Search

Objective: To examine the potential effect of the probiotic strain Lactobacillus plantarum LP45 on osteoporosis and to explore the involved molecular mechanisms. Methods: A rat model of glucocorticoid-induced osteoporosis (GIO) was established, which was also orally administered with increasing doses of LP45 for 8 weeks. After the termination of the 8-week treatment, the tibia and femur bones of rats were analyzed for bone histomorphometry, bone mineral content (BMC), and bone mineral density (BMD). Femoral biomechanics were assessed. In addition, levels of osteocalcin, tartrate-resistant acid phosphatase 5 (TRAP5), osteoprotegerin (OPG), and receptor activator of nuclear factor kappa-B ligand (RANKL) in the serum and bone marrow were also measured using ELISA, Western blot, and real time-polymerase chain reaction. Results: GIO caused obvious defects in tibia and femur bone structures, in terms of tissue/bone volume, trabecular separation, trabecular thickness, and trabecular number, which could be rescued by LP45 dose dependently. The GIO-induced reductions in BMC, BMD, osteoblast surfaces per bone surface (BS), as well as elevated osteoclast surface per BS were largely restored by LP45 administration dose-dependently. LP45 also increased femoral biomechanics of GIO rats. Importantly, LP45 dose-dependently restored the changes of osteocalcin, TRAP5, OPG, and RANKL in the serum as well as bone marrow of GIO rats. Conclusion: Oral LP45 administration could significantly prevent bone defects in GIO rats, suggesting its potential as a dietary supplement with beneficial effects against osteoporosis, which might involve the RANKL/OPG signaling pathway.

Breast ultrasound (BUS) imaging is commonly used in the early detection of breast cancer as a portable, valuable, and widely available diagnosis tool. Automated BUS image classification and segmentation can assist radiologists in making accurate and fast decisions. Recent studies illustrate that tumor, peritumoral, and background regions of BUS images provide valuable information for BUS image segmentation or classification. However, few studies have investigated the influence of these three regions on multi-task learning. In this study, we propose an RMTL-Net to simultaneously segment tumor regions and classify tumors in BUS images into benign or malignant categories. To improve both segmentation and classification performance, we design a regional attention (RA) module that employs the predicted probability maps to automatically guide the classifier to learn important category-sensitive information in the tumor, peritumoral, and background regions and seamlessly fuse them to obtain a better feature representation. We conduct detailed ablation experiments of the proposed RA module and comparative experiments with four recent state-of-the-art peer multi-task learning methods, three single-task segmentation methods, and four single-task classification methods on two public BUS datasets. Experimental results show that the proposed RMTL-Net achieves the best overall segmentation and classification accuracy in terms of five segmentation metrics and six classification metrics.

Objective: This study was designed to compare the effects of hamstring muscle injection of botulinum toxin type A (BoNT/A) combined with periarticular injection to those of simple periarticular injection alone, in terms of early pain reduction and functional recovery following total knee arthroplasty (TKA). Methods: This study included 40 patients who underwent elective unilateral TKA at our hospital from February 2021 to December 2021. The patients were randomly assigned to either the experimental group (BoNT/A injection into the hamstring muscle + periarticular injection, n = 20 cases) or the control group (0.9% normal saline injection into the hamstring muscle + periarticular injection, n = 20 cases). The preoperative and postoperative 3-day HSS scores were compared between the two groups. Postoperative visual analog scale (VAS) pain scores were observed and compared between the two groups. The Lovett grading method was used to evaluate the postoperative muscle strength of both groups. Additionally, postoperative knee joint pain scores, maximum active flexion angle, and any postoperative adverse reactions were observed and compared between the two groups. Results: The experimental group showed significantly lower VAS pain scores than the control group at 24 h, 48 h, and 72 h after the operation, with a statistically significant difference (P < 0.05). Furthermore, compared to the control group, the patients in the experimental group exhibited larger knee joint active flexion angles at 1 day, 3 days, and 5 days post-operation, also demonstrating a statistically significant difference (P < 0.05). These findings hold scientific significance (P < 0.05). Additionally, the combined administration of BoNT/A hamstring muscle injection with periarticular injection exhibited a low incidence of adverse reactions during the perioperative period and had no significant effect on quadriceps muscle strength. Conclusion: The combination of BoNT/A hamstring muscle injection with periarticular injection effectively alleviates early postoperative pain following TKA and promotes early functional recovery of the knee joint.

