Exploring the causal relationships between rheumatoid arthritis and oral phenotypes: a genetic correlation and Mendelian randomization study

BackgroundRheumatoid arthritis (RA) frequently presents with oral manifestations, including gingival inflammation, loose teeth, and mouth ulcers; however, the causal connections between these conditions remain unclear. This study aims to explore the genetic correlations and causal relationships between RA and prevalent oral phenotypes.MethodsUsing summary data from genome-wide association studies of European populations, a cross-trait linkage disequilibrium score regression was conducted to estimate the genetic correlations between RA and six oral phenotypes. Subsequently, a two-sample Mendelian randomization (MR) approach was employed to assess the causal relationships, corroborated by various sensitivity analyses. Heterogeneity was addressed through the RadialMR method, while potential covariates were corrected using the multivariable MR approach.ResultsA significant negative genetic correlation was detected between RA and denture usage (rg = −0.192, p = 4.88 × 10−8). Meanwhile, a heterogenous causal relationship between RA and mouth ulcers was observed (OR = 1.027 [1.005–1.05], p = 0.016, Pheterogeneity = 4.69 × 10−8), which remained robust across sensitivity analyses. After excluding outlier variants, the results demonstrated robustly consistent (OR = 1.021 [1.008–1.035], p = 1.99 × 10−3, Pheterogeneity = 0.044). However, upon adjusting for covariates such as smoking, alcohol consumption, body mass index, and obesity, the significance diminished, revealing no evidence to support independent genetic associations.ConclusionGenetically predicted RA increases the risk of mouth ulcers, and a negative genetic correlation is identified between RA and denture use. The observed heterogeneity suggests that shared immunological mechanisms and environmental factors may play significant roles. These findings highlight the importance of targeted dental management strategies for RA patients. Further clinical guidelines are required to improve oral health among vulnerable RA patients

Energy-efficient secure outsourcing decryption of attribute based encryption for mobile device in cloud computation

This is a copy of the author 's final draft version of an article published in the "Journal of ambient intelligence and humanized computing". The final publication is available at Springer via http://dx.doi.org/10.1007/s12652-017-0658-2In this paper two new ways for efficient secure outsourcing the decryption of key-policy attribute-based encryption (KP-ABE) with energy efficiency are proposed. Based on an observation about the permutation property of the access structure for the attribute based encryption schemes, we propose a high efficient way for outsourcing the decryption of KP-ABE, which is suitable for being used in mobile devices. But it can only be used for the ABE schemes having tree-like access structure for the self-enclosed system. The second way is motivated from the fact that almost all the previous work on outsourcing the decryption of KP-ABE cares little about the ciphertext length. Almost all the previous schemes for secure outsourcing the decryption of ABE have linear length ciphertext with the attributes or the policy. But transferring so long ciphertexts via wireless network for mobile phone can easily run out of battery power, therefore it can not be adapted to practical application scenarios. Thus another new scheme for outsourcing the decryption of ABE but with constant-size ciphertexts is proposed. Furthermore, our second proposal gives a new efficient way for secure outsourcing the decryptor’s secret key to the cloud, which need only one modular exponentiation while all the previous schemes need many. We evaluate the efficiency of our proposals and the results show that our proposals are practical.Peer ReviewedPostprint (author's final draft

Ultra rapid lispro improves postprandial glucose control versus lispro in combination with basal insulin: a study based on CGM in type 2 diabetes in China

AimTo evaluate the efficacy and safety of URLi (ultra rapid lispro insulin) compared to insulin lispro as bolus insulin with basal insulin using CGM in the individuals with type 2 diabetes(T2D) in China.MethodsThis was a double-blind, randomized, parallel, prospective, phase 3 study. Subjects with uncontrolled T2D were recruited and randomized 1:2 into the insulin lispro and URLi groups. Subjects received a consistent basal insulin regimen during the study and self-administered insulin lispro or URLi before each meal throughout the treatment period. Subjects underwent a 3-day continuous glucose monitoring (CGM) at the baseline and endpoint respectively, and then CGM data were analyzed. The primary endpoint was to compare the difference in postprandial glucose (PPG) control using CGM between the two groups.ResultsA total of 57 subjects with T2D completed the study. Our CGM data showed that postprandial glucose excursions after breakfast (BPPGE) in the URLi group was lower than that in the insulin lispro group (1.59 ± 1.57 mmol/L vs 2.51 ± 1.73 mmol/L, p = 0.046). 1-hour PPG was observed to decrease more in the URLi group than that in the insulin lispro group (-1.37 ± 3.28 mmol/L vs 0.24 ± 2.58 mmol/L, p = 0.047). 2-hour PPG was observed to decrease more in the URLi group than that in the insulin lispro group (-1.12 ± 4.00 mmol/L vs 1.22 ± 2.90 mmol/L, p = 0.021). The mean HbA1c level decreased by 1.1% in the URLi group and 0.99% in the insulin lispro group, with no treatment difference (p = 0.642). In the CGM profile, TBR was not significantly different between the two groups (p = 0.743). The weight gain also did not differ between the two groups (p = 0.303).ConclusionURLi can control breakfast PPG better than insulin lispro in adults with T2D in China, while it is non-inferior in improving HbA1c. The incidence of hypoglycemic and weight gain were similar between the two groups

