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As important geospatial data, point cloud collected from an aerial laser scanner (ALS) provides three-dimensional (3D) information for the study of the distribution of typical urban land cover, which is critical in the construction of a “digital city”. However, existing point cloud classification methods usually use a single machine learning classifier that experiences uncertainty in making decisions for fuzzy samples in confusing areas. This limits the improvement of classification accuracy. To take full advantage of different classifiers and reduce uncertainty, we propose a classification method based on possibility theory and multi-classifier fusion. Firstly, the feature importance measure was performed by the XGBoost algorithm to construct a feature space, and two commonly used support vector machines (SVMs) were the chosen base classifiers. Then, classification results from the two base classifiers were quantitatively evaluated to define the confusing areas in classification. Finally, the confidence degree of each classifier for different categories was calculated by the confusion matrix and normalized to obtain the weights. Then, we synthesize different classifiers based on possibility theory to achieve more accurate classification in the confusion areas. DALES datasets were utilized to assess the proposed method. The results reveal that the proposed method can significantly improve classification accuracy in confusing areas.

Dynamic target threat assessment can be regarded as a classical multiple attribute group decision-making (MAGDM) issue. Triangular fuzzy numbers (TFNs) can fully describe imprecise and fuzzy information for target threat assessment. Thus, to consider the changing trend of target detection information in a certain period and the influence of commanders’ psychological factors on target threat assessment results, this study proposes an extended CPT-TODIM method based on possibility theory (PT-CPT-TODIM) to solve target threat assessment. Of note, the CRITIC method is used to determine the attribute weights of the probability mean matrix and the probability standard deviation matrix, which can make the target threat assessment result more rational. A numerical example and comparison analysis demonstrate the efficiency and feasibility of the newly proposed CPT-TODIM method.

It is always a goal to fully exploit difference features and their importance in infrared polarization and intensity images to drive selection of fusion algorithms rather than using fixed algorithm, to improve pertinence and effectiveness of image fusion. However, it is difficult to obtain a better and more reasonable result for fused image due to the varied difference features and uncertain relationship between difference features and fusion algorithms. The study aims to investigate the two-tuple linguistic cloud model for possibility distribution to solve the fusion algorithm selection problem. Firstly, fusion validity distributions of difference feature amplitudes are constructed through giving consideration to both fuzziness and randomness. Secondly, in order to build set-valued mapping between difference feature amplitudes and fusion algorithms, a novel fusion validity degree transformation method into two-tuple linguistic cloud variable is proposed based on possibility theory. Thirdly, difference feature weights are calculated based on nonparametric estimation for the frequency of each corresponding feature in images. Next, a cloud weighted arithmetic averaging operator is constructed to rank the algorithms. Finally, a case of the fusion algorithm selection is provided to illustrate the implementation process and applicability of the method proposed in this paper. In addition, the proposed method can be utilized the comprehensive and multi-constrained optimal problem with clear and effective management process.

BackgroundIdentifying useful markers is essential for diagnosis and prevention of perioperative neurocognitive disorders (PNDs). Here, we attempt to understand the research basis and status, potential hotspots and trends of predictive markers associated with PNDs after cardiac surgery via bibliometric analysis.MethodsA total of 4,609 original research articles and reviews that cited 290 articles between 2001 and 2021 were obtained from the Web of Science Core Collection (WoSCC) as the data source. We used the software CiteSpace to generate and analyze visual networks of bibliographic information, including published years and journals, collaborating institutions, co-cited references, and co-occurring keywords.ResultsThe number of annual and cumulative publications from 2001 to 2021 has been increasing on the whole. The Harvard Medical School was a very prolific and important institution in this field. The journal of Ann Thorac Surg (IF 4.33) had the most publications, while New Engl J Med was the most cited journal. Neuron-specific enolase (NSE), S100b and kynurenic acid (KYNA) were frequently discussed as possible markers of PNDs in many references. Cardiopulmonary bypass (CPB) was a keyword with high frequency (430) and sigma (6.26), and inflammation was the most recent burst keyword.ConclusionPotential markers of PNDs has received growing attention across various disciplines for many years. The research basis mainly focuses on three classic biomarkers of S100b, NSE, and KYNA. The most active frontiers are the inflammation-related biomarkers (e.g., inflammatory cells, cytokines, or mediators) and surgery-related monitoring parameters (e.g., perfusion, oxygen saturation, and the depth of anesthesia).

