In this study, we have fabricated two types of non-invasive fiber-optic respiration sensors that can measure respiratory signals during magnetic resonance (MR) image acquisition. One is a nasal-cavity attached sensor that can measure the temperature variation of air-flow using a thermochromic pigment. The other is an abdomen attached sensor that can measure the abdominal circumference change using a sensing part composed of polymethyl-methacrylate (PMMA) tubes, a mirror and a spring. We have measured modulated light guided to detectors in the MRI control room via optical fibers due to the respiratory movements of the patient in the MR room, and the respiratory signals of the fiber-optic respiration sensors are compared with those of the BIOPAC ® system. We have verified that respiratory signals can be obtained without deteriorating the MR image. It is anticipated that the proposed fiber-optic respiration sensors would be highly suitable for respiratory monitoring during surgical procedures performed inside an MRI system.

BackgroundRecently, innovative attempts have been made to identify moyamoya disease (MMD) by focusing on the morphological differences in the head of MMD patients. Following the recent revolution in the development of deep learning (DL) algorithms, we designed this study to determine whether DL can distinguish MMD in plain skull radiograph images.MethodsThree hundred forty-five skull images were collected as an MMD-labeled dataset from patients aged 18 to 50 years with definite MMD. As a control-labeled data set, 408 skull images of trauma patients were selected by age and sex matching. Skull images were partitioned into training and test datasets at a 7:3 ratio using permutation. A total of six convolution layers were designed and trained. The accuracy and area under the receiver operating characteristic (AUROC) curve were evaluated as classifier performance. To identify areas of attention, gradient-weighted class activation mapping was applied. External validation was performed with a new dataset from another hospital.FindingsFor the institutional test set, the classifier predicted the true label with 84·1% accuracy. Sensitivity and specificity were both 0·84. AUROC was 0·91. MMD was predicted by attention to the lower face in most cases. Overall accuracy for external validation data set was 75·9%.InterpretationDL can distinguish MMD cases within specific ages from controls in plain skull radiograph images with considerable accuracy and AUROC. The viscerocranium may play a role in MMD-related skull features.FundThis work was supported by grant no. 18-2018-029 from the Seoul National University Bundang Hospital Research Fund.

An efficient method for identifying subjects at high risk of an intracranial aneurysm (IA) is warranted to provide adequate radiological screening guidelines and effectively allocate medical resources. We developed a model for pre-diagnosis IA prediction using a national claims database and health examination records. Data from the National Health Screening Program in Korea were utilized as input for several machine learning algorithms: logistic regression (LR), random forest (RF), scalable tree boosting system (XGB), and deep neural networks (DNN). Algorithm performance was evaluated through the area under the receiver operating characteristic curve (AUROC) using different test data from that employed for model training. Five risk groups were classified in ascending order of risk using model prediction probabilities. Incidence rate ratios between the lowest-and highest-risk groups were then compared. The XGB model produced the best IA risk prediction (AUROC of 0.765) and predicted the lowest IA incidence (3.20) in the lowest-risk group, whereas the RF model predicted the highest IA incidence (161.34) in the highest-risk group. The incidence rate ratios between the lowest-and highestrisk groups were 49.85, 35.85, 34.90, and 30.26 for the XGB, LR, DNN, and RF models, respectively. The developed prediction model can aid future IA screening strategies.

