Pain Forecasting using Self-supervised Learning and Patient Phenotyping: An attempt to prevent Opioid Addiction

Sickle Cell Disease (SCD) is a chronic genetic disorder characterized by recurrent acute painful episodes. Opioids are often used to manage these painful episodes; the extent of their use in managing pain in this disorder is an issue of debate. The risk of addiction and side effects of these opioid treatments can often lead to more pain episodes in the future. Hence, it is crucial to forecast future patient pain trajectories to help patients manage their SCD to improve their quality of life without compromising their treatment. It is challenging to obtain many pain records to design forecasting models since it is mainly recorded by patients' self-report. Therefore, it is expensive and painful (due to the need for patient compliance) to solve pain forecasting problems in a purely supervised manner. In light of this challenge, we propose to solve the pain forecasting problem using self-supervised learning methods. Also, clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines tailored to homogeneous patient subgroups. Hence, we propose a self-supervised learning approach for clustering time-series data, where each cluster comprises patients who share similar future pain profiles. Experiments on five years of real-world datasets show that our models achieve superior performance over state-of-the-art benchmarks and identify meaningful clusters that can be translated into actionable information for clinical decision-making.Comment: 8 page

Is Stent Placement Effective for Palliation of Right Ventricle to Pulmonary Artery Conduit Stenosis?

ObjectivesThis study was designed to evaluate the outcome of stent placement (SP) for conduit discrete stenosis using predefined criteria.BackgroundRight ventricle (RV) to pulmonary artery (PA) conduits are often associated with complications, such as stenosis, requiring multiple surgical replacements.MethodsPatients who underwent primary or repeat SP were included. Indications for SP were clinical symptoms and/or RV to systolic blood pressure (SBP) ratio (RV:SBP) >0.65 by echocardiography. Our definition of success was a decrease in RV:SBP by >20%, a final RV:SBP ratio of <0.65, or resolution of symptoms.ResultsStents were placed successfully in 28 of 31 patients (90%), including 3 patients who underwent the procedure solely for symptoms. The RV:SBP ratio decreased (0.75 ± 0.17 vs. 0.52 ± 0.12, p < 0.001), and the conduit diameter increased (postero-anterior 9.1 ± 2.9 vs. 12.0 ± 2.8 mm, lateral 8.3 ± 2.2 vs. 11.6 ± 2.4 mm, p < 0.001). In the 28 patients with successful SP, 8 (29%) remained free from second intervention. In the remaining patients, the median time to re-intervention was 16 months (range 6 to 44 months). Second transcatheter interventions (4 SP, 4 balloon dilation) were successful in 8 of 13 patients. Complications included balloon rupture (n = 4), stent fracture (n = 2), and pseudoaneurysm formation (n = 1).ConclusionsInitial SP has excellent intermediate outcomes, successfully postponing surgical intervention for the majority of patients. Conduit restenosis may be successfully treated with a second transcatheter intervention. On the basis of these data, SP is likely the procedure of choice for patients with a discrete stenosis of the RV to PA conduit

Searching for vector dark matter with an optomechanical accelerometer

We consider using optomechanical accelerometers as resonant detectors for ultralight dark matter. As a concrete example, we describe a detector based on a silicon nitride membrane fixed to a beryllium mirror, forming an optical cavity. The use of different materials gives access to forces proportional to baryon (B) and lepton (L) charge, which are believed to be coupling channels for vector dark matter particles ("dark photons"). The cavity meanwhile provides access to quantum-limited displacement measurements. For a centimeter-scale membrane pre-cooled to 10 mK, we argue that sensitivity to vector B-L dark matter can exceed that of the E\"{o}t-Wash experiment in integration times of minutes, over a fractional bandwidth of $\sim 0.1\%$ near 10 kHz (corresponding to a particle mass of $10^{-10}$eV/c$^2$). Our analysis can be translated to alternative systems such as levitated particles, and suggests the possibility of a new generation of table-top experiments

