Bicyclol attenuates pulmonary fibrosis with silicosis via both canonical and non-canonical TGF-ß1 signaling pathways.

BACKGROUND: Silicosis is an irreversible fibrotic disease of the lung caused by chronic exposure to silica dust, which manifests as infiltration of inflammatory cells, excessive secretion of pro-inflammatory cytokines, and pulmonary diffuse fibrosis. As the disease progresses, lung function further deteriorates, leading to poorer quality of life of patients. Currently, few effective drugs are available for the treatment of silicosis. Bicyclol (BIC) is a compound widely employed to treat chronic viral hepatitis and drug-induced liver injury. While recent studies have demonstrated anti-fibrosis effects of BIC on multiple organs, including liver, lung, and kidney, its therapeutic benefit against silicosis remains unclear. In this study, we established a rat model of silicosis, with the aim of evaluating the potential therapeutic effects of BIC. METHODS: We constructed a silicotic rat model and administered BIC after injury. The FlexiVent instrument with a forced oscillation system was used to detect the pulmonary function of rats. HE and Masson staining were used to assess the effect of BIC on silica-induced rats. Macrophages-inflammatory model of RAW264.7 cells, fibroblast-myofibroblast transition (FMT) model of NIH-3T3 cells, and epithelial-mesenchymal transition (EMT) model of TC-1 cells were established in vitro. And the levels of inflammatory mediators and fibrosis-related proteins were evaluated in vivo and in vitro after BIC treatment by Western Blot analysis, RT-PCR, ELISA, and flow cytometry experiments. RESULTS: BIC significantly improved static compliance of lung and expiratory and inspiratory capacity of silica-induced rats. Moreover, BIC reduced number of inflammatory cells and cytokines as well as collagen deposition in lungs, leading to delayed fibrosis progression in the silicosis rat model. Further exploration of the underlying molecular mechanisms revealed that BIC suppressed the activation, polarization, and apoptosis of RAW264.7 macrophages induced by SiO2. Additionally, BIC inhibited SiO2-mediated secretion of the inflammatory cytokines IL-1ß, IL-6, TNF-α, and TGF-ß1 in macrophages. BIC inhibited FMT of NIH-3T3 as well as EMT of TC-1 in the in vitro silicosis model, resulting in reduced proliferation and migration capability of NIH-3T3 cells. Further investigation of the cytokines secreted by macrophages revealed suppression of both FMT and EMT by BIC through targeting of TGF-ß1. Notably, BIC blocked the activation of JAK2/STAT3 in NIH-3T3 cells required for FMT while preventing both phosphorylation and nuclear translocation of SMAD2/3 in TC-1 cells necessary for the EMT process. CONCLUSION: The collective data suggest that BIC prevents both FMT and EMT processes, in turn, reducing aberrant collagen deposition. Our findings demonstrate for the first time that BIC ameliorates inflammatory cytokine secretion, in particular, TGF-ß1, and consequently inhibits FMT and EMT via TGF-ß1 canonical and non-canonical pathways, ultimately resulting in reduction of aberrant collagen deposition and slower progression of silicosis, supporting its potential as a novel therapeutic agent.

Dexborneol Amplifies Pregabalin's Analgesic Effect in Mouse Models of Peripheral Nerve Injury and Incisional Pain.

Pregabalin is a medication primarily used in the treatment of neuropathic pain and anxiety disorders, owing to its gabapentinoid properties. Pregabalin monotherapy faces limitations due to its variable efficacy and dose-dependent adverse reactions. In this study, we conducted a comprehensive investigation into the potentiation of pregabalin's analgesic effects by dexborneol, a neuroprotective bicyclic monoterpenoid compound. We performed animal experiments where pain models were induced using two methods: peripheral nerve injury, involving axotomy and ligation of the tibial and common peroneal nerves, and incisional pain through a longitudinal incision in the hind paw, while employing a multifaceted methodology that integrates behavioral pharmacology, molecular biology, neuromorphology, and lipidomics to delve into the mechanisms behind this potentiation. Dexborneol was found to enhance pregabalin's efficacy by promoting its transportation to the central nervous system, disrupting self-amplifying vicious cycles via the reduction of HMGB1 and ATP release, and exerting significant anti-oxidative effects through modulation of central lipid metabolism. This combination therapy not only boosted pregabalin's analgesic property but also notably decreased its side effects. Moreover, this therapeutic cocktail exceeded basic pain relief, effectively reducing neuroinflammation and glial cell activation-key factors contributing to persistent and chronic pain. This study paves the way for more tolerable and effective analgesic options, highlighting the potential of dexborneol as an adjuvant to pregabalin therapy.

