Association between neutrophil-to-lymphocyte ratio and postoperative fatigue in elderly patients with hip fracture.

Background: and purpose: Postoperative fatigue (POF) is a common and distressing post-operative symptom. This study aimed to explore the relationship between neutrophil-to-lymphocyte ratio (NLR) and POF in elderly patients with hip fracture. Method: Elderly patients (age ≥65 years) with acute hip fracture admitted to the Department of Orthopedics of Anqing Municipal Hospital from June 2018 to June 2020 were included. Fatigue was assessed using the Fatigue Severity Scale at the 3-month follow-up postoperatively. Univariate and multivariate analyses were performed to explore the associations between NLR and POF. The diagnostic performance of NLR was analysed using Receiver Operating Characteristic (ROC) curve analysis and the Delong test. Result: A total of 321 elderly patients with hip fractures were included; 120 (37.4 %) of them were diagnosed with POF. Univariate analysis indicated significant differences in NLR, platelet-to-lymphocyte ratio (PLR), education, neutrophil count, lymphocyte count, Hamilton Depression Scale (HAMD) and Insomnia Severity Index (ISI) scores (P < 0.05). Multivariate analysis indicated neutrophil count (odds ratio [OR], 1.46; 95 % confidence interval [CI] 1.27-1.67), lymphocyte count (OR 0.32, 95 % CI 0.19-0.53), NLR (OR1.81, 95 % CI 1.50-2.17) and PLR (OR 1.005, 95 % CI 1.001-1.009) were significantly associated with POF. The areas under the ROC curves (AUCs) of neutrophil count, lymphocyte count, NLR and PLR were 0.712, 0.667, 0.775 and 0.605, respectively. The Delong test indicated that NLR had the best diagnostic performance (p < 0.05). Conclusion: NLR independently predicts POF in elderly patients with acute hip fracture.

MicroRNA-410-3p modulates chondrocyte apoptosis and inflammation by targeting high mobility group box 1 (HMGB1) in an osteoarthritis mouse model.

BACKGROUND: Osteoarthritis (OA) is the most prevalent type of arthritis, which commonly involves inflammation in the articular cartilage in OA pathogenesis. MicroRNAs (miRNAs) play essential roles in the regulation and pathophysiology of various diseases including OA. MiR-410-3p has been demonstrated to mediate inflammatory pathways, however, the regulatory functions of miR-410-3p in OA remain largely unknown. METHODS: The regulations of miR-410-3p were investigated in OA. Mouse primary chondrocytes and mouse in vivo models were used. The expression levels of miR-410-3p and HMGB1 were measured by qPCR. The transcription activity of NF-κB was assessed by luciferase reporter assay. MTT assay was performed to assess cellular proliferation. Cell apoptosis was evaluated with the Fluorescein Isothiocyanate (FITC) Annexin V assay. Expression levels of proteins were determined by Western blot. RESULTS: The results demonstrated that miR-410-3p was markedly downregulated in articular cartilage tissues as well as in lipopolysaccharide (LPS)-treated chondrocytes in OA mice. In addition, upregulation of miR-410-3p markedly inhibited LPS-induced apoptosis of chondrocytes. The results also demonstrated that the high mobility group box 1 (HMGB1) was a target of miR-410-3p. LPS-induced upregulated expression of HMGB1 significantly suppressed expression of miR-410-3p. Furthermore, upregulation of miR-410-3p markedly inhibited HMGB1 expression, the nuclear factor (NF)-kB activity and pro-inflammatory cytokines production. Taken together, the results suggested that miR-410-3p targeted HMGB1 and modulated chondrocytes apoptosis and inflammation through the NF-κB signaling pathway. CONCLUSIONS: These findings provide insights into the potential of miR-410-3p/ HMGB1 as therapeutic targets for OA treatment.

A machine learning model to precisely immunohistochemically classify pituitary adenoma subtypes with radiomics based on preoperative magnetic resonance imaging.

PURPOSE: The type of pituitary adenoma (PA) cannot be clearly recognized with preoperative magnetic resonance imaging (MRI) but can be classified with immunohistochemical staining after surgery. In this study, a model to precisely immunohistochemically classify the PA subtypes by radiomic features based on preoperative MR images was developed. METHODS: Two hundred thirty-five pathologically diagnosed PAs, including t-box pituitary transcription factor (Tpit) family tumors (n = 55), pituitary transcription factor 1 (Pit-1) family tumors (n = 110), and steroidogenic factor 1 (SF-1) family tumors (n = 70), were retrospectively studied. T1-weighted, T2-weighted and contrast-enhanced T1-weighted images were obtained from all patients. Through imaging acquisition, feature extraction and radiomic data processing, 18 radiomic features were used to train support vector machine (SVM), k-nearest neighbors (KNN) and Naïve Bayes (NBs) models. Ten-fold cross-validation was applied to evaluate the performance of these models. RESULTS: The SVM model showed high performance (balanced accuracy 0.89, AUC 0.9549) whereas the KNN (balanced accuracy 0.83, AUC 0.9266) and NBs (balanced accuracy 0.80, AUC 0.9324) models displayed low performance based on the T2-weighted images. The performance of the T2-weighted images was better than that of the other two MR sequences. Additionally, significant sensitivity (P = 0.031) and specificity (P = 0.012) differences were observed when classifying the PA subtypes by T2-weighted images. CONCLUSIONS: The SVM model was superior to the KNN and NBs models and can potentially precisely immunohistochemically classify PA subtypes with an MR-based radiomic analysis. The developed model exhibited good performance using T2-weighted images and might offer potential guidance to neurosurgeons in clinical decision-making before surgery.

Automatic detection on intracranial aneurysm from digital subtraction angiography with cascade convolutional neural networks.

BACKGROUND: An intracranial aneurysm is a cerebrovascular disorder that can result in various diseases. Clinically, diagnosis of an intracranial aneurysm utilizes digital subtraction angiography (DSA) modality as gold standard. The existing automatic computer-aided diagnosis (CAD) research studies with DSA modality were based on classical digital image processing (DIP) methods. However, the classical feature extraction methods were badly hampered by complex vascular distribution, and the sliding window methods were time-consuming during searching and feature extraction. Therefore, developing an accurate and efficient CAD method to detect intracranial aneurysms on DSA images is a meaningful task. METHODS: In this study, we proposed a two-stage convolutional neural network (CNN) architecture to automatically detect intracranial aneurysms on 2D-DSA images. In region localization stage (RLS), our detection system can locate a specific region to reduce the interference of the other regions. Then, in aneurysm detection stage (ADS), the detector could combine the information of frontal and lateral angiographic view to identify intracranial aneurysms, with a false-positive suppression algorithm. RESULTS: Our study was experimented on posterior communicating artery (PCoA) region of internal carotid artery (ICA). The data set contained 241 subjects for model training, and 40 prospectively collected subjects for testing. Compared with the classical DIP method which had an accuracy of 62.5% and an area under curve (AUC) of 0.69, the proposed architecture could achieve accuracy of 93.5% and the AUC of 0.942. In addition, the detection time cost of our method was about 0.569 s, which was one hundred times faster than the classical DIP method of 62.546 s. CONCLUSION: The results illustrated that our proposed two-stage CNN-based architecture was more accurate and faster compared with the existing research studies of classical DIP methods. Overall, our study is a demonstration that it is feasible to assist physicians to detect intracranial aneurysm on DSA images using CNN.