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Cobalt alloys have numerous applications, especially as critical components in orthopedic biomedical implants. However, recent investigations have revealed potential hazards associated with the release of nanoparticles from cobalt-based implants during implantation. This can lead to their accumulation and migration within the body, resulting in adverse reactions such as organ toxicity. Despite being a primary interface for cobalt nanoparticle (CoNP) exposure, skeletal muscle lacks comprehensive long-term impact studies. This study evaluated whether selenium nanoparticles (SeNPs) could mitigate CoNP toxicity in muscle cells and zebrafish models. CoNPs dose-dependently reduced C2C12 viability while elevating reactive oxygen species (ROS) and apoptosis. However, low-dose SeNPs attenuated these adverse effects. CoNPs downregulated myogenic genes and α-smooth muscle actin (α-SMA) expression in C2C12 cells; this effect was attenuated by SeNP cotreatment. Zebrafish studies confirmed CoNP toxicity, as it decreased locomotor performance while inducing muscle injury, ROS generation, malformations, and mortality. However, SeNPs alleviated these detrimental effects. Overall, SeNPs mitigated CoNP-mediated cytotoxicity in muscle cells and tissue through antioxidative and antiapoptotic mechanisms. This suggests that SeNP-coated implants could be developed to eliminate cobalt nanoparticle toxicity and enhance the safety of metallic implants.

A surgical site infection (SSI) is one of the most common complications of spinal surgery. Malnutrition has also been linked to SSI after other surgical procedures. However, whether malnutrition is a risk factor for SSI after spinal surgery remains controversial. Therefore, we performed a meta‐analysis to comprehensively evaluate the relationship between malnutrition and SSI. Relevant studies of the correlation between malnutrition and SSI were retrieved from the Cochrane Library, EMBASE, PubMed, Web of Science, China National Knowledge Infrastructure and Wanfang Data from database inception to 21 May 2023. Two reviewers independently assessed the included studies, and a meta‐analysis was performed using STATA 17.0 software. A total of 24 articles with 179 388 patients were included: 3919 and 175 469 cases comprised the SSI and control groups, respectively. The meta‐analysis results showed that malnutrition significantly increased the SSI incidence (odds ratio, 1.811; 95% confidence interval, 1.512–2.111; p < 0.001). These results suggest that patients with malnutrition are at higher risk for SSI after surgery. However, because of significant differences in sample sizes among studies, and because some studies had limitations to their methodological quality, further validation of these results by additional high‐quality studies with larger sample sizes is necessary.

A surgical site infection (SSI) is one of the most common complications of spinal surgery. Malnutrition has also been linked to SSI after other surgical procedures. However, whether malnutrition is a risk factor for SSI after spinal surgery remains controversial. Therefore, we performed a meta‐analysis to comprehensively evaluate the relationship between malnutrition and SSI. Relevant studies of the correlation between malnutrition and SSI were retrieved from the Cochrane Library, EMBASE, PubMed, Web of Science, China National Knowledge Infrastructure and Wanfang Data from database inception to 21 May 2023. Two reviewers independently assessed the included studies, and a meta‐analysis was performed using STATA 17.0 software. A total of 24 articles with 179 388 patients were included: 3919 and 175 469 cases comprised the SSI and control groups, respectively. The meta‐analysis results showed that malnutrition significantly increased the SSI incidence (odds ratio, 1.811; 95% confidence interval, 1.512–2.111; p  < 0.001). These results suggest that patients with malnutrition are at higher risk for SSI after surgery. However, because of significant differences in sample sizes among studies, and because some studies had limitations to their methodological quality, further validation of these results by additional high‐quality studies with larger sample sizes is necessary.

Fully Homomorphic Encryption (FHE) enables Machine Learning as a Service (MLaaS) providers to perform inference over encrypted data, preserving user privacy. In the RNS-CKKS FHE scheme, however, ReLU activations must be replaced with polynomials. High-degree polynomial approximations preserve accuracy but consume more ciphertext levels, triggering costly bootstrapping operations. Existing mixed-degree methods reduce the bootstrapping count by assigning different polynomial degrees across layers. However, recent FHE compiler research shows that reducing the bootstrapping count alone is insufficient to fully accelerate inference, because bootstrapping placement and the level budget restored after each operation also significantly affect performance. Incorporating such execution-side factors into mixed-degree design substantially enlarges the search space, making straightforward extensions of existing methods computationally infeasible. We propose CipherAPR, an importance-guided framework for level-aware mixed-degree polynomial design. CipherAPR introduces the Low-Magnitude Activation Ratio (LMAR) to prioritize degree updates on accuracy-sensitive layers, combines Domain-Adaptive Approximation (DAAP) with Multi-Objective Coefficient Tuning (MOCT) to produce reusable polynomial approximations that consume fewer ciphertext levels, and applies ciphertext-level utilization (CLU) to filter configurations with poor restored-level utilization. Lightweight latency and accuracy estimators further accelerate offline candidate screening. Experiments on ResNet and VGG show that CipherAPR achieves a 1.09×–1.39× speedup over AutoFHE with comparable accuracy.

In recent years, distributed machine learning has garnered significant attention. However, privacy continues to be an unresolved issue within this field. Multi-key homomorphic encryption over torus (MKTFHE) is one of the promising candidates for addressing this concern. Nevertheless, there may be security risks in the decryption of MKTFHE. Moreover, to our best known, the latest works about MKTFHE only support Boolean operation and linear operation which cannot directly compute the non-linear function like Sigmoid. Therefore, it is still hard to perform common machine learning such as logistic regression and neural networks in high performance. In this paper, we first discover a possible attack on the existing distributed decryption protocol for MKTFHE and subsequently introduce secret sharing to propose a securer one. Next, we design a new MKTFHE-friendly activation function via homogenizer and compare quads. Finally, we utilize them to implement logistic regression and neural network training in MKTFHE. Comparing the efficiency and accuracy between using Taylor polynomials of Sigmoid and our proposed function as an activation function, the experiments show that the efficiency of our function is 10 times higher than using 7-order Taylor polynomials straightly and the accuracy of the training model is similar to using a high-order polynomial as an activation function scheme.