing complementary tyrosine to phenylalanine mutations in Y145 (Y145F) and Y112 and Y128 (Y112/128F) differentially regulate integrin and collagen receptor signaling. We show that mutation of Y145 leads to severe impairment of glycoprotein VI (GPVI)–mediated responses while preserving outside-in integrin signaling. Platelets from Y112/128F mice, although having mild defects in GPVI signaling, exhibit defective actin reorganization after GPVI or αIIbβ3 engagement. The in vivo consequences of these signaling defects correlate with the mild protection from thrombosis seen in Y112/128F mice and the near complete protection observed in Y145F mice. Using genetic complementation, we further demonstrate that all three phosphorylatable tyrosines are required within the same SLP76 molecule to support platelet activation by GPVI