Two Cases of Lyme Arthritis in Winter In New England: A Case Series

Case Diagnosis: Lyme Arthritis Case Description: Patient 1 is a 26 year old male who presented in March with severe right knee pain and swelling for two weeks. He had a similar episode a month prior, but it resolved. The second episode progressed with pain from knee to foot and numbness on top of the foot. He had no known history of tick bites, travel, or trauma, but endorsed contact with a dog. On physical exam, he had a right knee effusion with limited ROM, diffuse joint line tenderness, positive McMurray’s, and pain with ligamentous testing. Synovial fluid of the joint showed WBC count 44,467 and was positive for Lyme. He was treated with doxycycline. MRI findings were limited to ACL laxity and inflammation. Patient 2 is a 24 year old male who presented in December with progressive right knee and calf pain for one week. He had been fishing in the woods a few weeks prior with no trauma. Joint aspiration showed a positive Lyme PCR and WBC count 37,520, and he was treated with doxycycline. Aspiration was repeated for recurrent effusion, and an MRI was done due to persistent pain. MRI showed bone contusion, ACL laxity, and inflammation. Discussions: Lyme disease is transmitted by Ixodes scapularis ticks, which appear in late spring and early summer; however,Lyme arthritis may occur during any season. Ticks infected with the spirochete B. burgdorferi are primarily found in the Northeastern and upper Midwestern US. B. burgdorferi strains of Lyme often disseminate to joints, tendons, or bursae early in infection.Lyme arthritis presents later, with an adaptive immune response that results in spirochetal killing. Conclusions: Lyme arthritis can present at any time of year, and clinical suspicion in endemic regions should remain high even without a known history of tick exposure or erythema migrans rash

Prehabilitation for Shoulder Dysfunction in Breast Cancer

Objective: To evaluate prehabilitation exercises to improve shoulder pain and abduction range of motion (ROM) after breast cancer surgery; to evaluate methods of exercise teaching; to assess postsurgical seroma formation. Design: Pilot study Setting: Academic medical center Participants: 60 breast cancer patients were randomly assigned to either personal exercise instruction, group 1, n=36, or video only instruction, group 2, n=24. Interventions: Shoulder exercises were assigned to both groups 1 month prior to surgery at an outpatient visit. Group 1 received personal instruction on exercises, plus written exercise instruction, and a link to access an online video. Group 2 received only written exercise instruction and a link to access the online video. Main Outcome Measures: Exercise compliance, pain (via visual analog scale), shoulder abduction ROM (via goniometer), and presence or absence of seroma. Results or Clinical Course: 76% of study patients chose to exercise. There was no difference in exercise compliance between personal instruction versus video teaching. (75%, 24/32 in-person vs. 77%, 10/13 video only, OR=1.03). 66% of patients (20/30) lost greater than 10 degrees shoulder abduction ROM at 1 month post surgery. 29% of patients (9/31) had worse shoulder pain at one month post surgery than at baseline (24%, 6/25 exercisers, and 50%, 3/6 non-exercisers). 15% of patients (4/27) had worse shoulder pain at 3 months post surgery than at baseline (8%, 2/25 exercisers, and 100%, 2/2 non-exercisers). Prehabilitation exercise program inferred no additional risk of seroma formation (21%, 7/33 exercisers vs. 22%, 2/9 non-exercisers OR=.94). Conclusion: In-person teaching does not appear superior to video teaching for prehabilitation exercises in breast cancer. A high quality randomized controlled trial is necessary to assess efficacy of prehabilitation for improving post surgical outcomes. Prehabilitation exercises do not appear to increase risk of seroma formation in breast cancer surgery

Quantifying Cerebral Contributions to Pain beyond Nociception

Cerebral processes contribute to pain beyond the level of nociceptive input and mediate psychological and behavioural influences. However, cerebral contributions beyond nociception are not yet well characterized, leading to a predominant focus on nociception when studying pain and developing interventions. Here we use functional magnetic resonance imaging combined with machine learning to develop a multivariate pattern signature—termed the stimulus intensity independent pain signature-1 (SIIPS1)—that predicts pain above and beyond nociceptive input in four training data sets (Studies 1–4, N¼137). The SIIPS1 includes patterns of activity in nucleus accumbens, lateral prefrontal and parahippocampal cortices, and other regions. In cross-validated analyses of Studies 1–4 and in two independent test data sets (Studies 5–6, N¼46), SIIPS1 responses explain variation in trial-by-trial pain ratings not captured by a previous fMRI-based marker for nociceptive pain. In addition, SIIPS1 responses mediate the pain-modulating effects of three psychological manipulations of expectations and perceived control. The SIIPS1 provides an extensible characterization of cerebral contributions to pain and specific brain targets for interventions

