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Brachioradial pruritus (BRP) is a rare chronic neuropathy of the skin of the arms and forearms that presents with itching, burning or tingling, with no associated dermatological features. Sun exposure and cervical spine pathology have been described as causes for BRP; however, the exact aetiology is often unclear. Herein, we discuss the case of a 63-year-old female patient who presented with BRP with a C5-C6 distribution. Physical examination excluded skin conditions, thus magnetic resonance imaging was done and revealed a C5-C6 disc protrusion. Anterior cervical discectomy and fusion were performed leading to the resolution of symptoms. The case emphasizes the beneficial role of anterior cervical discectomy and fusion as a last resort in patients with refractory pruritus of discogenic cause.Acute massive gastric distension is a rare but potentially life-threatening surgical complication of bulimia nervosa. This results from repeated binge eating and is likely compounded by increased gastric compliance and delayed gastric emptying. We describe a case of acute massive gastric distension in a 26-year-old female with undiagnosed bulimia nervosa who underwent a laparotomy and anterior gastrotomy after failed conservative measures for gastric decompression. It highlights the importance of early recognition of a potentially life-threatening condition and that a multi-disciplinary approach is necessary to prevent the recurrence and morbidity associated with it.[This corrects the article DOI 10.1093/jscr/rjac280.].

Numerous studies have shown that mesenchymal stem cells (MSCs) promote cutaneous wound healing via paracrine signaling. Our previous study found that the secretome of MSCs was significantly amplified by treatment with IFN-γ and TNF-α (IT). It has been known that macrophages are involved in the initiation and termination of inflammation, secretion of growth factors, phagocytosis, cell proliferation and collagen deposition in wound, which is the key factor during wound healing. In the present study, we used a unique supernatant of MSCs from human umbilical cord-derived MSCs (UC-MSCs) pretreated with IT, designated S-IT MSCs, to explore whether S-IT MSCs have a better effect on improving wound healing by improving the biological function of macrophages than the control supernatant of MSCs (S-MSCs).

In the present study, we used a unique supernatant of MSCs pretreated with IT subcutaneously injected into a mice total skin excision. We evaluated the effect of S-IT MSCs on wound healing and the quality of woundd healing.

Teen motor vehicle crash fatality rates differ by geographic location. Studies assessing teen transportation risk behaviors by location are inconclusive. Therefore, we explored the role of census region and metropolitan status for driving prevalence and four transportation risk behaviors among U.S. public high school students.

Data from 2015 and 2017 national Youth Risk Behavior Surveys were combined and analyzed. Multivariable models controlled for sex, age, race/ethnicity, grades in school, and school socioeconomic status.

Overall, 41% of students did not always wear a seat belt. Students attending schools in the Northeast were 40%

likely than those in the Midwest to not always wear a seat belt. Among the 75% of students aged ≥16 years who had driven during the past 30 days, 47% texted/e-mailed while driving. Students in the Northeast were 20%

likely than those in the Midwest to text/e-mail while driving, and students attending suburban or town schools were

likely to text/e-mail while drivingtion risk behaviors by census region or metropolitan status. Age at licensure, time since licensure, driving experience, and the policy and physical driving environment might contribute more to variation in teen fatal crashes by location than differences in transportation risk behaviors. Regardless of location, teen transportation risk behaviors remain high. Future research could address developing effective strategies to reduce teen cell phone use while driving and enhancing community implementation of existing, effective strategies to improve seat belt use and reduce alcohol consumption and driving after drinking alcohol.

Subarachnoid Hemorrhage (SAH) is a lethal hemorrhagic stroke that account for 25% of cerebrovascular deaths. As a result of the initial bleed, a chain of physiological events are initiated which may lead to Delayed Cerebral Ischemia (DCI). As of now we have no diagnostic capability to identify patients which may present DCI a few weeks after initial presentation. We propose to investigate whether a data driven approach using angiographic parametric imaging (API) may predict occurrence of the DCI.

Digital Subtraction Angiographic (DSA) sequences from 125 SAH patients were used retrospectively to perform API assessment of the entire brain hemisphere where the hemorrhage was detected. Four Regions of Interests (ROIs) were placed to extract five average API biomarkers in the lateral and AP DSAs. Data driven analysis using Logistic Regression was performed for various API parameters and ROIs to find the optimal configuration to maximize the prognosis accuracy. Each model performance was evaluated using area under the curve of the receiver operator characteristic (AUROC).

Data driven approach with API has a 60% accuracy predicting DCI occurrence. We determined that location of the ROI for extraction of the API parameters is very important for the data driven model performance. Normalizing the values using the inlet velocities for each patient yield higher and more consistent results. Single API biomarkers models had poor prediction accuracies, barely better than chance.

This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.