Airborne light detection and ranging (LiDAR) has been recognized as a reliable and accurate measurement tool in forest volume estimation, urban scene reconstruction and land cover classification, where LiDAR data provide crucial and efficient features such as intensity, elevation and coordinates. Due to the complex urban environment, it is difficult to classify land cover accurately and quickly from remotely sensed data. Methods based on the Dempster–Shafer evidence theory (DS theory) offer a possible solution to this problem. However, the inconsistency in the correspondence between classification features and land cover attributes constrains the improvement of classification accuracy. Under the original DS evidence theory classification framework, we propose a novel method for constructing a basic probability assignment (BPA) function based on possibility distributions and apply it to airborne LiDAR land cover classification. The proposed approach begins with a feature classification subset selected by single-feature classification results. Secondly, the possibility distribution of the four features was established, and the uncertainty relationship between feature values and land cover attributes was obtained. Then, we selected suitable interval cut-off points and constructed a BPA function. Finally, DS evidence theory was used for land cover classification. LiDAR and its co-registration data acquired by Toposys Falcon II were used in the performance tests of the proposed method. The experimental results revealed that it can significantly improve the classification accuracy compared to the basic DS method.

In multi-extended target tracking, each target may generate more than one observation. The traditional probability hypothesis density (PHD)-based methods are no longer effective in such scenarios. Recently, the Gaussian mixture PHD approach for the extended target tracking (ET-GM-PHD) has been presented to solve such a problem. The tracking performance of this approach has been restricted by the following disadvantages. First, it only focuses on the linear models. When targets are moving with nonlinear models, it may lead to great tracking errors. Second, the birth intensities are commonly assumed as a prior. In practice, these intensities are always unknown. In order to improve the tracking performance of the traditional ET-GM-PHD approach, a novel extended target tracking approach, namely the ET-cubature information GM-PHD (ET-CIGM-PHD) approach, has been proposed in this paper. To be more specific, we, first, utilize the cubature information filter (CIF) and gating methods to predict and update the Gaussian mixture components of the ET-GM-PHD approach. In the merit of high estimating accuracy of the CIF method, the tracking accuracy of the traditional ET-GM-PHD approach can be significantly improved. Due to the gating method, only part of cells can be used to construct the observation set in the update stage. Thus, the computational load of our approach can be significantly reduced. Then, we propose an adaptive initiating method for the birth intensity initiating. In our method, we utilize the estimated target set to select the most possible partition. Then, we remove cells associated with the estimated targets from the selected partition. The left cells are used to initiate the birth intensity. With the help of the above implementations, the birth intensity can be adaptively initiated. Using such a method, our approach can solve the cases that the prior information of birth intensity is rather little. The simulation results demonstrate the effectiveness of our approach.

At present, infrared polarization and intensity image fusion algorithms often cause fused images to appear fuzzy, making the fused image unable to be processed further. In this paper, a new infrared polarization and intensity image fusion algorithm is proposed, and the fused image is divided into base layer image and detail layer image. The infrared intensity image is used as the base layer image, and the infrared polarization image is decomposed by a multi-scale Gaussian filter and residual method. Next, a structural similarity index is introduced as the constraint of the multi-scale decomposition layers, and the detail layer image is obtained by summing of feature images of infrared polarization image. Finally, the fused image is obtained by the superimposition of the base layer image and detail layer image. The fused image retains all the features of the infrared intensity image and the majority of the polarization image features. The experimental results demonstrated that the fused image obtained by the proposed method performed better in both subjective and objective qualities.

