Your Suffers from associated with Community-dwelling seniors through the COVID-19 Lockdown within Wuhan: A qualitative study.
 PURPOSE OF REVIEW Effective acute pain management has evolved considerably in recent years and is a primary area of focus in attempts to defend against the opioid epidemic. Persistent postsurgical pain (PPP) has an incidence of up to 30-50% and has negative outcome of quality of life and negative burden on individuals, family, and society. The 2016 American Society of Anesthesiologists (ASA) guidelines states that enhanced recovery after surgery (ERAS) forms an integral part of Perioperative Surgical Home (PSH) and is now recommended to use a multimodal opioid-sparing approach for management of postoperative pain. As such, dexmedetomidine is now being used as part of ERAS protocols along with regional nerve blocks and other medications, to create a satisfactory postoperative outcome with reduced opioid consumption in the Post anesthesia care unit (PACU).  https://www.selleckchem.com/pharmacological_epigenetics.html  RECENT FINDINGS Dexmedetomidine, a selective alpha2 agonist, possesses analgesic effects and has a different mechanism of action when compared with opioids. When dexmedetomidine is initiated at the end of a procedure, it has a better hemodynamic stability and pain response than ropivacaine. Dexmedetomidine can be used as an adjuvant in epidurals with local anesthetic sparing effects. Its use during nerve blocks results in reduced postoperative pain. Also, local infiltration of IV dexmedetomidine is associated with earlier discharge from PACU. Perioperative use of dexmedetomidine has significantly improved postoperative outcomes when used as part of ERAS protocols. An in-depth review of the use of dexmedetomidine in ERAS protocols is presented for clinical anesthesiologists.Sparse-view tomography has many applications such as in low-dose computed tomography (CT). Using under-sampled data, a perfect image is not expected. The goal of this paper is to obtain a tomographic image that is better than the naïve filtered backprojection (FBP) reconstruction that uses linear interpolation to complete the measurements. This paper proposes a method to estimate the un-measured projections by displacement function interpolation. Displacement function estimation is a non-linear procedure and the linear interpolation is performed on the displacement function (instead of, on the sinogram itself). As a result, the estimated measurements are not the linear transformation of the measured data. The proposed method is compared with the linear interpolation methods, and the proposed method shows superior performance.We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.In this study, we propose a deep-learning-based method to correct motion artifacts in optical resolution photoacoustic microscopy (OR-PAM). The method is a convolutional neural network that establishes an end-to-end map from input raw data with motion artifacts to output corrected images. First, we performed simulation studies to evaluate the feasibility and effectiveness of the proposed method. Second, we employed this method to process images of rat brain vessels with multiple motion artifacts to evaluate its performance for in vivo applications. The results demonstrate that this method works well for both large blood vessels and capillary networks. In comparison with traditional methods, the proposed method in this study can be easily modified to satisfy different scenarios of motion corrections in OR-PAM by revising the training sets.An accurate segmentation and quantification of the superficial foveal avascular zone (sFAZ) is important to facilitate the diagnosis and treatment of many retinal diseases, such as diabetic retinopathy and retinal vein occlusion. We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography (OCTA) images with robustness to brightness and contrast (B/C) variations. A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth (GT) was manually segmented subsequently. A deep learning network with an encoder-decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class. Subsequently, we applied largest-connected-region extraction and hole-filling to fine-tune the automatic segmentation results. A maximum mean dice similarity coefficient (DSC) of 0.976 ± 0.011 was obtained when the automatic segmentation results were compared against the GT. The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997. In all nine parameter groups with various brightness/contrast, all the DSCs of the proposed method were higher than 0.96. The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods. In conclusion, we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations.  https://www.selleckchem.com/pharmacological_epigenetics.html  For clinical applications, this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.Traditional 3D printing is based on stereolithography or standard tessellation language models, which contain many redundant data and have low precision. This paper proposes a slicing and support structure generation algorithm for 3D printing directly on boundary representation (B-rep) models. First, surface slicing is performed by efficiently computing the intersection curves between the faces of the B-rep models and each slicing plane. Then, the normals of the B-rep models are used to detect where the support structures should be located and the support structures are generated. Experimental results show the efficiency and stability of our algorithm.