Ankylosing spondylitis and glaucoma in European population: A Mendelian randomization study

BackgroundThe relationship between ankylosing spondylitis (AS) and glaucoma in the European population remains unclear. In the present study, we applied a two-sample Mendelian randomization (MR) method to investigate their causal relationship.MethodsMR analysis was conducted to validate the causal associations between AS with glaucoma using summary statistics from the genome-wide association studies of AS (9,069 cases and 13,578 control subjects) and glaucoma (8,591 cases and 210,201 control subjects). The inverse variance weighting method was performed to evaluate the causal relationship. The MR–Egger regression approach was applied to assess pleiotropy, while Cochran’s Q test was used to analyze heterogeneity. Subgroup analysis was performed according to primary open-angle glaucoma (POAG) and primary angle-closure glaucoma (PACG).ResultsThe results of the MR study reveal a risk-increasing causal relationship between AS and glaucoma among European populations (OR = 1.35, 95%CI = 1.16–1.57, P = 8.81 × 10-5). Pleiotropy and heterogeneity were not found in our study. In the subgroup analysis, AS was also causal with POAG (OR = 1.48, 95%CI = 1.17–1.86, P = 8.80 × 10-4) and PACG (OR = 1.91, 95%CI = 1.03–3.51, P = 3.88 × 10-2).ConclusionThe results of the MR analysis suggested a causal relationship between AS and glaucoma in the European population. Further studies are needed to identify the specific mechanism between these two diseases

Magnetic Resonance Spectroscopy Quantification Aided by Deep Estimations of Imperfection Factors and Macromolecular Signal

Objective: Magnetic Resonance Spectroscopy (MRS) is an important technique for biomedical detection. However, it is challenging to accurately quantify metabolites with proton MRS due to serious overlaps of metabolite signals, imperfections because of non-ideal acquisition conditions, and interference with strong background signals mainly from macromolecules. The most popular method, LCModel, adopts complicated non-linear least square to quantify metabolites and addresses these problems by designing empirical priors such as basis-sets, imperfection factors. However, when the signal-to-noise ratio of MRS signal is low, the solution may have large deviation. Methods: Linear Least Squares (LLS) is integrated with deep learning to reduce the complexity of solving this overall quantification. First, a neural network is designed to explicitly predict the imperfection factors and the overall signal from macromolecules. Then, metabolite quantification is solved analytically with the introduced LLS. In our Quantification Network (QNet), LLS takes part in the backpropagation of network training, which allows the feedback of the quantification error into metabolite spectrum estimation. This scheme greatly improves the generalization to metabolite concentrations unseen for training compared to the end-to-end deep learning method. Results: Experiments show that compared with LCModel, the proposed QNet, has smaller quantification errors for simulated data, and presents more stable quantification for 20 healthy in vivo data at a wide range of signal-to-noise ratio. QNet also outperforms other end-to-end deep learning methods. Conclusion: This study provides an intelligent, reliable and robust MRS quantification. Significance: QNet is the first LLS quantification aided by deep learning

CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://csrc.xmu.edu.cn/CloudBrain.htmlComment: 11 pages, 13 figure

Association of lncRNA H19 polymorphisms with cancer susceptibility: An updated meta-analysis based on 53 studies

The association between polymorphisms in lncRNA H19 and cancer susceptibility remains to be inconsistent. This study aimed to provide a more precise estimation of the relationship between lncRNA H19 polymorphisms and the risk of cancer based on all available published studies. 53 studies encompassing 32,376 cases and 43,659 controls were included in our meta-analysis by searching the Pubmed, Embase, Web of Science, WanFang, and China National Knowledge Infrastructure databases. Pooled ORs and their 95% CIs were used to estimate the strength between the SNPs in H19 (rs217727, rs2839698, rs2107425, rs3024270, rs2735971, rs3741216, and rs3741219) and cancer susceptibility. The results showed that H19 rs2839698 polymorphism was associated with increased cancer risk in all participants under three genetic models. However, no significant association was identified between the other six SNPs as well as an overall cancer risk. Stratification by ethnicity showed that rs2839698 mutation indicated to be an important hazardous factor for the Asian population. While rs2107425 mutation had a protective effect on the Caucasian population. Stratification by cancer type identified that rs217727 mutation was linked to increased susceptibility to oral squamous cell carcinoma, lung cancer, and hepatocellular carcinoma; whereas rs2839698 mutation was associated with an elevated risk of hematological tumor and digestive system tumor (p < 0.05). Besides, the rs2735971 mutation was connected with the digestive system tumor. In summary, the rs217727, rs2839698, rs2107425 and rs2735971 polymorphisms in H19 have associations with cancer susceptibility

Upregulation of miR-214 Induced Radioresistance of Osteosarcoma by Targeting PHLDA2 via PI3K/Akt Signaling