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard

Background Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. Objectives This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. Methods We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. Results The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. Conclusion A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.</p

Group-regularized individual prediction: theory and application to pain

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N = 180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals' data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study

Delivery of small interfering RNA for inhibition of endothelial cell apoptosis by hypoxia and serum deprivation

RNA interference (RNAi) for anti-angiogenic or pro-apoptotic factors in endothelial cells (ECs) has great potential for the treatment of ischemic diseases by promoting angiogenesis or inhibiting apoptosis. Here, we report the utility of small interfering RNA (siRNA) in inhibiting EC apoptosis induced by tumor necrosis factor-α (TNF-α). siRNA was designed and synthesized targeting tumor necrosis factor-α receptor-1 (TNFR-1) and Src homology 2 domain-containing protein tyrosine phosphatase-1 (SHP-1). Human umbilical vein endothelial cells (HUVECs) were cultured under in vitro hypoxic and serum-deprived conditions to simulate in vivo ischemic conditions. Two days after liposomal delivery of siRNA targeting TNFR-1 and SHP-1, significant silencing of each target (TNFR-1; 76.5 % and SHP-1; 97.2 %) was detected. Under serum-deprived hypoxic (1% oxygen) conditions, TNF-α expression in HUVECs increased relative to normoxic (20% oxygen) and serum-containing conditions. Despite enhanced TNF-α expression, suppression of TNFR-1 or SHP-1 by siRNA delivery not only enhanced expression of angiogenic factors (KDR/Flk-1 and eNOS) and anti-apoptotic factor (Bcl-xL) but also reduced expression of a pro-apoptotic factor (Bax). Transfection of TNFR-1 or SHP-1 siRNA significantly decreased the HUVEC apoptosis while significantly enhancing HUVEC proliferation and capillary formation. The present study demonstrates that TNFR-1 and SHP-1 may be useful targets for the treatment of myocardial or hindlimb ischemia

Detection of Group 1 Coronaviruses in Bats in North America

Bats of 2 species harbor group 1 coronaviruses

Classifying aerosol type using in situ surface spectral aerosol optical properties

Knowledge of aerosol size and composition is important for determining radiative forcing effects of aerosols, identifying aerosol sources and improving aerosol satellite retrieval algorithms. The ability to extrapolate aerosol size and composition, or type, from intensive aerosol optical properties can help expand the current knowledge of spatiotemporal variability in aerosol type globally, particularly where chemical composition measurements do not exist concurrently with optical property measurements. This study uses medians of the scattering Ångström exponent (SAE), absorption Ångström exponent (AAE) and single scattering albedo (SSA) from 24 stations within the NOAA/ESRL Federated Aerosol Monitoring Network to infer aerosol type using previously published aerosol classification schemes. Three methods are implemented to obtain a best estimate of dominant aerosol type at each station using aerosol optical properties. The first method plots station medians into an AAE vs. SAE plot space, so that a unique combination of intensive properties corresponds with an aerosol type. The second typing method expands on the first by introducing a multivariate cluster analysis, which aims to group stations with similar optical characteristics and thus similar dominant aerosol type. The third and final classification method pairs 3-day backward air mass trajectories with median aerosol optical properties to explore the relationship between trajectory origin (proxy for likely aerosol type) and aerosol intensive parameters, while allowing for multiple dominant aerosol types at each station. The three aerosol classification methods have some common, and thus robust, results. In general, estimating dominant aerosol type using optical properties is best suited for site locations with a stable and homogenous aerosol population, particularly continental polluted (carbonaceous aerosol), marine polluted (carbonaceous aerosol mixed with sea salt) and continental dust/biomass sites (dust and carbonaceous aerosol); however, current classification schemes perform poorly when predicting dominant aerosol type at remote marine and Arctic sites and at stations with more complex locations and topography where variable aerosol populations are not well represented by median optical properties. Although the aerosol classification methods presented here provide new ways to reduce ambiguity in typing schemes, there is more work needed to find aerosol typing methods that are useful for a larger range of geographic locations and aerosol populations