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 inter-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

Exposure of fibrinogen and thrombin to nitric oxide donor ProliNONOate affects fibrin clot properties

Fibrin fibers form the structural backbone of blood clots. The structural properties of fibrin clots are highly dependent on formation kinetics. Environmental factors such as protein concentration, pH, salt, and protein modification, to name a few, can affect fiber kinetics through altered fibrinopeptide release, monomer association, and/or lateral aggregation. The objective of our study was to determine the effect of thrombin and fibrinogen exposed to nitric oxide on fibrin clot properties. ProliNONOate (5 [mu]mol/l) was added to fibrinogen and thrombin before clot initiation and immediately following the addition of thrombin to the fibrinogen solution. Resulting fibrin fibers were probed with an atomic force microscope to determine their diameter and extensibility and fibrin clots were analyzed for clot density using confocal microscopy. Fiber diameters were also determined by confocal microscopy and the rate of clot formation was recorded using UV-vis spectrophotometry. Protein oxidation and S-nitrosation was determined by UV-vis, ELISA, and chemiluminescence. The addition of ProliNONOate to fibrinogen or thrombin resulted in a change in clot structure. ProliNONOate exposure produced clots with lower fiber density, thicker fibers, and increased time to maximum turbidity. The effect of the exposure of nitric oxide to thrombin and fibrinogen were measured independently and indicated that each plays a role in altering clot properties. We detected thrombin S-nitrosation and protein carbonyl formation after nitric oxide exposure. Our study reveals a regulation of fibrin clot properties by nitric oxide exposure and suggests a role of peroxynitrite in oxidative modifications of the proteins. These results relate nitric oxide bioavailability and oxidative stress to altered clot properties

3D printed PEEK/HA composites for bone tissue engineering applications: effect of material formulation on mechanical performance and bioactive potential

Polyetheretherketone (PEEK) is a biocompatible polymer widely used for biomedical applications. Because it is biologically inert, bioactive phases, such as nano-hydroxyapatite (HA), have been added to PEEK in order to improve its bioactivity. 3D printing (3DP) technologies are being increasingly used today to manufacture patient specific devices and implants. However, processing of PEEK is challenging due to its high melting point which is above 340 °C. In this study, PEEK-based filaments containing 10 wt% of pure nano-HA, strontium (Sr)- doped nano-HA and Zinc (Zn)-doped nano-HA were produced via hot-melt extrusion and subsequently 3D printed via fused deposition modelling (FDM), following an initial optimization process. The raw materials, extruded filaments and 3D printed samples were characterized in terms of physicochemical, thermal and morphological analysis. Moreover, the mechanical performance of 3D printed specimens was assessed via tensile tensing. Although an increase in the melting point and a reduction in crystallization temperature was observed with the addition of HA and doped HA to pure PEEK, there was no noticeable increase in the degree of crystallinity. Regarding the mechanical behavior, no significant differences were detected following the addition of the inorganic phases to the polymeric matrix, although a small reduction in the ultimate tensile strength (~14%) and Young's modulus (~5%) in PEEK/HA was observed in comparison to pure PEEK. Moreover, in vitro bioactivity of 3D printed samples was evaluated via a simulated body fluid immersion test for up to 28 days; the formation of apatite was observed on the surfaces of sample surfaces containing HA, SrHA and ZnHA. These results indicate the potential to produce bioactive, 3DP PEEK composites for challenging applications such as in craniofacial bone repair

The 2020s will be a crunch decade that will determine the UK’s trajectory into the mid-21st century

As vaccines roll out and restrictions are lifted, public debate is turning to the economic recovery from COVID-19 and the deepest annual downturn for 300 years that came in its wake. But viewing the years ahead simply as the post-pandemic period is far too limited a frame, say the authors of the Economy 2020 Inquiry. Instead, the 2020s look set to be the decisive decade during which the UK will need to renew its approach to achieving economic success