Osteosarcoma is an aggressive bone tumor with high resistance to radiotherapy. Pleckstrin homology-like domain family A member 2 (PHLDA2) displays low expression in human osteosarcoma as a proapoptosis factor. miRNAs have been shown to be important in modulating translation and therapeutic responsiveness in solid tumors. Herein, we used luciferase assay to show that miR-214 downregulates the PHLDA2 expression by targeting its 3′-untranslated region (UTR). A high level of miR-214 was identified in tumor tissues from 30 osteosarcoma patients via qPCR analysis, associated positively with lung metastasis. Ectopic expression miR-214 enhanced radioresistance in osteosarcoma cells, with decreased IR-induced apoptosis. Moreover, the depletion of miR-214 enhanced radiosensitivity in both osteosarcoma cells and mouse xenograft models. Importantly, we showed that miR-214 regulated the activation of phosphatidylinositol-3-kinase/Akt signaling pathway by inhibiting PHLDA2. Finally, the introduction of PHLDA2 cDNA lacking the 3′-UTR or treatment with Akt inhibitor LY294002 partially abrogated miR-214-induced radioresistance. In summary, our results reveal that the upregulation of miR-214 as a frequent event in osteosarcoma contributes to radioresistance by regulating the PHLDA2/Akt pathway. The miR-214/PHLDA2/Akt axis provides a new avenue toward understanding the mechanism of radiosensitivity and may be a potential target for osteosarcoma intervention

Compressed CO₂ mediated synthesis of bifunctional periodic mesoporous organosilicas with tunable porosity

A facile and green method is proposed for the fabrication of bifunctional periodic mesoporous organosilicas using compressed CO2.</p

CloudBrain-MRS: An Intelligent Cloud Computing Platform for in vivo Magnetic Resonance Spectroscopy Preprocessing, Quantification, and Analysis

Magnetic resonance spectroscopy (MRS) is an important clinical imaging method for diagnosis of diseases. MRS spectrum is used to observe the signal intensity of metabolites or further infer their concentrations. Although the magnetic resonance vendors commonly provide basic functions of spectra plots and metabolite quantification, the widespread clinical research of MRS is still limited due to the lack of easy-to-use processing software or platform. To address this issue, we have developed CloudBrain-MRS, a cloud-based online platform that provides powerful hardware and advanced algorithms. The platform can be accessed simply through a web browser, without the need of any program installation on the user side. CloudBrain-MRS also integrates the classic LCModel and advanced artificial intelligence algorithms and supports batch preprocessing, quantification, and analysis of MRS data from different vendors. Additionally, the platform offers useful functions: 1) Automatically statistical analysis to find biomarkers for diseases; 2) Consistency verification between the classic and artificial intelligence quantification algorithms; 3) Colorful three-dimensional visualization for easy observation of individual metabolite spectrum. Last, both healthy and mild cognitive impairment patient data are used to demonstrate the functions of the platform. To the best of our knowledge, this is the first cloud computing platform for in vivo MRS with artificial intelligence processing. We have shared our cloud platform at MRSHub, providing free access and service for two years. Please visit https://mrshub.org/software_all/#CloudBrain-MRS or https://csrc.xmu.edu.cn/CloudBrain.html.Comment: 11 pages, 12 figure

CagA-positive Helicobacter pylori may promote and aggravate scrub typhus

Helicobacter pylori (H. pylori) infection may alter the host’s resistance to tsutsugamushi disease pathogens through the Th1 immune response, leading to potential synergistic pathogenic effects. A total of 117 scrub typhus cases at Beihai People’s Hospital and affiliated hospitals of Youjiang University for Nationalities and Medical Sciences were studied from January to December 2022, alongside 130 healthy individuals forming the control group. All participants underwent serum H. pylori antibody testing. The prevalence of H. pylori infection was significantly higher among scrub typhus patients (89.7%) compared to healthy individuals (54.6%) (p &lt; 0.05). Moreover, type I H. pylori infection was notably more prevalent in scrub typhus cases (67.5%) compared to healthy individuals (30%) (p &lt; 0.05). Multifactorial analysis demonstrated type I H. pylori infection as an independent risk factor for scrub typhus (adjusted odds ratio: 2.407, 95% confidence interval: 1.249–4.64, p = 0.009). Among scrub typhus patients with multiple organ damage, the prevalence of type I H. pylori infection was significantly higher (50.6%) than type II H. pylori infection (15.4%) (χ2 = 4.735, p = 0.030). These results highlight a higher incidence of H. pylori infection in scrub typhus patients compared to the healthy population. Additionally, type I H. pylori strain emerged as an independent risk factor for scrub typhus development. Moreover, individuals infected with type I H. pylori are more susceptible to multiple organ damage. These findings suggest a potential role of H. pylori carrying the CagA gene in promoting and exacerbating scrub typhus

Radiomic signature of the FOWARC trial predicts pathological response to neoadjuvant treatment in rectal cancer

Background We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. Methods This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. Results We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990–1.000] and 0.822 [95% CI 0.649–0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. Conclusions The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making