Abstract The long-term benefits of direct-acting antiviral (DAA) therapy after achieving sustained virological response (SVR) remain uncertain in the Chinese population. This study evaluates the incidence of hepatocellular carcinoma (HCC), hepatic decompensation, and all-cause mortality among Chinese hepatitis C patients treated with DAAs. We included patients diagnosed since 2011 and followed them until November 1, 2022. The primary outcomes were HCC, hepatic decompensation, and mortality. Multivariable proportional hazards model was used to assess the impact of SVR and cirrhosis status. The cohort consisted of 1272 patients with SVR (92.1%) and 109 without SVR (7.9%), with a median follow-up of 61 months. The incidence of HCC was significantly lower in the SVR group (5.1 per 1000 person-years) compared to the no-SVR group (15.0 per 1000 person-years). Achieving SVR was associated with a significantly reduced risk of HCC (adjusted hazard ratio: 0.32; 95% CI 0.16–0.67). Cirrhosis was linked to an increased risk of developing HCC and hepatic decompensation. These findings highlight the importance of early DAA treatment, particularly for patients with cirrhosis.

Abstract Background Vaccination is effective in preventing viral respiratory infectious diseases through protective antibodies and the gut microbiome has been proven to regulate human immunity. This study explores the causal correlations between gut microbial features and serum-specific antiviral immunoglobulin G (IgG) levels. Methods We conduct a two-sample bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data to explore the causal relationships between 412 gut microbial features and four antiviral IgG (for influenza A, measles, rubella, and mumps) levels. To make the results more reliable, we used four robust methods and performed comprehensive sensitivity analyses. Results The MR analyses revealed 26, 13, 20, and 18 causal associations of the gut microbial features influencing four IgG levels separately. ​Interestingly, ten microbial features, like genus Collinsella, species Bifidobacterium longum, and the biosynthesis of L-alanine have shown the capacity to regulate multiple IgG levels with consistent direction (rise or fall). The ​reverse MR analysis suggested several potential causal associations of IgG levels affecting microbial features. Conclusions The human immune response against viral respiratory infectious diseases could be modulated by changing the abundance of gut microbes, which provided new approaches for the intervention of viral respiratory infections.

Background and objectivesSeveral recent clinical studies have indicated that vamorolone is comparable in effectiveness to glucocorticosteroids for treating Duchenne muscular dystrophy (DMD). However, there is a lack of extensive data regarding the efficacy and safety of various doses of vamorolone. We conducted a study to evaluate the efficacy of different doses of vamorolone in boys with DMD, and compare the safety of vamorolone vs. glucocorticosteroids, prednisone or deflazacort in boys with DMD.MethodsWe performed systematic searches of the PubMed, Embase, and Cochrane Library databases for vamorolone, glucocorticosteroids, prednisone or deflazacort in boys with DMD. We assessed statistical heterogeneity across trials based on the Newcastle Ottawa scale (NOS) tool test and I2 values, and mean differences were pooled using the random-effects model. We used traditional meta-analysis to evaluate efficacy and safety of vamorolone 6.0 mg/kg/d vs. vamorolone 2.0 mg/kg/d and vamorolone vs. prednisone. A network meta-analysis was applied to estimated the safety of vamorolone in comparison to glucocorticosteroids, prednisone and deflazacort. Our meta-analysis were performed using Revman 5.4 software, and our network meta-analysis were performed using Stata/MP 18.0.ResultsIn the meta-analysis, a total of 193 patients were analyzed across four clinical trials (97 patients receiving vamorolone 2 mg/kg per day; 96 patients receiving vamorolone 2 mg/kg per day). We observed that there were statistically significant differences in boys with DMD between vamorolone 6.0 mg/kg/d and vamorolone 2.0 mg/kg/d in TTSTANDV (MD = 0.03, 95%CI = 0.00–0.06, p = 0.04), TTRWV (MD = 0.13, 95%CI = 0.08–0.19, p

