Facile Prepared MOF-OH-PAN Nanofiber for Separation Co(II) from Waste Batteries.

Recovering cobalt from waste batteries is crucial for resource recycling and environmental protection. Here, MOF-OH, a Zr-based MOF, was synthesized and merged into a polyacrylonitrile (PAN) matrix to create MOF-OH-PAN nanofibers (NFs). These NFs showed a high cobalt ion adsorption capacity of 33.1 mg/g, retaining over 90% of the capacity after six cycles. The adsorption mechanism involves Co(II) surface diffusion followed by strong bonding with functional groups. This technology enables efficient cobalt recovery from waste batteries, supporting reuse and reducing resource depletion and environmental pollution. The study provides insights into waste battery resource management, highlighting environmental and economic benefits and contributing to green resource recovery and circular economy initiatives.

Spontaneous resolution of acute gout: mechanisms and therapeutic targets.

Gout is a common inflammatory arthritis that has been increasing in both prevalence and incidence. Consequently, management of refractory and chronic gout has been gaining attention. Onset of gout is related to the deposition of monosodium urate crystals under hyperuricaemia. Interestingly, acute gout attacks often resolve spontaneously within 7-10 days, and many studies have confirmed the notion that gout flares can be self-relieved. However, the underlying mechanism for spontaneous remission of gout requires further elucidation. In this article, we summarise the roles and mechanisms related to spontaneous remission of gout, which are essential for understanding its pathogenesis and developing potential targeted therapies.

Prediction of mortality in pneumonia patients with connective tissue disease treated with glucocorticoids or/and immunosuppressants by machine learning.

Objectives: The assessment of accurate mortality risk is essential for managing pneumonia patients with connective tissue disease (CTD) treated with glucocorticoids or/and immunosuppressants. This study aimed to construct a nomogram for predicting 90-day mortality in pneumonia patients using machine learning. Methods: Data were obtained from the DRYAD database. Pneumonia patients with CTD were screened. The samples were randomly divided into a training cohort (70%) and a validation cohort (30%). A univariate Cox regression analysis was used to screen for prognostic variables in the training cohort. Prognostic variables were entered into the least absolute shrinkage and selection operator (Lasso) and a random survival forest (RSF) analysis was used to screen important prognostic variables. The overlapping prognostic variables of the two algorithms were entered into the stepwise Cox regression analysis to screen the main prognostic variables and construct a model. Model predictive power was assessed using the C-index, the calibration curve, and the clinical subgroup analysis (age, gender, interstitial lung disease, diabetes mellitus). The clinical benefits of the model were assessed using a decision curve analysis (DCA). Similarly, the C-index was calculated and the calibration curve was plotted to verify the model stability in the validation cohort. Results: A total of 368 pneumonia patients with CTD (training cohort: 247; validation cohort: 121) treated with glucocorticoids or/and immunosuppressants were included. The univariate Cox regression analysis obtained 19 prognostic variables. Lasso and RSF algorithms obtained eight overlapping variables. The overlapping variables were entered into a stepwise Cox regression to obtain five variables (fever, cyanosis, blood urea nitrogen, ganciclovir treatment, and anti-pseudomonas treatment), and a prognostic model was constructed based on the five variables. The C-index of the construction nomogram of the training cohort was 0.808. The calibration curve, DCA results, and clinical subgroup analysis showed that the model also had good predictive power. Similarly, the C-index of the model in the validation cohort was 0.762 and the calibration curve had good predictive value. Conclusion: In this study, the nomogram developed performed well in predicting the 90-day risk of death in pneumonia patients with CTD treated with glucocorticoids or/and immunosuppressants.

Ataxin-10 Inhibits TNF-α-Induced Endothelial Inflammation via Suppressing Interferon Regulatory Factor-1.

Endothelial inflammation is a crucial event in the initiation of atherosclerosis. Here, we identify Ataxin-10 protein as a novel negative modulator of endothelial activation by suppressing IRF-1 transcription activity. The protein level of Ataxin-10 is relatively higher in human vascular endothelial cells, which can be significantly suppressed by TNF-α in both HUVECs and HLMECs. Overexpression of Ataxin-10 markedly inhibited the mRNA expressions of VCAM-1 and several cytokines including MCP-1, CXCL-1, CCL-5, and TNF-α; thus, it can also suppress monocyte adhesion to endothelial cells. Accordingly, Ataxin-10 silencing promoted endothelial inflammation. However, Ataxin-10 did not affect the MAPK/NF-κB signaling pathway stimulated by TNF-α in HUVECs. Using the yeast two-hybrid assay, we found that Ataxin-10 can directly bind to interferon regulatory factor-1 (IRF-1). Upon TNF-α stimulation, Ataxin-10 promoted the cytoplasmic localization of IRF-1, which inhibited the transcription of VCAM-1. Moreover, knockdown of IRF-1 can eliminate the effect of Ataxin-10 on the expression of VCAM-1 in HUVECs induced by TNF-α. Taken together, these results indicate that Ataxin-10 inhibits endothelial cell activation and may serve as a promising therapeutic target for some vascular inflammatory-related diseases such as atherosclerosis.

AIMP3 inhibits cell growth and metastasis of lung adenocarcinoma through activating a miR-96-5p-AIMP3-p53 axis.

Aminoacyl-tRNA synthetase-interacting multifunctional protein-3 (AIMP3) is a tumour suppressor, however, the roles of AIMP3 in non-small cell lung cancer (NSCLC) are not explored yet. Here, we reported that AIMP3 significantly inhibited the cell growth and metastasis of NSCLC (lung adenocarcinoma) in vitro and in vivo. We have firstly identified that AIMP3 was down-regulated in human NSCLC tissues compared with adjacent normal lung tissues using immunohistochemistry and western blot assays. Overexpression of AIMP3 markedly suppressed the proliferation and migration of cancer cells in a p53-dependent manner. Furthermore, we observed that AIMP3 significantly suppressed tumour growth and metastasis of A549 cells in xenograft nude mice. Mechanically, we identified that AIMP3 was a direct target of miR-96-5p, and we also observed that there was a negative correlation between AIMP3 and miR-96-5p expression in paired NSCLC clinic samples. Ectopic miR-96-5p expression promoted the proliferation and migration of cancer cells in vitro and tumour growth and metastasis in vivo which partially depended on AIMP3. Taken together, our results demonstrated that the axis of miR-96-5p-AIMP3-p53 played an important role in lung adenocarcinoma, which may provide a new strategy for the diagnosis and treatment of NSCLC.