Intermittent Fasting Targets Osteocyte Neuropeptide Y to Relieve Osteoarthritis.

Osteoarthritis is a highly prevalent progressive joint disease that still requires an optimal therapeutic approach. Intermittent fasting is an attractive dieting strategy for improving health. Here this study shows that intermittent fasting potently relieves medial meniscus (DMM)- or natural aging-induced osteoarthritic phenotypes. Osteocytes, the most abundant bone cells, secrete excess neuropeptide Y (NPY) during osteoarthritis, and this alteration can be altered by intermittent fasting. Both NPY and the NPY-abundant culture medium of osteocytes (OCY-CM) from osteoarthritic mice possess pro-inflammatory, pro-osteoclastic, and pro-neurite outgrowth effects, while OCY-CM from the intermittent fasting-treated osteoarthritic mice fails to induce significant stimulatory effects on inflammation, osteoclast formation, and neurite outgrowth. Depletion of osteocyte NPY significantly attenuates DMM-induced osteoarthritis and abolishes the benefits of intermittent fasting on osteoarthritis. This study suggests that osteocyte NPY is a key contributing factor in the pathogenesis of osteoarthritis and intermittent fasting represents a promising nonpharmacological antiosteoarthritis method by targeting osteocyte NPY.

Unsupervised Spectral Demosaicing With Lightweight Spectral Attention Networks.

This paper presents a deep learning-based spectral demosaicing technique trained in an unsupervised manner. Many existing deep learning-based techniques relying on supervised learning with synthetic images, often underperform on real-world images, especially as the number of spectral bands increases. This paper presents a comprehensive unsupervised spectral demosaicing (USD) framework based on the characteristics of spectral mosaic images. This framework encompasses a training method, model structure, transformation strategy, and a well-fitted model selection strategy. To enable the network to dynamically model spectral correlation while maintaining a compact parameter space, we reduce the complexity and parameters of the spectral attention module. This is achieved by dividing the spectral attention tensor into spectral attention matrices in the spatial dimension and spectral attention vector in the channel dimension. This paper also presents Mosaic 25 , a real 25-band hyperspectral mosaic image dataset featuring various objects, illuminations, and materials for benchmarking purposes. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed method outperforms conventional unsupervised methods in terms of spatial distortion suppression, spectral fidelity, robustness, and computational cost. Our code and dataset are publicly available at https://github.com/polwork/Unsupervised-Spectral-Demosaicing.

Tensor Completion Using Bilayer Multimode Low-Rank Prior and Total Variation.

In this article, we propose a novel bilayer low-rankness measure and two models based on it to recover a low-rank (LR) tensor. The global low rankness of underlying tensor is first encoded by LR matrix factorizations (MFs) to the all-mode matricizations, which can exploit multiorientational spectral low rankness. Presumably, the factor matrices of all-mode decomposition are LR, since local low-rankness property exists in within-mode correlation. In the decomposed subspace, to describe the refined local LR structures of factor/subspace, a new low-rankness insight of subspace: a double nuclear norm scheme is designed to explore the so-called second-layer low rankness. By simultaneously representing the bilayer low rankness of the all modes of the underlying tensor, the proposed methods aim to model multiorientational correlations for arbitrary N -way ( N ≥ 3 ) tensors. A block successive upper-bound minimization (BSUM) algorithm is designed to solve the optimization problem. Subsequence convergence of our algorithms can be established, and the iterates generated by our algorithms converge to the coordinatewise minimizers in some mild conditions. Experiments on several types of public datasets show that our algorithm can recover a variety of LR tensors from significantly fewer samples than its counterparts.

An update on recent progress of the epidemiology, etiology, diagnosis, and treatment of acute septic arthritis: a review.

Acute septic arthritis is on the rise among all patients. Acute septic arthritis must be extensively assessed, identified, and treated to prevent fatal consequences. Antimicrobial therapy administered intravenously has long been considered the gold standard for treating acute osteoarticular infections. According to clinical research, parenteral antibiotics for a few days, followed by oral antibiotics, are safe and effective for treating infections without complications. This article focuses on bringing physicians up-to-date on the most recent findings and discussions about the epidemiology, etiology, diagnosis, and treatment of acute septic arthritis. In recent years, the emergence of antibiotic-resistant, particularly aggressive bacterial species has highlighted the need for more research to enhance treatment approaches and develop innovative diagnosis methods and drugs that might combat better in all patients. This article aims to furnish radiologists, orthopaedic surgeons, and other medical practitioners with contemporary insights on the subject matter and foster collaborative efforts to improve patient outcomes. This review represents the initial comprehensive update encompassing patients across all age groups.

Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction.

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling k- t space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images. (2) they focus on preserving global information only, while ignoring the local details reconstruction such as the spatial piece-wise smoothness and sharp boundaries. To overcome these obstacles, we suggest a novel low-rank tensor decomposition approach by integrating tensor Qatar Riyal (QR) decomposition, low-rank tensor nuclear norm, and asymmetric total variation to reconstruct dMRI, named TQRTV. Specifically, while preserving the tensor inherent structure by utilizing tensor nuclear norm minimization to approximate tensor rank, QR decomposition reduces the dimensions in the low-rank constraint term, thereby improving the reconstruction performance. TQRTV further exploits the asymmetric total variation regularizer to capture local details. Numerical experiments demonstrate that the proposed reconstruction approach is superior to the existing ones.