No abstract

In this study, we describe an intratumoral injectable, electrostatic, cross-linkable curcumin (Cur) drug depot to enhance anticancer activity. The key concept in this work was the preparation of an electrostatic, cross-linked carboxymethyl cellulose (CMC) and chitosan (CHI) hydrogel containing Cur-loaded microcapsules (Cur-M). The CMC and CHI solutions existed as a liquid before mixing and formed a CMC and CHI (CCH) hydrogel as a drug depot after mixing via electrostatic interactions between the anionic CMC and cationic CHI. Compared with the individual CMC and CHI solutions, the electrostatic, cross-linked CCH depot persisted in vivo for an extended period. The prepared Cur-M was easily mixed with the CMC and CHI solutions. Cur-M/CMC and Cur-;M/CHI solutions easily formed Cur-M-loaded CCH depots after simple mixing. The in vitro and in vivo Cur-M-loaded CCH depot was designed with Cur-M dispersed inside an outer shell of electrostatically cross-linked CCH. The Cur-M-loaded CCH depot produced greater inhibition of tumor growth than did Cur-M, whereas single and repeated injections of free Cur had the weakest inhibitory effects. The results of this study indicate that the electrostatic, cross-linked, Cur-M-loaded CCH depot described in this study can synergistically enhance anticancer activity in chemotherapeutic delivery systems. Preparation of Cur-MCur-M was prepared using a monoaxial one-nozzle atomizer (Sono-Tek Crop, Milton, NY, USA). The typical preparation of Cur-M was achieved as follows: PLGA and Cur were dissolved in ethyl acetate and methanol, respectively. The concentrations of PLGA and Cur were 3% and 5% w/v, respectively. The mixtures of PLGA and Cur were fed into the ultrasonic atomizer at flow rates of 4 ml min − 1 . Microdroplets were produced by atomizing the mixed solutions of PLGA and Cur for~5 s at a vibration frequency of 3 W per 60 kHz and the microdroplets were then immediately collected in a 0.5% w/v poly(vinyl alcohol) solution for 2 min. The distance between the atomizer head and the aqueous poly(vinyl alcohol) solution was 1 cm, and the stirring speed of the poly(vinyl alcohol) solution was 1000 r.p.m. The resulting mixtures were gently stirred for 2 h to allow solidification of the microcapsules and were then filtered and washed with distilled water. The Cur-M was frozen at − 75°C, followed by freeze-drying over 4 days. The morphology of Cur-M was confirmed using an optical microscope (Carl Zeiss MicroimagingThe encapsulation efficiency of Cur was determined using acetonitrile and DW. Cur-M (4 mg) was placed into a test tube and 0.6 ml acetonitrile was added to Injectable, electrostatic, cross-linkable curcumin SH Park et al

Our purpose was to test whether a preparation of injectable formulations of dexamethasone (Dex)-loaded microspheres (Dex-Ms) mixed with click-crosslinked hyaluronic acid (Cx-HA) (or Pluronic (PH) for comparison) prolongs therapeutic levels of released Dex. Dex-Ms were prepared using a monoaxial-nozzle ultrasonic atomizer with an 85% yield of the Dex-Ms preparation, encapsulation efficiency of 80%, and average particle size of 57 μm. Cx-HA was prepared via a click reaction between transcyclooctene (TCO)-modified HA (TCO-HA) and tetrazine (TET)-modified HA (TET-HA). The injectable formulations (Dex-Ms/PH and Dex-Ms/Cx-HA) were fabricated as suspensions and became a Dex-Ms-loaded hydrogel drug depot after injection into the subcutaneous tissue of Sprague Dawley rats. Dex-Ms alone also formed a drug depot after injection. The Cx-HA hydrogel persisted in vivo for 28 days, but the PH hydrogel disappeared within six days, as evidenced by in vivo near-infrared fluorescence imaging. The in vitro and in vivo cumulative release of Dex by Dex-Ms/Cx-HA was much slower in the early days, followed by sustained release for 28 days, compared with Dex-Ms alone and Dex-Ms/PH. The reason was that the Cx-HA hydrogel acted as an external gel matrix for Dex-Ms, resulting in the retarded release of Dex from Dex-Ms. Therefore, we achieved significantly extended duration of a Dex release from an in vivo Dex-Ms-loaded hydrogel drug depot formed by Dex-Ms wrapped in an injectable click-crosslinked HA hydrogel in a minimally invasive manner. In conclusion, the Dex-Ms/Cx-HA drug depot described in this work showed excellent performance on extended in vivo delivery of Dex.

No abstract

No abstract