Orthopedic revolution: The emerging role of nanotechnology.

This review summarizes the latest progress in orthopedic nanotechnology, explores innovative applications of nanofibers in tendon repair, and evaluates the potential of selenium and cerium oxide nanoparticles in osteoarthritis and osteoblast differentiation. This review also describes the emerging applications of injectable hydrogels in cartilage engineering, emphasizing the critical role of interdisciplinary research and highlighting the challenges and future prospects of integrating nanotechnology into orthopedic clinical practice. This comprehensive approach provides a holistic perspective on the transformative impact of nanotechnology in orthopedics, offering valuable insights for future research and clinical applications.

Association between tobacco exposure and bladder cancer recurrence: A systematic review and meta-analysis.

Background: However, the connection between smoking and the prognosis of patients with bladder cancer remains unclear.

Aim: To determine whether smoking is linked to the recurrence and progression of bladder cancer.

Methods: As of July 20, 2022, relevant English-language research was identified by searching PubMed, the Web of Science, and the Cochrane Library.

Colorectal cancer screening: The value of early detection and modern challenges.

The screening of colorectal cancer (CRC) is pivotal for both the prevention and treatment of this disease, significantly improving early-stage tumor detection rates. This advancement not only boosts survival rates and quality of life for patients but also reduces the costs associated with treatment. However, the adoption of CRC screening methods faces numerous challenges, including the technical limitations of both noninvasive and invasive methods in terms of sensitivity and specificity.

[Spatial and temporal variations of vegetation phenology and its response to urbanization in central Yunnan urban agglomeration, Southwest China].

Vegetation phenology is an important sensor that responds to environmental changes. Based on MOD13Q1 EVI data, we used the dynamic threshold method to extract vegetation phenological parameters of the central Yunnan urban agglomeration from 2001 to 2020, namely the start of growing season, the end of growing season, and the length of growing season, aiming to reveal the spatiotemporal variations in vegetation phenology and urban-rural differences. The results showed that vegetation phenology of the central Yunnan urban agglomeration from 2001 to 2020 generally showed a phenomenon of delayed start of growing season, delayed the end of growing season (0.

Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients.

The study aims to develop AICare, an interpretable mortality prediction model, using electronic medical records (EMR) from follow-up visits for end-stage renal disease (ESRD) patients. AICare includes a multichannel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform personalized health context representation learning.

Enhancing robustness in video recognition models: Sparse adversarial attacks and beyond.

Recent years have witnessed increasing interest in adversarial attacks on images, while adversarial video attacks have seldom been explored. In this paper, we propose a sparse adversarial attack strategy on videos (DeepSAVA). Our model aims to add a small human-imperceptible perturbation to the key frame of the input video to fool the classifiers.

Development of a performance measurement system for general practitioners' office in China's primary healthcare.

Background: General practitioners are the main providers of primary care services. To better strengthen the important role of general practitioners in primary healthcare services, China is promoting the general practitioners' office system. There is a lack of well-accepted methods to measure the performance of general practitioner offices in China.

Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task.

The black-box nature of machine learning models hinders the deployment of some high-accuracy medical diagnosis algorithms. It is risky to put one's life in the hands of models that medical researchers do not fully understand or trust. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may have overlooked due to the surge of infected patients in the COVID-19 pandemic.

Structural and biochemical insights into small RNA 3' end trimming by Arabidopsis SDN1.

A family of DEDDh 3'→5' exonucleases known as Small RNA Degrading Nucleases (SDNs) initiates the turnover of ARGONAUTE1 (AGO1)-bound microRNAs in Arabidopsis by trimming their 3' ends. Here, we report the crystal structure of Arabidopsis SDN1 (residues 2-300) in complex with a 9 nucleotide single-stranded RNA substrate, revealing that the DEDDh domain forms rigid interactions with the N-terminal domain and binds 4 nucleotides from the 3' end of the RNA via its catalytic pocket. Structural and biochemical results suggest that the SDN1 C-terminal domain adopts an RNA Recognition Motif (RRM) fold and is critical for substrate binding and enzymatic processivity of SDN1.

Evolution of two modes of intrinsic RNA polymerase transcript cleavage.

During gene transcription, the RNA polymerase (Pol) active center can catalyze RNA cleavage. This intrinsic cleavage activity is strong for Pol I and Pol III but very weak for Pol II. The reason for this difference is unclear because the active centers of the polymerases are virtually identical.