This effectiveness exploratory study demonstrates for the first time, that prognosis of the DCI in SAH patients, is feasible and warrants a more in-depth investigation.Quantitative angiography is a 2D/3D x-ray imaging modality that summarizes hemodynamic information using time density curve (TDC) based parameters. Estimation of the TDC parameters are susceptible to errors due to various factors including, patient motion, incomplete temporal data, imaging trigger errors etc. In this study, we tested the feasibility of using recurrent neural networks (RNN) to recover complete TDC temporal information from incomplete sequences and evaluate quantitative parameters generated from the corrected TDCs. Digital subtraction angiograms (DSAs) were collected from patients undergoing endovascular treatments and angiographic parametric imaging (API) parameters were calculated from each DSA. Each set of API parameters was used to simulate a TDC resulting in a dataset of 760 TDCs. One-third of each TDC was continuously masked from pseudo-random points past the peak height (PH) point to simulate missing/artifact information. An RNN was developed, trained and tested to generate completed/corrected TDCs. The RNN recovered complete TDC temporal information with an average mean squared error of 0.0086±0.002. Average mean absolute errors were calculated between each API parameter generated from the ground truth TDCs and RNN corrected TDCs, these were 11.02%±0.91 for time to peak, 10.97%±0.69 for mean transit time, 5.65%±0.76 for PH, and 15.08%±0.98 for area under the TDC. The change in API parameters was not clinically significant and the predictive power of the API parameters was retained. This study proved the feasibility of using RNNs to mitigate motion artifacts and incomplete angiographic acquisitions to extract accurate quantitative parameters.Cerebral aneurysms (CA) affect nearly 6% of the US population and its rupture is one of the major causes of hemorrhagic stroke. Neurointerventionalists performing endovascular therapy (ET) to treat CA rely on qualitative image sequences obtained under fluoroscopy guidance alone, and do not have access to crucial quantitative information regarding blood flow before, during and after treatment - partially contributing to a failure rate of up to 30%. Computational fluid dynamics (CFD) is a powerful tool that can provide a wealth of quantitative data; however, CFD has found limited utility in the clinic due to the challenges in obtaining hemodynamic boundary conditions for each patient. In this work, we present a novel CFD-based simulated angiogram approach (SAA) that resolves the blood flow physics and interaction between blood and injected contrast agent to extract quantitative hemodynamic parameters which can be used to design real-time parametric imaging analysis. The SAA enables correlating contrast agent transport to the underlying hemodynamic conditions via time-density curves (TDC) obtained at several points in the region of interest. The ability of the TDC and the SAA to provide critical hemodynamic parameters in and around CA anatomies, such as washout and local flow changes is explored and presented. This provides invaluable quantitative data to the clinician at the time of intervention, since it incorporates the physics of blood flow and correlates the contrast transport to hemodynamic parameters quantitatively - thereby enabling the clinician to take informed decisions that improve treatment outcomes.

Data-driven methods based on x-ray angiographic parametric imaging (API) have been successfully used to provide prognosis for intracranial aneurysm (IA) treatment outcome. Previous studies have mainly focused on embolization devices where the flow pattern visualization is in the aneurysm dome; however, this is not possible in IAs treated with endovascular coils due to high x-ray attenuation of the devices. To circumvent this challenge, we propose to investigate whether flow changes in the parent artery distal to the coil-embolized IAs could be used to achieve the same accuracy of surgical outcome prognosis.

Eighty digital subtraction angiography sequences were acquired from patients with IA embolized with coils. Five API parameters were recorded from a region of interest (ROI) placed distal to the IA neck in the main artery. Average API values were recorded and pre-treatment values. A supervised machine learning algorithm was trained to provide a six-month post procedure binary outcome (occluded/not occluded). Receiver operating characteristic (ROC) analysis was used to assess the accuracy of the method.

Use of API parameters with data driven methods yielded an area under the ROC curve of 0.77 ±0.11 and accuracy of 78.6%. Single parameter-based analysis yielded accuracies which were suboptimal for clinical acceptance.

We determined that data-driven method based on API analysis of flow in the parent artery of IA treated with coils provide clinically acceptable accuracy for the prognosis of six months occlusion outcome.

We determined that data-driven method based on API analysis of flow in the parent artery of IA treated with coils provide clinically acceptable accuracy for the prognosis of six months occlusion outcome.Digital subtraction angiography (DSA) remains the clinical standard for detailed visualization of the neurovasculature due to its high-spatial resolution; however, detailed blood-flow quantification is impaired by its low-temporal resolution. Advances in photon-counting detector technology have led us to develop High-Speed Angiography (HSA), where x-ray images are acquired at 1000 fps for more accurate visualization and quantification of blood flow. We have implemented a physics-based optical flow method to extract such information from HSA, but validation of the angiography-derived velocity distributions is not straightforward. Computational fluid dynamics (CFD) is widely regarded as the benchmark for hemodynamic analysis, as it provides a multitude of quantitative flow parameters throughout the volume of interest. JAK assay However, there are several limitations with this method related to over-simplification of boundary conditions and suboptimal meshing (spatial resolution), that make CFD simulation results an inexact criterion for validation.