Abstract Nearly a quarter of bipolar disorder (BD) patients were misdiagnosed as major depressive disorder (MDD) patients, which cannot be corrected until mania/hypomania develops. It is important to recognize these obstacles so that the appropriate treatment can be initiated. Thus, we sought to distinguish patients with BD from MDD, especially to identify misdiagnosed BD before mania/hypomania, and further explore potential trait features that allow accurate differential diagnosis independent of state matters. Functional magnetic resonance imaging scans were performed at baseline on 92 MDD patients and 48 BD patients. The MDD patients were then followed up for more than two years. After follow-up, 23 patients transformed into BD (tBD), and 69 patients whose diagnoses remained unchanged were eligible for unipolar depression (UD). A support vector machine classifier was trained on the amygdala-based functional connectivity (FC) of 48 BD and 50 UD patients using a novel region-based feature selection. Then, the classifier was tested on the dataset, encompassing tBD and the remaining UD. It performed well for known BD and UD and can also distinguish tBD from UD with an accuracy of 81%, sensitivity of 82.6%, specificity of 79%, and AUC of 74.6%, respectively. Feature selection results revealed that ten regions within the cortico-limbic neural circuit contributed most to classification. Furthermore, in the FC comparisons among diseases, BD and tBD shared almost overlapped FC patterns in the cortico-limbic neural circuit, and both of them presented pronounced differences in most regions within the circuit compared with UD. The FC values of the most discriminating brain regions had no prominent correlations with the severity of depression, anxiety, and mania/hypomania (FDR correction). It suggests that BD possesses some trait features in the cortico-limbic neural circuit, rendering it dichotomized by the classifier based on known-diagnosis data.

Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure–activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides.

ObjectivesObstructive sleep apnea (OSA) and osteoarthritis (OA) are common comorbidities that significantly impact individuals’ quality of life. However, the relationship between OSA and OA remains unclear. This study aims to explore the connection between OSA and OA and evaluate causality using Mendelian randomization (MR).MethodsA total of 12,454 participants from the National Health and Nutrition Examination Survey (2009–2012) were included. OSA participants were identified based on self-reported interviews. The association between OA and OSA was assessed through multivariable logistic regression analysis. A two-sample MR was employed to investigate the relationship between OSA and OA, specifically hip OA and knee OA, utilizing the inverse variance-weighted (IVW) approach.ResultsBased on the observational study, individuals with OSA exhibited a higher risk of OA (OR = 1.67, 95% CI = 1.40–1.98). IVW demonstrated that the risk of OA (OR = 1.13, 95% CI: 1.05–1.21, p = 0.001), hip OA (OR = 1.11, 95% CI: 1.04–1.18, p = 0.002), and knee OA (OR = 1.08, 95% CI: 1.02–1.14, p = 0.005) was significantly associated with OSA. Reverse MR analyses indicated no effect of OA on OSA. Additionally, body mass index (BMI) was found to mediate 36.9% (95% CI, 4.64–73.2%, p = 0.026) of the OSA effects on OA risk.ConclusionThe cross-sectional observational analysis unveiled noteworthy associations between OSA and OA. Meanwhile, findings from the MR study provide support for a causal role.

Abstract Background Most existing predictive models of hepatocellular carcinoma (HCC) risk after sustained virologic response (SVR) are built on data collected at baseline and therefore have limited accuracy. The current study aimed to construct an accurate predictive model incorporating longitudinal data using a novel modeling strategy. The predictive performance of the longitudinal model was also compared with a baseline model. Methods A total of 400 patients with HCV-related cirrhosis who achieved SVR with direct-acting antivirals (DAA) were enrolled in the study. Patients were randomly divided into a training set (70%) and a validation set (30%). Informative features were extracted from the longitudinal variables and then put into the random survival forest (RSF) to develop the longitudinal model. A baseline model including the same variables was built for comparison. Results During a median follow-up time of approximately 5 years, 25 patients (8.9%) in the training set and 11 patients (9.2%) in the validation set developed HCC. The areas under the receiver-operating characteristics curves (AUROC) for the longitudinal model were 0.9507 (0.8838–0.9997), 0.8767 (0.6972,0.9918), and 0.8307 (0.6941,0.9993) for 1-, 2- and 3-year risk prediction, respectively. The brier scores of the longitudinal model were also relatively low for the 1-, 2- and 3-year risk prediction (0.0283, 0.0561, and 0.0501, respectively). In contrast, the baseline model only achieved mediocre AUROCs of around 0.6 (0.6113, 0.6213, and 0.6480, respectively). Conclusions Our longitudinal model yielded accurate predictions of HCC risk in patients with HCV-relate cirrhosis, outperforming the baseline model. Our model can provide patients with valuable prognosis information and guide the intensity of surveillance in clinical practice.

