Outcome of arthroscopic triple release combined with rotator cuff repair in the treatment of rotator cuff injury combined with frozen shoulder.

Objective: To investigate the effect of arthroscopic triple release combined with rotator cuff repair in the treatment of rotator cuff injury combined with frozen shoulder and its influence on the range of motion and pain score of shoulder joint, and the levels of serum pain mediators. Methods: This was prospective study. A total of 132 patients with rotator cuff injury combined with frozen shoulder admitted to The Third Affiliated Hospital of Gansu University of Traditional Chinese Medicine from December 2020 to December 2022 were prospectively selected and divided into two groups according to the random number table method: control group (n=67) and observation group (n=65). Patients in the control group were treated with arthroscopic rotator cuff repair alone, while those in the observation group were treated with arthroscopic triple release combined with rotator cuff repair, and the surgical effects of the two groups were compared. Results: Three months after treatment, the external rotation, internal rotation, abduction, forward flexion, ß-endorphin(ß-EP), prostagranin E2 (PGE2) and substance P(SP)in the observation group were better than those in the control group (P<0.05), while the weight-bearing strength of the affected limb in internal rotation, external rotation and forward flexion was higher than that of the control group(P<0.05). Meanwhile, the Visual Analogue Scale (VAS) score of the observation group was lower than that of the control group at one month and three months after treatment, while the University of California at Los Angeles shoulder rating scale (UCLA) score and Constant-Murley Score (CMS) were higher than those of the control group (P< 0.05). Conclusion: Arthroscopic triple release combined with rotator cuff repair improves various effects for patients with rotator cuff injury combined with frozen shoulder, such as ameliorating the muscle strength of the affected limb and improving the level of pain mediators.

Effect of cooling rate on the microstructure and luminescence properties of Sr2 MgSi2 O7 :Eu2+ ,Dy3+ materials.

Sr2 MgSi2 O7 :Eu2+ ,Dy3+ long afterglow materials were prepared by a high-temperature solid-state reaction method with different cooling rates. The cooling rate had a slight effect on X-ray diffraction patterns and photoluminescence performance, but significantly modified the grain boundaries and long afterglow properties of the Sr2 MgSi2 O7 :Eu2+ ,Dy3+ materials. When the cooling rate was 1°C/min, grains remained intact with clear grain boundaries. As the cooling rate increased from 1°C/min to 5°C/min, some grain boundaries became indistinguishable. The afterglow properties were optimized, presenting best performance at the cooling rate of 3°C/min. The trap state was investigated and illustrated through thermoluminescence curves. The depths of the traps of all the samples were unchanged, whereas densities changed to a large extent, leading to different afterglow properties. The retrapping process is discussed based on the afterglow curves.

Development and validation of a fully automated system using deep learning for opportunistic osteoporosis screening using low-dose computed tomography scans.

Background: Bone density measurement is an important examination for the diagnosis and screening of osteoporosis. The aim of this study was to develop a deep learning (DL) system for automatic measurement of bone mineral density (BMD) for osteoporosis screening using low-dose computed tomography (LDCT) images. Methods: This retrospective study included 500 individuals who underwent LDCT scanning from April 2018 to July 2021. All images were manually annotated by a radiologist for the cancellous bone of target vertebrae and post-processed using quantitative computed tomography (QCT) software to identify osteoporosis. Patients were divided into the training, validation, and testing sets in a ratio of 6:2:2 using a 4-fold cross validation method. A localization model using faster region-based convolutional neural network (R-CNN) was trained to identify and locate the target vertebrae (T12-L2), then a 3-dimensional (3D) AnatomyNet was trained to finely segment the cancellous bone of target vertebrae in the localized image. A 3D DenseNet was applied for calculating BMD. The Dice coefficient was used to evaluate segmentation performance. Linear regression and Bland-Altman (BA) analyses were performed to compare the calculated BMD values using the proposed system with QCT. The diagnostic performance of the system for osteoporosis and osteopenia was evaluated with receiver operating characteristic (ROC) curve analysis. Results: Our segmentation model achieved a mean Dice coefficient of 0.95, with Dice coefficients greater than 0.9 accounting for 96.6%. The correlation coefficient (R2) and mean errors between the proposed system and QCT in the testing set were 0.967 and 2.21 mg/cm3, respectively. The area under the curve (AUC) of the ROC was 0.984 for detecting osteoporosis and 0.993 for distinguishing abnormal BMD (osteopenia and osteoporosis). Conclusions: The fully automated DL-based system is able to perform automatic BMD calculation for opportunistic osteoporosis screening with high accuracy using LDCT scans.

A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions.

OBJECTIVE: To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT. METHODS: This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0. RESULTS: Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05). CONCLUSION: DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction. ADVANCES IN KNOWLEDGE: (1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images.