Ultrasound-triggered functional hydrogel promotes multistage bone regeneration.

The dysfunction of bone mesenchymal stem cells (BMSCs), caused by the physical and chemical properties of the inflammatory and repair phases of bone regeneration, contributes to the failure of bone regeneration. To meet the spatiotemporal needs of BMSCs in different phases, designing biocompatible materials that respond to external stimuli, improve migration in the inflammatory phase, reduce apoptosis in the proliferative phase, and clear the hurdle in the differentiation phase of BMSCs is an effective strategy for multistage repair of bone defects. In this study, we designed a cascade-response functional composite hydrogel (Gel@Eb/HA) to regulate BMSCs dysfunction in vitro and in vivo. Gel@Eb/HA improved the migration of BMSCs by upregulating the expression of chemokine (C-C motif) ligand 5 (CCL5) during the inflammatory phase. Ultrasound (US) triggered the rapid release of Ebselen (Eb), eliminating the accumulation of reactive oxygen species (ROS) in BMSCs, and reversing apoptosis under oxidative stress. Continued US treatment accelerated the degradation of the materials, thereby providing Ca2+ for the osteogenic differentiation of BMSCs. Altogether, our study highlights the prospects of US-controlled intelligent system, that provides a novel strategy for addressing the complexities of multistage bone repair.

Paracetamol Combination Therapy for Back Pain and Osteoarthritis: A Systematic Review and Meta-Analyses.

BACKGROUND AND OBJECTIVE: Although paracetamol (acetaminophen) combined with other analgesics can reduce pain intensity in some pain conditions, its effectiveness in managing low back pain and osteoarthritis is unclear. This systematic review investigated whether paracetamol combination therapy is more effective and safer than monotherapy or placebo in low back pain and osteoarthritis. METHODS: Online database searches were conducted for randomised trials that evaluated paracetamol combined with another analgesic compared to a placebo or the non-paracetamol ingredient in the combination (monotherapy) in low back pain and osteoarthritis. The primary outcome was a change in pain. Secondary outcomes were (serious) adverse events, changes in disability and quality of life. Follow-up was immediate (≤ 2 weeks), short (> 2 weeks but ≤ 3 months), intermediate (> 3 months but < 12 months) or long term (≥ 12 months). A random-effects meta-analysis was conducted. Risk of bias was assessed using the original Cochrane tool, and quality of evidence using Grading of Recommendations Assessment, Development and Evaluation (GRADE). RESULTS: Twenty-two studies were included. Pain was reduced with oral paracetamol plus a non-steroidal anti-inflammatory drug (NSAID) at immediate term in low back pain (paracetamol plus ibuprofen vs ibuprofen [mean difference (MD) - 6.2, 95% confidence interval (CI) -10.4 to -2.0, moderate evidence]) and in osteoarthritis (paracetamol plus aceclofenac vs aceclofenac [MD - 4.7, 95% CI - 8.3 to - 1.2, moderate certainty evidence] and paracetamol plus etodolac vs etodolac [MD - 15.1, 95% CI - 18.5 to - 11.8; moderate certainty evidence]). Paracetamol plus oral tramadol reduced pain compared with placebo at intermediate term for low back pain (MD - 11.7, 95% CI - 19.2 to - 4.3; very low certainty evidence) and osteoarthritis (MD - 6.8, 95% CI - 12.7 to -0.9; moderate certainty evidence). Disability scores improved in half the comparisons. Quality of life was infrequently measured. All paracetamol plus NSAID combinations did not increase the risk of adverse events compared to NSAID monotherapy. CONCLUSIONS: Low-to-moderate quality evidence supports the oral use of some paracetamol plus NSAID combinations for short-term pain relief with no increased risk of harm for low back pain and osteoarthritis compared to its non-paracetamol monotherapy comparator.

DA-LSTM-VAE: Dual-Stage Attention-Based LSTM-VAE for KPI Anomaly Detection.

To ensure the normal operation of the system, the enterprise's operations engineer will monitor the system through the KPI (key performance indicator). For example, web page visits, server memory utilization, etc. KPI anomaly detection is a core technology, which is of great significance for rapid fault detection and repair. This paper proposes a novel dual-stage attention-based LSTM-VAE (DA-LSTM-VAE) model for KPI anomaly detection. Firstly, in order to capture time correlation in KPI data, long-short-term memory (LSTM) units are used to replace traditional neurons in the variational autoencoder (VAE). Then, in order to improve the effect of KPI anomaly detection, an attention mechanism is introduced into the input stage of the encoder and decoder, respectively. During the input stage of the encoder, a time attention mechanism is adopted to assign different weights to different time points, which can adaptively select important input sequences to avoid the influence of noise in the data. During the input stage of the decoder, a feature attention mechanism is adopted to adaptively select important latent variable representations, which can capture the long-term dependence of time series better. In addition, this paper proposes an adaptive threshold method based on anomaly scores measured by reconstruction probability, which can minimize false positives and false negatives and avoid adjustment of the threshold manually. Experimental results in a public dataset show that the proposed method in this paper outperforms other baseline methods.

Hippocampus Segmentation Method Based on Subspace Patch-Sparsity Clustering in Noisy Brain MRI.

Since the hippocampus is of small size, low contrast, and irregular shape, a novel hippocampus segmentation method based on subspace patch-sparsity clustering in brain MRI is proposed to improve the segmentation accuracy, which requires that the representation coefficients in different subspaces should be as sparse as possible, while the representation coefficients in the same subspace should be as average as possible. By restraining the coefficient matrix with the patch-sparse constraint, the coefficient matrix contains a patch-sparse structure, which is helpful to the hippocampus segmentation. The experimental results show that our proposed method is effective in the noisy brain MRI data, which can well deal with hippocampus segmentation problem.

Log Sequence Anomaly Detection Method Based on Contrastive Adversarial Training and Dual Feature Extraction.

The log messages generated in the system reflect the state of the system at all times. The realization of autonomous detection of abnormalities in log messages can help operators find abnormalities in time and provide a basis for analyzing the causes of abnormalities. First, this paper proposes a log sequence anomaly detection method based on contrastive adversarial training and dual feature extraction. This method uses BERT (Bidirectional Encoder Representations from Transformers) and VAE (Variational Auto-Encoder) to extract the semantic features and statistical features of the log sequence, respectively, and the dual features are combined to perform anomaly detection on the log sequence, with a novel contrastive adversarial training method also used to train the model. In addition, this paper introduces the method of obtaining statistical features of log sequence and the method of combining semantic features with statistical features. Furthermore, the specific process of contrastive adversarial training is described. Finally, an experimental comparison is carried out, and the experimental results show that the method in this paper is better than the contrasted log sequence anomaly detection method.