Multi-modal image fusion can more accurately describe the features of a scene than a single image. Because of the different imaging mechanisms, the difference between multi-modal images is great, which leads to poor contrast of the fused images. Therefore, a simple and effective spatial domain fusion algorithm based on variable parameter fractional difference enhancement is proposed. Based on the characteristics of fractional difference enhancement, a variable parameter fractional difference is introduced, the multi-modal images are repeatedly enhanced, and multiple enhanced images are obtained. A correlation coefficient is applied to constrain the number of enhancement cycles. In addition, an energy contrast is used to extract the contrast features of the image, and the tangent function is simultaneously used to obtain the fusion weight to attain multiple contrast-enhanced initialization fusion images. Finally, the weighted average is applied to obtain the final fused image. Experimental results demonstrate that the proposed fusion algorithm can effectively preserve the contrast features between images and improve the quality of fused images. [ABSTRACT FROM AUTHOR]

Objective: We explore the candidate susceptibility genes for influenza A virus (IAV), measles, rubella, and mumps and their underlying biological mechanisms.Methods: We downloaded the genome-wide association study summary data of four virus-specific immunoglobulin G (IgG) level data sets (anti-IAV IgG, anti-measles IgG, anti-rubella IgG, and anti-mumps virus IgG levels) and integrated them with reference models of three potential tissues from the Genotype-Tissue Expression (GTEx) project, namely, whole blood, lung, and transformed fibroblast cells, to identify genes whose expression is predicted to be associated with IAV, measles, mumps, and rubella.Results: We identified 19 significant genes (ULK4, AC010132.11, SURF1, NIPAL2, TRAP1, TAF1C, AC000078.5, RP4-639F20.1, RMDN2, ATP1B3, SRSF12, RP11-477D19.2, TFB1M, XXyac-YX65C7_A.2, TAF1C, PCGF2, and BNIP1) associated with IAV at a Bonferroni-corrected threshold of p < 0.05; 14 significant genes (SOAT1, COLGALT2, AC021860.1, HCG11, METTL21B, MRPL10, GSTM4, PAQR6, RP11-617D20.1, SNX8, METTL21B, ANKRD27, CBWD2, and TSFM) associated with measles at a Bonferroni-corrected threshold of p < 0.05; 15 significant genes (MTOR, LAMC1, TRIM38, U91328.21, POLR2J, SCRN2, Smpd4, UBN1, CNTROB, SCRN2, HOXB-AS1, SLC14A1, AC007566.10, AC093668.2, and CPD) associated with mumps at a Bonferroni-corrected threshold of p < 0.05; and 13 significant genes (JAGN1, RRP12, RP11-452K12.7, CASP7, AP3S2, IL17RC, FAM86HP, AMACR, RRP12, PPP2R1B, C11orf1, DLAT, and TMEM117) associated with rubella at a Bonferroni-corrected threshold of p < 0.05.Conclusions: We have identified several candidate genes for IAV, measles, mumps, and rubella in multiple tissues. Our research may further our understanding of the pathogenesis of infectious respiratory diseases.