Tuberoinfundibular dopamine (TIDA) neurons have cell bodies located in the arcuate nucleus of the mediobasal hypothalamus. They project to the external zone of the median eminence, and the dopamine (DA) released there is carried by the hypophysial portal vasculature to the anterior pituitary. The DA then activates D2 receptors to inhibit prolactin (PRL) secretion from lactotrophs. The TIDA neuronal population is the principal regulatory factor controlling PRL secretion. The neuroendocrine role subserved by TIDA neurons sets them apart from other dopaminergic populations like the nigrostriatal and mesolimbic DA neurons. TIDA neurons exhibit intrinsic oscillatory fluctuations in their membrane potential that give rise to phasic firing and bursting activity. TIDA neuronal activity is sexually differentiated and modulated by gonadal hormones and PRL, as well as an array of small molecule and peptide neurotransmitters. This review covers these characteristics.

This study aimed to report on a method for injecting botulinum toxin type A into the hamstring muscles combined with periarticular injection in total knee arthroplasty (TKA) patients. We enrolled patients who underwent elective unilateral TKA at our hospital from February 2021 to December 2021 and administered botulinum toxin type A hamstring muscle injection combined with periarticular injection. We established and reported a detailed method for this combined approach, which could provide an alternative analgesic regimen after TKA in clinical practice.

Abstract One of the primary challenges of visual tracking is the variable appearance of the target object. As tracking proceeds, the target object can change its appearance due to illumination changes, rotations, deformations etc. Modern trackers incorporate online updating to learn how the target changes over time. However, they do not use the history of target appearance. To address this shortcoming, we uniquely use domain adaptation with the target appearance history to efficiently learn a temporally matching filter (TMF) during online updating. This TMF emphasizes the persistent features found in different appearances of the target object. It also improves the classification accuracy of the convolutional neural network by assisting the training of the classification layers without incurring the runtime overhead of updating the convolutional layers. Extensive experimental results demonstrate that the proposed TMF‐based tracker, which incorporates domain adaptation with the target appearance history, improves tracking performance on three benchmark video databases (OTB‐50, OTB‐100 and VOT2016) over other online learning trackers. Specifically, it improves the overlap success of VITAL and MDNet by 0.44 % and 1.03 % on the OTB‐100 dataset and improves the accuracy of VITAL and MDNet by 0.55 % and 0.06 % on the VOT2016 dataset, respectively.

Traffic sign detection is considered as one of the active research topics in transportation and computer vision. The previous works mainly focus on detecting traffic signs in images or in mobile light detection and ranging (LiDAR) data. In this paper, we propose a novel deep learning method to accurately detect traffic signs by fusing the complementary features from registered airborne geo-referenced color images and noisy airborne LiDAR data. Specifically, we first segment the airborne color images to road and non-road segments by integrating various local features in an inequality constraint quadratic optimization model. Next, we find the corresponding road regions in LiDAR data and extract high elevated objects above the road. We then segment the extracted objects to different regions corresponding to traffic sign candidates using Euclidean distance-based clustering. Finally, we find the corresponding traffic sign candidates in color images, extract their deep features, and represent them in a convex optimization model to classify the candidates. A set of extensive experiments have been carried out on the airborne geo-referenced color images and noisy airborne LiDAR data captured by Utah State University from I-15 highway. The results show the effectiveness of the proposed method in detecting traffic signs.

This study proposes an illumination‐invariant face‐recognition method called adaptive homomorphic eight local directional pattern (AH‐ELDP). AH‐ELDP first uses adaptive homomorphic filtering to reduce the influence of illumination from an input face image. It then applies an interpolative enhancement function to stretch the filtered image. Finally, it produces eight directional edge images using Kirsch compass masks and uses all the directional information to create an illumination‐insensitive representation. The author's extensive experiments show that the AH‐ELDP technique achieves the best face recognition accuracy of 99.45% for CMU‐PIE face images, 96.67% for Yale B face images and 84.42% for Extended Yale B face images using one image per subject for training when compared to seven representative state‐of‐the‐art techniques.

Abstract All-perovskite tandem solar cells have shown great promise in breaking the Shockley–Queisser limit of single-junction solar cells. However, the efficiency improvement of all-perovskite tandem solar cells is largely hindered by the surface defects induced non-radiative recombination loss in Sn–Pb mixed narrow bandgap perovskite films. Here, we report a surface reconstruction strategy utilizing a surface polishing agent, 1,4-butanediamine, together with a surface passivator, ethylenediammonium diiodide, to eliminate Sn-related defects and passivate organic cation and halide vacancy defects on the surface of Sn–Pb mixed perovskite films. Our strategy not only delivers high-quality Sn–Pb mixed perovskite films with a close-to-ideal stoichiometric ratio surface but also minimizes the non-radiative energy loss at the perovskite/electron transport layer interface. As a result, our Sn–Pb mixed perovskite solar cells with bandgaps of 1.32 and 1.25 eV realize power conversion efficiencies of 22.65% and 23.32%, respectively. Additionally, we further obtain a certified power conversion efficiency of 28.49% of two-junction all-perovskite tandem solar cells.