An almond-based low carbohydrate diet improves depression and glycometabolism in patients with Type 2 Diabetes through modulating gut microbiota and GLP-1: A randomized controlled trial

A low carbohydrate diet (LCD) is more beneficial for the glycometabolism in type 2 diabetes (T2DM) and may be effective in reducing depression. Almond, which is a common nut, has been shown to effectively improve hyperglycemia and depression symptoms. This study aimed to determine the effect of an almond-based LCD (a-LCD) on depression and glycometabolism, as well as gut microbiota and fasting glucagon-like peptide 1 (GLP-1) in patients with T2DM. Methods: This was a randomized controlled trial which compared an a-LCD with a low-fat diet (LFD). Forty-five participants with T2DM at a diabetes club and the Endocrine Division of the First and Second Affiliated Hospital of Soochow University between December 2018 to December 2019 completed each dietary intervention for 3 months, including 22 in the a-LCD group and 23 in the LFD group. The indicators for depression and biochemical indicators including glycosylated hemoglobin (HbA1c), gut microbiota, and GLP-1 concentration were assessed at the baseline and third month and compared between the two groups. Results: A-LCD significantly improved depression and HbA1c (p <0.01). Meanwhile, a-LCD significantly increased the short chain fatty acid (SCFAs)-producing bacteria Roseburia, Ruminococcus and Eubacterium. The GLP-1 concentration in the a-LCD group was higher than that in the LFD group (p <0.05). Conclusions: A-LCD could exert a beneficial effect on depression and glycometabolism in patients with T2DM. We speculate that the role of a-LCD in improving depression in patients with T2DM may be associated with it stimulating the growth of SCFAs-producing bacteria, increasing SCFAs production and GPR43 activation, and further maintaining GLP-1 secretion. In future studies, the SCFAs and GPR43 activation should be further examined

The Effect of Iron Oxide Magnetic Nanoparticles on Smooth Muscle Cells

Recently, magnetic nanoparticles of iron oxide (Fe3O4, γ-Fe2O3) have shown an increasing number of applications in the field of biomedicine, but some questions have been raised about the potential impact of these nanoparticles on the environment and human health. In this work, the three types of magnetic nanoparticles (DMSA-Fe2O3, APTS-Fe2O3, and GLU-Fe2O3) with the same crystal structure, magnetic properties, and size distribution was designed, prepared, and characterized by transmission electronic microscopy, powder X-ray diffraction, zeta potential analyzer, vibrating sample magnetometer, and Fourier transform Infrared spectroscopy. Then, we have investigated the effect of the three types of magnetic nanoparticles (DMSA-Fe2O3, APTS-Fe2O3, and GLU-Fe2O3) on smooth muscle cells (SMCs). Cellular uptake of nanoparticles by SMC displays the dose, the incubation time and surface property dependent patterns. Through the thin section TEM images, we observe that DMSA-Fe2O3is incorporated into the lysosome of SMCs. The magnetic nanoparticles have no inflammation impact, but decrease the viability of SMCs. The other questions about metabolism and other impacts will be the next subject of further studies

On the detection and refinement of transcription factor binding sites using ChIP-Seq data

Coupling chromatin immunoprecipitation (ChIP) with recently developed massively parallel sequencing technologies has enabled genome-wide detection of protein–DNA interactions with unprecedented sensitivity and specificity. This new technology, ChIP-Seq, presents opportunities for in-depth analysis of transcription regulation. In this study, we explore the value of using ChIP-Seq data to better detect and refine transcription factor binding sites (TFBS). We introduce a novel computational algorithm named Hybrid Motif Sampler (HMS), specifically designed for TFBS motif discovery in ChIP-Seq data. We propose a Bayesian model that incorporates sequencing depth information to aid motif identification. Our model also allows intra-motif dependency to describe more accurately the underlying motif pattern. Our algorithm combines stochastic sampling and deterministic ‘greedy’ search steps into a novel hybrid iterative scheme. This combination accelerates the computation process. Simulation studies demonstrate favorable performance of HMS compared to other existing methods. When applying HMS to real ChIP-Seq datasets, we find that (i) the accuracy of existing TFBS motif patterns can be significantly improved; and (ii) there is significant intra-motif dependency inside all the TFBS motifs we tested; modeling these dependencies further improves the accuracy of these TFBS motif patterns. These findings may offer new biological insights into the mechanisms of transcription factor regulation