Backgrounds: Suicidal ideation (SI) is one of the most serious consequences of major depressive disorder (MDD). Understanding the unique mechanism of MDD with SI (MDD + S) is crucial for treatment development. While abundant research has studied MDD, past studies have not reached a consensus on the mechanism of MDD + S. The study aimed to investigate the abnormalities of the gray matter volumes (GMVs) and plasma IL-6 level in MDD + S to further reveal the mechanism of MDD + S. Methods: We tested the plasma IL-6 level using Luminex multifactor assays and collected the Structural Magnetic Resonance Imaging (SMRI) data from 34 healthy controls (HCs), 36 MDD patients without SI (MDD − S) and 34 MDD + S patients. We performed a partial correlation between the GMVs of the brain regions with significant differences and plasma IL-6 level with age, sex, medication, scores of HAMD-17 and HAMA as the covariates. Results: Compared with HCs and MDD − S, MDD + S had significantly decreased GMVs in the left cerebellum Crus I/II and significantly increased plasma IL-6 level; compared with HCs, both the MDD + S and MDD − S had significantly decreased GMVs in right precentral and postcentral gyri. No significant correlation was found between the GMVs and the plasma IL-6 level in the MDD + S and MDD − S, respectively. While the GMVs of the right precentral and postcentral gyri negatively correlated with the level of IL-6 in the whole MDD (r = −0.28, P = 0.03). The GMVs of the left cerebellum Crus I/II (r = −0.47, P = 0.02), and the right precentral and postcentral gyri (r = −0.42, P = 0.04) negatively correlated with the level of IL-6 in HCs. Conclusion: The altered GMVs and the plasma IL-6 level may provide a scientific basis to understand the pathophysiological mechanisms of MDD + S.

BackgroundInsomnia is considered one of the manifestations of sleep disorders, and its intensity is linked to the treatment effect or suicidal thoughts. Major depressive disorder (MDD) is classified into various subtypes due to heterogeneous symptoms. Melancholic MDD has been considered one of the most common subtypes with special sleep features. However, the brain functional mechanisms in melancholic MDD with insomnia remain unclear.Materials and methodsMelancholic MDD and healthy controls (HCs, n = 46) were recruited for the study. Patients were divided into patients with melancholic MDD with low insomnia (mMDD-LI, n = 23) and patients with melancholic MDD with high insomnia (mMDD-HI, n = 30), according to the sleep disturbance subscale of the 17-item Hamilton Depression Rating Scale. The dynamic amplitude of low-frequency fluctuation was employed to investigate the alterations of brain activity among the three groups. Then, the correlations between abnormal dALFF values of brain regions and the severity of symptoms were investigated.ResultsLower dALFF values were found in the mMDD-HI group in the right middle temporal gyrus (MTG)/superior temporal gyrus (STG) than in the mMDD-LI (p = 0.014) and HC groups (p < 0.001). Melancholic MDD groups showed decreased dALFF values than HC in the right middle occipital gyri (MOG)/superior occipital gyri (SOG), the right cuneus, the bilateral lingual gyrus, and the bilateral calcarine (p < 0.05). Lower dALFF values than HC in the left MOG/SOG and the left cuneus in melancholic MDD groups were found, but no significant difference was found between the mMDD-LI group and HC group (p = 0.079). Positive correlations between the dALFF values in the right MTG/STG and HAMD-SD scores (the sleep disturbance subscale of the HAMD-17) in the mMDD-HI group (r = 0.41, p = 0.042) were found. In the pooled melancholic MDD, the dALFF values in the right MOG/SOG and the right cuneus (r = 0.338, p = 0.019), the left MOG/SOG and the left cuneus (r = 0.299, p = 0.039), and the bilateral lingual gyrus and the bilateral calcarine (r = 0.288, p = 0.047) were positively correlated with adjusted HAMD scores.ConclusionThe occipital cortex may be related to depressive symptoms in melancholic MDD. Importantly, the right MTG/STG may play a critical role in patients with melancholic MDD with more severe insomnia.