Integrating Radiomics and Neural Networks for Knee Osteoarthritis Incidence Prediction.

OBJECTIVE: Accurately predicting knee osteoarthritis (KOA) is essential for early detection and personalized treatment. We aimed to develop and test a magnetic resonance imaging (MRI)-based joint space (JS) radiomic model (RM) to predict radiographic KOA incidence through neural networks by integrating meniscus and femorotibial cartilage radiomic features. METHODS: In the Osteoarthritis Initiative cohort, participants with knees without radiographic KOA at baseline but at high risk for radiographic KOA were included. Patients' knees developed radiographic KOA, whereas control knees did not over four years. We randomly split the participants into development and test cohorts (8:2) and extracted features from baseline three-dimensional double-echo steady-state sequence MRI. Model performance was evaluated using an area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in both cohorts. Nine resident surgeons performed the reader experiment without/with the JS-RM aid. RESULTS: Our study included 549 knees in the development cohort (275 knees of patients with KOA vs 274 knees of controls) and 137 knees in the test cohort (68 knees of patients with KOA vs 69 knees of controls). In the test cohort, JS-RM had a favorable accuracy for predicting the radiographic KOA incidence with an AUC of 0.931 (95% confidence interval [CI] 0.876-0.963), a sensitivity of 84.4% (95% CI 83.9%-84.9%), and a specificity of 85.6% (95% CI 85.2%-86.0%). The mean specificity and sensitivity of resident surgeons through MRI reading in predicting radiographic KOA incidence were increased from 0.474 (95% CI 0.333-0.614) and 0.586 (95% CI 0.429-0.743) without the assistance of JS-RM to 0.874 (95% CI 0.847-0.901) and 0.812 (95% CI 0.742-0.881) with JS-RM assistance, respectively (P < 0.001). CONCLUSION: JS-RM integrating the features of the meniscus and cartilage showed improved predictive values in radiographic KOA incidence.

Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.

Objective.In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. However, the process of 'signal to image' inevitably introduces information distortion, ultimately leading to irrecoverable biases in the 'image to knowledge' process. Our goal is to skip reconstruction and build a diagnostic model directly from the raw data (signal).Approach. This study focuses on computed tomography (CT) and its raw data (sinogram) as the research subjects. We simulate the real-world process of 'human-signal-image' using the workflow 'CT-simulated data- reconstructed CT,' and we develop a novel AI predictive model directly targeting raw data (RCTM). This model comprises orientation, spatial, and global analysis modules, embodying the fusion of local to global information extraction from raw data. We selected 1994 patients with retrospective cases of solid lung nodules and modeled different types of data.Main results. We employed predefined radiomic features to assess the diagnostic feature differences caused by reconstruction. The results indicated that approximately 14% of the features had Spearman correlation coefficients below 0.8. These findings suggest that despite the increasing maturity of CT reconstruction algorithms, they still introduce perturbations to diagnostic features. Moreover, our proposed RCTM achieved an area under the curve (AUC) of 0.863 in the diagnosis task, showcasing a comprehensive superiority over models constructed from secondary reconstructed CTs (0.840, 0.822, and 0.825). Additionally, the performance of RCTM closely resembled that of models constructed from original CT scans (0.868, 0.878, and 0.866).Significance. The diagnostic and therapeutic approach directly based on CT raw data can enhance the precision of AI models and the concept of 'signal-to-image' can be extended to other types of imaging. AI diagnostic models tailored to raw data offer the potential to disrupt the traditional paradigm of 'signal-image-knowledge', opening up new avenues for more accurate medical diagnostics.