Deep CTS: a Deep Neural Network for Identification MRI of Carpal Tunnel Syndrome.

Carpal tunnel syndrome (CTS) is a common peripheral nerve disease in adults; it can cause pain, numbness, and even muscle atrophy and will adversely affect patients' daily life and work. There are no standard diagnostic criteria that go against the early diagnosis and treatment of patients. MRI as a novel imaging technique can show the patient's condition more objectively, and several characteristics of carpal tunnel syndrome have been found. However, various image sequences, heavy artifacts, small lesion characteristics, high volume of imagine reading, and high difficulty in MRI interpretation limit its application in clinical practice. With the development of automatic image segmentation technology, the algorithm has great potential in medical imaging. The challenge is that the segmentation target is too small, and there are two categories of images with the proximal border of the carpal tunnel as the boundary. To meet the challenge, we propose an end-to-end deep learning framework called Deep CTS to segment the carpal tunnel from the MR image. The Deep CTS consists of the shape classifier with a simple convolutional neural network and the carpal tunnel region segmentation with simplified U-Net. With the specialized structure for the carpal tunnel, Deep CTS can segment the carpal tunnel region efficiently and improve the intersection over union of results. The experimental results demonstrated that the performance of the proposed deep learning framework is better than other segmentation networks for small objects. We trained the model with 333 images, tested it with 82 images, and achieved 0.63 accuracy of intersection over union and 0.17 s segmentation efficiency, which indicate great promise for the clinical application of this algorithm.

Iodosobenzene-Mediated α-Acyloxylation of 1,3-Dicarbonyl Compounds with Carboxylic Acids and Insight into the Reaction Mechanism.

A highly efficient direct α-acyloxylation of 1,3-dicarbonyl compounds with carboxylic acids mediated by hypervalent iodine reagent is presented. Treatment of a variety of 1,3-dicarbonyl compounds with carboxylic acids in the presence of iodosobenzene provides the corresponding α-acyloxylated products in good to excellent yields. The mechanistic investigation by means of NMR spectroscopy reveals that the in situ-generated phenyliodine biscarboxylate proves to be the key intermediate for the α-acyloxylation, and the loading sequence of reactants and oxidant is crucial for the generation of the active species. The mild reaction conditions, wide substrate scope, short reaction time, good yields, high chemoselectivity, excellent functional group tolerance, and metal catalyst-free conversion make this acyloxylation a significant synthetic protocol.

A deep learning approach for medical waste classification.

As the demand for health grows, the increase in medical waste generation is gradually outstripping the load. In this paper, we propose a deep learning approach for identification and classification of medical waste. Deep learning is currently the most popular technique in image classification, but its need for large amounts of data limits its usage. In this scenario, we propose a deep learning-based classification method, in which ResNeXt is a suitable deep neural network for practical implementation, followed by transfer learning methods to improve classification results. We pay special attention to the problem of medical waste classification, which needs to be solved urgently in the current environmental protection context. We applied the technique to 3480 images and succeeded in correctly identifying 8 kinds of medical waste with an accuracy of 97.2%; the average F1-score of five-fold cross-validation was 97.2%. This study provided a deep learning-based method for automatic detection and classification of 8 kinds of medical waste with high accuracy and average precision. We believe that the power of artificial intelligence could be harnessed in products that would facilitate medical waste classification and could become widely available throughout China.

Deep HT: A deep neural network for diagnose on MR images of tumors of the hand.

BACKGROUND: There are many types of hand tumors, and it is often difficult for imaging diagnosticians to make a correct diagnosis, which can easily lead to misdiagnosis and delay in treatment. Thus in this paper, we propose a deep neural network for diagnose on MR Images of tumors of the hand in order to better define preoperative diagnosis and standardize surgical treatment. METHODS: We collected MRI figures of 221 patients with hand tumors from one medical center from 2016 to 2019, invited medical experts to annotate the images to form the annotation data set. Then the original image is preprocessed to get the image data set. The data set is randomly divided into ten parts, nine for training and one for test. Next, the data set is input into the neural network system for testing. Finally, average the results of ten experiments as an estimate of the accuracy of the algorithm. RESULTS: This research uses 221 images as dataset and the system shows an average confidence level of 71.6% in segmentation of hand tumors. The segmented tumor regions are validated through ground truth analysis and manual analysis by a radiologist. CONCLUSIONS: With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder decoder deep architectures. Therefore, in this paper, we propose an automatic segmentation method based on DeepLab v3+ and achieved a good diagnostic accuracy rate.

Indocyanine green-functionalized bottle brushes of poly(2-oxazoline) on cellulose nanocrystals for photothermal cancer therapy.

Deriving from the most abundant and renewable material, cellulose nanocrystals (CNCs) have drawn increasing attention in nanomedicine as well as other biomedical fields due to their excellent physicochemical properties and unique rod-like geometry. In this work, we report an efficient procedure to create biocompatible poly(2-oxazoline)s with a defined bottle brush architecture on CNCs through UV-induced photopolymerization and subsequent living cationic ring-opening polymerization. Characterization of the poly(2-oxazoline) modified CNCs indicates an improved thermal stability with the crystalline structure as well as the rod-like contour intact. The side chain terminal piperidinium of the bottle brush was utilized to immobilize indocyanine green (ICG) via electrostatic interactions. The resulting nanorods exhibit low toxicity in the dark and an efficient photothermal therapeutic effect against HeLa cells under 808 nm near infrared laser irradiation after being internalized by cancer cells. Moreover, bottle brushes on CNCs with amphiphilic poly(2-n-propyl-2-oxazoline) side chains show a higher efficiency in stabilizing ICG in comparison to those with hydrophilic poly(2-methyl-2-oxazoline) side chains, thus a higher photostability and a greater therapeutic effect were revealed for amphiphilic polymer modified CNCs. This work indicates that poly(2-oxazoline) functionalized CNCs represent a novel and promising platform for the design of other CNC-based drug delivery systems.

Monodisperse magnetizable silica composite particles from heteroaggregate of carboxylic polystyrene latex and Fe(3)O(4) nanoparticles.

Monodisperse magnetizable silica composite particles were prepared from heteroaggregates of carboxylic polystyrene latex and Fe(3)O(4) nanoparticles. It was found that the heteroaggregation of the carboxylic latex and Fe(3)O(4) nanoparticles is dependent on the pH of the solution. At low pH value (pH = 2-4), the aggregation proceeds effectively due to opposite charges on the surfaces of the latex and the magnetic nanoparticles. At high pH value (pH>8), no aggregation was observed due to the negative charge on both the surface of the latex and the magnetic nanoparticles. The heteroaggregate of the latex and magnetic nanoparticles was found to be stable in a wide range of pH values, due to the existence of coordination interactions at the interface of the latex and magnetic nanoparticles. After silica layer coating on the heteroaggregate by the Stöber process and removal of the latex by calcination, hollow monodisperse magnetizable silica composite particles are obtained.