ObjectiveBipolar disorder (BD) has a higher lifetime rate of suicide attempts (SA) than other psychiatric disorders. Furthermore, BD patients with SA (BD + S) are prone to a worse quality of life. However, the pathophysiology of BD + S is poorly understood. To further reveal the potential mechanisms of BD + S, abnormalities in peripheral plasma inflammatory cytokines and brain white matter (WM) in BD + S, as well as the correlation between them are investigated.MethodsWe tested the levels of TNF-α, IL-1β, and IL-6 in peripheral plasma and collected the diffusion tensor imaging (DTI) data from 14 BD + S, 24 BD patients without SA (BD-S), and 26 healthy controls (HCs). The three groups were matched by age and gender. The levels of TNF-α, IL-1β, and IL-6 were detected by Luminex multifactor detection technology, and the fractional anisotropy (FA) values were employed to depict the alterations of WM. Partial correlation analyses were conducted to detect correlations between levels of TNF-α, IL-1β, and IL-6 and changes of WM, and the relationships between severity of clinical symptoms, including scores of HAMD-17 and YMRS, and cytokine levels or FA values in all groups.ResultsFor plasma inflammatory cytokines, there was no significant difference in their levels except for IL-6 among the three groups. Post-hoc analyses revealed that increased IL-6 level was only detected in BD + S (p < 0.05, Bonferroni correction). For DTI, BD + S showed specifically decreased FA in the bilateral middle cerebellar peduncle and the left superior corona radiata compared to BD-S and HCs (p < 0.05, Bonferroni correction). Additionally, both BD + S and BD-S groups revealed decreased FA in the bilateral body and genu of corpus callosum (CC) compared to HCs (p < 0.05, Bonferroni correction). No significant correlation between plasma inflammatory cytokines and WM integrity was found. In the BD + S group, we found negative correlation between the scores of YMRS and FA values of the left middle cerebellar peduncle (r = −0.74, p = 0.035).ConclusionThe inflammation and impaired WM integrity may provide a scientific basis to understand the potential mechanisms of BD + S.

N6-methyladenosine (m6A) is one of the most prevalent mRNA modifications in eukaryotes. Here the authors show that the m6A writer protein methyltransferase-like (METTL) 3 plays an important role for the development of brown adipose tissue.

Objective: Enhanced glucagon signaling and hepatic glucose production (HGP) can account for hyperglycemia in patients with obesity and type 2 diabetes. However, the detailed molecular mechanisms underlying the enhanced HGP in these patients are not fully understood. Here, we identify Purβ as a positive regulator of HGP and study its molecular mechanisms in the regulation of HGP both in vivo and in vitro. Methods: Adenovirus-mediated knockdown or overexpression of Purβ was performed in either primary hepatocytes or the livers of db/db mice. Glucose metabolism, insulin sensitivity, and HGP were determined by glucose, insulin, and lactate tolerance tests, respectively. Purβ/ADCY6 protein levels, glucagon signaling (p-CREB/CREB), and insulin signaling (p-Akt/Akt) were measured by immunoblotting. Gene expression was measured by RNA-seq and real-time quantitative polymerase chain reaction. Luciferase reporter and chromatin immunoprecipitation assays were used to study the interaction between Purβ and the Adcy6 promoter. Results: Purβ was abnormally elevated in obese mice and was also increased under fasting conditions or via the glucagon signaling pathway, which promoted HGP by increasing Adcy6 expression. Liver-specific knockdown of Purβ in db/db mice significantly ameliorated hyperglycemia and glucose intolerance by suppressing the glucagon/ADCY6/cAMP/PKA/CREB signaling pathway. Consistent with this observation, the knockdown of Purβ also inhibited glucose production in isolated primary hepatocytes by inhibiting the glucagon/ADCY6/cAMP/PKA/CREB signaling pathway, whereas the overexpression of Purβ promoted glucose production by activating this signaling pathway. Mechanistically, Purβ directly binds to the promoter of the Adcy6 gene and thereby promotes its transcription. Conclusions: Taken together, these results illustrate a new model in which Purβ functions to regulate the glucagon/ADCY6/cAMP/PKA/CREB signaling pathway to help maintain glucose homeostasis. Keywords: Purβ, Liver, Gluconeogenesis, Glucagon signaling, ADCY6, Obesity